用高光谱图像技术结合化学计量学方法, 实现了番茄叶片中过氧化物酶(POD)活性的快速检测。 利用高光谱图像的光谱特征建立预测模型步骤为: 采集高光谱图像数据、 获取光谱曲线、 光谱数据预处理、 提取特征波段、 建立POD酶活性预测模型。 与预处理方法(SG, SNV, MSC, 1-Der和2-Der)相比, DOSC预处理对POD酶活性预测效果最好。 研究表明: 以443, 464, 413, 410, 401, 402, 426和926 nm这八个特征波段的光谱数据建立的DOSC-SPA-PLS模型对POD酶活性预测结果为Rp=0.935 3, RMSEP=37.80 U·g-1。 这说明高光谱图像技术测定番茄叶片POD活性具有可行性, 且预测结果令人满意, 这为抗氧化酶活性和番茄植株生长状况的动态检测提供了新的方法。

Activities of POD in tomato leaves were measured rapidly using hyperspectral imaging technology combined with chemometrics method. Operation process was: extracting spectra curve, pretreatment of spectra data, extracting characteristic wavelengths with SPA, and establishing prediction model for determining POD activities. In comparison with other methods such as SG, SNV, MSC, 1-Der and 2-Der, DOSC was the optimal pretreatment. It was shown in this research that SPA-PLS model was the optimal effective model among all models (SPA-MLR, SPA-PLS, SPA-BPNN and SPA-LS-SVM) for forecasting POD activities. The model was based on reflectance information of effective wavelengths (443, 464, 413, 410, 401, 402, 426 and 926 nm) extracted by SPA. Rp and RMSEP were 0.935 3 and 37.80 U·g-1, respectively. The result indicated that it was feasible to determine the POD activities with hyperspectral imaging technology, and the prediction accuracy of model was satisfactory. It was a new method for dynamic observation of POD activities and growth state of tomato.