This study presents an optimized workflow for rapid quantification of core quality traits in edible oils, including trans-fatty acids (TFA), saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), and selected additives, by combining 80 MHz benchtop 1H NMR and Fourier transform infrared (FTIR) spectroscopy. Seventy commercial margarine and butter products from Denmark and Uzbekistan were analyzed. FTIR-based TFA quantification showed that nearly 50 % of the Uzbek margarines exceeded the 2 % regulatory threshold, while all Danish samples remained below 1.3 %. Butter, which naturally contains rumen-derived TFA, exhibited greater variability (up to 6 %) but no country-specific differences. NMR analysis revealed clear compositional contrasts between butters and margarines, and between the same product types across the two countries. Butter consistently contained more SFA and less PUFA than margarine, while MUFA and PUFA showed the greatest geographical variation. Benchtop NMR also enabled detection of additives, such as stanol esters and sorbic acid, in several butter samples. Overall, this work demonstrates a workflow based on green analytical technologies that provides robust chemical insights into core quality traits of edible oils, enabling efficient monitoring in both research and quality control laboratories.
{"title":"Characterization of butter and margarine oil composition using benchtop NMR and FTIR: A comparative study of products from Uzbekistan and Denmark","authors":"Umrbek Mavlanov , Tomasz Pawel Czaja , Sardorjon Shukurov Salimovich , Sarvar Khodjaev , Bekzod Khakimov","doi":"10.1016/j.jfca.2025.108842","DOIUrl":"10.1016/j.jfca.2025.108842","url":null,"abstract":"<div><div>This study presents an optimized workflow for rapid quantification of core quality traits in edible oils, including <em>trans</em>-fatty acids (TFA), saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), and selected additives, by combining 80 MHz benchtop <sup>1</sup>H NMR and Fourier transform infrared (FTIR) spectroscopy. Seventy commercial margarine and butter products from Denmark and Uzbekistan were analyzed. FTIR-based TFA quantification showed that nearly 50 % of the Uzbek margarines exceeded the 2 % regulatory threshold, while all Danish samples remained below 1.3 %. Butter, which naturally contains rumen-derived TFA, exhibited greater variability (up to 6 %) but no country-specific differences. NMR analysis revealed clear compositional contrasts between butters and margarines, and between the same product types across the two countries. Butter consistently contained more SFA and less PUFA than margarine, while MUFA and PUFA showed the greatest geographical variation. Benchtop NMR also enabled detection of additives, such as stanol esters and sorbic acid, in several butter samples. Overall, this work demonstrates a workflow based on green analytical technologies that provides robust chemical insights into core quality traits of edible oils, enabling efficient monitoring in both research and quality control laboratories.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108842"},"PeriodicalIF":4.6,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882305","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 : 2025-12-24DOI: 10.1016/j.jfca.2025.108838
Wenhao Zhou , Jianhong Sun , Li Jin , Shuchao Huang , Yandong Xie , Xiting Yang , Zhe Zhang , Jiyuan Cui , Ning Jin , Shuya Wang , Jihua Yu , Jian Lyu
Nitrogen deficiency and improper nitrogen form ratios often limit tomato fruit quality and yield. Optimizing nitrogen form and ratio may provide an effective solution for improving tomato nutritional value and production quality. In this study, to identify the optimal nitrogen form and ratio that can improve tomato fruit quality, 13 treatments were established under the same nitrogen level (15 mM) with varying ratios of nitrogen forms (nitrate nitrogen: ammonium nitrogen: urea nitrogen). The treatments included CK (0:0:0), T1 (100 %:0:0), T2 (0:100 %:0), T3 (0:0:100 %), T4 (75 %:25 %:0), T5 (50 %:50 %:0), T6 (25 %:75 %:0), T7 (0:25 %:75 %), T8 (0:50 %:50 %), T9 (0:75 %:25 %), T10 (75 %:0:25 %), T11 (50 %:0:50 %), and T12 (25 %:0:75 %). The results showed that, compared with CK, the contents of total amino acids, glutamic acid, soluble sugars, N, Cu, Mn, and Zn were significantly increased under T4 treatment. Principal component analysis based on 20 quality indicators ranked T4 treatment as the highest. In conclusion, the T4 treatment (75 % nitrate-N: 25 % ammonium-N: 0 % urea-N) was identified as the most favorable for improving overall tomato fruit quality, providing a theoretical basis for the scientific application of nitrogen in high-quality tomato cultivation.
{"title":"Combined application of different nitrogen forms enhances nutritional quality and secondary metabolite accumulation in tomato (Solanum lycopersicum L.) fruits","authors":"Wenhao Zhou , Jianhong Sun , Li Jin , Shuchao Huang , Yandong Xie , Xiting Yang , Zhe Zhang , Jiyuan Cui , Ning Jin , Shuya Wang , Jihua Yu , Jian Lyu","doi":"10.1016/j.jfca.2025.108838","DOIUrl":"10.1016/j.jfca.2025.108838","url":null,"abstract":"<div><div>Nitrogen deficiency and improper nitrogen form ratios often limit tomato fruit quality and yield. Optimizing nitrogen form and ratio may provide an effective solution for improving tomato nutritional value and production quality. In this study, to identify the optimal nitrogen form and ratio that can improve tomato fruit quality, 13 treatments were established under the same nitrogen level (15 mM) with varying ratios of nitrogen forms (nitrate nitrogen: ammonium nitrogen: urea nitrogen). The treatments included CK (0:0:0), T1 (100 %:0:0), T2 (0:100 %:0), T3 (0:0:100 %), T4 (75 %:25 %:0), T5 (50 %:50 %:0), T6 (25 %:75 %:0), T7 (0:25 %:75 %), T8 (0:50 %:50 %), T9 (0:75 %:25 %), T10 (75 %:0:25 %), T11 (50 %:0:50 %), and T12 (25 %:0:75 %). The results showed that, compared with CK, the contents of total amino acids, glutamic acid, soluble sugars, N, Cu, Mn, and Zn were significantly increased under T4 treatment. Principal component analysis based on 20 quality indicators ranked T4 treatment as the highest. In conclusion, the T4 treatment (75 % nitrate-N: 25 % ammonium-N: 0 % urea-N) was identified as the most favorable for improving overall tomato fruit quality, providing a theoretical basis for the scientific application of nitrogen in high-quality tomato cultivation.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108838"},"PeriodicalIF":4.6,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839716","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 : 2025-12-24DOI: 10.1016/j.jfca.2025.108844
Kottur Senthilkumar Navin Venketeish , Nagamaniammai Govindarajan , Ravi Pandiselvam
The study explored the impact of processing techniques such as sprouting, autoclaving, and a combination of Sprouting and Autoclaving on physico-chemical properties, anti-nutritional factors (ANFs), amino acid composition of chickpea (CP) and fava bean (FB). Significant changes were observed in proximate composition, with sprouting generally enhanced protein content to 22.88 % fava bean in and 22.91 % in Chickpea. Autoclaving reduced ANFs. Sprouting of FB and CP notably improved protein content, reduced ANFs, while texture profile analysis (TPA) showed differences in hardness, adhesiveness, resilience. Autoclaved legumes became softer while sprouting influenced adhesiveness and springiness. Amino acid composition differed depending on processing method, with sprouting having a significant impact. Essential amino acid was higher in Sprouted Fava bean, Sprouted chickpea for about 37.52 g/100 gm and 37.79 g/100 gm respectively. Essential amino acid index (EAAI), Nutritional Index (NI), Amino acid scores (AAS), E/T (%) (Essential amino acid to Total amino acids), Scanned Electron Microscope (SEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) were analyzed for legumes. The novelty of this study lies in its comprehensive evaluation of effects on sprouting, autoclaving, and combination on CP and FB, which has not been extensively explored in previous research, providing valuable insights for optimizing nutritional benefits.
{"title":"Mechanistic insights of germination and autoclaving effects on Chickpea (Cicer arietinum L.) and Fava Bean (Vicia faba L.): Molecular, structural, and functional perspectives","authors":"Kottur Senthilkumar Navin Venketeish , Nagamaniammai Govindarajan , Ravi Pandiselvam","doi":"10.1016/j.jfca.2025.108844","DOIUrl":"10.1016/j.jfca.2025.108844","url":null,"abstract":"<div><div>The study explored the impact of processing techniques such as sprouting, autoclaving, and a combination of Sprouting and Autoclaving on physico-chemical properties, anti-nutritional factors (ANFs), amino acid composition of chickpea (CP) and fava bean (FB). Significant changes were observed in proximate composition, with sprouting generally enhanced protein content to 22.88 % fava bean in and 22.91 % in Chickpea. Autoclaving reduced ANFs. Sprouting of FB and CP notably improved protein content, reduced ANFs, while texture profile analysis (TPA) showed differences in hardness, adhesiveness, resilience. Autoclaved legumes became softer while sprouting influenced adhesiveness and springiness. Amino acid composition differed depending on processing method, with sprouting having a significant impact. Essential amino acid was higher in Sprouted Fava bean, Sprouted chickpea for about 37.52 g/100 gm and 37.79 g/100 gm respectively. Essential amino acid index (EAAI), Nutritional Index (NI), Amino acid scores (AAS), E/T (%) (Essential amino acid to Total amino acids), Scanned Electron Microscope (SEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) were analyzed for legumes. The novelty of this study lies in its comprehensive evaluation of effects on sprouting, autoclaving, and combination on CP and FB, which has not been extensively explored in previous research, providing valuable insights for optimizing nutritional benefits.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108844"},"PeriodicalIF":4.6,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882311","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 : 2025-12-23DOI: 10.1016/j.jfca.2025.108821
Yihang Feng , Yi Wang , Xinhao Wang , Bo Zhao , Jinbo Bi , Song Han , Zhenlei Xiao , Yangchao Luo
Accurate estimation of absolute nutrient values in food remains a significant challenge for automated dietary assessment. We propose a novel multi-modal deep learning framework that integrates RGB-D imaging with vision-language contrastive learning for food nutrient estimation. Our dual-pathway architecture employs Swin Transformer V2 Tiny backbones to process RGB and depth information separately, followed by hierarchical feature mixing across multiple scales to capture both fine-grained details and global food representations. We integrate the FLAVA (Foundational Language And Vision Alignment) model to enable vision-text contrastive learning, aligning visual features with ingredient descriptions to enhance semantic understanding of food composition. Additionally, we implement vision-vision contrastive learning to ensure consistency between different visual representations. Evaluated on the Nutrition5k dataset containing 3490 RGB-D images with precise nutritional measurements, our approach achieves state-of-the-art performance with a mean Percentage Mean Absolute Error (PMAE) of 14.43 % across all nutritional components, representing significant improvement over the previous best of 15.9 %. FLAVA integration guides model training with ingredient information but is not employed during testing, significantly reducing computational demands. With only 0.44-second inference time, our approach is suitable for real-time applications including mobile deployment. While achieving state-of-the-art results on Nutrition5k, the model's performance on diverse global cuisines requires further validation.
{"title":"RGB-D food nutrient estimation supported by FLAVA contrastive learning","authors":"Yihang Feng , Yi Wang , Xinhao Wang , Bo Zhao , Jinbo Bi , Song Han , Zhenlei Xiao , Yangchao Luo","doi":"10.1016/j.jfca.2025.108821","DOIUrl":"10.1016/j.jfca.2025.108821","url":null,"abstract":"<div><div>Accurate estimation of absolute nutrient values in food remains a significant challenge for automated dietary assessment. We propose a novel multi-modal deep learning framework that integrates RGB-D imaging with vision-language contrastive learning for food nutrient estimation. Our dual-pathway architecture employs Swin Transformer V2 Tiny backbones to process RGB and depth information separately, followed by hierarchical feature mixing across multiple scales to capture both fine-grained details and global food representations. We integrate the FLAVA (Foundational Language And Vision Alignment) model to enable vision-text contrastive learning, aligning visual features with ingredient descriptions to enhance semantic understanding of food composition. Additionally, we implement vision-vision contrastive learning to ensure consistency between different visual representations. Evaluated on the Nutrition5k dataset containing 3490 RGB-D images with precise nutritional measurements, our approach achieves state-of-the-art performance with a mean Percentage Mean Absolute Error (PMAE) of 14.43 % across all nutritional components, representing significant improvement over the previous best of 15.9 %. FLAVA integration guides model training with ingredient information but is not employed during testing, significantly reducing computational demands. With only 0.44-second inference time, our approach is suitable for real-time applications including mobile deployment. While achieving state-of-the-art results on Nutrition5k, the model's performance on diverse global cuisines requires further validation.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108821"},"PeriodicalIF":4.6,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882304","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 : 2025-12-23DOI: 10.1016/j.jfca.2025.108835
Tongtong Yang , Bo Tang , Junyi Tang , Chenqi Xu , Yanlong Hong , Fei Wu , Xiao Lin
Pheretima products are widely used, but the stenchy odor is a constraint to their application. In this study, 86 and 673 volatile components (VOCs) were identified in Pheretima and its extracts using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), respectively. An increase in acid and amine contents after processing was found, which may explain the increased stenchy odor. Through orthogonal partial least squares discriminant analysis (OPLS-DA), 14 differential markers between aqueous and alcoholic extracts were screened, and 3-methylbutanal, pentanal, and trimethylamine were identified as the key differential odor components. Combined with relative odor activity value (ROAV) analysis, 10 key odor components were identified, including: 3-methylbutanal, 1-octen-3-ol, dimethyl trisulfide, (2E,6Z)-nona-2,6-dienal, methyl mercaptan, guaiacol, isobutyraldehyde, 1,8-cineole, 2-pentylfuran, and pentanal. This study provides theoretical support for the optimization of the odor of Pheretima-containing products and promotes their application.
{"title":"Analysis of volatile components in Pheretima and its extracts using HS-GC-IMS and HS-SPME-GC-MS combined with ROAV","authors":"Tongtong Yang , Bo Tang , Junyi Tang , Chenqi Xu , Yanlong Hong , Fei Wu , Xiao Lin","doi":"10.1016/j.jfca.2025.108835","DOIUrl":"10.1016/j.jfca.2025.108835","url":null,"abstract":"<div><div>Pheretima products are widely used, but the stenchy odor is a constraint to their application. In this study, 86 and 673 volatile components (VOCs) were identified in Pheretima and its extracts using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), respectively. An increase in acid and amine contents after processing was found, which may explain the increased stenchy odor. Through orthogonal partial least squares discriminant analysis (OPLS-DA), 14 differential markers between aqueous and alcoholic extracts were screened, and 3-methylbutanal, pentanal, and trimethylamine were identified as the key differential odor components. Combined with relative odor activity value (ROAV) analysis, 10 key odor components were identified, including: 3-methylbutanal, 1-octen-3-ol, dimethyl trisulfide, (2<em>E</em>,6<em>Z</em>)-nona-2,6-dienal, methyl mercaptan, guaiacol, isobutyraldehyde, 1,8-cineole, 2-pentylfuran, and pentanal. This study provides theoretical support for the optimization of the odor of Pheretima-containing products and promotes their application.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108835"},"PeriodicalIF":4.6,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824140","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 : 2025-12-23DOI: 10.1016/j.jfca.2025.108832
Qing Li , Xinyi Wen , Yongjie Zhou , Huawei Ma , Yuqing Tan , Hui Hong , Yongkang Luo
Traditional chemical methods for evaluating fish freshness are time-consuming and destructive, underscoring the need for rapid and non-invasive predictive models. In this study, random forest (RF), backpropagation neural network (BPNN), and long short-term memory (LSTM) models were employed to predict muscle freshness of four bighead carp cuts during storage at different temperatures (0, 3, 6, 12, 24℃). A total of 108 data points were collected for TVC, TVB-N, K-value, and sensory evaluation indicators. BPNN outperformed both RF and LSTM in TVC, TVB-N and sensory evaluation, with R2 in testing sets being 0.8857 0.9998, and 0.9312, respectively. Data augmentation using exponential decay function (EDF) and Arrhenius function (AF) improved performance for all models. EDF-LSTM was the best for predicting TVC in eye muscle, with an average validation error of 19.22 %. AF-RF provided the best predictions for K-value in dorsal, belly, and tail muscles, with errors of 17.27 %, 15.93 %, and 15.53 %. EDF-RF and AF-LSTM were optimal for sensory evaluation in eye and dorsal muscles, with errors of 11.01 % and 21.52 %. These findings demonstrate that integrating machine learning with data augmentation offers a promising approach for non-destructive freshness prediction in fish cuts across a range of storage temperatures.
{"title":"Integration of machine learning algorithms and empirical formula-driven data augmentation for freshness prediction of bighead carp cutting products","authors":"Qing Li , Xinyi Wen , Yongjie Zhou , Huawei Ma , Yuqing Tan , Hui Hong , Yongkang Luo","doi":"10.1016/j.jfca.2025.108832","DOIUrl":"10.1016/j.jfca.2025.108832","url":null,"abstract":"<div><div>Traditional chemical methods for evaluating fish freshness are time-consuming and destructive, underscoring the need for rapid and non-invasive predictive models. In this study, random forest (RF), backpropagation neural network (BPNN), and long short-term memory (LSTM) models were employed to predict muscle freshness of four bighead carp cuts during storage at different temperatures (0, 3, 6, 12, 24℃). A total of 108 data points were collected for TVC, TVB-N, K-value, and sensory evaluation indicators. BPNN outperformed both RF and LSTM in TVC, TVB-N and sensory evaluation, with R<sup>2</sup> in testing sets being 0.8857 0.9998, and 0.9312, respectively. Data augmentation using exponential decay function (EDF) and Arrhenius function (AF) improved performance for all models. EDF-LSTM was the best for predicting TVC in eye muscle, with an average validation error of 19.22 %. AF-RF provided the best predictions for K-value in dorsal, belly, and tail muscles, with errors of 17.27 %, 15.93 %, and 15.53 %. EDF-RF and AF-LSTM were optimal for sensory evaluation in eye and dorsal muscles, with errors of 11.01 % and 21.52 %. These findings demonstrate that integrating machine learning with data augmentation offers a promising approach for non-destructive freshness prediction in fish cuts across a range of storage temperatures.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108832"},"PeriodicalIF":4.6,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927826","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 : 2025-12-22DOI: 10.1016/j.jfca.2025.108827
Hongkun Lin , Yilan Sun , Xiaolin Li , Zunren Chen , Qinhua Zhang , Qinghui Chen , Huiyue Zhang , Jie Pang , Shiguo Huang
Accurate identification of aroma-active compounds is essential for evaluating food quality, sensory characteristics, and authenticity. However, predicting multi-label odor attributes directly from volatile molecular structures remains challenging due to data sparsity and imbalance. In this study, we develop a graph neural network (GNN)-based ensemble framework to support odor profiling in high-aroma tea products. Using two public odor databases (GoodScents and Leffingwell PMP 2001; 4983 molecules, 138 labels), we systematically train and compare multiple GNN architectures (MPNN, GINE, EdgeGAT, PNA) and ensemble strategies. To assess real-world applicability, we analyze four batches of Osmanthus Oolong tea (single and double scented, 2021–2022), identifying 48 volatiles, 16 of which contain odor annotations measured using proton transfer reaction time of flight mass spectrometry (PTR-TOF-MS). Feature-level fusion ensembles achieve the best overall performance, reaching an AUC–ROC of 89.1, precision 66.7, recall 30.2, and an F1-score of 41.6, outperforming individual GNNs (e.g., MPNN 88.7) and traditional machine-learning models such as XGBoost (85.2) and Random Forest (83.3). Incorporating molecular similarity further improves predictions for new compounds. This study demonstrates the potential of integrating odor databases with deep learning to enable data-driven sensory analysis and quality monitoring in flavored foods, offering a scalable solution for aroma evaluation.
{"title":"Multi-label odor profiling of Osmanthus Oolong tea using graph neural networks: Integrating public databases and PTR-TOF-MS-based aroma compound analysis","authors":"Hongkun Lin , Yilan Sun , Xiaolin Li , Zunren Chen , Qinhua Zhang , Qinghui Chen , Huiyue Zhang , Jie Pang , Shiguo Huang","doi":"10.1016/j.jfca.2025.108827","DOIUrl":"10.1016/j.jfca.2025.108827","url":null,"abstract":"<div><div>Accurate identification of aroma-active compounds is essential for evaluating food quality, sensory characteristics, and authenticity. However, predicting multi-label odor attributes directly from volatile molecular structures remains challenging due to data sparsity and imbalance. In this study, we develop a graph neural network (GNN)-based ensemble framework to support odor profiling in high-aroma tea products. Using two public odor databases (GoodScents and Leffingwell PMP 2001; 4983 molecules, 138 labels), we systematically train and compare multiple GNN architectures (MPNN, GINE, EdgeGAT, PNA) and ensemble strategies. To assess real-world applicability, we analyze four batches of Osmanthus Oolong tea (single and double scented, 2021–2022), identifying 48 volatiles, 16 of which contain odor annotations measured using proton transfer reaction time of flight mass spectrometry (PTR-TOF-MS). Feature-level fusion ensembles achieve the best overall performance, reaching an AUC–ROC of 89.1, precision 66.7, recall 30.2, and an F1-score of 41.6, outperforming individual GNNs (e.g., MPNN 88.7) and traditional machine-learning models such as XGBoost (85.2) and Random Forest (83.3). Incorporating molecular similarity further improves predictions for new compounds. This study demonstrates the potential of integrating odor databases with deep learning to enable data-driven sensory analysis and quality monitoring in flavored foods, offering a scalable solution for aroma evaluation.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108827"},"PeriodicalIF":4.6,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824141","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 : 2025-12-22DOI: 10.1016/j.jfca.2025.108816
Heyun Zhang , Huan Zhang , Jihong Huang , Liping Du , Lijuan Ma , Juan Wang , Yurong Xing , Xiaorui Song
Fu brick tea (FBT) is prized for its unique aroma, yet the dynamic evolution of its key aroma-active compounds during storage remains unclear. To investigate this dynamic change, headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-olfactometry-mass spectrometry (GC-O-MS) and sensory omics analysis were employed for the comprehensive characterization of aroma-active compounds in five FBT samples. A total of 47 aroma-active compounds were detected, 15 of which were further determined as the key aroma-active compounds based on high flavor dilution (FD) factors, aroma intensities (AI), and odor activity value (OAV). Notably, although dihydroactindiolide exhibited high contents in HF15-HF19 (1733–2156 µg/kg), it was not classified as a key aroma compound. Multivariate statistical analysis revealed that the “stale” aroma attribute was strongly associated with aged FBT samples, while “minty” and “grassy” attributes were characteristic of newly produced FBT. Furthermore, aroma recombination and omission tests combined sensory evaluation confirmed that hexanal, 2-hexenal, (E, E)-2,4-hexadienal, (E, Z)-2,6-nonadienal, safranal, (E, E)-2,4-nonadien-1-al, β-ionone, linalool and cedrol played decisive roles in constructing the overall aroma profile of FBT. This research provides detailed insights into the evolution of FBT aroma during storage, which can serve as a theoretical basis for optimizing FBT storage processes and improving its quality stability.
{"title":"Characterization of the key odorants of Fu Brick tea with different storage years using GC-O-MS combined with sensory evaluation","authors":"Heyun Zhang , Huan Zhang , Jihong Huang , Liping Du , Lijuan Ma , Juan Wang , Yurong Xing , Xiaorui Song","doi":"10.1016/j.jfca.2025.108816","DOIUrl":"10.1016/j.jfca.2025.108816","url":null,"abstract":"<div><div>Fu brick tea (FBT) is prized for its unique aroma, yet the dynamic evolution of its key aroma-active compounds during storage remains unclear. To investigate this dynamic change, headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-olfactometry-mass spectrometry (GC-O-MS) and sensory omics analysis were employed for the comprehensive characterization of aroma-active compounds in five FBT samples. A total of 47 aroma-active compounds were detected, 15 of which were further determined as the key aroma-active compounds based on high flavor dilution (FD) factors, aroma intensities (AI), and odor activity value (OAV). Notably, although dihydroactindiolide exhibited high contents in HF15-HF19 (1733–2156 µg/kg), it was not classified as a key aroma compound. Multivariate statistical analysis revealed that the “stale” aroma attribute was strongly associated with aged FBT samples, while “minty” and “grassy” attributes were characteristic of newly produced FBT. Furthermore, aroma recombination and omission tests combined sensory evaluation confirmed that hexanal, 2-hexenal, (<em>E, E</em>)-2,4-hexadienal, (<em>E, Z</em>)-2,6-nonadienal, safranal, (<em>E, E</em>)-2,4-nonadien-1-al, <em>β</em>-ionone, linalool and cedrol played decisive roles in constructing the overall aroma profile of FBT. This research provides detailed insights into the evolution of FBT aroma during storage, which can serve as a theoretical basis for optimizing FBT storage processes and improving its quality stability.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108816"},"PeriodicalIF":4.6,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839713","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 : 2025-12-22DOI: 10.1016/j.jfca.2025.108834
Chunmei Jian , Zhengkuan Tan , Ruo Xu , Zhihong Gong , Xiaoyu Zhang , Yuling Zeng , Haichang Ding , Congbin Fan , Gang Liu , Shouzhi Pu
Accurate monitoring of bisulfite (HSO3−) in biological and complex matrices is crucial for elucidating its physiological and pathological roles. Here, a turn-on fluorescent probe QA was developed based on a quinoline platform. Upon reaction with HSO3− via a specific nucleophilic addition reaction, QA exhibits high selectivity for HSO3− and remarkable turn-on fluorescence response at 628 nm, with high sensitivity (LOD=0.263 μM) and good linear response ranging from 2 to 15 μM. Moreover, a portable platform was constructed by integrating QA into test strip for rapid on-site detection using a smartphone-based system under UV light. In addition, QA can effectively monitor HSO3− in diverse food samples (liquor, beer, wine, rock sugar, and canned fruits), achieving spike recovery rates of 89.60–105.80 %. Furthermore, QA was successfully applied for the fluorescence imaging of exogenous HSO3− in live HeLa cells and zebrafish, confirming its good cell permeability and biocompatibility. These findings establish QA as a reliable tool for HSO3− detection in both food safety and bioimaging applications.
{"title":"Smartphone-assisted fluorescent probe based on quinoline salts for detecting bisulfite and its application in cells and zebrafish imaging","authors":"Chunmei Jian , Zhengkuan Tan , Ruo Xu , Zhihong Gong , Xiaoyu Zhang , Yuling Zeng , Haichang Ding , Congbin Fan , Gang Liu , Shouzhi Pu","doi":"10.1016/j.jfca.2025.108834","DOIUrl":"10.1016/j.jfca.2025.108834","url":null,"abstract":"<div><div>Accurate monitoring of bisulfite (HSO<sub>3</sub><sup>−</sup>) in biological and complex matrices is crucial for elucidating its physiological and pathological roles. Here, a turn-on fluorescent probe <strong>QA</strong> was developed based on a quinoline platform. Upon reaction with HSO<sub>3</sub><sup>−</sup> via a specific nucleophilic addition reaction, <strong>QA</strong> exhibits high selectivity for HSO<sub>3</sub><sup>−</sup> and remarkable turn-on fluorescence response at 628 nm, with high sensitivity (LOD=0.263 μM) and good linear response ranging from 2 to 15 μM. Moreover, a portable platform was constructed by integrating <strong>QA</strong> into test strip for rapid on-site detection using a smartphone-based system under UV light. In addition, <strong>QA</strong> can effectively monitor HSO<sub>3</sub><sup>−</sup> in diverse food samples (liquor, beer, wine, rock sugar, and canned fruits), achieving spike recovery rates of 89.60–105.80 %. Furthermore, <strong>QA</strong> was successfully applied for the fluorescence imaging of exogenous HSO<sub>3</sub><sup>−</sup> in live HeLa cells and zebrafish, confirming its good cell permeability and biocompatibility. These findings establish <strong>QA</strong> as a reliable tool for HSO<sub>3</sub><sup>−</sup> detection in both food safety and bioimaging applications.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108834"},"PeriodicalIF":4.6,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839718","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 : 2025-12-22DOI: 10.1016/j.jfca.2025.108833
Liangliang Xie , Anying Cai , Jianping Tian , Xiang Wan , Jianping Yang , Haili Yang , Xinjun Hu , Manjiao Chen , Rongzhi Wang , Hao Zhang , Yuansong Peng , Kaiyang Yuan , Haonan Yi
Accurate identification of brewing sorghum varieties is the key to guaranteeing the stability of liquor quality. In response to the problem that the detail loss and band distortion caused by imaging system noise lead to a decrease in spectral reconstruction accuracy, this study proposes a detection algorithm based on the Spectral Adaptive Feature Enhancement-based Multi-stage Spectral-wise Transformer (ASTE-MST++) network —an improved version of the original Multi-stage Spectral-wise Transformer (MST++) network, which integrates a self-designed spectral adaptive feature enhancement (ASTE) module. This module adopts dynamic threshold denoising and frequency domain attention enhancement to enhance the local detailed features in the image, thereby optimizing spectral reconstruction quality. To evaluate the spectral reconstruction performance, the proposed spectral reconstruction model is compared with the existing Hyperspectral Convolutional Neural Network-Dense (HSCNN-D) and the MST+ + network. Subsequently, classification models including Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), and Sparrow Search Algorithm-Optimized Random Forest (SSA-RF) are constructed to test the reconstructed data. The results show that the ASTE-MST++ model integrated with SSA-RF achieves the best performance in sorghum variety detection (accuracy 92.66 %, recall 92.31 %, F1-score 92.12 %). The proposed ASTE-MST++ detection model provides an efficient and practical solution for the online sorting of brewing sorghum.
{"title":"Spectral reconstruction and variety identification of brewing sorghum based on spectral adaptive feature enhancement network","authors":"Liangliang Xie , Anying Cai , Jianping Tian , Xiang Wan , Jianping Yang , Haili Yang , Xinjun Hu , Manjiao Chen , Rongzhi Wang , Hao Zhang , Yuansong Peng , Kaiyang Yuan , Haonan Yi","doi":"10.1016/j.jfca.2025.108833","DOIUrl":"10.1016/j.jfca.2025.108833","url":null,"abstract":"<div><div>Accurate identification of brewing sorghum varieties is the key to guaranteeing the stability of liquor quality. In response to the problem that the detail loss and band distortion caused by imaging system noise lead to a decrease in spectral reconstruction accuracy, this study proposes a detection algorithm based on the Spectral Adaptive Feature Enhancement-based Multi-stage Spectral-wise Transformer (ASTE-MST++) network —an improved version of the original Multi-stage Spectral-wise Transformer (MST++) network, which integrates a self-designed spectral adaptive feature enhancement (ASTE) module. This module adopts dynamic threshold denoising and frequency domain attention enhancement to enhance the local detailed features in the image, thereby optimizing spectral reconstruction quality. To evaluate the spectral reconstruction performance, the proposed spectral reconstruction model is compared with the existing Hyperspectral Convolutional Neural Network-Dense (HSCNN-D) and the MST+ + network. Subsequently, classification models including Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), and Sparrow Search Algorithm-Optimized Random Forest (SSA-RF) are constructed to test the reconstructed data. The results show that the ASTE-MST++ model integrated with SSA-RF achieves the best performance in sorghum variety detection (accuracy 92.66 %, recall 92.31 %, F1-score 92.12 %). The proposed ASTE-MST++ detection model provides an efficient and practical solution for the online sorting of brewing sorghum.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108833"},"PeriodicalIF":4.6,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839715","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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