应用近红外透射光谱和人工神经网络的豆油脂良莠鉴别

Qualitative discrimination of bean oil by near-infrared transmission spectra and artificial neural network

提出了一种利用近红外透射光谱结合BP神经网络识别未知豆油脂良莠类别的方法。在10 000～3500 cm-1范围内分别采集合格油、不合格油(精炼垃圾油、煎炸油和变质合格油)的透射光谱,对光谱数据依次作出Savitzky-Golay平滑、基线校正预处理,采用SPSS 11.0抽取出9个主成分(累计贡献率达到99.89%)作为神经网络输入神经元,建立3层BP神经网络模型,模型能够有效辨识未知豆油脂的良莠以及不合格具体种类,类别预测正确率为100%。

A new method was developed to discriminate the quality of bean oil based on near-infrared transmission spectra and BP neural network.The near-infrared transmission spectra of qualified oil and unqualified oil（refined waste oil,fried oil and degenerative oil） were obtained from 10 000 to 3 500 cm^-1.Spectral data were preprocessed by Savitky-Golay and baseline correction.Nine principal components（Cumulative contribution rate is 99.502%） extracted by SPSS 11.0 acted as input nerve cell of neural network,and the BP neural network model was build.The model could discriminate qualified oil from unqualified oil,even unqualified kind.Calculation results showed that the distinguishing rate was 100%.