Study on Classification of Shampoo Products by Infrared Spectroscopy Combined with Bayesian Discrimination
To establish a rapid and non-destructive analytical method for testing shampoo products,sixty common shampoo samples were tested using Fourier transform infrared spectroscopy.The spectral data were preprocessed using Savitzky Golay smoothing,fast Fourier transform FFT,and noise reduction methods,respectively.The spectral data were then dimensionally reduced using principal component analysis.At the same time,two classification models,multi-layer perceptron neural network and Bayesian discriminant analysis,were established to analyze and verify spectral data.The classification accuracy rates of the multi-layer perceptron neural network for raw data,S-G smoothing,FFT,and noise reduction are 86.67％,88.33％,80％,and 90％,respectively.The classification accuracy rates of Bayesian discrimination are 83.33％,85％,83.33％,and 95％.The results show that the effect of noise reduction processing is better,and Bayesian discrimination has a higher accuracy rate.The method has good sample reproducibility,small sample consumption,and non-destructive samples,which can provide scientific basis for the identification of physical evidence of shampoo products.
万方数据
维普
