Journal of Food Engineering最新文献

{"title":"Impact of extrusion cooking on tree bean (Parkia timoriana) seed protein emulsion gels with natural deep eutectic solvents and its use as an extrusion-based 3D food printing ink","authors":"Prostuti Chakravorty ,&nbsp;Laxmikant S. Badwaik ,&nbsp;Amit Baran Das","doi":"10.1016/j.jfoodeng.2025.112722","DOIUrl":"10.1016/j.jfoodeng.2025.112722","url":null,"abstract":"<div><div>This study investigates the effects of single-screw extrusion on the stability and functional properties of emulsion gels containing tree bean seed protein (45–60 % w/w), starch, lipids, and natural deep eutectic solvents (NADES)- a novel, green solvent system known to enhance protein interactions. The extrusion process was optimized by evaluating the influence of temperature, screw speed, moisture content, and NADES concentration on critical parameters such as viscosity, compression energy, and peak force. The optimized conditions (93.33 °C, 155.41 rpm, 48.38 % moisture content, and 3.33 % NADES) yielded emulsion gels with improved textural and thermal properties. Notably, the inclusion of NADES enhanced protein thermal stability, with a denaturation temperature range of 136.5–147.2 °C, indicates the formation of stable quaternary complexes. Rheological analysis revealed that emulsion gel with NADES exhibited superior structural uniformity, reduced viscosity, and enhanced flow properties, making them ideal candidates for extrusion-based 3D food printing. Furthermore, 3D-printed meat analogues derived from these emulsion gels demonstrated high shape retention and softness comparable to raw chicken meat. This research highlights the potential of integrating NADES with extrusion technology to develop sustainable, plant-based meat analogues with improved structural and functional characteristics, addressing key challenges in the field of alternative protein foods.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112722"},"PeriodicalIF":5.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An experimental and molecular dynamics analysis of Listeria monocytogenes inactivation in peach puree influenced by moderate electric field and temperature","authors":"Chaminda P. Samaranayake,&nbsp;Mohamed M. Ali,&nbsp;Sudhir K. Sastry","doi":"10.1016/j.jfoodeng.2025.112734","DOIUrl":"10.1016/j.jfoodeng.2025.112734","url":null,"abstract":"<div><div>Nonthermal food preservation methods are increasingly recognized for their potential, with moderate electric fields (MEF) identified as a viable alternative. This study explores the synergistic effect of MEF and temperature on the inactivation of <em>Listeria monocytogenes</em> in peach puree. An electro-processing device was employed to minimize ohmic heating and improve cooling during electric field application. Inactivation was investigated at a field strength of 105 V/cm and isothermal treatment temperatures of 45 and 50 °C, with results compared to corresponding control treatments performed without electric field application. The results revealed a significant (<em>P</em> ≤ 0.05) increase in inactivation induced by MEF at 50 °C within the initial 10 min of treatment, while at 45 °C, a comparable effect was noted only after 20 min of MEF exposure. Moreover, a reduction exceeding 5-log₁₀ was achieved with MEF treatment at 50 °C for durations exceeding 10 min. Molecular dynamics analysis also demonstrated the effectiveness of the combined influence of electric field and temperature on electroporation and membrane lipid dynamics, with a significant (<em>P</em> ≤ 0.05) enhancement observed at 50 °C. The results of the present study highlight the applicability of MEF processing technology to fruit-based matrices.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112734"},"PeriodicalIF":5.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a digital twin for beer quality control: development of a digital model integrating industrial process data and model-based fermentation descriptors","authors":"Colomba Dazzarola ,&nbsp;Ronald Tighe ,&nbsp;José R. Pérez-Correa ,&nbsp;Pedro A. Saa","doi":"10.1016/j.jfoodeng.2025.112726","DOIUrl":"10.1016/j.jfoodeng.2025.112726","url":null,"abstract":"<div><div>Beer production consists of a series of complex chemical, physical, and biological transformations. Although modern industrial production protocols are highly standardized, external and process disturbances often lead to degradation in beer quality. While models are available to predict beer quality, their widespread use is currently limited. Typically, these models rely on input variables from products that are almost complete; therefore, corrective measures cannot be implemented on time. Advanced modeling tools, such as digital twins, are effective alternatives to tackle this limitation, as they can integrate real-time process data into a digital model of the physical system to provide online predictions. To advance the development of such a tool, we have developed a hybrid model for beer quality prediction by combining different modeling frameworks and industrial process data. First, a dynamic model of industrial beer fermentation was calibrated that satisfactorily captured the kinetics of primary (extract) and quality-associated volatile compounds (pentanedione and butanedione). Second, a Naïve Bayes classifier for predicting beer quality was trained using physicochemical variables that identified the most critical attributes in a high-quality beer (ethyl acetate, total esters, foam stability, bitterness, and isoamyl acetate). Lastly, a hybrid regression model was constructed using fermentation model descriptors and external process data to predict the latter attributes with high prediction fidelity (less than 10% relative root mean squared error). The model identified yeast handling—including storage and propagation—and wort preparation as critical determinants of final product quality. Overall, this work represents a step toward developing a digital twin that can provide real-time process descriptors and integrate industrial data to optimize production and enhance beer quality.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112726"},"PeriodicalIF":5.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a radio frequency disinfestation technology for red jujubes with BaTiO3 as a filling material: Pest mortality, energy efficiency, and product quality","authors":"Xin Ye ,&nbsp;Junjie Zheng ,&nbsp;Jinsong Zhang ,&nbsp;Longlong Jiang ,&nbsp;Rui Li ,&nbsp;Shaojin Wang","doi":"10.1016/j.jfoodeng.2025.112727","DOIUrl":"10.1016/j.jfoodeng.2025.112727","url":null,"abstract":"<div><div>The Indianmeal moth (<em>Plodia interpunctella</em>), characterized by its rapid infestation rate and short developmental cycle, poses a grave threat to red jujubes during the storage period thereby resulting in substantial economic losses. Previous study showed that incorporating BaTiO₃ (BT) in different volumes into food-grade silicone (FGS) for generating FGS/BT composite particles with diverse dielectric properties can significantly augment the heating uniformity of red jujubes during radio frequency (RF) treatment. This study adopted RF technology for disinfesting red jujubes, while evaluating effects of the filling materials and target temperatures on the disinfestation efficacy and product quality. Results indicated that the fifth-instar larvae of the Indianmeal moth were completely killed under the RF treatment with a target temperature of 52 °C and a holding time of 1 min. Meanwhile, the filling FGS/BT material significantly improved the utilization efficiency of RF energy, and effectively prevented the surface of red jujubes from charring, without significant impact on the product quality. These findings demonstrated the unique advantages of RF technology combined with filling materials for disinfesting red jujubes, providing the theoretical basis for further industrial applications.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112727"},"PeriodicalIF":5.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bacterial inactivation and bactericidal mechanisms of pulsed ohmic heating against foodborne pathogens in milk depending on the applied frequency","authors":"Eun-Rae Cho ,&nbsp;Dong-Hyun Kang","doi":"10.1016/j.jfoodeng.2025.112729","DOIUrl":"10.1016/j.jfoodeng.2025.112729","url":null,"abstract":"<div><div>Conflicting results have been reported regarding whether the relationship between frequency and microbial inactivation in ohmic heating (OH) is direct or inverse. The main purpose of this study was to investigate the bacterial inactivation and bactericidal mechanism of pulsed ohmic heating (POH) depending on the applied frequency. Milk samples inoculated with foodborne pathogens were subjected to POH at a constant voltage of 70 V_rms with applied frequencies of 0.06, 0.1, 1, or 10 kHz. The samples were heated to 60°C using POH and held at that temperature for up to 10 min. The bacterial inactivation levels of POH at 0.06 kHz were significantly greater (<em>P</em> &lt; 0.05) than those at 0.1–10 kHz. At 0.06 kHz, for instance, <em>Salmonella</em> Typhimurium reduced more than 2 log CFU/ml compared with that at 0.1–10 kHz. Cell membrane depolarization, analyzed using bis-(1,3-dibutylbarbituric acid)trimethine oxonol (DiBAC₄(3)), was significantly greater after POH at 0.06 kHz compared to other frequencies. The value of crystal violet uptake, which reflects cell permeability, was also highest at 0.06 kHz. Because POH at 0.06 kHz fatally damaged cell membranes and increased permeability, the largest amount of intracellular material leaked. Through a DNA integrity assessment, POH at 0.06 kHz provoked severe DNA damage to all the pathogens. It was speculated that these results occurred because POH at 0.06 kHz provided enough time for the buildup of charges around the cell membranes and DNA. This research provides a fundamental scientific basis for determining the mechanism by which low-frequency POH results in high bacterial inactivation.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112729"},"PeriodicalIF":5.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of classical kinetic and Bayesian approaches for predicting food shelf life by accelerated testing: the case of biobased coffee capsules","authors":"Marco Lopriore ,&nbsp;Lara Manzocco ,&nbsp;Sonia Calligaris ,&nbsp;Marilisa Alongi ,&nbsp;Maria Cristina Nicoli ,&nbsp;Giovanni Fonseca","doi":"10.1016/j.jfoodeng.2025.112733","DOIUrl":"10.1016/j.jfoodeng.2025.112733","url":null,"abstract":"<div><div>Predicting the shelf life (SL) of food products is still challenging from a computational point of view. In the present work, the classical kinetic (K) approach was compared with the Bayesian (B) methodology, whose application in SL studies is largely unexplored.</div><div>Data relevant to pH changes of coffee brews obtained from coffee packaged in bio-based capsules and stored at different relative humidity (54, 65, 75 %) and temperature (20, 30, 45 °C) were considered as a case study, assuming pH 5.1 as the acceptability limit of the coffee brew. Data were elaborated according to K or B methodologies and SL estimates were compared. Although B better fit coffee pH decay at all environmental conditions, this methodology provided SL estimates comparable to those obtained by K. However, the B methodology produced SL estimates with considerably smaller uncertainty intervals and gave the opportunity to interpret shelf life from a probabilistic point of view.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112733"},"PeriodicalIF":5.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic RuS2/CeS2 nano-hybrid for electrochemical detection of brilliant blue dye","authors":"Sreelekshmi ,&nbsp;Raril Chenthattil ,&nbsp;Mani Govindasamy ,&nbsp;Beena Saraswathyamma","doi":"10.1016/j.jfoodeng.2025.112731","DOIUrl":"10.1016/j.jfoodeng.2025.112731","url":null,"abstract":"<div><div>Accurate identification and quantification of brilliant blue (BB) in commercial food samples are essential due to the detrimental effects caused by BB on human health. This work presents the construction of an electrochemical sensor for BB detection utilizing hybrid nanomaterial RuS₂/CeS₂ as modifier. The synergistic interaction between RuS₂ and CeS₂ nanoparticles enabled the fabricated sensor to directly detect the analyte in real food samples, demonstrating its significant and noteworthy feature of the constructed sensor. The developed sensor exhibits an exceptionally wide linear range of 5.0–4351.0 μM with an LOD value 0.22 μM, surpassing all other previously reported works. The electrochemical study displayed that BB showed an irreversible, diffusion-controlled process on the designed electrode, resulting in an enhancement in peak current compared to RuS₂ alone. The outcomes obtained from the investigation pave the way for development of portable sensors.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112731"},"PeriodicalIF":5.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microwave vacuum evaporation of sugar solutions: Analysis of dielectric and microwave absorption properties","authors":"Yuan Tao ,&nbsp;Bowen Yan ,&nbsp;Nana Zhang ,&nbsp;Jianxin Zhao ,&nbsp;Wei Chen ,&nbsp;Daming Fan","doi":"10.1016/j.jfoodeng.2025.112728","DOIUrl":"10.1016/j.jfoodeng.2025.112728","url":null,"abstract":"<div><div>Microwave vacuum evaporation (MVE) is a promising alternative over traditional vacuum evaporation, offering rapid evaporation rates and high efficiency. This study investigated the MVE processes and the resulting quality of sugar solutions under varying microwave power densities (10–50 W/g) and vacuum levels (40–80 kPa). The results indicated that a moderate microwave power density (∼30 W/g in this study) was optimal for MVE, balancing both evaporation efficiency and sugar quality. Furthermore, the dielectric properties of the sugar solutions during MVE (over the temperature range of approximately 25-110°C) exhibited a time-dependent decrease in the dielectric constant (ε′), whereas both the dielectric loss factor (ε′′) and loss tangent (tanδε) initially decreased before increasing. Similar values of ε′′ and tanδε were observed for sugar solutions evaporated at high power densities, accounting for the phenomenon of saturated evaporation. The penetration depth, reflection loss, and relative input impedance results indicated that the 4 mm-thick sugar solutions exhibited good microwave absorption during the MVE, except at the initial and final stages, confirming the feasibility of utilizing a liquid film configuration in MVE applications. In conclusion, the analysis of dielectric and microwave absorption properties offers a robust approach for optimizing the MVE process, facilitating the production of high-quality sugars.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112728"},"PeriodicalIF":5.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel quantitative analysis method for printability in 3D food printing for surimi","authors":"Sol Kim ,&nbsp;Jaehwi Seol ,&nbsp;Eunji Ju ,&nbsp;Jeonghyeon Pak ,&nbsp;Hyoung Il Son ,&nbsp;Soo-Jung Kim","doi":"10.1016/j.jfoodeng.2025.112725","DOIUrl":"10.1016/j.jfoodeng.2025.112725","url":null,"abstract":"<div><div>The success of 3D food printing (3DFP) relies heavily on material printability, defined by smooth extrusion and structural stability. However, many food materials struggle to meet these criteria, leading to challenges in shaping and printing precision. Conventional evaluation methods for evaluating key printability indicators like water holding capacity (WHC) and gel strength are often costly, require specialized equipment, and are destructive. To overcome these limitations, this study proposes a non-destructive, computer vision-based approach for printability evaluation using digital images. Grayscale images were analyzed to extract texture features based on the gray-level co-occurrence matrix (GLCM). These texture features, along with additive concentration data, then served as input for a Random Forest model. Three distinct models were developed: top view (TOP), side view (SIDE), and combined view (TS). The TOP model demonstrated the highest predictive performance for WHC (MAE = 0.674, R² = 0.875, RMSE = 0.831), while the TS model showed superior accuracy for gel strength (MAE = 0.391, R² = 0.691, RMSE = 0.556). This novel approach enables rapid, automated, and cost-effective assessments, thereby significantly aiding in 3DFP optimization and the development of customized food products.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112725"},"PeriodicalIF":5.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approach to inline monitoring of apple puree consistency through process data and fruit characteristics","authors":"Aref Sepehr ,&nbsp;Maciej Zaborowicz ,&nbsp;Carlo Gabardi ,&nbsp;Nicola Gabardi ,&nbsp;Elisa Biada ,&nbsp;Marco Luzzini ,&nbsp;Alessandro Zanchin ,&nbsp;Lorenzo Guerrini","doi":"10.1016/j.jfoodeng.2025.112712","DOIUrl":"10.1016/j.jfoodeng.2025.112712","url":null,"abstract":"<div><div>Consistency is a key attribute for apple purée producers, since it affects the product quality and acceptability, and can be used to regulate the process settings. This study aims to build a statistical model to predict puree consistency using data collected from an industrial production line. The dataset includes 14 variables across 524 samples, comprising process parameters (e.g., pump performance, temperature) and characteristics of the apples. Three predictive models were developed incrementally. Model 1 relied on the mechanical energy conservation law to establish a relationship between a measured pressure drop and the consistency. In this study, we quantify consistency using the Bostwick flow distance, a widely adopted practical proxy for apparent viscosity in the food industry. Model 2 incorporated the machinery-related parameters, and Model 3 further integrated the characteristics of the apples to account for raw material variability. For models training and validation, generalized linear models (GLM), gradient boosting machines (GBM), and deep learning (DL) architectures were compared. The effect of the different input variables on the predictive performance was also assessed. In all models, pressure difference emerged as the most influential variable. Model 3, particularly with GBM, resulted in the highest predictive accuracy (R² = 0.78; MAPE = 9.2 %), demonstrating the importance of incorporating ripening information of the apples in the model. The model outperforms traditional laboratory-based viscosity measurements, which typically have greater variability. The model considers the interactions between processing conditions and raw material properties that influence puree rheology, enabling a real-time inline consistency monitoring of apple puree in industrial settings, offering manufacturers operational benefits. These include the potential for reducing manual testing time and decreasing production costs through minimized batch rejections, and the ability to implement timely process adjustments. These advantages collectively contribute to reduced product waste, enhanced quality control, and improved economic outcomes in commercial apple puree production.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"403 ","pages":"Article 112712"},"PeriodicalIF":5.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}