Development of An Advanced Quality Assessment System for Specialized Flour for Fresh Rice Noodles Employing Multivariate Statistical Techniques

WU Miaoyuan; LIAO Luyan; REN Xianlong; LIU Cao; WU Weiguo

doi:10.13386/j.issn1002-0306.2024060249

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WU Miaoyuan, LIAO Luyan, REN Xianlong, et al. Development of An Advanced Quality Assessment System for Specialized Flour for Fresh Rice Noodles Employing Multivariate Statistical Techniques[J]. Science and Technology of Food Industry, 2025, 46(9): 1−10. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2024060249.

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Development of An Advanced Quality Assessment System for Specialized Flour for Fresh Rice Noodles Employing Multivariate Statistical Techniques

1.
College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China
2.
Hunan Jiaoshan Rice Co., Ltd., Hengyang 421200, China
3.
Taoyuan Xinglong Rice Technology Development Co., Ltd., Changde 415705, China

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Received Date: June 16, 2024
Available Online: March 01, 2025

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Abstract

This study aimed to elucidate the complicated relationship between diverse characteristics of rice flour and the quality indicators of fresh rice noodles, while at the same time as figuring out key parameters and threshold ranges for flour for fresh rice noodles production. Gui Chao rice was used as the foremost uncooked material. Rice flour underwent processing through a range of milling methods (dry, wet, and semi-dry) and grinding sizes (60, 80, 100, and 120 mesh), yielding 28 unique rice flour samples. The rice flours had been comprehensively with the aid of assessing a vary of imperative parameters, together with amylose content, crude protein levels, damaged starch proportion, particle dimension distribution (D₅₀), water absorption properties, and pasting behavior. Furthermore, the quality of the fresh rice noodles was meticulously assessed via texture profile analysis, cooking quality attributes, and sensory assessment scores. An exhaustive and systematic evaluation was once conducted to elucidate the tricky relationship between rice flour traits and the best of fresh rice noodles, involving an array of state-of-the-art multivariate statistical methods, which included descriptive statistics, correlation analysis, principal component analysis, stepwise regression, and cluster analysis. A tailored theoretical assessment model specifically designed for the specialized flour for fresh rice noodles was once developed, and the results characteristic indicators alongside with their corresponding threshold degrees were identified and delineated. The results indicated no statistically significant differences in amylose content and crude protein levels amongst rice flours produced using a variety of milling methods. Under identical grinding size conditions, wet-milled rice flour achieved superior overall scores in comparison to dry and semi-dry milled flours, resulting in accelerated sensory satisfactory in fresh rice noodles. Among the number of grinding sizes, flours with a mesh dimension of 100~120 demonstrated the highest basic and sensory scores. The evaluation model for the specialized flour intended for fresh rice noodles was represented by the equation Y=−28.141−0.06X₄+1.035X₅+0.05X₈ (R²=0.937, P<0.01). The most reliable threshold values for characteristic indicators in the processing of fresh rice noodles were as follows: semi-dry milled flour needed to have a D₅₀ ranging from 88.65 to 146.21 µm, whereas wet-milled flour had a D₅₀ ranging from 10.34 to 47.36 µm. Additionally, rice flour should demonstrate a water absorption exceeding 10.15 g/g and a peak viscosity surpassing 3235.00 cp. This learn about appreciably contributes to the advancement of flour for fresh rice noodles, enables the development of on-line flour blending systems, and has substantial implications for the standardization and industrial production of flour used in fresh rice noodles.

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Science and Technology of Food Industry

Development of An Advanced Quality Assessment System for Specialized Flour for Fresh Rice Noodles Employing Multivariate Statistical Techniques

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