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Zong W, Zhao S, Li Y, Yang X, Qie M, Zhang P, Zhao Y. Trace the origin of yak meat in Xizang based on stable isotope combined with multivariate statistics. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171949. [PMID: 38537817 DOI: 10.1016/j.scitotenv.2024.171949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/05/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
In this study, the feasibility of tracing the origin of yak meat in Xizang Autonomous Region based on stable isotope combined with multivariable statistics was researched. The δ13C, δ15N, δ2H and δ18O in yak meat were determined by stable isotope ratio mass spectrometry, and the data were analyzed by analysis of variance, fisher discriminant analysis (FDA), back propagation (BP) neural network and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the δ13C, δ15N, δ2H and δ18O had significant differences among different origins (P < 0.05). The overall original correct discrimination rate of fisher discriminant analysis was 89.7 %, and the correct discrimination rate of cross validation was 88.2 %. The correct classification rate of BP neural network based on training set was 93.38 %, and the correct classification rate of BP neural network based on test set was 89.83 %. The OPLS-DA model interpretation rate parameter R2Y was 0.67, the model prediction rate parameter Q2 was 0.409, which could distinguish yak meat from seven different producing areas in Xizang Autonomous Region. The results showed that the origin of yak meat in Xizang Autonomous Region can be traced based on stable isotope combined with multivariate statistics.
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Affiliation(s)
- Wanli Zong
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Weihai Institute for Food and Drug Control, Weihai Key Laboratory of Food and Drug Quality Evaluation and Technical Research, Weihai 264210, 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 100081, 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 100081, China
| | - Xiaoting Yang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, 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 100081, China
| | - Ping Zhang
- Menzies Health Institute, Griffith University, Australia
| | - 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 100081, China.
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Kang X, Zhao Y, Yao L, Tan Z. Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis. Curr Res Food Sci 2024; 8:100738. [PMID: 38659973 PMCID: PMC11039350 DOI: 10.1016/j.crfs.2024.100738] [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: 01/11/2024] [Revised: 04/06/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.
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Affiliation(s)
- Xuming Kang
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | - Yanfang Zhao
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | - Lin Yao
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | - Zhijun Tan
- Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
- Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266071, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian, 116034, China
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Varrà MO, Zanardi E, Serra M, Conter M, Ianieri A, Ghidini S. Isotope Fingerprinting as a Backup for Modern Safety and Traceability Systems in the Animal-Derived Food Chain. Molecules 2023; 28:4300. [PMID: 37298773 PMCID: PMC10254398 DOI: 10.3390/molecules28114300] [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: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
In recent years, due to the globalization of food trade and certified agro-food products, the authenticity and traceability of food have received increasing attention. As a result, opportunities for fraudulent practices arise, highlighting the need to protect consumers from economic and health damages. In this regard, specific analytical techniques have been optimized and implemented to support the integrity of the food chain, such as those targeting different isotopes and their ratios. This review article explores the scientific progress of the last decade in the study of the isotopic identity card of food of animal origin, provides the reader with an overview of its application, and focuses on whether the combination of isotopes with other markers increases confidence and robustness in food authenticity testing. To this purpose, a total of 135 studies analyzing fish and seafood, meat, eggs, milk, and dairy products, and aiming to examine the relation between isotopic ratios and the geographical provenance, feeding regime, production method, and seasonality were reviewed. Current trends and major research achievements in the field were discussed and commented on in detail, pointing out advantages and drawbacks typically associated with this analytical approach and arguing future improvements and changes that need to be made to recognize it as a standard and validated method for fraud mitigation and safety control in the sector of food of animal origin.
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Affiliation(s)
- Maria Olga Varrà
- Department of Food and Drug, University of Parma, 43126 Parma, Italy
| | - Emanuela Zanardi
- Department of Food and Drug, University of Parma, 43126 Parma, Italy
| | - Matteo Serra
- Department of Food and Drug, University of Parma, 43126 Parma, Italy
| | - Mauro Conter
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | - Adriana Ianieri
- Department of Food and Drug, University of Parma, 43126 Parma, Italy
| | - Sergio Ghidini
- Department of Food and Drug, University of Parma, 43126 Parma, Italy
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Guo B, Zou Z, Huang Z, Wang Q, Qin J, Guo Y, Pan S, Wei J, Guo H, Zhu D, Su Z. A simple and green method for simultaneously determining the geographical origin and glycogen content of oysters using ATR–FTIR and chemometrics. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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Recent advances in Chinese food authentication and origin verification using isotope ratio mass spectrometry. Food Chem 2023; 398:133896. [DOI: 10.1016/j.foodchem.2022.133896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 08/03/2022] [Accepted: 08/06/2022] [Indexed: 11/20/2022]
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Thomatou AA, Psarra E, Mazarakioti EC, Katerinopoulou K, Tsirogiannis G, Zotos A, Kontogeorgos A, Patakas A, Ladavos A. Stable Isotope Analysis for the Discrimination of the Geographical Origin of Greek Bottarga ‘Avgotaracho Messolongiou’: A Preliminary Research. Foods 2022; 11:foods11192960. [PMID: 36230036 PMCID: PMC9564321 DOI: 10.3390/foods11192960] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/10/2022] [Accepted: 09/19/2022] [Indexed: 11/30/2022] Open
Abstract
Consumers are increasingly interested in the geographical origin of the foodstuff they consume as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products such as wine, oil, meat, while only a few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products. Analysis of the stable isotopes of C, N and S of Bottarga samples from four different origins were carried out. The values of δ15N (5.45‰) and δ34S (4.66‰) for the Greek Bottarga Product named ‘Avgotaracho Messolongiou’, from Messolongi lagoon were lower than other areas while δ13C values were higher (−14.84‰). The first results show that the stable isotopes ratios of carbon, nitrogen and sulphur could be used to discriminate the Greek Protected Designations of Origin Bottarga product ‘Avgotaracho Messolongiou’ from other similar products.
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Affiliation(s)
- Anna-Akrivi Thomatou
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
| | - Eleni Psarra
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
| | - Eleni C. Mazarakioti
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
| | - Katerina Katerinopoulou
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
| | - Georgios Tsirogiannis
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
| | - Anastasios Zotos
- Department of Biosystems Science and Agricultural Engineering, University of Patras, 30200 Messolongi, Greece
| | - Achilleas Kontogeorgos
- Department of Agriculture, International Hellenic University, 57001 Thessaloniki, Greece
| | - Angelos Patakas
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
| | - Athanasios Ladavos
- Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
- Correspondence: ; Tel.: +30-26410-74126
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Kang X, Zhao Y, Tan Z, Ning J, Zhai Y, Zheng G. Evaluation of multivariate data analysis for marine mussels Mytilus edulis authentication in China: Based on stable isotope ratio and compositions of C, N, O and H. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104627] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Kang X, Zhao Y, Peng J, Ding H, Tan Z, Han C, Sheng X, Liu X, Zhai Y. Authentication of the Geographical Origin of Shandong Scallop Chlamys farreri Using Mineral Elements Combined with Multivariate Data Analysis and Machine Learning Algorithm. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02346-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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