Nutrient clustering, NOVA classification, and nutrient profiling: How do they overlap, and what do they predict about...

We compared the performance of three food categorisation metrics in predicting palatability (taste pleasantness) using a dataset of 52 foods, each rated virtually (online) by 72-224 participants familiar with the foods in question, as described in Appetite 193 (2024) 107124. The metrics were nutrient clustering, NOVA, and nutrient profiling. The first two of these metrics were developed to identify, respectively: 'hyper-palatable' foods (HPFs); and ultra-processed foods (UPFs), which are claimed to be 'made to be hyper-palatable'. The third metric categorises foods as high fat, sugar, salt (HFSS) foods versus non-HFSS foods. There were overlaps, but also significant differences, in categorisation of the foods by the three metrics: of the 52 foods, 35 (67%) were categorised as HPF, and/or UPF, and/or HFSS, and 17 (33%) were categorised as none of these. There was no significant difference in measured palatability between HPFs and non-HPFs, nor between UPFs and non-UPFs (p ≥ 0.412). HFSS foods were significantly more palatable than non-HFSS foods (p = 0.049). None of the metrics significantly predicted food reward (desire to eat). These results do not support the use of hypothetical combinations of food ingredients as proxies for palatability, as done explicitly by the nutrient clustering and NOVA metrics. To discover what aspects of food composition predict palatability requires measuring the palatability of a wide range of foods that differ in composition, as we do here.