Using machine learning models to predict the quality of plant-based foods

Plant-based foods (PBFs) are considered healthy, especially, minimally processed whole foods, fruits, whole grains, and legumes while highly processed PBFs maybe less nutritious. Educating consumers on nurient quality will help to guide their choices. This study was aimed at estimating and predicting the nutrient quality of PBFs using their Nutri-Score and micronutrient content. The NHANES (2017–2020) data shows the output for foods consumed in the US and their nutrient composition based on a 24-h recall. Though the Nutri-Score label has been used to discriminate food quality, it still needs to be implemented in most countries. It computes mostly macronutrients with less consideration for micronutrients which also contributes to product quality. ML methods used in this study combine the Nutri-Score grade and micronutrient content in predicting food quality. The FNDDS data of PBFs for 2017–2020 were split into training (n = 300) and testing (n = 74) datasets. Eight ML models were used to predict the Nutri-Score and the Nutri-Score grade of PBFs. Random forest (RF) and light gradient boost model (LightGBM) performed best with accuracy and coefficient of determination (R2) scores of 0.88 and 0.96, respectively, while DT had the least scores in predicting the Nutri-Score grade (0.81) and Nutri-Score (0.93). These results suggest that ML can be effectively leveraged to predict PBFs quality