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Abstract：
The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.
摘要：
该研究旨在使用高光谱成像技术测量胡萝卜的类胡萝卜素（Car）和pH值。使用高光谱成像系统共采集了300幅图像,覆盖从400到1000nm的472波段。定义感兴趣区域(ROI)以从高光谱图像(HIS)提取平均光谱。我们开发了两种模型：最小二乘支持向量机（LS-SVM）和偏最小二乘回归（PLSR），以建立颜料含量和光谱之间的定量分析。使用这些模型对光谱和色素含量进行了预测和关联。使用连续投影算法(SPA)进行建模的EW的选择，PLSR模型的回归系数(RC)，和LS-SVM。结果表明，高光谱成像可以有效地评价胡萝卜皮层和木质部的内部属性。此外，这些模型准确地预测了胡萝卜零件的汽车和pH值含量。本研究为胡萝卜高光谱成像研究中的变量选择和建模提供了一种有价值的方法。
