Mohammadi Saeedeh, Gowen Aoife, O'Donnell Colm
School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Heliyon. 2024 Aug 15;10(16):e36385. doi: 10.1016/j.heliyon.2024.e36385. eCollection 2024 Aug 30.
The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies.
本研究的目的是使用振动光谱方法（近红外（NIR）、中红外（MIR）和拉曼光谱）对某特定公司供应的七种爱尔兰牛奶（黄油奶、鲜奶、有益心脏健康奶、无乳糖奶、低脂奶、高蛋白奶和低热卡奶）进行分类。在这方面，使用了化学计量学方法，并评估了光谱数据融合对预测准确性的影响。总共测试了105个样本，其中21个用于测试集。该研究评估了主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）以及顺序和正交化偏最小二乘线性判别分析（SO-PLS-LDA）对不同牛奶类型的分类效果。当将PLS-DA应用于单个数据块和低水平融合数据时，预测准确率不超过85.71%。然而，实施SO-PLS-LDA策略可将准确率显著提高到95%，这表明数据融合策略为开发牛奶分类模型提供了一种有前景的方法。
