Operationally realistic validation for prediction of cocoa sensory qualities by high-throughput mass spectrometry.

The potential of analytical chemistry to predict sensory qualities of food materials is a major current theme. Standard practice is cross-validation (CV), where a set of chemical and associated sensory data is partitioned so chemometric models can be developed on training subsets, and validated on held-out subsets. CV demonstrates prediction, but is an unlikely scenario for industrial operations, where concomitant data acquisition for model development and test materials would be unwieldy.