Classification of olive cultivars by machine learning based on olive oil chemical composition
•Classification of olive cultivars by machine learning based on olive oil chemical composition.•Olive oil chemical characteristics of Greek and Italian cultivars were studied.•Exploratory data analysis was used to reveal discriminative patterns.•The XGBoost algorithm was used for olive cultivar and...
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| Published in | Food chemistry Vol. 429; p. 136793 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
15.12.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | •Classification of olive cultivars by machine learning based on olive oil chemical composition.•Olive oil chemical characteristics of Greek and Italian cultivars were studied.•Exploratory data analysis was used to reveal discriminative patterns.•The XGBoost algorithm was used for olive cultivar and geographic classification.•Olive Cultivars and country of origin were predicted with high accuracy.
Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples. |
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| AbstractList | Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples.Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples. Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples. •Classification of olive cultivars by machine learning based on olive oil chemical composition.•Olive oil chemical characteristics of Greek and Italian cultivars were studied.•Exploratory data analysis was used to reveal discriminative patterns.•The XGBoost algorithm was used for olive cultivar and geographic classification.•Olive Cultivars and country of origin were predicted with high accuracy. Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples. |
| ArticleNumber | 136793 |
| Author | Gkisakis, Vasileios Katsaris, Panagiotis Skiada, Vasiliki Kambouris, Manousos E. Manoussopoulos, Yiannis |
| Author_xml | – sequence: 1 givenname: Vasiliki surname: Skiada fullname: Skiada, Vasiliki organization: Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece – sequence: 2 givenname: Panagiotis surname: Katsaris fullname: Katsaris, Panagiotis organization: Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece – sequence: 3 givenname: Manousos E. surname: Kambouris fullname: Kambouris, Manousos E. organization: Department of Pharmacy, University of Patras, Rio Patras 26504 Patras, Greece – sequence: 4 givenname: Vasileios surname: Gkisakis fullname: Gkisakis, Vasileios organization: Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece – sequence: 5 givenname: Yiannis surname: Manoussopoulos fullname: Manoussopoulos, Yiannis email: inminz@gmail.com organization: Plant Protection Division of Patras, Hellenic Agricultural Organization – DEMETER, N.E.O & Amerikis, 264 42 Patras, Greece |
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| Cites_doi | 10.1016/j.foodcont.2022.108939 10.1016/j.foodres.2018.02.010 10.1016/j.chemolab.2003.12.007 10.3390/s22249651 10.3389/fchem.2019.00929 10.1016/j.tifs.2019.02.025 10.1016/j.lwt.2017.07.050 10.3390/horticulturae7110493 10.1016/j.talanta.2016.08.033 10.1111/1541-4337.12949 10.1080/10408398.2018.1433628 10.1016/j.talanta.2018.11.033 10.21105/joss.01686 10.1016/j.foodres.2019.03.063 10.1016/j.foodchem.2018.04.019 10.17660/ActaHortic.2008.791.106 10.1016/j.foodchem.2021.130633 10.1021/jf070523r 10.1002/jsfa.7616 10.1002/ejlt.201500293 10.1002/ejlt.200900015 10.21105/joss.03139 10.1002/(SICI)1097-0010(20000515)80:7<939::AID-JSFA644>3.0.CO;2-C 10.1111/ijfs.12245 10.1021/jf049894n 10.1016/j.foodchem.2022.133945 10.1016/j.foodchem.2016.09.041 10.1016/j.foodchem.2022.134543 10.1016/j.foodcont.2021.108318 10.1016/j.foodchem.2012.08.005 |
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| Keywords | Authenticity Cultivar classification Artificial intelligent models Chemical composition Olive oil Machine learning |
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| References | Di Bella, Maisano, La Pera, Lo Turco, Salvo, Dugo (b0035) 2007; 55 Jiménez-Carvelo, Cruz, Olivieri, González-Casado, Cuadros-Rodríguez (b0050) 2019; 195 Wickham, Averick, Bryan, Chang, D’Agostino McGowan, Francois, Yutani (b0190) 2019; 4 Lukić, Lukić, Moslavac (b0100) 2021; 7 Chen, Guestrin (b0020) 2016; 11 Kassambara, A., & Mundt, F. (2020). https://CRAN.R-project.org/package=caret. Vega-Márquez, Nepomuceno-Chamorro, Jurado-Campos, Rubio-Escudero (b0185) 2019; 7 Piironen, Lindsay, Miettinen, Toivo, Lampi (b0115) 2000; 80 https://CRAN.R-project.org/package=factoextra. García-González, Luna, Morales, Aparicio (b0040) 2009; 111 Jiménez-Carvelo, Osorio, Koidis, González-Casado, Cuadros-Rodríguez (b0060) 2017; 86 Marini, Balestrieri, Bucci, Magrı̀, Magrı̀, Marini (b0105) 2004; 73 R Core Team (2021). Conte, Bendini, Valli, Lucci, Moret, Maquet, Gallina Toschi (b0025) 2020; 105 Aroca-Santos, Cancilla, Pariente, Torrecilla (b0005) 2016; 161 Tahir, Arslan, Komla Mahunu, Adam Mariod, B H Hashim, Xiaobo, Jiyong, El-Seedi, Musa (b0165) 2022; 366 Binetti, Del Coco, Ragone, Zelasco, Perri, Montemurro, Schena (b0015) 2017; 219 Rodrigues, N., Ferreiro, N., Veloso, A. C. A., Pereira, J. A., & Peres, A. M. (2022). An Electronic Nose as a Non-Destructive Analytical Tool to Identify the Geographical Origin of Portuguese Olive Oils from Two Adjacent Regions. https://CRAN.R-project.org/package=openxlsx. Covas, Fitó, de La Torre (b0030) 2015 Ludecke, Ben-Shachar, Patil, Waggoner, Makowski (b0090) 2021; 6 Lozano-Castellón, López-Yerena, Domínguez-López, Siscart-Serra, Fraga, Sámano, Pérez (b0085) 2022; 21 Schauberger, P., & Walker, A. (2021). Stefas, Gyftokostas, Kourelias, Nanou, Kokkinos, Bouras, Couris (b0160) 2021; 130 Jiménez-Carvelo, González-Casado, Bagur-González, Cuadros-Rodríguez (b0055) 2019; 122 Senizza, Ganugi, Trevisan, Lucini (b0155) 2023; 404 Ranalli, Lucera, Contento, Fontanazza, Patumi (b0125) 2008 , Gonzalez-Fernandez, Iglesias-Otero, Esteki, Moldes, Mejuto, Simal-Gandara (b0045) 2019; 59 Tang, Polari, Green, Wang, Hatzakis (b0170) 2022; 137 Mohamed, Rocchetti, Montesano, Ali, Guasmi, Grati-Kamoun, Lucini (b0110) 2018; 106 . Lukić, Lukić, Krapac, Sladonja, Piližota (b0095) 2013; 136 Beltrán, Del Rio, Sánchez, Martínez (b0010) 2004; 52 Lenth, R. V. (2021). Torrecilla, Cancilla, Matute, Díaz-Rodríguez (b0180) 2013; 48 Kuhn, M. (2020). Rodrigues, Peres, Casal, Santamaria-Echart, Barreiro, Peres, Alberto Pereira (b0135) 2023; 398 Vienna, Austria. https://www.R-project.org/. Kyçyk, Aguilera, Gaforio, Jiménez, Beltrán (b0080) 2016; 96 Sayago, González-Domínguez, Beltrán, Fernández-Recamales (b0145) 2018; 261 Kosma, Badeka, Vatavali, Kontakos, Kontominas (b0070) 2016; 118 Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., & Li, Y. (2021). Ranalli (10.1016/j.foodchem.2023.136793_b0125) 2008 Senizza (10.1016/j.foodchem.2023.136793_b0155) 2023; 404 Covas (10.1016/j.foodchem.2023.136793_b0030) 2015 Tahir (10.1016/j.foodchem.2023.136793_b0165) 2022; 366 Conte (10.1016/j.foodchem.2023.136793_b0025) 2020; 105 Wickham (10.1016/j.foodchem.2023.136793_b0190) 2019; 4 10.1016/j.foodchem.2023.136793_b0140 10.1016/j.foodchem.2023.136793_b0065 10.1016/j.foodchem.2023.136793_b0120 Rodrigues (10.1016/j.foodchem.2023.136793_b0135) 2023; 398 Mohamed (10.1016/j.foodchem.2023.136793_b0110) 2018; 106 García-González (10.1016/j.foodchem.2023.136793_b0040) 2009; 111 Stefas (10.1016/j.foodchem.2023.136793_b0160) 2021; 130 Vega-Márquez (10.1016/j.foodchem.2023.136793_b0185) 2019; 7 Binetti (10.1016/j.foodchem.2023.136793_b0015) 2017; 219 Marini (10.1016/j.foodchem.2023.136793_b0105) 2004; 73 Torrecilla (10.1016/j.foodchem.2023.136793_b0180) 2013; 48 Beltrán (10.1016/j.foodchem.2023.136793_b0010) 2004; 52 Piironen (10.1016/j.foodchem.2023.136793_b0115) 2000; 80 Ludecke (10.1016/j.foodchem.2023.136793_b0090) 2021; 6 Chen (10.1016/j.foodchem.2023.136793_b0020) 2016; 11 Lukić (10.1016/j.foodchem.2023.136793_b0095) 2013; 136 Aroca-Santos (10.1016/j.foodchem.2023.136793_b0005) 2016; 161 Jiménez-Carvelo (10.1016/j.foodchem.2023.136793_b0050) 2019; 195 Jiménez-Carvelo (10.1016/j.foodchem.2023.136793_b0060) 2017; 86 Sayago (10.1016/j.foodchem.2023.136793_b0145) 2018; 261 Di Bella (10.1016/j.foodchem.2023.136793_b0035) 2007; 55 Lukić (10.1016/j.foodchem.2023.136793_b0100) 2021; 7 Tang (10.1016/j.foodchem.2023.136793_b0170) 2022; 137 Jiménez-Carvelo (10.1016/j.foodchem.2023.136793_b0055) 2019; 122 Lozano-Castellón (10.1016/j.foodchem.2023.136793_b0085) 2022; 21 10.1016/j.foodchem.2023.136793_b0150 10.1016/j.foodchem.2023.136793_b0075 10.1016/j.foodchem.2023.136793_b0130 10.1016/j.foodchem.2023.136793_b0175 Kosma (10.1016/j.foodchem.2023.136793_b0070) 2016; 118 Kyçyk (10.1016/j.foodchem.2023.136793_b0080) 2016; 96 Gonzalez-Fernandez (10.1016/j.foodchem.2023.136793_b0045) 2019; 59 |
| References_xml | – reference: Kuhn, M. (2020). – volume: 21 start-page: 2639 year: 2022 end-page: 2664 ident: b0085 article-title: Extra virgin olive oil: A comprehensive review of efforts to ensure its authenticity, traceability, and safety publication-title: Comprehensive Reviews in Food Science and Food Safety – volume: 118 start-page: 849 year: 2016 end-page: 861 ident: b0070 article-title: Differentiation of Greek extra virgin olive oils according to cultivar based on volatile compound analysis and fatty acid composition publication-title: European Journal of Lipid Science and Technology – reference: . Vienna, Austria. https://www.R-project.org/. – reference: . https://CRAN.R-project.org/package=caret. – volume: 6 start-page: 3139 year: 2021 ident: b0090 article-title: performance: An R package for assessment, comparison and testing of statistical models publication-title: Journal of Open Source Software – reference: , – volume: 111 start-page: 1003 year: 2009 end-page: 1013 ident: b0040 article-title: Stepwise geographical traceability of virgin olive oils by chemical profiles using artificial neural network models publication-title: European Journal of Lipid Science and Technology – volume: 48 start-page: 2528 year: 2013 end-page: 2534 ident: b0180 article-title: Neural network models to classify olive oils within the protected denomination of origin framework publication-title: International Journal of Food Science and Technology – volume: 161 start-page: 304 year: 2016 end-page: 308 ident: b0005 article-title: Neural networks applied to characterize blends containing refined and extra virgin olive oils publication-title: Talanta – reference: . https://CRAN.R-project.org/package=openxlsx. – volume: 59 start-page: 1913 year: 2019 end-page: 1926 ident: b0045 article-title: A critical review on the use of artificial neural networks in olive oil production, characterization and authentication publication-title: Critical Reviews in Food Science and Nutrition – volume: 136 start-page: 251 year: 2013 end-page: 258 ident: b0095 article-title: Sterols and triterpene diols in olive oil as indicators of variety and degree of ripening publication-title: Food Chemistry – start-page: 697 year: 2008 end-page: 704 ident: b0125 article-title: Assessment of physico-chemical, sensory and nutritional parameters in virgin olive oil from the new genotype Favolosa (FS17) publication-title: Acta Horticulturae – volume: 55 start-page: 6568 year: 2007 end-page: 6574 ident: b0035 article-title: Statistical characterization of sicilian olive oils from the Peloritana and Maghrebian zones according to the fatty acid profile publication-title: Journal of Agricultural and Food Chemistry – volume: 86 start-page: 174 year: 2017 end-page: 184 ident: b0060 article-title: Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy publication-title: LWT – volume: 7 start-page: 493 year: 2021 ident: b0100 article-title: Sterols and triterpene diols in virgin olive oil: A comprehensive review on their properties and significance, with a special emphasis on the influence of variety and ripening degree publication-title: Horticulturae – volume: 404 year: 2023 ident: b0155 article-title: Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure publication-title: Food Chemistry – volume: 137 year: 2022 ident: b0170 article-title: NMR-based metabolomics for olive oil cultivar classification: A comparison with standard targeted analysis of fatty acids and triglycerides publication-title: Food Control – volume: 366 year: 2022 ident: b0165 article-title: The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013–2020) publication-title: Food Chemistry – volume: 4 start-page: 1686 year: 2019 ident: b0190 article-title: Welcome to the tidyverse publication-title: Journal of Open Source Software – reference: Rodrigues, N., Ferreiro, N., Veloso, A. C. A., Pereira, J. A., & Peres, A. M. (2022). An Electronic Nose as a Non-Destructive Analytical Tool to Identify the Geographical Origin of Portuguese Olive Oils from Two Adjacent Regions. – volume: 261 start-page: 42 year: 2018 end-page: 50 ident: b0145 article-title: Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition publication-title: Food Chemistry – reference: R Core Team (2021). – volume: 122 start-page: 25 year: 2019 end-page: 39 ident: b0055 article-title: Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review publication-title: Food research international (Ottawa, Ont.) – volume: 7 start-page: 929 year: 2019 ident: b0185 article-title: Deep learning techniques to improve the performance of olive oil classification publication-title: Frontiers in Chemistry – volume: 11 start-page: 785 year: 2016 end-page: 794 ident: b0020 publication-title: XGBoost – reference: . https://CRAN.R-project.org/package=factoextra. – volume: 130 year: 2021 ident: b0160 article-title: Discrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data publication-title: Food Control – reference: . – volume: 105 start-page: 483 year: 2020 end-page: 493 ident: b0025 article-title: Olive oil quality and authenticity: A review of current EU legislation, standards, relevant methods of analyses, their drawbacks and recommendations for the future publication-title: Trends in Food Science and Technology – volume: 52 start-page: 3434 year: 2004 end-page: 3440 ident: b0010 article-title: Influence of harvest date and crop yield on the fatty acid composition of virgin olive oils from cv. Picual publication-title: Journal of Agricultural and Food Chemistry – volume: 398 year: 2023 ident: b0135 article-title: Geographical discrimination of olive oils from Cv. 'Galega Vulgar' publication-title: Food Chemistry – volume: 96 start-page: 4143 year: 2016 end-page: 4150 ident: b0080 article-title: Sterol composition of virgin olive oil of forty-three olive cultivars from the World Collection Olive Germplasm Bank of Cordoba publication-title: Journal of the Science of Food and Agriculture – reference: Schauberger, P., & Walker, A. (2021). – reference: Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., & Li, Y. (2021). – start-page: 31 year: 2015 end-page: 52 ident: b0030 article-title: Minor Bioactive Olive Oil Components and Health: Key Data for Their Role in Providing Health Benefits in Humans publication-title: Olive and Olive Oil Bioactive Constituents – volume: 195 start-page: 69 year: 2019 end-page: 76 ident: b0050 article-title: Classification of olive oils according to their cultivars based on second-order data using LC-DAD publication-title: Talanta – volume: 80 start-page: 939 year: 2000 end-page: 966 ident: b0115 article-title: Plant sterols: Biosynthesis, biological function and their importance to human nutrition publication-title: Journal of the Science of Food and Agriculture – reference: Lenth, R. V. (2021). – volume: 106 start-page: 920 year: 2018 end-page: 927 ident: b0110 article-title: Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints publication-title: Food Research International (Ottawa, Ont.) – volume: 219 start-page: 131 year: 2017 end-page: 138 ident: b0015 article-title: Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data publication-title: Food Chemistry – volume: 73 start-page: 85 year: 2004 end-page: 93 ident: b0105 article-title: Supervised pattern recognition to authenticate Italian extra virgin olive oil varieties publication-title: Chemometrics and Intelligent Laboratory Systems – reference: . – reference: Kassambara, A., & Mundt, F. (2020). – ident: 10.1016/j.foodchem.2023.136793_b0120 – ident: 10.1016/j.foodchem.2023.136793_b0065 – volume: 137 year: 2022 ident: 10.1016/j.foodchem.2023.136793_b0170 article-title: NMR-based metabolomics for olive oil cultivar classification: A comparison with standard targeted analysis of fatty acids and triglycerides publication-title: Food Control doi: 10.1016/j.foodcont.2022.108939 – volume: 106 start-page: 920 year: 2018 ident: 10.1016/j.foodchem.2023.136793_b0110 article-title: Discrimination of Tunisian and Italian extra-virgin olive oils according to their phenolic and sterolic fingerprints publication-title: Food Research International (Ottawa, Ont.) doi: 10.1016/j.foodres.2018.02.010 – volume: 73 start-page: 85 year: 2004 ident: 10.1016/j.foodchem.2023.136793_b0105 article-title: Supervised pattern recognition to authenticate Italian extra virgin olive oil varieties publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/j.chemolab.2003.12.007 – ident: 10.1016/j.foodchem.2023.136793_b0130 doi: 10.3390/s22249651 – volume: 7 start-page: 929 year: 2019 ident: 10.1016/j.foodchem.2023.136793_b0185 article-title: Deep learning techniques to improve the performance of olive oil classification publication-title: Frontiers in Chemistry doi: 10.3389/fchem.2019.00929 – volume: 11 start-page: 785 year: 2016 ident: 10.1016/j.foodchem.2023.136793_b0020 publication-title: XGBoost – volume: 105 start-page: 483 year: 2020 ident: 10.1016/j.foodchem.2023.136793_b0025 article-title: Olive oil quality and authenticity: A review of current EU legislation, standards, relevant methods of analyses, their drawbacks and recommendations for the future publication-title: Trends in Food Science and Technology doi: 10.1016/j.tifs.2019.02.025 – volume: 86 start-page: 174 year: 2017 ident: 10.1016/j.foodchem.2023.136793_b0060 article-title: Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-ATR and Raman spectroscopy publication-title: LWT doi: 10.1016/j.lwt.2017.07.050 – volume: 7 start-page: 493 year: 2021 ident: 10.1016/j.foodchem.2023.136793_b0100 article-title: Sterols and triterpene diols in virgin olive oil: A comprehensive review on their properties and significance, with a special emphasis on the influence of variety and ripening degree publication-title: Horticulturae doi: 10.3390/horticulturae7110493 – volume: 161 start-page: 304 year: 2016 ident: 10.1016/j.foodchem.2023.136793_b0005 article-title: Neural networks applied to characterize blends containing refined and extra virgin olive oils publication-title: Talanta doi: 10.1016/j.talanta.2016.08.033 – volume: 21 start-page: 2639 year: 2022 ident: 10.1016/j.foodchem.2023.136793_b0085 article-title: Extra virgin olive oil: A comprehensive review of efforts to ensure its authenticity, traceability, and safety publication-title: Comprehensive Reviews in Food Science and Food Safety doi: 10.1111/1541-4337.12949 – start-page: 31 year: 2015 ident: 10.1016/j.foodchem.2023.136793_b0030 article-title: Minor Bioactive Olive Oil Components and Health: Key Data for Their Role in Providing Health Benefits in Humans – volume: 59 start-page: 1913 year: 2019 ident: 10.1016/j.foodchem.2023.136793_b0045 article-title: A critical review on the use of artificial neural networks in olive oil production, characterization and authentication publication-title: Critical Reviews in Food Science and Nutrition doi: 10.1080/10408398.2018.1433628 – ident: 10.1016/j.foodchem.2023.136793_b0075 – volume: 195 start-page: 69 year: 2019 ident: 10.1016/j.foodchem.2023.136793_b0050 article-title: Classification of olive oils according to their cultivars based on second-order data using LC-DAD publication-title: Talanta doi: 10.1016/j.talanta.2018.11.033 – ident: 10.1016/j.foodchem.2023.136793_b0140 – volume: 4 start-page: 1686 year: 2019 ident: 10.1016/j.foodchem.2023.136793_b0190 article-title: Welcome to the tidyverse publication-title: Journal of Open Source Software doi: 10.21105/joss.01686 – volume: 122 start-page: 25 year: 2019 ident: 10.1016/j.foodchem.2023.136793_b0055 article-title: Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review publication-title: Food research international (Ottawa, Ont.) doi: 10.1016/j.foodres.2019.03.063 – volume: 261 start-page: 42 year: 2018 ident: 10.1016/j.foodchem.2023.136793_b0145 article-title: Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition publication-title: Food Chemistry doi: 10.1016/j.foodchem.2018.04.019 – start-page: 697 year: 2008 ident: 10.1016/j.foodchem.2023.136793_b0125 article-title: Assessment of physico-chemical, sensory and nutritional parameters in virgin olive oil from the new genotype Favolosa (FS17) publication-title: Acta Horticulturae doi: 10.17660/ActaHortic.2008.791.106 – volume: 366 year: 2022 ident: 10.1016/j.foodchem.2023.136793_b0165 article-title: The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils: A systematic review (2013–2020) publication-title: Food Chemistry doi: 10.1016/j.foodchem.2021.130633 – volume: 55 start-page: 6568 year: 2007 ident: 10.1016/j.foodchem.2023.136793_b0035 article-title: Statistical characterization of sicilian olive oils from the Peloritana and Maghrebian zones according to the fatty acid profile publication-title: Journal of Agricultural and Food Chemistry doi: 10.1021/jf070523r – volume: 96 start-page: 4143 year: 2016 ident: 10.1016/j.foodchem.2023.136793_b0080 article-title: Sterol composition of virgin olive oil of forty-three olive cultivars from the World Collection Olive Germplasm Bank of Cordoba publication-title: Journal of the Science of Food and Agriculture doi: 10.1002/jsfa.7616 – volume: 118 start-page: 849 year: 2016 ident: 10.1016/j.foodchem.2023.136793_b0070 article-title: Differentiation of Greek extra virgin olive oils according to cultivar based on volatile compound analysis and fatty acid composition publication-title: European Journal of Lipid Science and Technology doi: 10.1002/ejlt.201500293 – ident: 10.1016/j.foodchem.2023.136793_b0175 – volume: 111 start-page: 1003 year: 2009 ident: 10.1016/j.foodchem.2023.136793_b0040 article-title: Stepwise geographical traceability of virgin olive oils by chemical profiles using artificial neural network models publication-title: European Journal of Lipid Science and Technology doi: 10.1002/ejlt.200900015 – ident: 10.1016/j.foodchem.2023.136793_b0150 – volume: 6 start-page: 3139 year: 2021 ident: 10.1016/j.foodchem.2023.136793_b0090 article-title: performance: An R package for assessment, comparison and testing of statistical models publication-title: Journal of Open Source Software doi: 10.21105/joss.03139 – volume: 80 start-page: 939 year: 2000 ident: 10.1016/j.foodchem.2023.136793_b0115 article-title: Plant sterols: Biosynthesis, biological function and their importance to human nutrition publication-title: Journal of the Science of Food and Agriculture doi: 10.1002/(SICI)1097-0010(20000515)80:7<939::AID-JSFA644>3.0.CO;2-C – volume: 48 start-page: 2528 year: 2013 ident: 10.1016/j.foodchem.2023.136793_b0180 article-title: Neural network models to classify olive oils within the protected denomination of origin framework publication-title: International Journal of Food Science and Technology doi: 10.1111/ijfs.12245 – volume: 52 start-page: 3434 year: 2004 ident: 10.1016/j.foodchem.2023.136793_b0010 article-title: Influence of harvest date and crop yield on the fatty acid composition of virgin olive oils from cv. Picual publication-title: Journal of Agricultural and Food Chemistry doi: 10.1021/jf049894n – volume: 398 year: 2023 ident: 10.1016/j.foodchem.2023.136793_b0135 article-title: Geographical discrimination of olive oils from Cv. 'Galega Vulgar' publication-title: Food Chemistry doi: 10.1016/j.foodchem.2022.133945 – volume: 219 start-page: 131 year: 2017 ident: 10.1016/j.foodchem.2023.136793_b0015 article-title: Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data publication-title: Food Chemistry doi: 10.1016/j.foodchem.2016.09.041 – volume: 404 year: 2023 ident: 10.1016/j.foodchem.2023.136793_b0155 article-title: Combining untargeted profiling of phenolics and sterols, supervised multivariate class modelling and artificial neural networks for the origin and authenticity of extra-virgin olive oil: A case study on Taggiasca Ligure publication-title: Food Chemistry doi: 10.1016/j.foodchem.2022.134543 – volume: 130 year: 2021 ident: 10.1016/j.foodchem.2023.136793_b0160 article-title: Discrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data publication-title: Food Control doi: 10.1016/j.foodcont.2021.108318 – volume: 136 start-page: 251 year: 2013 ident: 10.1016/j.foodchem.2023.136793_b0095 article-title: Sterols and triterpene diols in olive oil as indicators of variety and degree of ripening publication-title: Food Chemistry doi: 10.1016/j.foodchem.2012.08.005 |
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| SubjectTerms | algorithms artificial intelligence Artificial intelligent models Authenticity Chemical composition coasts Cultivar classification cultivar identification cultivars extra-virgin olive oil food chemistry Greece Italy Machine learning Olive oil olives principal component analysis traceability |
| Title | Classification of olive cultivars by machine learning based on olive oil chemical composition |
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