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Zhang Y, Wang Y. Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects. Food Chem X 2023; 19:100860. [PMID: 37780348 PMCID: PMC10534232 DOI: 10.1016/j.fochx.2023.100860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Abstract
The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.
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Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
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Liu S, Liu H, Li J, Wang Y. Building deep learning and traditional chemometric models based on Fourier transform mid-infrared spectroscopy: Identification of wild and cultivated Gastrodia elata. Food Sci Nutr 2023; 11:6249-6259. [PMID: 37823161 PMCID: PMC10563693 DOI: 10.1002/fsn3.3565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 10/13/2023] Open
Abstract
To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.
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Affiliation(s)
- Shuai Liu
- College of Agronomy and BiotechnologyYunnan Agricultural UniversityKunmingChina
- Medicinal Plants Research InstituteYunnan Academy of Agricultural SciencesKunmingChina
| | - Honggao Liu
- Yunnan Key Laboratory of Gastrodia and Fungi Symbiotic BiologyZhaotong UniversityZhaotongChina
| | - Jieqing Li
- College of Agronomy and BiotechnologyYunnan Agricultural UniversityKunmingChina
| | - Yuanzhong Wang
- Medicinal Plants Research InstituteYunnan Academy of Agricultural SciencesKunmingChina
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3
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Untargeted HPLC-MS-based metabolomics approach to reveal cocoa powder adulterations. Food Chem 2023; 402:134209. [DOI: 10.1016/j.foodchem.2022.134209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022]
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4
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Wei X, Kong D, Zhu S, Li S, Zhou S, Wu W. Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine. FRONTIERS IN PLANT SCIENCE 2022; 13:823865. [PMID: 35360340 PMCID: PMC8963758 DOI: 10.3389/fpls.2022.823865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.
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Affiliation(s)
- Xiao Wei
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- College of Engineering and Technology, Southwest University, Chongqing, China
| | - Dandan Kong
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Shiping Zhu
- College of Engineering and Technology, Southwest University, Chongqing, China
| | - Song Li
- College of Engineering and Technology, Southwest University, Chongqing, China
| | - Shengling Zhou
- College of Engineering and Technology, Southwest University, Chongqing, China
| | - Weiji Wu
- China Tianjin Grain and Oil Wholesale Trade Market, Tianjin, China
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Qi Z, Wu X, Yang Y, Wu B, Fu H. Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis. Foods 2022; 11:foods11050763. [PMID: 35267396 PMCID: PMC8909659 DOI: 10.3390/foods11050763] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 02/06/2023] Open
Abstract
In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects.
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Affiliation(s)
- Zuxuan Qi
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Yangjian Yang
- Research Institute of Zhejiang University-Taizhou, Taizhou 317700, China
- Correspondence:
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China;
| | - Haijun Fu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Z.Q.); (X.W.); (H.F.)
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Rapid screening of unground cocoa beans based on their content of bioactive compounds by NIR spectroscopy. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques. INTERNATIONAL JOURNAL OF FOOD SCIENCE 2021; 2021:1844675. [PMID: 34845434 PMCID: PMC8627362 DOI: 10.1155/2021/1844675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/08/2021] [Accepted: 10/25/2021] [Indexed: 11/18/2022]
Abstract
The global market for organic cocoa beans continues to show sturdy growth. A low-cost handheld NIR spectrometer (900-1700 nm) combined with multivariate classification algorithms was used for rapid differentiation analysis of organic cocoa beans' integrity. In this research, organic and conventionally cultivated cocoa beans were collected from different locations in Ghana and scanned nondestructively with a handheld spectrometer. Different preprocessing treatments were employed. Principal component analysis (PCA) and classification analysis, RF (random forest), KNN (K-nearest neighbours), LDA (linear discriminant analysis), and PLS-DA (partial least squares-discriminant analysis) were performed comparatively to build classification models. The performance of the models was evaluated by accuracy, specificity, sensitivity, and efficiency. Second derivative preprocessing together with PLS-DA algorithm was superior to the rest of the algorithms with a classification accuracy of 100.00% in both the calibration set and prediction set. Second derivative algorithm was found to be the best preprocessing tool. The identification rates for the calibration set and prediction set were 96.15% and 98.08%, respectively, for RF, 91.35% and 92.31% for KNN, and 90.38% and 98.08% for LDA. Generally, the results showed that a handheld NIR spectrometer coupled with an appropriate multivariate algorithm could be used in situ for the differentiation of organic cocoa beans from conventional ones to ensure food integrity along the cocoa bean value chain.
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Potential Applicability of Cocoa Pulp ( Theobroma cacao L) as an Adjunct for Beer Production. ScientificWorldJournal 2020; 2020:3192585. [PMID: 32934606 PMCID: PMC7484685 DOI: 10.1155/2020/3192585] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 07/17/2020] [Indexed: 01/13/2023] Open
Abstract
The aim of this study was to evaluate the application of cocoa pulp as an adjunct for malt in beer production. The cocoa pulp was analyzed for humidity, proteins, lipids, sugars, total soluble solids, organic acids, and minerals. A study was carried out to reduce the cocoa pulp viscosity by enzymatic depectinization, making its use viable in beer production. The cocoa pulp showed relevant quantities of compounds important in fermentation, such as sugars, acids, and minerals. In fermentation using the adjunct, the proportions of pulp used were 10, 30, and 49%. A significant difference was found between the adjunct and all-malt worts. The 30% cocoa pulp concentration as an adjunct for malt in the fermentation medium contributed the most to the fermentative performance of the yeasts at both 15 and 22°C based on the consumption of apparent extract (°Plato), ethanol production, and cellular growth.
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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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Aheto JH, Huang X, Tian X, Ren Y, Bonah E, Alenyorege EA, Lv R, Dai C. Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13225] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Joshua H. Aheto
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xingyi Huang
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xiaoyu Tian
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Yi Ren
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Suzhou Polytechnic Institute of Agriculture; Suzhou China
| | - Ernest Bonah
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Laboratory Services Department; Food and Drugs Authority; Accra Ghana
| | - Evans A. Alenyorege
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Faculty of Agriculture; University for Development Studies; Tamale Ghana
| | - Riqin Lv
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Biological Science and Food Engineering; Chuzhou University; No. 1528 Fengle Avenue, Yu District, Zhangzhou City China
| | - Chunxia Dai
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang Jiangsu China
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12
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13
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Dankowska A. Data fusion of fluorescence and UV spectroscopies improves the detection of cocoa butter adulteration. EUR J LIPID SCI TECH 2017. [DOI: 10.1002/ejlt.201600268] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Anna Dankowska
- Faculty of Commodity Science; Poznań University of Economics and Business; Poznań Poland
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14
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Oliveira LF, Braga SC, Augusto F, Hashimoto JC, Efraim P, Poppi RJ. Differentiation of cocoa nibs from distinct origins using comprehensive two-dimensional gas chromatography and multivariate analysis. Food Res Int 2016; 90:133-138. [DOI: 10.1016/j.foodres.2016.10.047] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 10/25/2016] [Accepted: 10/29/2016] [Indexed: 12/17/2022]
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Teye E, Uhomoibhi J, Wang H. Nondestructive Authentication of Cocoa Bean Cultivars by FT-NIR Spectroscopy and Multivariate Techniques. ACTA ACUST UNITED AC 2016. [DOI: 10.21859/focsci-020347] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Kiani S, Minaei S, Ghasemi-Varnamkhasti M. Fusion of artificial senses as a robust approach to food quality assessment. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2015.10.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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