用机器学习的共聚焦拉曼显微镜区分植物和牛奶蛋白及其在混合奶酪中的相互作用
Di Lu1, Cushla McGoverin2, Debashree Roy1
1Riddet Institute, Massey University, Private Bag 11 222, Palmerston North 4442, New Zealand.
Food chemistry
|November 22, 2025
概括
混合加工奶酪类似物 (HPCAs) 结合了植物和牛奶蛋白质. 先进的光谱学和机器学习揭示了素如何影响这些新型食品系统中的豆和大麻蛋白质结构.
科学领域:
- 食品科学 食品科学 食品科学
- 材料科学 材料科学 材料科学
- 生物物理学的生物物理.
背景情况:
- 对植物乳制品替代品的日益增长的需求推动了混合加工奶酪类似品 (HPCAs) 的创新.
- 了解HPCA内部复杂的微观结构和分子相互作用对于产品开发至关重要.
- HPCA结合了植物蛋白 (如豆蛋白分离物 - MPI,或大麻蛋白分离物 - HPI) 与素,以模仿乳酪的特性.
研究的目的:
- 在将MPI或HPI与素结合时,研究HPCA的结构和分子变化.
- 阐明复杂的食物矩阵中的植物蛋白和素之间的相互作用.
- 建立一个强大的分析方法来描述HPCAs中的蛋白质行为.
主要方法:
- 整合共聚焦拉曼光谱技术,用于高分辨率的空间绘图.
- 应用先进的机器学习算法用于分子表征.
- 对具有不同MPI/HPI和素比例的HPCA进行比较分析 (例如,HPI100,HPI50,MPI100,MPI30).
主要成果:
- 对焦拉曼光谱和机器学习成功地区分了蛋白质来源和绘制的分子变化.
- 素添加诱导HPI中的结构障碍和二硫化键重组,显著降低了氨酸双倍比率.
- 素与MPI的相互作用导致了微结构分离,并显著减少了β-sheet含量.
结论:
- 综合光谱和机器学习方法为蛋白质结构和HPCA等复杂食品系统中的相互作用提供了强大的洞察力.
- 了解这些分子相互作用是优化植物性奶酪替代品质感,功能和感觉特性的关键.
- 这项研究为合理设计新型杂交食品产品奠定了基础.
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