Journal of Food Science最新文献

With the rising demand for plant-based foods, this study formulated plant-based salad dressings by emulsifying 50% v/v olive oil with 50% v/v lemon juice containing pea protein-alginate conjugates (2.0% w/v) and lecithin (1.0% w/v). The conjugates were prepared by reacting proteins with propylene glycol alginate at pH 11.0. Physical stability and sensory attributes of these salad dressings were compared to those prepared with propylene glycol alginate or gum arabic, two conventional emulsifiers. When stored at 4°C for 30 days, salad dressings emulsified with the conjugates exhibited a smooth and homogenous appearance, stable droplet diameter (16.69-20.06 µm), negative zeta-potential (from -54.0 to -51.2 mV), and consistent L* (82.64-85.49), a* (-0.58 to 1.77), and b* (14.02-19.07) values. In contrast, salad dressings emulsified with 2% w/v propylene glycol alginate or gum arabic exhibited significant changes for all parameters except a* during storage. However, salad dressings emulsified with the conjugates exhibited significant variations in browning index and viscosity during storage at 4°C. Sensory evaluation indicated no distinctions in flavor, sour/tartness, sweetness, consistency, or overall acceptability among the different formulations. These findings show that pea protein-alginate conjugates are effective plant-based emulsifiers for salad dressings. PRACTICAL APPLICATIONS: Pea protein-alginate conjugates are novel and sustainable biopolymeric surfactants that can be used in a wide variety of emulsion-based food products. The conjugates enhance the physical stability of model salad dressings during 4°C storage by maintaining droplet diameter, zeta-potential, color, and appearance and perform better than the conventional emulsifiers. Their potential use as novel emulsifiers for salad dressings is further supported by their sensory attributes being comparable to conventional emulsifiers.

Microplastics (MPs) are frequently detected in seafood. However, the extent to which seafood consumption contributes to MP intake remains uncertain. Previous studies on MP intake in humans did not consider interindividual variability in exposure. The present study aimed to identify which types of seafood contribute most to the long-term average MP intake of residents of Semarang city in Indonesia, and to determine which groups have the highest exposure level. We utilized published data on MP concentrations in locally caught seafood, where MPs were quantified by microscopy and polymer types were confirmed by μ-FTIR under strict analytical quality assurance measures. These data were then combined with seafood consumption data from questionnaires and 24 h dietary recalls in 982 respondents of various age groups. Estimates of microplastic intake were derived using a Monte Carlo simulation (10,000 iterations), with species-specific mean microplastic concentrations as fixed inputs and seafood consumption sampled from a lognormal distribution. Parameter importance was explored through a sensitivity analysis. Bivalves significantly contribute to MP intake, especially for adolescents (31%) and adults (26.4%). Crabs are the main contributor for toddlers (49.4%) and the elderly (32.7%). Shrimps also contribute substantially to children's MP intake (30.6%). Adolescents are the most exposed age group, with daily intakes at the 99.5th percentile reaching up to 427 particles per person. In contrast, children and toddlers, although consuming smaller per-person amounts, showed the highest intake of MPs per kilogram bodyweight (MP·kg^-1·bw·y^-1), that is, 1.9 and 1.6 times higher than adults, respectively. Hence, children and toddlers are the most vulnerable groups in terms of exposure.

The integration of hyperspectral imaging (HSI) with machine learning enables non-destructive prediction and visualization of food quality. However, multicollinearity and redundant features in spectral data can reduce model accuracy and increase computational time, emphasizing the need for key wavelength selection. In response, this study presents an inventive method combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) to select key wavelengths for predicting apple dry matter content (DMC). A partial least squares regression (PLSR) model using the selected features outperformed recursive feature elimination (RFE) and competitive adaptive reweighted sampling (CARS), achieving a coefficient of determination (R²) of 0.46 and a root mean squared error (RMSE) of 0.70%. The approach was further applied to hyperspectral images to visualize pixelwise DMC distribution, providing spatial insights into fruit composition. Results demonstrate that integrating XAI with evolutionary feature selection offers a noninvasive, transparent, and efficient strategy for assessing and visualizing fruit quality.

This study investigated the centesimal composition, physicochemical properties, bioactive compounds, antimicrobial activity, and anti-quorum sensing (QS) potential of red pulp (RP; Hylocereus polyrhizus) and white pulp (WP; Hylocereus undulatus) pitayas. Both pulps exhibited high moisture content and low protein levels, with ash contents of 0.78% (RP) and 0.70% (WP), titratable acidity of 0.26% for both, and pH values of 5.75 (RP) and 5.12 (WP). RP pitaya presented a higher total phenolic content and contained 3.15 mg/100 g of betalains. Antioxidant capacity differed according to the assay employed: RP showed higher activity in the ABTS•⁺ and β-carotene/linoleic acid methods, whereas WP exhibited greater radical scavenging capacity in the DPPH• assay. LC-MS/MS analysis revealed phyllocactin as the major betalain in RP pitaya, along with 4'-O-malonyl-betanin and isophyllocactin. Four flavonoids were identified, with quercetin 3-rutinoside as the predominant compound. Both extracts displayed mild antimicrobial activity against foodborne pathogens and inhibited violacein production by Chromobacterium violaceum, indicating anti-QS activity. However, no inhibitory effects were observed on swarming motility or biofilm formation under subinhibitory concentration. Overall, the results highlight pitaya pulps as sources of bioactive compounds with antioxidant, antimicrobial, and anti-QS properties, supporting their potential application in food systems and as natural ingredients for microbial control strategies.

Paratuberculosis is an infectious disease mainly affecting domestic ruminants, caused by Mycobacterium avium subsp. paratuberculosis (MAP). Affected animals shed the pathogen through feces, also during the subclinical stages of the disease. Although there is still no conclusive evidence of MAP involvement in human diseases, particularly Crohn's disease and autoimmune disorders, exposure to MAP through the consumption of contaminated dairy products has become a public health concern. Therefore, in this study, the survival of MAP during the production of Pecorino, an Italian cheese made from raw sheep's milk, was investigated by a microbial challenge test. Three batches of raw milk were artificially contaminated with MAP to obtain a concentration of approximately 10⁴ CFU/mL, higher than natural conditions. The Pecorino cheesemaking process can reduce the initial MAP concentration by about 1 log₁₀ after 60 or 90 days of ripening. The process may act as a hurdle factor against the pathogen, thereby contributing to product safety. Nonetheless, no scientific evidence is currently available regarding MAP contamination levels in raw bulk-tank milk from flocks affected by paratuberculosis, nor quantification of contamination risk relative to flock prevalence. Consequently, as a precautionary measure, milk pasteurization at 72°C for 25 s is recommended to produce Pecorino in flocks with clinical cases of paratuberculosis, regardless of the estimated seroprevalence. PRACTICAL APPLICATIONS: This study provides relevant insights for artisanal producers of Pecorino and similar cheeses, giving evidence of MAP inactivation during the cheesemaking process. These findings support product safety assessment and the competent authority for managing potential public health risks.

The increasing demand for artisanal chocolates highlights the importance of reliable analytical strategies for quality assurance and product authentication. In this study, 45 Brazilian artisanal chocolates (36%-100% cocoa) were characterized by integrated chromatographic and chemometric analyses. Seventeen representative samples were evaluated for volatile compounds using HS-SPME/GC-MS, and methylxanthines were quantified by HPLC-DAD after optimization of an ultrasound-assisted liquid-liquid extraction method. The procedure achieved recoveries of 92% ± 10% for theobromine and 95% ± 3% for caffeine, meeting international validation criteria. In total, 72 volatile compounds were identified, mainly acids, esters, pyrazines, and aldehydes, associated with descriptors such as roasted, nutty, floral, and fruity. Theobromine (1.158-21.033 g kg^-1) and caffeine (0.058-1.997 g kg^-1) concentrations showed strong positive correlations with declared cocoa content. Principal component analysis revealed clear separation between low- and high-cocoa chocolates, with theobromine as the main discriminating variable. The results demonstrate that methylxanthines are robust chemical markers for chocolate authentication and classification, and that the combined use of GC-MS, HPLC, and multivariate analysis provides a reliable workflow for quality control, traceability, and prevention of commercial fraud in the chocolate industry. PRACTICAL APPLICATIONS: The combined use of chromatographic and chemometric approaches demonstrated in this study can be applied in food laboratories to improve the authentication, classification, and quality monitoring of chocolates. The optimized analytical methods for volatile and methylxanthine profiling are suitable for both artisanal and industrial products, supporting standardization, traceability, and prevention of commercial fraud in the chocolate industry.

The application of Poria cocos polysaccharides (PCP) is largely determined by their unique structural characteristics and functional properties. However, the effect of their derivatives, particularly Poria cocos oligosaccharides (PCO) and Poria cocos polysaccharide nanoparticles (PCNPs) on these properties and their applications is not well understood. In this study, PCO and PCNPs were synthesized and compared with PCP in terms of structure and function to clarify their mechanisms of influence. The molecular weight of PCO, enzymatic digestion by PCP, was determined to range from m/z 504.32 to 992.79, which corresponds to a polymerization degree of 3 to 6. The primary monosaccharide of PCO was glucose, with smaller amounts of arabinose, galactose, and mannose. The PCNPs had an average size of 108.50 ± 2.15 nm and zeta potential of -10.25 ± 0.44 mV, accompanied with notable structural changes observed in Fourier transform infrared (FTIR) spectroscopy. Functionally, the solubility of PCP significantly increased to over 90% after conversion into PCO and PCNPs, leading to improved water and oil holding capacities. Conversely, the apparent viscosity decreased from 161.46 ± 2.77 mPa·s to 76.05 ± 1.25 mPa·s in PCNPs and 36.45 ± 2.33 mPa·s in PCO. Additionally, these derivatives enhanced the antioxidant activity of PCP, notably, PCNPs exhibited the highest DPPH radicals scavenging capacity at 75.27% ± 0.93%, hydroxyl radicals scavenging capacity at 35.45% ± 0.78%, and superoxide anion radicals scavenging capacity at 59.01% ± 0.67%, whereas PCO merely increased the scavenging of superoxide anion radicals in these assays. In general, PCNPs enhanced functionality and a broader range of potential applications in functional foods and pharmaceuticals.

In this study, the extracted polysaccharides from passion fruit peel were employed in the development of a nanoparticle thin film and resulted in the formation of a polysaccharide-based composite film comprising zinc oxide-nanoparticles (ZnO-NPs). The effects of ZnO-NPs (0%-0.75%) on the composite film's physicochemical properties, ultrastructure, and antimicrobial properties were comprehensively investigated. Moreover, the effect of the application of composite film coating on the freshness of cherry tomatoes was evaluated. Results showed that the incorporation of ZnO-NPs into the polysaccharide-based thin film significantly modified its light transmittance, mechanical properties, and water vapor permeability. The thin film containing 0.5% ZnO-NPs also exhibited superior overall physicochemical properties compared to other concentrations of ZnO-NPs. The composite film of polysaccharide/ZnO-NPs showed strong antimicrobial activity against Staphylococcus aureus and Escherichia coli, with the greatest effectiveness observed at 0.5% ZnO-NPs. The application of the composite film (0.5% ZnO-NPs) to preserve cherry tomatoes was found to potentially delay senescence of cherry tomatoes or quality deterioration during 10 days storage. This study highlights the potential of using fruit peel polysaccharides to develop environmentally friendly composite films incorporating ZnO-NPs, providing valuable insights for future fruit preservation strategies.

Potato is a globally important crop with a wide range of applications in fresh consumption and industrial processing. Potato quality depends on a complex interplay of biochemical and physical attributes. This study evaluates diverse potato cultivars through integrated biochemical, thermal, and elemental profiling, aiming to establish selection criteria for industrial applications. Biochemical analysis targeted cell wall enzymes, pectin methyl esterase (PME) and polygalacturonase (PG), as determinants of firmness and texture. Lignin content was measured to assess tissue rigidity and structural integrity. Starch composition was evaluated via amylose percentage (ranges from 22% to 34%), a key factor in gelatinization and retrogradation. Differential scanning calorimetry (DSC) provided insights into starch thermal properties, including gelatinization temperatures and enthalpy, essential to optimize industrial cooking processes. Elemental profiling using ICP-MS quantified elements which are known to modulate enzymatic activity and starch structure. Pearson correlation and hierarchical cluster analyses revealed significant associations among the parameters studied. The observed correlations between amylose content, starch granule size distribution, enzyme activity, and thermal properties highlight the complex interplay of biochemical traits that influence potato quality. Specifically, amylose shows a positive correlation with enthalpy (0.23), K (0.27), and Fe (0.26) and starch granules with size ranges from 0.0 to 0.5 µm² (0.26). This integrated approach facilitates targeted cultivar selection for specific applications, providing a framework for breeders and processors to optimize end-use quality and consumer satisfaction.

Milk adulteration remains a significant global food safety concern, given the widespread consumption of milk by billions worldwide. This study presents a soft sensor for quantifying concentrations of common milk adulterants, including ammonia, urea, sugar, and hydrogen peroxide. The approach integrates near-infrared (NIR) spectroscopy with a chemometric framework for real-time measurement. Orthogonal partial least squares (OPLS) was coupled with the XGBoost algorithm, leveraging its ensemble boosting mechanism and resistance to overfitting. K-means random clustering was used to organize experimental trials during model development. The soft sensor demonstrated high predictive performance, with root mean squared error (RMSE), normalized root mean squared error (NRMSE), and cross validation - coefficient of correlation (CV-R²) values of 0.01, 0.02, and 0.97 for urea; 0.01, 0.02, and 0.96 for ammonium sulfate; 0.07, 0.13, and 0.95 for sugar; and 0.01, 0.03, and 0.94 for H₂O₂. For all the adulterants, R² and the other variants of R² (adjusted and multiple) values were higher than 0.95, thereby demonstrating the exceptional performance of the developed soft sensor. The soft sensor provided accurate real-time values with an average error rate of less than 10%. Overall, the results demonstrate that the developed NIR-chemometric soft sensor provides a rapid, non-destructive, and reliable method for detecting and quantifying multiple milk adulterants in real time, offering significant potential for food safety monitoring.