Nutritional and potential health benefits of fermented food proteins

BACKGROUND

Protein fermentation continues to gain popularity as a result of several factors, including the cost-effectiveness of the process and the positive correlation of fermented protein consumption, with a reduced risk of developing diet-related diseases such as diabetes and cardiovascular disorders, as well as their enhanced nutritional and techno-functional properties. Nonetheless, the nutritional and health benefits of food protein fermentation such as enhanced nutrient bioavailability, reduced antinutritional factors (ANFs) and enriched bioactive peptides (BAPs) are often overlooked. The present study reviewed recent work on the influence of protein fermentation on nutrition and health. In total, 322 eligible studies were identified on the Scopus and Google Scholar databases out of which 69 studies were evaluated based on our inclusion criteria.

RESULTS

Fermented protein ingredients and products show reduced ANF content, enhanced digestibility and bioavailability, and increased antioxidant and other biological activities, such as probiotic, prebiotic, angiotensin-converting enzyme inhibitory and antihypertensive properties. In addition, co-products in protein fermentation such as BAPs possess and could contribute additional sensory and flavor properties, degrade toxins, and reduce allergens in foods.

CONCLUSION

Thus, fermentation is not only a method for food preservation, but also serves as a means for producing functional food products for consumer health promotion and nutrition enrichment. © 2023 Society of Chemical Industry.

Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning

This study assessed the combined utility of ATR-FTIR spectroscopy and machine learning (ML) techniques for identifying and classifying pure njangsa seed oil (NSO), palm kernel oil (PKO), coconut oil (CCO), njangsa seed oil-palm kernel oil (NSOPKO) and njangsa seed oil-coconut oil (NSOCCO) margarine. Additionally, it quantified the degree of adulteration in each oil and margarine using ML regression models and sunflower oil and canola-flaxseed oil margarine as adulterants. Fingerprints of the oils and the margarines derived in the spectra region 4000-600 cm-1 were combined with ML models. The first two principal components explained 99.4% and 98% of the variance of pure oils and margarines and 90.1, 88.3, 88, 97.3 and 98.3% of adulterated PKO, NSO, CCO, NSOCCO and NSOPKO, respectively while enabling visualization. Pure margarines were classified accurately (100%) in all models. KNN was most effective in classifying pure oil at 97% followed by LR (93%), SVM (83%), LightGBM (53%) and DT (50%). The R2 obtained from all the models for adulterated PKO, NSO, CCO, NSOPKO and NSOCCO ranged from 59-99%, 55-99%, 45-94%, 69-98% and 59-94%, respectively. SVM and DT underperformed, while KNN was the best model.

Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data

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.