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Galindo-Luján R, Pont L, Quispe F, Sanz-Nebot V, Benavente F. Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Combined with Chemometrics for Protein Profiling and Classification of Boiled and Extruded Quinoa from Conventional and Organic Crops. Foods 2024; 13:1906. [PMID: 38928847 PMCID: PMC11203106 DOI: 10.3390/foods13121906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/03/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.
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Affiliation(s)
- Rocío Galindo-Luján
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain; (R.G.-L.); (L.P.); (V.S.-N.)
| | - Laura Pont
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain; (R.G.-L.); (L.P.); (V.S.-N.)
- Serra Húnter Program, Generalitat de Catalunya, 08007 Barcelona, Spain
| | - Fredy Quispe
- National Institute of Agricultural Innovation (INIA), Lima 15024, Peru;
| | - Victoria Sanz-Nebot
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain; (R.G.-L.); (L.P.); (V.S.-N.)
| | - Fernando Benavente
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain; (R.G.-L.); (L.P.); (V.S.-N.)
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2
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Li Y, Chen B, Ye S, Wu Q, Zhu L, Ding Y. Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2024; 41:587-600. [PMID: 38648105 DOI: 10.1080/19440049.2024.2341104] [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: 01/28/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.
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Affiliation(s)
- Yaxin Li
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Baoguo Chen
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Shuhong Ye
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Qi Wu
- China National Institute of Standardization, Beijing, China
| | - Lin Zhu
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Yan Ding
- SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
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3
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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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RAHMAWATI L, ZAHRA AM, LISTANTI R, MASITHOH RE, HARIADI H, ADNAN, SYAFUTRI MI, LIDIASARI E, AMDANI RZ, PUSPITAHATI, AGUSTINI S, NURAINI L, VOLKANDARI SD, KARIMY MF, SURATNO, WINDARSIH A, PAHLAWAN MFR. Necessity of Log(1/R) and Kubelka-Munk transformation in chemometrics analysis to predict white rice flour adulteration in brown rice flour using visible-near-infrared spectroscopy. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.116422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
| | | | | | | | - Hari HARIADI
- National Research and Innovation Agency, Indonesia
| | - ADNAN
- National Research and Innovation Agency, Indonesia
| | | | | | | | | | - Sri AGUSTINI
- National Research and Innovation Agency, Indonesia
| | | | | | | | - SURATNO
- National Research and Innovation Agency, Indonesia
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5
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Galindo-Luján R, Pont L, Sanz-Nebot V, Benavente F. Protein profiling and classification of commercial quinoa grains by MALDI-TOF-MS and chemometrics. Food Chem 2023; 398:133895. [DOI: 10.1016/j.foodchem.2022.133895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/28/2022] [Accepted: 08/06/2022] [Indexed: 11/29/2022]
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Detection of sodium hydrosulfite adulteration in wheat flour by FT-MIR spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01763-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Tsagkaris AS, Kalogiouri N, Hrbek V, Hajslova J. Spelt authenticity assessment using a rapid and simple Fourier transform infrared spectroscopy (FTIR) method combined to advanced chemometrics. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04128-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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8
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Wang Z, Wu Q, Kamruzzaman M. Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108970] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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9
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Yang X, Xing B, Guo Y, Wang S, Guo H, Qin P, Hou C, Ren G. Rapid, accurate and simply-operated determination of laboratory-made adulteration of quinoa flour with rice flour and wheat flour by headspace gas chromatography-ion mobility spectrometry. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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10
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Faith Ndlovu P, Samukelo Magwaza L, Zeray Tesfay S, Ramaesele Mphahlele R. Destructive and rapid non-invasive methods used to detect adulteration of dried powdered horticultural products: A review. Food Res Int 2022; 157:111198. [DOI: 10.1016/j.foodres.2022.111198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 03/25/2022] [Accepted: 03/27/2022] [Indexed: 01/17/2023]
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11
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Kong D, Sun D, Qiu R, Zhang W, Liu Y, He Y. Rapid and nondestructive detection of marine fishmeal adulteration by hyperspectral imaging and machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:120990. [PMID: 35183858 DOI: 10.1016/j.saa.2022.120990] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Pure fishmeal (PFM) from whole marine-origin fish is an expensive and indispensable protein ingredient in most aquaculture feeds. In China, the supply shortage of domestically produced PFM has caused frequent PFM adulteration with low-cost protein sources such as feather meal (FTM) and fishmeal from by-products (FBP). The aim of this study was to develop a rapid and nondestructive detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms for the identification of PFM adulterated with FTM, FBP, and the binary adulterant (composed of FTM and FBP). A hierarchical modelling strategy was adopted to acquire a better classification accuracy. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) coupled with four spectral preprocessing methods were employed to construct classification models. The SVM with baseline offset (BO-SVM) model using 20 effective wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) achieved classification accuracy of 100% and 99.43% for discriminating PFM from the adulterants (FTM, FBP) and adulterated fishmeal (AFM), respectively. This study confirmed that NIR-HSI offered a promising technique for feed mills to identify AFM containing FTM, FBP, or binary adulterants.
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Affiliation(s)
- Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Dawei Sun
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Ruicheng Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
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12
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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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13
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Biancolillo A, Foschi M, Di Micco M, Di Donato F, D'Archivio A. ATR-FTIR-based rapid solution for the discrimination of lentils from different origins, with a special focus on PGI and Slow Food typical varieties. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107327] [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]
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14
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Lixourgioti P, Goggin KA, Zhao X, Murphy DJ, van Ruth S, Koidis A. Authentication of cinnamon spice samples using FT-IR spectroscopy and chemometric classification. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112760] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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15
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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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16
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Vis-NIR spectroscopic and chemometric models for detecting contamination of premium green banana flour with wheat by quantifying resistant starch content. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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17
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Stanschewski CS, Rey E, Fiene G, Craine EB, Wellman G, Melino VJ, S. R. Patiranage D, Johansen K, Schmöckel SM, Bertero D, Oakey H, Colque-Little C, Afzal I, Raubach S, Miller N, Streich J, Amby DB, Emrani N, Warmington M, Mousa MAA, Wu D, Jacobson D, Andreasen C, Jung C, Murphy K, Bazile D, Tester M. Quinoa Phenotyping Methodologies: An International Consensus. PLANTS (BASEL, SWITZERLAND) 2021; 10:1759. [PMID: 34579292 PMCID: PMC8472428 DOI: 10.3390/plants10091759] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 11/30/2022]
Abstract
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
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Affiliation(s)
- Clara S. Stanschewski
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Elodie Rey
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Gabriele Fiene
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Evan B. Craine
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Gordon Wellman
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Vanessa J. Melino
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Dilan S. R. Patiranage
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kasper Johansen
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia;
| | - Sandra M. Schmöckel
- Department Physiology of Yield Stability, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Daniel Bertero
- Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires C1417DSE, Argentina;
| | - Helena Oakey
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Carla Colque-Little
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Irfan Afzal
- Department of Agronomy, University of Agriculture, Faisalabad 38000, Pakistan;
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee AB15 8QH, UK;
| | - Nathan Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Dr, Madison, WI 53706, USA;
| | - Jared Streich
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Daniel Buchvaldt Amby
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Nazgol Emrani
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Mark Warmington
- Department of Primary Industries and Regional Development, Agriculture and Food, Kununurra, WA 6743, Australia;
| | - Magdi A. A. Mousa
- Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Department of Vegetables, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
| | - David Wu
- Shanxi Jiaqi Agri-Tech Co., Ltd., Taiyuan 030006, China;
| | - Daniel Jacobson
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Christian Andreasen
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Christian Jung
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kevin Murphy
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Didier Bazile
- CIRAD, UMR SENS, 34398 Montpellier, France;
- SENS, CIRAD, IRD, University Paul Valery Montpellier 3, 34090 Montpellier, France
| | - Mark Tester
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
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Mshayisa VV, Van Wyk J, Zozo B, Rodríguez SD. Structural properties of native and conjugated black soldier fly ( Hermetia illucens) larvae protein via Maillard reaction and classification by SIMCA. Heliyon 2021; 7:e07242. [PMID: 34189301 PMCID: PMC8215222 DOI: 10.1016/j.heliyon.2021.e07242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/21/2021] [Accepted: 06/03/2021] [Indexed: 12/24/2022] Open
Abstract
Black soldier fly (Hermetia illucens) has received considerable interest as an alternative protein source. Aqueous solutions of black soldier fly larvae (BSFL) protein and glucose (2:1 w.w−1, pH 9) were heated at 50, 70 and 90 °C, for 2–10 h at 2 h intervals, respectively. The zeta-potential (ζ) of BSFL-Glu conjugates heat-treated at 70 °C ranged from -10.25 to -25.25 mV while the native BSFL protein ranged from -12.84 to -16.70 mV. The ζ-potential analysis revealed that the glycation reaction modified the surface charge density of the BSFL protein as a function of reaction time and temperature. In addition, an increase in thermal stability of the BSFL-Glu conjugates was observed by means of Thermo-gravimetric analysis (TGA) and differential scanning calorimetry (DSC). Fourier transform infrared spectroscopy (FT-IR) analysis indicated that the most apparent structural changes in the BSFL protein were in the amide I and amide II region. Well-separated clusters permitting differentiation between native BSFL and BSFL-Glu conjugates were observed by using principal component analysis (PCA) on FT-IR spectra. At 50, 70 and 90 °C the first two principal components (PC1 and PC2) showed an accumulated total variance of 91, 96 and 95%, respectively. A classification efficiency of 91% was obtained when using soft independent modelling of class analogy (SIMCA). Infrared spectroscopy combined with SIMCA is a powerful tool to monitor the formation of edible insect protein–sugar conjugates by Maillard reaction. As a result, combining FT-IR spectroscopy with multivariate techniques (PCA and SIMCA) exhibited a strong potential to differentiate between native and glycated protein samples from black soldier fly larvae.
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Affiliation(s)
- Vusi Vincent Mshayisa
- Department of Food Science and Technology, Cape Peninsula University of Technology, Bellville, 7535, South Africa
| | - Jessy Van Wyk
- Department of Food Science and Technology, Cape Peninsula University of Technology, Bellville, 7535, South Africa
| | - Bongisiwe Zozo
- Department of Chemistry, Cape Peninsula University of Technology, Bellville, 7535, South Africa
| | - Silvio D Rodríguez
- Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA), CONICET -Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
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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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21
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Cleaner and faster method to detect adulteration in cassava starch using Raman spectroscopy and one-class support vector machine. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.107917] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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22
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Khamsopha D, Woranitta S, Teerachaichayut S. Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107781] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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23
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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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24
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Faqeerzada MA, Lohumi S, Kim G, Joshi R, Lee H, Kim MS, Cho BK. Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5855. [PMID: 33081195 PMCID: PMC7589775 DOI: 10.3390/s20205855] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/17/2020] [Accepted: 10/11/2020] [Indexed: 11/16/2022]
Abstract
The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.
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Affiliation(s)
- Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
| | - Santosh Lohumi
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
| | - Geonwoo Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, BARC-East, Beltsville, MD 20705, USA;
| | - Rahul Joshi
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea;
| | - Moon Sung Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, BARC-East, Beltsville, MD 20705, USA;
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea; (M.A.F.); (S.L.); (R.J.)
- Department of Smart Agriculture System, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
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25
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Galindo-Luján R, Pont L, Sanz-Nebot V, Benavente F. Classification of quinoa varieties based on protein fingerprinting by capillary electrophoresis with ultraviolet absorption diode array detection and advanced chemometrics. Food Chem 2020; 341:128207. [PMID: 33035861 DOI: 10.1016/j.foodchem.2020.128207] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 08/10/2020] [Accepted: 09/23/2020] [Indexed: 12/24/2022]
Abstract
Quinoa (Chenopodium quinoa Willd.) is an andean grain with exceptional nutritional properties that has been progressively introduced in western countries as a protein-rich super food with a broad amino acid spectrum. Quinoa is consumed as whole grain, but it is also milled to produce high-value flour, which is susceptible to adulteration. Therefore, there is a growing interest in developing novel analytical methods to get further information about quinoa at the chemical level. In this study, we developed a rapid and simple capillary electrophoresis-ultraviolet absorption diode array detection (CE-UV-DAD) method to obtain characteristic multiwavelength electrophoretic profiles of soluble protein extracts from different quinoa grain varieties. Then, advanced chemometric methods (i.e. multivariate curve resolution alternating least squares, MCR-ALS, followed by principal component analysis, PCA, and partial least squares discriminant analysis, PLS-DA) were applied to deconvolute the components present in the electropherograms and classify the quinoa varieties according to their differential protein composition.
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Affiliation(s)
- Rocío Galindo-Luján
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Laura Pont
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Victoria Sanz-Nebot
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Fernando Benavente
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain.
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26
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Arendse E, Nieuwoudt H, Magwaza LS, Nturambirwe JFI, Fawole OA, Opara UL. Recent Advancements on Vibrational Spectroscopic Techniques for the Detection of Authenticity and Adulteration in Horticultural Products with a Specific Focus on Oils, Juices and Powders. FOOD BIOPROCESS TECH 2020. [DOI: 10.1007/s11947-020-02505-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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27
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Wallace ED, Todd DA, Harnly JM, Cech NB, Kellogg JJ. Identification of adulteration in botanical samples with untargeted metabolomics. Anal Bioanal Chem 2020; 412:4273-4286. [PMID: 32347364 PMCID: PMC7321857 DOI: 10.1007/s00216-020-02678-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/27/2020] [Accepted: 04/21/2020] [Indexed: 01/28/2023]
Abstract
Adulteration remains an issue in the dietary supplement industry, including botanical supplements. While it is common to employ a targeted analysis to detect known adulterants, this is difficult when little is known about the sample set. With this study, untargeted metabolomics using liquid chromatography coupled to ultraviolet-visible spectroscopy (LC-UV) or high-resolution mass spectrometry (LC-MS) was employed to detect adulteration in botanical dietary supplements. A training set was prepared by combining Hydrastis canadensis L. with a known adulterant, Coptis chinensis Franch., in ratios ranging from 5 to 95% adulteration. The metabolomics datasets were analyzed using both unsupervised (principal component analysis and composite score) and supervised (SIMCA) techniques. Palmatine, a known H. canadensis metabolite, was quantified as a targeted analysis comparison. While the targeted analysis was the most sensitive method tested in detecting adulteration, statistical analyses of the untargeted metabolomics datasets detected adulteration of the goldenseal samples, with SIMCA providing the greatest discriminating potential. Graphical abstract.
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Affiliation(s)
- E Diane Wallace
- Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA
| | - Daniel A Todd
- Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA
| | - James M Harnly
- U.S. Department of Agriculture, Agricultural Research Service, Food Composition and Methods Development Laboratory, Beltsville Human Nutrition Research Center, Beltsville, MD, 20705, USA
| | - Nadja B Cech
- Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA
| | - Joshua J Kellogg
- Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA.
- Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, 16802, USA.
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28
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He Y, Bai X, Xiao Q, Liu F, Zhou L, Zhang C. Detection of adulteration in food based on nondestructive analysis techniques: a review. Crit Rev Food Sci Nutr 2020; 61:2351-2371. [PMID: 32543218 DOI: 10.1080/10408398.2020.1777526] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.
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Affiliation(s)
- Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ministry of Agriculture and Rural Affairs, Key Laboratory of Spectroscopy Sensing, Hangzhou, Zhejiang, China
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29
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Ayseli MT, Yilmaz MT, Cebi N, Sagdic O, Ozmen D, Capanoglu E. Physicochemical, rheological, molecular, thermal and sensory evaluation of newly developed complementary infant (6–24 months old) foods prepared with quinoa (Chenopodium quinoa Willd.) flour. Food Chem 2020; 315:126208. [DOI: 10.1016/j.foodchem.2020.126208] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 12/29/2019] [Accepted: 01/11/2020] [Indexed: 11/16/2022]
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30
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de Magalhães BEA, Santana DDA, Silva IMDJ, Minho LAC, Gomes MA, Almeida JRGDS, Lopes dos Santos WN. Determination of phenolic composition of oilseed whole flours by HPLC-DAD with evaluation using chemometric analyses. Microchem J 2020. [DOI: 10.1016/j.microc.2020.104683] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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31
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Shen T, Yu H, Wang YZ. Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization. Molecules 2020; 25:molecules25061442. [PMID: 32210010 PMCID: PMC7144467 DOI: 10.3390/molecules25061442] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 03/15/2020] [Accepted: 03/20/2020] [Indexed: 01/09/2023] Open
Abstract
Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm-1) and Fourier transform mid-infrared (MIR: 4000-600 cm-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.
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Affiliation(s)
- Tao Shen
- Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China;
- The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresources in China (Yunnan) and Southeast Asia, Yunnan University, Kunming 650091, China
- College of Chemistry, Biological and Environment, Yuxi Normal University, Yu’xi 653100, China
| | - Hong Yu
- Yunnan Herbal Laboratory, Institute of Herb Biotic Resources, School of Life and Sciences, Yunnan University, Kunming 650091, China;
- The International Joint Research Center for Sustainable Utilization of Cordyceps Bioresources in China (Yunnan) and Southeast Asia, Yunnan University, Kunming 650091, China
- Correspondence: ; Tel.: +86-1370-067-6633
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China;
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32
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Romano N, Ureta MM, Guerrero-Sánchez M, Gómez-Zavaglia A. Nutritional and technological properties of a quinoa (Chenopodium quinoa Willd.) spray-dried powdered extract. Food Res Int 2020; 129:108884. [DOI: 10.1016/j.foodres.2019.108884] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 11/30/2019] [Accepted: 12/01/2019] [Indexed: 01/26/2023]
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33
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Rodríguez SD, Gagneten M, Farroni AE, Percibaldi NM, Buera MP. FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils. Food Control 2019. [DOI: 10.1016/j.foodcont.2019.05.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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34
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Identification of fiber added to semolina by near infrared (NIR) spectral techniques. Food Chem 2019; 289:195-203. [DOI: 10.1016/j.foodchem.2019.03.057] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 03/07/2019] [Accepted: 03/11/2019] [Indexed: 01/11/2023]
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35
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36
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Jiao X, Meng Y, Wang K, Huang W, Li N, Liu TCY. Rapid Detection of Adulterants in Whey Protein Supplement by Raman Spectroscopy Combined with Multivariate Analysis. Molecules 2019; 24:E1889. [PMID: 31100965 PMCID: PMC6571825 DOI: 10.3390/molecules24101889] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 04/29/2019] [Accepted: 05/14/2019] [Indexed: 11/21/2022] Open
Abstract
The growing demand for whey protein supplements has made them the target of adulteration with cheap substances. Therefore, Raman spectroscopy in tandem with chemometrics was proposed to simultaneously detect and quantify three common adulterants (creatine, l-glutamine and taurine) in whey protein concentrate (WPC) powder. Soft independent modeling class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) models were built based on two spectral regions (400-1800 cm-1 and 500-1100 cm-1) to classify different types of adulterated samples. The most effective was the SIMCA model in 500-1100 cm-1 with an accuracy of 96.9% and an error rate of 5%. Partial least squares regression (PLSR) models for each adulterant were developed using two different Raman spectral ranges (400-1800 cm-1 and selected specific region) and data pretreatment methods. The determination coefficients (R2) of all models were higher than 0.96. PLSR models based on typical Raman regions (500-1100 cm-1 for creatine and taurine, the combination of range 800-1000 cm-1 and 1300-1500 cm-1 for glutamine) were superior to models in the full spectrum. The lowest root mean squared error of prediction (RMSEP) was 0.21%, 0.33%, 0.42% for creatine, taurine and glutamine, and the corresponding limit of detection (LOD) values for them were 0.53%, 0.71% and 1.13%, respectively. This proves that Raman spectroscopy with the help of multivariate approaches is a powerful method to detect adulterants in WPC.
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Affiliation(s)
- Xianzhi Jiao
- MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine, College of Biophotonics, South China Normal University, Guangdong 510631, China.
| | - Yaoyong Meng
- MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine, College of Biophotonics, South China Normal University, Guangdong 510631, China.
| | - Kangkang Wang
- MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine, College of Biophotonics, South China Normal University, Guangdong 510631, China.
| | - Wei Huang
- MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine, College of Biophotonics, South China Normal University, Guangdong 510631, China.
| | - Nan Li
- MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine, College of Biophotonics, South China Normal University, Guangdong 510631, China.
| | - Timon Cheng-Yi Liu
- Laboratory of Laser Sports Medicine, South China Normal University, Guangdong 510631, China.
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