The feasibility of combining spectral and textural information from hyperspectral imaging to improve the prediction of the C16:0 and C18:1 n9 contents for lamb was explored. 29 and 22 optimal wavelengths were selected for the C16:0 and C18:1 n9 contents, respectively, by conducting the variable combination population analysis–iteratively retaining informative variables (VCPA-IRIV) algorithm. To extract the textural features of images, a gray-level co-occurrence matrix (GLCM) analysis was implemented in the first principal component image. The least squares support vector machine (LSSVM) model and the partial least squares regression (PLSR) model were developed to predict the C16:0 and C18:1 n9 contents from the spectra and the fusion data. The distribution map was visualized using the best model with the imaging process. The results showed that the combination of the spectral and textural information of hyperspectral imaging coupled with the VCPA-IRIV algorithm had strong potential for the prediction and visualization of the C16:0 and C18:1 n9 contents of lamb.

探索了将来自高光谱成像的光谱和纹理信息相结合以改善对羔羊C16：0和C18：1 n9含量的预测的可行性。通过进行变量组合总体分析-迭代保留信息变量（VCPA-IRIV）算法，分别为C16：0和C18：1 n9含量选择了29和22个最佳波长。为了提取图像的纹理特征，在第一主成分图像中实施了灰度共现矩阵（GLCM）分析。开发了最小二乘支持向量机（LSSVM）模型和偏最小二乘回归（PLSR）模型，以根据光谱和融合数据预测C16：0和C18：1 n9的含量。使用具有成像过程的最佳模型将分布图可视化。