Application of improved partial least squares regression method to microorganism culture medium optimization

Abstract

The invention discloses an application of an improved partial least squares regression method to microorganism culture medium optimization. According to the application, a cross validation technologyand a variable screening technology are integrated to reduce potential overfitting problem of PLSR, namely, a proper number of potential variables are extracted by carrying out cross validation through a leave-one-out method, and a steady principle of coefficients is decided by utilizing a variable projection importance technology-based variable screening method and a regression model so as to establish simple and steady models with high prediction precision. According to the application, the VIP regression model is capable of effectively establishing the simple and steady models which are high in prediction precision and can be further popularized.

Description

technical field [0001] The invention relates to the application of a multivariate statistical method, in particular to the application of an improved partial least square regression method in the optimization of microbial culture medium. Background technique [0002] Partial least squares regression (PLSR) is a new type of multivariate statistical method because it combines the advantages of multiple linear regression, principal component analysis and canonical correlation analysis. Studies have shown that PLSR is good at dealing with multicollinearity that cannot be solved by conventional linear regression. , small samples, etc., and can use limited data to perform regression operations of multiple independent variables on multiple dependent variables at the same time. [0003] On the other hand, however, PLSR often exhibits a tendency to overfit due to the large number of latent variables that extract confounding noise. Overfitting is a statistical phenomenon, which gener...

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Problems solved by technology

[0003] However, on the other hand, PLSR often shows a tendency of overfitting, which is caused by the large number of latent variables for extracting confounding noise.

Overfitting is a statistical phenomenon, which generally means that the modeling data can be perfectly fitted, but the newly added data cannot be reliably predicted, that is, the fitting accuracy is high but the prediction accuracy is poor

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