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Yang Y, Harrison RC, Zhang D, Shen B, Yan Y, Kang D. Effect of genetic distances of different genotypes of maize on the authenticity of single seeds detected by NIR spectroscopy. FRONTIERS IN PLANT SCIENCE 2024; 15:1361328. [PMID: 38486851 PMCID: PMC10937569 DOI: 10.3389/fpls.2024.1361328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Introduction NIR spectroscopy combined with chemometric algorithms has been widely used for seed authenticity detection. However, the study of seed genetic distance, an internal feature that affects the discriminative performance of classification models, has rarely been reported. Methods Therefore, maize seed samples of different genotypes were selected to investigate the effect of genetic distance on the authenticity of single seeds detected by NIR spectroscopy. Firstly, the Support vector machine (SVM) model was established using spectral information combined with a preprocessing algorithm, and then the DNA of the samples was extracted to study the correlation between genetic and relative spectral distances, the model identification performance, and finally to compare the similarities and differences between the results of genetic clustering and relative spectral clustering. Results The results were as follows: the average accuracy of the models was 93.6% (inbred lines) and 93.7% (hybrids), respectively; Genetic distance and correlation spectral distance exhibited positive correlation significantly (inbred lines: r=0.177, p<0.05; hybrids: r=0.238, p<0.05), likewise genetic distance and model accuracy also showed positive correlation (inbred lines: r=0.611, p<0.01; hybrids: r=0.6158, p<0.01); Genetic clustering and spectral clustering results were essentially uniform for 94.3% (inbred lines) and 93.9% (hybrids), respectively. Discussion This study reveals the relationship between the genetic and relative spectral distances of seeds and the accuracy of the model, which provides theoretical basis for the establishment of the standardized system for detecting the authenticity of seeds by NIR spectroscopic techniques.
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Affiliation(s)
- Yongqin Yang
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Rashaun Candace Harrison
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Dun Zhang
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Binghui Shen
- Department of Applied Physics, College of Science, China Agricultural University, Beijing, China
| | - Yanlu Yan
- Department of Electrical Engineering, College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Dingming Kang
- Ministry of Education of the People's Republic of China (MOE) Key Laboratory of Crop Heterosis and Utilization, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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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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3
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Kharbach M, Alaoui Mansouri M, Taabouz M, Yu H. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches. Foods 2023; 12:2753. [PMID: 37509845 PMCID: PMC10379817 DOI: 10.3390/foods12142753] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/30/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
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Affiliation(s)
- Mourad Kharbach
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
- Department of Computer Sciences, University of Helsinki, 00560 Helsinki, Finland
| | - Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, 90014 Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Mohammed Taabouz
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Rabat BP 6203, Morocco
| | - Huiwen Yu
- Shenzhen Hospital, Southern Medical University, Shenzhen 518005, China
- Chemometrics group, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
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Wu Q, Oliveira MM, Achata EM, Kamruzzaman M. Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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5
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Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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Martínez-Martín I, Hernández-Jiménez M, Revilla I, Vivar-Quintana AM. Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:1491. [PMID: 36772530 PMCID: PMC9920201 DOI: 10.3390/s23031491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.
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Unuvar A, Boyaci I, Yazar S, Koksel H. Rapid detection of common wheat flour addition to durum wheat flour and pasta using spectroscopic methods and chemometrics. J Cereal Sci 2023. [DOI: 10.1016/j.jcs.2022.103604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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8
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Polarized light microscopy guarantees the use of autochthonous wheat in the production of flour for the Protected Geographical Indication ‘Galician Bread’. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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9
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Near infrared techniques applied to analysis of wheat-based products: Recent advances and future trends. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109115] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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10
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Guadalupi C, Braglia L, Gavazzi F, Morello L, Breviario D. A Combinatorial Q-locus and Tubulin-Based Polymorphism (TBP) Approach Helps in Discriminating Triticum Species. Genes (Basel) 2022; 13:genes13040633. [PMID: 35456439 PMCID: PMC9029001 DOI: 10.3390/genes13040633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 01/27/2023] Open
Abstract
The simple and straightforward recognition of Triticum species is not an easy task due to their complex genetic origins. To provide a recommendation, we have compared the performance of different PCR-based methods relying on the discrimination ability of the Q- and γ-gliadin (GAG56D) genes, as well as TBP (Tubulin-Based Polymorphism), a method based on the multiple amplification of genes of the β-tubulin family. Among these approaches, the PCR-RFLP (Restriction Fragment Length Polymorphism) assay based on a single-nucleotide polymorphism (SNP) present in the Q gene is the only one capable of fully discerning hexaploid spelt and common wheat species, while both γ-gliadin and TBP fail with similar error frequencies. The Q-locus assay results in the attainment of either a single fragment or a doublet, depending on the presence of a suitable restriction site, which is affected by the mutation. This dual pattern of resolution limits both the diagnostic effectiveness, when additional Triticum species are assayed and compared to each other, and its usefulness, when commercially available flours are analyzed. These limitations are overtaken by flanking the Q-locus assay with the TBP analysis. In this way, almost all of the Triticum species can be accurately identified.
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11
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Identification of Baha'sib mung beans based on Fourier transform near infrared spectroscopy and partial least squares. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review. Curr Res Food Sci 2022; 5:1305-1312. [PMID: 36065198 PMCID: PMC9440252 DOI: 10.1016/j.crfs.2022.08.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022] Open
Abstract
The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products. NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature. Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis. Online and handheld NIR spectrometers for wheat flour detection are the future trends.
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13
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A novel approach for rapid discrimination of common and durum wheat flours using spectroscopic analyses combined with chemometrics. J Cereal Sci 2021. [DOI: 10.1016/j.jcs.2021.103269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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14
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Liu HY, Wadood SA, Xia Y, Liu Y, Guo H, Guo BL, Gan RY. Wheat authentication:An overview on different techniques and chemometric methods. Crit Rev Food Sci Nutr 2021; 63:33-56. [PMID: 34196234 DOI: 10.1080/10408398.2021.1942783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.
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Affiliation(s)
- Hong-Yan Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Syed Abdul Wadood
- Department of Food and Nutrition, University of Home Economics, Lahore, Pakistan
| | - Yu Xia
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Yi Liu
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Huan Guo
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China
| | - Bo-Li Guo
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ren-You Gan
- Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu, China.,Chengdu National Agricultural Science & Technology Center, Chengdu, China.,Key Laboratory of Coarse Cereal Processing (Ministry of Agriculture and Rural Affairs), Sichuan Engineering & Technology Research Center of Coarse Cereal Industrialization, Chengdu University, Chengdu, China
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15
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Feng L, Wu B, Zhu S, He Y, Zhang C. Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins. Front Nutr 2021; 8:680357. [PMID: 34222304 PMCID: PMC8247466 DOI: 10.3389/fnut.2021.680357] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/25/2021] [Indexed: 01/25/2023] Open
Abstract
Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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Pandiselvam R, Sruthi NU, Kumar A, Kothakota A, Thirumdas R, Ramesh S, Cozzolino D. Recent Applications of Vibrational Spectroscopic Techniques in the Grain Industry. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1904253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- R. Pandiselvam
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - N. U. Sruthi
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Ankit Kumar
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science & Technology, Telangana, India
| | - S.V. Ramesh
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), the University of Queensland, Brisbane, Australia
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De Girolamo A, Arroyo MC, Cervellieri S, Cortese M, Pascale M, Logrieco AF, Lippolis V. Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109368] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Badaró AT, Garcia-Martin JF, López-Barrera MDC, Barbin DF, Alvarez-Mateos P. Determination of pectin content in orange peels by near infrared hyperspectral imaging. Food Chem 2020; 323:126861. [PMID: 32334320 DOI: 10.1016/j.foodchem.2020.126861] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 04/03/2020] [Accepted: 04/17/2020] [Indexed: 12/15/2022]
Abstract
Pectin has several purposes in the food and pharmaceutical industry making its quantification important for further extraction. Current techniques for pectin quantification require its extraction using chemicals and producing residues. Determination of pectin content in orange peels was investigated using near infrared hyperspectral imaging (NIR-HSI). Hyperspectral images from orange peel (140 samples) with different amounts of pectin were acquired in the range of 900-2500 nm, and the spectra was used for calibration models using multivariate statistical analyses. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed better results considering three groups: low (0-5%), intermediate (10-40%) and high (50-100%) pectin content. Partial least squares regression (PLSR) models based on full spectra showed higher precision (R2 > 0.93) than those based on few selected wavelengths (R2 between 0.92 and 0.94). The results demonstrate the potential of NIR-HSI to quantify pectin content in orange peels, providing a valuable technique for orange producers and processing industries.
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Affiliation(s)
- Amanda Teixeira Badaró
- Department of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas-SP 13083-862, Brazil.
| | | | | | - Douglas Fernandes Barbin
- Department of Food Engineering, University of Campinas (UNICAMP), Rua Monteiro Lobato, 80, Cidade Universitária, Campinas-SP 13083-862, Brazil.
| | - Paloma Alvarez-Mateos
- Departamento de Ingeniería Química, Facultad de Química, Universidad de Sevilla, Sevilla 41012, Spain.
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19
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Arslan FN, Akin G, Karuk Elmas ŞN, Üner B, Yilmaz I, Janssen HG, Kenar A. FT-IR spectroscopy with chemometrics for rapid detection of wheat flour adulteration with barley flour. J Verbrauch Lebensm 2020. [DOI: 10.1007/s00003-019-01267-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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20
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The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods. SENSORS 2019; 20:s20010230. [PMID: 31906139 PMCID: PMC6982964 DOI: 10.3390/s20010230] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/18/2019] [Accepted: 12/27/2019] [Indexed: 02/05/2023]
Abstract
Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.
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Rodríguez SD, López-Fernández MP, Maldonado S, Buera MP. Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2019; 56:4457-4464. [PMID: 31686677 DOI: 10.1007/s13197-019-03948-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 06/22/2019] [Accepted: 07/11/2019] [Indexed: 12/31/2022]
Abstract
Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C-H, C-H2, C-H3), alkene group (-CH=CH-), hydroxyl group (O-H) and Amides I and II vibrational modes.
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Affiliation(s)
- Silvio D Rodríguez
- 1Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,2Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA), CONICET - Universidad de Buenos Aires, Intendente Güiraldes 2160, Pabellón 2, 4to Piso, Ciudad Universitaria, Buenos Aires, Argentina
| | - M P López-Fernández
- 1Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,2Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA), CONICET - Universidad de Buenos Aires, Intendente Güiraldes 2160, Pabellón 2, 4to Piso, Ciudad Universitaria, Buenos Aires, Argentina
| | - S Maldonado
- 1Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,2Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA), CONICET - Universidad de Buenos Aires, Intendente Güiraldes 2160, Pabellón 2, 4to Piso, Ciudad Universitaria, Buenos Aires, Argentina
| | - M P Buera
- 1Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
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22
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Seo Y, Lee H, Mo C, Kim MS, Baek I, Lee J, Cho BK. Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses. SENSORS 2019; 19:s19163483. [PMID: 31395841 PMCID: PMC6720503 DOI: 10.3390/s19163483] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 07/19/2019] [Accepted: 07/30/2019] [Indexed: 11/30/2022]
Abstract
Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.
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Affiliation(s)
- Youngwook Seo
- Rural Development Administration, National Institute of Agricultural Sciences, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea.
| | - Changyeun Mo
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea
| | - Moon S Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
| | - Jayoung Lee
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
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23
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Curzon AY, Chandrasekhar K, Nashef YK, Abbo S, Bonfil DJ, Reifen R, Bar-El S, Avneri A, Ben-David R. Distinguishing between Bread Wheat and Spelt Grains Using Molecular Markers and Spectroscopy. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:3837-3841. [PMID: 30807140 DOI: 10.1021/acs.jafc.9b00131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The increasing demand for spelt products requires the baking industry to develop accurate and efficient tools to differentiate between spelt and bread wheat grains. We subjected a 272-sample spelt-bread wheat set to several potential diagnostic methods. DNA markers for γ-gliadin-D ( GAG56D), γ-gliadin-B ( GAG56B), and the Q-gene were used, alongside phenotypic assessment of ease-of-threshing and near-infrared spectroscopy (NIRS). The GAG56B and GAG56D markers demonstrated low diagnostic power in comparison to the Q-gene genotyping, which showed full accordance with the threshing phenotype, providing a highly accurate distinction between bread wheat and spelt kernels. A highly reliable Q classification was based on a three-waveband NIR model [Kappa (0.97), R-square (0.93)], which suggested that this gene influences grain characteristics. Our data ruled out a protein concentration bias of the NIRS-based diagnosis. These findings highlight the Q gene and NIRS as important, valuable, but simple tools for distinguishing between bread wheat and spelt.
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Affiliation(s)
- A Y Curzon
- Department of Vegetable and Field Crops, Institute of Plant Sciences , Agricultural Research Organization (ARO)-Volcani Center , Rishon LeZion 7528809 , Israel
- The Levi Eshkol School of Agriculture , The Hebrew University of Jerusalem , Rehovot 7610001 , Israel
| | - K Chandrasekhar
- Department of Vegetable and Field Crops, Institute of Plant Sciences , Agricultural Research Organization (ARO)-Volcani Center , Rishon LeZion 7528809 , Israel
| | - Y K Nashef
- Department of Vegetable and Field Crops, Institute of Plant Sciences , Agricultural Research Organization (ARO)-Volcani Center , Rishon LeZion 7528809 , Israel
| | - S Abbo
- The Levi Eshkol School of Agriculture , The Hebrew University of Jerusalem , Rehovot 7610001 , Israel
| | - D J Bonfil
- Department of Vegetable and Field Crops, Institute of Plant Sciences , Agricultural Research Organization (ARO)-Gilat Research Center , Gilat 8531100 , Israel
| | - R Reifen
- The School of Nutritional Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment , The Hebrew University of Jerusalem , Rehovot 7610001 , Israel
| | - S Bar-El
- The School of Nutritional Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment , The Hebrew University of Jerusalem , Rehovot 7610001 , Israel
| | - A Avneri
- The Levi Eshkol School of Agriculture , The Hebrew University of Jerusalem , Rehovot 7610001 , Israel
| | - R Ben-David
- Department of Vegetable and Field Crops, Institute of Plant Sciences , Agricultural Research Organization (ARO)-Volcani Center , Rishon LeZion 7528809 , Israel
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24
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Sadat A, Corradini MG, Joye IJ. Molecular spectroscopy to assess protein structures within cereal systems. Curr Opin Food Sci 2019. [DOI: 10.1016/j.cofs.2019.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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25
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Detection of quinoa flour adulteration by means of FT-MIR spectroscopy combined with chemometric methods. Food Chem 2019; 274:392-401. [DOI: 10.1016/j.foodchem.2018.08.140] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 07/30/2018] [Accepted: 08/31/2018] [Indexed: 12/31/2022]
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26
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Basati Z, Jamshidi B, Rasekh M, Abbaspour-Gilandeh Y. Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 203:308-314. [PMID: 29879646 DOI: 10.1016/j.saa.2018.05.123] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 05/16/2018] [Accepted: 05/29/2018] [Indexed: 05/23/2023]
Abstract
The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R2 = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers.
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Affiliation(s)
- Zahra Basati
- Department of Bio-systems Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Bahareh Jamshidi
- Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
| | - Mansour Rasekh
- Department of Bio-systems Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Yousef Abbaspour-Gilandeh
- Department of Bio-systems Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
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27
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Salimi Khorshidi A, Storsley J, Malunga LN, Thandapilly SJ, Ames N. Advancing the science of wheat quality evaluation using nuclear magnetic resonance (NMR) and ultrasound-based techniques. Cereal Chem 2018. [DOI: 10.1002/cche.10040] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
| | - Joanne Storsley
- Cereal Research Centre; Agriculture & Agri-Food Canada; Winnipeg MB Canada
| | | | - Sijo Joseph Thandapilly
- Cereal Research Centre; Agriculture & Agri-Food Canada; Winnipeg MB Canada
- Department of Food and Human Nutritional Sciences; University of Manitoba; Winnipeg MB Canada
| | - Nancy Ames
- Cereal Research Centre; Agriculture & Agri-Food Canada; Winnipeg MB Canada
- Department of Food and Human Nutritional Sciences; University of Manitoba; Winnipeg MB Canada
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28
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Su WH, Sun DW. Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review. Compr Rev Food Sci Food Saf 2017; 17:104-122. [DOI: 10.1111/1541-4337.12314] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 09/12/2017] [Accepted: 09/14/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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