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Online Detection of Sugar Content in Watermelon Based on Full-Transmission Visible and Near-Infrared Spectroscopy

文献类型： 外文期刊

作者： Wang He-gong 1 ; Huang Wen-qian 2 ; Cai Zhong-lei 2 ; Yan Zhong-wei 2 ; Li Sheng 2 ; Li Jiang-bo 2 ;

作者机构： 1.Xinjiang Agr Univ, Coll Mech & Elect Engn, Urumqi 830052, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

关键词： Full-transmittance spectrum; Online detection; Watermelon; Sugar content; Modeling

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.7； 五年影响因子：0.6 ）

ISSN： 1000-0593

年卷期： 2024 年 44 卷 6 期

页码：

收录情况： SCI

摘要： 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 similar to 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 degrees 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 degrees 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 degrees Brix. Correspondingly, the prediction correlation coefficient (RP) and the root mean square error of prediction (RMSEP) were 0.91 and 0.37 degrees 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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