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使用近红外和中红外光谱学结合常规和机器学习算法对替代蛋白质混合物的组合分析

R Dos Santos¹, J Cruz², I Muñoz¹

¹IRTA, Food Quality and Technology, Finca Camps i Armet, s/n, 17121 Monells, Girona, Spain.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|April 3, 2025

概括

近红外和中红外光谱学可以分析植物性蛋白质混合物的成分和质存在. 这项技术可确保肉类模拟产品的质量,并有效地识别无质选项.

科学领域:

食品科学与技术食品科学与技术
分析化学分析化学
频谱学是一种光谱学.

背景情况:

对植物性蛋白质的准确分析对于肉类模拟生产中的高湿度挤出工艺至关重要.
确保植物性挤出材料的质量和质地,需要精确的成分数据.
在替代蛋白质来源中检测谷蛋白对于过敏原管理和产品标签至关重要.

研究的目的:

分析来自豆类,谷物和伪谷物来源的混合植物性蛋白质的组成和质存在.
为了评估近红外光谱 (NIRS) 和中红外光谱 (MIRS) 与机器学习算法相结合的有效性.
开发用于确定蛋白质,水分,碳水化合物和脂肪含量的预测模型,以及用于质识别.

主要方法:

使用五种替代蛋白质来源制备混合物:大麦,小麦,豆,狼和麦.
使用低成本和基板近红外光谱仪以及中红外光谱仪获取光谱数据.
使用部分最小平方回归 (PLSR),支持矢量机差异分析 (SVM-DA),部分最小平方差异分析 (PLS-DA) 和卷积神经网络 (CNN) 开发预测模型.

主要成果:

蛋白质,水分,碳水化合物和脂肪含量是通过使用基板NIR和PLSR.的低误差确定的 (RMSEP分别为1.59%,0.18%,1.41%,0.19%).
使用PLS-DA.使用高灵敏度 (0.85) 和精度 (0.93) 识别无质样本.
红外光谱学有效地分析了各种植物性蛋白质混合物的组成.

结论:

红外光谱学与机器学习相结合,是一种可行的非侵入性方法,用于分析植物性蛋白质混合物.
这种技术可以准确地确定这些混合物的营养成分 (蛋白质,水分,碳水化合物,脂肪).
这项研究成功地证明了红外光谱学在识别无质样品方面的能力,支持食品生产的质量控制.

关键词:

化学测量化学测量化学测量机器学习是机器学习.部分最小正方形.植物性蛋白质是一种植物性蛋白质.过程监控过程监控大致的组成.

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