Design of a high-tech system to evaluate fresh strawberry quality-based on the principal component analysis

Abstract

This study was designed to investigate the differences in the physicochemical properties among twelve strawberry cultivars by using pattern recognition tools, to provide a theoretical basis for quality variation among samples. The data of 14 indicators were subjected to principal component analysis (PCA), descriptive statistical analysis, correlation analysis, and hierarchical cluster analysis, to filter core evaluation factors. Quality evaluation index weights and quality comprehensive evaluation were determined by an analytic hierarchy process. Of the 14 indicators selected as indices of fresh strawberry quality evaluation index, including a value, sugar-acid ratio, firmness, vitamin C, TAC, and TPC. The data were deployed to adjust the multivariate kinetics using Analytic Hierarchy Process (AHP), and the results were compared to those sensory score using sensory personnel. Results showed that the correlation coefficient of sensory scores and AHP comprehensive score is 0.9239. This high correlation coefficient indicates that the use of our mathematical model for strawberry quality evaluation is feasible. The information herein provides a practical strategy for the evaluation of strawberry quality.