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Online Detection of Sugar Content in Watermelon Based on
Full-Transmission Visible and Near-Infrared Spectroscopy |
WANG He-gong1, 2, HUANG Wen-qian2, CAI Zhong-lei2, YAN Zhong-wei2, LI Sheng2, LI Jiang-bo2* |
1. College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2. Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097,China
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Abstract Sugar content is a crucial parameter for assessing watermelon quality, influencing watermelon's marketability and commercial value. However, the natural biological characteristics of large volume and thick skin pose challenges for rapid and non-destructive evaluation of the sugar content of watermelon. In this study, 230 watermelons were selected for investigation. A custom-designed full-transmission visible-near-infrared detection system was developed. Spectral data of all samples were acquired online. Each sample spectral data comes from the equatorial part of the watermelon. The overall watermelon sugar content and the central sugar content were measured separately to provide reference values for the assessment of sugar content. In the data processing phase, the spectral data of each sample was averaged, and spectral data in the 690~1 100 nm was selected. The Monte Carlo method was implemented to remove abnormal samples, and preprocessing, such as Standard Normal Variate correction and Savitzky-Golay smoothing, was applied to optimize the spectral data. The SPXY algorithm was used to divide the calibration and prediction sets. Utilizing the optimized spectral data, linear Partial Least Squares Regression (PLSR) and non-linear Least Squares Support Vector Machine (LS-SVM) models were developed to forecast each sample's center sugar content and overall sugar content. The results revealed that, Combined with standard normal variate correction and Savitzky-golay smoothing, the LS-SVM model yielded the most favorable results in predicting the overall watermelon sugar content. The calibration correlation coefficient (RC) of 0.92 and root mean square error of calibration (RMSEC) of 0.37°Brix were obtained for the calibration set. Correspondingly, the prediction correlation coefficient (RP) of 0.88 and root mean square error of prediction (RMSEP) of 0.40°Brix were obtained for the prediction set. Furthermore, feature wavelength selection algorithms (e.g., Competitive Adaptive Reweighted Sampling, Uninformative Variable Elimination, Successive Projections Algorithm) were used for variable selection. Study found that the LS-SVM model combined with Competitive Adaptive Reweighted Sampling and Uninformative Variable Elimination methods has the optimal performance in predicting the overall watermelon sugar content with a calibration correlation coefficient (RC) of 0.94 and a calibration root mean square error of 0.31°Brix. Correspondingly, the prediction correlation coefficient (RP) and the root mean square error of prediction (RMSEP) were 0.91 and 0.37 °Brix, respectively. Additionally, the number of variables was significantly reduced from 1 524 to 39. This study provides a reference for the practical application of rapid and non-destructive testing of sugar content in watermelon.
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Received: 2023-05-05
Accepted: 2023-08-07
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Corresponding Authors:
LI Jiang-bo
E-mail: jbli2011@163.com; lijb@nercita.org.cn
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[1] ZHANG Lin, YAN Shen, LI Qiong-hua, et al(张 琳, 闫 燊, 李琼华, 等). Chinese Scientific Data(中国科学数据),2021, 6(4): 8.
[2] ZUO Jie-wen, PENG Yan-kun, LI Yong-yu, et al(左杰文, 彭彦昆, 李永玉, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2022, 53(S1): 316.
[3] Jie D, Xie L, Rao X, et al. Postharvest Biology and Technology, 2014, 90: 1.
[4] Chen Y, Xu Z, Tang W, et al. Artificial Intelligence in Agriculture, 2021, 5: 125.
[5] Raghavendra A, Guru D S, Rao M K. Aritificial Intelligence in Agriculture, 2021, 5: 43.
[6] Rejeb A, Rejeb K, Zailani S, et al. Artificial Intelligence in Agriculture,2022, 6: 111.
[7] LU Yong, LI Zhen-feng, LI Jing, et al(陆 勇, 李臻峰, 李 静, 等). Fujian Agricultural Science and Technology(福建农业科技), 2014,(10): 77.
[8] Liu D, Wang E, Wang G, et al. Journal of the Science of Food and Agriculture, 2021, 101(10): 4308.
[9] Jie D F, Wei X. Computers and Electronics in Agriculutre, 2018, 151: 156.
[10] Phuangsombut K, Phuangsombut A, Talabnark A, et al. Postharvest Biology and Technology, 2018, 142: 55.
[11] Jie D, Zhou W, Wei X. Scientia Horticulturae, 2019, 257: 108718.
[12] Zhang D Y, Xu L, Wang Q Y, et al. Food Analytical Methods, 2019, 12: 136.
[13] Jie D, Xie L, Fu X, et al. Journal of Food Engineering, 2013, 118(4): 387.
[14] Onsawai P, Phetpan K, Khurnpoon L, et al. Measurement, 2021,174: 108684.
[15] Olarewaju O O, Magwaza L S, Nieuwoudt H, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 209: 62.
[16] WANG Shi-fang, HAN Ping, CUI Guang-lu, et al(王世芳, 韩 平, 崔广禄, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(3): 738.
[17] Hu R, Zhang L X, Yu Z Y, et al. Infrared Physics & Technology, 2019, 102: 102999.
[18] Li M, Han D, Liu W. Biosystems Engineering, 2019, 188: 31.
[19] JIE Deng-fei, XIE Li-juan, RAO Xiu-qin, et al(介邓飞, 谢丽娟, 饶秀勤, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(12): 264.
[20] GUO Yang, SHI Yong, GUO Jun-xian, et al(郭 阳, 史 勇, 郭俊先, 等). Food and Fermentation Industries(食品与发酵工业), 2022, 48(2): 248.
[21] GUO Yang, GUO Jun-xian, SHI Yong, et al(郭 阳, 郭俊先, 史 勇, 等). Food & Machinery(食品与机械), 2021, 37(6): 81.
[22] Zhang Y, Wang Z, Tian X, et al. Infrared Physics and Technology, 2022, 122: 104090.
[23] Song J, Li G, Yang X. Journal of the Science of Food and Agriculture, 2019, 99(11): 4898.
[24] LONG Fei, YU Zheng, LIU Fen, et al(龙 霏, 余 铮, 刘 芬, 等). Computer Systems & Applications(计算机系统应用), 2021, 30(7): 283.
[25] Tamburini E, Costa S, Rugiero I, et al. Sensors, 2017, 17(4): 746.
[26] Qi S, Song S, Jiang S, et al. Journal of Innovative Optical Health Sciences, 2014, 7(4): 1350034.
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