Multiobjective process optimization for betaine enriched spelt flour based extrudates

Reduction of Alternaria Toxins via the Extrusion Processing of Whole-Grain Red Sorghum Flour

This study delved into the impact of two extrusion processing parameters-screw speed (SS at 400, 600, 800 RPM) and material moisture content in the extruder barrel (M at 12, 15, 18%) at constant feed rate (50 kg/h)-on reducing the content of alternariol (AOH), alternariol monomethyl ether (AME), tenuazonic acid (TeA), and tentoxin (TEN) in whole-grain red sorghum flour. Ultra-performance liquid chromatography combined with a triple-quadrupole mass spectrometer (UPLC-MS/MS) was employed for the determination of Alternaria toxin levels. The extruder die temperature fluctuated between 136 and 177 °C, with die pressures ranging from 0.16 to 6.23 MPa. The specific mechanical energy spanned from 83.5 to 152.3 kWh/t, the torque varied between 88 and 162.8 Nm, and the average material retention time in the barrel ranged from 5.6 to 13 s. The optimal parameters for reducing the concentration of all Alternaria toxins with a satisfactory quality of the sorghum snacks were: SS = 400 RPM, M = 12%, with a reduction of 61.4, 76.4, 12.1, and 50.8% for AOH, AME, TeA, and TEN, respectively.

Unlocking the Potential of the ANN Optimization in Sweet Potato Varieties Drying Processes

This study explores the unexploited potential of Artificial Neural Network (ANN) optimization techniques in enhancing different drying methods and their influence on the characteristics of various sweet potato varieties. Focusing on the intricate interplay between drying methods and the unique characteristics of white, pink, orange, and purple sweet potatoes, the presented experimental study indicates the impact of ANN-driven optimization on food-related characteristics such as color, phenols content, biological activities (antioxidant, antimicrobial, anti-hyperglycemic, and anti-inflammatory), chemical, and mineral contents. The results unveil significant variations in drying method efficacy across different sweet potato types, underscoring the need for tailored optimization strategies. Specifically, purple sweet potatoes emerge as robust carriers of phenolic compounds, showcasing superior antioxidant activities. Furthermore, this study reveals the optimized parameters of dried sweet potato, such as total phenols content of 1677.76 mg/100 g and anti-inflammatory activity of 8.93%, anti-hyperglycemic activity of 24.42%. The upgraded antioxidant capability is presented through DPPH●, ABTS●+, RP, and SoA assays with values of 1500.56, 10,083.37, 3130.81, and 22,753.97 μg TE/100 g, respectively. Additionally, the moisture content in the lyophilized sample reached a minimum of 2.97%, holding favorable chemical and mineral contents. The utilization of ANN optimization proves instrumental in interpreting complex interactions and unlocking efficiencies in sweet potato drying processes, thereby contributing valuable insights to food science and technology.

Betaine as a Functional Ingredient: Metabolism, Health-Promoting Attributes, Food Sources, Applications and Analysis Methods

Betaine is a non-essential amino acid with proven functional properties and underutilized potential. The most common dietary sources of betaine are beets, spinach, and whole grains. Whole grains-such as quinoa, wheat and oat brans, brown rice, barley, etc.-are generally considered rich sources of betaine. This valuable compound has gained popularity as an ingredient in novel and functional foods due to the demonstrated health benefits that it may provide. This review study will provide an overview of the various natural sources of betaine, including different types of food products, and explore the potential of betaine as an innovative functional ingredient. It will thoroughly discuss its metabolic pathways and physiology, disease-preventing and health-promoting properties, and further highlight the extraction procedures and detection methods in different matrices. In addition, gaps in the existing scientific literature will be emphasized.

Food physics insight: the structural design of foods

Understanding food materials from the classical realm of physics including soft condensed matter physics has been an area of interest especially in the structural design engineering of food products. The contents of this review would help the reader in understanding the thermodynamics of food polymer, structural design principles, structural hierarchy, steps involved in food structuring, newer structural design technologies, and structure measurement techniques. Understanding the concepts of free volume would help the food engineers and technologists to study the food structural changes, manipulate process parameters and, the optimum amount of nutraceuticals/ingredients to be loaded in the food matrix. Such understanding helps in reducing food ingredient wastage while designing a food product.

Development of a Novel Rice-Based Snack Enriched with Chicory Root: Physicochemical and Sensory Properties

A novel rice-based snack enriched with chicory root flour (CRF) was developed by twin-screw extrusion. Chicory (Cichorium intybus L.) is one of the promising medicinal plants for the development of innovative food and may be considered a functional food ingredient. Central composite design (CCD) was employed to generate snack formulations by varying feed moisture (M, 16.3–22.5%), screw speed (SS, 500–900 rpm) and CRF content (20–40%). The optimization according to artificial neural network modeling and a genetic algorithm was applied to define optimal process conditions (17.6% moisture, 820 rpm and 24.1% of CRF) for obtaining the product with the highest expansion (3.34), crispiness (3.22 × 10−3), volume (2040 m3), degree of gelatinization (69.70%) and good color properties. Bulk density (110.33 g/L), density (250 kg/m3), and hardness (98.74 N) resulted in low values for the optimal sample. The descriptive sensory analysis evaluated low hardness and bitterness, with high crispiness for the optimal extrudate. This study points to the possibility of a novel chicory enriched extrudate production with desirable physicochemical and sensory properties.

Encapsulation of bioactive compounds extracted from Cucurbita moschata pumpkin waste: The multi-objective optimization study

AIM

Artificial neural network (ANN) development to find optimal carriers (pea protein-P, maltodextrin-M, and inulin-I) mixture for encapsulation of pumpkin waste bioactives (β-carotene and phenolics).

METHODS

Freeze-drying encapsulation and encapsulates characterisation in terms of bioactives contents and encapsulation efficiencies, water activity, hygroscopicity, densities, flowability, cohesiveness, particle size (laser diffraction), solubility, color (CIELab), morphological (SEM), stability and release properties.

RESULTS

Optimal encapsulates, OE-T (with highest total bioactives contents; P, M, and I of 53.9, 46.1, and 0%w/w) and OE-EE (with highest bioactives encapsulation efficiencies; P, M, and I of 45.5, 32.0, and 22.5%w/w) had particle diameters of 94.561 ± 1.341µm and 90.206 ± 0.571µm, span of 1.777 ± 0.094 and 1.588 ± 0.089, highest release at pH 7.4 of phenolics of 71.03%w/w after 72h and 66.22%w/w after 48h, and β-carotene of 43.67%w/w after 8h and 48.62%w/w after 6h, respectively.

CONCLUSION

ANN model for prediction of encapsulates' preparation, showed good anticipation properties (with gained determination coefficients of 1.000).

Comparison of Nutritional Profiles of Super Worm (Zophobas morio) and Yellow Mealworm (Tenebrio molitor) as Alternative Feeds Used in Animal Husbandry: Is Super Worm Superior?

Edible insects are acknowledged as a valuable nutritional source and promising alternative to traditional feed ingredients, while the optimization of rearing conditions is required for their wider utilization in the animal feed industry. The main goal of this study was to compare and optimize the rearing conditions of the two species' larvae and identify the most favorable nutritive composition of the full-fat larval meal. For that purpose, Tenebrio molitor (TM) and Zophobas morio (ZM) were reared on three different substrates and harvested after three time periods. An artificial neural network (ANN) with multi-objective optimization (MOO) was used to investigate the influence between the observed parameters as well as to optimize and determine rearing conditions. The optimization of the larval rearing conditions showed that the best nutritive composition of full-fat larval meal was obtained for ZM larvae reared on a mixture of cabbage, carrot and flaxseed and harvested after 104 days. The best nutritive composition contained 39.52% protein, 32% crude fat, 44.01% essential amino acids, 65.21 mg/100 g Ca and 651.15 mg/100 g P with a favorable ratio of 1.5 of n6/n3 fatty acids. Additionally, the incorporation of flaxseed in the larval diet resulted in an increase in C18:3n3 content in all samples.

Horned Melon Pulp, Peel, and Seed: New Insight into Phytochemical and Biological Properties

Artificial neural intelligence was established for the estimation, prediction, and optimization of many agricultural and food processes to enable enhanced and balanced utilization of fresh and processed fruits. The predictive capabilities of artificial neural networks (ANNs) are evaluated to estimate the phytochemical composition and the antioxidant and antimicrobial activity of horned melon (Cucumis metuliferus) pulp, peel, and seed. Using multiobjective optimization, the main goals were successively achieved through analysis of antimicrobial potential against sensitive microorganisms for peel (Bacillus cereus, Pseudomonas aeruginosa, Aspergillus brasiliensis, and Penicillium aurantiogriseum), pulp (Salmonella enterica subsp. enterica serotype Typhimurium), and seed samples (Saccharomyces cerevisiae and Candida albicans), and its connection with phytochemical and nutritional composition and antioxidant activity. The highly potent extracts were obtained from peels which represent a waste part with strong antioxidant and antifungal capacity. Briefly, the calculated inhibition zone minimums for sensitive microorganisms were 25.3−30.7 mm, while the optimal results achieved with carotenoids, phenolics, vitamin C, proteins, lipids, DPPH, ABTS, and RP were: 332.01 mg β-car/100 g, 1923.52 mg GAE/100 g, 928.15 mg/100 g, 5.73 g/100 g, 2.3 g/100 g, 226.56 μmol TE/100 g, 8042.55 μmol TE/100 g, and 7526.36 μmol TE/100 g, respectively. These results imply the possibility of using horned melon peel extract as an antioxidant and antifungal agent for food safety and quality.

Textural, Color and Sensory Features of Spelt Wholegrain Snack Enriched with Betaine

The influence of different extrusion parameters, including screw speed (250–750 rpm), feed rate (15–25 kg/h) and feed moisture content (15–25%), on the textural and color properties of spelt wholegrain snack products produced on a co-rotating twin-screw extruder with added betaine was investigated. In order to determine the relative influence of input variables in the artificial neural network (ANN) model, Yoon’s interpretation method was used, and it was concluded that feed moisture content has the greatest influence on L* values, while screw speed has the greatest influence on a* and b* values. The softest samples were obtained at the lowest moisture content. Sensory analysis was carried out on selected samples, and it showed that betaine addition did not intensify the bitter taste. The sample with the largest expansion exhibited the lowest hardness and chewiness before and after immersion in milk, and this sample is the most suitable for enrichment with betaine.