Fusion of food profiling data from very different analytical techniques

Abstract

The increasing demand for safe, authentic, and high-quality food requires high-resolution and rapid analytical methods to reliably verify these properties. As a result, a variety of approaches based on different analytical techniques, for example, based on mass spectrometry, spectroscopy, or imaging, have been developed. However, these approaches often focus on specific aspects of the complex composition of food and thus only consider a small part of food properties. In order to gain a comprehensive understanding and to obtain powerful approaches for food testing, it is particularly advantageous to combine data from very different analytical techniques. The combination of data sets with different properties in particular poses challenges, and there are different approaches for their fusion. In this article, we analyze and evaluate the current state of the art for fusing very different food data from various analytical techniques and make recommendations for approaches that can usefully be applied to data fusion.