Journal of Food Process Engineering最新文献

Pinus nigra Ten. (black pine) bark, a by-product of the timber industry, is rich in bioactive compounds with high antioxidant activity and various health benefits. This study investigated the effects of the addition of pine bark extract (PBE) into bread at different concentrations (0%, 0.25%, 0.50%, and 1.00%; w/w) on the hydrolysis index (HI), predicted glycemic index (pGI), antioxidant activity (DPPH and FRAP), total phenolic content (TPC), in vitro phenolic digestibility, mineral composition, textural properties, and sensory attributes. Bioaccessibility levels of the control bread were 6.79% for TPC, 8.99% for FRAP activity, and 6.44% for DPPH radical scavenging activity, whereas 1.00% PBE-enriched bread exhibited significantly higher values of 28.63%, 30.14%, and 22.75%, respectively. Incorporation of 0.50% and 1.00% of PBE into bread also reduced their pGI significantly; thereby these breads were classified as medium (pGI = 66.28 and 59.29, respectively) glycemic index. A strong linear correlation was observed between TPC and antioxidant activities. Mineral analysis (mg/100 g) confirmed the presence of essential macro- (Ca [19.79–38.95], K [4.80–25.72], Mg [5.78–12.38], Na [7.12–28.29]) and microelements (Zn [2.62–3.61], Cu [< 2.5], Fe [< 2.5], Mn [< 2.5]) in especially PBE-enriched breads. Texture analysis showed that the breads became softer (304.43–394.09 N) in proportion to the PBE level while sensory evaluations did not show any negative effect of PBE addition. Overall, these findings highlighted the potential of PBE as a valuable functional ingredient for enhancing the nutritional and technological properties of bakery products.

This study reviewed recent advances in electronic eye, electronic nose and electronic tongue in sensory evaluation of food. Significant progress has been made in using electronic eyes, noses, and tongues for objective food sensory evaluation, but research gaps include improving sensor technology for stability and selectivity, standardizing testing protocols, and developing more robust, low-cost portable devices. The novelty lies in the move from single-device applications to integrated, multisensory systems (e-nose, e-tongue, e-eye) that provide a more holistic and accurate picture of sensory properties. A novel approach is to combine the data from multiple artificial senses (electronic nose, tongue, and eye) to create a more comprehensive and reliable evaluation, often resulting in 8%–25% higher accuracy than a single instrument. The application of more advanced AI and machine learning techniques beyond traditional chemometrics like principal component analysis (PCA) will be a novel approach. This includes deep learning, neural networks, and specialized algorithms that can process high-dimensional sensor data more efficiently and accurately for tasks such as food authentication and quality prediction. Novel sensor designs, such as ratiometric electrochemical sensors, offer improved signal stability and noise cancelation. These dual-signal processing capabilities provide more accurate and reproducible measurements by internally correcting for background interference. Future outlooks point toward advanced data processing with deep learning, real-time online quality control in production, and a greater role in rapid food safety analysis. Although electronic senses are effective for many food types, their performance is still limited by complex food matrices. Factors like water vapor, high fat content, and complex volatile profiles can interfere with electronic nose sensors, requiring more research to enhance selectivity. While portable options exist, making electronic sensors affordable for widespread consumer and small-business use requires significant progress in cost-effective manufacturing and miniaturization. Incorporating biological recognition elements, such as enzymes and antibodies, into electronic sensor platforms allows for highly specific detection of complex analytes like pathogenic microorganisms and mycotoxins. The future will see electronic senses integrated into smart systems for real-time, remote monitoring throughout the food supply chain.

Traditional dairy sweets are highly perishable due to their high moisture content (~36%), moderate pH (5.5–6.5), and nutrient-rich composition, which leads to rapid microbial spoilage. This study aimed to develop a natural, extended-release vapor-phase antimicrobial system using eugenol as an antimicrobial agent and an expanded porous starch matrix of puffed rice (EPSMPR) as a carrier matrix to preserve the quality of such perishable products. This study investigates the vapor-phase antimicrobial efficacy of eugenol and extends its applications as a food preservation option to extend the shelf life of dairy-based sweets through the development of an ethyl cellulose-coated eugenol-loaded EPSMPR (ELEPSMPR). Eugenol exhibited broad-spectrum vapor-phase antimicrobial activity against Gram-positive, Gram-negative, and fungal strains, with minimum inhibitory concentration, minimum bactericidal concentration, and minimum inhibitory dose values in the ranges of 0.195–1.56 μg/mL, 0.39–3.12 μg/mL, and 100–171.42 mL/L, respectively. ELEPSMPR was developed through the vacuum impregnation process (encapsulation efficiency 82.64%), and it offered sustained release of eugenol vapors (30.22% release at 36 h compared to 40% in the uncoated control sample). ELEPSMPR was placed on the top inner surface of rectangular storage packets (Kraft paper, volume 500 cm³) of dairy sweets, ensuring the eugenol release without direct product contact at ambient storage. The ELEPSMPR modified the headspace environment of the storage packets, effectively suppressing microbial growth while maintaining the physicochemical quality of dairy sweet—Sandesh (pH, moisture content, and lipid stability). The ELEPSMPR system represents a sustainable food preservation technology that can be a safe, natural, and contactless strategy for dairy sweets and similar foods.

This experimental study compares the performance and cost-effectiveness of an indirect solar dryer (ISD) with a Screw Conveyor Based Solar Dryer (SBSD) used to dry ivy gourd. The drying procedure is followed at temperatures between 33°C and 49°C, with each dryer requiring 16 h to attain the necessary moisture reduction. Moreover, the average drying efficiency for ISD and SBSD is 7.82% and 7.48%, respectively. The final moisture content in ISD is measured at 8%, while SBSD is 15.97%, thereby demonstrating the superior moisture removal capabilities of ISD. Besides, the average drying rates of ISD and SBSD are 1.16 and 1.05 kg/h, respectively. The average heat supply of ISD and SBSD is determined to be 0.88 and 0.80 kW, respectively, with ISD providing 9.09% more heat than SBSD. The average mass transfer coefficient (h_m) for ISD is 0.0049 m/s, while it is 0.0036 m/s for SBSD, showing that ISD has a 26.53% higher h_m value. The specific energy consumption (SEC) values of SBSD and ISD are 0.931 and 0.924 kWh/kg, respectively. The Payback Period (PBP) for ISD is 2.16 and 7.45 years for SBSD, indicating a faster return on investment for ISD. Therefore, from the study, the findings show that the ISD has improved the drying efficiency, energy utilization, and economic feasibility for small-scale farmers in rural areas.

Milk is one of the nutritious foods consumed regularly by people. It is highly perishable and prone to spoilage quickly after milking from animals. The study aimed to develop a Milk Quality Index (MQI) comprising physicochemical, microbiological, and volatile parameters and to create a predictive model based on this index for evaluating milk quality. In this study, milk was stored for 0–240 min, and its physicochemical properties (fat, SNF, protein, lactose, density, color, and pH), microbial analysis (MBRT and TPC), and volatile compounds were assessed using an E-nose at 30-min intervals. The major volatile compounds responsible for spoilage were identified using the Partial Least Squares regression—Variable Importance in Projection method. Furthermore, Principal Component Analysis was performed on physicochemical, microbial, and volatile compounds obtained from the VIP score to group the milk samples based on spoilage. The MQI was developed for grouped milk samples and found that an MQI ≤ 3.0 indicates a fresh sample, an MQI between 3 and 6 indicates the beginning of spoilage and requires heat treatment for consumption, and an MQI ≥ 6 indicates spoiled. Multiple linear regression and Artificial Neural Network models were developed by relating physicochemical and microbial analysis to MQI to predict milk quality. This study provides insight into the development of MQI models for real-time milk spoilage prediction and decision-making in the dairy industry.

This study evaluated the impact of optimized thermosonication pretreatment (UT) (743.1 J/g, 16.6°C) on the physical and chemical quality of dried black grapes, comparing it with the traditional potash treatment. Dried grapes showed high dry matter (89.53%–91.41%) and controlled water activity (0.415–0.467), with significant changes in color parameters. Thermosonication pretreatment significantly reduced rehydration capacity, unlike potash. Potash increased sucrose content and negatively impacted fructose, while UT significantly reduced sucrose. UT-treated samples retained the highest organic acid contents and exhibited the lowest HMF and furfural contents, demonstrating an advantage over potash in minimizing these undesirable compounds. Potash enhanced total phenolic content (TPC) and total monomeric anthocyanin content (TMAC), whereas UT only increased antioxidant activity. Untreated control samples generally scored highest in sensory attributes, particularly appearance and overall acceptability. This research indicates that optimized thermosonication offers specific benefits like HMF/furfural reduction and organic acid preservation, though its overall quality impact varies compared to conventional treatments.

Building on previous research that optimized thermosonication (TS) conditions to enhance the functional compounds of yacon (Smallanthus sonchifolius) tuber juice, this study explores the underlying extraction kinetics and thermodynamic mechanisms governing the process. Following the determination of optimal TSYJ processing parameters using the integrated ANN-GA model (50% amplitude, 40°C, and 45 min), responses were evaluated across a temperature range of 30°C–50°C, with measurements recorded at 5-min intervals during sonication. By evaluating the release patterns of functional compounds such as total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (AOA), ascorbic acid (AA), and total carotenoid content (TCC), the study applies kinetic modeling to identify the most suitable mathematical fit. The pseudo second-order model was identified as the most suitable model with the highest R² (0.999) and lower χ² (0.072) value obtained for all five responses. Additionally, key thermodynamic parameters, including activation energy (E_a: 24.89–28.23), enthalpy (ΔH: 14.86–27.44 kJ/mol), entropy (ΔS: 22.27–58.42 J/mol·K), and Gibbs free energy (ΔG: −6.73 to 18.86 kJ/mol), were obtained to understand the energy requirements and feasibility of thermosonication-assisted extraction.

The human gut microbiome plays a crucial role in synthesizing immune-modulating metabolites, such as short-chain fatty acids (SCFAs), which offer multiple health benefits, including fuelling gut cells and modulating the immune system. Individual variations in nutrition, diet, and genetics significantly influence the body's response to various foods, affecting digestion, absorption, and metabolism processes. These factors, in turn, impact the accessibility of nutrients to the gut microbiota. Recent studies have elucidated diet-microbiome interaction mechanisms, demonstrating how gut microbiota respond to dietary changes, with specific nutrients selectively promoting the growth of certain microbial species. The emerging field of nutrigenomics examines the effects of genetic variants on nutritional responses, eating habits, and overall health. This discipline enhances our understanding of how diet influences the composition and activities of gut microbes. The interplay between genes, gut microbiomes, and host physiology forms a complex, highly interconnected network. Nutrigenomics research focuses on personalized nutrition, aiming to develop diets tailored to an individual's genetic profile and gut bacterial composition. This approach may unlock potential treatments for metabolically associated disorders in the future. This research aims to explore the role of nutrigenomics in identifying genetic variables that influence an individual's susceptibility to certain illnesses and how dietary choices can modulate these risks through interactions with the gut flora. Additionally, the review addresses the challenges associated with microbial dynamics, ethical and privacy concerns, and practical applications of this emerging field.

This study investigates the thermal performance and drying kinetics of banana slices under Indoor Forced Convection Drying. The experimental analyses focus on variations in surface temperature, moisture evaporation, and relative humidity over time. The results show that the maximum surface temperature of 2 and 5 mm slices reaches 50.77°C, and 56.23°C, respectively. The moisture evaporation trend indicates a significant reduction, with the mass of evaporated moisture decreasing from 16.0 to 1.30 g for 2 mm slices and from 22.3 to 1.60 g for 5 mm slices. The convective heat transfer coefficient (h_c) is higher for thicker slices (1.67 W/m² °C) as compared to thinner ones (0.79 W/m² °C), demonstrating improved heat transfer efficiency. A comparative analysis with existing drying models identifies the best-fitting model (the Modified Page model) is recommended for 2 mm banana slices, while the Lewis model is better suited for 5 mm banana slices predicting drying behavior. The finding highlights are the critical role of slice thickness in drying efficiency and thermal behavior, providing valuable insights for optimizing drying processes in the food industry. These results contribute to advancements in drying technology for agricultural products, ensuring improved energy efficiency and quality retention. These findings align with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production) by improving food preservation and reducing post-harvest losses, contributing to sustainable agricultural processing.