可见/近红外光谱结合软化指标快速判定沙果货架期

Detection of Shelf Life of Malus asiatica Using Near-infrared Spectroscopy and Softening Index

基于可见/近红外光谱技术探讨利用软化指标对不同货架期沙果进行分类的可行性。以SNV为最优预处理结合PLS建立软化指标模型,经优化后其校正和预测模型的决定系数R2分别为0.847和0.813。证实软化指标与光谱数据之间存在很高的相关性,可以以软化为指标对不同货架期的沙果进行分类。分别采用非线性的LS-SVM和线性的PLS-LDA建立分类模型,结果表明:利用LS-SVM所建模型得到的效果要优于PLS-LDA模型,其正确识别率和正确拒识率都达到了94%。

The classified feasibility of Malus asiatica fruit by using near-infrared spectroscopy（VIS /NIR spectrometer） and softening index during different shelf life was analyzed.A mathematical model was established by combining partial least square（PLS） and standard normalized variate（SNV） which was regarded as the best pre-processing technology.The determination coefficients（R2） of calibration set and prediction set were 0.847 and 0.813 respectively,which illustrated that there was a high correlation between spectrum and the softening index.It showed that the softening index could be used to classify Malus asiatica samples during different shelf life.The LS-SVM and PLS-LDA were applied to build classification models,respectively.The results indicated that non-linear LS-SVM model was more suitable for classification of Malus asiatica samples than linear PLS-LDA model.The average correct recognition rate and average correct rejection rate were above 94%.