| Title: | Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness |
| Author(s): | Liang Y; Lin H; Kang W; Shao X; Cai J; Li H; Chen Q; |
| Address: | "School of Food and Biological Engineering, Jiangsu University, Jiangsu, China. College of Food and Biological Engineering, Jimei University, Xiamen, China" |
| ISSN/ISBN: | 1097-0010 (Electronic) 0022-5142 (Linking) |
| Abstract: | "BACKGROUND: Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. RESULTS: Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. CONCLUSION: The method developed could be used for non-destructive detection of grain freshness. (c) 2023 Society of Chemical Industry" |
| Keywords: | colorimetric sensor array freshness degree grain image processing visible-near-infrared spectroscopy; |
| Notes: | "PublisherLiang, Yue Lin, Hao Kang, Wencui Shao, Xiaokang Cai, Jianrong Li, Huanhuan Chen, Quansheng eng SCX 203321/Jiangsu Agricultural independent innovation fund/ BE2021343/Key R&D program of Jiangsu Province/ 51975259/National Natural Science Foundation of China/ England 2023/06/13 J Sci Food Agric. 2023 Jun 12. doi: 10.1002/jsfa.12777" |
