Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system |
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| Institution: | 1. Centre for Postharvest and Refrigeration Research, Massey University, Palmerston North, New Zealand;2. Department of Statistics, Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand;3. Zespri International Ltd., Mt. Maunganui, New Zealand;1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, 100097, China;2. National Research Center of Intelligent Equipment for Agriculture, Beijing, 100097, China;3. Key Laboratory of Agri-Informatics, Ministry of Agriculture, Beijing, 100097, China;4. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, China;1. Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia;2. Department of Process and Food Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia;1. College of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China;2. Department of External Liaison, East China Jiaotong University, Nanchang 330013, China;1. Tianjin Key Laboratory of Food Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, No. 409 Guang Rong Road, Beichen District, Tianjin, 300134, China;2. College of Food Science and Nutritional Engineering, China Agricultural University, No. 17 Tsing Hua East Road, Haidian District, Beijing, 100083, China;3. School of Chemical Engineering, Xiangtan University, Xiangtan, Hunan, 411105, China |
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| Abstract: | Sugar content is one of the most important factors determining the eating quality of watermelon fruit. In order to detect the fruit soluble solids content (SSC) on-line, this work develops a nondestructive on-line detection prototype system using visible and near-infrared (Vis/NIR) technology. For the acquisition of the diffuse transmittance spectrum of watermelon, the conveyor was set at a speed of 0.3 m/s and ten 150 W tungsten halogen lamps were used as the light source. The crucial model for SSC value prediction was optimized by chemometrics. Partial least squares regression (PLSR), stepwise multiple linear regressions (SMLR), Monte-Carlo uninformative variable elimination (MC-UVE) and genetic algorithms (GA) were applied to the spectra in the range of 687–920 nm. The data pre-processing methods were optimized to transmittance spectra with baseline offset correction (BOC), and the BOC-MC-UVE-SMLR calibration model was the best with a correlation coefficient (rpre) of 0.70, root mean square error of prediction (RMSEP) of 0.33 °Brix for the prediction set. In on-line testing of 30 samples, the rpre was 0.66 and RMSEP was 0.39 °Brix. The results showed that a nondestructive on-line SSC value determination prototype based on Vis/NIR technology was feasible. |
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| Keywords: | Near infrared spectroscopy Monte Carlo-uninformative variable elimination Watermelon On-line determination Stepwise multiple linear regression |
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