基于图谱融合技术的红地球葡萄总酸无损检测

摘要：总酸含量是衡量红地球葡萄内在品质的重要指标。本文提出了一种基于高光谱融合技术的红地球葡萄总酸无损检 测方法。分别基于光谱信息、图像信息以及两者融合信息建立了总酸的非线性LSSVM预测模型。结果表明，采用MSC- CARS-SPA算法提取特征波长后的光谱信息与PCA算法降维后的图像信息，通过图像融合技术构建的MSC-CARS-SPA- LSSVM模型效果最佳。最优LSSVM模型预测集的相关系数达到0.9907，相较于单一光谱或图像信息模型，精度显著提 高。本研究为红地球葡萄总酸无损检测提供了一种基于图像融合技术的新方法。

关键词：红地球葡萄；高光谱成像技术；总酸含量；图像融合；无损检测

期刊：Smart Agricultural Technology

DOI：https://doi.org/10.1016/j.atech.2024.100406

设备：GaiaField

Non-destructive detection of total acid of red globe grapes based on map fusion technique

Abstract：Total acid is an important indicator of the internal quality of red globe grapes. This paper proposed a nondestructive method for the determination of total acid of red globe grapes based on hyperspectral fusion technique. A non-linear LSSVM prediction model based on spectral information, image information and the fusion of the two was built respectively for total acid. The results showed that the MSC-CARS-SPA-LSSVM model of samples about total acid built by fusing the spectra after feature wavelength extraction using the MSC-CARS-SPA algorithm and the image information after dimensionality reduction by the PCA algorithm using the graph fusion technique worked best. The correlation coefffcients of the prediction sets of the optimal LSSVM model was 0.9907, which improved the accuracy over the unilateral models based on spectral or image information. A new method for nondestructive detection of total acid of red globe grapes by map fusion technique was discovered.

Keywords：Red globe grapes; Hyperspectral imaging; Total acid; Map fusion; Non-destructive detection