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Zhang J, Ma N, Xu G, Kuang L, Shen Y. Discrimination of apples from the surrounding Bohai Bay and the loess plateau: A combined study of ICP-MS and UPLC-Q-TOF-MS based element and metabolite fingerprints. Food Chem 2024; 459:140279. [PMID: 38991451 DOI: 10.1016/j.foodchem.2024.140279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/13/2024]
Abstract
Apples are important fruits in China, and their authentication is beneficial for quality control. However, the differentiation between apples from two primary producing regions, the surrounding Bohai Bay (BHB) and the Loess Plateau (LP), has not been well studied. This study used element and metabolite fingerprints combined with mathematical recognition techniques to discriminate between BHB and LP apples. A total of 235 samples were collected from these regions during 2018-2019. The apple element and metabolite profiles were obtained via instrument analysis. Differential elements and metabolites between BHB and LP apples were identified, and linear and nonlinear discriminant models were constructed. Nonlinear models demonstrated higher accuracy and effectiveness in model optimization. The final random forest (RF) model, constructed with 11 elements and 51 metabolites, achieved a training accuracy of 91.51% and a validation accuracy of 98.57%. This study discriminated between BHB and LP apples, providing a foundation for apple authentication.
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Affiliation(s)
- Jianyi Zhang
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China
| | - Ning Ma
- College of Veterinary Medicine, Agricultural University of Hebei, Baoding 071000, Hebei Province, PR China
| | - Guofeng Xu
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China
| | - Lixue Kuang
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China
| | - Youming Shen
- Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs (Xingcheng); Research Institute of Pomology Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning Province, PR China..
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Xu S, Guo Y, Liang X, Lu H. Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review. Foods 2024; 13:1116. [PMID: 38611420 PMCID: PMC11012010 DOI: 10.3390/foods13071116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.
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Affiliation(s)
- Sai Xu
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;
| | - Yinghua Guo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China;
| | - Xin Liang
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;
- College of Engineering, South China Agricultural University, Guangzhou 510642, China;
| | - Huazhong Lu
- Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
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Kang Z, Fan R, Zhan C, Wu Y, Lin Y, Li K, Qing R, Xu L. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning. Molecules 2024; 29:682. [PMID: 38338424 PMCID: PMC10856461 DOI: 10.3390/molecules29030682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
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Affiliation(s)
- Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rongsheng Fan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Chunyi Zhan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Youli Wu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Yi Lin
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Kunyu Li
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rui Qing
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
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