基于可见-近红外光谱技术对市售紫薯粉的品质评价

Quality evaluation of purple potato powder in the market based on visible-near infrared spectroscopy

目的建立可见-近红外光谱法结合偏最小二乘回归法对市售紫薯粉的品质进行评价。方法以市售紫薯粉为研究对象,对其原始光谱进行S-G 9点卷积平滑(savitzky-golay smoothing,S-G)、标准正态变量变换(standard normal variable transform,SNV)预处理,建立碘蓝值、花青素以及水分含量的偏最小二乘模型。结果花青素模型校正集和预测集的相关系数分别为0.9750和0.9461,均方根误差分别为0.1052 mg/g和0.1918 mg/g;碘蓝值模型校正集和预测集的相关系数分别为0.9687和0.9673,均方根误差分别为7.0256和7.1848;水分含量校正集和预测集的相关系数分别为0.9397和0.9219,均方根误差分别为0.5589%和0.5965%。结论基于可见-近红外光谱技术可以实现对市售紫薯粉的花青素、碘蓝值以及水分含量的快速无损检测,对市售紫薯粉的品质评价提供理论参考。

Objective To establish a method for evaluating the quality of purple potato powder in the market by visible-near infrared （NIR） spectroscopy with partial least square regression. Methods Taking the purple potato powder in the market as the experimental objects. The savitzky-golay smoothing and standard normal variable transform （SNV） was used to process the original spectra data, then the partial least square regression method was used to establish the prediction model for iodine blue value, anthocyanin and moisture content. Results The correlation coefficients of correction set and prediction set of anthocyanin model were 0.9750 and 0.9461, and the root mean square errors were 0.1052 mg/g and 0.1918 mg/g, respectively. The correlation coefficients of calibration set and prediction set of iodineblue value model were 0.9687 and 0.9673, and the root mean square errors were 7.0256 and 7.1848, respectively. The correlation coefficients of correction set and prediction set of water content were 0.9397 and 0.9219, and the mean square root errors were 0.5589% and 0.5965%, respectively. Conclusion Rapid nondestructive detection for anthocyanins, iodine blue value and moisture contentof purple potato powder in the market based on visible-near infrared spectroscopy can be achieved, and this method can provide theoretical references for the quality evaluation of purple potato powder.