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Liu K, Fan P, Jia Z, Wang Z, Qi S. Analysis of four heavy metal concentrations in sediments fromthe Jiaozhou Bay, China by visible and near infrared spectroscopy (225-975 nm). SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 316:124367. [PMID: 38692111 DOI: 10.1016/j.saa.2024.124367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 04/20/2024] [Accepted: 04/27/2024] [Indexed: 05/03/2024]
Abstract
As an important component ofbiogeochemical cyclein coastal ecosystems, sediments are the sink of heavy metals. Therefore, distribution and dynamics of heavy metals in sediments could assess ecological quality and predict ecological risks. In the new era, rapid and green technology are highly needed, especially that could determine multi-parameters simultaneously. Here, we explored a new method to rapidly determine concentrations of heavy metals in sediments by visible and near infrared reflectance spectroscopy (VIRS).We sampled sediments in the Jiaozhou Bay, China, collected their reflectance spectra, and measured concentrations of four heavy metals (As, Cr, Cu, and Zn). Heavy metal models were established and evaluated using substances highly correlated with heavy metals. This study provides an effective reference for rapid analysis of As, Cr, Cu, and Zn simultaneously in sediments, at least in the Jiaozhou Bay, and for ecological environment protection and resource development of the Jiaozhou Bay.
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Affiliation(s)
- Kai Liu
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China; Key Laboratory for Ocean Environment Monitoring Technology of Shandong Province, Qingdao 266061, China
| | - Pingping Fan
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China; Key Laboratory for Ocean Environment Monitoring Technology of Shandong Province, Qingdao 266061, China.
| | - Zongchao Jia
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China; Key Laboratory for Ocean Environment Monitoring Technology of Shandong Province, Qingdao 266061, China
| | - Zijian Wang
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China; Key Laboratory for Ocean Environment Monitoring Technology of Shandong Province, Qingdao 266061, China
| | - Suiping Qi
- Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China; Key Laboratory for Ocean Environment Monitoring Technology of Shandong Province, Qingdao 266061, China
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Guo Z, Chen X, Zhang Y, Sun C, Jayan H, Majeed U, Watson NJ, Zou X. Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms. Foods 2024; 13:1698. [PMID: 38890926 PMCID: PMC11171995 DOI: 10.3390/foods13111698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/20/2024] Open
Abstract
Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.
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Affiliation(s)
- Zhiming Guo
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China;
| | - Xuan Chen
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Yiyin Zhang
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Chanjun Sun
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Heera Jayan
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
| | - Usman Majeed
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China;
| | - Nicholas J. Watson
- School of Food Science and Nutrition, University of Leeds, Leeds LS2 9JT, UK;
| | - Xiaobo Zou
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China; (X.C.); (Y.Z.); (C.S.); (H.J.); (X.Z.)
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Products Processing, Jiangsu University, Zhenjiang 212013, China;
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Pan W, Cheng X, Du R, Zhu X, Guo W. Detection of chlorophyll content based on optical properties of maize leaves. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 309:123843. [PMID: 38215563 DOI: 10.1016/j.saa.2024.123843] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 01/14/2024]
Abstract
The chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status. Maize is one of the three widely planted gain crops in the world. In order to offer useful information for the development of chlorophyll content detectors of maize leaves, a single integrating sphere system was used to measure the transmittance and reflectance spectra of maize leaves over the wavelength range of 500-950 nm. The linear relationships of transmittance and reflectance with chlorophyll content were investigated. The feature wavelengths (FWs) sensitive to chlorophyll content were extracted from the full transmittance and reflectance spectra using the successive projections algorithm (SPA). The partial least squares regression (PLSR) models for predicting the chlorophyll content were established using the full spectra and extracted FWs. The results showed that there were obvious linear relationships between transmittance and reflectance with chlorophyll content of maize leaves and the best linear relationships were found at 709 nm and 714 nm, respectively, with the linear correlation coefficients of 0.801 and 0.696, and the root-mean-squares error (RMSEP) of 0.321 mg·g-1 and 0.405 mg·g-1, respectively. Eight and 6 FWs were extracted from the transmittance and reflectance spectra, respectively. The PLSR model established using the selected FWs from transmittance spectra had better prediction performance with RMSEP of 0.208 mg·g-1 than using full transmittance spectra. The built PLSR models using the full reflectance spectra and extracted FWs had poor robustness. This research offers some theoretical basis for developing a maize leaf chlorophyll content detector based on transmittance or reflectance.
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Affiliation(s)
- Weidong Pan
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaodong Cheng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Rongyu Du
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xinhua Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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4
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Carabajal MD, Bortolato SA, Lisandrini FT, Olivieri AC. An exhaustive analysis of the use of image moments for second-order calibration. A comparison with multivariate curve resolution-alternating least-squares. Anal Chim Acta 2024; 1288:342177. [PMID: 38220307 DOI: 10.1016/j.aca.2023.342177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). Recently, an alternative procedure was described based on the estimation of image moments for each data matrix and subsequent application of multiple linear regression after suitable variable selection. RESULTS The analysis of both simulated and experimental data leads to the conclusion that the image moment method, although can cope with chromatographic lack of reproducibility across injections, it only performs well in the absence of uncalibrated interferents. MCR-ALS, on the other hand, provides good analytical results in all studied situations, whether the test samples contain uncalibrated interferents or not. SIGNIFICANCE The results are useful to assess the real usefulness of newly proposed methodologies for second-order calibration in the case of chromatographic-spectral data sets, especially when samples contain unexpected chemical constituents.
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Affiliation(s)
- Maira D Carabajal
- Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina; Instituto de Química Rosario (CONICET-UNR), 27 de Febrero 210 Bis, 2000, Rosario, Argentina
| | - Santiago A Bortolato
- Departamento de Matemática, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina; Instituto de Química Rosario (CONICET-UNR), 27 de Febrero 210 Bis, 2000, Rosario, Argentina
| | - Franco T Lisandrini
- Physikalisches Institut, University of Bonn, Nussallee 12, 53115, Bonn, Germany
| | - Alejandro C Olivieri
- Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000, Rosario, Argentina; Instituto de Química Rosario (CONICET-UNR), 27 de Febrero 210 Bis, 2000, Rosario, Argentina.
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5
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Xu W, Wei L, Cheng W, Yi X, Lin Y. Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering. FRONTIERS IN PLANT SCIENCE 2024; 15:1292365. [PMID: 38357269 PMCID: PMC10864577 DOI: 10.3389/fpls.2024.1292365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an R M S E p of 0.721, a M A P E p of 0.046, and an R P D p of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.
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Affiliation(s)
- Wei Xu
- Institute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai, China
- Institute for Six-sector Economy, Fudan University, Shanghai, China
| | - Liangzhuang Wei
- Academy for Engineering & Technology, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xiangwei Yi
- Academy for Engineering & Technology, Fudan University, Shanghai, China
| | - Yandan Lin
- Institute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai, China
- Institute for Six-sector Economy, Fudan University, Shanghai, China
- Academy for Engineering & Technology, Fudan University, Shanghai, China
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Wang Z, Zuo C, Chen M, Song J, Tu K, Lan W, Li C, Pan L. A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics. Foods 2023; 12:4435. [PMID: 38137239 PMCID: PMC10743185 DOI: 10.3390/foods12244435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/04/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.
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Affiliation(s)
- Zhenjie Wang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Changzhou Zuo
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Min Chen
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Jin Song
- College of Artificial Intelligence, Nanjing Agricultural University, No. 40 Dianjiangtai Road, Nanjing 210095, China;
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Weijie Lan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
| | - Chunyang Li
- Institute of Agro-Products Processing, Jiangsu Academy of Agricultural Sciences, No. 50 Zhongling Road, Nanjing 210014, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; (Z.W.); (C.Z.); (M.C.); (K.T.); (W.L.)
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Mokari A, Guo S, Bocklitz T. Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning. Molecules 2023; 28:6886. [PMID: 37836728 PMCID: PMC10574384 DOI: 10.3390/molecules28196886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/13/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.
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Affiliation(s)
- Azadeh Mokari
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
- Institute of Physical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Shuxia Guo
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
- Institute of Physical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany
- Institute of Computer Science, Faculty of Mathematics, Physics & Computer Science, University Bayreuth, Universitaet sstraße 30, 95447 Bayreuth, Germany
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Canova LDS, Vallese FD, Pistonesi MF, de Araújo Gomes A. An improved successive projections algorithm version to variable selection in multiple linear regression. Anal Chim Acta 2023; 1274:341560. [PMID: 37455078 DOI: 10.1016/j.aca.2023.341560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/07/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023]
Abstract
The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.
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Affiliation(s)
- Luciana Dos Santos Canova
- Instituto de Química, IQ, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 Agronomia, 91501970, Porto Alegre, RS, Brazil
| | - Federico Danilo Vallese
- Dpto. de Química, Universidad Nacional del Sur, INQUISUR, Av. Alem 1253, B8000CPB, Bahía Blanca, Buenos Aires, Argentina
| | - Marcelo Fabian Pistonesi
- Dpto. de Química, Universidad Nacional del Sur, INQUISUR, Av. Alem 1253, B8000CPB, Bahía Blanca, Buenos Aires, Argentina
| | - Adriano de Araújo Gomes
- Instituto de Química, IQ, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500 Agronomia, 91501970, Porto Alegre, RS, Brazil.
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Freitas RVDM, de Freitas DLD, de Oliveira IRD, Dos Santos Gomes C, Guerra GCB, Dantas PMS, da Silva TG, Duque G, de Lima KMG, Guerra RO. Fourier-Transform Infrared Spectroscopy as a Screening Tool for Osteosarcopenia in Community-Dwelling Older Women. J Gerontol A Biol Sci Med Sci 2023; 78:1543-1549. [PMID: 36905160 DOI: 10.1093/gerona/glad081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Indexed: 03/12/2023] Open
Abstract
Osteosarcopenia is a complex geriatric syndrome characterized by the presence of both sarcopenia and osteopenia/osteoporosis. This condition increases rates of disability, falls, fractures, mortality, and mobility impairments in older adults. The purpose of this study was to analyze the Fourier-transform infrared (FTIR) spectroscopy diagnostic power for osteosarcopenia in community-dwelling older women (n = 64; 32 osteosarcopenic and 32 non-osteosarcopenia). FTIR is a fast and reproducible technique highly sensitive to biological tissues, and a mathematical model was created using multivariate classification techniques that denoted the graphic spectra of the molecular groups. Genetic algorithm and support vector machine regression (GA-SVM) was the most feasible model, achieving 80.0% of accuracy. GA-SVM identified 15 wave numbers responsible for class differentiation, in which several amino acids (responsible for the proper activation of the mammalian target of rapamycin) and hydroxyapatite (an inorganic bone component) were observed. Imaging tests and low availability of instruments that allow the observation of osteosarcopenia involve high health costs for patients and restrictive indications. Therefore, FTIR can be used to diagnose osteosarcopenia due to its efficiency and low cost and to enable early detection in geriatric services, contributing to advances in science and technology that are potential "conventional" methods in the future.
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Affiliation(s)
| | | | | | | | | | - Paulo Moreira Silva Dantas
- Postgraduation Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil
- Postgraduation Program in Physical Education, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Tales Gomes da Silva
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gustavo Duque
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Bone, Muscle & Geroscience Group, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Kassio Michell Gomes de Lima
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ricardo Oliveira Guerra
- Postgraduation Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil
- Postgraduation Program in Physiotherapy, Federal University of Rio Grande do Norte, Natal, Brazil
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10
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Lu Z, Huang S, Zhang X, Shi Y, Yang W, Zhu L, Huang C. Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion. PLANT METHODS 2023; 19:75. [PMID: 37516875 PMCID: PMC10385904 DOI: 10.1186/s13007-023-01056-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 07/15/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND Verticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively. RESULT The results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%. CONCLUSIONS The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.
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Affiliation(s)
- Zhihao Lu
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Shihao Huang
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xiaojun Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Yuxuan Shi
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Longfu Zhu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Chenglong Huang
- College of Engineering, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518000, People's Republic of China.
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11
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Jin P, Fu Y, Niu R, Zhang Q, Zhang M, Li Z, Zhang X. Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy. Foods 2023; 12:2756. [PMID: 37509847 PMCID: PMC10379075 DOI: 10.3390/foods12142756] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/05/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky-Golay smoothing, SG; Savitzky-Golay 1 derivative, SG-1st; and Savitzky-Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R2p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R2p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.
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Affiliation(s)
- Peilin Jin
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Yifan Fu
- Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China
| | - Renzhong Niu
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Qi Zhang
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Mingyue Zhang
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Zhigang Li
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Xiaoshuan Zhang
- Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China
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12
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Ji J, He Y, Zhao K, Zhang M, Li M, Li H. Quality Information Detection of Agaricus bisporus Based on a Portable Spectrum Acquisition Device. Foods 2023; 12:2562. [PMID: 37444303 DOI: 10.3390/foods12132562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R2 values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus.
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Affiliation(s)
- Jiangtao Ji
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
- Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China
| | - Yongkang He
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Kaixuan Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
| | - Mengke Zhang
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Mengsong Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Hongzhen Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
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13
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Li L, Sheng X, Zan J, Yuan H, Zong X, Jiang Y. Monitoring the dynamic change of catechins in black tea drying by using near-infrared spectroscopy and chemometrics. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
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14
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Wang W, Man Z, Li X, Chen R, You Z, Pan T, Dai X, Xiao H, Liu F. Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress. JOURNAL OF HAZARDOUS MATERIALS 2023; 449:131010. [PMID: 36801724 DOI: 10.1016/j.jhazmat.2023.131010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (Rp=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (Rp=0.9161) for SP and CARS-ELM (Rp=0.9021) for MDA, all with Rp higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.
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Affiliation(s)
- Wei Wang
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Zun Man
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Zhengkai You
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaorong Dai
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo 315100, China
| | - Hang Xiao
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
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15
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Zuo J, Peng Y, Li Y, Zou W, Chen Y, Huo D, Chao K. Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique. Meat Sci 2023; 202:109204. [PMID: 37146500 DOI: 10.1016/j.meatsci.2023.109204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/22/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.
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Affiliation(s)
- Jiewen Zuo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Wenlong Zou
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yahui Chen
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Daoyu Huo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Kuanglin Chao
- Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD 20705, United States
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16
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Chen T, Zhang T, Tang H, Cheng X, Li H. Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning. Anal Chem 2023; 95:4819-4827. [PMID: 36857731 DOI: 10.1021/acs.analchem.3c00487] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R2P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.
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Affiliation(s)
- Tingting Chen
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an, 710127, China
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an, 710127, China
| | - Hongsheng Tang
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an, 710127, China
| | - Xuemei Cheng
- Technology and Nano Functional Materials, Institute of Photonics & Photon-Technology, Northwest University, Xi'an 710127, PR China
| | - Hua Li
- Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an, 710127, China.,College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an, 710065, China
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17
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Cao Z, Zhang S, Liu Y, Smith CJ, Sherman AM, Hwang Y, Simpson GJ. Spectral classification by generative adversarial linear discriminant analysis. Anal Chim Acta 2023; 1261:341129. [PMID: 37147049 DOI: 10.1016/j.aca.2023.341129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/20/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023]
Abstract
Generative adversarial linear discriminant analysis (GALDA) is formulated as a broadly applicable tool for increasing classification accuracy and reducing overfitting in spectrochemical analysis. Although inspired by the successes of generative adversarial neural networks (GANs) for minimizing overfitting artifacts in artificial neural networks, GALDA was built around an independent linear algebra framework distinct from those in GANs. In contrast to feature extraction and data reduction approaches for minimizing overfitting, GALDA performs data augmentation by identifying and adversarially excluding the regions in spectral space in which genuine data do not reside. Relative to non-adversarial analogs, loading plots for dimension reduction showed significant smoothing and more prominent features aligned with spectral peaks following generative adversarial optimization. Classification accuracy was evaluated for GALDA together with other commonly available supervised and unsupervised methods for dimension reduction in simulated spectra generated using an open-source Raman database (Romanian Database of Raman Spectroscopy, RDRS). Spectral analysis was then performed for microscopy measurements of microsphereroids of the blood thinner clopidogrel bisulfate and in THz Raman imaging of common constituents in aspirin tablets. From these collective results, the potential scope of use for GALDA is critically evaluated relative to alternative established spectral dimension reduction and classification methods.
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Affiliation(s)
- Ziyi Cao
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Shijie Zhang
- Takeda Pharmaceuticals International Co, 35 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Youlin Liu
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Casey J Smith
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Alex M Sherman
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Yechan Hwang
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA
| | - Garth J Simpson
- Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907, USA.
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18
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Zhang W, Lin M, He H, Wang Y, Wang J, Liu H. Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm. Molecules 2023; 28:molecules28041681. [PMID: 36838670 PMCID: PMC9966128 DOI: 10.3390/molecules28041681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Citrus peels are rich in bioactive compounds such as vitamin C and extraction of vitamin C is a good strategy for citrus peel recycling. It is essential to evaluate the levels of vitamin C in citrus peels before reuse. In this study, a near-infrared (NIR)-based method was proposed to quantify the vitamin C content of citrus peels in a rapid way. The spectra of 249 citrus peels in the 912-1667 nm range were acquired, preprocessed, and then related to measured vitamin C values using the linear partial least squares (PLS) algorithm, indicating that normalization correction (NC) was more suitable for spectral preprocessing and NC-PLS model built with full NC spectra (375 wavelengths) showed a better performance in predicting vitamin C. To accelerate the predictive process, wavelength selection was conducted, and 15 optimal wavelengths were finally selected from NC spectra using the stepwise regression (SR) method, to predict vitamin C using the multiple linear regression (MLR) algorithm. The results showed that SR-NC-MLR model had the best predictive ability with correlation coefficients (rP) of 0.949 and root mean square error (RMSEP) of 14.814 mg/100 mg in prediction set, comparable to the NC-PLS model in predicting vitamin C. External validation was implemented using 40 independent citrus peels samples to validate the suitability of the SR-NC-MLR model, obtaining a good correlation (R2 = 0.9558) between predicted and measured vitamin C contents. In conclusion, it was reasonable and feasible to achieve the rapid estimation of vitamin C in citrus peels using NIR spectra coupled with MLR algorithm.
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Affiliation(s)
- Weiqing Zhang
- Zhejiang Citrus Research Institute, Zhejiang Academy of Agricultural Sciences, Taizhou 318026, China
| | - Mei Lin
- Zhejiang Citrus Research Institute, Zhejiang Academy of Agricultural Sciences, Taizhou 318026, China
| | - Hongju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
- Correspondence:
| | - Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Jingru Wang
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hongjie Liu
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
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19
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Silva HKTDA, Barbosa TM, Santos MCD, Jales JT, de Araújo AMU, Morais CLM, de Lima LAS, Bicudo TC, Gama RA, Marinho PA, Lima KMG. Detection of terbufos in cases of intoxication by means of entomotoxicological analysis using ATR-FTIR spectroscopy combined with chemometrics. Acta Trop 2023; 238:106779. [PMID: 36442528 DOI: 10.1016/j.actatropica.2022.106779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/27/2022]
Abstract
The detection of toxic substances in larvae from carcasses in an advanced stage of decomposition may help criminal expertise in elucidating the cause of death in suspected cases of poisoning. Terbufos (Counter®) or O,O-diethyl-S-[(tert-butylsulfanyl)methyl] phosphorodithioate is an insecticide and systemic nematicide, which has very high toxicity from an acute point of view (oral LD50 in rodents ranging from 1.4 to 9.2 mg/kg) that has been marketed irregularly and indiscriminately in Brazil as a rodenticide, often being used to practice homicides. The present study aims to evaluate the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to detect traces of terbufos pesticide in fly larvae (Sarcophagidae). ATR-FTIR spectra of scavenger fly larvae from control (n = 31) and intoxicated (n = 80) groups were collected and submitted to chemometric analysis by means of multivariate classification using principal component analysis with quadratic discriminant analysis (PCA-QDA), successive projections algorithm with quadratic discriminant analysis (SPA-QDA) and genetic algorithm with quadratic discriminant analysis (GA-QDA) in order to distinguish between control and intoxicated groups. All discriminant models showed sensitivity and specificity above 90%, with the GA-QDA model showing the best performance with 98.9% sensitivity and specificity. The proposed methodology proved to be sensitive and promising for the detection of terbufos in scavenger fly larvae from intoxicated rat carcasses. In addition, the non-destructive nature of the ATR-FTIR technique may be useful in preserving the forensic evidence, meeting the precepts of the chain of custody and allowing for counter-proof.
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Affiliation(s)
- Hellyda K T de Andrade Silva
- Laboratório de Química Biológica e Quimiometria, Departamento de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Taciano M Barbosa
- Laboratório de Insetos e Vetores, Departamento de Microbiologia e Parasitologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Marfran C D Santos
- Laboratório de Química Biológica e Quimiometria, Departamento de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil; Ciência e Tecnologia do Sertão Pernambucano - Campus Floresta, Instituto Federal de Educação, Floresta 56400-000, Brasil
| | - Jessica T Jales
- Laboratório de Insetos e Vetores, Departamento de Microbiologia e Parasitologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Antonio M U de Araújo
- Escola de Ciências e Tecnologia, Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Camilo L M Morais
- Laboratório de Química Biológica e Quimiometria, Departamento de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Leomir A S de Lima
- Laboratório de Química Biológica e Quimiometria, Departamento de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Tatiana C Bicudo
- Escola de Ciências e Tecnologia, Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Renata A Gama
- Laboratório de Insetos e Vetores, Departamento de Microbiologia e Parasitologia, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil
| | - Pablo Alves Marinho
- Polícia Civil do Estado de Minas Gerais, Instituto de Criminalística, Belo Horizonte, MG, Brasil
| | - Kássio M G Lima
- Laboratório de Química Biológica e Quimiometria, Departamento de Química, Universidade Federal do Rio Grande do Norte, Natal, RN, Brasil.
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20
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Park E, Kim YS, Faqeerzada MA, Kim MS, Baek I, Cho BK. Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng ( Panax ginseng Meyer). FRONTIERS IN PLANT SCIENCE 2023; 14:1109060. [PMID: 36818876 PMCID: PMC9930644 DOI: 10.3389/fpls.2023.1109060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.
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Affiliation(s)
- Eunsoo Park
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
| | - Yun-Soo Kim
- R&D Headquarters, Korea Ginseng Corporation, Yuseong, Daejeon, Republic of Korea
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural System, College of Agricultural and Life Science, Chungnam National University, Daejeon, Republic of Korea
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21
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Chemometrics-assisted inductively coupled plasma-optical emission spectrometry method for determination of natural zinc isotopes. J Radioanal Nucl Chem 2023. [DOI: 10.1007/s10967-022-08756-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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22
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ZHOU M, LONG T, ZHAO Z, CHEN J, WU Q, WANG Y, ZOU Z. Honey quality detection based on near-infrared spectroscopy. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.98822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Man ZHOU
- Sichuan Agricultural University, China
| | - Tao LONG
- Sichuan Agricultural University, China
| | | | - Jie CHEN
- Sichuan Agricultural University, China
| | | | - Yue WANG
- Sichuan Agricultural University, China
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23
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Detection Limits of Antibiotics in Wastewater by Real-Time UV–VIS Spectrometry at Different Optical Path Length. Processes (Basel) 2022. [DOI: 10.3390/pr10122614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Real-time monitoring of antibiotics in hospital and pharmaceutical wastewater using ultraviolet–visible (UV–Vis) spectroscopy is considered a promising method. Although gas chromatography–mass spectrometry (GC–MS) and other methods can detect antibiotics with quite low limits of detection (LOD), they possess various limitations. UV–Vis spectroscopy combined with chemometric methods is a promising choice for monitoring antibiotics. In this study, two immersed in situ UV–Vis sensors were used to explore the relationship between absorption spectra and antibiotics and study the influence of the optical path length on the LOD. The LODs of sensor 2 using a 10 cm optical path is up to 300 times lower than that of sensor 1 using a 0.5 mm optical path. Moreover, multiple antibiotics in the wastewater were investigated in real-time manner. The absorption spectra of 70 groups of wastewater samples containing different concentrations of tetracycline, ofloxacin, and chloramphenicol were measured. The results indicate that the nine wavelengths selected by interval partial least squares (iPLS) after the second derivative pretreatment have better predictability for ofloxacin and the six wavelengths selected by competitive adaptive reweighted sampling (CARS) after the first derivative. The multi-fold cross-validation results indicate that the model has a good predictive ability.
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24
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A critical review of recent trends in sample classification using Laser-Induced Breakdown Spectroscopy (LIBS). Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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25
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Sun X, Deng D, Liu J, Feng S. Model development and update of portable NIRS instrument for assessment of internal quality attributes of two navel orange varieties. Front Nutr 2022; 9:976178. [PMID: 36091234 PMCID: PMC9450129 DOI: 10.3389/fnut.2022.976178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
The variation of fruit among batches influences the performance of the portable near infrared spectroscopy (NIRS) instrument and then determines the success or failure for practical application in fruit industry. Model development and update methods were investigated for determining soluble solids contents (SSC) and titrable acidity (TA) of navel orange. The pretreatment and variable selection methods were explored for building partial least square regression (PLSR) models. The best models, developed by the combination of second derivative (2D) and variable sorting for normalization (VSN), could predict SSC but not TA. The root mean square error of prediction (RMSEP), coefficient of determination for prediction (Rp2) and ratio of prediction to deviation (RPD) for SSC were 0.66 °Brix, 0.66 and 1.73. Model maintain methods of model update (MU) and slope and bias correction (SBC) achieved the best results in predicting SSC for two external validation sets with Rp2, RMSEP and RPD of 0.54, 0.83 °Brix, 1.60 and 0.52, 0.83 °Brix, 1.65, respectively. The results suggested model development and update with MU and SBC could improve the robustness of the portable NIRS instrument in predicting SSC of navel orange.
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Affiliation(s)
- Xudong Sun
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China
- Ministry of Education, Key Laboratory of Conveyance Equipment (East China Jiaotong University), Nanchang, China
- *Correspondence: Xudong Sun ; ;
| | - Di Deng
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China
| | - Jiacheng Liu
- Cultivation Laboratory, Ganzhou Citrus Research Institute, Ganzhou, China
| | - Shaoran Feng
- Beijing Sunlight Yishida Technology Co., Ltd., Beijing, China
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26
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Xue X, Chen Z, Wu H, Gao H. Identification of Guiboutia species by NIR-HSI spectroscopy. Sci Rep 2022; 12:11507. [PMID: 35798833 PMCID: PMC9262927 DOI: 10.1038/s41598-022-15719-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/28/2022] [Indexed: 11/09/2022] Open
Abstract
Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate, G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.
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Affiliation(s)
- Xiaoming Xue
- Nanjing Forest Police College, Nanjing, Jiangsu, China.
| | - Zhenan Chen
- Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Haoqi Wu
- Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Handong Gao
- Nanjing Forestry University, Nanjing, Jiangsu, China
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27
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Li XK, Li ZY, Yang ZY, Qiu D, Li JM, Li BQ. A hybrid variable selection and modeling strategy for the determination of target compounds in different spectral datasets. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 275:121123. [PMID: 35313172 DOI: 10.1016/j.saa.2022.121123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/15/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
In this paper, a hybrid technique is proposed to establish quantitative models for the determination of target compounds in different spectral datasets. The proposed hybrid method is the hybridization of interval partial least squares (iPLS) method with gradient descent (GD) algorithm. Here, the novelty of the proposed method is that the iPLS method is applied to variable selection and the GD algorithm is employed to establish quantitative models based on the selected optimal variables. In the application of the hybrid iPLS-GD method, the factors, i.e., the number of the interval for the iPLS method and the learning rate, the number of iterations for the GD method, that affect the quantitative accuracy of the method are optimized and determined. Then three spectral datasets, including the near-infrared spectroscopy (NIR) dataset, nuclear magnetic resonance (1H NMR) dataset and excitation-emission matrix fluorescence (EEM) dataset, are used to test and verify the performance of the iPLS-GD method. We compare the hybrid iPLS-GD method with the PLS and iPLS methods from the aspects of modeling ability and predictive ability. The results demonstrated that the iPLS-GD method can be used as an effective and promising tool for the determination of target compounds in complex samples in practice.
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Affiliation(s)
- Xin Kang Li
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, PR China
| | - Ze Ying Li
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, PR China
| | - Zhuo Ying Yang
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, PR China
| | - Dian Qiu
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, PR China
| | - Jia Min Li
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, PR China
| | - Bao Qiong Li
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, PR China.
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28
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Zhang J, Wang Z, Qu M, Cheng F. Research on physicochemical properties, microscopic characterization and detection of different freezing-damaged corn seeds. Food Chem X 2022; 14:100338. [PMID: 35634222 PMCID: PMC9133772 DOI: 10.1016/j.fochx.2022.100338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 04/19/2022] [Accepted: 05/18/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
| | | | | | - Fang Cheng
- Corresponding author at: Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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29
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Xie S, Ding F, Chen S, Wang X, Li Y, Ma K. Prediction of soil organic matter content based on characteristic band selection method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:120949. [PMID: 35183857 DOI: 10.1016/j.saa.2022.120949] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/19/2022] [Accepted: 01/22/2022] [Indexed: 06/14/2023]
Abstract
Soil organic matter (SOM) is a key index for evaluating soil fertility and plays a vital role in the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy is an effective method for determining soil properties and is often used to predict SOM content. However, the key prerequisite for effective prediction of SOM content by Vis-NIR spectroscopy lies in the selection of appropriate preprocessing methods and effective data mining techniques. Therefore, in this study, six commonly used spectral preprocessing methods and effective characteristic band selection methods were selected to process the spectrum to predict SOM content. This study aims to determine a stable spectral preprocessing method and explore the predictive performance of different characteristic band selection methods. The results showed that: (i) The first derivative (FD) is the most stable spectral preprocessing method that can effectively improve the spectral characteristic information and the prediction effect of the model. (ii) The prediction effect of SOM content based on characteristic band selection methods is generally better than the full-spectra data. (iii) The precision of FD preprocessing spectrum combined with successive projections algorithm (SPA) in the partial least square regression prediction model of SOM content is the best. (iv) Although the prediction effect of the model based on the optimal band combination algorithm is slightly lower than that of SPA, it shows stable prediction performance, which provides a feasible method for SOM content prediction. In summary, the characteristic band selection method combined with FD can significantly improve the prediction accuracy of SOM content.
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Affiliation(s)
- Shugang Xie
- College of Resources and Environment, Shandong Agricultural University, Taian 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian 271018, China
| | - Fangjun Ding
- Shandong Agricultural University Fertilizer Technology Co. Ltd, Taian 271600, China
| | - Shigeng Chen
- Shandong Agricultural University Fertilizer Technology Co. Ltd, Taian 271600, China
| | - Xi Wang
- College of Resources and Environment, Shandong Agricultural University, Taian 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian 271018, China
| | - Yuhuan Li
- College of Resources and Environment, Shandong Agricultural University, Taian 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Taian 271018, China.
| | - Ke Ma
- Shandong Agricultural University Fertilizer Technology Co. Ltd, Taian 271600, China.
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30
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Zhang Z, Liu H, Chen D, Zhang J, Li H, Shen M, Pu Y, Zhang Z, Zhao J, Hu J. SMOTE-based method for balanced spectral nondestructive testing of moldy apple core. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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31
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Plant Viral Disease Detection: From Molecular Diagnosis to Optical Sensing Technology—A Multidisciplinary Review. REMOTE SENSING 2022. [DOI: 10.3390/rs14071542] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.
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32
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Chen Z, Ren S, Qin R, Nie P. Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging. Molecules 2022; 27:molecules27062017. [PMID: 35335381 PMCID: PMC8950398 DOI: 10.3390/molecules27062017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 12/01/2022] Open
Abstract
Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH4-N), sodium nitrate (nitrate nitrogen, NO3-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH4-N, R2p = 0.92, RMSEP = 0.77% and RPD = 3.63; for NO3-N, R2p = 0.92, RMSEP = 0.74% and RPD = 4.17; for urea-N, R2p = 0.96, RMSEP = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.
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Affiliation(s)
- Zhuoyi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.C.); (S.R.); (R.Q.)
- Key Laboratory of Sensors Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China
| | - Shijie Ren
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.C.); (S.R.); (R.Q.)
- Key Laboratory of Sensors Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China
| | - Ruimiao Qin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.C.); (S.R.); (R.Q.)
- Key Laboratory of Sensors Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China
| | - Pengcheng Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (Z.C.); (S.R.); (R.Q.)
- Key Laboratory of Sensors Sensing, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-0571-8898-2456
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33
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Malvandi A, Feng H, Kamruzzaman M. Application of NIR spectroscopy and multivariate analysis for Non-destructive evaluation of apple moisture content during ultrasonic drying. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 269:120733. [PMID: 34920303 DOI: 10.1016/j.saa.2021.120733] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/14/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R2p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.
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Affiliation(s)
- Amir Malvandi
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA
| | - Hao Feng
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA; Department of Food Science and Human Nutrition, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL 61801, USA.
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34
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Liu Y, Long Y, Liu H, Lan Y, Long T, Kuang R, Wang Y, Zhao J. Polysaccharide prediction in Ganoderma lucidum fruiting body by hyperspectral imaging. Food Chem X 2022; 13:100199. [PMID: 35498961 PMCID: PMC9039882 DOI: 10.1016/j.fochx.2021.100199] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 11/03/2022] Open
Abstract
Predicting the concentration of polysaccharides by hyperspectral images of the Ganoderma lucidum cap is feasible. Establishing calibration models using visible and near-infrared spectroscopy respectively to find out the characteristic spectrum. Exploring the influence of different tissue parts on prediction through ROI selection. Prediction of polysaccharide concentration in the full life cycle of the Ganoderma lucidum fruiting body.
Ganoderma lucidum is a traditional Chinese healthy food with many kinds of nutritious activities, and polysaccharide is one of its main active components. Ganoderma lucidum polysaccharide plays a vital role in improving human immunity and anti-oxidation. At present, the methods of detecting polysaccharide content of Ganoderma lucidum are destructive, and the steps are complicated and time-consuming. This study aims to explore the possibility of using hyperspectral imaging (HSI) to predict polysaccharide content in a nondestructive way during the growth of Ganoderma lucidum. The partial least square regression (PLSR) model shows good performance for Ganoderma lucidum (Rp2 = 0.924, RPDp = 3.622) with pretreatment method of Savitzky-Golay (SG) and standard normal variate (SNV), and feature selection method of successive projections algorithm (SPA). This study indicates that HSI can quickly and nondestructive detect the polysaccharide content of Ganoderma lucidum, provide guidance for the cultivation industry and improve the economic benefits of Ganoderma lucidum.
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35
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Shan P, Li Z, Wang Q, He Z, Wang S, Zhao Y, Wu Z, Peng S. Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process. Anal Chim Acta 2021; 1188:339205. [PMID: 34794558 DOI: 10.1016/j.aca.2021.339205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 12/01/2022]
Abstract
When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.
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Affiliation(s)
- Peng Shan
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China.
| | - Zhigang Li
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Qiaoyun Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Zhonghai He
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Shuyu Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Yuhui Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Zhui Wu
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China
| | - Silong Peng
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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36
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Cao K, Wei W, Xing S, Ai X, Zhao Z, Zhang C. Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.16081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Kai Cao
- College of Quality and Technology Supervision Hebei University Baoding China
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Wensong Wei
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Shujuan Xing
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Xin Ai
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
| | - Zhilei Zhao
- College of Quality and Technology Supervision Hebei University Baoding China
| | - Chunjiang Zhang
- Institute of Food Science and Technology Chinese Academy of Agricultural Sciences (CAAS) Beijing China
- Comprehensive Key Laboratory of Agro‐Products Processing Ministry of Agriculture and Rural Affairs Beijing China
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37
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Zhu J, Fan X, Han L, Zhang C, Wang J, Pan L, Tu K, Peng J, Zhang M. Quantitative analysis of caprolactam in sauce-based food using infrared spectroscopy combined with data fusion strategies. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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38
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Near-infrared spectroscopy of blood plasma with chemometrics towards HIV discrimination during pregnancy. Sci Rep 2021; 11:22609. [PMID: 34799631 PMCID: PMC8604940 DOI: 10.1038/s41598-021-02105-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/02/2021] [Indexed: 11/21/2022] Open
Abstract
Prevention of mother-to-child transmission programs have been one of the hallmarks of success in the fight against HIV/AIDS. In Brazil, access to antiretroviral therapy (ART) during pregnancy has increased, leading to a reduction in new infections among children. Currently, lifelong ART is available to all pregnant, however yet challenges remain in eliminating mother-to-child transmission. In this paper, we focus on the role of near-infrared (NIR) spectroscopy to analyse blood plasma samples of pregnant women with HIV infection to differentiate pregnant women without HIV infection. Seventy-seven samples (39 HIV-infected patient and 38 healthy control samples) were analysed. Multivariate classification of resultant NIR spectra facilitated diagnostic segregation of both sample categories in a fast and non-destructive fashion, generating good accuracy, sensitivity and specificity. This method is simple and low-cost, and can be easily adapted to point-of-care screening, which can be essential to monitor pregnancy risks in remote locations or in the developing world. Therefore, it opens a new perspective to investigate vertical transmission (VT). The approach described here, can be useful for the identification and exploration of VT under various pathophysiological conditions of maternal HIV. These findings demonstrate, for the first time, the potential of NIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost HIV detection.
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39
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Sem V. Interpretability of selected variables and performance comparison of variable selection methods in a polyethylene and polypropylene NIR classification task. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119850. [PMID: 33957449 DOI: 10.1016/j.saa.2021.119850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/08/2021] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
Near infrared (NIR) spectra are collected as a high amount of absorption values which usually greatly exceeds the sample size. Variable selection methods are employed in NIR spectroscopy to avoid "curse of dimensionality" related issues. In this paper, we examined the interpretability of selected variables, that is, how much selected wavelengths are related to the chemical structure of the materials studied, and if the relation is important for classification performance. Additionally, we examined classification performance in dependence on the number of selected variables. For this purpose, relative standard deviation (RSD), successive projection algorithm (SPA), stepwise decorrelation of variables (SELECT), genetic algorithm (GA), principal component analysis (PCA), and random (RANDOM) variable selection were applied in two-class classification modelling using linear discriminant analysis (LDA) or a support vector machine (SVM). Different pre-treatments and sample sizes were considered. Variable selection improved classification performance and variables selected by a majority of the methods considered were conveniently related to chemical structure. Interpretability and performance increase/decrease depend greatly on the number of selected variables, however. Since selected variables reveal great chemical interpretability, some variable selection methods could be employed to determine material characteristic absorption bands. SELECT and SPA displayed the best properties among the methods considered. To avoid faulty results, optimization of the number of selected variables should become the crucial stage in the variable selection process.
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Affiliation(s)
- Vilma Sem
- Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia.
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Park E, Kim YS, Omari MK, Suh HK, Faqeerzada MA, Kim MS, Baek I, Cho BK. High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng ( Panax ginseng Meyer) Using a Hyperspectral Reflectance Image. SENSORS 2021; 21:s21165634. [PMID: 34451076 PMCID: PMC8402434 DOI: 10.3390/s21165634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/15/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022]
Abstract
Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.
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Affiliation(s)
- Eunsoo Park
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Yun-Soo Kim
- R&D Headquarters, Korea Ginseng Corporation, Daejeon 34128, Korea;
| | - Mohammad Kamran Omari
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Hyun-Kwon Suh
- Department of Life Resources Industry, Dong-A University, Busan 49315, Korea;
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
- Department of Smart Agriculture System, Chungnam National University, Daejeon 34134, Korea
- Correspondence:
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41
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He W, He H, Wang F, Wang S, Li R, Chang J, Li C. Rapid and Uninvasive Characterization of Bananas by Hyperspectral Imaging with Extreme Gradient Boosting (XGBoost). ANAL LETT 2021. [DOI: 10.1080/00032719.2021.1952214] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Weiwen He
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Fanglin Wang
- Institute of Forensic Science, Ministry of Public Security, Beijing, China
| | - Shuyue Wang
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Runkang Li
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Jing Chang
- Institute of Forensic Science, Ministry of Public Security, Beijing, China
| | - Chunyu Li
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
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42
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Jales JT, Barbosa TM, de Medeiros JR, de Lima LAS, de Lima KMG, Gama RA. Infrared spectroscopy and forensic entomology: Can this union work? A literature review. J Forensic Sci 2021; 66:2080-2091. [PMID: 34291458 DOI: 10.1111/1556-4029.14800] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/11/2021] [Accepted: 06/01/2021] [Indexed: 12/01/2022]
Abstract
For more than two decades, infrared spectroscopy techniques combined with multivariate analysis have been efficiently applied in several entomological fields, such as Taxonomy and Toxicology. However, little is known about its use and applicability in Forensic entomology (FE) field, with vibrational techniques such as Near-infrared spectroscopy (NIRS) and Medium-infrared spectroscopy (MIRS) underutilized in forensic sciences. Thus, this work describes the potential of NIRS, MIRS, and other spectroscopic methodologies, for entomological analysis in FE, as well as discusses its future uses for criminal or civil investigations. After a thorough research on scientific journals database, a total of 33 publications were found in scientific journals, with direct or indirect application to FE, including experimental applications of NIRS and MIRS in taxonomic discrimination of species, larval age prediction, detection of toxic substances in insects from environments or crime scenes, and detection of internal or external infestations by live or dead insects in stored products. Besides, NIRS and MIRS combined with multivariate analysis were efficient, inexpensive, fast, and non-destructive analytical tools. However, more than 51% of the spectroscopic publications are concentrated in the stored products field, and so we discuss the need for expansion and more direct application in other FE areas. We hope the number of articles continues to increase, and as NIRS and MIRS technology progress, they advance in forensic research and routine use.
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Affiliation(s)
- Jessica T Jales
- Laboratory of Insect and Vectors, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil.,Biochemistry and Molecular Biology post-graduation program, Department of Biochemistry, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Taciano M Barbosa
- Laboratory of Insect and Vectors, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil.,Parasitic biology post-graduation program, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Jucélia R de Medeiros
- Laboratory of Insect and Vectors, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil.,Parasitic biology post-graduation program, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Leomir A S de Lima
- Laboratory of Biological Chemistry and Chemometric, Department of Chemistry, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Kássio M G de Lima
- Laboratory of Biological Chemistry and Chemometric, Department of Chemistry, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Renata A Gama
- Laboratory of Insect and Vectors, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil.,Parasitic biology post-graduation program, Department of Microbiology and Parasitology, Federal University of Rio Grande do Norte, Natal, RN, Brazil
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43
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Zhang Y, Chen Y, Wu Y, Cui C. Accurate and nondestructive detection of apple brix and acidity based on visible and near-infrared spectroscopy. APPLIED OPTICS 2021; 60:4021-4028. [PMID: 33983342 DOI: 10.1364/ao.423994] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/06/2021] [Indexed: 06/12/2023]
Abstract
Rapid, nondestructive and accurate detection of internal qualities of the apple is an important research interest. In this study, the brix, acidity and brix/acidity ratio of the apple were rapidly detected by visible and near-infrared spectroscopy (VIS-NIRS). By scanning spectra and measuring the reference values of brix and acidity of apple samples, the relationship models between the spectra and brix, acidity, brix/acidity ratio were, respectively, established. Sample division, characteristic wavelength optimization, and modeling methods were compared systematically, and the optimal prediction model of each quality index was determined. The experimental results show that the competitive adaptive reweighted sampling method can effectively select characteristic wavelengths, which not only improves the prediction speed, but also greatly enhances the prediction accuracy. The established partial least squares models based on these selected characteristic wavelengths all have high accuracy and robustness for the three quality indices. The determination coefficients of the models are 0.9899, 0.9615, 0.9535, and the relative percent deviation are 9.9269, 5.0987, 4.6374, respectively. All this work proves that VIS-NIRS can be used for rapid and nondestructive detection of the internal qualities of an apple.
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44
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Liang W, Zhu Z, Yang B, Zhu X, Guo W. Detecting melamine‐adulterated raw milk by using near‐infrared transmission spectroscopy. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Wenting Liang
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Zhuozhuo Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Biao Yang
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Xinhua Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs Yangling Shaanxi China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling Shaanxi China
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45
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Sun H, Feng M, Xiao L, Yang W, Ding G, Wang C, Jia X, Wu G, Zhang S. Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes. FRONTIERS IN PLANT SCIENCE 2021; 12:631573. [PMID: 33719305 PMCID: PMC7952645 DOI: 10.3389/fpls.2021.631573] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/19/2021] [Indexed: 06/01/2023]
Abstract
Real-time, nondestructive, and accurate estimation of plant water status is important to the precision irrigation of winter wheat. The objective of this study was to develop a method to estimate plant water content (PWC) by using canopy spectral proximal sensing data. Two experiments under different water stresses were conducted in 2014-2015 and 2015-2016. The PWC and canopy reflectance of winter wheat were collected at different growth stages (the jointing, booting, heading, flowering, and filling stages in 2015 and the jointing, booting, flowering, and filling stages in 2016). The performance of different spectral transformation approaches was further compared. Based on the optimal pretreatment, partial least squares regression (PLSR) and four combination methods [i.e., PLSR-stepwise regression (SR), PLSR-successive projections algorithm (SPA), PLSR-random frog (RF), and PLSR-uninformative variables elimination (UVE)] were used to extract the sensitive bands of PWC. The results showed that all transformed spectra were closely correlated to PWC. The PLSR models based on the first derivative transformation method exhibited the best performance (coefficient of determination in calibration, R 2 C = 0.96; root mean square error in calibration, RMSEC = 20.49%; ratio of performance to interquartile distance in calibration, RPIQC = 9.19; and coefficient of determination in validation, R 2 V = 0.86; root mean square error in validation, RMSEV = 46.27%; ratio of performance to interquartile distance in validation, RPIQV = 4.34). Among the combination models, the PLSR model established with the sensitive bands from PLSR-RF demonstrated a good performance for calibration and validation (R 2 C = 0.99, RMSEC = 11.53%, and RPIQC = 16.34; and R 2 V = 0.84, RMSEV = 44.40%, and RPIQV = 4.52, respectively). This study provides a theoretical basis and a reference for estimating PWC of winter wheat by using canopy spectral proximal sensing data.
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Affiliation(s)
- Hui Sun
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
- College of Resource and Environment, Shanxi Agricultural University, Taigu, China
| | - Meichen Feng
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
| | - Lujie Xiao
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
| | - Wude Yang
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
| | - Guangwei Ding
- Department of Chemistry, Northern State University, Aberdeen, SD, United States
| | - Chao Wang
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
| | - Xueqin Jia
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
| | - Gaihong Wu
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
| | - Song Zhang
- Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu, China
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46
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A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition. Molecules 2021; 26:molecules26030749. [PMID: 33535494 PMCID: PMC7867108 DOI: 10.3390/molecules26030749] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 11/23/2022] Open
Abstract
Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.
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47
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Shu M, Shen M, Zuo J, Yin P, Wang M, Xie Z, Tang J, Wang R, Li B, Yang X, Ma Y. The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9890745. [PMID: 33889850 PMCID: PMC8054988 DOI: 10.34133/2021/9890745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/19/2021] [Indexed: 05/19/2023]
Abstract
Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.
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Affiliation(s)
- Meiyan Shu
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Mengyuan Shen
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jinyu Zuo
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Pengfei Yin
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
| | - Min Wang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
| | - Ziwen Xie
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jihua Tang
- College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Ruili Wang
- Agricultural Artificial Intelligence and Crop Phenotype Engineering Research Center, Inner Mongolia Institute of Biotechnology, Huhhot 010070, China
| | - Baoguo Li
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xiaohong Yang
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100193, China
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48
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ATR-FTIR spectroscopy in blood plasma combined with multivariate analysis to detect HIV infection in pregnant women. Sci Rep 2020; 10:20156. [PMID: 33214678 PMCID: PMC7677535 DOI: 10.1038/s41598-020-77378-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 11/04/2020] [Indexed: 11/09/2022] Open
Abstract
The primary concern for HIV-infected pregnant women is the vertical transmission that can occur during pregnancy, in the intrauterine period, during labour or even breastfeeding. The risk of vertical transmission can be reduced by early diagnosis. Therefore, it is necessary to develop new methods to detect this virus in a quick and low-cost fashion, as colorimetric assays for HIV detection tend to be laborious and costly. Herein, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with multivariate analysis was employed to distinguish HIV-infected patients from healthy uninfected controls in a total of 120 blood plasma samples. The best sensitivity (83%) and specificity (92%) values were obtained using the genetic algorithm with linear discriminant analysis (GA-LDA). These good classification results in addition to the potential for high analytical frequency, the low cost and reagent-free nature of this method demonstrate its potential as an alternative tool for HIV screening during pregnancy.
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49
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Bernardes-Oliveira E, de Freitas DLD, de Morais CDLM, Cornetta MDCDM, Camargo JDDAS, de Lima KMG, Crispim JCDO. Spectrochemical differentiation in gestational diabetes mellitus based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and multivariate analysis. Sci Rep 2020; 10:19259. [PMID: 33159100 PMCID: PMC7648639 DOI: 10.1038/s41598-020-75539-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 09/30/2020] [Indexed: 11/09/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is a hyperglycaemic imbalance first recognized during pregnancy, and affects up to 22% of pregnancies worldwide, bringing negative maternal–fetal consequences in the short- and long-term. In order to better characterize GDM in pregnant women, 100 blood plasma samples (50 GDM and 50 healthy pregnant control group) were submitted Attenuated Total Reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, using chemometric approaches, including feature selection algorithms associated with discriminant analysis, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM), analyzed in the biofingerprint region between 1800 and 900 cm−1 followed by Savitzky–Golay smoothing, baseline correction and normalization to Amide-I band (~ 1650 cm−1). An initial exploratory analysis of the data by Principal Component Analysis (PCA) showed a separation tendency between the two groups, which were then classified by supervised algorithms. Overall, the results obtained by Genetic Algorithm Linear Discriminant Analysis (GA-LDA) were the most satisfactory, with an accuracy, sensitivity and specificity of 100%. The spectral features responsible for group differentiation were attributed mainly to the lipid/protein regions (1462–1747 cm−1). These findings demonstrate, for the first time, the potential of ATR-FTIR spectroscopy combined with multivariate analysis as a screening tool for fast and low-cost GDM detection.
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Affiliation(s)
- Emanuelly Bernardes-Oliveira
- Post-Graduate Program in Technological Development and Innovation in Medicines, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil.
| | - Daniel Lucas Dantas de Freitas
- Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil
| | - Camilo de Lelis Medeiros de Morais
- Lancashire Teaching Hospitals NHS Trust, Royal Preston Hospital, Fulwood, Preston, PR2 9HT, UK.,School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, PR1 2HE, UK
| | | | | | - Kassio Michell Gomes de Lima
- Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil
| | - Janaina Cristiana de Oliveira Crispim
- Post-Graduate Program in Technological Development and Innovation in Medicines, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil. .,Januario Cicco Maternity School, Federal University of Rio Grande do Norte, Natal, RN, 59072-970, Brazil.
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50
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Zhang B, Gao S, Jia F, Liu X, Li X. Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13553] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Binhui Zhang
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
| | - Song Gao
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
| | - Fei Jia
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
| | - Xue Liu
- College of Information and Electrical Engineering China Agricultural University Beijing China
| | - Xingmin Li
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
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