Discrimination of varieties of cola using visual-near infrared spectra

作者：Qiu Zheng jun; Lu Jiang feng; Mao Jing yuan; He Yong^*

来源：Spectroscopy and Spectral Analysis, 2007, 27(8): 1543-1546.

• visual-near infrared spectral
• cola
• principal component analysis (PCA)
• artificial neural network
• discrimination

A new method was developed to fast discriminate the brands of cola by means of visual-near infrared spectroscopy (NIRS). Three different brands of cola (Coca-cola, Pepsi-cola and Future-cola) were analyzed using a handheld near infrared spectrometer produced by ASD Company. Fifty five samples were used for each brand of cola, and they were divided randomly into a group of 150 samples as calibrated samples and one of 15 samples as prediction samples. The samples data were pretreated using average smoothing and standard normal variable method, and then the pretreated spectra data were analyzed using principal component analysis (PCA). The principal component data of calibrated samples were used as the inputs of back-propagation artificial neural network (ANN-BP), while the values of cola brands used as the outputs of ANN-BP, and then the three layers ANN-BP discrimination model was built. The 15 unknown prediction samples were analyzed by the ANN-BP model. The result showed that the distinguishing rate was 100%; it was realized to discriminate different brands of Cola rapidly and exactly.

• 出版日期2007-8
• 单位浙江大学