Prediction model for the quality of fresh wet rice noodles and study on raw material selection

In response to the issue of unstable product quality in China's fresh wet rice noodle production due to the lack of scientific standards for selecting raw rice, this study aims to systematically analyze the relationship between the physicochemical properties of different indica rice varieties and the quality of the resulting fresh wet rice noodles. The goal is to establish an evaluation system for processing suitability and a quality prediction model, providing a theoretical basis and methodological support for selecting specialized raw materials for high-quality fresh wet rice noodles. The study selected 11 indica rice varieties, analyzing key indicators such as main Components, pasting properties, and gel properties. These were correlated with the texture, cooking quality, and sensory evaluation of the prepared fresh wet rice noodles using principal component analysis and cluster analysis. This led to the identification of raw materials with superior comprehensive quality, Finally, stepwise regression was used to construct a quality prediction model for rice noodles, The results indicate that raw material No. 4 exhibits the best overall quality. Materials with a comprehensive score greater than 1.0 are considered suitable for rice noodle processing. Key indicators affecting the comprehensive quality of rice noodles include sensory score, damping coefficient, amylose content, and final viscosity. Suitable rice raw materials for producing fresh wet rice noodles should meet the following criteria: amylose content greater than 22.32%, sensory evaluation score higher than 85 points, final viscosity greater than 4796 mPa·s, and damping coefficient greater than 15.56 MPa·s. This quality prediction model holds significant practical importance for achieving standardization and quality control in rice noodle production.