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Shu N, Chen X, Sun X, Cao X, Liu Y, Xu YJ. Metabolomics identify landscape of food sensory properties. Crit Rev Food Sci Nutr 2022; 63:8478-8488. [PMID: 35435783 DOI: 10.1080/10408398.2022.2062698] [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] [Indexed: 11/03/2022]
Abstract
Sensory evaluation is a key component of food production strategy. The classical food sensory evaluation method is time-consuming, laborious, costly, and highly subjective. Since flavor (taste and smell), texture, and mouthfeel are all related to the chemical properties of food, there has been a growing interest in how they affect the senses of food. In the past decades, emerging metabolomics has received much attention in the field of sensory evaluation, because it not only offers a broad picture of chemical composition for sensory properties but also revealed their changes and functions in food proceeding. This article reviewed food chemicals regarding the flavor, smell, and texture of foods, and discussed the advantages and limitations of applying metabolomics approaches to sensory evaluation, including GC-MS, LC-MS, and NMR. Taken together, this review gives a comprehensive, critical overview of the current state, future challenges, and trends in metabolomics on food sensory properties.
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Affiliation(s)
- Nanxi Shu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Xiaoying Chen
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Xian Sun
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Xinyu Cao
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Yuanfa Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Yong-Jiang Xu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
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2
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Çetin N. Machine Learning for Varietal Binary Classification of Soybean (Glycine max (L.) Merrill) Seeds Based on Shape and Size Attributes. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02286-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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3
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Quelal‐Vásconez MA, Lerma‐García MJ, Pérez‐Esteve É, Talens P, Barat JM. Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods. Compr Rev Food Sci Food Saf 2020; 19:448-478. [DOI: 10.1111/1541-4337.12522] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/06/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023]
Affiliation(s)
| | | | - Édgar Pérez‐Esteve
- Departamento de Tecnología de AlimentosUniversitat Politècnica de València Valencia Spain
| | - Pau Talens
- Departamento de Tecnología de AlimentosUniversitat Politècnica de València Valencia Spain
| | - José Manuel Barat
- Departamento de Tecnología de AlimentosUniversitat Politècnica de València Valencia Spain
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4
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Aheto JH, Huang X, Tian X, Ren Y, Ernest B, Alenyorege EA, Dai C, Hongyang T, Xiaorui Z, Wang P. Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat. Anal Bioanal Chem 2020; 412:1169-1179. [PMID: 31912184 DOI: 10.1007/s00216-019-02345-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 12/03/2019] [Accepted: 12/10/2019] [Indexed: 01/20/2023]
Abstract
The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute "combined attribute features" (CAF) which yielded acceptable results with (Rc2 = 0.877, 0.891; RMSEC = 2.410, 1.109; Rp2 = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (Rc2 = 0.783, 0.877; RMSEC = 4.591, 0.653; Rp2 = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (Rc2 = 0.936, 0.955; RMSEC = 1.209, 0.997; Rp2 = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat. Graphical abstract.
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Affiliation(s)
- Joshua Harrington Aheto
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China.
| | - Xiaoyu Tian
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China.
| | - Yi Ren
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
- Suzhou Polytechnic Institute of Agriculture, School of Smart Agriculture, No.279 Xiyuan Road, Suzhou, 215008, China
| | - Bonah Ernest
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
- Food and Drugs Authority, Laboratory Services Department, P. O. Box CT 2783, Cantonments, Accra, Ghana
| | - Evans Adingba Alenyorege
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
- Faculty of Agriculture, University for Development Studies, Tamale, Ghana
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
- School of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
| | - Tu Hongyang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
| | - Zhang Xiaorui
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
| | - Peichang Wang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013, Jiangsu, China
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Fang S, Ning J, Huang WJ, Zhang G, Deng WW, Zhang Z. Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:4344-4352. [PMID: 30828822 DOI: 10.1002/jsfa.9668] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 02/20/2019] [Accepted: 02/27/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head-space solid phase microextraction approach and gas chromatography-mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS Thirty-one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k-nearest neighbor (k-NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross-validation prediction abilities (84.6%) was obtained for the PCA-k-NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry.
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Affiliation(s)
- Shimao Fang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Wen-Jing Huang
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Gang Zhang
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Wei-Wei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
- School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, China
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Jiménez-Carvelo AM, González-Casado A, Bagur-González MG, Cuadros-Rodríguez L. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review. Food Res Int 2019; 122:25-39. [PMID: 31229078 DOI: 10.1016/j.foodres.2019.03.063] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 12/31/2022]
Abstract
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.
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Affiliation(s)
- Ana M Jiménez-Carvelo
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain.
| | - Antonio González-Casado
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - M Gracia Bagur-González
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
| | - Luis Cuadros-Rodríguez
- Department of Analytical Chemistry, Faculty of Science, University of Granada, C/ Fuentenueva s/n, E-18071 Granada, Spain
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7
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Zhang L, Wang X, Huang GB, Liu T, Tan X. Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:947-960. [PMID: 29994190 DOI: 10.1109/tcyb.2018.2789889] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.
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Li B, Kimatu BM, Pei F, Chen S, Feng X, Hu Q, Zhao L. Non-volatile flavour components in Lentinus edodes after hot water blanching and microwave blanching. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2018. [DOI: 10.1080/10942912.2017.1373667] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Biao Li
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Benard Muinde Kimatu
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, China
- Department of Dairy and Food Science and Technology, Egerton University, Egerton, Kenya
| | - Fei Pei
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing, China
| | - Shuangyang Chen
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Xin Feng
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, China
| | - Qiuhui Hu
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, China
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing, China
| | - Liyan Zhao
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, China
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Wang L, Nägele T, Doerfler H, Fragner L, Chaturvedi P, Nukarinen E, Bellaire A, Huber W, Weiszmann J, Engelmeier D, Ramsak Z, Gruden K, Weckwerth W. System level analysis of cacao seed ripening reveals a sequential interplay of primary and secondary metabolism leading to polyphenol accumulation and preparation of stress resistance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 87:318-32. [PMID: 27136060 DOI: 10.1111/tpj.13201] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 03/04/2016] [Accepted: 04/22/2016] [Indexed: 05/19/2023]
Abstract
Theobroma cacao and its popular product, chocolate, are attracting attention due to potential health benefits including antioxidative effects by polyphenols, anti-depressant effects by high serotonin levels, inhibition of platelet aggregation and prevention of obesity-dependent insulin resistance. The development of cacao seeds during fruit ripening is the most crucial process for the accumulation of these compounds. In this study, we analyzed the primary and the secondary metabolome as well as the proteome during Theobroma cacao cv. Forastero seed development by applying an integrative extraction protocol. The combination of multivariate statistics and mathematical modelling revealed a complex consecutive coordination of primary and secondary metabolism and corresponding pathways. Tricarboxylic acid (TCA) cycle and aromatic amino acid metabolism dominated during the early developmental stages (stages 1 and 2; cell division and expansion phase). This was accompanied with a significant shift of proteins from phenylpropanoid metabolism to flavonoid biosynthesis. At stage 3 (reserve accumulation phase), metabolism of sucrose switched from hydrolysis into raffinose synthesis. Lipids as well as proteins involved in lipid metabolism increased whereas amino acids and N-phenylpropenoyl amino acids decreased. Purine alkaloids, polyphenols, and raffinose as well as proteins involved in abiotic and biotic stress accumulated at stage 4 (maturation phase) endowing cacao seeds the characteristic astringent taste and resistance to stress. In summary, metabolic key points of cacao seed development comprise the sequential coordination of primary metabolites, phenylpropanoid, N-phenylpropenoyl amino acid, serotonin, lipid and polyphenol metabolism thereby covering the major compound classes involved in cacao aroma and health benefits.
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Affiliation(s)
- Lei Wang
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
- Vienna Metabolomics Center (VIME); University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Hannes Doerfler
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Lena Fragner
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Palak Chaturvedi
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Ella Nukarinen
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Anke Bellaire
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
- Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030, Vienna, Austria
| | - Werner Huber
- Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030, Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Doris Engelmeier
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria
| | - Ziva Ramsak
- Department of Systems Biology and Biotechnology, National Institute of Biology, Vecna pot 111, 1000, Ljubljana, Slovenia
| | - Kristina Gruden
- Department of Systems Biology and Biotechnology, National Institute of Biology, Vecna pot 111, 1000, Ljubljana, Slovenia
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.
- Vienna Metabolomics Center (VIME); University of Vienna, Althanstrasse 14, 1090, Vienna, Austria.
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Kim K, Seo M, Kang H, Cho S, Kim H, Seo KS. Application of LogitBoost Classifier for Traceability Using SNP Chip Data. PLoS One 2015; 10:e0139685. [PMID: 26436917 PMCID: PMC4593556 DOI: 10.1371/journal.pone.0139685] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Accepted: 09/16/2015] [Indexed: 12/03/2022] Open
Abstract
Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.
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Affiliation(s)
- Kwondo Kim
- Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151–921, Republic of Korea
- C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4–2 Bongcheon-dong, Gwanak-gu, Seoul 151–919, Republic of Korea
| | - Minseok Seo
- C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4–2 Bongcheon-dong, Gwanak-gu, Seoul 151–919, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151–747, Republic of Korea
| | - Hyunsung Kang
- Department of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Suncheon, 540–742, Republic of Korea
| | - Seoae Cho
- C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4–2 Bongcheon-dong, Gwanak-gu, Seoul 151–919, Republic of Korea
| | - Heebal Kim
- Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151–921, Republic of Korea
- C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4–2 Bongcheon-dong, Gwanak-gu, Seoul 151–919, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151–747, Republic of Korea
| | - Kang-Seok Seo
- Department of Animal Science and Technology, College of Life Science and Natural Resources, Sunchon National University, Suncheon, 540–742, Republic of Korea
- * E-mail:
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11
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Regueiro J, Negreira N, Simal-Gándara J. Challenges in relating concentrations of aromas and tastes with flavor features of foods. Crit Rev Food Sci Nutr 2015; 57:2112-2127. [DOI: 10.1080/10408398.2015.1048775] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jorge Regueiro
- Nutrition and Bromatology Group, Food Science and Technology Faculty, Department of Analytical and Food Chemistry, University of Vigo—Ourense Campus, Ourense, Spain
| | - Noelia Negreira
- International Iberian Nanotechnology Laboratory, Braga, Portugal
| | - Jesús Simal-Gándara
- Nutrition and Bromatology Group, Food Science and Technology Faculty, Department of Analytical and Food Chemistry, University of Vigo—Ourense Campus, Ourense, Spain
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12
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A Liquid Chromatography Time-of-Flight Mass Spectrometry-Based Metabolomics Approach for the Discrimination of Cocoa Beans from Different Growing Regions. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0245-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Han F, Huang X, Teye E, Gu H. Quantitative Analysis of Fish Microbiological Quality Using Electronic Tongue Coupled with Nonlinear Pattern Recognition Algorithms. J Food Saf 2015. [DOI: 10.1111/jfs.12180] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Fangkai Han
- School of Food and Biological Engineering; Jiangsu University; Xuefu Road 301 Zhenjiang 212013 Jiangsu China
| | - Xingyi Huang
- School of Food and Biological Engineering; Jiangsu University; Xuefu Road 301 Zhenjiang 212013 Jiangsu China
| | - Ernest Teye
- School of Agriculture; Department of Agricultural Engineering; College of Agriculture and Natural Science; University of Cape Coast; Cape Coast Ghana
| | - Haiyang Gu
- School of Food and Biological Engineering; Jiangsu University; Xuefu Road 301 Zhenjiang 212013 Jiangsu China
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14
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Li Y, Lei J, Liang D. Identification of Fake Green Tea by Sensory Assessment and Electronic Tongue. FOOD SCIENCE AND TECHNOLOGY RESEARCH 2015. [DOI: 10.3136/fstr.21.207] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Yanjie Li
- College of life science and engineering, Chongqing Three Gorges University
| | - Jincan Lei
- Postdoctoral Station of Science and Technology of Instrumentation, College of Optoelectronic Engineering, Chongqing University
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16
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Teye E, Huang X, Takrama J, Haiyang G. Integrating NIR Spectroscopy and Electronic Tongue Together with Chemometric Analysis for Accurate Classification of Cocoa Bean Varieties. J FOOD PROCESS ENG 2014. [DOI: 10.1111/jfpe.12109] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ernest Teye
- School of Food and Biological Engineering, Xuefu Road; Jiangsu University; Zhenjiang 212013 Jiangsu Province China
- School of Agriculture; University of Cape Coast; Cape Coast Ghana
| | - Xingyi Huang
- School of Food and Biological Engineering, Xuefu Road; Jiangsu University; Zhenjiang 212013 Jiangsu Province China
| | - Jemmy Takrama
- Physiology & Biochemistry Division; Cocoa Research Institute of Ghana; New Tafo Akim Ghana
| | - Gu Haiyang
- School of Food and Biological Engineering, Xuefu Road; Jiangsu University; Zhenjiang 212013 Jiangsu Province China
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17
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Li Y, Lei J, Yang J, Liu R. Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition. ANAL LETT 2014. [DOI: 10.1080/00032719.2014.908381] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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Huang X, Teye E, Owusu-Sekyere JD, Takrama J, Sam-Amoah LK, Yao L, Firempong CK. Simultaneous Measurement of Titratable Acidity and Fermentation Index in Cocoa Beans by Electronic Tongue Together with Linear and Non-linear Multivariate Technique. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-9862-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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