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Giussani B, Gorla G, Riu J. Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Crit Rev Anal Chem 2024; 54:11-43. [PMID: 35286178 DOI: 10.1080/10408347.2022.2047607] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.
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Affiliation(s)
- Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Giulia Gorla
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, Como, Italy
| | - Jordi Riu
- Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Tarragona, Spain
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2
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Chen Y, Guo M, Chen K, Jiang X, Ding Z, Zhang H, Lu M, Qi D, Dong C. Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion. Talanta 2024; 273:125892. [PMID: 38493609 DOI: 10.1016/j.talanta.2024.125892] [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: 12/30/2023] [Revised: 02/16/2024] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.
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Affiliation(s)
- Yong Chen
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China
| | - Mengqi Guo
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Kai Chen
- Shangrao Normal University, The Innovation Institute of Agricultural Technology, College of Life Science, Shangrao, 334001, China
| | - Xinfeng Jiang
- Jiangxi Institute of Economic Crops, Nanchang, 330046, China
| | - Zezhong Ding
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Haowen Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Min Lu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Dandan Qi
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
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3
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Liu Y, Pan K, Liu Z, Dai Y, Duan X, Wang M, Shen Q. Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:3362. [PMID: 38894153 PMCID: PMC11174505 DOI: 10.3390/s24113362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/14/2024] [Accepted: 05/18/2024] [Indexed: 06/21/2024]
Abstract
As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.
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Affiliation(s)
| | | | | | | | | | | | - Qiang Shen
- Tea Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550025, China; (Y.L.); (K.P.); (Z.L.); (Y.D.); (X.D.); (M.W.)
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4
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Zhang J, Wu X, He C, Wu B, Zhang S, Sun J. Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties. Foods 2024; 13:1439. [PMID: 38790739 PMCID: PMC11119828 DOI: 10.3390/foods13101439] [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: 04/12/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.
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Affiliation(s)
- Jiawei Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shuyu Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
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Ke Q, Yin L, Jayan H, El-Seedi HR, Zou X, Guo Z. Ag-coated tetrapod gold nanostars (Au@AgNSs) for acetamiprid determination in tea using SERS combined with microfluidics. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:2721-2731. [PMID: 38629244 DOI: 10.1039/d4ay00297k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
Acetamiprid is an organic and highly toxic compound. Despite being widely used as a pesticide agent on a large scale, acetamiprid poses numerous health risks to living organisms, particularly humans. Herein, a strategy for the detection of acetamiprid in tea employing surface-enhanced Raman scattering (SERS) technology incorporated with a microfluidic chip was developed. Significantly, a seed-mediated growth approach was utilized to engineer Ag-coated tetrapod gold nanostars (core-shell Au@AgNSs) with four sharp tips. The synthesized Au@AgNSs showed an enhancement factor of 7.2 × 106. Solid works was used to figure out the two-channel microfluidic chip featuring four circular split hybrid structures, and COMSOL (Software for Multiphysics Simulation) was utilized to model the fusion effect between the substrate (Au@AgNSs) and the sample (acetamiprid). For the first time, the core-shell Au@AgNSs and acetamiprid were fused in the microfluidic channel to facilitate the detection of acetamiprid using SERS. The outcomes pointed out that the standard curve correlation coefficient between SERS intensity (876 cm-1) and the concentration of acetamiprid in tea specimens was calculated as 0.991, while the limit of detection (LOD) was 0.048 ng mL-1, which is well below the minimum limit set by the European Union (10 ng mL-1). Thus, the developed technique combining SERS and microfluidics demonstrated high potential for the rapid and efficient detection of acetamiprid in tea.
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Affiliation(s)
- Qian Ke
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Limei Yin
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
- International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
| | - Heera Jayan
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Hesham R El-Seedi
- Pharmacognosy Group, Department of Pharmaceutical Biosciences, BMC, Uppsala University, Box 591, SE 751 24 Uppsala, Sweden
- International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Xiaobo Zou
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
- International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Zhiming Guo
- China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
- International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing, Jiangsu University, Zhenjiang 212013, China
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6
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Yang Z, Zhu A, Adade SYSS, Ali S, Chen Q, Wei J, Chen X, Jiao T, Chen Q. Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish. Food Chem 2024; 438:138026. [PMID: 37983993 DOI: 10.1016/j.foodchem.2023.138026] [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: 05/19/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/22/2023]
Abstract
The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.
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Affiliation(s)
- Zhiwei Yang
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Afang Zhu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Shujat Ali
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, PR China
| | - Qingmin Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Jie Wei
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Xiaomei Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Tianhui Jiao
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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7
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Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
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Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
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8
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Ren Z, Hou Z, Deng G, Huang L, Liu N, Ning J, Wang Y. Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea. Food Chem 2023; 427:136673. [PMID: 37364316 DOI: 10.1016/j.foodchem.2023.136673] [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: 12/28/2022] [Revised: 05/29/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023]
Abstract
Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.
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Affiliation(s)
- Zhengyu Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Zhiwei Hou
- College of Tea Science and Tea Culture, Zhejiang A&F University, China
| | - Guojian Deng
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Lunfang Huang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Nanfeng Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China.
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China; Key Laboratory of Tea Biology and Tea Processing of Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, China; International Joint Research Laboratory of Tea Chemistry and Health Effects of Ministry of Education, Anhui Agricultural University, China.
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Huang J, Wang P, Wu Y, Zeng L, Ji X, Zhang X, Wu M, Tong H, Yang Y. Rapid determination of triglyceride and glucose levels in Drosophila melanogaster induced by high-sugar or high-fat diets based on near-infrared spectroscopy. Heliyon 2023; 9:e17389. [PMID: 37426790 PMCID: PMC10329124 DOI: 10.1016/j.heliyon.2023.e17389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Abstract
Triglyceride and glucose levels are important indicators for determining metabolic syndrome, one of the leading public-health burdens worldwide. Drosophila melanogaster is an ideal model for investigating metabolic diseases because it has 70% homology to human genes and its regulatory mechanism of energy metabolism homeostasis is highly similar to that of mammals. However, traditional analytical methods of triglyceride and glucose are time-consuming, laborious, and costly. In this study, a simple, practical, and reliable near-infrared (NIR) spectroscopic analysis method was developed for the rapid determination of glucose and triglyceride levels in an in vivo model of metabolic disorders using Drosophila induced by high-sugar or high-fat diets. The partial least squares (PLS) model was constructed and optimized using different spectral regions and spectral pretreatment methods. The overall results had satisfactory prediction performance. For Drosophila induced by high-sugar diets, the correlation coefficient (RP) and root mean square error of prediction (RMSEP) were 0.919 and 0.228 mmoL gprot-1 for triglyceride and 0.913 and 0.143 mmoL gprot-1 for glucose respectively; for Drosophila induced by high-fat diets, the RP and RMSEP were 0.871 and 0.097 mmoL gprot-1 for triglyceride and 0.853 and 0.154 mmoL gprot-1 for glucose, respectively. This study demonstrated the potential of using NIR spectroscopy combined with PLS in the determination of triglyceride and glucose levels in Drosophila, providing a rapid and effective method for monitoring metabolite levels during disease development and a possibility for evaluating metabolic diseases in humans in clinical practice.
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Affiliation(s)
- Jiamin Huang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Pengwei Wang
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China
| | - Yu Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Li Zeng
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Xiaoliang Ji
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China
| | - Xu Zhang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Mingjiang Wu
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Haibin Tong
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
| | - Yue Yang
- Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China
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10
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Xu Y, Yang M, Yang T, Yang W, Wang Y, Zhang J. Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum. FRONTIERS IN PLANT SCIENCE 2023; 14:1140691. [PMID: 37223798 PMCID: PMC10200983 DOI: 10.3389/fpls.2023.1140691] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/11/2023] [Indexed: 05/25/2023]
Abstract
Introduction Polygonatum kingianum is a traditional medicinal plant, and processing has significantly impacts its quality. Methods Therefore, untargeted gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near-infrared spectroscopy (FT-NIR) were used to analyze the 14 processing methods commonly used in the Chinese market.It is dedicated to analyzing the causes of major volatile metabolite changes and identifying signature volatile components for each processing method. Results The untargeted GC-MS technique identified a total of 333 metabolites. The relative content accounted for sugars (43%), acids (20%), amino acids (18%), nucleotides (6%), and esters (3%). The multiple steaming and roasting samples contained more sugars, nucleotides, esters and flavonoids but fewer amino acids. The sugars are predominantly monosaccharides or small molecular sugars, mainly due to polysaccharides depolymerization. The heat treatment reduces the amino acid content significantly, and the multiple steaming and roasting methods are not conducive to accumulating amino acids. The multiple steaming and roasting samples showed significant differences, as seen from principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on GC-MS and FT-NIR. The partial least squares discriminant analysis (PLS-DA) based on FT-NIR can achieve 96.43% identification rate for the processed samples. Discussion This study can provide some references and options for consumers, producers, and researchers.
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Affiliation(s)
- Yulin Xu
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- School of Agriculture, Yunnan University, Kunming, China
| | - Meiquan Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Tianmei Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Weize Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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11
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Zhang Y, Yuan W, Ren Z, Ning J, Wang Y. Indicator displacement assay for freshness monitoring of green tea during storage. Food Res Int 2023; 167:112668. [PMID: 37087209 DOI: 10.1016/j.foodres.2023.112668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/16/2023] [Accepted: 03/05/2023] [Indexed: 03/30/2023]
Abstract
Aging of green tea leads to reductions in its flavor and health value, yet in situ testing methods for green tea freshness are lacking. A novel sensitive indicator displacement assay (IDA) sensor was constructed and applied for monitoring of green tea freshness during storage. Low-cost pH dyes and metal ions were used as indicators and receptors, respectively, for the targeted detection of catechins in tea samples. The feasibility of the IDA reaction was verified using images and UV-vis spectroscopy, respectively. IDA combined with supervised algorithms achieved accurate identification of green tea freshness with an accuracy of 86.67%, and acceptable accuracies in the prediction of catechin monomers and total catechins with ratio of prediction to deviation values over 1.5. Thus, the developed IDA sensor is capable of qualitative and quantitative monitoring of the green tea freshness during storage, providing a new option for quality evaluation and control of green teas.
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12
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Chen Y, Wu H, Liu Y, Wang Y, Lu C, Li T, Wei Y, Ning J. Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3093-3101. [PMID: 36418909 DOI: 10.1002/jsfa.12350] [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: 08/09/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Yuyu Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Huiting Wu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Chengye Lu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yuming Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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13
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He R, Ma TT, Gong MX, Xie KL, Wang ZM, Li J. The correlation between pharmacological activity and contents of eight constituents of Glycyrrhiza uralensis Fisch. Heliyon 2023; 9:e14570. [PMID: 36967897 PMCID: PMC10036654 DOI: 10.1016/j.heliyon.2023.e14570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 03/01/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Licorice (Glycyrrhiza uralensis Fisch. (GUF), Leguminosae) has been extensively applied in traditional Chinese medicine (TCM) to treat diseases, exactly, in almost half of Chinese herbal prescription. However, the relationship between chemical contents and efficacy has not been established, which could evaluate GUF quality. To create a simple and effective quality-evaluation method, 33 batches of GUF from different habitats in China were collected. The correlation between eight constituents (liquiritin, isoliquiritin, liquiritigenin, isoliquiritigenin, glycyrrhizic acid, licochalcone A, glabridin and glycyrrhetinic acid) and pharmacological activities (anti-inflammatory, antioxidant and immunoregulatory) was analyzed per the partial least squares regression method. Results showed that eight constituents correlated significantly with the pharmacological activity. The correlation equation modes between pharmacological activity and contents of eight constituents were constructed and verified to be reliable. In GUF extract, the main constituents liquiritin, isoliquiritin and glycyrrhizic acid exhibited positive influence on anti-inflammatory and antioxidant effect with different potent, while the metabolites liquiritigenin and isoliquiritigenin exhibited positive effect on the immunoregulatory activity and glycyrrhetinic acid exhibited positive effect on all the tested activities. Thus, our chemical-efficacy correlation method is reliable and feasible to predict the pharmacological activity based on its eight constituents. It could be powerful in quality control of GUF and provides a useful way for quality evaluation of other medicinal herbs.
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Affiliation(s)
- Rui He
- Beijing Key Laboratory of Traditional Chinese Medicine Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing, 100069, China
| | - Ting-ting Ma
- Beijing Key Laboratory of Traditional Chinese Medicine Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing, 100069, China
| | - Mu-xin Gong
- Beijing Key Laboratory of Traditional Chinese Medicine Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing, 100069, China
- Corresponding author.
| | - Kai-li Xie
- Beijing Key Laboratory of Traditional Chinese Medicine Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing, 100069, China
| | - Zhi-min Wang
- National Engineering Laboratory for Quality Control Technology of Chinese Herbal Medicines, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Jing Li
- Beijing Key Laboratory of Traditional Chinese Medicine Collateral Disease Theory Research, School of Traditional Chinese Medicine, Capital Medical University, Beijing, 100069, China
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14
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Cost-effective and sensitive indicator-displacement array (IDA) assay for quality monitoring of black tea fermentation. Food Chem 2023; 403:134340. [DOI: 10.1016/j.foodchem.2022.134340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/08/2022] [Accepted: 09/16/2022] [Indexed: 11/21/2022]
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15
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Wang Y, Ren Z, Li M, Lu C, Deng WW, Zhang Z, Ning J. From lab to factory: A calibration transfer strategy from HSI to online NIR optimized for quality control of green tea fixation. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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17
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Wang Y, Ren Z, Chen Y, Lu C, Deng WW, Zhang Z, Ning J. Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation. Food Chem 2023; 401:134090. [DOI: 10.1016/j.foodchem.2022.134090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 08/13/2022] [Accepted: 08/29/2022] [Indexed: 01/30/2023]
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18
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A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms. Sci Rep 2022; 12:21418. [PMID: 36496531 PMCID: PMC9741623 DOI: 10.1038/s41598-022-25671-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022] Open
Abstract
Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.
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19
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Li T, Lu C, Huang J, Chen Y, Zhang J, Wei Y, Wang Y, Ning J. Qualitative and quantitative analysis of the pile fermentation degree of Pu-erh tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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20
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Estimation of the sensory properties of black tea samples using non-destructive near-infrared spectroscopy sensors. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109260] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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21
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Riu J, Vega A, Boqué R, Giussani B. Exploring the Analytical Complexities in Insect Powder Analysis Using Miniaturized NIR Spectroscopy. Foods 2022; 11:foods11213524. [PMID: 36360137 PMCID: PMC9659064 DOI: 10.3390/foods11213524] [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: 09/22/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022] Open
Abstract
Insects have been a food source for humans for millennia, and they are actively consumed in various parts of the world. This paper aims to ascertain the feasibility of portable near-infrared (NIR) spectroscopy as a reliable and fast candidate for the classification of insect powder samples and the prediction of their major components. Commercially-available insect powder samples were analyzed using two miniaturized NIR instruments. The samples were analyzed as they are and after grinding, to study the effect of the granulometry on the spectroscopic analyses. A homemade sample holder was designed and optimized for making reliable spectroscopic measurements. Classification was then performed using three classification strategies, and partial least squares (PLS) regression was used to predict the macronutrients. The results obtained confirmed that both spectroscopic sensors were able to classify insect powder samples and predict macronutrients with an adequate detection limit.
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Affiliation(s)
- Jordi Riu
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain
| | - Alba Vega
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain
| | - Ricard Boqué
- Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Carrer Marcel·lí Domingo 1, 43007 Tarragona, Spain
| | - Barbara Giussani
- Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell’Insubria, Via Valleggio, 9, 22100 Como, Italy
- Correspondence: ; Tel.: +39-031-238-6434
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22
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Yin XL, Fu WJ, Chen Y, Zhou RF, Sun W, Ding B, Peng XT, Gu HW. GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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23
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Wang Y, Yang J, Yu S, Fu H, He S, Yang B, Nan T, Yuan Y, Huang L. Prediction of chemical indicators for quality of Zanthoxylum spices from multi-regions using hyperspectral imaging combined with chemometrics. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2022. [DOI: 10.3389/fsufs.2022.1036892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fruits of Zanthoxylum bungeanum Maxim (Red “Huajiao,” RHJ) and Z. schinifolium Sieb. et Zucc. (Green “Huajiao,” GHJ) are famous spices around the world. Antioxidant capability (AOC), total alkylamides content (TALC) and volatile oil content (VOC) in HJ are three important quality indicators and lack rapid and effective methods for detection. Non-destructive, time-saving, and effective technology of hyperspectral imaging (HSI) combined with chemometrics was adopted to improve the indicators prediction in this study. Results showed that the three chemical indexes exhibited significant differences between different regions and varieties (P < 0.05). Specifically, the mass percentages of TALC were 11–22% in RHJ group and 21–36% in GHJ group. The mass percentages of VOC content were 23–31% and 16–24% in RHJ and GHJ groups, respectively. More importantly, these indicators could be well predicted based on the full or effective HSI wavelengths via model adaptive space shrinkage (MASS) and iteratively variable subset optimization (IVSO) selections combined with wavelet transform (WT) method for noise reduction. The best prediction results of AOC, TALC, and VOC indicators were achieved with the highest residual predictive deviation (RPD) values of 7.43, 7.82, and 3.73 for RHJ, respectively, and 6.82, 2.66, and 4.64 for GHJ, respectively. The above results highlight the great potential of HSI assisted with chemometrics in the rapid and effective prediction of chemical indicators of Zanthoxylum spices.
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24
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Improved Model for Starch Prediction in Potato by the Fusion of Near-Infrared Spectral and Textural Data. Foods 2022; 11:foods11193133. [PMID: 36230208 PMCID: PMC9563719 DOI: 10.3390/foods11193133] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/20/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a partial least squares regression (PLSR) prediction model was then established. The results revealed that low-level data fusion could not improve accuracy in predicting starch content. Therefore, to improve prediction accuracy, key variables were selected from the spectral and textural data through competitive adaptive reweighted sampling (CARS) and correlation analysis, respectively, and mid-level data fusion was performed. With a residual predictive deviation (RPD) value > 2, the established PLSR model achieved satisfactory prediction accuracy. Therefore, this study demonstrated that appropriate data fusion can effectively improve the prediction accuracy for starch content and thus aid the sorting of potato starch content in the production line.
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25
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NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea. Foods 2022; 11:foods11192976. [PMID: 36230052 PMCID: PMC9563823 DOI: 10.3390/foods11192976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/29/2022] Open
Abstract
Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (RP = 0.9407, RPD = 3.00), FAA (RP = 0.9110, RPD = 2.21) and TP/FAA (RP = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.
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26
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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman. Foods 2022; 11:foods11182928. [PMID: 36141056 PMCID: PMC9498461 DOI: 10.3390/foods11182928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.
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27
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Rapid detection of fumonisin B1 and B2 in ground corn samples using smartphone-controlled portable near-infrared spectrometry and chemometrics. Food Chem 2022; 384:132487. [DOI: 10.1016/j.foodchem.2022.132487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/11/2022]
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28
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Wang F, Wang C, Song S. Rapid and Low-Cost Detection of Millet Quality by Miniature Near-Infrared Spectroscopy and Iteratively Retaining Informative Variables. Foods 2022; 11:foods11131841. [PMID: 35804657 PMCID: PMC9265786 DOI: 10.3390/foods11131841] [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: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
Traditional chemical methods for testing the fat content of millet, a widely consumed grain, are time-consuming and costly. In this study, we developed a low-cost and rapid method for fat detection and quantification in millet. A miniature NIR spectrometer connected to a smartphone was used to collect spectral data from millet samples of different origins. The standard normal variate (SNV) and first derivative (1D) methods were used to preprocess spectral signals. Variable selection methods, including bootstrapping soft shrinkage (BOSS), the variable iterative space shrinkage approach (VISSA), iteratively retaining informative variables (IRIV), iteratively variable subset optimization (IVSO), and competitive adaptive reweighted sampling (CARS), were used to select characteristic wavelengths. The partial least squares regression (PLSR) algorithm was employed to develop the regression models aimed at predicting the fat content in millet. The results showed that the proposed 1D-IRIV-PLSR model achieved optimal accuracy for fat detection, with a correlation coefficient for prediction (Rp) of 0.953, a root mean square error for prediction (RMSEP) of 0.301 g/100 g, and a residual predictive deviation (RPD) of 3.225, by using only 18 characteristic wavelengths. This result highlights the feasibility of using this low-cost and high-portability assessment tool for millet quality testing, which provides an optional solution for in situ inspection of millet quality in different scenarios, such as production lines or sales stores.
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Affiliation(s)
- Fuxiang Wang
- School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China;
| | - Chunguang Wang
- School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China;
- Correspondence: ; Tel.: +86-0471-4304788
| | - Shiyong Song
- Mongolia Lvtao Detection Technology Company Limited, Hohhot 010000, China;
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29
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Nagy MM, Wang S, Farag MA. Quality analysis and authentication of nutraceuticals using near IR (NIR) spectroscopy: A comprehensive review of novel trends and applications. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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30
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Wang Y, Ren Z, Li M, Yuan W, Zhang Z, Ning J. pH indicator-based sensor array in combination with hyperspectral imaging for intelligent evaluation of withering degree during processing of black tea. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 271:120959. [PMID: 35121474 DOI: 10.1016/j.saa.2022.120959] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Withering is one of the most critical steps in the processing of black tea. The degree of withering affects the aroma quality of the finished tea. In this study, we used a pH indicator-based colorimetric sensor array in combination with hyperspectral imaging to intelligently evaluate the withering degree. After analyzing the difference between images taken before and after the reaction of pH indicators with withered leaves, six pH indicators were selected to build a sensor array. Then, the hyperspectral image of each pH indicator was obtained at wavelengths between 400 and 1000 nm. Nonlinear support vector machine (SVM) and least-squares (LS) SVM models were established to determine the degree of withering. Results revealed that the spectral information from single pH indicator failed to accurately evaluate the withering degree. The LS-SVM model achieved satisfactory discriminant results with the low-level data fusion of six pH indicators followed by principal component analysis for dimensionality reduction. The optimal model yielded accuracies of 93.75% and 90.00% for the calibration and prediction sets, respectively. The results indicated that colorimetric sensor array in combination with hyperspectral imaging can effectively determine the withering degree, thus providing a novel method for the intelligent processing of food and tea.
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Affiliation(s)
- Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China
| | - Zhengyu Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China
| | - Maoyu Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China
| | - Wenxuan Yuan
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China.
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China.
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31
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Optimizing the Quality and Commercial Value of Gyokuro-Styled Green Tea Grown in Australia. BEVERAGES 2022. [DOI: 10.3390/beverages8020022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Gyokuro is a style of Japanese green tea produced by employing agricultural shading in the weeks before harvest. This method results in a tea product with different organoleptic and chemical properties than common Japanese green tea. In an effort to yield the highest quality and commercially valuable green tea product, the present study explores the influence of shading treatments and the duration of shading on the natural biochemistry of the green tea plant. This study applied shading treatments at light intensity conditions of 40%, 16%, 10% and 1% of available ambient light and the application of a red-colored shade cloth of 60% opacity. The Quality Index Tool was used to measure the quality and commercial value of the green tea, using individual target constituents (theanine, caffeine and the catechins) quantified from HPLC analysis. This study shows that very high levels of total visible spectrum light shading (~99%) is required to achieve improvements in quality and commercial value. Specifically, this improvement is a direct result of changes in the mood- modifying bioactive metabolites theanine and caffeine. This study concludes that in green tea growing regions with more hours of sunlight per year, such as on the Central Coast of Australia, more intense shading will achieve products with improved quality and commercial value, which has more potential to be marketed as a functional ingredient.
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32
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Luo D, Gao Y, Wang Y, Shi Y, Chen S, Ding Z, Fan K. Using UAV image data to monitor the effects of different nitrogen application rates on tea quality. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:1540-1549. [PMID: 34424545 DOI: 10.1002/jsfa.11489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/21/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field. RESULTS We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2 = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2 = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10. CONCLUSION The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Danni Luo
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Yuan Gao
- Jinan Agricultural Technology Promotion Service Center, Jinan, China
| | - Yu Wang
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Rizhao, China
| | - Yujie Shi
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Sizhou Chen
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Zhaotang Ding
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Rizhao, China
| | - Kai Fan
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
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33
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Li L, Wang Y, Cui Q, Liu Y, Ning J, Zhang Z. Qualitative and quantitative quality evaluation of black tea fermentation through noncontact chemical imaging. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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34
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Hou Y, Gao X, Li S, Cai X, Li P, Li W, Li Z. Variable Selection Based on Gray Wolf Optimization Algorithm for the Prediction of Saponin Contents in Xuesaitong Dropping Pills Using NIR Spectroscopy. J Pharm Innov 2022. [DOI: 10.1007/s12247-022-09620-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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35
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Andrade MKDS, Santana MAD, Assunção Ferreira MR, dos Santos WP, Lira Soares LA. Determination of Libidibia ferrea Markers Using Spectrophotometry and Chemometric Tools with Comparison to a Standard High-Performance Liquid Chromatography (HPLC) Protocol. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2032123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Maria Karoline da Silva Andrade
- Pharmacognosy Laboratory, Department of Pharmaceutical Sciences, Federal University of Pernambuco, Brazil
- Post-Graduate Program in Pharmaceutical Sciences, Federal University of Pernambuco, Brazil
| | - Maíra Araújo de Santana
- Polytechnic School of Pernambuco, Program on Computing Engineering, University of Pernambuco, Brazil
| | | | | | - Luiz Alberto Lira Soares
- Pharmacognosy Laboratory, Department of Pharmaceutical Sciences, Federal University of Pernambuco, Brazil
- Post-Graduate Program in Pharmaceutical Sciences, Federal University of Pernambuco, Brazil
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36
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37
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Untargeted and targeted metabolomics reveals potential marker compounds of an tea during storage. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112791] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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38
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Determination of caffeine in dietary supplements by miniaturized portable liquid chromatography. J Chromatogr A 2021; 1664:462770. [PMID: 34979283 DOI: 10.1016/j.chroma.2021.462770] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023]
Abstract
In this study three miniaturized liquid chromatography (LC) instruments have been evaluated and compared for the analysis of caffeine in dietary supplements, namely a benchtop capillary LC (capLC) system, a benchtop nano LC (nanoLC)system and a portable LC system. Commercial products derived from different sources of caffeine have been analyzed. Under optimized conditions, the methods based on benchtop systems were superior in terms of sensitivity. The limits of detection (LODs) found with the capLC and nanoLC systems were 0.01 and 0.003 µg mL-1, respectively, whereas the LOD obtained with the portable LC instrument was of 1 µg mL-1. The portable LC-based method was superior in terms of simplicity and throughput (total analysis time < 15 min). On the basis of the results obtained, a new method for the rapid measurement of caffeine in dietary supplements by portable miniaturized LC is presented. This method provided good linearity within the 1-20 µg mL-1 interval, and it allowed the quantification of caffeine even in products derived from decaffeinated green coffee extracts. The contents of caffeine found with the proposed portable LC method in the real samples analyzed ranged from 1.38 to 7 mg per gram of product, which were values statistically equivalent to those found with the benchtop capLC and nanoLC methods, being the precision, expressed as relative standard deviation (RDS), of 2 -14% (n = 3). The proposed portable LC based method can be used as a simple and rapid alternative to estimate the quality, effectiveness and safety of dietary supplements, regarding their caffeine content.
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39
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Wang W, Dou H, Zhang G, Xie L, Wang Z, Deng G. An approach for simultaneous monitoring the content of insensitive agent in the double-base oblate spherical propellant by application of near-infrared spectroscope and partial least squares. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119851. [PMID: 33940569 DOI: 10.1016/j.saa.2021.119851] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/20/2021] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
The content of insensitive agent is an important parameter that has been shown correlated with the combustion characteristic of double-base oblate spherical propellant (DOSP). This work focused on the feasibility of simultaneous monitoring the content of insensitive agent (dibutyl phthalate (DBP) and N, N'-dimethyl-N, N'-diphenylurea (C2)) in DOSP by using near-infrared (NIR) spectroscope coupled with partial least squares (PLS). The optimal spectral intervals for creating models of DBP and C2 corresponded to 5964 cm-1-4212 cm-1 and 6240 cm-1-4380 cm-1, respectively. It had been demonstrated that derivative tools were more suitable for spectral preprocessing as which had the lowest root mean squares error of cross-validation (RMSECV). The best-performance models of DBP and C2 were built under 4 and 7 PLS factors, respectively. The results showed that the determination coefficients of calibration (Rc2) and the root mean squares error of calibration (RMSEC) were 0.9771 and 0.0173 for DBP; 0.984 and 0.0072 for C2, respectively. Besides, the developed models exhibited excellent ability in prediction with the determination coefficients in prediction (RP2) and the root mean squares error in prediction (RMSEP) of 0.9681 and 0.0275 for DBP, and of 0.9554 and 0.0107 for C2, respectively. The residual predictive deviation (RPD) of prediction set were 5.68 and 5.12 for DBP and C2, respectively. The average relative errors of the proposed and reference methods were 0.652% for DBP, and 0.429% for C2, revealing a good correlation between the reference values and predicted values. Therefore, it concluded that the proposed plan has shown to be an attractive means since its efficient and highly accurate which could provide a better option for quality control in the large-scale production of DOSP.
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Affiliation(s)
- Weibin Wang
- National Special Superfine Powder Engineering Research Center, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
| | - Haixu Dou
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, 5988 Renmin Street, Changchun 130022, China
| | - Gaofeng Zhang
- National Special Superfine Powder Engineering Research Center, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
| | - Liang Xie
- National Special Superfine Powder Engineering Research Center, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
| | - Zhaoxuan Wang
- National Special Superfine Powder Engineering Research Center, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
| | - Guodong Deng
- National Special Superfine Powder Engineering Research Center, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China.
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40
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Wang Y, Li L, Liu Y, Cui Q, Ning J, Zhang Z. Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2021.110599] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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41
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Wang Y, Liu Y, Chen Y, Cui Q, Li L, Ning J, Zhang Z. Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111737] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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42
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Wang F, Wang C, Song S, Xie S, Kang F. Study on starch content detection and visualization of potato based on hyperspectral imaging. Food Sci Nutr 2021; 9:4420-4430. [PMID: 34401090 PMCID: PMC8358368 DOI: 10.1002/fsn3.2415] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/20/2021] [Accepted: 06/02/2021] [Indexed: 02/06/2023] Open
Abstract
Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least-squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS-SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo-color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.
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Affiliation(s)
- Fuxiang Wang
- Inner Mongolia Agriculture UniversityHohhotChina
| | | | - Shiyong Song
- Inner Mongolia Agriculture UniversityHohhotChina
| | - Shengshi Xie
- Inner Mongolia Agriculture UniversityHohhotChina
| | - Feilong Kang
- Inner Mongolia Agriculture UniversityHohhotChina
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43
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Jin G, Wang YJ, Li M, Li T, Huang WJ, Li L, Deng WW, Ning J. Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system. Food Chem 2021; 358:129815. [PMID: 33915424 DOI: 10.1016/j.foodchem.2021.129815] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wen-Jing Huang
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Wei-Wei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science & Technology, Anhui Agricultural University, Hefei, Anhui 230036, PR China.
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