Bilinear surface model-enhanced determination of gluten quality in whole wheat flour by NIR spectroscopy.

IF 9.2 1区农林科学 Q1 CHEMISTRY, APPLIED

Food Chemistry: X Pub Date : 2026-10-05 eCollection Date: 2026-10-01 DOI:10.1016/j.fochx.2026.104541

Rui Zhang, Pingping Qu, Hongyan Sun, Guoqing Feng, Lihua Liu, Dayu Pan, Bin Luo

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引用次数: 0

Abstract

Gluten content and strength are key indicators of whole wheat flour processing quality, and their rapid non-destructive detection is crucial for quality screening and wheat variety breeding. This paper proposed a covariance guided stratified sampling (CGSS) algorithm for partitioning multi-indicator small sample sets. Furthermore, a novel analytical framework combining a bilinear surface model with machine learning algorithms is presented to simultaneously detect gluten content and strength indicators. The results showed that the novel modeling approach combined with CARS-LASSO achieved optimal prediction, with test $R^{2}$ values of 0.98, 0.93, 0.89, 0.84, and 0.83 and RPD values of 6.73, 3.77, 3.08, 2.47, and 2.40 for WGS, GI, DGC, WGC, and WGC (14% MB), respectively. The proposed model, incorporating machine learning algorithms, provides a novel approach for the quantitative and rapid simultaneous detection of gluten properties in whole wheat flour.

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双线性表面模型增强近红外光谱法测定全麦面粉中面筋质量。

谷蛋白含量和强度是小麦面粉加工品质的关键指标，其快速无损检测对品质筛选和小麦品种选育具有重要意义。提出了一种多指标小样本集的协方差引导分层抽样（CGSS）算法。此外，提出了一种结合双线性曲面模型和机器学习算法的分析框架，用于同时检测面筋含量和强度指标。结果表明，结合CARS-LASSO模型的预测效果最佳，WGS、GI、DGC、WGC和WGC （14% MB）的检验r2分别为0.98、0.93、0.89、0.84和0.83，RPD分别为6.73、3.77、3.08、2.47和2.40。该模型结合了机器学习算法，为全麦面粉中麸质特性的定量和快速同时检测提供了一种新的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Food Chemistry: X CHEMISTRY, APPLIED-

CiteScore

4.90

自引率

6.60%

发文量

315

审稿时长

55 days

期刊介绍： Food Chemistry: X, one of three Open Access companion journals to Food Chemistry, follows the same aims, scope, and peer-review process. It focuses on papers advancing food and biochemistry or analytical methods, prioritizing research novelty. Manuscript evaluation considers novelty, scientific rigor, field advancement, and reader interest. Excluded are studies on food molecular sciences or disease cure/prevention. Topics include food component chemistry, bioactives, processing effects, additives, contaminants, and analytical methods. The journal welcome Analytical Papers addressing food microbiology, sensory aspects, and more, emphasizing new methods with robust validation and applicability to diverse foods or regions.