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.

In the US, people frequently snack between meals, consuming calorie-dense foods including baked goods (cakes), sweets, and desserts (ice cream) high in lipids, salt, and sugar. Monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) are reasonably healthy; however, excessive consumption of food high in saturated fatty acid (SFA) has been related to an elevated risk of cardiovascular diseases. The National Health and Nutrition Survey (NHANES) uses a 24 h recall to collect information on people’s food habits in the US. The complexity of the NHANES data necessitates using machine learning (ML) methods, a branch of data science that uses algorithms to collect large, unstructured, and structured data sets and identify correlations between the data variables. This study focused on determining the ability of ML regression models including artificial neural networks (ANNs), decision trees (DTs), k-nearest neighbors (KNNs), and support vector machines (SVMs) to assess the variability in total fat content concerning the classes (SFA, MUFA, and PUFA) of US-consumed snacks between 2017 and 2018. KNNs and DTs predicted SFA, MUFA, and PUFA with mean squared error (MSE) of 0.707, 0.489, 0.612, and 1.172, 0.846, 0.738, respectively. SVMs failed to predict the fatty acids accurately; however, ANNs performed satisfactorily. Using ensemble methods, DTs (10.635, 5.120, 7.075) showed higher error values for MSE than linear regression (LiR) (9.086, 3.698, 5.820) for SFA, MUFA, and PUFA prediction, respectively. R² score ranged between −0.541 to 0.983 and 0.390 to 0.751 for models one and two, respectively. Extreme gradient boost (XGR), Light gradient boost (LightGBM), and random forest (RF) performed better than LiR, with RF having the lowest score for MSE in predicting all the fatty acid classes.

Artificial Intelligence (AI) is increasingly used in mental health care, but its equitability is a pressing concern. This paper examines the potential biases in AI-driven mental health tools and their impact on marginalized communities. It explores several strategies to mitigate bias in AI-driven mental health tools, focusing on promoting equity and inclusivity.