基于可见/近红外能量光谱的苹果褐腐病和水心鉴别

快速无损鉴别苹果内部品质的优劣是当前苹果行业亟待解决的一项重要课题.针对这一现状,提出了直接采用可见一近红外能量光谱对苹果褐腐病、水心鉴别的新方法,考察了不同判别分析方法对苹果类别判定的准确性.在能量光谱经MSC或者一阶导数处理后,分别采用了峰面积判别法(PADA)、主成分分析判别法(PCADA)、偏最小二乘判别法(PLSDA)建立判别模型.结果显示,三种方法对褐腐病苹果判别正确率都为100%;对水心苹果分别是79.2%,95.0%和96.7%;对正常苹果分别是88.6%,98.2%和98.8%.其中,PCADA和PLSDA明显优于PADA,而PLSDA总判别率最高,达到98.1%,其建模标准差RMSEC为0.449,预测标准差RMSEP为0.392,说明可见一近红外能量光谱结合化学计量学算法可以快速、无损鉴别苹果褐腐病和水心.

Discrimination of Brownheart and Watercore of Apples Based on Energy Spectrum of Visible/Near Infrared Transmittance

It is urgent to develop a quick and precise method for the discrimination of the internal quality of apple. Vis/NIR spectroscopy combined with multivariate analysis after the appropriate spectral data pre-treatment has been proved to be a very powerful tool for judgment of objects that have very similar exterior properties. In the present study, peak area discriminant analysis (PADA), principal component analysis discriminant analysis (PCADA) and partial least squares discriminant analysis (PLSDA) were applied to classify apples with different internal properties such as brownherat and watercore. Energy spectra were processed using MSC or one-order derivative, and three models using PADA, PCADA and PLSDA were built, respectively. The accuracy rates of prediction for brownheart apple were 100%, for watercore apple were 79.6%, 95.0% and 96.7%, and for natural apple were 88.4%, 98.2% and 98.8%, respectively. The PLSDA model was better than the others remarkably. And the overall correct ratio of PLSDA was 98.1%, with RMSEC=0.449 and RMSEP=0.392. The results in the present study show that Vis/NIR spectroscopy together with chemometrics techniques could be used to differentiate brownheart and watercore apple, which offers the benefit of avoiding time-consuming, costly and sensory analysis.