Wolffia globosa is an interesting alternative plant-based protein source containing up to 40% protein dry weight. Dried duckweed protein extract (PE) was obtained using ultrasound-assisted extraction (UAE) before isoelectric precipitation (pH 3.5) to yield protein concentrate (PC) and protein solution (PS). The PC was hydrolyzed using Alcalase enzyme to obtain protein concentrate hydrolysate (PCH). Among all fractions, PCH exhibited antimicrobial properties by decreasing populations of Vibrio parahaemolyticus and Candida albicans at 0.43 ± 1.31 log reduction (66.21%) and 3.70 ± 0.11 log reduction (99.98%), respectively. The PE and PS also showed high solubilities at pH 8 of 90.49% and 86.84%, respectively. The PE demonstrated the highest emulsifying capacity (EC) (71.29%) at pH 4, while the highest emulsifying stability (ES) (~98%) was obtained from the PE and PS at pH 6 and pH 2, respectively. The major molecular weights (Mw) of the PE, PC, PCH and PS were observed at 25, 45, 63 and 100 kDa, with a decrease in the Mw of the PCH (<5 kDa). The PCH contained the highest total amino acids, with aspartic acid and glutamic acid being the major components. The results revealed the antimicrobial and functional properties of duckweed protein and hydrolysate for the first time and showed their potential for further development as functional food ingredients.

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R²) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.

This study investigated the combined influence of hydrothermal treatment and particle size on the techno-functional properties of defatted coconut residue (DCR) to optimize its use as a hydrocolloid fat replacer. A 3 × 2 factorial design evaluated boiling and autoclaving treatments in combination with coarse and fine milling. Fine particle fractions (boiling-fine [BF] and autoclaved-fine [AF]) were identified as optimal, exhibiting peak water-holding capacity (WHC) (10.95 g/g) and oil-holding capacity (4.57 g/g) due to maximized surface area and thermal unblocking of capillary networks. When incorporated into cookies, all DCR formulations qualified as “reduced-fat” (30% reduction) and “high-fiber” (6 g/100 g) products. Crucially, the extreme WHC of fine fractions induced severe water competition within the dough, leading to a direct inverse correlation with quality, characterized by a restricted spread ratio (6.9) and increased hardness (27 N). Furthermore, thermal leaching of Maillard precursors suppressed excessive browning, improving cookie color. While the BF fraction provided the best functional balance, future research should optimize dough moisture to mitigate the impact of high fiber hydration on texture. These findings demonstrate DCR’s potential for agro-food valorization and improved human health.