Training method of food borne pathogenic bacteria Raman spectrum identification method established based on PCA-Stacking

Abstract

The invention provides a training method of a food borne pathogenic bacteria Raman spectrum identification method established based on PCA-Stacking. The method aims at a problem of insufficient identification precision for two food borne pathogenic bacterium with similar Raman peaks, namely Escherichia coli 0157:H7 and Brucella S2, and provides a Ramon integrated classification algorithm based onPCA-Stacking, wherein a mathematical statistics model with better robustness and a calculating method are found. For aiming at problems of burr and baseline drift in the Raman spectrum, spectrum pre-processing is realized by means of a Savitzky-Golay filter and an asymmetric least squares. The model parameters are searched through grids. Higher classification accuracy of a Stacking integrated algorithm relative to K neighboring, logic regression, and support vector machine single algorithm model is proved.

Description

technical field [0001] The invention relates to a training method for a Raman spectrum recognition model of food-borne pathogenic bacteria established based on PCA-Stacking. Background technique [0002] At present, the methods used to detect foodborne pathogens include: morphological identification, immunological detection and polymerase chain reaction. However, these methods have complicated operation steps and a long cycle, and cannot effectively monitor and prevent them. [0003] Raman spectrum is generated based on the interaction of light and chemical bonds in materials. Through the analysis of Raman spectrum signals of food-borne pathogens, the identification of pathogenic bacteria can be quickly realized. Because there are many noises in the original Raman spectrum, and different substances may have similar peak shapes at the same wavelength, these factors reduce the accuracy of current artificial peak identification and lead to wrong identification of food-borne pa...

AI Technical Summary

Problems solved by technology

Because there is a lot of noise in the original Raman spectrum, and different substances may have similar peak shapes at the same wavelength, these factors reduce the accuracy of current artificial peak identification, leading to wrong identification of foodborne pathogens

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