Traditional biscuits are considered products with poor nutritional value because of their large share of rapidly digested starch, which results in an elevated glycaemic index. This paper explores the improvement of the nutritional value of biscuits by adding yellow mealworm (Tenebrio molitor) powder. Four biscuit recipes containing 0%(R1), 10%(R2), 15%(R3), and 20%(R4) of yellow mealworm powder were prepared and subjected to sensorial analysis. The R3 biscuits were selected for further investigation, as they had the highest acceptability. Compared to the reference R1, the R3 biscuits showed an improved nutritional profile in terms of protein, fat, ash, minerals, fibres, essential amino acids, and unsaturated fatty acids, and lower amounts of carbohydrates and 5-hydroxymethylfurfural. The in vitro protein digestibility in R3 improved 1.12-fold compared to R1. No significant difference was found between the digestibility of the lipids released from R1 and R3. A higher fraction of slowly digestible starch was present in R3 compared to R1. The starch digestibility and estimated glycaemic index were 72.96% and 79.56% in R3, which can be compared to 78.79% and 90.14%, respectively, in R1. Due to their enhanced nutritional profile, higher bioaccessible protein fraction, and lower glycaemic index, yellow mealworm powder biscuits can be considered a more nutritious alternative to traditional biscuits.

The study investigates the structural and chemical properties of brown rice flour (WRF), black rice flour (BRF) and their mixtures in ratios of 25%, 50% and 75% to provide reference information for the gluten-free bakery industry. BRF contains higher concentrations of proteins, lipids, total minerals, crude fiber, total polyphenols, proanthocyanidins and flavonoids than WRF. A higher amylose content in BRF than in WRF resulted in flour mixtures with slower starch digestion and a lower glycemic response depending on the BRF ratio added. Differences in the chemical composition of WRF and BRF led to improved composition of the flour mixtures depending on the BRF ratio. The presence of anthocyanidins and phenolic acids in higher concentrations in the BRF resulted in a red–blue color shift within the flour mixtures. The deconvoluted FTIR spectra showed a higher proportion of α-helixes in the amide I band of BRF proteins, indicating their tighter folding. An analysis of the FTIR spectra revealed a more compact starch structure in BRF than in WRF. By processing reflection spectra, nine optically active compound groups were distinguished in rice flour, the proportion in BRF being 83.02% higher than in WRF. Due to co-pigmentation, the bathochromic shift to higher wavelengths was expressed by the proanthocyanins and phenolic acids associated with the wavelengths 380 nm to 590 nm and at 695 nm. Anthocyanins, protein–tannin complexes, methylated anthocyanins and acylated anthocyanins, associated with wavelengths 619, 644 and 668 nm, exhibited a hypsochromic effect by shifting the wavelengths to lower values. This research represents a first step in the development of rice-based products with increased nutritional value and a lower glycemic index.

The present study describes the enhancement of the nutritional values of gluten-free rice crackers by adding whole black rice grain flour. The crackers were prepared by combining whole brown rice flour (WRF) and whole black rice flour (BRF) in ratios of 0% (WRC), 25% (25-BRC), 50% (50-BRC), 75% (75-BRC), and 100% (BRC). The resulting samples underwent in-vivo effects on postprandial blood glucose levels as well as physicochemical and sensory analysis. In comparison to WRC, the samples containing 100% added black rice flour presented higher nutritional qualities in terms of protein, by 16.61%, 8.64% for lipids, 5.61% for ash, 36.94% for crude fiber, 58.04% for total polyphenols, 95.49% for proanthocyanidins, and 88.07% for flavonoids. The addition of BRF had a suppressing effect on lightness (L*) and yellowness (b*), while redness (a*) increased. The results of the glycemic measurements confirmed that consumption of crackers made from brown or black whole-grain rice grain flour does not generate glycemic peaks above the limit of 30 mg/dL in baseline blood glucose levels. The results of developing rice crackers from black and brown flour blends showed promising physicochemical and nutritional properties and could provide a good alternative to wheat flour as a gluten-free product.

The purpose of this study was to obtain a biodegradable film based on polyvinyl alcohol with reduced water vapor permeability. The hydrophobic character of the films was achieved by incorporating beeswax, vegetable bio-surfactant, citric acid as a cross-linking agent, and glycerol to provide elasticity, along with the application of thermal treatment. Water vapor permeability was determined gravimetrically. The results indicated that all films produced had lower water vapor permeability compared to unmodified or untreated polyvinyl alcohol films. The barrier to water vapor varied directly with the mass of beeswax used, and the homogeneous dispersion of beeswax in the polyvinyl alcohol matrix was essential for achieving an efficient hydrophobic film. The best performing-material exhibited a water vapor permeability 5.15 times lower than that of the neat polyvinyl alcohol and 15 times higher than that of polyethylene. Considering the fact that the water vapor barrier property of neat polyvinyl alcohol was 78 times lower than that of polyethylene, the combination of beeswax, citric acid, and vegetable bio-surfactant—along with thermal treatment—can be a viable solution to reduce the hygroscopicity of polyvinyl alcohol-based films.

This study investigated the effect of chickpea flour (CF) on the staling behavior of gluten-free bread (GFB) by comparing a formulation containing 50% rice flour (RF) and 50% CF (CFB) with a control bread based on rice flour supplemented with whey protein (RFB). Bread samples were stored at room temperature for up to 7 days. Changes in color, reflectance, starch and protein structure, specific volume, crumb structure, texture, and staling kinetics were monitored. CFB exhibited a darker and more yellow crumb and crust, with lower reflectance intensity, and showed greater color stability during storage. Fourier-transform infrared (FTIR) spectroscopy revealed higher overall starch crystallinity and more stable relative crystallinity degree (RCD) values in CFB (58.74–59.05%) compared to RFB (46.19–40.52%) throughout storage, indicating early amylose-driven ordering and a more stable molecular organization of starch. Protein secondary structure analysis showed that CFB had a higher β-sheet content (35.05–37.49) than the RFB formulation (30.37–31.16), indicating stronger protein aggregation. In contrast, macroscopic quality parameters showed that CFB had lower specific volume (1.65 vs. 1.93) and porosity (17.17 vs. 21.01 cm³/g) than RFB, resulting in higher hardness (15.92 vs. 6.15 N) and accelerated staling kinetics (k_corr) (0.28 vs. 0.14 day⁻¹), indicating faster crumb firming despite the observed molecular-level stability. Overall, the results demonstrated that CF contributes to enhanced molecular organization of starch and increased nutritional value of GFB, while its technological performance at the macroscopic level remains formulation-dependent. These findings highlight the need for targeted formulation and process optimization to balance molecular stability with desirable textural properties in CFB.

The paper explores the accuracy of gender and age prediction of human subjects based on the chemometric analysis of FTIR spectra from fingernails. The baseline and scaling over the 0–1 range were applied to FTIR spectra from fingernails of 123 subjects, and wavenumbers for which absorbance values showed a statistically significant correlation with gender and age were identified. The prediction accuracy was analyzed using: Multiple linear regression, Forward stepwise regression, Backward stepwise regression, Principal component regression, and Partial least squares regression. As regard the gender prediction, the principal component regression model proved to be the most accurate, with 8 extracted components allowing a prediction of 93.50 % (86.00 % for women, 98.63 % for men). The predictive power of the model showed that in case of new subjects, gender classification could be done in 91.06 % of cases (84.00 % for women, 95.89 % for men). For age prediction, the optimal backward stepwise regression model with 6 statistically significant predictors showed an average error of 11.39 % (13.70 % for women, 9.81 % for men). The predictive power of the model was 12.13 % (11.73 % for women, 10.35 % for men). The age prediction was less accurate, with a maximum error of 10.00 % achieved only in the case of 65.04 % of subjects.