高光谱技术结合CARS算法的库尔勒香梨可溶性固形物定量测定

由于高光谱数据量大、维数高，光谱噪声明显、散射严重等特征导致光谱建模时关键变量提取较为困难，同时，高光谱图像的获取会受非单色光、杂散光、温度等多种因素的影响，从而使高光谱数据与待测性质之间有一定非线性关系。为此，提出采用正自适应加权算法(CARS)对可见-近红外高光谱高维数据进行关键变量筛选，并与全光谱和经典变量提取方法SPA，MC-UVE，GA和GA-SPA方法进行比较。以200个库尔勒香梨为研究对象，采用SPXY方法将样本划分为校正集和预测集，校正集和预测集分别包含150个和50个样本。基于不同方法筛选的变量，分别建立线性PLS模型及非线性LS-SVM模型，r2，RMSEP和RPD用于模型性能的评估。综合比较发现，GA，GA-SPA和CARS变量筛选方法能够有效地筛选出原始高光谱数据中具有强信息且对外界影响因素不敏感的变量，适用于高光谱数据关键变量的提取，其中CARS变量筛选效果最佳，基于CARS获取的关键变量构建的非线性LS-SVM库尔勒香梨SSC含量预测模型获得了最优的预测结果，r2_pre，RMSEP和RPD分别为0.851 2，0.291 3和2.592 4。研究表明，CARS方法是一种有效的高光谱关键变量筛选方法，利用高光谱数据，非线性LS-SVM模型比线性PLS模型更适合于香梨品质的定量预测。

Hyperspectral Technology Combined with CARS Algorithm to Quantitatively Determine the SSC in Korla Fragrant Pear

Hyperspectral imaging has large data volume and high dimensionality, and original spectra data includes a lot of noises and severe scattering. And, quality of acquired hyperspectral data can be influenced by non-monochromatic light, external stray light and temperature, which resulted in having some non-linear relationship between the acquired hyperspectral data and the predicted quality index. Therefore, the present study proposed that competitive adaptive reweighted sampling (CARS) algorithm is used to select the key variables from visible and near infrared hyperspectral data. The performance of CARS was compared with full spectra, successive projections algorithm (SPA), Monte Carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and GA-SPA (genetic algorithm-successive projections algorithm). Two hundred Korla fragrant pears were used as research object. SPXY algorithm was used to divided sample set to correction set with 150 samples and prediction set with 50 samples, respectively. Based on variables selected by different methods, linear PLS and nonlinear LS-SVM models were developed, respectively, and the performance of models was assessed using parameters r2, RMSEP and RPD. A comprehensive comparison found that GA, GA-SPA and CARS can effectively select the variables with strong and useful information. These methods can be used for selection of Vis-NIR hyperspectral data variables, particularly for CARS. LS-SVM model can obtain the best results for SSC prediction of Korla fragrant pear based on variables obtained from CARS method. r2, RMSEP and RPD were 0.851 2, 0.291 3 and 2.592 4, respectively. The study showed that CARS is an effectively hyperspectral variable selection method, and nonlinear LS-SVM model is more suitable than linear PLS model for quantitatively determining the quality of fragrant pear based on hyperspectral information.