School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.
School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu, 213164, China.
J Food Sci. 2022 Jan;87(1):326-338. doi: 10.1111/1750-3841.16004. Epub 2021 Dec 23.
Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68-1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter-based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD-RC-LSSVM model obtained the best prediction accuracy for TSS, with of 0.93, with of 0.6 %, with of 18.14 and of 3.69. The overall results indicated that the VMD-RC algorithm can be used as an alternative to handle high-dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. PRACTICAL APPLICATION: Traditional methods of evaluating grape quality attributes are destructive, time-consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.
总可溶性固体（TSS）是决定水果品质和价格的最重要属性之一。本研究旨在使用高光谱成像（HSI）和波长选择技术对葡萄中的 TSS 进行无损检测。提出了一种新的方法，即变分模态分解和回归系数（VMD-RC），用于选择特征波长。VMD 用于分解具有（400.68-1001.61nm）波段的样本的高光谱图像数据，以获得一系列特征分量。然后，分别对这些分量进行回归分析，以获得每个分量的 RC 稳定性值。最后，使用基于滤波器的选择策略筛选关键波长。分别在全波长和特征波长下构建最小二乘支持向量机（LSSVM）和偏最小二乘（PLS）模型，用于预测 TSS。VMD-RC-LSSVM 模型对 TSS 的预测精度最高，其值为 0.93，相对误差（RE）为 0.6%，预测均方根误差（RMSEP）为 18.14，预测标准差（RPD）为 3.69。结果表明，VMD-RC 算法可以作为处理高维高光谱图像数据的替代方法，HSI 在水果品质属性的无损、快速评价方面具有很大的潜力。实际应用：传统的葡萄品质属性评价方法具有破坏性、耗时耗力。因此，使用 HSI 实现了对葡萄 TSS 的快速无损测定。结果表明，使用 HSI 和变量选择预测 TSS 是可行的。这对提高葡萄品质、指导采后处理具有重要意义，为非侵入性评估水果其他内部属性提供了有价值的参考。
