Research on the Method of Online Detection of Hollow Watermelons Based on Full-Transmission Near-Infrared Spectroscopy

文献类型： 外文期刊

第一作者： Li, Jia-qi

作者： Li, Jia-qi;Li, Jia-qi;Tian, Xi;Wang, Qing-yan;He, Xin;Huang, Wen-qian;Li, Jia-qi;Tian, Xi;Wang, Qing-yan;He, Xin;Huang, Wen-qian

作者机构：

关键词： Full-transmission near-infrared spectroscopy; Hollow watermelons; Weighted spectra; 1D-CNN

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.8； 五年影响因子：0.7 ）

ISSN： 1000-0593

年卷期： 2025 年 45 卷 5 期

页码：

收录情况： SCI

摘要： Watermelon has high nutritional value and is known for its effectiveness in relieving heat in medical applications, Key indicators for evaluating watermelon include ripeness, sweetness, and whether it is hollow. These factors significantly influence market competitiveness, Screening for hollow watermelons ensures higher quality, thereby enhancing market competitiveness. In this study, 307 watermelon spectra were collected using a fully transmissive near-infrared (NIR) spectroscopy device developed independently in our laboratory. Based on the characteristic that hollow arcas in watermelons primarily occur at the center of the fruit, we innovatively propose segmenting and weighting the spectra. The optimal two weighted spectra were selected using Support Vector Machine (SVM) and Partial Least Squares Discriminant Analysis (PLSDA) algorithms. Classification models for hollow watermelons were then built using the original spectra and preprocessed with Multiplicative Scatter Correction (MSC) and Savitzky-Golay Smoothing (SGS) in combination with SVM and PLSDA. The results showed that preprocessing the spectra did not necessarily improve the model performance and could even decrease it compared to models built with the original spectra. The models established using the two weighted spectra achieved the best performance, with accuracies of 96.74% (SVM) and 92.39% (PLSDA). The weighted spectra provided better modeling performance than the original and other preprocessed spectra. The weighted spectra were selected using SVM and PLSDA algorithms, and the original spectra were used to establish classification models with one-dimensional convolutional neural networks (ID-CNN), The model accuracies were 98.92% (SVM), 96.77% (PLSDA), and 95.70% (original spectra). The results indicated that ID-CNN provided better modeling performance than SVM and PLSDA. Additionally, the segmented and weighted spectra remained effective in ID-CNN and performed better than the original spectra, This study provides important technical support for non-destructive online grading detection of watermelons.

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