一种基于近红外光谱快速鉴别染色橙的新方法

本文应用近红外光谱仪(NIRS)测定染色橙样品的光谱数据,采用多种方式对光谱数据进行预处理,用主成分分析法(PCA)对不同染色橙样品进行聚类分析并获得染色橙的近红外光谱数据的主成分,在此基础上建立了偏最小二乘(PLS)回归模型,并根据均方根校准误差(RMSEC)和相关系数(R2)对模型性能进行评价。结果表明,主成分分析可以快速鉴别染色橙样品,模型识别率达到94%。将主成分分析(PCA)与偏最小二乘(PLS)相结合建立的回归模型,均方根校准误差(RMSEC)为0.26,决定系数R2为0.96,模型效果较好。表明利用近红外光谱鉴别染色橙是可行的,这为染色橙的鉴别提供了一种快速无损的新方法。

Application PCA Method to Fast Discrimination of Dyed Navel Oranges Using Near Infrared Spectroscopy

The spectral data of dyed navel oranges was achieved by near infrared spectrum instrument(NIRS).The data was preprocessed by different means and analyzed with principal component analysis(PCA).A PLS model was established based on this,and the performance of the model was evauated according to root mean squared error of calibration ( RMSEC) and correction coefficient(R 2 ).Results show that:It appeared to provide PCA could be used to identify the dyed navel oranges,the recognition rate achieved 94%.RMSEC of the PLS regression model established based on PCA was 0.26,R 2 was 0.96,the model was well. It was feasible to identify the dyed orange by near infrared spectroscopy,which would provide a new and nondestructive method for the identification of dyed orange.