Spectroscopic technologies and data fusion: Applications for the dairy industry

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.

Automated summary

Increasing consumer demand for safety, quality, and sustainability in food products has driven the need for rapid and accurate analytical techniques. Spectroscopy, combined with AI-enabled sensors and chemometric techniques, has enabled the fusion of data sources for dairy analysis. This article provides an overview of spectroscopic technologies in the dairy industry and introduces data fusion methods. It discusses the relevance of data fusion in improving predictions for processing traits using chemometric techniques and machine learning algorithms.