基于高光谱成像的葡萄可溶性固形物含量和 pH 值预测及成熟度分级

摘要：红地球葡萄的可溶性固形物含量（（ SSC）和pH值是衡量其品质的关键指标。本研究采用高光谱成像技术，实现了 对红地球葡萄SSC和pH值的无损检测及分布可视化。首先，采集样品的高光谱图像；随后，运用CARS、SPA、GA和 IRIV方法从原始光谱（（ RAW）信息中提取特征变量，并建立PLSR预测模型。通过对比不同预测模型，确定RAW-IRIV- PLSR为最优模型。最后，通过计算样品的SSC和pH值，生成灰度图像并进行伪彩色变换，实现了SSC和pH值的分布 可视化。通过对样品成熟度的分级研究，得出RAW-IRIV-ELM为最佳的成熟度判别分类模型。高光谱成像技术为红地球 葡萄成熟度分级提供了一种新方法。

关键词：红地球葡萄；可溶性固形物含量；可视化技术；高光谱成像；成熟度分级

期刊：Smart Agricultural Technology

DOI：https://doi.org/10.1016/j.atech.2024.100457

设备：GaiaField

SSC and pH prediction and maturity classiffcation of grapes based on hyperspectral imaging

Abstract：Soluble solids content (SSC) and pH of red globe grapes are crucial measures of quality. In this paper, we used hyperspectral imaging technology to achieve nondestructive detection and distribution visualization of SSC and pH of red globe grapes. First, the hyperspectral images of samples were collected. Then, CARS, SPA, GA, IRIV were used to extract feature variables from raw spectral (RAW) information. The PLSR prediction models of samples were developed. By comparing the different prediction models, RAW-IRIV-PLSR was selected as the optimal model. Finally, the SSC and pH of the samples were calculated to obtain a grayscale image and perform a pseudo-color transformation to visualize the distribution of SSC and pH. By studying the classiffcation of the maturity of samples, it was concluded that the best discriminant classiffcation model of maturity was RAW-IRIVELM. Hyperspectral also provided a new method for maturity stage classiffcation of red globe grapes.

Keywords：Red globe grapes; SSC; Visualization; Hyperspectral imaging; Maturity stage