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Zhang T, Wang Y, Sun J, Liang J, Wang B, Xu X, Xu J, Liu L. Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS). SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124112. [PMID: 38518439 DOI: 10.1016/j.saa.2024.124112] [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: 12/05/2023] [Revised: 02/28/2024] [Accepted: 03/02/2024] [Indexed: 03/24/2024]
Abstract
Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images - EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.
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Affiliation(s)
- Tianrui Zhang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Yifan Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jiansong Sun
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jing Liang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Bin Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
| | - Xiaoxuan Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Yunnan Research Institute, Nankai University, Kunming 650091, China
| | - Jing Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Lei Liu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
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2
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Thantar S, Mihailova A, Islam MD, Maxwell F, Hamed I, Vlachou C, Kelly SD. Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics. Talanta 2024; 273:125910. [PMID: 38492284 DOI: 10.1016/j.talanta.2024.125910] [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: 01/30/2024] [Revised: 03/06/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.
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Affiliation(s)
- Saw Thantar
- Department of Nuclear Technology, Kyaukse Technological University, Kyaukse, Myanmar
| | - Alina Mihailova
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria.
| | - Marivil D Islam
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Florence Maxwell
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Islam Hamed
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Christina Vlachou
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Simon D Kelly
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
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3
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Wang T, Xu L, Lan T, Deng Z, Yun YH, Zhai C, Qian C. Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:123976. [PMID: 38330764 DOI: 10.1016/j.saa.2024.123976] [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: 11/21/2023] [Revised: 01/16/2024] [Accepted: 01/27/2024] [Indexed: 02/10/2024]
Abstract
Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.
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Affiliation(s)
- Tao Wang
- School of Food Science and Engineering, Hainan University, Haikou 570228, PR China
| | - Lilan Xu
- School of Food Science and Engineering, Hainan University, Haikou 570228, PR China
| | - Tao Lan
- School of Food Science and Engineering, Hainan University, Haikou 570228, PR China
| | - Zhuowen Deng
- School of Food Science and Engineering, Hainan University, Haikou 570228, PR China
| | - Yong-Huan Yun
- School of Food Science and Engineering, Hainan University, Haikou 570228, PR China; Hainan Institute for Food Control, Key Laboratory of Tropical Fruits and Vegetables Quality and Safety for State Market Regulation, Haikou 570314, PR China.
| | - Chen Zhai
- COFCO Nutrition and Health Research Institute, Beijing Key Laboratory of Nutrition and Health and Food Safety, Beijing 102209, PR China.
| | - Chengjing Qian
- COFCO Nutrition and Health Research Institute, Beijing Key Laboratory of Nutrition and Health and Food Safety, Beijing 102209, PR China
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Park B, Wi S, Chung H, Lee H. Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops. SENSORS (BASEL, SWITZERLAND) 2024; 24:1442. [PMID: 38474977 DOI: 10.3390/s24051442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/15/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.
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Affiliation(s)
- Beomjin Park
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si 28644, Republic of Korea
| | - Seunghwan Wi
- Vegetable Research Division, National Institute of Horticultural & Herbal Science, Wanju 55365, Republic of Korea
| | - Hwanjo Chung
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si 28644, Republic of Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju-si 28644, Republic of Korea
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5
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Kang Z, Fan R, Zhan C, Wu Y, Lin Y, Li K, Qing R, Xu L. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning. Molecules 2024; 29:682. [PMID: 38338424 PMCID: PMC10856461 DOI: 10.3390/molecules29030682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
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Affiliation(s)
- Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rongsheng Fan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Chunyi Zhan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Youli Wu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Yi Lin
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Kunyu Li
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rui Qing
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
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6
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Hong Y, Birse N, Quinn B, Li Y, Jia W, van Ruth S, Elliott CT. MALDI-ToF MS and chemometric analysis as a tool for identifying wild and farmed salmon. Food Chem 2024; 432:137279. [PMID: 37657341 DOI: 10.1016/j.foodchem.2023.137279] [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: 05/09/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023]
Abstract
In this study, the difference between wild and farmed salmon production was successfully profiled and differentiated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) combined with chemometric analysis. The established method based on multivariate analysis mainly involved principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) as the screening and verifying tools to provide insights into the distinctive features found in wild and farmed salmon products, respectively. The discrimination between farmed and wild salmon was accomplished with 100% classification accuracy using chemometric models, 100% identification accuracy was also achieved in distinguishing wild Salmo salar and Oncorhynchus nerka samples. The results of the present work suggest that the proposed method could serve as a reference for detecting salmon fraud relating to wild or farmed production and expand the application of MALDI-ToF technology further into food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom.
| | - Brian Quinn
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, Netherlands; School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani 12120, Thailand
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7
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Zhang Z, Li Y, Zhao S, Qie M, Bai L, Gao Z, Liang K, Zhao Y. Rapid analysis technologies with chemometrics for food authenticity field: A review. Curr Res Food Sci 2024; 8:100676. [PMID: 38303999 PMCID: PMC10830540 DOI: 10.1016/j.crfs.2024.100676] [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: 07/24/2023] [Revised: 12/15/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024] Open
Abstract
In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.
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Affiliation(s)
- Zixuan Zhang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yalan Li
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shanshan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Mengjie Qie
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lu Bai
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhiwei Gao
- Hangzhou Nutritome Biotech Co., Ltd., Hangzhou, China
| | - Kehong Liang
- Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yan Zhao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-Product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China
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8
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Díaz EO, Iino H, Koyama K, Kawamura S, Koseki S, Lyu S. Non-destructive quality classification of rice taste properties based on near-infrared spectroscopy and machine learning algorithms. Food Chem 2023; 429:136907. [PMID: 37487393 DOI: 10.1016/j.foodchem.2023.136907] [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: 03/04/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/26/2023]
Abstract
The taste quality of rice is determined by protein and amylose percentages, with low levels indicating high-quality taste in Japan. However, accurate non-destructive screening remains a challenge for the industry. We explored the use of machine learning models and near-infrared spectra to classify rice taste quality. Three models were optimized using 796 brown rice samples from Hokkaido, Japan, produced between 2008 and 2016, and tested on 278 distinct samples from the same region produced between 2017 and 2019. Logistic regression and support vector machine models outperformed the partial least-squares discriminant analysis model, achieving high accuracy (94%), f1-score (90%), average precision (0.94), and low classification error (4%) and allowing accurate non-destructive classification of rice quality. These results not only improve rice quality, post-harvest technology, and producer output in Japan but also could enhance quality control processes and foster the production of high-quality products for other agricultural goods and food commodities worldwide.
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Affiliation(s)
- Edenio Olivares Díaz
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan.
| | - Haruka Iino
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Kento Koyama
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Shuso Kawamura
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Shigenobu Koseki
- Graduate School of Agricultural Science, Hokkaido University, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Suxing Lyu
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa 277-8561, Japan
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9
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Mehdizadeh SA, Noshad M, Chaharlangi M, Ampatzidis Y. Development of an Innovative Optoelectronic Nose for Detecting Adulteration in Quince Seed Oil. Foods 2023; 12:4350. [PMID: 38231827 DOI: 10.3390/foods12234350] [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: 11/01/2023] [Revised: 11/27/2023] [Accepted: 11/30/2023] [Indexed: 01/19/2024] Open
Abstract
In this study, an innovative odor imaging system capable of detecting adulteration in quince seed edible oils mixed with sunflower oil and sesame oil based on their volatile organic compound (VOC) profiles was developed. The system comprises a colorimetric sensor array (CSA), a data acquisition unit, and a machine learning algorithm for identifying adulterants. The CSA was created using a method that involves applying a mixture of six different pH indicators (methyl violet, chlorophenol red, Nile blue, methyl orange, alizarin, cresol red) onto a Thin Layer Chromatography (TLC) silica gel plate. Subsequently, difference maps were generated by subtracting the "initial" image from the "final" image, with the resulting color changes being converted into digital data, which were then further analyzed using Principal Component Analysis (PCA). Following this, a Support Vector Machine was employed to scrutinize quince seed oil that had been adulterated with varying proportions of sunflower oil and sesame oil. The classifier was progressively supplied with an increasing number of principal components (PCs), starting from one and incrementally increasing up to five. Each time, the classifier was optimized to determine the hyperparameters utilizing a random search algorithm. With one to five PCs, the classification error accounted for a range of 37.18% to 1.29%. According to the results, this novel system is simple, cost-effective, and has potential applications in food quality control and consumer protection.
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Affiliation(s)
- Saman Abdanan Mehdizadeh
- Department of Mechanics of Biosystems Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
| | - Mohammad Noshad
- Department of Food Science & Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
| | - Mahsa Chaharlangi
- Central Laboratory, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
| | - Yiannis Ampatzidis
- Southwest Florida Research and Education Center, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
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10
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Hao Y, Li X, Zhang C. Improving prediction model robustness with virtual sample construction for near-infrared spectra analysis. Anal Chim Acta 2023; 1279:341763. [PMID: 37827664 DOI: 10.1016/j.aca.2023.341763] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/22/2023] [Accepted: 08/28/2023] [Indexed: 10/14/2023]
Abstract
In a qualitative analysis of near-infrared spectroscopy (NIRS), when the samples to be analyzed are difficult to obtain or there are few counterexamples, the robustness of the models is poor, resulting in the decline of the generalization ability of the models. In this case, the effective method is to construct virtual samples to achieve the balance of categories. In this contribution, three virtual spectrum construction strategies including Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Deep Convolutional Generative Adversarial Network (DCGAN) were explored to deal with the problem of insufficient or imbalanced sample numbers in NIRS analysis. The strategies were tested with the melamine and Yali pears two spectral datasets. The PLS-DA and Correct Recognition Rate (CRR) were used for discriminant model construction and accuracy evaluation, respectively. The results show that SMOTE, ADASYN, and DCGAN processing strategies can all improve the global CRR (CRRglob). The SMOTE and ADASYN can improve the CRR for majority class sample (CRRmaj), but the CRR for minority class sample (CRRmin) has decreased. For the DCGAN method, the CRRglob, CRRmaj, and CRRmin were all improved. The standard deviation of the results of the multiple parallel calculations demonstrates the robustness of DCGAN generation method. Therefore, the DCGAN method has good reliability and practicability, and can increase the robustness and generalization ability of the NIRS model.
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Affiliation(s)
- Yong Hao
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China; Key Laboratory of Conveyance Equipment of the Ministry of Education, Nanchang, 330013, China.
| | - Xiyan Li
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China
| | - Chengxiang Zhang
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China
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11
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El Maouardi M, Alaoui Mansouri M, De Braekeleer K, Bouklouze A, Vander Heyden Y. Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions. Molecules 2023; 28:5698. [PMID: 37570667 PMCID: PMC10419999 DOI: 10.3390/molecules28155698] [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: 06/29/2023] [Revised: 07/22/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods-including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)-were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.
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Affiliation(s)
- Meryeme El Maouardi
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat 10100, Morocco; (M.E.M.)
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
| | | | - Kris De Braekeleer
- Pharmacognosy, Bioanalysis & Drug Discovery Unit, Faculty of Pharmacy, University Libre Brussels, 1050 Brussels, Belgium
| | - Abdelaziz Bouklouze
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V, Rabat 10100, Morocco; (M.E.M.)
| | - Yvan Vander Heyden
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
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12
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Chakkumpulakkal Puthan Veettil T, Duffin RN, Roy S, Vongsvivut J, Tobin MJ, Martin M, Adegoke JA, Andrews PC, Wood BR. Synchrotron-Infrared Microspectroscopy of Live Leishmania major Infected Macrophages and Isolated Promastigotes and Amastigotes. Anal Chem 2023; 95:3986-3995. [PMID: 36787387 DOI: 10.1021/acs.analchem.2c04004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The prevalence of neglected tropical diseases (NTDs) is advancing at an alarming rate. The NTD leishmaniasis is now endemic in over 90 tropical and sub-tropical low socioeconomic countries. Current diagnosis for this disease involves serological assessment of infected tissue by either light microscopy, antibody tests, or culturing with in vitro or in vivo animal inoculation. Furthermore, co-infection by other pathogens can make it difficult to accurately determine Leishmania infection with light microscopy. Herein, for the first time, we demonstrate the potential of combining synchrotron Fourier-transform infrared (FTIR) microspectroscopy with powerful discrimination tools, such as partial least squares-discriminant analysis (PLS-DA), support vector machine-discriminant analysis (SVM-DA), and k-nearest neighbors (KNN), to characterize the parasitic forms of Leishmania major both isolated and within infected macrophages. For measurements performed on functional infected and uninfected macrophages in physiological solutions, the sensitivities from PLS-DA, SVM-DA, and KNN classification methods were found to be 0.923, 0.981, and 0.989, while the specificities were 0.897, 1.00, and 0.975, respectively. Cross-validated PLS-DA models on live amastigotes and promastigotes showed a sensitivity and specificity of 0.98 in the lipid region, while a specificity and sensitivity of 1.00 was achieved in the fingerprint region. The study demonstrates the potential of the FTIR technique to identify unique diagnostic bands and utilize them to generate machine learning models to predict Leishmania infection. For the first time, we examine the potential of infrared spectroscopy to study the molecular structure of parasitic forms in their native aqueous functional state, laying the groundwork for future clinical studies using more portable devices.
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Affiliation(s)
| | - Rebekah N Duffin
- School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | - Supti Roy
- Centre for Biospectroscopy, School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | | | - Mark J Tobin
- Australian Synchrotron, 800 Blackburn Rd, Clayton, Victoria 3168, Australia
| | - Miguela Martin
- School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | - John A Adegoke
- School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | - Philip C Andrews
- School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
| | - Bayden R Wood
- Centre for Biospectroscopy, School of Chemistry, Faculty of Science, Monash University, Wellington Road, Clayton, Victoria 3800, Australia
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13
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A rapid identification based on FT-NIR spectroscopies and machine learning for drying temperatures of Amomum tsao-ko. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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14
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Geographical Origin Identification of Chinese Tomatoes Using Long-Wave Fourier-Transform Near-Infrared Spectroscopy Combined with Deep Learning Methods. FOOD ANAL METHOD 2023. [DOI: 10.1007/s12161-023-02444-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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15
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Application of Logistic Regression and Artificial Intelligence in the Risk Prediction of Acute Aortic Dissection Rupture. J Clin Med 2022; 12:jcm12010179. [PMID: 36614979 PMCID: PMC9821290 DOI: 10.3390/jcm12010179] [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: 11/07/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Logistic regression (LR) and artificial intelligence algorithms were used to analyze the risk factors for the early rupture of acute type A aortic dissection (ATAAD). Data from electronic medical records of 200 patients diagnosed with ATAAD from the Department of Emergency of Guangdong Provincial People’s Hospital from April 2012 to March 2017 were collected. Logistic regression and artificial intelligence algorithms were used to establish prediction models, and the prediction effects of four models were analyzed. According to the LR models, we elucidated independent risk factors for ATAAD rupture, which included age > 63 years (odds ratio (OR) = 1.69), female sex (OR = 1.77), ventilator assisted ventilation (OR = 3.05), AST > 80 U/L (OR = 1.59), no distortion of the inner membrane (OR = 1.57), the diameter of the aortic sinus > 41 mm (OR = 0.92), maximum aortic diameter > 48 mm (OR = 1.32), the ratio of false lumen area to true lumen area > 2.12 (OR = 1.94), lactates > 1.9 mmol/L (OR = 2.28), and white blood cell > 14.2 × 109 /L (OR = 1.23). The highest sensitivity and accuracy were found with the convolutional neural network (CNN) model. Its sensitivity was 0.93, specificity was 0.90, and accuracy was 0.90. In this present study, we found that age, sex, select biomarkers, and select morphological parameters of the aorta are independent predictors for the rupture of ATAAD. In terms of predicting the risk of ATAAD, the performance of random forests and CNN is significantly better than LR, but the performance of the support vector machine (SVM) is worse than LR.
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16
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Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249764. [PMID: 36560133 PMCID: PMC9784128 DOI: 10.3390/s22249764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 06/01/2023]
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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Affiliation(s)
- Wenwen Zhang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Qi Jie Wang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yuanjin Zheng
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
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17
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Liu Y, Zhao S, Gao X, Fu S, Chao Song, Dou Y, Shaozhong Song, Qi C, Lin J. Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin. RSC Adv 2022; 12:34520-34530. [PMID: 36545607 PMCID: PMC9710531 DOI: 10.1039/d2ra06892c] [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: 10/31/2022] [Accepted: 11/23/2022] [Indexed: 12/02/2022] Open
Abstract
With the events of fake and inferior rice and food products occurring frequently, how to establish a rapid and high accuracy monitoring method for rice food identification becomes an urgent problem. In this work, we investigate using combined laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) with machine learning algorithms to identify the place of origin of rice production. Six geographical origin rice samples grown in different parts of China are selected and pretreated, and measured by the atomic emission spectra of LIBS and the reflection spectra of HSI, respectively. The principal component analysis (PCA) is utilized to realize data dimensionality and extract the data feat of LIBS, HSI and fusion data, and based on this, three models employing the partial least squares discriminant analysis (PLS-DA), the support vector machine (SVM) and the extreme learning machine (ELM) are used to identify the rice geographical origin. The results show that the accuracy of LIBS and HSI analysis with the SVM machine learning algorithm can reach 93.06% and 88.07%, respectively, and the accuracy of combined LIBS and HSI data fusion recognition can reach 99.85%. Besides, the classification accuracy of the three models measured after pretreatment is basically all above 95%, and up to 99.85%. This study proves the effectiveness of using the combined LIBS and HSI with the machine learning algorithm in rice geographical origin identification, which can achieve rapid and accurate rice quality and identity detection.
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Affiliation(s)
- Yuanyuan Liu
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Shangyong Zhao
- Department of Energy and Power Engineering, Tsinghua UniversityBeijing100084China
| | - Xun Gao
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Shaoyan Fu
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Chao Song
- School of Chemistry and Environmental Engineering, Changchun University of Science and TechnologyJilin130022China
| | - Yinping Dou
- School of Physics, Changchun University of Science and TechnologyJilin130022China
| | - Shaozhong Song
- School of Data Science and Artificial Intelligence, Jilin Engineering Normal UniversityJilin130052China
| | - Chunyan Qi
- Jilin Academy of Agricultural SciencesJilin130033China
| | - Jingquan Lin
- School of Physics, Changchun University of Science and TechnologyJilin130022China
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18
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Son S, Kim D, Choul Choi M, Lee J, Kim B, Min Choi C, Kim S. Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy. Food Chem X 2022; 15:100430. [PMID: 36211751 PMCID: PMC9532771 DOI: 10.1016/j.fochx.2022.100430] [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: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 12/02/2022] Open
Abstract
ANN model was build based on NIR spectra and nutrient values of 110 rice samples. Good correlation between ANN predicted and experimental nutrient values observed. Scientific interpretation of weights agreed well with previously reported results. Interpretation of weights was also in good agreement with conventional PLS analysis.
Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.
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19
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Bui MQ, Quan TC, Nguyen QT, Tran-Lam TT, Dao YH. Geographical origin traceability of Sengcu rice using elemental markers and multivariate analysis. FOOD ADDITIVES & CONTAMINANTS. PART B, SURVEILLANCE 2022; 15:177-190. [PMID: 35722667 DOI: 10.1080/19393210.2022.2070932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/24/2022] [Indexed: 06/15/2023]
Abstract
Multi-element analysis combined with chemometric method has been used to investigate the distinguish between Sengcu rice and other types of rice origins in Vietnam. In Sengcu rice, As, Ba Sr, Pb, Ca, Se were confirmed as the key elements for geographical traceability among three fields of Lao Cai, whereas Al, Ca, Fe, Mg, Ag, As were major factors to distinguish between Sengcu and other types of rice. Based on linear discriminant analysis and partial least squares-discriminant analysis model, overall correct identification rates distinguishing between Sengcu and other types of rice were approximately 100% in both training and validation test. Moreover, to distinguish geographical origin of Sengcu rice samples, these rates vary from 80% to 99%. These results suggest the presence of food adulteration illustrated in the latter.
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Affiliation(s)
- Minh Quang Bui
- Center for Research and Technology Transfer, Vietnam Academy of Science and Technology, Ha Noi, Vietnam
| | - Thuy Cam Quan
- Department of Analytical Chemistry, Faculty of Chemistry, Viet Tri University of Industry, Phu Tho, Vietnam
| | - Quang Trung Nguyen
- Center for Research and Technology Transfer, Vietnam Academy of Science and Technology, Ha Noi, Vietnam
| | - Thanh-Thien Tran-Lam
- Institute of Mechanics and Applied Informatics, Vietnam Academy of Science and Technology, Ho Chi Minh City, Vietnam
| | - Yen Hai Dao
- Institute of Chemistry, Vietnam Academy of Science and Technology, Ha Noi, Vietnam
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20
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Wan Q, Ouyang A, Liu Y, Xiong Z, Li X, Li L. Detection of infestation by striped stem‐borer (Chilo suppressalis) in rice based on hyperspectral imaging. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Qiming Wan
- School of Mechatronics & Vehicle Engineering East China Jiaotong University Nanchang China
- National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment East China Jiaotong University Nanchang China
| | - Aiguo Ouyang
- School of Mechatronics & Vehicle Engineering East China Jiaotong University Nanchang China
- National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment East China Jiaotong University Nanchang China
| | - Yande Liu
- School of Mechatronics & Vehicle Engineering East China Jiaotong University Nanchang China
- National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment East China Jiaotong University Nanchang China
| | - Zhiyi Xiong
- School of Mechatronics & Vehicle Engineering East China Jiaotong University Nanchang China
- National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment East China Jiaotong University Nanchang China
| | - Xiong Li
- School of Mechatronics & Vehicle Engineering East China Jiaotong University Nanchang China
- National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment East China Jiaotong University Nanchang China
| | - Lisha Li
- School of Mechatronics & Vehicle Engineering East China Jiaotong University Nanchang China
- National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment East China Jiaotong University Nanchang China
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21
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Varrà MO, Ghidini S, Fabrile MP, Ianieri A, Zanardi E. Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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22
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Development of Certified Reference Materials for the Determination of Apparent Amylose Content in Rice. Molecules 2022; 27:molecules27144647. [PMID: 35889518 PMCID: PMC9322866 DOI: 10.3390/molecules27144647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/16/2022] Open
Abstract
Apparent amylose content (AAC) is one of the most important parameters in rice quality evaluation. In this study, four rice reference materials used to test rice AAC were developed. The AAC of rice reference materials were measured by a spectrophotometric method with a defatting procedure, calibrated from potato amylose and waxy rice amylopectin at the absorption wavelengths of 620 and 720 nm. Homogeneity test (n = 20) was judged by F-test based on the mean squares of among and within bottles, and short- and long-term stability monitoring was performed by T-test to check if there was significant degradation at the delivery temperature of under 40 °C (14 days) and at 0–4 °C storage condition (18 months), respectively. After joint evaluation by ten laboratories, Dixion and Cochran statistical analyses were presented. The expanded uncertainties were calculated based on the uncertainty of homogeneity, short- and long-term stability, and inter-laboratory validation containing factor k = 2. It found that the four reference materials were homogenous and stable, and had the AAC (g/100 g, k = 2) of 2.96 ± 1.01, 10.68 ± 0.66, 17.18 ± 1.04, and 16.09 ± 1.29, respectively, at 620 nm, and 1.46 ± 0.49, 10.44 ± 0.56, 16.82 ± 0.75, and 24.33 ± 0.52, respectively, at 720 nm. It was indicated that 720 nm was more suitable for the determination of rice AAC with lower uncertainties. The determinations of the AAC of 11 rice varieties were carried out by two methods, the method without defatting and with calibration from the four rice reference materials and the method with a defatting procedure and calibrating from potato amylose and waxy rice amylopectin. It confirmed that the undefatted rice reference materials could achieve satisfactory results to test the rice samples with the AAC ranging from 1 to 25 g/100 g. It would greatly reduce the time cost and improve testing efficiency and applicability, and provide technical support for the high-quality development of the rice industry.
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23
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Chadalavada K, Anbazhagan K, Ndour A, Choudhary S, Palmer W, Flynn JR, Mallayee S, Pothu S, Prasad KVSV, Varijakshapanikar P, Jones CS, Kholová J. NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals. SENSORS 2022; 22:s22103710. [PMID: 35632119 PMCID: PMC9146900 DOI: 10.3390/s22103710] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 01/20/2023]
Abstract
Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.
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Affiliation(s)
- Keerthi Chadalavada
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
- Department of Botany, Bharathidasan University, Tiruchirappalli 620 024, India
| | - Krithika Anbazhagan
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
| | - Adama Ndour
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Bamako BP 320, Mali;
| | - Sunita Choudhary
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
| | | | | | - Srikanth Mallayee
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
| | - Sharada Pothu
- South Asia Regional Center, International Livestock Research Institute, Patancheru 502 324, India; (S.P.); (K.V.S.V.P.); (P.V.)
| | | | - Padmakumar Varijakshapanikar
- South Asia Regional Center, International Livestock Research Institute, Patancheru 502 324, India; (S.P.); (K.V.S.V.P.); (P.V.)
| | - Chris S. Jones
- Feed and Forage Development, International Livestock Research Institute, Addis Ababa P.O. Box 5689, Ethiopia;
| | - Jana Kholová
- Crop Physiology & Modeling, International Crops Research Institute for Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India; (K.C.); (K.A.); (S.C.); (S.M.)
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
- Correspondence:
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24
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Hu Y, Sjoberg SM, Chen CJ, Hauvermale AL, Morris CF, Delwiche SR, Cannon AE, Steber CM, Zhang Z. As the number falls, alternatives to the Hagberg-Perten falling number method: A review. Compr Rev Food Sci Food Saf 2022; 21:2105-2117. [PMID: 35411636 DOI: 10.1111/1541-4337.12959] [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: 06/19/2021] [Revised: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 11/28/2022]
Abstract
This review examines the application, limitations, and potential alternatives to the Hagberg-Perten falling number (FN) method used in the global wheat industry for detecting the risk of poor end-product quality mainly due to starch degradation by the enzyme α-amylase. By viscometry, the FN test indirectly detects the presence of α-amylase, the primary enzyme that digests starch. Elevated α-amylase results in low FN and damages wheat product quality resulting in cakes that fall, and sticky bread and noodles. Low FN can occur from preharvest sprouting (PHS) and late maturity α-amylase (LMA). Moist or rainy conditions before harvest cause PHS on the mother plant. Continuously cool or fluctuating temperatures during the grain filling stage cause LMA. Due to the expression of additional hydrolytic enzymes, PHS has a stronger negative impact than LMA. Wheat grain with low FN/high α-amylase results in serious losses for farmers, traders, millers, and bakers worldwide. Although blending of low FN grain with sound wheat may be used as a means of moving affected grain through the marketplace, care must be taken to avoid grain lots from falling below contract-specified FN. A large amount of sound wheat can be ruined if mixed with a small amount of sprouted wheat. The FN method is widely employed to detect α-amylase after harvest. However, it has several limitations, including sampling variability, high cost, labor intensiveness, the destructive nature of the test, and an inability to differentiate between LMA and PHS. Faster, cheaper, and more accurate alternatives could improve breeding for resistance to PHS and LMA and could preserve the value of wheat grain by avoiding inadvertent mixing of high- and low-FN grain by enabling testing at more stages of the value stream including at harvest, delivery, transport, storage, and milling. Alternatives to the FN method explored here include the Rapid Visco Analyzer, enzyme assays, immunoassays, near-infrared spectroscopy, and hyperspectral imaging.
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Affiliation(s)
- Yang Hu
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA
| | - Stephanie M Sjoberg
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA
| | - Chunpen James Chen
- Department of Animal and Poultry Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - Amber L Hauvermale
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA
| | - Craig F Morris
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA.,USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, Washington, USA
| | - Stephen R Delwiche
- USDA, Agricultural Research Service, Beltsville Agricultural Research Center, Food Quality, Laboratory, Beltsville, Maryland, USA
| | - Ashley E Cannon
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA.,USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, Washington, USA
| | - Camille M Steber
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA.,USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, Washington, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA
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25
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Chen T, Chen X, Meng L, Wei Z, Chen B, Wang Y, Chen H, Cheng Q. Characteristic Fingerprint Analysis of the Moldy Odor in Guangxi Fragrant Rice by Gas Chromatography - Ion Mobility Spectrometry (GC-IMS). ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2043337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Tong Chen
- School of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou, China
| | - Xinyu Chen
- Department of Physical Chemistry, University of Duisburg-Essen, Essen, Germany
| | - Luli Meng
- School of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou, China
| | - Ziyu Wei
- School of Economics and Management, Guangxi University of Science and Technology, Liuzhou, China
| | - Bin Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Yong Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Hui Chen
- School of Animal Science and Food Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, China
| | - Qianwei Cheng
- School of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou, China
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26
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Classification of pulse flours using near-infrared hyperspectral imaging. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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27
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Sampaio PS, Almeida AS, Brites CM. Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters. Foods 2021; 10:3016. [PMID: 34945567 PMCID: PMC8701132 DOI: 10.3390/foods10123016] [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: 10/25/2021] [Revised: 11/15/2021] [Accepted: 12/01/2021] [Indexed: 11/16/2022] Open
Abstract
The main goal of this study was to test the ability of an artificial neural network (ANN) for rice quality prediction based on grain physical parameters and to conduct a comparison with multiple linear regression (MLR) using 66 samples in duplicate. The parameters used for rice quality prediction are related to biochemical composition (starch, amylose, ash, fat, and protein concentration) and pasting parameters (peak viscosity, trough, breakdown, final viscosity, and setback). These parameters were estimated based on grain appearance (length, width, length/width ratio, total whiteness, vitreous whiteness, and chalkiness), and milling yield (husked, milled, head) data. The MLR models were characterized by very low coefficient determination (R2 = 0.27-0.96) and a root-mean-square error (RMSE) (0.08-0.56). Meanwhile, the ANN models presented a range for R2 = 0.97-0.99, being characterized for R2 = 0.98 (training), R2 = 0.88 (validation), and R2 = 0.90 (testing). According to these results, the ANN algorithms could be used to obtain robust models to predict both biochemical and pasting profiles parameters in a fast and accurate form, which makes them suitable for application to simultaneous qualitative and quantitative analysis of rice quality. Moreover, the ANN prediction method represents a promising approach to estimate several targeted biochemical and viscosity parameters with a fast and clean approach that is interesting to industry and consumers, leading to better assessment of rice classification for authenticity purposes.
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Affiliation(s)
- Pedro Sousa Sampaio
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal; (A.S.A.); (C.M.B.)
- GREEN-IT Bioresources for Sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
- DREAMS-Centre for Interdisciplinary Development and Research on Environment, Applied Management, and Space, Faculty of Engineering, Lusófona University (ULHT), Campo Grande, 376, 1749-024 Lisbon, Portugal
| | - Ana Sofia Almeida
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal; (A.S.A.); (C.M.B.)
- GREEN-IT Bioresources for Sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
| | - Carla Moita Brites
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. da República, Quinta do Marquês, 2780-157 Oeiras, Portugal; (A.S.A.); (C.M.B.)
- GREEN-IT Bioresources for Sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
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28
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Xue SS, Tan J, Xie JY, Li MF. Rapid, simultaneous and non-destructive determination of maize flour and soybean flour adulterated in quinoa flour by front-face synchronous fluorescence spectroscopy. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108329] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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29
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Srinuttrakul W, Mihailova A, Islam MD, Liebisch B, Maxwell F, Kelly SD, Cannavan A. Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy. Foods 2021; 10:foods10081951. [PMID: 34441727 PMCID: PMC8392001 DOI: 10.3390/foods10081951] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 11/20/2022] Open
Abstract
Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.
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Affiliation(s)
- Wannee Srinuttrakul
- Research and Development Division, Thailand Institute of Nuclear Technology, Sai Mun, Ongkharak, Nakhon Nayok 26120, Thailand;
| | - Alina Mihailova
- Food and Environmental Protection Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria; (M.D.I.); (B.L.); (F.M.); (S.D.K.); (A.C.)
- Correspondence:
| | - Marivil D. Islam
- Food and Environmental Protection Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria; (M.D.I.); (B.L.); (F.M.); (S.D.K.); (A.C.)
| | - Beatrix Liebisch
- Food and Environmental Protection Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria; (M.D.I.); (B.L.); (F.M.); (S.D.K.); (A.C.)
| | - Florence Maxwell
- Food and Environmental Protection Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria; (M.D.I.); (B.L.); (F.M.); (S.D.K.); (A.C.)
| | - Simon D. Kelly
- Food and Environmental Protection Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria; (M.D.I.); (B.L.); (F.M.); (S.D.K.); (A.C.)
| | - Andrew Cannavan
- Food and Environmental Protection Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, P.O. Box 100, 1400 Vienna, Austria; (M.D.I.); (B.L.); (F.M.); (S.D.K.); (A.C.)
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30
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Song H, Li F, Guang P, Yang X, Pan H, Huang F. Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models. J Food Prot 2021; 84:1315-1320. [PMID: 33710323 DOI: 10.4315/jfp-20-447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS
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Affiliation(s)
- Han Song
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Feng Li
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Peiwen Guang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Xinhao Yang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
| | - Huanyu Pan
- Guangzhou Huibiao Testing Technology Center, Guangzhou 510700, People's Republic of China
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, People's Republic of China
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32
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Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11010362] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).
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33
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Bwambok DK, Siraj N, Macchi S, Larm NE, Baker GA, Pérez RL, Ayala CE, Walgama C, Pollard D, Rodriguez JD, Banerjee S, Elzey B, Warner IM, Fakayode SO. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6982. [PMID: 33297345 PMCID: PMC7730680 DOI: 10.3390/s20236982] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/23/2022]
Abstract
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
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Affiliation(s)
- David K. Bwambok
- Chemistry and Biochemistry, California State University San Marcos, 333 S. Twin Oaks Valley Rd, San Marcos, CA 92096, USA;
| | - Noureen Siraj
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Samantha Macchi
- Department of Chemistry, University of Arkansas at Little Rock, 2801 S. University Ave, Little Rock, AR 72204, USA; (N.S.); (S.M.)
| | - Nathaniel E. Larm
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Gary A. Baker
- Department of Chemistry, University of Missouri, 601 S. College Avenue, Columbia, MO 65211, USA; (N.E.L.); (G.A.B.)
| | - Rocío L. Pérez
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Caitlan E. Ayala
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Charuksha Walgama
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - David Pollard
- Department of Chemistry, Winston-Salem State University, 601 S. Martin Luther King Jr Dr, Winston-Salem, NC 27013, USA;
| | - Jason D. Rodriguez
- Division of Complex Drug Analysis, Center for Drug Evaluation and Research, US Food and Drug Administration, 645 S. Newstead Ave., St. Louis, MO 63110, USA;
| | - Souvik Banerjee
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
| | - Brianda Elzey
- Science, Engineering, and Technology Department, Howard Community College, 10901 Little Patuxent Pkwy, Columbia, MD 21044, USA;
| | - Isiah M. Warner
- Department of Chemistry, Louisiana State University, 232 Choppin Hall, Baton Rouge, LA 70803, USA; (R.L.P.); (C.E.A.); (I.M.W.)
| | - Sayo O. Fakayode
- Department of Physical Sciences, University of Arkansas-Fort Smith, 5210 Grand Ave, Fort Smith, AR 72913, USA; (C.W.); (S.B.)
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34
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Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01885-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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35
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Pérez-Rodríguez M, Dirchwolf PM, Rodríguez-Negrín Z, Pellerano RG. Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion. Food Chem 2020; 339:128125. [PMID: 33152892 DOI: 10.1016/j.foodchem.2020.128125] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/12/2020] [Accepted: 09/14/2020] [Indexed: 11/17/2022]
Abstract
The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and LDA were applied. Binary and multiclass associations were considered to assess rice flour authenticity through fraud identification. Models based on element predictors showed accuracies ranging from 72 to 88% to distinguish adulterated and unadulterated samples. The fusion of the mineral features with the principal components (PCs) obtained from PCA provided classification rates of 100% in training samples, and 91-100% in test samples. The proposed method proved to be a useful tool for quality control in the rice industry since a perfect success rate was achieved for rice flour fraud detection.
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Affiliation(s)
- Michael Pérez-Rodríguez
- Centre of Chemical Bioactive (CBQ), Central University of Las Villas - UCLV, Highway to Camajuaní Km 5½, 54830 Santa Clara, VC, Cuba; Institute of Basic and Applied Chemistry of the Northeast of Argentina (IQUIBA-NEA), National Scientific and Technical Research Council (CONICET), Faculty of Exact and Natural Science and Surveying, National University of the Northeast - UNNE, Av. Libertad 5470, 3400 Corrientes, Argentina.
| | - Pamela Maia Dirchwolf
- Faculty of Agricultural Sciences, UNNE, Sgto. Cabral 2131, 3400 Corrientes, Argentina
| | - Zenaida Rodríguez-Negrín
- Centre of Chemical Bioactive (CBQ), Central University of Las Villas - UCLV, Highway to Camajuaní Km 5½, 54830 Santa Clara, VC, Cuba
| | - Roberto Gerardo Pellerano
- Institute of Basic and Applied Chemistry of the Northeast of Argentina (IQUIBA-NEA), National Scientific and Technical Research Council (CONICET), Faculty of Exact and Natural Science and Surveying, National University of the Northeast - UNNE, Av. Libertad 5470, 3400 Corrientes, Argentina
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