Journal of Food Process Engineering最新文献

{"title":"Advances in 3D Food Printing: Exploring Technological Progress and Rheology of 3D Printing Ink","authors":"Anupriya Senthilkumaran,&nbsp;Vijayasri Kadirvel,&nbsp;Kirthika Suresh,&nbsp;Nandhini Devi Ganesan","doi":"10.1111/jfpe.70187","DOIUrl":"https://doi.org/10.1111/jfpe.70187","url":null,"abstract":"<div>\u0000 \u0000 <p>Additive manufacturing, also known as three-dimensional food printing (3DFP), is a rapidly advancing digital technology that constructs food layer by layer. It has recently surged in popularity, influencing sectors beyond the food industry. The ability to create complex designs through this technology is correlated with the rheological properties, mechanical characteristics, and particle sizes, which are determined by the selection and interaction of ingredients. Despite the significant potential of 3DFP, the relationship between printing materials and their associated processing parameters remains limited and insufficiently documented. This review consolidates current scientific advancements related to 3DFP with detailed insights into developing food products using cereals, pulses, chocolates, fruits, and vegetables. Additionally, a comprehensive overview of printing technologies, post-processing methods, and their impact on the rheological characteristics of food inks has been discussed. The authors highlight the key benefits, major findings, and promising applications of 3D-printed food products in the food sector. The efficiency and precision of 3DFP coupled with its capacity for innovation will spark interest among industries in the future.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Semantic Segmentation Method for Segmenting Chicken Parts Based on a Lightweight DeepLabv3+","authors":"Yan Chen,&nbsp;Chenchen Xu,&nbsp;Peng Zhang,&nbsp;Xianhui Peng,&nbsp;Dandan Fu,&nbsp;Zhigang Hu","doi":"10.1111/jfpe.70180","DOIUrl":"https://doi.org/10.1111/jfpe.70180","url":null,"abstract":"<div>\u0000 \u0000 <p>Research on poultry part partitioning techniques is crucial for the advancement of automated poultry partitioning equipment. In this study, a semantic segmentation method for chicken parts, based on a lightweight DeepLabv3+, was introduced to cater to real-time and precise requirements of segmenting varying poultry sizes. Initially, the backbone network was replaced with an improved lightweight MobileNetV2, enhancing the predictive speed and decreasing computational parameters. Subsequently, the SENet was incorporated, enhancing the capacity to discern high-level features and negate irrelevant information. Furthermore, two shallow feature layers of different scales were integrated into the decoder, augmenting the richness of shallow features and mitigating inaccuracies at segmentation edges. Finally, the Dice Loss and Cross Entropy Loss (CE Loss) functions were combined to minimize the imbalance between positive and negative samples. Experimental findings demonstrated that the lightweight DeepLabv3+ improved the MIoU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) scores of the original model by 5.42% and 3%, respectively, and amplified the detection speed by 1.89 times. Remarkably, the model size was a mere 10.95% of the original, indicating substantial enhancements in segmentation accuracy and detection speed. Therefore, the proposed algorithm could potentially provide certain technical insights for automatic segmentation of different poultry.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination of Hydrocolloid and Ultrasound Assistance to Improve the Stability of Lemon Juice After Thawing","authors":"Jingyi Huo,&nbsp;Min Zhang,&nbsp;Qing Sun,&nbsp;Yiping Chen","doi":"10.1111/jfpe.70181","DOIUrl":"https://doi.org/10.1111/jfpe.70181","url":null,"abstract":"<div>\u0000 \u0000 <p>In order to reduce the delamination and flocculation of frozen lemon juice after thawing, this study investigated the effect of ultrasound-assisted hydrocolloids on the stability of lemon juice after thawing. By comparing the effects of different hydrocolloids, it was found that pectin, xanthan gum, and konjac gum significantly improved the stability of lemon juice, whereas other hydrocolloids such as gum arabic and acacia bean gum were not effective in improving the stability and even exacerbated the flocculation and delamination phenomenon in some cases. In addition, the effects of pectin, xanthan gum, and konjac gum were studied in depth. The effects of different combinations of concentrations and hydrophilic colloids were evaluated by determining parameters such as particle size, zeta potential, viscosity, and cloud stability of the juices using one-way and response surface tests. The results showed that the use of any one hydrocolloid alone could not completely solve the problems such as delamination and flocculation of fruit juice. The hydrophilic colloids were more effective when used in combination, and the optimal composite stabilizer additions were when the additions of pectin, xanthan gum, and konjac gum were 0.32%, 0.25%, and 0.15%, respectively, at which time the stability of lemon juice after thawing was better. In addition, the use of ultrasound-assisted treatment also significantly improved the stability of lemon juice, and the use of 1500 W ultrasound for 10 min resulted in a 15.4% increase in its cloud stability. Practical Application: This study provides a simple and novel method to improve the stability of frozen lemon juice after thawing. Using the addition of hydrophilic colloids combined with ultrasonic treatment, the stability of lemon juice after thawing can be improved, which provides a new method to improve the quality of fruit juice products.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Study on Shell-Breaking Characteristics and Crack Formation Patterns of Walnuts (Juglans Sigillata D.) Under Static Loading","authors":"Tianyu Zhang,&nbsp;Jingwei Wang,&nbsp;Daochun Xu,&nbsp;Xiaopeng Bai,&nbsp;Wenbin Li,&nbsp;Jiale Cao,&nbsp;Xu Niu","doi":"10.1111/jfpe.70182","DOIUrl":"https://doi.org/10.1111/jfpe.70182","url":null,"abstract":"<div>\u0000 \u0000 <p>Obtaining highly intact walnut kernels after shell-breaking is a key step in the deep processing of walnuts. <i>Juglans sigillata</i> D., characterized by tightly bonded internal septa and irregular shell geometry, poses significant challenges for achieving kernel integrity. In this study, a three-dimensional walnut model was developed using 120 walnut samples. Four types of compression heads—flat, concave groove-shaped, inverted conical, and protruding stud-shaped—were employed to perform static loading tests at a constant rate of 10 mm/min from three directions: the head, belly, and umbilical regions. The shell-breaking characteristics and crack formation patterns were systematically investigated. Results showed that the shell-breaking process involves an initial elastic deformation followed by plastic failure. The initial shell-breaking force ranged from 172.3 N to 739.7 N, significantly influenced by both compression head type and loading direction. The highest probability of obtaining intact kernels (over 70%) was achieved by applying a belly-directional load using the concave groove-shaped compression head. Initial crack formation patterns were categorized into six types across three main models, with longitudinal cracks perpendicular to the suture being most favorable for intact kernel acquisition. A finite element-based predictive model for crack initiation demonstrated an accuracy of 85.3%, showing high consistency with observed stress concentration zones and actual crack propagation paths. This study elucidates the mechanical behavior and crack formation patterns during walnut shell-breaking and highlights the relationship between crack formation and kernel integrity, offering valuable insights for the development of efficient walnut shelling equipment.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fabrication of Composite Membrane via Electrospinning: A Novel Membrane Bioreactor Design for Enzymatic Synthesis of Prebiotic Carbohydrates","authors":"Haci Ali Gulec,&nbsp;Kadir Cinar,&nbsp;Ufuk Bagci,&nbsp;Pelin Onsekizoglu Bagci","doi":"10.1111/jfpe.70168","DOIUrl":"https://doi.org/10.1111/jfpe.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents the fabrication and performance assessment of a composite membrane produced via electrospinning to optimize separation efficiency in the enzymatic synthesis of galactooligosaccharides (GOS). GOS production, facilitated by β-galactosidase, is often hindered by inhibitory effects from monosaccharides like glucose and galactose. Conventional nanofiltration (NF) systems struggle with saccharide selectivity, leading to substrate and product losses. In this study, an electrospun composite membrane, composed of Nylon 6/6, polyethyleneimine (PEI), and polyvinyl alcohol (PVA), was developed and compared with a commercial NP030 NF membrane having a polyethersulfone structure. The production process (PP-III) using the composite electrospun membrane demonstrated improved lactose retention compared to the process (PP-II) using the commercial membrane. The PP-III method achieved a maximum GOS yield of 37.2 g L⁻¹, close to the performance of the conventional stirred-tank reactor system (PP-I) and 18% higher than the yield from the PP-II. Additionally, the composite membrane exhibited an initial permeate flux of 25 kg m² h⁻¹, approximately three times lower than that of the commercial membrane at 78 kg m² h⁻¹. This lower flux minimized lactose loss, allowing more substrate to remain within the reactor, which led to an increased GOS yield and improved reaction efficiency. The PP-III method supported higher GOS yields at early lactose conversion (15%–35%). At later stages (over 45% conversion), monosaccharide removal enhanced lactose hydrolysis but reduced GOS productivity due to lactose loss. Overall, the composite electrospun membrane offers a promising solution for scalable, cost-effective GOS production, balancing enzyme activity and product yield while reducing substrate waste.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Elliptical Fourier Analysis and Color Properties in Hazelnut Classification Using Machine Learning Algorithms","authors":"Laith Ghanem,&nbsp;Alper Taner,&nbsp;Hüseyin Sauk","doi":"10.1111/jfpe.70179","DOIUrl":"https://doi.org/10.1111/jfpe.70179","url":null,"abstract":"<p>The accurate classification of hazelnut cultivars is critical for ensuring product consistency, quality control, and market competitiveness in the food industry. Conventional identification methods remain manual, time-consuming, and error-prone, highlighting the need for automated alternatives. This study presents a novel, real-time machine vision system for classifying 11 hazelnut cultivars using a single side-view image. The proposed approach integrates three complementary feature extraction techniques: Elliptical Fourier Analysis (EFA) for contour and shape decomposition, circular masking for curvature quantification, and brown color gradient analysis for surface tone assessment. The extracted features—fully normalized and dimensionless to account for variations in imaging angle, distance, and nut positioning—were classified using three machine learning algorithms: Support Vector Machine with Radial Basis Function (SVM-RBF), Multilayer Perceptron (MLP), and Extreme Learning Machine (ELM-RBF). Among the classifiers, SVM-RBF achieved the highest performance with an F1-score of 0.92 for multi-view images and 0.89 for side-view only. MLP and ELM-RBF followed with competitive yet slightly lower scores. The system demonstrated high robustness, computational efficiency, and interpretability. Overall, the proposed method offers a lightweight, scalable, and non-destructive solution for hazelnut cultivar classification and demonstrates strong potential for real-time deployment in industrial sorting lines and embedded systems in precision agriculture.</p>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfpe.70179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking of Infrared Emitters for Small-Scale Milk Heating: Performance and Heat Flux Analysis","authors":"Gursharn Singh Saini,&nbsp;Prashant Saurabh Minz,&nbsp;Chitranayak Sinha","doi":"10.1111/jfpe.70173","DOIUrl":"https://doi.org/10.1111/jfpe.70173","url":null,"abstract":"<div>\u0000 \u0000 <p>An infrared (IR) based heating system was developed, and the suitability of different IR emitters was explored for small scale milk heating. The heating system was designed specifically to fit the different infrared emitters: Ceramic Full Trough Emitter (CFTE), Halogen Infrared Emitter (HIRE), and Pillared Quartz Emitter (PQE). The interface temperatures, rate of change of temperature, time required for heating, convective heat flux, radiative heat flux, total heat flux, energy consumption, and thermal efficiency during milk heating (10°C to 90°C) were analyzed. Analysis of interface temperatures provided key insights into the temperature gradients responsible for driving heat transfer across different regions of the system. The maximum temperature at the emitter–air interface varied between 733°C and 790°C, while the heating rate ranged from 110°C/min to 340°C/min, showing rapid heat generation. Heat transfer characteristics exhibited high radiative heat flux (26,869–64,243.58 W/m²) compared to convective heat flux (6778–9077 W/m²). These differences highlight the critical role of emitter selection in achieving the optimal balance between radiative and convective heat transfer for specific thermal applications. The time required to attain the target temperature of 90°C spanned from 15.25 to 19.87 min. The results validate the applicability of IR heating technology for small scale milk heating.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Light From Compound Light Emitting Diodes on Nutrient Content and Storage Quality of Broccoli Plants","authors":"Hong Liu,&nbsp;Qi Wang,&nbsp;Shanwen Zhou,&nbsp;Yage Xing,&nbsp;Qinglian Xu,&nbsp;Quan Zhang,&nbsp;Zhiwei Chen,&nbsp;Yuan Tian,&nbsp;Xinrou Zhou","doi":"10.1111/jfpe.70167","DOIUrl":"https://doi.org/10.1111/jfpe.70167","url":null,"abstract":"<div>\u0000 \u0000 <p>This study systematically investigated the effects of red-blue and red-green LED light durations on postharvest broccoli quality under low-temperature storage, proposing an innovative approach for vegetable preservation through non-illumination parameter optimization in LED applications. The results showed that the storage period of broccoli could be extended by 4 days at a temperature of 10°C using red-blue light for 24 h every 48 h. The 10-days storage period using red-blue light revealed a sensory score of 61.8, 0.117 mg/g chlorophyll content, 14.7% weight-loss, 0.46% titratable acid content, 26.4 mg/100 g vitamin C (VC) content, and a color difference of 10.5. The 10-days storage period with red-green light exposure for 24 h every 48 h demonstrated a sensory score of 56.3, 0.102 mg/g chlorophyll content, 13.23% weight loss, 0.33% titratable acid content, 25.53 mg/100 g VC content, and a color difference of 13.17. Therefore, red-blue light irradiation for 24 h every 48 h at 10°C enhanced the appearance of broccoli, effectively improving its commercial value. Furthermore, this process effectively reduced VC, chlorophyll, and the degradation of titratable acids.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-Destructive Estimation of Water Fractions by Machine Learning Models During Freeze-Drying","authors":"Weijie Li,&nbsp;Shoaib Younas,&nbsp;Farhan Ali,&nbsp;Ukasha Arqam,&nbsp;Muhammad Safeer Abbas,&nbsp;Muhammad Yousaf,&nbsp;Zeshan Ali,&nbsp;Mian Anjum Murtaza,&nbsp;Jin Tao,&nbsp;Muhammad Imran","doi":"10.1111/jfpe.70174","DOIUrl":"https://doi.org/10.1111/jfpe.70174","url":null,"abstract":"<div>\u0000 \u0000 <p>Prediction of water status in post-harvested agriculture products enduring drying is critical to maintain storage conditions. This study focused on the efficiency of multispectral imaging a novel nondestructive analytical tool by combining various machine-learning models such as Feedforward Neural Network (FNN), Decision Tree Regression, Support Vector Regression, and k-nearest neighbors in the prediction of water fractions during freeze-drying of mushrooms. Spectra from multispectral imaging of the Vis–NIR (405–970 nm) region were combined with machine learning models for the quantification of free water (FW), immobilized water (IM), bound water (BW) and total water (TW) during freeze-drying (FD) of shiitake mushrooms. Water distribution tests through low-field nuclear magnetic resonance demonstrated that 36 h of drying sublimates 90.55% freezable water. The modeling approach performed well, and FNN was found to be the best compared to the others. Its prediction efficiency was 97.77% and 95.95% in BW and TW, respectively. In terms of root mean square error, this model obtained the lowest prediction errors compared to the rest of the models for all water fractions. However, the FNN model prediction deviation is determined with the best bias value of 0.1312 for FW. This study provides an excellent platform in predicting the water status and food quality with a rapid and nondestructive multispectral Vis–NIR spectroscopic approach during drying. The techniques successfully handled the complex spectral data when combined with chemometrics and could be useful in the future for the detection of the chemical composition of agricultural products.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral Reconstruction in Combination With a Crown Porcupine Optimization-Optimized Support Vector Regression (CPO-SVR) Machine Learning Model for Predicting the Total Acid Content of Daqu","authors":"Yuanyuan Xia,&nbsp;Jianping Tian,&nbsp;Dan Huang,&nbsp;Jun Wang,&nbsp;Kangling He,&nbsp;Liangliang Xie,&nbsp;Xinjun Hu,&nbsp;Haili Yang","doi":"10.1111/jfpe.70172","DOIUrl":"https://doi.org/10.1111/jfpe.70172","url":null,"abstract":"<div>\u0000 \u0000 <p>The total acid content (TAC) of Daqu during fermentation is an important index for evaluating the quality of Daqu. In order to overcome the problem of low detection accuracy of RGB image and strict environmental requirements of HSI detection. Therefore, this study proposes a real-time and rapid detection method for the total acid content of Daqu by integrating spectral reconstruction technology with an optimized support vector regression (SVR) model. In this approach, RGB image data are acquired using an industrial camera, and hyperspectral data of the sample are generated via the MST++ reconstruction algorithm. These data serve as the input for the Daqu total acid content detection model. Additionally, the Crown Porcupine Optimization (CPO) algorithm is employed to optimize the parameters of the SVR model, thereby establishing a predictive model for the total acid content of Daqu. The experimental results show that the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msubsup>\u0000 <mi>R</mi>\u0000 <mi>p</mi>\u0000 <mn>2</mn>\u0000 </msubsup>\u0000 </mrow>\u0000 <annotation>$$ {R}_p^2 $$</annotation>\u0000 </semantics></math> of the CPO-SVR model based on the reconstructed hyperspectral was 0.9449, the RPD was 4.2592, and the RMSEP was 0.0332. When compared to the CPO-SVR model based on original hyperspectral, the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msubsup>\u0000 <mi>R</mi>\u0000 <mi>p</mi>\u0000 <mn>2</mn>\u0000 </msubsup>\u0000 </mrow>\u0000 <annotation>$$ {R}_p^2 $$</annotation>\u0000 </semantics></math> and the RPD were only 0.0185 and 1.0335 lower, while the RMSEP increased by 0.0062. The study showed that the MST++ hyperspectral reconstruction algorithm combined with the CPO-SVR model can realize real-time and rapid detection of the TAC of Daqu.</p>\u0000 </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}