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Zhang J, Ma N, Xu G, Kuang L, Shen Y. Discrimination of apples from the surrounding Bohai Bay and the loess plateau: A combined study of ICP-MS and UPLC-Q-TOF-MS based element and metabolite fingerprints. Food Chem 2024; 459:140279. [PMID: 38991451 DOI: 10.1016/j.foodchem.2024.140279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/13/2024]
Abstract
Apples are important fruits in China, and their authentication is beneficial for quality control. However, the differentiation between apples from two primary producing regions, the surrounding Bohai Bay (BHB) and the Loess Plateau (LP), has not been well studied. This study used element and metabolite fingerprints combined with mathematical recognition techniques to discriminate between BHB and LP apples. A total of 235 samples were collected from these regions during 2018-2019. The apple element and metabolite profiles were obtained via instrument analysis. Differential elements and metabolites between BHB and LP apples were identified, and linear and nonlinear discriminant models were constructed. Nonlinear models demonstrated higher accuracy and effectiveness in model optimization. The final random forest (RF) model, constructed with 11 elements and 51 metabolites, achieved a training accuracy of 91.51% and a validation accuracy of 98.57%. This study discriminated between BHB and LP apples, providing a foundation for apple authentication.
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Affiliation(s)
- Jianyi Zhang
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China
| | - Ning Ma
- College of Veterinary Medicine, Agricultural University of Hebei, Baoding 071000, Hebei Province, PR China
| | - Guofeng Xu
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China
| | - Lixue Kuang
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China
| | - Youming Shen
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China..
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2
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Song D, Dong K, Liu S, Fu S, Zhao F, Man C, Jiang Y, Zhao K, Qu B, Yang X. Research advances in detection of food adulteration and application of MALDI-TOF MS: A review. Food Chem 2024; 456:140070. [PMID: 38917694 DOI: 10.1016/j.foodchem.2024.140070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/28/2024] [Accepted: 06/09/2024] [Indexed: 06/27/2024]
Abstract
Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.
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Affiliation(s)
- Danliangmin Song
- Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, China
| | - Kai Dong
- Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, China
| | - Shiyu Liu
- Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, China
| | - Shiqian Fu
- Zhejiang-Malaysia Joint Research Laboratory for Agricultural Product Processing and Nutrition, Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China
| | - Feng Zhao
- Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, China
| | - Chaoxin Man
- Key Laboratory of Dairy Science, Ministry of Education, Harbin 150030, China
| | - Yujun Jiang
- Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, China; Food Laboratory of Zhongyuan, Luohe 462300, Henan, China
| | - Kuangyu Zhao
- Fang zheng comprehensive Product quality inspection and testing center, Harbin 150030, China
| | - Bo Qu
- Key Laboratory of Dairy Science, Ministry of Education, Department of Food Science, Northeast Agricultural University, Harbin 150030, China.
| | - Xinyan Yang
- Key Laboratory of Dairy Science, Ministry of Education, Harbin 150030, China.
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3
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Li X, Li P, Tang W, Zheng J, Fan F, Jiang X, Li Z, Fang Y. Simultaneous determination of subspecies and geographic origins of 110 rice cultivars by microsatellite markers. Food Chem 2024; 445:138657. [PMID: 38354640 DOI: 10.1016/j.foodchem.2024.138657] [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: 11/28/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
Rice varieties of different subspecies types (indica rice and japonica rice) across various geographical origins (Hunan, Jiangsu, and Northeast China) were monitored using microsatellite markers (simple sequence repeats, SSR). 110 representative rice cultivars were collected from the main crop areas. Multiple methods including clustering analysis (neighbor-joining (NJ) method, unweighted pair-group method with arithmetic mean (UPGMA) method), principal component analysis (PCA) and model-based grouping were applied. The study revealed that 25 pairs of SSR markers exhibited a broad range of polymorphism information content (PIC) values, ranging from 0.240 to 0.830. Furthermore, our study successfully achieved a higher overall mean correct rate of 99.09% in determining the geographical origin of rice. Simultaneously, it accurately classified indica rice and japonica rice. These findings are significant as they provide an SSR fingerprint of 110 high-quality rice cultivars, serving as a valuable scientific resource for the detection of rice adulteration and traceability of its origin.
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Affiliation(s)
- Xinyue Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Peng Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Wenqian Tang
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Jiayu Zheng
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Fengjiao Fan
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Xiaoyi Jiang
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Ziqian Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China
| | - Yong Fang
- College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, China.
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4
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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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5
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Kang Z, Fan R, Zhan C, Wu Y, Lin Y, Li K, Qing R, Xu L. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning. Molecules 2024; 29:682. [PMID: 38338424 PMCID: PMC10856461 DOI: 10.3390/molecules29030682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
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Affiliation(s)
- Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rongsheng Fan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Chunyi Zhan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Youli Wu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Yi Lin
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Kunyu Li
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rui Qing
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
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6
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Thuy Bui T, Jeong S, Jeong H, Truong Le G, Quynh Nguyen H, Chung H. Authentication of ST25 rice using temperature-perturbed Raman measurement with variable selection by Incremental Association Markov Blanket. Food Chem 2023; 429:136985. [PMID: 37517227 DOI: 10.1016/j.foodchem.2023.136985] [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: 05/22/2023] [Revised: 07/13/2023] [Accepted: 07/22/2023] [Indexed: 08/01/2023]
Abstract
A temperature-perturbed transmission Raman measurement was demonstrated for the discrimination of ST25 and non-ST25 rice samples. ST25 rice is a premium long-grain Vietnamese rice with the aroma of pandan leaves and the scent of early sticky rice. Raman spectra of rice samples were acquired with temperature perturbation ranging from 20 to 50 °C, and the variables (intensities of peaks) with greater discrimination were selected from the spectra using Incremental Association Markov Blanket (IAMB) for authentication. The combination of four, seven, and four variables selected from the spectra at 20, 30, and 50 °C, respectively, yielded the highest accuracy of 97.9%. The accuracies in the single-temperature measurements were lower, suggesting that the combination of mutually complementary spectral features acquired at these temperatures is synergetic to recognize the compositional differences between two sample groups, such as in the amylose/amylopectin ratio and the protein constituent.
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Affiliation(s)
- Thu Thuy Bui
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Seongsoo Jeong
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Haeseong Jeong
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea
| | - Giang Truong Le
- Institute of Chemistry, Vietnam Academy of Science and Technology, Hanoi, Viet Nam
| | - Hoa Quynh Nguyen
- Department of General Education, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Viet Nam.
| | - Hoeil Chung
- Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea.
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7
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Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
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Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
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8
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Wu S, Zheng J, Chen Y, Yi L, Liu C, Li G. Chemometrics-based Discrimination of Virgin and Recycled Acrylonitrile-Butadiene-Styrene Plastics Toys via Non-targeted Screening of Volatile Substances. J Chromatogr A 2023; 1711:464442. [PMID: 37844445 DOI: 10.1016/j.chroma.2023.464442] [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: 08/05/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/18/2023]
Abstract
Owing to the growing emphasis on child safety, it is greatly urgent to identify and assess the unknown compounds and discriminate the recycled materials for plastic toys. In this study, gas chromatography mass spectrometry coupled with static headspace has been optimized by response surface methodology for non-targeted screening of unknown volatiles in acrylonitrile-butadiene-styrene (ABS) plastic toys. Optimum conditions for static headspace were 120 °C for extraction temperature and 48 min for extraction time. A total of 83 volatiles in 11 categories were qualitatively identified by matching the NIST database library, retention index and standard materials. Considering high positive rate and potential toxicity, high-risk volatiles in ABS plastic toys were listed and traced for safety pre-warning. Moreover, the differential volatiles between virgin and recycled ABS plastics were screened out by orthogonal partial least-squares discrimination analysis. Principal component analysis, hierarchical cluster analysis and linear discrimination analysis were employed to successfully discriminate recycled ABS plastic toys based on the differential volatiles. The proposed strategy represents an effective and promising analytical method for non-targeted screening and risk assessment of unknown volatiles and discrimination of recycled materials combining with various chemometric techniques for children's plastic products to safeguard children's health.
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Affiliation(s)
- Shanshan Wu
- Toys & Juvenile Products Testing Institute, Guangzhou Customs Technology Center, Guangzhou 510623, China; School of chemistry, Sun Yat-sen University, Guangzhou 510006, China
| | - Jianguo Zheng
- Toys & Juvenile Products Testing Institute, Guangzhou Customs Technology Center, Guangzhou 510623, China
| | - Yang Chen
- Toys & Juvenile Products Testing Institute, Guangzhou Customs Technology Center, Guangzhou 510623, China
| | - Lezhou Yi
- Toys & Juvenile Products Testing Institute, Guangzhou Customs Technology Center, Guangzhou 510623, China
| | - Chonghua Liu
- Toys & Juvenile Products Testing Institute, Guangzhou Customs Technology Center, Guangzhou 510623, China.
| | - Gongke Li
- School of chemistry, Sun Yat-sen University, Guangzhou 510006, China.
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9
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Multi-stable isotope and multi-element origin traceability of rice from the main producing regions in Asia: A long-term investigation during 2017-2020. Food Chem 2023; 412:135417. [PMID: 36753940 DOI: 10.1016/j.foodchem.2023.135417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 12/28/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023]
Abstract
Stable isotope and multi-element analytical techniques with chemometrics were developed to trace the origin authenticity of rice in China market. In the long-term study from 2017 to 2020, a total of 115 batches of rice samples from 8 main producing areas of 7 Asian countries were determined 5 stable isotope ratios and 18 elemental contents. One-way analysis of variance (ANOVA) and various multivariate modeling methods were performed for the origin discrimination. Supervised multivariate modeling including partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) can realize more satisfactory identification of 8 rice origins than ANOVA comparison and unsupervised methods, their leave-one-out cross-validation accuracies approach 85.0 % and 90.9 %, respectively. δ2H, δ13C, Ba, Al, Mg, δ34S, Pb and δ18O were screened as the most important variables for rice origin traceability (VIP > 1 or AUC > 0.5). This analytical strategy combining maybe promising to ensure the origin authenticity and combat illegal mislabeling in rice trade.
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10
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Shi S, Feng J, Yang L, Xing J, Pan G, Tang J, Wang J, Liu J, Cao C, Jiang Y. Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122343. [PMID: 36657285 DOI: 10.1016/j.saa.2023.122343] [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: 10/16/2022] [Revised: 12/19/2022] [Accepted: 01/07/2023] [Indexed: 06/17/2023]
Abstract
Storage is necessary for rice to ensure the year-round consumption of rice. With the increase in storage time, the taste quality and commercial value of rice gradually decrease. The accurate determination of the freshness of rice is critical to the rice trade. However, it is difficult to distinguish aging rice from fresh rice, so a quick and simple method is needed to identify the freshness of the rice. In this study, a combination of near-infrared spectroscopy (NIR) and various algorithms, such as partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and classification and regression trees (CART), were used to differentiate the freshness of rice. PLS-DA and SVM demonstrated excellent classification ability in identifying the freshness of rice, with sensitivity and specificity of 1. The original spectra were used with 100% accuracy in the test set to determine the freshness of the rice. As a result, PLS-DA and SVM can be used to determine the freshness of the rice.
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Affiliation(s)
- Shijie Shi
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Junheng Feng
- Cangzhou Academy of Agriculture and Forestry Sciences, Cangzhou 061001, China
| | - Lichao Yang
- College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
| | - Junyang Xing
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Gaofeng Pan
- Xiangyang Academy of Agricultural Sciences, Xiangyang 441022, China
| | - Jichao Tang
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jing Wang
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Juan Liu
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Cougui Cao
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China; Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan 430070, China
| | - Yang Jiang
- College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China; Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan 430070, China.
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11
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Van De Steene J, Ruyssinck J, Fernandez-Pierna JA, Vandermeersch L, Maes A, Van Langenhove H, Walgraeve C, Demeestere K, De Meulenaer B, Jacxsens L, Miserez B. Fingerprinting methods for origin and variety assessment of rice: Development, validation and data fusion experiments. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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12
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An Automated Image Processing Module for Quality Evaluation of Milled Rice. Foods 2023; 12:foods12061273. [PMID: 36981200 PMCID: PMC10048426 DOI: 10.3390/foods12061273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/19/2023] Open
Abstract
The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice.
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13
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Liu Y, Zuo M, Wang K, Jiao L, Yang G, Yang C, Zhao X, Dong D. Rapid identification of artificial fragrant rice based on volatile organic compounds: From PTR-MS to FTIR. Food Chem 2023; 418:135952. [PMID: 36940544 DOI: 10.1016/j.foodchem.2023.135952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/28/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
The volatile organic compounds (VOCs) released from foods can reflect their internal properties. Artificial fragrant rice (AFR) is a fraudulent food product in which the flavor of low-quality rice is artificially enhanced by addition of essence. In this study, proton-transfer reaction mass spectrometry, long optical path gas phase FTIR spectroscopy and fiber optic evanescent wave were used to analyze the characteristic mass-charge ratios signal and infrared fingerprint signal of four essence which may be used to make AFR, and the prepared AFR samples with different essence levels (0.001 %-0.3 %) were used to verify the detection performance of the detection methods. The results show that the three detection methods effectively identified AFR containing the minimum recommended dose of essence (≥0.1 %, w/w). The above detection methods can provide detection results in real time without complex sample pretreatment and provide options as rapid screening methods for food regulatory authorities to identify AFR.
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Affiliation(s)
- Yachao Liu
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Min Zuo
- National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
| | - Ke Wang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Leizi Jiao
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Guiyan Yang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Chongshan Yang
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xiande Zhao
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Daming Dong
- Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, China; Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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14
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Li Z, Song J, Ma Y, Yu Y, He X, Guo Y, Dou J, Dong H. Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables. Food Chem X 2022; 17:100539. [PMID: 36845513 PMCID: PMC9943763 DOI: 10.1016/j.fochx.2022.100539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/10/2022] [Accepted: 12/03/2022] [Indexed: 12/13/2022] Open
Abstract
The long-term storage of rice will inevitably be involved in the deterioration of edible quality, and aged rice poses a great threat to food safety and human health. The acid value can be employed as a sensitive index for the determination of rice quality and freshness. In this study, near-infrared spectra of three kinds of rice (Chinese Daohuaxiang, southern japonica rice, and late japonica rice) mixed with different proportions of aged rice were collected. The partial least squares regression (PLSR) model with different preprocessing was constructed to identify the aged rice adulteration. Meanwhile, a competitive adaptive reweighted sampling (CARS) algorithm was used to extract the optimization model of characteristic variables. The constructed CARS-PLSR model method could not only reduce greatly the number of characteristic variables required by the spectrum but also improve the identification accuracy of three kinds of aged-rice adulteration. As above, this study proposed a rapid, simple, and accurate detection method for aged-rice adulteration, providing new clues and alternatives for the quality control of commercial rice.
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Affiliation(s)
- Zhanming Li
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Jiahui Song
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yinxing Ma
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Yue Yu
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China,Corresponding authors.
| | - Xueming He
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
| | - Yuanxin Guo
- School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China
| | - Jinxin Dou
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China
| | - Hao Dong
- College of Light Industry and Food Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China,Corresponding authors.
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15
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:8655. [PMID: 36433249 PMCID: PMC9697730 DOI: 10.3390/s22228655] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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16
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Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics. Foods 2022; 11:foods11213318. [DOI: 10.3390/foods11213318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/09/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022] Open
Abstract
Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC–MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.
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17
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Chen T, Li H, Chen X, Wang Y, Cheng Q, Qi X. Construction and application of exclusive flavour fingerprints from fragrant rice based on gas chromatography – ion mobility spectrometry (
GC‐IMS
). FLAVOUR FRAG J 2022. [DOI: 10.1002/ffj.3716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Tong Chen
- School of Biological and Chemical Engineering Guangxi University of Science and Technology Liuzhou China
| | - Haiyu Li
- School of Biological and Chemical Engineering Guangxi University of Science and Technology Liuzhou China
| | - Xinyu Chen
- Department of Physical Chemistry University of Duisburg‐Essen Essen Germany
| | - Yong Wang
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Qianwei Cheng
- School of Biological and Chemical Engineering Guangxi University of Science and Technology Liuzhou China
| | - Xingpu Qi
- School of Food Science and Technology Jiangsu Agri‐animal Husbandry Vocational College Taizhou China
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18
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Liu X, Bai B, Rogers KM, Wu D, Qian Q, Qi F, Zhou J, Yao C, Song W. Determining the geographical origin and cultivation methods of Shanghai special rice using NIR and IRMS. Food Chem 2022; 394:133425. [DOI: 10.1016/j.foodchem.2022.133425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/15/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022]
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19
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Quinn B, McCarron P, Hong Y, Birse N, Wu D, Elliott CT, Ch R. Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam. Food Chem 2022; 386:132738. [PMID: 35349900 DOI: 10.1016/j.foodchem.2022.132738] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 11/17/2022]
Abstract
The COVID-19 pandemic has impacted the food industry and consumers, with production gaps, shipping delays, and changes in supply and demand leading to an increased risk of food fraud. Rice has a high probability for adulteration by food fraudsters, being a staple commodity for more than half the global population, making the assessment of geographical origins of rice for authenticity important in terms of protecting businesses and consumers. In this study, we describe ICP-MS elemental profiling coupled with elementomic modelling to identify the geographical indications of Indian, Chinese, and Vietnamese rice. A PLS-DA model exhibited good discrimination (R2 = 0.8393, Q2 = 0.7673, accuracy = 1.0). Data-driven soft independent modelling of class analogy (dd-SIMCA) and K-nearest neighbours (K-NN) models have good sensitivity (98%) and specificity (100%).
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Affiliation(s)
- Brian Quinn
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Philip McCarron
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Yunhe Hong
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Nicholas Birse
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Di Wu
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Christopher T Elliott
- ASSET Technology Centre, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, United Kingdom
| | - Ratnasekhar Ch
- Central Institute of Medicinal and Aromatic Plants, P.O. CIMAP, Kukrail Picnic Spot Road, Lucknow, Utter Pradesh 226015, India
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20
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Chen T, Chen X, Meng L, Wei Z, Chen B, Wang Y, Chen H, Cheng Q. Characteristic Fingerprint Analysis of the Moldy Odor in Guangxi Fragrant Rice by Gas Chromatography - Ion Mobility Spectrometry (GC-IMS). ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2043337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Tong Chen
- School of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou, China
| | - Xinyu Chen
- Department of Physical Chemistry, University of Duisburg-Essen, Essen, Germany
| | - Luli Meng
- School of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou, China
| | - Ziyu Wei
- School of Economics and Management, Guangxi University of Science and Technology, Liuzhou, China
| | - Bin Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Yong Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Hui Chen
- School of Animal Science and Food Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, China
| | - Qianwei Cheng
- School of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou, China
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21
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DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview. Foods 2022; 11:foods11030258. [PMID: 35159410 PMCID: PMC8834242 DOI: 10.3390/foods11030258] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.
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