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从单个牛奶光谱预测奶酪制造特征的有针对性的iPLS

Arnaud Molle¹, Claudio Cipolat-Gotet¹, Giorgia Stocco¹

¹Department of Veterinary Science, University of Parma, 43126 Parma, Italy.

|January 20, 2026

概括

与传统方法相比,有针对性的间隔部分最小平方 (iPLS) 模型显著改善了从牛奶光谱中预测奶酪制造特征. 这种方法通过选择相关的光谱区域和优化乳制品加工前处理来提高准确性.

科学领域:

乳制品科学乳制品科学
化学测量化学测量化学测量
频谱学是一种光谱学.

背景情况:

中红外 (MIR) 光谱对于分析牛奶成分至关重要.
传统的全谱部分最小方程 (PLS) 回归在预测奶酪制造特征方面存在局限性,原因是光谱冗余和多线性.
准确预测牛奶特征对于高效的乳制品加工和质量控制至关重要.

研究的目的:

评估目标区间部分最小方程 (iPLS) 的有效性,用于从牛奶MIR光谱中预测奶酪制造特征.
为了比较目标iPLS的预测性能与传统的全谱PLS回归.
确定最佳的光谱区域和预处理方法,以提高预测准确度.

主要方法:

使用高分辨率的里埃变换红外光谱技术进行牛奶样本分析.
应用目标区间部分最小平方 (iPLS) 通过选择特定的光谱区域.
与传统的全谱PLS回归模型比较iPLS结果.
研究了定制预处理方法对模型性能的影响.

主要成果:

传统的PLS模型显示预测能力较差 (R²_VAL: 0.000.15).
针对性的IPLS显著提高了奶酪制造特征的预测准确性 (R²_VAL:0.590.91对于%CY特征,高达0.87对于%REC).
最佳的iPLS性能需要选择与蛋白质,脂肪和乳糖相关的光谱区域,同时排除与水相关的区域.
定制的预治疗策略进一步提高了特征特定模型的性能.

结论:

针对性IPLS为预测牛奶奶酪制造特征提供了比全谱PLS更好的替代方案.
对光谱区域的战略选择和量身定制的预处理对于最大限度地提高牛奶分析的预测准确性至关重要.
针对性IPLS框架为乳制品行业的实时监控和优化提供了一个实用的工具.

关键词:

奶酪制造的特点奶酪制造的特点美国的FTIR是FTIR.频谱预处理的方法波长选择波长的选择.

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