苹果糖度近红外光谱分析模型的温度补偿

温度变化对水果品质近红外评价有很大影响,需要补偿温度波动对模型的影响.文章研究了温度变化(2～42℃)对苹果近红外漫反射光谱的影响,采用剔除温度变量法和内校正法补偿温度对模型的影响,提高预测精度.研究表明,温度与光谱信息存在一定相关性,其模型R2=0.985,RMSEC=1.88,RMSEP=2.32;未进行温度校正模型的预测标准偏差达到2.55;采用复合预处理方法和改进的遗传算法对光谱数据优化,剔除温度变量法模型的R2=0.954,RMSEC=0.63,RMSEP1=0.72,RMSEP2=0.74;内校正法的模型R2=0.952,RMSEC=0.64,RMSEP1=0.69,RMSEP2=0.68;相比未进行温度补偿模型均提高了预测精度.结果显示:温度对苹果近红外光谱影响呈非线性变化,剔除温度变鼍法和内校正法可用于补偿温度对模型的影响,可提高模型预测精度.

Temperature Compensation for Calibration Model of Apple Fruit Soluble Solids Contents by Near Infrared Reflectance

The detection precision of soluble solids in apple fruit by near infrared reflectance (NIR) spectroscopy was affected by sample temperature. The NIR technique needs to be able to compensate for fruit temperature fluctuations. In the present study, it was observed that the sample temperature (2-42 ℃) affects the NIR spectrum in a nonlinear way. The temperature model was built with R2=0.985, RMSEC=1.88, and RMSEP=2.32. When no precautions are taken, the error in the SSC reading may be as large as 2.55%°Brix. Two techniques were found well suited to control the accuracy of the calibration models for soluble solids with respect to temperature fluctuations, such as temperature variable-eliminating calibration model and global robust calibration model to cover the temperature range. And an improved genetic algorithms (GAs) was used to implement an automated variables selection procedure for use in building multivariate calibration models based on partial least squares regression (PLS). The two compensation methods were found to perform well with RMSEP1=0.72/0.69 and RMSEP2=0.74/0.68, respectively. This work proved that the compensation techniques could emend the temperature effect for NIR spectra and improve the precision of models for apple SSC by NIR.