Strategies for Accurate Food Data Mining and Optimizing Information Generation

Abstract

Food evaluation is performed using techniques linked to various sciences – physics, chemistry and sensory science. In addition to developments in instrumentation and applied methods, research is also focusing on how to better extract information from extant data. Many of the information-rich techniques used in food quality evaluation produce vast amounts of data. Having an appropriate statistical strategy to analyse them is paramount, especially when working with multimodal data or data from different fields. This chapter presents some of the relevant aspects when working with data from analytical chemistry (targeted or untargeted, discrete or continuous), sensory science (with a focus on rapid methods) and statistical modelling (data fusion at various levels from basic to multimodal/multiblock), from the perspective of optimizing the analytical workflow and strategy. Smart approaches to data, such as those described here, can contribute to improving not only new product development activities (for example, in rapid sensory methods), but also the current understanding of the phenomena underlying food manufacturing practices or product shelf life (for example, in chemical fingerprinting).