Pub Date : 2025-12-11DOI: 10.1016/j.jfca.2025.108778
Muhammad Imran , Bingfang Huang , Muhammad Adil , Kainat Aleem , Muhammad Waseem , Muhammad Faisal Manzoor , Muhammad Rizwan Javed , Muhammad Saleem , Nabindra Kumar Shrestha , Gulsah Karabulut , Gulden Goksen
Plant-based meat analogs (PBMAs) have gained increasing attention in recent years as sustainable alternatives to animal-derived products. Nutritional, environmental, and ethical considerations drive their development. The advancement of PBMAs relies on understanding the compositional and functional characteristics of plant-derived proteins that determine product quality. This review critically examines emerging novel protein sources, including legumes, hemp, zein, duckweed, and microalgae, emphasizing their amino acid balance, digestibility, and techno-functional properties relevant to meat analog formulation. The compositional effects of processing technologies such as extrusion, shear structuring, and 3D printing on protein structure and nutrient retention are also discussed. Furthermore, the nutritional profile, bioactive potential, and environmental implications of PBMAs are evaluated in relation to consumer perception and market acceptance. Overall, this study provides an integrated overview linking food composition, functionality, and sustainability to advance the next generation of plant-based meat analogs.
{"title":"Harnessing novel proteins for advancing plant-based meat analogs","authors":"Muhammad Imran , Bingfang Huang , Muhammad Adil , Kainat Aleem , Muhammad Waseem , Muhammad Faisal Manzoor , Muhammad Rizwan Javed , Muhammad Saleem , Nabindra Kumar Shrestha , Gulsah Karabulut , Gulden Goksen","doi":"10.1016/j.jfca.2025.108778","DOIUrl":"10.1016/j.jfca.2025.108778","url":null,"abstract":"<div><div>Plant-based meat analogs (PBMAs) have gained increasing attention in recent years as sustainable alternatives to animal-derived products. Nutritional, environmental, and ethical considerations drive their development. The advancement of PBMAs relies on understanding the compositional and functional characteristics of plant-derived proteins that determine product quality. This review critically examines emerging novel protein sources, including legumes, hemp, zein, duckweed, and microalgae, emphasizing their amino acid balance, digestibility, and techno-functional properties relevant to meat analog formulation. The compositional effects of processing technologies such as extrusion, shear structuring, and 3D printing on protein structure and nutrient retention are also discussed. Furthermore, the nutritional profile, bioactive potential, and environmental implications of PBMAs are evaluated in relation to consumer perception and market acceptance. Overall, this study provides an integrated overview linking food composition, functionality, and sustainability to advance the next generation of plant-based meat analogs.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108778"},"PeriodicalIF":4.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746926","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 : 2025-12-11DOI: 10.1016/j.jfca.2025.108779
Hye-Jin Cho , Ju-Hyun Park , Young-Ran Song , Mina. K. Kim
This study investigated the volatile profiles of traditional Korean doenjang made from meju sourced in six regions, using HS-SPME-GC–MS and electronic nose (E-nose) analysis. A total of 55 volatile compounds were identified and grouped into 12 chemical categories. Key contributors to compositional variation included acetic acid, 3-methylbutanoic acid, and trimethylpyrazine. Notably, region-specific volatiles such as 1-octen-3-ol and 2-methylbutanal were detected only in selected samples, suggesting potential as origin markers. E-nose data clearly distinguished samples by origin and showed strong correlations with GC–MS results via partial least squares regression (PLS-R). These findings highlight the combined use of GC–MS and E-nose as a powerful strategy for the classification, authentication, and quality assessment of traditionally fermented foods.
{"title":"Compositional differentiation of volatile compounds in traditional Korean doenjang according to meju origin: GC–MS and E-nose correlation study","authors":"Hye-Jin Cho , Ju-Hyun Park , Young-Ran Song , Mina. K. Kim","doi":"10.1016/j.jfca.2025.108779","DOIUrl":"10.1016/j.jfca.2025.108779","url":null,"abstract":"<div><div>This study investigated the volatile profiles of traditional Korean doenjang made from meju sourced in six regions, using HS-SPME-GC–MS and electronic nose (E-nose) analysis. A total of 55 volatile compounds were identified and grouped into 12 chemical categories. Key contributors to compositional variation included acetic acid, 3-methylbutanoic acid, and trimethylpyrazine. Notably, region-specific volatiles such as 1-octen-3-ol and 2-methylbutanal were detected only in selected samples, suggesting potential as origin markers. E-nose data clearly distinguished samples by origin and showed strong correlations with GC–MS results via partial least squares regression (PLS-R). These findings highlight the combined use of GC–MS and E-nose as a powerful strategy for the classification, authentication, and quality assessment of traditionally fermented foods.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108779"},"PeriodicalIF":4.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747716","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 : 2025-12-11DOI: 10.1016/j.jfca.2025.108774
Yongxian Wang , Junsheng Liu , Kaisen Zhang , Yi Liu , Ruofei Liu , Bo Ma , Linlong Jing , Xinpeng Cao , Hongjian Zhang , Linlin Sun , Jinxing Wang
Accurate monitoring of kiwifruit storage time is crucial for reducing postharvest losses. A method combining hyperspectral imaging (HSI) and convolutional neural networks (CNN) was developed for storage day identification. Three cultivars— ‘Xuxiang’, ‘Cuixiang’, and ‘Hongyang’ —were imaged at 0, 3, 6, and 9 days of storage. Principal component analysis extracted the first three components, and clustering plots highlighted spectral differences. Competitive adaptive reweighted sampling (CARS) and sequential forward selection (SFS) were employed to select characteristic bands. Classification models based on partial least squares discriminant analysis, least squares support vector machine, random forest, and CNN were constructed. The CARS–CNN model achieved 100 % accuracy on the calibration set and prediction accuracies of 95.0 %, 97.5 %, and 97.5 % for three cultivars, respectively, outperforming other models. These results validate the reliability of HSI–CNN for classifying kiwifruit storage day and support the development of online fruit quality monitoring systems for storage time management.
{"title":"Classification of kiwifruit storage day based on hyperspectral imaging and convolutional neural networks","authors":"Yongxian Wang , Junsheng Liu , Kaisen Zhang , Yi Liu , Ruofei Liu , Bo Ma , Linlong Jing , Xinpeng Cao , Hongjian Zhang , Linlin Sun , Jinxing Wang","doi":"10.1016/j.jfca.2025.108774","DOIUrl":"10.1016/j.jfca.2025.108774","url":null,"abstract":"<div><div>Accurate monitoring of kiwifruit storage time is crucial for reducing postharvest losses. A method combining hyperspectral imaging (HSI) and convolutional neural networks (CNN) was developed for storage day identification. Three cultivars— ‘Xuxiang’, ‘Cuixiang’, and ‘Hongyang’ —were imaged at 0, 3, 6, and 9 days of storage. Principal component analysis extracted the first three components, and clustering plots highlighted spectral differences. Competitive adaptive reweighted sampling (CARS) and sequential forward selection (SFS) were employed to select characteristic bands. Classification models based on partial least squares discriminant analysis, least squares support vector machine, random forest, and CNN were constructed. The CARS–CNN model achieved 100 % accuracy on the calibration set and prediction accuracies of 95.0 %, 97.5 %, and 97.5 % for three cultivars, respectively, outperforming other models. These results validate the reliability of HSI–CNN for classifying kiwifruit storage day and support the development of online fruit quality monitoring systems for storage time management.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108774"},"PeriodicalIF":4.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747719","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 : 2025-12-11DOI: 10.1016/j.jfca.2025.108781
Chao Zhao , Jiaheng Zhang , Duangsamorn Suthisut , Chunqi Bai , Lei Yan , Dianxuan Wang , Jianhua Lü , Peng Li
To address the issues of poor model generalization caused by sample scarcity and class imbalance in hyperspectral detection of storage Astragalus pests, this study proposes a detection method that integrates near-infrared hyperspectral imaging with Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) data augmentation. By constructing a dataset containing 1023 samples, the study systematically compared four generative models: Variational Autoencoder (VAE), Generative Adversarial Network (GAN), WGAN, and WGAN-GP. The results showed that WGAN-GP performed best in terms of Root Mean Square Error (RMSE), Maximum Mean Discrepancy (MMD), and Sliced Wasserstein Distance (SWD) evaluation metrics, and the generated data highly overlapped with the real data in Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) visualizations. Through systematic optimization, the optimal data augmentation ratio was determined to be 0.75 times, under which the performance of five classifiers—Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Transformer, and Partial Least Squares-Discriminant Analysis (PLS-DA)—was significantly improved. This study confirms that WGAN-GP data augmentation can effectively address the challenges of small sample sizes and class imbalance, providing a reliable technical solution for intelligent and non-destructive pest grading of Astragalus and other traditional Chinese medicinal materials.
{"title":"WGAN-GP augmented hyperspectral framework for pest infestation grading in stored Astragalus membranaceus","authors":"Chao Zhao , Jiaheng Zhang , Duangsamorn Suthisut , Chunqi Bai , Lei Yan , Dianxuan Wang , Jianhua Lü , Peng Li","doi":"10.1016/j.jfca.2025.108781","DOIUrl":"10.1016/j.jfca.2025.108781","url":null,"abstract":"<div><div>To address the issues of poor model generalization caused by sample scarcity and class imbalance in hyperspectral detection of storage Astragalus pests, this study proposes a detection method that integrates near-infrared hyperspectral imaging with Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) data augmentation. By constructing a dataset containing 1023 samples, the study systematically compared four generative models: Variational Autoencoder (VAE), Generative Adversarial Network (GAN), WGAN, and WGAN-GP. The results showed that WGAN-GP performed best in terms of Root Mean Square Error (RMSE), Maximum Mean Discrepancy (MMD), and Sliced Wasserstein Distance (SWD) evaluation metrics, and the generated data highly overlapped with the real data in Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) visualizations. Through systematic optimization, the optimal data augmentation ratio was determined to be 0.75 times, under which the performance of five classifiers—Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Transformer, and Partial Least Squares-Discriminant Analysis (PLS-DA)—was significantly improved. This study confirms that WGAN-GP data augmentation can effectively address the challenges of small sample sizes and class imbalance, providing a reliable technical solution for intelligent and non-destructive pest grading of Astragalus and other traditional Chinese medicinal materials.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108781"},"PeriodicalIF":4.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747717","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 : 2025-12-09DOI: 10.1016/j.jfca.2025.108770
Xiang Li , Shijia Wu , Qianjin Li , Jianlin Li , Zhouping Wang
Foodborne pathogens represent a major threat to food safety and public health. Escherichia coli O157:H7, widely present in sources such as bovine feces, undercooked ground beef, fresh produce, and drinking water, can cause diarrhea, hemorrhagic colitis, and even death upon infection. Therefore, developing sensitive and portable detection methods is crucial for food safety monitoring and outbreak response. This study established a detection method combining loop-mediated isothermal amplification (LAMP) with a CRISPR/Cas12b system for the highly sensitive and rapid detection of E. coli O157:H7. Using elaborately designed LAMP primers and Cas12b sgRNA targeting the FliC gene, we developed and optimized two assay formats: a two-tube method and a one-tube method. The two-tube method, involving separate LAMP amplification followed by CRISPR/Cas12b detection, demonstrated high sensitivity with a limit of detection (LOD) of 10 copies/μL for plasmid DNA. To prevent cross-contamination from tube opening, we further developed a one-tube system that integrates LAMP and CRISPR detection into a single closed-tube reaction, achieving an LOD of 10 copies/μL. At the same concentration, the two-tube method demonstrates a higher detection rate for plasmids, with both methods having the same limit of detection (LOD). Both methods exhibited excellent specificity, showing no cross-reactivity with other tested common foodborne bacteria. The established method is characterized by simple operation, low cost, rapid turnaround, and high accuracy, offering a reliable tool for on-site food safety monitoring and emergency detection.
{"title":"Novel detection strategies of Escherichia. coli O157:H7 based on CRISPR/Cas12b- and LAMP- platform","authors":"Xiang Li , Shijia Wu , Qianjin Li , Jianlin Li , Zhouping Wang","doi":"10.1016/j.jfca.2025.108770","DOIUrl":"10.1016/j.jfca.2025.108770","url":null,"abstract":"<div><div>Foodborne pathogens represent a major threat to food safety and public health. <em>Escherichia coli</em> O157:H7, widely present in sources such as bovine feces, undercooked ground beef, fresh produce, and drinking water, can cause diarrhea, hemorrhagic colitis, and even death upon infection. Therefore, developing sensitive and portable detection methods is crucial for food safety monitoring and outbreak response. This study established a detection method combining loop-mediated isothermal amplification (LAMP) with a CRISPR/Cas12b system for the highly sensitive and rapid detection of <em>E. coli</em> O157:H7. Using elaborately designed LAMP primers and Cas12b sgRNA targeting the <em>FliC</em> gene, we developed and optimized two assay formats: a two-tube method and a one-tube method. The two-tube method, involving separate LAMP amplification followed by CRISPR/Cas12b detection, demonstrated high sensitivity with a limit of detection (LOD) of 10 copies/μL for plasmid DNA. To prevent cross-contamination from tube opening, we further developed a one-tube system that integrates LAMP and CRISPR detection into a single closed-tube reaction, achieving an LOD of 10 copies/μL. At the same concentration, the two-tube method demonstrates a higher detection rate for plasmids, with both methods having the same limit of detection (LOD). Both methods exhibited excellent specificity, showing no cross-reactivity with other tested common foodborne bacteria. The established method is characterized by simple operation, low cost, rapid turnaround, and high accuracy, offering a reliable tool for on-site food safety monitoring and emergency detection.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108770"},"PeriodicalIF":4.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747718","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 : 2025-12-09DOI: 10.1016/j.jfca.2025.108769
Jie Zhu , Wenjie Yu , Yunping Wang , Yifan Zhang , Jingze Cao , Ruowen Wang , Qiuning Wang , Xiali Guo , Shuai Zhuang , Liping Luo
The metabolic regulatory mechanisms underlying quality variations in red ‘Fuji’ apples from different geographical origins remain unclear. In this study, we systematically analyzed red ‘Fuji’ apples from four geographical origins, identifying altitude as the primary driver of their quality variations. Red ‘Fuji’ apples from higher altitudes (HA, 1013–1037 m) showed significantly higher soluble sugar, acidity, and phenolics than those from lower altitudes (LA, 675–728 m). The key aroma compound 2-methylbutyl acetate was significantly elevated in HA, contributing to their enhanced fruity aroma, whereas hexanal and 1-hexanol were identified as the predominant contributors to the characteristic grassy notes in LA. Ferulic acid was a key differential compound in PCA, with significantly higher abundance in the HA group (PC1: 11.54 %; PC2: 9.42 %). Metabolic analysis revealed that flavonoid biosynthesis pathways as primary drivers of quality variation between different geographical origins. By identifying the critical roles of flavonoid biosynthesis and aroma compound accumulation in red ‘Fuji’ apples from different regions, this study clarifies the metabolic basis for their nutritional (phenolics) and sensory (fruity/grassy notes) differences. These findings inform both cultivation strategies and marketing practices, enabling producers to leverage “high altitude” labeling to highlight the superior flavor and nutritional benefits for consumers.
{"title":"Integrated physicochemical and metabolomic profiling reveals quality variations in red ‘Fuji’ apples from different geographical origins","authors":"Jie Zhu , Wenjie Yu , Yunping Wang , Yifan Zhang , Jingze Cao , Ruowen Wang , Qiuning Wang , Xiali Guo , Shuai Zhuang , Liping Luo","doi":"10.1016/j.jfca.2025.108769","DOIUrl":"10.1016/j.jfca.2025.108769","url":null,"abstract":"<div><div>The metabolic regulatory mechanisms underlying quality variations in red ‘Fuji’ apples from different geographical origins remain unclear. In this study, we systematically analyzed red ‘Fuji’ apples from four geographical origins, identifying altitude as the primary driver of their quality variations. Red ‘Fuji’ apples from higher altitudes (HA, 1013–1037 m) showed significantly higher soluble sugar, acidity, and phenolics than those from lower altitudes (LA, 675–728 m). The key aroma compound 2-methylbutyl acetate was significantly elevated in HA, contributing to their enhanced fruity aroma, whereas hexanal and 1-hexanol were identified as the predominant contributors to the characteristic grassy notes in LA. Ferulic acid was a key differential compound in PCA, with significantly higher abundance in the HA group (PC1: 11.54 %; PC2: 9.42 %). Metabolic analysis revealed that flavonoid biosynthesis pathways as primary drivers of quality variation between different geographical origins. By identifying the critical roles of flavonoid biosynthesis and aroma compound accumulation in red ‘Fuji’ apples from different regions, this study clarifies the metabolic basis for their nutritional (phenolics) and sensory (fruity/grassy notes) differences. These findings inform both cultivation strategies and marketing practices, enabling producers to leverage “high altitude” labeling to highlight the superior flavor and nutritional benefits for consumers.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108769"},"PeriodicalIF":4.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747720","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}
To address the challenges of accurately assessing the fermentation degree of black tea using traditional manual methods and issues such as sample destruction and low accuracy associated with other methods, we propose a model named CVK-Net, which integrates a Convolutional Neural Network (CNN), Vision Transformer (ViT), and Kolmogorov-Arnold Network (KAN). By obtaining sensor data and extracting RGB, HSV, and Lab color components from images, numerical data is constructed and aligned with image data to build a bimodal dataset. First, dynamic snake convolution was introduced to improve the dense connection block for extracting shallow image features, while KAN was introduced to extract numerical modal features. The ESE attention mechanism was then used to generate bimodal feature weights and achieved adaptive fusion. Finally, ViT was used to construct a bimodal global feature representation. In addition, we developed a black tea fermentation identification system. The experimental results show that the model achieves an accuracy of 98.15 %, a precision of 98.20 %, a recall rate of 98.30 %, and an F1-score of 98.11 %, demonstrating good identification performance. CVK-Net can accurately identify the fermentation stages of black tea, providing effective technical support for non-destructive testing during the black tea fermentation process.
{"title":"CVK-Net: A non-destructive identification method for black tea fermentation stages using multi-source data fusion","authors":"Juntao Xiong , Kangning Liao , Youcong Hou , Shuli Zheng , Zhuolun Liao , Jiatong Tang , Wenjie Qiu , Guanghua Hu","doi":"10.1016/j.jfca.2025.108767","DOIUrl":"10.1016/j.jfca.2025.108767","url":null,"abstract":"<div><div>To address the challenges of accurately assessing the fermentation degree of black tea using traditional manual methods and issues such as sample destruction and low accuracy associated with other methods, we propose a model named CVK-Net, which integrates a Convolutional Neural Network (CNN), Vision Transformer (ViT), and Kolmogorov-Arnold Network (KAN). By obtaining sensor data and extracting RGB, HSV, and Lab color components from images, numerical data is constructed and aligned with image data to build a bimodal dataset. First, dynamic snake convolution was introduced to improve the dense connection block for extracting shallow image features, while KAN was introduced to extract numerical modal features. The ESE attention mechanism was then used to generate bimodal feature weights and achieved adaptive fusion. Finally, ViT was used to construct a bimodal global feature representation. In addition, we developed a black tea fermentation identification system. The experimental results show that the model achieves an accuracy of 98.15 %, a precision of 98.20 %, a recall rate of 98.30 %, and an F1-score of 98.11 %, demonstrating good identification performance. CVK-Net can accurately identify the fermentation stages of black tea, providing effective technical support for non-destructive testing during the black tea fermentation process.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108767"},"PeriodicalIF":4.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747721","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 : 2025-12-08DOI: 10.1016/j.jfca.2025.108764
Zhongyu Li , Zhaolong Gao , Jiaxin Yu , Huaijie Shi , Jianya Ling , Guoying Zhang
This study reviews applications of Electronic Nose (E-nose), Chromatography-Mass Spectrometry (GC-MS), and Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) in tea industry research. The E-nose offers rapid, non-destructive analysis for real-time quality assessment through odor fingerprint pattern recognition, yet cannot identify or quantify individual compounds. Gas GC-MS, established as the industry gold standard, enables precise qualitative/quantitative analysis of complex volatiles with enhanced efficacy for higher molecular weight compounds. GC-IMS emerges as a user-friendly tool for highly sensitive detection of low molecular weight volatile compounds and rapid two-dimensional gas separation, effectively compensating for GC-MS limitations in trace-level small molecule analysis; however, its compound identification accuracy remains inferior to GC-MS due to incomplete spectral libraries. Collectively, these technologies enable comprehensive tea quality evaluation by characterizing aroma profiles across varieties, authenticating geographical origins, and tracking volatile changes during processing and storage. Their complementary strengths in speed (E-nose), specificity for macromolecules (GC-MS), and sensitivity to small molecules (GC-IMS) establish a multidimensional framework for tea research. This integration provides robust solutions for classification, process optimization, and shelf-life studies, offering theoretical and practical insights to advance quality control, product development, and origin traceability in tea production chains.
{"title":"Applications of E-nose, GC-MS, and GC-IMS in tea volatile components analysis","authors":"Zhongyu Li , Zhaolong Gao , Jiaxin Yu , Huaijie Shi , Jianya Ling , Guoying Zhang","doi":"10.1016/j.jfca.2025.108764","DOIUrl":"10.1016/j.jfca.2025.108764","url":null,"abstract":"<div><div>This study reviews applications of Electronic Nose (E-nose), Chromatography-Mass Spectrometry (GC-MS), and Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) in tea industry research. The E-nose offers rapid, non-destructive analysis for real-time quality assessment through odor fingerprint pattern recognition, yet cannot identify or quantify individual compounds. Gas GC-MS, established as the industry gold standard, enables precise qualitative/quantitative analysis of complex volatiles with enhanced efficacy for higher molecular weight compounds. GC-IMS emerges as a user-friendly tool for highly sensitive detection of low molecular weight volatile compounds and rapid two-dimensional gas separation, effectively compensating for GC-MS limitations in trace-level small molecule analysis; however, its compound identification accuracy remains inferior to GC-MS due to incomplete spectral libraries. Collectively, these technologies enable comprehensive tea quality evaluation by characterizing aroma profiles across varieties, authenticating geographical origins, and tracking volatile changes during processing and storage. Their complementary strengths in speed (E-nose), specificity for macromolecules (GC-MS), and sensitivity to small molecules (GC-IMS) establish a multidimensional framework for tea research. This integration provides robust solutions for classification, process optimization, and shelf-life studies, offering theoretical and practical insights to advance quality control, product development, and origin traceability in tea production chains.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108764"},"PeriodicalIF":4.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747714","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 : 2025-12-07DOI: 10.1016/j.jfca.2025.108765
Xunan Zhang , Zhenzhen Cai , Wei Zong
Aqueous two-phase systems (ATPS) offer a sustainable and residue-free approach for food processing. Herein, a stable PEG-K₃PO₄ ATPS was developed and its phase behavior shown to depend on PEG molecular weight, temperature, and pH. The system enabled efficient recovery of acesulfame potassium (Ace-K), which preferentially partitioned into the PEG-rich phase through electrostatic and hydrophobic interactions. Applied to real samples including cola, Babao congee, and oral solutions, the method achieved recovery rates of 94.58–110.92 % with detection and quantification limits of 0.22 and 0.67 mg/L, respectively. This phosphate-PEG ATPS provides a safe, efficient, and cost-effective platform for sweetener recovery and detection in food and pharmaceutical products.
{"title":"Extraction of acesulfame potassium from sweetened foods using a K₃PO₄-PEG aqueous two-phase system","authors":"Xunan Zhang , Zhenzhen Cai , Wei Zong","doi":"10.1016/j.jfca.2025.108765","DOIUrl":"10.1016/j.jfca.2025.108765","url":null,"abstract":"<div><div>Aqueous two-phase systems (ATPS) offer a sustainable and residue-free approach for food processing. Herein, a stable PEG-K₃PO₄ ATPS was developed and its phase behavior shown to depend on PEG molecular weight, temperature, and pH. The system enabled efficient recovery of acesulfame potassium (Ace-K), which preferentially partitioned into the PEG-rich phase through electrostatic and hydrophobic interactions. Applied to real samples including cola, Babao congee, and oral solutions, the method achieved recovery rates of 94.58–110.92 % with detection and quantification limits of 0.22 and 0.67 mg/L, respectively. This phosphate-PEG ATPS provides a safe, efficient, and cost-effective platform for sweetener recovery and detection in food and pharmaceutical products.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108765"},"PeriodicalIF":4.6,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746923","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}
The current study aims to evaluate the nutrients, antinutrients, and health risks associated with consuming mung bean cultivars collected from Melo Koza Woreda, South Ethiopia. A multi-factorial experimental design (3 × 2 × 2) is employed, in which samples are collected from three kebeles of farmland located at two different altitudes, and the cultivar and processing factors (hulled and unhulled) of mung beans are examined. The obtained data were analyzed using SPSS software, which revealed a statistically significant difference (p ≤ 0.05) between cultivars, locations, and processing factors. Proximate composition is estimated to be in the following range: fiber (2.50–7.83 %), fat (0.33–2.45 %), protein (25.62–35.24 %), and digestible carbohydrates (51.92–60.09 %). The mineral concentration (mg kg⁻¹) ranges were K (27.97–85.49), Mg (33.74–47.82), Na (1.65–12.89), Ca (6.36–9.92), Fe (4.50–14.06), Zn (0.50–1.13), Cd (0.16–0.33), Pb (4.40–16.07), and Cr (1.73–5.43). Statistical analyses, including Principal Component Analysis (PCA), are employed to interpret the data. PCA revealed that four and three principal components accounted for 82.9 % and 88.2 % of the total variance for metals and proximate composition, respectively. PCA accounted for a high eigenvalue (0.61) of Pb, which shared high on the cumulative variance, 82.9 % loading by all metals. Furthermore, the results of the bioavailability of minerals, hazard quotient values of metals, and the hazard index of combined toxic metals are found to be within safe limits (< 1). The carcinogenic risk values of metals are also within the acceptable limit. The study's findings demonstrated that mung bean varieties possess potential health-contributing factors that can enhance food quality and human health. Furthermore, the assessment recommends that the studied Mung bean cultivars are safe for consumption.
{"title":"Nutritional composition, metal content, and health risks of mung beans (Vigna radiata L.) from Melo Koza Woreda, Ethiopia","authors":"Tolera Badessa, Ayele Amenu, Dessie Ezez, Ramesh Duraisamy","doi":"10.1016/j.jfca.2025.108760","DOIUrl":"10.1016/j.jfca.2025.108760","url":null,"abstract":"<div><div>The current study aims to evaluate the nutrients, antinutrients, and health risks associated with consuming mung bean cultivars collected from Melo Koza Woreda, South Ethiopia. A multi-factorial experimental design (3 × 2 × 2) is employed, in which samples are collected from three kebeles of farmland located at two different altitudes, and the cultivar and processing factors (hulled and unhulled) of mung beans are examined. The obtained data were analyzed using SPSS software, which revealed a statistically significant difference (p ≤ 0.05) between cultivars, locations, and processing factors. Proximate composition is estimated to be in the following range: fiber (2.50–7.83 %), fat (0.33–2.45 %), protein (25.62–35.24 %), and digestible carbohydrates (51.92–60.09 %). The mineral concentration (mg kg⁻¹) ranges were K (27.97–85.49), Mg (33.74–47.82), Na (1.65–12.89), Ca (6.36–9.92), Fe (4.50–14.06), Zn (0.50–1.13), Cd (0.16–0.33), Pb (4.40–16.07), and Cr (1.73–5.43). Statistical analyses, including Principal Component Analysis (PCA), are employed to interpret the data. PCA revealed that four and three principal components accounted for 82.9 % and 88.2 % of the total variance for metals and proximate composition, respectively. PCA accounted for a high eigenvalue (0.61) of Pb, which shared high on the cumulative variance, 82.9 % loading by all metals. Furthermore, the results of the bioavailability of minerals, hazard quotient values of metals, and the hazard index of combined toxic metals are found to be within safe limits (< 1). The carcinogenic risk values of metals are also within the acceptable limit. The study's findings demonstrated that mung bean varieties possess potential health-contributing factors that can enhance food quality and human health. Furthermore, the assessment recommends that the studied Mung bean cultivars are safe for consumption.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"149 ","pages":"Article 108760"},"PeriodicalIF":4.6,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746928","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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