Determination of soluble solids content in loquat using near-infrared spectroscopy coupled with broad learning system and hybrid wavelength selection strategy

文献类型： 外文期刊

第一作者： Li, Peng

作者： Li, Peng;Liu, Huaming;Han, Liguo;Li, Chuanzong;Luo, Yizhi;Li, Peng;Liu, Huaming;Han, Liguo;Li, Chuanzong;Jin, Qingting

作者机构：

关键词： Fruit; Nondestructive detection; Machine learning; Chemometrics; Spectral analysis

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.0； 五年影响因子：6.0 ）

ISSN： 0023-6438

年卷期： 2024 年 206 卷

页码：

收录情况： SCI

摘要： Soluble solids content (SSC) serves as a crucial metric in assessing loquat quality. To achieve fast, nondestructive, and accurate detection of SSC, the combination of near-infrared (NIR) spectroscopy and broad learning system (BLS) model was developed in this study. Meanwhile, the influence of six spectral preprocessing methods and six wavelength selection algorithms on SSC prediction was explored, and the performance of the BLS model was compared with the widely used partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models. The results showed that Savitzky-Golay smoothing combined with standard normal transformation (SG-SNV) offered the optimal pretreatment, and the hybrid method, namely interval variable iterative space shrinking analysis and successive projections algorithm (iVISSA-SPA), emerged as the most effective wavelength selection method. The BLS model outperformed both PLSR and LS-SVM models. Specifically, the BLS model based on the iVISSA-SPA method achieved the optimal SSC prediction results, with R2P = 0.8646, RMSEP = 0.5104, and RPD = 2.7481. Therefore, NIR spectroscopy coupled with the BLS model and hybrid wavelength selection strategy could rapidly, non-destructively and accurately detect the SSC of loquat, providing a viable alternative for SSC prediction in fruit.

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