蓝莓可溶性固形物、总酸近红外无损检测模型的建立

建立了近红外漫反射光谱技术检测蓝莓可溶性固形物、总酸的数学模型，并对其进行评价。实验比较了在近红外全波长范围400~2 500 nm内，不同的光谱预处理方法对模型的影响。结果表明利用偏最小二乘法（PLS）、一阶导数（D1Log（1/R））和加权多元离散校正处理（WMSC）建立的可溶性固形物含量（SSC）定标模型预测结果相对较好。其预测相关系数Rp2为0.8518，预测标准误差（SEP）为0.351，相对分析误差（RPD）为2.05。总酸的最佳模型处理条件为改进偏最小二乘法（MPLS）、二阶导数（D2Log（1/R））和WMSC，其Rp2为0.8776，SEP 为0.042，RPD为2.10。由此确定近红外漫反射技术可用于蓝莓可溶性固形物、总酸含量的快速损检测。
The purpose of this research is to establish mathematical model between near infrared diffuse reflection（NIR） spectroscopy and soluble solids and total acidity in blueberry，and to evaluate its application value. The influence of different spectral preprocessing methods on the model was compared in the spectral region between 400~2500 nm. The results showed that the calibration model with the partial least squares（PLS），the first derivative D1lg（1/R） and weighted multiple scatter correction（WMSC） could provide better prediction performance for SSC，with the correlation coefficient of prediction（Rp2）of 0.8518 and the root mean square error of prediction（SEP） of 0.351 and relative percent deviation of prediction（RPD） of 2.05. The best model of total acidity is the modified partial least squares（MPLS） model，the second derivative D2lg（1/R） and WMSC，with the correlation coefficient of prediction（Rp2）of 0.8776 and the root mean square error of prediction（SEP） of 0.042and relative percent deviation of prediction（RPD） of 2.10. So the near-infrared diffuse reflectance spectroscope can be used for fast nondestructive measurement of soluble solids and total acidity in blueberry