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Dibakoane SR, Meiring B, Dube BA, Wokadala OC, Mlambo V. The application of multi-elemental fingerprints and chemometrics for discriminating between cage and free-range table eggs based on atomic absorption spectrometry (AAS) and colorimetry. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01899-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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2
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Chen Z, He P, He Y, Wu F, Rao X, Pan J, Lin H. Eggshell biometrics for individual egg identification based on convolutional neural networks. Poult Sci 2023; 102:102540. [PMID: 36863120 PMCID: PMC10006506 DOI: 10.1016/j.psj.2023.102540] [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: 10/07/2022] [Revised: 01/15/2023] [Accepted: 01/21/2023] [Indexed: 02/03/2023] Open
Abstract
Individual egg identification technology has potential applications in breeding, product tracking/tracing, and anti-counterfeit. This study developed a novel method for individual egg identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was collected from the blunt-end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1,540 images. The testing results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate and the equal error rate reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual chicken egg identification, and can be extended to eggs of other poultry species for product tracking/tracing and anti-counterfeit.
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Affiliation(s)
- Zhonghao Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Pengguang He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Yefan He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Fan Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Xiuqin Rao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Jinming Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Hongjian Lin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China.
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Gajek M, Pawlaczyk A, Maćkiewicz E, Albińska J, Wysocki P, Jóźwik K, Szynkowska-Jóźwik MI. Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis. Foods 2022; 11:foods11182810. [PMID: 36140938 PMCID: PMC9498178 DOI: 10.3390/foods11182810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of 88Sr/86Sr, 84Sr/86Sr, 87Sr/86Sr and 63Cu/65Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.
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Affiliation(s)
- Magdalena Gajek
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
- Correspondence: ; Tel.: +48-42-631-30-95
| | - Aleksandra Pawlaczyk
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Elżbieta Maćkiewicz
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Jadwiga Albińska
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Piotr Wysocki
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
| | - Krzysztof Jóźwik
- Faculty of Mechanical Engineering, Institute of Turbomachinery, Lodz University of Technology, Wolczanska 219/223, 90-924 Lodz, Poland
| | - Małgorzata Iwona Szynkowska-Jóźwik
- Faculty of Chemistry, Institute of General and Ecological Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland
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Varrà MO, Husáková L, Patočka J, Ghidini S, Zanardi E. Classification of transformed anchovy products based on the use of element patterns and decision trees to assess traceability and country of origin labelling. Food Chem 2021; 360:129790. [PMID: 33971507 DOI: 10.1016/j.foodchem.2021.129790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/02/2021] [Accepted: 04/09/2021] [Indexed: 10/21/2022]
Abstract
Quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS) and direct mercury analysis were used to determine the elemental composition of 180 transformed (salt-ripened) anchovies from three different fishing areas before and after packaging. To this purpose, four decision trees-based algorithms, corresponding to C5.0, classification and regression trees (CART), chi-squareautomatic interaction detection (CHAID), and quick unbiased efficient statistical tree (QUEST) were applied to the elemental datasets to find the most accurate data mining procedure to achieve the ultimate goal of fish origin prediction. Classification rules generated by the trained CHAID model optimally identified unlabelled testing bulk anchovies (93.9% F-score) by using just 6 out of 52 elements (As, K, P, Cd, Li, and Sr). The finished packaged product was better modelled by the QUEST algorithm which recognised the origin of anchovies with F-score of 97.7%, considering the information carried out by 5 elements (B, As, K. Cd, and Pd). Results obtained suggested that the traceability system in the fishery sector may be supported by simplified machine learning techniques applied to a limited but effective number of inorganic predictors of origin.
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Affiliation(s)
- Maria Olga Varrà
- Department of Food and Drug, University of Parma, Parma Via del Taglio, 10, Parma 43126, Italy.
| | - Lenka Husáková
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573 HB/D, Pardubice CZ-532 10, Czech Republic.
| | - Jan Patočka
- Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573 HB/D, Pardubice CZ-532 10, Czech Republic.
| | - Sergio Ghidini
- Department of Food and Drug, University of Parma, Parma Via del Taglio, 10, Parma 43126, Italy.
| | - Emanuela Zanardi
- Department of Food and Drug, University of Parma, Parma Via del Taglio, 10, Parma 43126, Italy.
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Mi S, Shang K, Zhang CH, Fan YQ. Characterization and discrimination of selected chicken eggs in China's retail market based on multi-element and lipidomics analysis. Food Res Int 2019; 126:108668. [DOI: 10.1016/j.foodres.2019.108668] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 08/30/2019] [Accepted: 09/09/2019] [Indexed: 10/26/2022]
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Campmajó G, Cayero L, Saurina J, Núñez O. Classification of Hen Eggs by HPLC-UV Fingerprinting and Chemometric Methods. Foods 2019; 8:foods8080310. [PMID: 31374995 PMCID: PMC6723454 DOI: 10.3390/foods8080310] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/23/2019] [Accepted: 07/29/2019] [Indexed: 12/05/2022] Open
Abstract
Hen eggs are classified into four groups according to their production method: Organic, free-range, barn, or caged. It is known that a fraudulent practice is the misrepresentation of a high-quality egg with a lower one. In this work, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were proposed as a source of potential chemical descriptors to achieve the classification of hen eggs according to their labelled type. A reversed-phase separation was optimized to obtain discriminant enough chromatographic fingerprints, which were subsequently processed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Particular trends were observed for organic and caged hen eggs by PCA and, as expected, these groupings were improved by PLS-DA. The applicability of the method to distinguish egg manufacturer and size was also studied by PLS-DA, observing variations in the HPLC-UV fingerprints in both cases. Moreover, the classification of higher class eggs, in front of any other with one lower, and hence cheaper, was studied by building paired PLS-DA models, reaching a classification rate of at least 82.6% (100% for organic vs. non-organic hen eggs) and demonstrating the suitability of the proposed method.
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Affiliation(s)
- Guillem Campmajó
- Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain.
| | - Laura Cayero
- Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain
| | - Javier Saurina
- Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain
- Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, E08921 Barcelona, Spain
| | - Oscar Núñez
- Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain
- Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, E08921 Barcelona, Spain
- Serra Húnter Fellow, Generalitat de Catalunya, Rambla de Catalunya 19-21, E08007 Barcelona, Spain
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Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree. ENTROPY 2019; 21:e21020198. [PMID: 33266913 PMCID: PMC7514679 DOI: 10.3390/e21020198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 02/08/2019] [Accepted: 02/13/2019] [Indexed: 11/30/2022]
Abstract
Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization. Firstly, the calculation and analysis of single- and multi-value event uncertainty distributions of information entropy is followed by an enhanced property of single-value event entropy kernel and multi-value event entropy peaks as well as a reciprocal relationship between peak location and the number of possible events. Secondly, this study proposed an estimated method for information entropy whose entropy kernel is replaced with a peak-shift sine function to establish a decision tree learning (CGDT) algorithm on the basis of constraint gain. Finally, by combining branch convergence and fan-out indices under an inductive depth of a decision tree, we built a constraint gained and depth inductive improved decision tree (CGDIDT) learning algorithm. Results show the benefits of the CGDT and CGDIDT algorithms.
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Methods of Authentication of Food Grown in Organic and Conventional Systems Using Chemometrics and Data Mining Algorithms: a Review. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-018-01413-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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9
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Potortì AG, Di Bella G, Mottese AF, Bua GD, Fede MR, Sabatino G, Salvo A, Somma R, Dugo G, Lo Turco V. Traceability of Protected Geographical Indication (PGI) Interdonato lemon pulps by chemometric analysis of the mineral composition. J Food Compost Anal 2018. [DOI: 10.1016/j.jfca.2018.03.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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10
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Chen C, Husny J, Rabe S. Predicting fishiness off-flavour and identifying compounds of lipid oxidation in dairy powders by SPME-GC/MS and machine learning. Int Dairy J 2018. [DOI: 10.1016/j.idairyj.2017.09.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Maione C, de Oliveira Souza VC, Togni LR, da Costa JL, Campiglia AD, Barbosa F, Barbosa RM. Using Cluster Analysis and ICP-MS to Identify Groups of Ecstasy Tablets in Sao Paulo State, Brazil. J Forensic Sci 2017; 62:1479-1486. [DOI: 10.1111/1556-4029.13448] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 12/25/2016] [Accepted: 12/28/2016] [Indexed: 11/30/2022]
Affiliation(s)
- Camila Maione
- Instituto de Informática; Universidade Federal de Goiás; Goiânia Goiás Brazil
| | - Vanessa Cristina de Oliveira Souza
- Laboratório de Toxicologia e Essencialidade de Metais; Departamento de Análises Clínicas, Toxicológicas e Bromatológicas; Faculdade de Ciências Farmacêuticas de Ribeirão Preto-USP; Ribeirão Preto São Paulo Brazil
| | - Loraine Rezende Togni
- Laboratório de Química e Toxicologia Forense; Instituto de Criminalística de São Paulo; São Paulo São Paulo Brazil
| | - José Luiz da Costa
- Centro de Controle de Intoxicação; Universidade de Campinas; Campinas São Paulo Brazil
| | - Andres Dobal Campiglia
- Department of Chemistry; University of Central Florida; Orlando FL
- National Center for Forensic Science; University of Central Florida; Orlando FL
| | - Fernando Barbosa
- Laboratório de Toxicologia e Essencialidade de Metais; Departamento de Análises Clínicas, Toxicológicas e Bromatológicas; Faculdade de Ciências Farmacêuticas de Ribeirão Preto-USP; Ribeirão Preto São Paulo Brazil
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Danezis GP, Tsagkaris AS, Brusic V, Georgiou CA. Food authentication: state of the art and prospects. Curr Opin Food Sci 2016. [DOI: 10.1016/j.cofs.2016.07.003] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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13
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Barbosa RM, de Paula ES, Paulelli AC, Moore AF, Souza JMO, Batista BL, Campiglia AD, Barbosa F. Recognition of organic rice samples based on trace elements and support vector machines. J Food Compost Anal 2016. [DOI: 10.1016/j.jfca.2015.09.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Borges EM, Gelinski JMLN, de Oliveira Souza VC, Barbosa Jr. F, Batista BL. Monitoring the authenticity of organic rice via chemometric analysis of elemental data. Food Res Int 2015. [DOI: 10.1016/j.foodres.2015.06.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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15
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Georgiou CA, Danezis GP. Elemental and Isotopic Mass Spectrometry. ADVANCED MASS SPECTROMETRY FOR FOOD SAFETY AND QUALITY 2015. [DOI: 10.1016/b978-0-444-63340-8.00003-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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