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This is a Quantitative Study study.

October 9, 2025

Developing an augmented nutrient profiling system in the perspective of healthy and sustainable diets

LMLuca MuzzioliSapienza University of Rome OVOlivia Di VincenzoSapienza University of Rome DCDiego Casado–MansillaUniversidad de Deusto

Key Points

The machine learning-based nutrient profiling system showed significant correlations with established diets, enhancing public health strategies.
Strong connections to the Mediterranean diet and the Eat-Lancet diet demonstrate the system's ability to assess dietary health.
The proposed augmented system offers guidance on food consumption frequencies to promote sustainable dietary practices.
Findings indicate a balance of nutritional quality and sustainability can be achieved through advanced deep learning methods.

Abstract

Nutrient profiling systems (NPSs) are widely used to guide public health policies. However, the urgent threat posed to human life by climate change highlights the need to expand the concept of healthy diets to include sustainability. Thus, this study aimed at developing an NPS that integrates nutritional quality, sustainability and food processing by creating a machine learning (ML)-based artificial neural network. ML-based NPSs showed strong Spearman's correlations with established healthy and sustainable diets (Mediterranean diet: rho = 0.891, p < 0.001; Eat-Lancet reference diet: rho = 0.783, p < 0.001), as well as a composite environmental sustainability index (rho = 0.710, p < 0.001). In contrast, ML-based NPSs showed weak correlations with NOVA classification, revealing an inverse relationship between the Sustainability Index and NOVA. These findings suggest that deep learning methods can effectively balance multiple dimensions in NPS design. In conclusion, the proposed method can classify any food product and offer guidance on appropriate consumption frequencies to consumers, promoting healthier and more sustainable diets.

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