Non-contact and accurate determination of product moisture content during drying is essential for maintaining quality and evaluating drying performance. In this study, a specific drying chamber was equipped with a laser light backscattering imaging (LLBI) setup to capture real-time backscattering images of quince slices. Two diode-pumped lasers, operating at green (532 nm) and near-infrared (NIR) (980 nm) wavelengths, were implemented for this purpose. In addition to extracting color features from backscattered regions, state-of-the-art shape features were also extracted from both saturated and backscattered regions of the lasers by measuring radial profiles (RPs). Furthermore, two pre-trained convolutional neural networks, namely ResNet50 and VGG19, were utilized to extract new deep features. The color and shape features of both lasers were assessed individually and in a fusion strategy to maximize the predictability of moisture content using two regression methods: partial least squares (PLS) and artificial neural networks (ANN). The results demonstrated excellent predictability of moisture content when color and shape features of the green laser were fused into an ANN model (SDR of 3.00). However, the NIR laser yielded moderate predictions individually, particularly when utilizing VGG19 deep features (SDR of 2.08). Moreover, the fusion of color and shape features from both lasers exhibited strong synergy, resulting in the best ANN predictive model (R2p of 0.920, RMSEP of 7.24%, and SDR of 3.56). Through the utilization of these novel features, this study highlights the significant potential of the LLBI technique for real-time monitoring of moisture content in quince slices during drying.
{"title":"Novel feature extraction in laser light backscattering imaging for real-time monitoring of quince moisture content during hot-air drying","authors":"Nadia Sadat Aghili , Seyed Ahmad Mireei , Morteza Sadeghi , Mehrnoosh Jafari , Rouzbeh Abbaszadeh","doi":"10.1016/j.jfoodeng.2025.112496","DOIUrl":"10.1016/j.jfoodeng.2025.112496","url":null,"abstract":"<div><div>Non-contact and accurate determination of product moisture content during drying is essential for maintaining quality and evaluating drying performance. In this study, a specific drying chamber was equipped with a laser light backscattering imaging (LLBI) setup to capture real-time backscattering images of quince slices. Two diode-pumped lasers, operating at green (532 nm) and near-infrared (NIR) (980 nm) wavelengths, were implemented for this purpose. In addition to extracting color features from backscattered regions, state-of-the-art shape features were also extracted from both saturated and backscattered regions of the lasers by measuring radial profiles (RPs). Furthermore, two pre-trained convolutional neural networks, namely ResNet50 and VGG19, were utilized to extract new deep features. The color and shape features of both lasers were assessed individually and in a fusion strategy to maximize the predictability of moisture content using two regression methods: partial least squares (PLS) and artificial neural networks (ANN). The results demonstrated excellent predictability of moisture content when color and shape features of the green laser were fused into an ANN model (SDR of 3.00). However, the NIR laser yielded moderate predictions individually, particularly when utilizing VGG19 deep features (SDR of 2.08). Moreover, the fusion of color and shape features from both lasers exhibited strong synergy, resulting in the best ANN predictive model (<em>R</em><sup>2</sup><sub>p</sub> of 0.920, RMSEP of 7.24%, and SDR of 3.56). Through the utilization of these novel features, this study highlights the significant potential of the LLBI technique for real-time monitoring of moisture content in quince slices during drying.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112496"},"PeriodicalIF":5.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183635","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}
Pigeon pea (Cajanus cajan) offers a viable alternative to conventional plant proteins, but optimizing its functionality remains crucial. This study evaluates the effects of microwave-assisted extraction (MAE) on the physicochemical, structural, and functional properties of pigeon pea protein isolates (PPIs). Under varied MAE conditions, solubility, water, and oil holding capacities improved, with foaming and emulsifying properties comparable to commercial soy and pea proteins. High microwave power (900 W, 120 s) significantly boosted extraction and protein yields to 15.67% and 61.92%, respectively. Structural modifications in the electrophoretic profile of small bands were observed via SDS-PAGE, which revealed a predominance of vicilin proteins. Despite a high thermal stability (denaturation at 95.6 °C), the onset temperature shifts also indicate some degree of protein denaturation. These findings highlight the potential of MAE to tailor the functional properties of PPIs, making them valuable for various food and beverage applications and advancing their use as sustainable protein sources.
{"title":"Exploring microwave-assisted extraction on physicochemical and functional properties of pigeon pea protein for food applications","authors":"Gabriela Silva Mendes Coutinho , Priscylla Martins Carrijo Prado , Alline Emannuele Chaves Ribeiro , Michael T. Nickerson , Márcio Caliari , Manoel Soares Soares Júnior","doi":"10.1016/j.jfoodeng.2025.112497","DOIUrl":"10.1016/j.jfoodeng.2025.112497","url":null,"abstract":"<div><div>Pigeon pea (<em>Cajanus cajan</em>) offers a viable alternative to conventional plant proteins, but optimizing its functionality remains crucial. This study evaluates the effects of microwave-assisted extraction (MAE) on the physicochemical, structural, and functional properties of pigeon pea protein isolates (PPIs). Under varied MAE conditions, solubility, water, and oil holding capacities improved, with foaming and emulsifying properties comparable to commercial soy and pea proteins. High microwave power (900 W, 120 s) significantly boosted extraction and protein yields to 15.67% and 61.92%, respectively. Structural modifications in the electrophoretic profile of small bands were observed via SDS-PAGE, which revealed a predominance of vicilin proteins. Despite a high thermal stability (denaturation at 95.6 °C), the onset temperature shifts also indicate some degree of protein denaturation. These findings highlight the potential of MAE to tailor the functional properties of PPIs, making them valuable for various food and beverage applications and advancing their use as sustainable protein sources.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112497"},"PeriodicalIF":5.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183636","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}
Pub Date : 2025-01-25DOI: 10.1016/j.jfoodeng.2025.112495
Suhong Li , Yu Liu , Chenfei Liu , Chunyan Wang
The impact of dynamic high-pressure microfluidization (DHPM) (30–150 MPa) on the structure of peanut protein isolate (PPI) and PPI gel behavior and microstructural properties were studied. The research found that DHPM treatment significantly decreased the volume-mean diameter of PPI (p < 0.05), and induced the β-sheets, β-turn and random coils to transform into α-helix structures. With increased pressure, the hydrophobic groups of PPI were exposed to the outside, thus the surface hydrophobicity of PPI enhanced, and the sulfhydryl content and disulfide bond content increased significantly (p < 0.05) compared to control samples. Furthermore, the hardness of MTGase-induced gel increased gradually from 0.14 N (0.1 MPa) to 0.35 N (150 MPa). And the water-holding capacity of PPI gel reached its maximum at 90 MPa. The types of intermolecular interactions in the PPI gel were mainly hydrophobic interactions and disulfide bonds. Gʹ and Gʹʹ increased significantly from 30 MPa to 90 MPa. The microstructure of the gel systems upon 90 MPa and 120 MPa treatment showed more optimal surface appearance. These results indicate that the DHPM treatment could provide a new way to enhance the gel properties of PPI.
{"title":"Impact of dynamic high-pressure microfluidization on conformation and gel properties of peanut protein isolates","authors":"Suhong Li , Yu Liu , Chenfei Liu , Chunyan Wang","doi":"10.1016/j.jfoodeng.2025.112495","DOIUrl":"10.1016/j.jfoodeng.2025.112495","url":null,"abstract":"<div><div>The impact of dynamic high-pressure microfluidization (DHPM) (30–150 MPa) on the structure of peanut protein isolate (PPI) and PPI gel behavior and microstructural properties were studied. The research found that DHPM treatment significantly decreased the volume-mean diameter of PPI (<em>p</em> < 0.05), and induced the β-sheets, β-turn and random coils to transform into α-helix structures. With increased pressure, the hydrophobic groups of PPI were exposed to the outside, thus the surface hydrophobicity of PPI enhanced, and the sulfhydryl content and disulfide bond content increased significantly (<em>p</em> < 0.05) compared to control samples. Furthermore, the hardness of MTGase-induced gel increased gradually from 0.14 N (0.1 MPa) to 0.35 N (150 MPa). And the water-holding capacity of PPI gel reached its maximum at 90 MPa. The types of intermolecular interactions in the PPI gel were mainly hydrophobic interactions and disulfide bonds. Gʹ and Gʹʹ increased significantly from 30 MPa to 90 MPa. The microstructure of the gel systems upon 90 MPa and 120 MPa treatment showed more optimal surface appearance. These results indicate that the DHPM treatment could provide a new way to enhance the gel properties of PPI.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112495"},"PeriodicalIF":5.3,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183633","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}
In the era of rapid advancements in computing and the Internet of Things, the food fermentation sector is undergoing a digital and intelligent transformation. This research developed a food fermentation prediction and control system based on digital twin technology. The system employs multi-scale feature extraction and convolution feature fusion to establish partial least squares (PLS) prediction models for C source and bacterial concentration. The results showed that the PLS prediction models of C source and bacterial concentration exhibited excellent performance, with RMSEP of 0.5538 mg/mL and 0.0558 (Au), and RPD of 5.63 and 6.52, respectively. An optimal control system for the fermentation process was constructed by integrating the prediction models with a genetic algorithm (GA), yielding satisfactory simulation and testing outcomes. The study showed that the proposed digital twin-based fermentation prediction and control system offers superior robustness and reliability, advancing the digital and intelligent development of the food fermentation industry.
{"title":"Digital twin for predicting and controlling food fermentation: A case study of kombucha fermentation","authors":"Songguang Zhao , Tianhui Jiao , Selorm Yao-Say Solomon Adade , Zhen Wang , Qin Ouyang , Quansheng Chen","doi":"10.1016/j.jfoodeng.2025.112467","DOIUrl":"10.1016/j.jfoodeng.2025.112467","url":null,"abstract":"<div><div>In the era of rapid advancements in computing and the Internet of Things, the food fermentation sector is undergoing a digital and intelligent transformation. This research developed a food fermentation prediction and control system based on digital twin technology. The system employs multi-scale feature extraction and convolution feature fusion to establish partial least squares (PLS) prediction models for C source and bacterial concentration. The results showed that the PLS prediction models of C source and bacterial concentration exhibited excellent performance, with RMSEP of 0.5538 mg/mL and 0.0558 (Au), and RPD of 5.63 and 6.52, respectively. An optimal control system for the fermentation process was constructed by integrating the prediction models with a genetic algorithm (GA), yielding satisfactory simulation and testing outcomes. The study showed that the proposed digital twin-based fermentation prediction and control system offers superior robustness and reliability, advancing the digital and intelligent development of the food fermentation industry.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"393 ","pages":"Article 112467"},"PeriodicalIF":5.3,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175039","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}
Pub Date : 2025-01-23DOI: 10.1016/j.jfoodeng.2025.112494
Neha Tanwar , Sandeep N. Mudliar , Roopavathi C , Prasanna Vasu , Sukumar Debnath
This study examines the impact of cold plasma treatment on the microbiological safety and quality characteristics of geographical indication (GI)-tagged Byadagi chili powder, known for its vibrant color and mild spiciness. Plasma treatment was varied by voltage (10–20 kV), duration (1–10 min) and electrode distance (5–7 cm) to assess effects on composition, vitamins, water activity, phenolic content, antioxidant activity, color, capsaicin content, pungency and structural integrity. The most effective treatment viz., 20 kV for 10 min at 5 cm, contributed achievement of a 3.7 log reduction in total microbial load, 3.1 log reduction in coliforms and 2.6 log reduction in yeast and mold. FTIR and SEM analyses revealed subtle but non-significant changes, including increased surface oxygen content (indicated by the C-H and C-O stretching regions), with minimal microstructural differences. The results indicate that cold plasma treatment effectively maintained core properties of Byadagi chili powder, including color and capsaicin and also preserving other physicochemical attributes. This method offers a promising solution for enhancing the safety and quality of Byadagi chili powder, addressing export concerns and maintaining its global market competitiveness.
{"title":"Effect of multipin atmospheric cold plasma treatment on color, capsaicin and microbial content of geographical indication-tagged Byadagi chili powder","authors":"Neha Tanwar , Sandeep N. Mudliar , Roopavathi C , Prasanna Vasu , Sukumar Debnath","doi":"10.1016/j.jfoodeng.2025.112494","DOIUrl":"10.1016/j.jfoodeng.2025.112494","url":null,"abstract":"<div><div>This study examines the impact of cold plasma treatment on the microbiological safety and quality characteristics of geographical indication (GI)-tagged <em>Byadagi chili</em> powder, known for its vibrant color and mild spiciness. Plasma treatment was varied by voltage (10–20 kV), duration (1–10 min) and electrode distance (5–7 cm) to assess effects on composition, vitamins, water activity, phenolic content, antioxidant activity, color, capsaicin content, pungency and structural integrity. The most effective treatment viz., 20 kV for 10 min at 5 cm, contributed achievement of a 3.7 log reduction in total microbial load, 3.1 log reduction in coliforms and 2.6 log reduction in yeast and mold. FTIR and SEM analyses revealed subtle but non-significant changes, including increased surface oxygen content (indicated by the C-H and C-O stretching regions), with minimal microstructural differences. The results indicate that cold plasma treatment effectively maintained core properties of <em>Byadagi chili</em> powder, including color and capsaicin and also preserving other physicochemical attributes. This method offers a promising solution for enhancing the safety and quality of <em>Byadagi chili</em> powder, addressing export concerns and maintaining its global market competitiveness.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112494"},"PeriodicalIF":5.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183511","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}
Pub Date : 2025-01-22DOI: 10.1016/j.jfoodeng.2025.112486
Jerzy Stangierski , Ryszard Rezler , Przemysław Siejak , Katarzyna Walkowiak , Łukasz Masewicz , Krzysztof Kawecki , Hanna Maria Baranowska
The aim of the study was to determine changes in selected physicochemical characteristics during 60-day storage of Milano smoked salami (SS) and Milano mould salami (MS). The study included basic analyses as well as the analyses of texture, rheological properties, Fourier-transform infrared spectroscopy (FTIR), and low-field nuclear magnetic resonance (LF-NMR). Additionally, the influence of the place of sampling (from under the casing and from the centre of the salami bar) on the analyses was assessed. The analyses showed that the type of salami and storage time reduced the water content, Aw, and increased pH. The hardness of the SS was greater than that of the MS. In the initial period of storage there were differences in the values of rheological parameters in both types of salami, but they became equal over time. The FTIR analysis of the samples revealed characteristic amide I and amide II bands, as well as bands typical of fats and nucleic acids. The lack of visible shifts in the fatty bands suggests limited oxidation of unsaturated fatty acids during storage. The LF NMR analysis showed that the relaxation parameters depended on the type of salami and the place of sampling. The water bound in the salamis did not change its molecular dynamics during the storage of the products. Changes in the molecular rotational movements of protons showed that after 60 days of storage these movements were more limited in the smoked salami. Our study showed that the smoking process, the addition of mould, and the packaging method influenced the dynamics of water migration inside the product and its physicochemical characteristics.
{"title":"An instrumental analysis of changes in the physicochemical and mechanical properties of smoked and mould salamis during storage","authors":"Jerzy Stangierski , Ryszard Rezler , Przemysław Siejak , Katarzyna Walkowiak , Łukasz Masewicz , Krzysztof Kawecki , Hanna Maria Baranowska","doi":"10.1016/j.jfoodeng.2025.112486","DOIUrl":"10.1016/j.jfoodeng.2025.112486","url":null,"abstract":"<div><div>The aim of the study was to determine changes in selected physicochemical characteristics during 60-day storage of Milano smoked salami (SS) and Milano mould salami (MS). The study included basic analyses as well as the analyses of texture, rheological properties, Fourier-transform infrared spectroscopy (FTIR), and low-field nuclear magnetic resonance (LF-NMR). Additionally, the influence of the place of sampling (from under the casing and from the centre of the salami bar) on the analyses was assessed. The analyses showed that the type of salami and storage time reduced the water content, Aw, and increased pH. The hardness of the SS was greater than that of the MS. In the initial period of storage there were differences in the values of rheological parameters in both types of salami, but they became equal over time. The FTIR analysis of the samples revealed characteristic amide I and amide II bands, as well as bands typical of fats and nucleic acids. The lack of visible shifts in the fatty bands suggests limited oxidation of unsaturated fatty acids during storage. The LF NMR analysis showed that the relaxation parameters depended on the type of salami and the place of sampling. The water bound in the salamis did not change its molecular dynamics during the storage of the products. Changes in the molecular rotational movements of protons showed that after 60 days of storage these movements were more limited in the smoked salami. Our study showed that the smoking process, the addition of mould, and the packaging method influenced the dynamics of water migration inside the product and its physicochemical characteristics.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112486"},"PeriodicalIF":5.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces an innovative method combining acoustic vibration technology with machine learning (ML) to non-destructively assess the freshness of closed-shell oysters. An acoustic vibration system, developed in-house, gathers vibration signals, which are then processed through a fusion strategy that integrates two time-domain features, six frequency-domain features, and one time-frequency domain feature based on an improved MFCC. Utilizing these fused features, the Stacking ensemble learning algorithm was employed to integrate six mainstream machine learning classification algorithms, leveraging their strengths in signal analysis to build a high-performance freshness detection model. Cross-validation assessments reveal the model's accuracy at 98%, highlighting how multi-feature fusion and ensemble learning algorithms significantly improve detection precision. Comparative studies further demonstrate that fusion features notably enhance classification accuracy over using domain-specific features alone. The experimental results indicate that the proposed method successfully navigates the challenge posed by the oyster shell to internal detection, achieving dynamic decay detection of oyster freshness. This provides a new technological approach for quality control in shellfish products like oysters, contributing meaningfully to food safety and quality enhancement.
{"title":"NDT of closed-shell oyster freshness by acoustic vibration signals","authors":"Jiahao Yu , Yuankun Song , Shaohua Xing , Xinqing Xiao , Yongman Zhao , Xiaoshuan Zhang","doi":"10.1016/j.jfoodeng.2025.112492","DOIUrl":"10.1016/j.jfoodeng.2025.112492","url":null,"abstract":"<div><div>This study introduces an innovative method combining acoustic vibration technology with machine learning (ML) to non-destructively assess the freshness of closed-shell oysters. An acoustic vibration system, developed in-house, gathers vibration signals, which are then processed through a fusion strategy that integrates two time-domain features, six frequency-domain features, and one time-frequency domain feature based on an improved MFCC. Utilizing these fused features, the Stacking ensemble learning algorithm was employed to integrate six mainstream machine learning classification algorithms, leveraging their strengths in signal analysis to build a high-performance freshness detection model. Cross-validation assessments reveal the model's accuracy at 98%, highlighting how multi-feature fusion and ensemble learning algorithms significantly improve detection precision. Comparative studies further demonstrate that fusion features notably enhance classification accuracy over using domain-specific features alone. The experimental results indicate that the proposed method successfully navigates the challenge posed by the oyster shell to internal detection, achieving dynamic decay detection of oyster freshness. This provides a new technological approach for quality control in shellfish products like oysters, contributing meaningfully to food safety and quality enhancement.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112492"},"PeriodicalIF":5.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183510","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}
Pub Date : 2025-01-20DOI: 10.1016/j.jfoodeng.2025.112493
Kate Waldert, Sandra Bittermann, Nina Martinović, Felix Schottroff, Henry Jäger
The potential of ohmic baking as an energy-efficient alternative to conventional baking was investigated in wheat bread production, with a particular focus on bread quality parameters. The effects of specific power input (1–5 kW/kg) and various parallel plate treatment chamber configurations were evaluated regarding resulting physico-chemical parameters. Ohmic baking at high power inputs (5 kW/kg) reduced baking times by 98 % compared to conventional baking. Concerning product quality, however, lower power input levels ( 3 kW/kg) revealed specific benefits in terms of a more uniform crumb structure and higher degrees of starch cooking. Treatment chambers with thick electrodes ( 5 mm) and decreased product surface area were associated with treatment inhomogeneities that resulted in higher product losses and lower bread volume. The results underline the importance of power input and treatment chamber design as control tools for tailored ohmic baking concepts to attain specific product properties. The necessity for a subsequent holding time after the heating phase was ascertained to achieve product quality comparable to conventionally baked breads.
{"title":"Ohmic baking of wheat bread – effect of process parameters on physico-chemical quality attributes","authors":"Kate Waldert, Sandra Bittermann, Nina Martinović, Felix Schottroff, Henry Jäger","doi":"10.1016/j.jfoodeng.2025.112493","DOIUrl":"10.1016/j.jfoodeng.2025.112493","url":null,"abstract":"<div><div>The potential of ohmic baking as an energy-efficient alternative to conventional baking was investigated in wheat bread production, with a particular focus on bread quality parameters. The effects of specific power input (1–5 kW/kg) and various parallel plate treatment chamber configurations were evaluated regarding resulting physico-chemical parameters. Ohmic baking at high power inputs (5 kW/kg) reduced baking times by 98 % compared to conventional baking. Concerning product quality, however, lower power input levels (<span><math><mrow><mo>≤</mo></mrow></math></span> 3 kW/kg) revealed specific benefits in terms of a more uniform crumb structure and higher degrees of starch cooking. Treatment chambers with thick electrodes (<span><math><mrow><mo>≥</mo></mrow></math></span> 5 mm) and decreased product surface area were associated with treatment inhomogeneities that resulted in higher product losses and lower bread volume. The results underline the importance of power input and treatment chamber design as control tools for tailored ohmic baking concepts to attain specific product properties. The necessity for a subsequent holding time after the heating phase was ascertained to achieve product quality comparable to conventionally baked breads.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112493"},"PeriodicalIF":5.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-19DOI: 10.1016/j.jfoodeng.2025.112485
Yuhe Fan , Lixun Zhang , Canxing Zheng , Zekun Yang , Huaiyu Che , Zhenhan Wang , Feng Xue , Xingyuan Wang
<div><div>Accurate volume measurement and posture estimation of meals have significant applications in meal-assisting robotics, food engineering, and food analysis. Traditional multi-view image acquisition techniques have proven effective in reconstructing the 3D morphology of meals. However, these methods encounter significant challenges when applied to real-time posture and volume estimation for meal-assisting robots due to computational complexity and time constraints. Furthermore, the multi-view image acquisition methods require precise calibration and synchronization of multiple cameras, which can be cumbersome and impractical in dynamic environments of meal-assisting robots. Moreover, the irregular shapes of dinner plates and complex rheological properties of fluid and solid foods pose substantial hurdles to achieving accurate measurements. Aiming at the above problems, this paper proposes a new method for fitting, posture estimation, and volume measurement of multiple classes of foods from a single viewpoint (FPV-MCFs). The method utilizes the RGB-D images of meals from a single viewpoint as input to reconstruct and fit different kinds of meals in three dimensions and then estimates the posture and volume of each meal separately by combining with the geometric models of meals. Specifically, for the non-Newtonian fluid sticky meals (non-Newtonian FSM) and non-Newtonian fluid-solid interaction sticky meals (non-Newtonian FSISM), the principal component analysis (PCA), iterative closest point algorithm (ICP), and optimization method with chamfer distance as the objective function are used in this paper to fit the point cloud of meals into a plate-like sector geometric model. For block meals (BM) and diced mixed meals (DMM), the least squares and randomized sampling consistency (RANSAC) algorithms are used to fit them to get the sphere and super-ellipsoid models, respectively. Finally, the volume and posture of each meal are estimated by combining the geometric approach with the FPV-MCFs algorithm, respectively. To evaluate the performance of the FPV-MCFs algorithm, some comprehensive measurement experiments of the actual volumes and actual postures of multiple meals are carried out, which cover single classes, mixed classes, and different orientations. The experimental results show that the FPV-MCFs algorithm exhibits smaller absolute relative deviations and average deviations in both volume measurement and posture estimation of meals. Specifically, the FPV-MCFs algorithm achieves 2.95% and 2.53% in <span><math><mrow><mover><mrow><mi>A</mi><mi>R</mi><mi>E</mi><mrow><mo>(</mo><mi>V</mi><mo>)</mo></mrow></mrow><mo>‾</mo></mover></mrow></math></span> and <span><math><mrow><mover><mrow><mi>ε</mi><mi>β</mi></mrow><mo>‾</mo></mover></mrow></math></span> metrics for non-Newtonian FSM or non-Newtonian FSISM, respectively, and 6.6 ms and 1.2 ms in processing time metrics for DM and DMM, respectively. Moreover, experiments involving different voxel num
{"title":"Measuring posture and volume of meals for meal-assisting robotics","authors":"Yuhe Fan , Lixun Zhang , Canxing Zheng , Zekun Yang , Huaiyu Che , Zhenhan Wang , Feng Xue , Xingyuan Wang","doi":"10.1016/j.jfoodeng.2025.112485","DOIUrl":"10.1016/j.jfoodeng.2025.112485","url":null,"abstract":"<div><div>Accurate volume measurement and posture estimation of meals have significant applications in meal-assisting robotics, food engineering, and food analysis. Traditional multi-view image acquisition techniques have proven effective in reconstructing the 3D morphology of meals. However, these methods encounter significant challenges when applied to real-time posture and volume estimation for meal-assisting robots due to computational complexity and time constraints. Furthermore, the multi-view image acquisition methods require precise calibration and synchronization of multiple cameras, which can be cumbersome and impractical in dynamic environments of meal-assisting robots. Moreover, the irregular shapes of dinner plates and complex rheological properties of fluid and solid foods pose substantial hurdles to achieving accurate measurements. Aiming at the above problems, this paper proposes a new method for fitting, posture estimation, and volume measurement of multiple classes of foods from a single viewpoint (FPV-MCFs). The method utilizes the RGB-D images of meals from a single viewpoint as input to reconstruct and fit different kinds of meals in three dimensions and then estimates the posture and volume of each meal separately by combining with the geometric models of meals. Specifically, for the non-Newtonian fluid sticky meals (non-Newtonian FSM) and non-Newtonian fluid-solid interaction sticky meals (non-Newtonian FSISM), the principal component analysis (PCA), iterative closest point algorithm (ICP), and optimization method with chamfer distance as the objective function are used in this paper to fit the point cloud of meals into a plate-like sector geometric model. For block meals (BM) and diced mixed meals (DMM), the least squares and randomized sampling consistency (RANSAC) algorithms are used to fit them to get the sphere and super-ellipsoid models, respectively. Finally, the volume and posture of each meal are estimated by combining the geometric approach with the FPV-MCFs algorithm, respectively. To evaluate the performance of the FPV-MCFs algorithm, some comprehensive measurement experiments of the actual volumes and actual postures of multiple meals are carried out, which cover single classes, mixed classes, and different orientations. The experimental results show that the FPV-MCFs algorithm exhibits smaller absolute relative deviations and average deviations in both volume measurement and posture estimation of meals. Specifically, the FPV-MCFs algorithm achieves 2.95% and 2.53% in <span><math><mrow><mover><mrow><mi>A</mi><mi>R</mi><mi>E</mi><mrow><mo>(</mo><mi>V</mi><mo>)</mo></mrow></mrow><mo>‾</mo></mover></mrow></math></span> and <span><math><mrow><mover><mrow><mi>ε</mi><mi>β</mi></mrow><mo>‾</mo></mover></mrow></math></span> metrics for non-Newtonian FSM or non-Newtonian FSISM, respectively, and 6.6 ms and 1.2 ms in processing time metrics for DM and DMM, respectively. Moreover, experiments involving different voxel num","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112485"},"PeriodicalIF":5.3,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183534","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}
Pub Date : 2025-01-18DOI: 10.1016/j.jfoodeng.2025.112474
I. Sam Saguy , Cristina L.M. Silva , Eli Cohen
The rapid advancement of science and technology, driven by digitalization and artificial intelligence, underscores the need to reevaluate food science, technology, and engineering (FST&E) education. A global study with 688 respondents examined key challenges and opportunities in this evolving field, gathering input from professionals and students in Africa, China, Eastern and Western Europe, USA & Canada, and South America & Mexico. The study aimed to identify strategies such as hybrid teaching, project-based learning, interdisciplinary collaboration, and internships to meet future educational demands. Principal Component Analysis highlighted two key factors: professional development (PC1), which grouped adaptability, employability, soft skills, and apprenticeships; and future-oriented education (PC2), clustering hybrid teaching, curriculum revisions, nutrition integration, and research projects. African participants placed greater emphasis on these factors compared to USA respondents. A notable finding was the lower engagement of food engineering (FE) professionals with both principal component factors compared to their food science and technology (FST) counterparts. This suggests a possible resistance to change or higher satisfaction with the status quo, which could limit FE professionals' ability to meet future business and innovation requirements. This is concerning given the rapid technological and science progress and the necessity for new curricula that foster innovation. The study underscores the importance of adapting FST&E education to regional differences and evolving industry expectations. It advocates for strategic educational transformations that integrate emerging technologies, interdisciplinary approaches, and practical learning opportunities to equip students for future challenges and capitalize on new opportunities in the FST&E field.
{"title":"Innovative curriculum strategies for managing the future of food science, technology and engineering education","authors":"I. Sam Saguy , Cristina L.M. Silva , Eli Cohen","doi":"10.1016/j.jfoodeng.2025.112474","DOIUrl":"10.1016/j.jfoodeng.2025.112474","url":null,"abstract":"<div><div>The rapid advancement of science and technology, driven by digitalization and artificial intelligence, underscores the need to reevaluate food science, technology, and engineering (FST&E) education. A global study with 688 respondents examined key challenges and opportunities in this evolving field, gathering input from professionals and students in Africa, China, Eastern and Western Europe, USA & Canada, and South America & Mexico. The study aimed to identify strategies such as hybrid teaching, project-based learning, interdisciplinary collaboration, and internships to meet future educational demands. Principal Component Analysis highlighted two key factors: professional development (PC1), which grouped adaptability, employability, soft skills, and apprenticeships; and future-oriented education (PC2), clustering hybrid teaching, curriculum revisions, nutrition integration, and research projects. African participants placed greater emphasis on these factors compared to USA respondents. A notable finding was the lower engagement of food engineering (FE) professionals with both principal component factors compared to their food science and technology (FST) counterparts. This suggests a possible resistance to change or higher satisfaction with the status quo, which could limit FE professionals' ability to meet future business and innovation requirements. This is concerning given the rapid technological and science progress and the necessity for new curricula that foster innovation. The study underscores the importance of adapting FST&E education to regional differences and evolving industry expectations. It advocates for strategic educational transformations that integrate emerging technologies, interdisciplinary approaches, and practical learning opportunities to equip students for future challenges and capitalize on new opportunities in the FST&E field.</div></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"392 ","pages":"Article 112474"},"PeriodicalIF":5.3,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183509","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}
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