Hayes Elena, Greene Derek, O'Donnell Colm, O'Shea Norah, Fenelon Mark A
University College Dublin (UCD) School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland.
Teagasc Food Research Centre, Moorepark, Fermoy, Ireland.
Front Nutr. 2023 Jan 11;9:1074688. doi: 10.3389/fnut.2022.1074688. eCollection 2022.
Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.
消费者意识的提高、生产规模的扩大以及对确保食品安全性、质量和可持续性的需求，加速了对用于食品的快速、可靠和准确分析技术的需求。光谱学与人工智能传感器和化学计量技术相结合，实现了乳制品分析应用中数据源的融合。本文概述了乳制品行业目前使用的光谱技术，并介绍了数据融合以及基于光谱的数据融合中使用的相关方法。我们考虑了数据融合在乳制品行业中的相关性，重点关注其通过化学计量技术（如主成分分析（PCA）、偏最小二乘回归（PLS）和其他机器学习算法）改善加工特性预测的潜力。
