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Lapcharoensuk R, Moul C. Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124480. [PMID: 38781824 DOI: 10.1016/j.saa.2024.124480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 05/11/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.
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Affiliation(s)
- Ravipat Lapcharoensuk
- Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
| | - Chen Moul
- Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
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de Oliveira Costa T, Rangel Botelho J, Helena Cassago Nascimento M, Krause M, Tereza Weitzel Dias Carneiro M, Coelho Ferreira D, Roberto Filgueiras P, de Oliveira Souza M. A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS). Food Chem 2024; 442:138268. [PMID: 38242000 DOI: 10.1016/j.foodchem.2023.138268] [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: 08/10/2023] [Revised: 11/27/2023] [Accepted: 12/22/2023] [Indexed: 01/21/2024]
Abstract
Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).
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Affiliation(s)
- Tayná de Oliveira Costa
- Laboratório de Analítica, Metabolômica e Quimiometria (LAMeQui), Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo, Campus Alegre (IFES), Brazil; Programa de Pós-Graduação em Ciências Naturais (PPGCN), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Brazil
| | | | | | - Maiara Krause
- Departamento de Química, Universidade Federal do Espírito Santo (UFES), Brazil
| | | | | | | | - Murilo de Oliveira Souza
- Laboratório de Analítica, Metabolômica e Quimiometria (LAMeQui), Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo, Campus Alegre (IFES), Brazil; Programa de Pós-Graduação em Ciências Naturais (PPGCN), Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Brazil.
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Thantar S, Mihailova A, Islam MD, Maxwell F, Hamed I, Vlachou C, Kelly SD. Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics. Talanta 2024; 273:125910. [PMID: 38492284 DOI: 10.1016/j.talanta.2024.125910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/06/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.
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Affiliation(s)
- Saw Thantar
- Department of Nuclear Technology, Kyaukse Technological University, Kyaukse, Myanmar
| | - Alina Mihailova
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria.
| | - Marivil D Islam
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Florence Maxwell
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Islam Hamed
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Christina Vlachou
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
| | - Simon D Kelly
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria
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Hong Y, Birse N, Quinn B, Li Y, Jia W, van Ruth S, Elliott CT. MALDI-ToF MS and chemometric analysis as a tool for identifying wild and farmed salmon. Food Chem 2024; 432:137279. [PMID: 37657341 DOI: 10.1016/j.foodchem.2023.137279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023]
Abstract
In this study, the difference between wild and farmed salmon production was successfully profiled and differentiated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) combined with chemometric analysis. The established method based on multivariate analysis mainly involved principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) as the screening and verifying tools to provide insights into the distinctive features found in wild and farmed salmon products, respectively. The discrimination between farmed and wild salmon was accomplished with 100% classification accuracy using chemometric models, 100% identification accuracy was also achieved in distinguishing wild Salmo salar and Oncorhynchus nerka samples. The results of the present work suggest that the proposed method could serve as a reference for detecting salmon fraud relating to wild or farmed production and expand the application of MALDI-ToF technology further into food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom.
| | - Brian Quinn
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, Netherlands; School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani 12120, Thailand
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Hong Y, Birse N, Quinn B, Li Y, Jia W, McCarron P, Wu D, da Silva GR, Vanhaecke L, van Ruth S, Elliott CT. Data fusion and multivariate analysis for food authenticity analysis. Nat Commun 2023; 14:3309. [PMID: 37291121 PMCID: PMC10250487 DOI: 10.1038/s41467-023-38382-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/27/2023] [Indexed: 06/10/2023] Open
Abstract
A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Brian Quinn
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Philip McCarron
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Gonçalo Rosas da Silva
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
| | - Lynn Vanhaecke
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom
- Laboratory of Integrative Metabolomics, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, The Netherlands
- School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, United Kingdom.
- School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani, 12120, Thailand.
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Mazarakioti EC, Zotos A, Thomatou AA, Kontogeorgos A, Patakas A, Ladavos A. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), a Useful Tool in Authenticity of Agricultural Products' and Foods' Origin. Foods 2022; 11:foods11223705. [PMID: 36429296 PMCID: PMC9689705 DOI: 10.3390/foods11223705] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
Fraudulent practices are the first and foremost concern of food industry, with significant consequences in economy and human's health. The increasing demand for food has led to food fraud by replacing, mixing, blending, and mislabeling products attempting to increase the profits of producers and companies. Consequently, there was the rise of a multidisciplinary field which encompasses a large number of analytical techniques aiming to trace and authenticate the origins of agricultural products, food and beverages. Among the analytical strategies have been developed for the authentication of geographical origin of foodstuff, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) increasingly dominates the field as a robust, accurate, and highly sensitive technique for determining the inorganic elements in food substances. Inorganic elements are well known for evaluating the nutritional composition of food products while it has been shown that they are considered as possible tracers for authenticating the geographical origin. This is based on the fact that the inorganic component of identical food type originating from different territories varies due to the diversity of matrix composition. The present systematic literature review focusing on gathering the research has been done up-to-date on authenticating the geographical origin of agricultural products and foods by utilizing the ICP-MS technique. The first part of the article is a tutorial about food safety/control and the fundaments of ICP-MS technique, while in the second part the total research review is discussed.
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Affiliation(s)
- Eleni C. Mazarakioti
- Department of Food Science and Technology, University of Patras, 30100 Agrinio, Greece
- Correspondence: (E.C.M.); (A.L.); Tel.: +30-26410-74126 (A.L.)
| | - Anastasios Zotos
- Department of Sustainable Agriculture, University of Patras, 30100 Agrinio, Greece
| | - Anna-Akrivi Thomatou
- Department of Food Science and Technology, University of Patras, 30100 Agrinio, Greece
| | - Achilleas Kontogeorgos
- Department of Agriculture, International Hellenic University, 57001 Thessaloniki, Greece
| | - Angelos Patakas
- Department of Food Science and Technology, University of Patras, 30100 Agrinio, Greece
| | - Athanasios Ladavos
- Department of Food Science and Technology, University of Patras, 30100 Agrinio, Greece
- Correspondence: (E.C.M.); (A.L.); Tel.: +30-26410-74126 (A.L.)
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