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.

Improving nutrition through biofortification-A systematic review.

Nutritious foods are essential for human health and development. However, malnutrition and hidden hunger continue to be a challenge globally. In most developing countries, access to adequate and nutritious food continues to be a challenge. Although hidden hunger is less prevalent in developed countries compared to developing countries where iron (Fe) and zinc (Zn) deficiencies are common. The United Nations (UN) 2nd Sustainable Development Goal was set to eradicate malnutrition and hidden hunger. Hidden hunger has led to numerous cases of infant and maternal mortalities, and has greatly impacted growth, development, cognitive ability, and physical working capacity. This has influenced several countries to develop interventions that could help combat malnutrition and hidden hunger. Interventions such as dietary diversification and food supplementation are being adopted. However, fortification but mainly biofortification has been projected to be the most sustainable solution to malnutrition and hidden hunger. Plant-based foods (PBFs) form a greater proportion of diets in certain populations; hence, fortification of PBFs is relevant in combating malnutrition and hidden hunger. Agronomic biofortification, plant breeding, and transgenic approaches are some currently used strategies in food crops. Crops such as cereals, legumes, oilseeds, vegetables, and fruits have been biofortified through all these three strategies. The transgenic approach is sustainable, efficient, and rapid, making it suitable for biofortification programs. Omics technology has also been introduced to improve the efficiency of the transgenic approach.

Valorization of seed and kernel marcs and evaluation of their antioxidant potential.

The processing and consumption of mango (Mangifera indica) generate a sizeable amount of kernel waste with enormous and largely unexplored potential, while by-products from njangsa (Ricinodendron heudelotii) seed and bush mango (Irvingia gabonensis) kernel oil extraction are often discarded. This study aims to repurpose these kernels and seed wastes into added/high-value products and evaluate the ethanolic and methanolic extracts of their pressed marcs for polyphenolic content and potential antioxidant activity. The total phenolic content (TPC) and total flavonoid content (TFC) in the marc extracts ranged between 47.87 and 376.0 mg GAE/g and 4.85 and 13.70 mg Rutin/g, respectively. Both mango kernel marc extracts showed higher potent reducing power, ABTS+ radical and DPPH radical scavenging activities with half effective concentration (EC50) values (0.20-0.22 mg/mL) comparable to the reference compound; ascorbic acid (0.20 mg/mL). The TPC and TFC of the marc extracts generally strongly correlated with antioxidant activity. Relatively higher contents of xanthophyll and ß-carotene were detected in bush mango kernel methanolic extract than in the other extracts. Extraction solvent affected the composition and content of bioactives in pressed marcs of njangsa seed and mango kernel.

Cell Culture-Based Assessment of Toxicity and Therapeutics of Phytochemical Antioxidants.

Plant-derived natural products are significant resources for drug discovery and development including appreciable potentials in preventing and managing oxidative stress, making them promising candidates in cancer and other disease therapeutics. Their effects have been linked to phytochemicals such as phenolic compounds and their antioxidant activities. The abundance and complexity of these bio-constituents highlight the need for well-defined in vitro characterization and quantification of the plant extracts/preparations that can translate to in vivo effects and hopefully to clinical use. This review article seeks to provide relevant information about the applicability of cell-based assays in assessing anti-cytotoxicity of phytochemicals considering several traditional and current methods.

Influence of conventional and recent extraction technologies on physicochemical properties of bioactive macromolecules from natural sources: A review.

The incorporation of bioactive macromolecules from natural sources into marketable functional foods and nutraceuticals is of major significance to the agri-food sector. Interest in this area of research stems from the application of purified bioactive macromolecules in enhancing food quality and as an alternative to some pharmaceutical drugs for delivery of potential health benefits, with less associated adverse effects. To obtain bioactive macromolecules of high quality, appropriate use of extraction techniques and its influence on sensory and physicochemical properties is paramount. With the advent of technology-aided processes, there has been remarkable improvement in the extraction efficiency of these bioactive agents. An overview of the influence of these new techniques on extraction efficiency and physicochemical properties of proteins, lipids and fibers, which this detailed review provides, will prove to be a valuable resource to food industries aiming to maximize production of bioactive macromolecules from natural sources as well as the scientific community.

Identification and quantitation of reaction intermediates and residuals in lipase-catalyzed transesterified oils by HPLC.

A high-performance liquid chromatography (HPLC) unit equipped with size exclusion column and a refractive index detector was used for simultaneous monitoring, identification, and quantitation of the reaction components from lipase-catalyzed transesterification of three oils. The procedure simultaneously separated and detected the unreacted triacylglycerols (TAG), diacyl-, and monoacyl-glycerol (DAG and MAG) co-products, residual alcohol as well as free fatty acid (FFA) based on retention times. The chromatograms showed well separated and resolved peaks. The elution of the components from the transesterification reaction in increasing order was: TAG < DAG < FFA < MAG. Generally, higher alcohol ratios decreased the conversion of TAG in all the oils studied with between 14% and 94% of TAG remaining at all the treatment combinations. Higher amount of salmon skin oil (SSO) TAG was generally converted to DAG than Rothsay composite (RC) and olive oil (OO) TAG. Relatively higher amount of OO DAG was converted to MAG than SSO and RC with only 5-14% DAG remaining in OO. RC and OO generally accumulated less MAG, and this was reflected as lower MAG levels in RC (<6%) and OO (<14%) compared with SSO (<27%). For the various treatment combinations and the three oils used in this study, the least amount of FFA was recorded in transesterified OO with a maximum of approximately 4%. This HPLC method can be used as a simple and fast technique to analyze the reaction components and products of transesterification reactions without the need for additional derivatization steps.