Identification of rice storage time based on colorimetric sensor array combined hyperspectral imaging technology |
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| Affiliation: | 1. Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan;2. Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA;3. USDA-ARS, Beltsville Agricultural Research Center, Food Quality Laboratory, Bldg. 303, BARC-East, Beltsville, MD 20705-2350, USA;4. Bioenergy Research Center, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan;1. Chungbuk National University, Republic of Korea;2. Korea Food Research Institute, Republic of Korea;1. Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran;2. Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Iran;1. School of Food and Biological Engineering, Jiangsu university, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China;2. College of Sciences and Arts-Alkamil, University of Jeddah, P.O. Box 110, Alkamil 21931, Saudi Arabia;3. Department of Food Science & Technology, College of Agricultural Studies, Sudan University of Science & Technology, P.O. Box 71, Khartoum North, Sudan;1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, PR China;2. School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, PR China |
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| Abstract: | The study reports a novel colorimetric sensor array (CSA) based hyperspectral imaging (HSI) system and chemometrics algorithms for the identification of rice storage time. CSA fabricated by boron-dipyrromethene (BODIPY) dyes was used to capture the volatile organic compounds (VOCs) of rice samples. CSA hypercube before and after the reaction were obtained with HSI. Genetic synergy interval partial least square algorithm (GA-Si-PLS) was used to filter spectral information. Fifty-four spectral data variables and five dominant wavelength images was selected from CSA hypercube. Then three grayscale difference values were extracted from each dominant wavelength image, thus totaling to 15 variables as imaging data variables. Linear discriminant analysis (LDA) and k-Nearest Neighbor (KNN) model were established to comparing the performance of spectral variables, imaging variables and combined datasets. The result showed the optimal model was linear discriminant analysis (LDA) model built by using spectral variables and the correct rate of calibration set for rice storage time discrimination was 92.73% and the obtained rate of prediction set was 90.91%. It is indicated the applicability of the proposed CSA combined with HSI technology towards rice storage time identification. |
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| Keywords: | Rice aging Colorimetric sensor array BODIPYs Hyperspectral imaging GA-Si-PLS |
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