A grey comprehensive evaluation model for wheat quality incorporating PCA-EWM

Abstract

The objective and accurate evaluation of wheat quality is the key to ensuring the safety of wheat storage. Due to the complexity and variability of wheat quality, traditional methods, such as the evaluation of single physical and chemical index, have the problems of insufficient information and poor differentiation of fuzzy intervals. Based on grey systems theory, this paper proposes a new comprehensive wheat quality grading model. Firstly, eight physical and chemical indices are selected as the factor set of the evaluation model, and the key quality indices are analyzed and screened by principal component analysis (PCA). The entropy weight method (EWM) is then used to measure the importance of the contribution of selected indices to wheat quality information. Finally, the grey evaluation model is then used to determine the grey correlation of the evaluation levels of different batches of wheat, and to rank the superiority and inferiority. The model is applied to the comprehensive analysis and evaluation of wheat samples, verifying the feasibility and validity of the model and providing some theoretical guidance for the evaluation of wheat quality.