苹果品种及损伤苹果的FT-NIR鉴别研究

用傅里叶近红外光谱技术(FT-NIR)对不同品种的苹果以及损伤嘎啦和完好嘎啦进行快速、无损检测,比较不同判别方法对所建立的区分苹果品种及苹果损伤模型的影响。结果表明:损伤嘎啦和完好嘎啦的近红外图谱经小波分析预处理后,用12000～4000cm-1波数范围的前5个主成分分别结合多层感知神经网络、径向基神经网络、Fisher判别3种方法所建立的判别模型对未知样本的正确判别率分别为97.8%、87.2%和84.8%,基于权重法用多元线性回归(MLR)所选择的特征波长所建立的Fisher判别模型对未知样本的正确判别率为89.1%;用偏最小二乘判别(PLS-DA)所建立的判别模型对未知样本的正确判别率为100%,由于PLS-DA模型对训练集和验证集的正确判别率均为100%,因此PLS-DA模型优于其他模型。不同品种苹果的光谱经平滑预处理后,用全波数范围12000～4000cm-1的前6个主成分所建立的判别模型优于经验波数范围8000～4500cm-1所建立的判别模型,其较优模型对建模集和验证集的正确判别率分别为90.9%和92.1%。近红外光谱技术结合化学计量学可以快速、无损鉴别苹果是否有损伤以及不同品种的苹果。

Discrimination and Identification of Bruised Apples and Apple Varieties by FT-NIR

Near-infrared(NIR) spectroscopy was applied to rapidly and non-destructively distinguish between bruised and intact apples and identify different apple varieties.Besides,the effect of different discrimination methods on the distinguishing and identification models obtained was investigated.The results indicated that discrimination models were developed based on the first five principal components in the range from 12000 cm-1 to 4000 cm-1 from the NIR spectra of bruised and intact apples subjected to wavelet pretreatment using three different discrimination methods,multilayer perceptron(MLP) neural network,radial basis function(RBF) neural network and Fisher line discriminant analysis(Fisher-DA) with discrimination accuracy rates of 97.8%,87.2% and 84.8%,respectively for unknown samples.The Fisher linear discriminant analysis model established based on multiple linear regression and the loading weights showed a discrimination accuracy rate of 89.1% compared with 100% for the model established using partial least squares discriminant analysis(PLS-DA).The discrimination accuracy rates of the PLS-DA model for the training and validation sets were both 100%,and therefore the PLS-DA model was superior to others.A better discrimination model was established based on the first six principal components of the NIR spectra of different apple varieties subjected to smoothing pretreatment in the full wavelength range of 12000-4000 cm-1 than in the empirical range of 8000-4500 cm-1 with discrimination accuracy rates of 90.9% and 92.1% for the predication and validation sets,respectively.In conclusion,the combination of NIR and chemometrics can provide a rapid and non-destructive approach to discriminate whether apples are bruised and identify different apple varieties.