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. 2024 Apr 19;13(8):1259.
doi: 10.3390/foods13081259.

Precision Food Composition Data as a Tool to Decipher the Riddle of Ultra-Processed Foods and Nutritional Quality

Antonis Vlassopoulos  1 , Alexandra Katidi  1 , Stamoulis Noutsos  1 , Maria Kapsokefalou  1
Affiliations

Precision Food Composition Data as a Tool to Decipher the Riddle of Ultra-Processed Foods and Nutritional Quality

Antonis Vlassopoulos et al. Foods. .

Abstract

Background: Epidemiology supports a link between ultra-processed foods (UPFs) and health, mediated mainly through the clustering of foods with suboptimal nutrient profiles within UPFs. However, successful NOVA categorization requires access to a food's ingredient list, which we hypothesized can impact both UPF identification and the link between processing and composition.

Methods: Foods (n = 4851) in the HelTH branded food composition database were classified as NOVA1-4, with or without using the ingredient lists (generic and branded approach, respectively), to identify differences in NOVA classification (chi-square test) and the estimated average nutritional composition of each NOVA group (Kruskal-Willis U test).

Results: Using the ingredients list increased UPF identification by 30%. More than 30% of foods commonly assumed to be minimally processed (NOVA1-plain dairy, frozen vegetables, etc.) were reclassified as UPFs when using ingredient lists. These reclassified foods, however, had nutritional compositions comparable to NOVA1 foods and better than UPFs for energy, fat, sugars, and sodium (p < 0.001). In fact, UPFs did not show a uniform nutritional composition covering foods from Nutri-Score A (~10%) to Nutri-Score E (~20%).

Conclusions: The assumption that all UPFs have the same unfavorable nutritional composition is challenged when NOVA is applied using the appropriate branded food composition database.

Keywords: NOVA; composition; food composition database; formulation; ingredients; ultra-processed foods.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
(a,b) Nutritional composition per 100 g for energy (i), protein, saturated fats (SFA), total sugars (ii) and sodium (iii) per NOVA group as assigned (a) using only the product name and (b) using both the product name and ingredient list. Differences in distribution were tested using the Kruskal–Wallis test. Pairwise comparisons were carried out using the Mann–Whitney U with Bonferroni correction. * Indicates p-value < 0.001 vs. NOVA1, § indicates p-value < 0.001 vs. NOVA2, ‡ indicates p-value < 0.001 vs. NOVA3. Magnified view of the low content values (near-zero values) for clarity provided at the bottom of each graph. Indicative examples presented for foods that were reclassified using the branded approach and their non-reclassified counterparts (for illustration purposes).
Figure 2
Figure 2
Nutritional composition per 100 g for energy, protein, saturated fats (SFA), total sugars and sodium for foods identified as NOVA4 based on the product name (generic) and products reclassified as NOVA4 after searching the ingredient list (originally grouped as NOVA1 or NOVA3 based on their name). Pairwise comparisons carried out using the Man-Whitney U with Bonferroni correction. * Indicates p-value < 0.02 vs. NOVA4 from NOVA1, § indicates p-value < 0.001 vs. NOVA4 from NOVA3.
Figure 3
Figure 3
Performance in the Nutri-Score system expressed as FSAm-NPS score and Nutri-Score grade per NOVA group and per NOVA classification methodology, using either only the product name (by name) or a combination of the product name and the ingredient list (by ingredient) as input. * Indicates p-value < 0.001 for pairwise comparisons by name and by ingredient within the same NOVA group either using the Kruskal–Wallis test (left) or the chi-square test (right).
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