不同类型近红外模型在苹果汁检测中的应用

Application of different of models near infrared spectroscopy in detection of apple juice

为了探讨近红外光谱分析中模型的稳定性和适应范围,该研究建立了4种模型(局部模型、转移的局部模型、全局模型、优化全局模型),分别对5种鲜榨苹果汁的3种品质参数(可溶性固形物SSC、pH值、电导率)进行分析。在对苹果汁品质的分析中,SSC的预测准确度较高(r=0.93,相对预测标准差3.7%);电导率与近红外光谱之间间接相关,其预测准确度较低(r=0.84,相对预测标准差12.7%);pH全局模型的相关系数较高(r=0.94),但其分辨能力较差(其局部模型的参考值标准偏差与预测标准差的比值为1.1)。采用相对预测标准差、参考值标准偏差与预测标准差的比值等参数来评价各种模型的稳定性和适应范围,通过对4种近红外模型的稳定性、适配性及准确度的比较,发现模型的适应范围对预测结果的影响很大,对不同的分析要求应该建立不同的模型,具体为:采用单一品种建立的局部模型准确度高,但稳定性较差,一般只适用于本品种样品的预测。通过在现有局部模型中加入少数几个待测品种的样本重新建立模型,可以实现模型的转移,使之适用于其它品种样品的预测。采用多个品种建立的全局模型稳定性高,其准确度较之局部模型稍有下降。通过挑选有代表性的样品来建立优化全局模型,可以在保持模型性能的同时降低建模工作量,是值得推荐的建模方法。

In order to investigate the stability and application range of near infrared spectroscopy(NIR)models,four calibration models(local model,transferred local model,global model,and optimized global model)were constructed.They were applied to detect three quality parameters(soluble solids content,pH,and electric conductivity)for five varieties of fresh apple juice.The results showed that the prediction accuracy for soluble solids content(SSC)was high(r=0.93,SEP%=3.7%).While electric conductivity was indirectly correlated to NIR,it could hardly be measured by NIR(r=0.84,SEP%=12.7%).The global model of pH had high correlation coefficient(r=0.94),but the differentiation ability of the local model was low(RPD=1.1).The relative standard error of prediction(SEP%),the ratio between standard deviation of quality parameters and SEP were applied to evaluate the stability and application range.The performance of four kinds of NIR models were compared from the aspects of stability,suitability and accuracy.It was found that the application range of model had high influence on the prediction results.Specifically,local model constructed by individual variety samples had high accuracy but poor stability;usually it was only applicable to its own variety and not suitable for other varieties.The method of adding a few samples from the other variety to the current local model so as to construct a new one could broaden its application.The global model constructed with several varieties of samples had high stability although its accuracy was a bit lower than the local model.The method of selecting the representative samples to optimize the global model could reduce the work and the cost for a practical model,and keep its excellent performance as well.Thus it is a worthy method to be recommended.