Wang Jia-hua, Sun Xu-dong, Pan Lu, Sun Qian, Han Dong-hai
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Sep;28(9):2098-102.
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.
开发一种快速、精确鉴别苹果内部品质的方法迫在眉睫。可见/近红外光谱结合适当的光谱数据预处理后的多元分析，已被证明是判断具有非常相似外观特性物体的有力工具。在本研究中，应用峰面积判别分析（PADA）、主成分分析判别分析（PCADA）和偏最小二乘判别分析（PLSDA）对具有不同内部特性（如褐变和水心）的苹果进行分类。能谱采用多元散射校正（MSC）或一阶导数进行处理，并分别建立了使用PADA、PCADA和PLSDA的三种模型。对褐变苹果的预测准确率分别为100%，对水心苹果的预测准确率分别为79.6%、95.0%和96.7%，对正常苹果的预测准确率分别为88.4%、98.2%和98.8%。PLSDA模型明显优于其他模型。PLSDA的总体正确比率为98.1%，RMSEC = 0.449，RMSEP = 0.392。本研究结果表明，可见/近红外光谱结合化学计量学技术可用于区分褐变和水心苹果，这有助于避免耗时、昂贵的感官分析方法。
