Pub Date : 2026-01-08DOI: 10.1016/j.jfca.2026.108888
Peiqin Shi , Mengfei Yang , Rui Chang , Hongli Yao
The identification of sweet and bitter compounds is essential for improving the sensory quality of foods, yet traditional analytical methods remain time-consuming and costly. To overcome this limitation, we developed a machine learning framework for high-throughput taste prediction of sweet, bitter, and tasteless compounds. Our optimized model achieved an F1 score of 88.0 %, and its reliability was validated through electronic tongue analysis and molecular docking. Furthermore, we implemented this model into an online platform named TasteMolNet (http://www.bstchem.fun/), which serves as a practical tool to accelerate sweetener discovery and bitterness modulation in food research.
{"title":"TasteMolNet: A machine learning-driven platform for sweet, bitter, and tasteless compounds prediction in food chemistry","authors":"Peiqin Shi , Mengfei Yang , Rui Chang , Hongli Yao","doi":"10.1016/j.jfca.2026.108888","DOIUrl":"10.1016/j.jfca.2026.108888","url":null,"abstract":"<div><div>The identification of sweet and bitter compounds is essential for improving the sensory quality of foods, yet traditional analytical methods remain time-consuming and costly. To overcome this limitation, we developed a machine learning framework for high-throughput taste prediction of sweet, bitter, and tasteless compounds. Our optimized model achieved an F1 score of 88.0 %, and its reliability was validated through electronic tongue analysis and molecular docking. Furthermore, we implemented this model into an online platform named TasteMolNet (<span><span>http://www.bstchem.fun/</span><svg><path></path></svg></span>), which serves as a practical tool to accelerate sweetener discovery and bitterness modulation in food research.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108888"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927811","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 : 2026-01-08DOI: 10.1016/j.jfca.2026.108880
Ilknur Alibas, Servet Sami Ipek
This study investigates the effects of five drying methods—freeze, vacuum, microwave, convective, and natural—on the nutritional and visual quality of kiwifruit, focusing on the vitamin C, B-group vitamins, fat-soluble vitamins, and carotenoids. Fresh samples contained 5.57 mg/g ascorbic acid, and freeze drying retained the highest level (3.13 mg/g), followed by vacuum drying (2.63 mg/g), while natural and convective drying resulted in severe reductions (1.06 and 1.21 mg/g). Riboflavin, initially 1.59 μg/g, decreased to 0.91 μg/g after freeze drying, 0.82 μg/g after vacuum drying, and 0.54 μg/g under natural drying. Carotenoids followed similar trends, with β-carotene declining from 2.88 μg/g in fresh samples to 2.24 μg/g after freeze drying and below 1 μg/g with natural drying. Freeze and vacuum drying best preserved vitamin content and color by minimizing oxidation and thermal damage, whereas natural and convective drying led to greater losses due to prolonged heat exposure and oxidative stress. Color changes strongly correlated with nutrient degradation. Notably, ΔE was negatively associated with ascorbic acid (−0.96) and thiamine (−0.95), while hue angle correlated positively with carotenoids and fat-soluble vitamins. These results show that color parameters reliably indicate nutritional retention, offering a practical approach for quality assessment in dried fruits.
{"title":"Effects of major drying methods on the stability and retention of vitamin C, B group vitamins, fat-soluble vitamins, and carotenoids in kiwifruits","authors":"Ilknur Alibas, Servet Sami Ipek","doi":"10.1016/j.jfca.2026.108880","DOIUrl":"10.1016/j.jfca.2026.108880","url":null,"abstract":"<div><div>This study investigates the effects of five drying methods—freeze, vacuum, microwave, convective, and natural—on the nutritional and visual quality of kiwifruit, focusing on the vitamin C, B-group vitamins, fat-soluble vitamins, and carotenoids. Fresh samples contained 5.57 mg/g ascorbic acid, and freeze drying retained the highest level (3.13 mg/g), followed by vacuum drying (2.63 mg/g), while natural and convective drying resulted in severe reductions (1.06 and 1.21 mg/g). Riboflavin, initially 1.59 μg/g, decreased to 0.91 μg/g after freeze drying, 0.82 μg/g after vacuum drying, and 0.54 μg/g under natural drying. Carotenoids followed similar trends, with β-carotene declining from 2.88 μg/g in fresh samples to 2.24 μg/g after freeze drying and below 1 μg/g with natural drying. Freeze and vacuum drying best preserved vitamin content and color by minimizing oxidation and thermal damage, whereas natural and convective drying led to greater losses due to prolonged heat exposure and oxidative stress. Color changes strongly correlated with nutrient degradation. Notably, ΔE was negatively associated with ascorbic acid (−0.96) and thiamine (−0.95), while hue angle correlated positively with carotenoids and fat-soluble vitamins. These results show that color parameters reliably indicate nutritional retention, offering a practical approach for quality assessment in dried fruits.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108880"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978724","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 : 2026-01-08DOI: 10.1016/j.jfca.2026.108885
Zeyi Cai , Xue Guo , Mengyu He , Cheng Li , Hengnian Qi , Ruibin Bai , Jian Yang , Chu Zhang
Chrysanthemum morifolium (Hangbaiju), as a flower tea, contains various bioactive components, exhibiting numerous pharmacological effects. Establishing a rapid-adaptation model for multiple components quantification is challenging. This study utilized hyperspectral imaging (HSI) and machine learning (ML) to quantify 28 Hangbaiju components. When modeled on individual bioactive components, Partial Least Squares Regression (PLSR) showed superior overall prediction, whereas deep learning (DL) excels on specific components. For some components, Residual Predictive Deviation (RPD) values of testing set shifted by more than 6. Task-incremental learning (task-IL) was applied to achieve continual learning for various regression tasks. The model rapidly adapted to new tasks by adding and training only task-specific modules within an already trained base network, while maintaining performance on previous prediction tasks. A linear relationship exists between the performance of the task-IL model and its base network, highlighting the importance of the feature extraction and cross-task generalization capabilities of base network in task-IL. This study demonstrates the potential of DL in the field of quality detection of flower teas and provides a framework for integrating HSI and DL in handling real-world scenarios across various fields.
{"title":"Determination of multiple bioactive components in Chrysanthemum morifolium (Hangbaiju) using hyperspectral imaging and task-incremental learning","authors":"Zeyi Cai , Xue Guo , Mengyu He , Cheng Li , Hengnian Qi , Ruibin Bai , Jian Yang , Chu Zhang","doi":"10.1016/j.jfca.2026.108885","DOIUrl":"10.1016/j.jfca.2026.108885","url":null,"abstract":"<div><div><em>Chrysanthemum morifolium</em> (Hangbaiju), as a flower tea, contains various bioactive components, exhibiting numerous pharmacological effects. Establishing a rapid-adaptation model for multiple components quantification is challenging. This study utilized hyperspectral imaging (HSI) and machine learning (ML) to quantify 28 Hangbaiju components. When modeled on individual bioactive components, Partial Least Squares Regression (PLSR) showed superior overall prediction, whereas deep learning (DL) excels on specific components. For some components, Residual Predictive Deviation (RPD) values of testing set shifted by more than 6. Task-incremental learning (task-IL) was applied to achieve continual learning for various regression tasks. The model rapidly adapted to new tasks by adding and training only task-specific modules within an already trained base network, while maintaining performance on previous prediction tasks. A linear relationship exists between the performance of the task-IL model and its base network, highlighting the importance of the feature extraction and cross-task generalization capabilities of base network in task-IL. This study demonstrates the potential of DL in the field of quality detection of flower teas and provides a framework for integrating HSI and DL in handling real-world scenarios across various fields.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108885"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927810","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 : 2026-01-08DOI: 10.1016/j.jfca.2026.108891
Yasser Alharbi , Kusum Yadav , Lulwah M. Alkwai , Debashis Dutta , Samim Sherzod
Caloric estimation remains a cornerstone of nutritional modeling, yet formula‑based methods often fail to capture nonlinear interactions among nutrients that influence real energy yield. This study aimed to develop and evaluate machine learning models capable of accurately predicting food caloric content from standardized nutrient profiles, thereby improving estimation precision for diverse food types. A dataset of 410 food items from the USDA FoodData Central was standardized and examined for outliers using leverage statistics, ensuring uniform predictor distribution and data integrity. Seven nutritional variables, including protein, fat, carbohydrates, sugar, fiber, sodium, and potassium, served as predictors of caloric content. Six algorithms were trained and validated: Decision Tree, AdaBoost, Random Forest, Support Vector Regression, and Multi‑Layer Perceptron, with model optimization via 5‑fold cross‑validation. Among tested algorithms, the MLP achieved the highest coefficient of determination (R²≈0.996) reflecting the strong deterministic relationship between nutrient composition and calories within the standardized USDA dataset. SHAP analysis identified carbohydrates, sugar, fat, and protein as the most influential predictors, consistent with physiological expectations. The findings demonstrate that data‑driven models can replicate theoretical caloric relationships with high fidelity while capturing minor nonlinear effects absent in conventional methods. This work contributes a transparent and reproducible framework for machine learning‑based caloric estimation and underscores the potential of deep learning architectures for enhanced nutritional prediction.
{"title":"Data driven modeling of nutritional profiles for caloric prediction using advanced machine learning techniques","authors":"Yasser Alharbi , Kusum Yadav , Lulwah M. Alkwai , Debashis Dutta , Samim Sherzod","doi":"10.1016/j.jfca.2026.108891","DOIUrl":"10.1016/j.jfca.2026.108891","url":null,"abstract":"<div><div>Caloric estimation remains a cornerstone of nutritional modeling, yet formula‑based methods often fail to capture nonlinear interactions among nutrients that influence real energy yield. This study aimed to develop and evaluate machine learning models capable of accurately predicting food caloric content from standardized nutrient profiles, thereby improving estimation precision for diverse food types. A dataset of 410 food items from the USDA FoodData Central was standardized and examined for outliers using leverage statistics, ensuring uniform predictor distribution and data integrity. Seven nutritional variables, including protein, fat, carbohydrates, sugar, fiber, sodium, and potassium, served as predictors of caloric content. Six algorithms were trained and validated: Decision Tree, AdaBoost, Random Forest, Support Vector Regression, and Multi‑Layer Perceptron, with model optimization via 5‑fold cross‑validation. Among tested algorithms, the MLP achieved the highest coefficient of determination (R²≈0.996) reflecting the strong deterministic relationship between nutrient composition and calories within the standardized USDA dataset. SHAP analysis identified carbohydrates, sugar, fat, and protein as the most influential predictors, consistent with physiological expectations. The findings demonstrate that data‑driven models can replicate theoretical caloric relationships with high fidelity while capturing minor nonlinear effects absent in conventional methods. This work contributes a transparent and reproducible framework for machine learning‑based caloric estimation and underscores the potential of deep learning architectures for enhanced nutritional prediction.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108891"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023624","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 : 2026-01-08DOI: 10.1016/j.jfca.2026.108889
Zheng-Yun He , Jun Xiang , Xing-Zi Ding , Qing-Qing Luo , Hui-Wen Gu , Xiao-Li Yin
This study investigated the distribution of signature components in renowned Chinese green teas to develop a method for authenticity identification. By employing high-performance liquid chromatography-diode array detection (HPLC-DAD) coupled with the alternating trilinear decomposition (ATLD) algorithm, a second-order calibration model was constructed that effectively addressed the challenge of co-elution peaks in complex tea matrices. The method demonstrated satisfactory accuracy, with spiked recoveries ranging from 93.5 % to 119.6 % and relative standard deviations (RSDs) below 1.10 % for all analytes. Using the established ATLD-enhanced HPLC method, a quantitative analysis of ten major components in 37 Chinese green teas was conducted, and their distribution patterns were visualized. Based on these quantitative results, multivariate statistical analysis methods, principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were subsequently employed to effectively utilize the data for authenticity discrimination concerning the geographical origin, variety, and grade of the teas. The results revealed that characteristic components varied significantly with tea origin, variety, and grade, enabling the authenticity identification of green tea. These findings not only confirm the efficacy of key catechins for authenticity identification but also establish an analytical strategy for quality evaluation, providing a powerful framework for industrial quality control and origin verification.
{"title":"Distribution characteristics of catechins, alkaloids and gallic acid in Chinese famous green tea and its application in authenticity identification","authors":"Zheng-Yun He , Jun Xiang , Xing-Zi Ding , Qing-Qing Luo , Hui-Wen Gu , Xiao-Li Yin","doi":"10.1016/j.jfca.2026.108889","DOIUrl":"10.1016/j.jfca.2026.108889","url":null,"abstract":"<div><div>This study investigated the distribution of signature components in renowned Chinese green teas to develop a method for authenticity identification. By employing high-performance liquid chromatography-diode array detection (HPLC-DAD) coupled with the alternating trilinear decomposition (ATLD) algorithm, a second-order calibration model was constructed that effectively addressed the challenge of co-elution peaks in complex tea matrices. The method demonstrated satisfactory accuracy, with spiked recoveries ranging from 93.5 % to 119.6 % and relative standard deviations (RSDs) below 1.10 % for all analytes. Using the established ATLD-enhanced HPLC method, a quantitative analysis of ten major components in 37 Chinese green teas was conducted, and their distribution patterns were visualized. Based on these quantitative results, multivariate statistical analysis methods, principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were subsequently employed to effectively utilize the data for authenticity discrimination concerning the geographical origin, variety, and grade of the teas. The results revealed that characteristic components varied significantly with tea origin, variety, and grade, enabling the authenticity identification of green tea. These findings not only confirm the efficacy of key catechins for authenticity identification but also establish an analytical strategy for quality evaluation, providing a powerful framework for industrial quality control and origin verification.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108889"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978757","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 : 2026-01-08DOI: 10.1016/j.jfca.2026.108883
Amir Kazemi , Mostafa Khojastehnazhand
Precise authentication of saffron types in Iran, as the main producer not only prevents economic losses for producers and consumers, but also minimizes the threat of adulteration. Therefore, in the present research, combination of machine vision method and texture feature extraction algorithms was applied to classify 3 main types of Iran commercial saffron including Sargol, Negin, and Pushal. Three texture feature extraction algorithms including Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRM) and combination of them were employed. Various machine learning models including Discriminant Analysis (DA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) models were applied to evaluate the results of classification. SVM model with Linear kernel and all features had the best outcome with 87.22 % for test dataset. Furthermore, four feature selection algorithms including Chi-Square Test (CST), Minimum Redundancy Maximum Relevance (MRMR), ReliefF, and Kruskal Wallis were used to select the most important features. Selected features by CST algorithm with SVM model had the best outcome with the accuracy of 76.7 %. The results of present research confirm the applicability of machine vision technique for classification of commercial saffron types which is significant in saffron industry.
{"title":"Classification of Iranian king of spices (saffron) types by texture features","authors":"Amir Kazemi , Mostafa Khojastehnazhand","doi":"10.1016/j.jfca.2026.108883","DOIUrl":"10.1016/j.jfca.2026.108883","url":null,"abstract":"<div><div>Precise authentication of saffron types in Iran, as the main producer not only prevents economic losses for producers and consumers, but also minimizes the threat of adulteration. Therefore, in the present research, combination of machine vision method and texture feature extraction algorithms was applied to classify 3 main types of Iran commercial saffron including Sargol, Negin, and Pushal. Three texture feature extraction algorithms including Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRM) and combination of them were employed. Various machine learning models including Discriminant Analysis (DA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) models were applied to evaluate the results of classification. SVM model with Linear kernel and all features had the best outcome with 87.22 % for test dataset. Furthermore, four feature selection algorithms including Chi-Square Test (CST), Minimum Redundancy Maximum Relevance (MRMR), ReliefF, and Kruskal Wallis were used to select the most important features. Selected features by CST algorithm with SVM model had the best outcome with the accuracy of 76.7 %. The results of present research confirm the applicability of machine vision technique for classification of commercial saffron types which is significant in saffron industry.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"151 ","pages":"Article 108883"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076089","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 : 2026-01-08DOI: 10.1016/j.jfca.2026.108886
Xiaoyu Xu , Yanwen Yang , Siyuan Zhang , Zhiwei Ye , Xuejun Li , Yanyu Jin , Trungtín Hoàng , Tao Liu , Qing Liu , Xudong Wu , Hong Li
The rapid expansion of the global coffee industry has positioned China as a dynamic and evolving market, transitioning from a traditionally tea-centric culture to an emerging centre of coffee production and consumption. This review focuses on the science of coffee flavour with particular attention to Yunnan province, a leading region for high-quality Coffea arabica industry. Chemical foundations of flavour are examined, highlighting the contributions of a variety of compounds in shaping sensory perception. Advances in analytical methodologies, including gas and liquid chromatography-mass spectrometry, nuclear magnetic resonance, and electronic sensory systems, are reviewed for their roles in flavour profiling. Beyond chemistry, influence of genetics, climate, post-harvest processing, and microbial fermentation were discussed, as well as the emerging applications of machine learning and artificial intelligence, especially their use in flavour prediction and process optimization. Compared to traditional origins, Yunnan Arabica often exhibits a smoother body and milder acidity than the bright, wine-like acidity characteristic of Ethiopian coffees, a less pronounced nutty chocolate profile than some Brazilian counterparts, and a distinct floral and herbal complexity that differs from the balanced, caramel-like notes of high-quality Colombian beans. This unique profile positions Yunnan as a distinct and valuable origin within the global specialty coffee spectrum.
{"title":"Exploring the chemistry and craft behind coffee flavour underpinned by the growing coffee industry in China (Yunnan): A review","authors":"Xiaoyu Xu , Yanwen Yang , Siyuan Zhang , Zhiwei Ye , Xuejun Li , Yanyu Jin , Trungtín Hoàng , Tao Liu , Qing Liu , Xudong Wu , Hong Li","doi":"10.1016/j.jfca.2026.108886","DOIUrl":"10.1016/j.jfca.2026.108886","url":null,"abstract":"<div><div>The rapid expansion of the global coffee industry has positioned China as a dynamic and evolving market, transitioning from a traditionally tea-centric culture to an emerging centre of coffee production and consumption. This review focuses on the science of coffee flavour with particular attention to Yunnan province, a leading region for high-quality <em>Coffea arabica</em> industry. Chemical foundations of flavour are examined, highlighting the contributions of a variety of compounds in shaping sensory perception. Advances in analytical methodologies, including gas and liquid chromatography-mass spectrometry, nuclear magnetic resonance, and electronic sensory systems, are reviewed for their roles in flavour profiling. Beyond chemistry, influence of genetics, climate, post-harvest processing, and microbial fermentation were discussed, as well as the emerging applications of machine learning and artificial intelligence, especially their use in flavour prediction and process optimization. Compared to traditional origins, Yunnan Arabica often exhibits a smoother body and milder acidity than the bright, wine-like acidity characteristic of Ethiopian coffees, a less pronounced nutty chocolate profile than some Brazilian counterparts, and a distinct floral and herbal complexity that differs from the balanced, caramel-like notes of high-quality Colombian beans. This unique profile positions Yunnan as a distinct and valuable origin within the global specialty coffee spectrum.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108886"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978061","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}
Date syrup, a traditional Saharan product, is attracting attention as a natural sugar alternative for the North African food sector. Its beneficial dietary and medicinal effects are attributed to its natural antioxidants, sugars, and minerals. However, poor agricultural practices and environmental factors may cause contaminants to be released into date syrup. Therefore, mineral content of seven commercial date syrups from various Algerian areas was analyzed by ICP-MS, including for the first time toxic and potentially toxic elements. Estimated dietary intakes (EDIs) were calculated for children (1–3 years) and adults, based on a daily serving of 10 g/day and 30 g/day. The plausibility of chronic non-carcinogenic risks was assessed by calculating the hazard quotient (HQ). Mineral profile was dominated by K, followed by Ca, Mg, Na, Fe, and Zn in most samples. Pb concentrations were always below the maximum limit permitted by European Regulation 915/2023 (0.1 mg/kg). EDIs did not exceed the reference limits. However, since the As percentage absorbed by children in some cases covered 38 % of its TDI, it is essential to strenghten monitor programs on this natural sweeting agent and establish evidence-based guidelines for its correct consumption.
{"title":"Macro-, micro- and potential toxic elements in commercial Algerian date syrup: Safety aspects and dietary risk assessment","authors":"Qada Benameur , Angela Giorgia Potortì , Vincenzo Nava , Federica Litrenta , Nadra Rechidi-Sidhoum , Meki Boutaiba Benklaouz , Benedetta Sgrò , Ambrogina Albergamo , Giuseppa Di Bella","doi":"10.1016/j.jfca.2026.108887","DOIUrl":"10.1016/j.jfca.2026.108887","url":null,"abstract":"<div><div>Date syrup, a traditional Saharan product, is attracting attention as a natural sugar alternative for the North African food sector. Its beneficial dietary and medicinal effects are attributed to its natural antioxidants, sugars, and minerals. However, poor agricultural practices and environmental factors may cause contaminants to be released into date syrup. Therefore, mineral content of seven commercial date syrups from various Algerian areas was analyzed by ICP-MS, including for the first time toxic and potentially toxic elements. Estimated dietary intakes (EDIs) were calculated for children (1–3 years) and adults, based on a daily serving of 10 g/day and 30 g/day. The plausibility of chronic non-carcinogenic risks was assessed by calculating the hazard quotient (HQ). Mineral profile was dominated by K, followed by Ca, Mg, Na, Fe, and Zn in most samples. Pb concentrations were always below the maximum limit permitted by European Regulation 915/2023 (0.1 mg/kg). EDIs did not exceed the reference limits. However, since the As percentage absorbed by children in some cases covered 38 % of its TDI, it is essential to strenghten monitor programs on this natural sweeting agent and establish evidence-based guidelines for its correct consumption.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108887"},"PeriodicalIF":4.6,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978726","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 : 2026-01-07DOI: 10.1016/j.jfca.2026.108878
Eunice Afia Amponsah , Amma Larbi , Moses Etsey , Gifty Yeboah , Linda Nana Esi Aduku , Charles Apprey , Herman Erick Lutterodt , Reginald Adjetey Annan
Genetic diversity, which encompasses variations in gene sequences within a species, significantly impacts traits such as yield, nutrient content, resilience, and adaptability. This systematic review focuses on the nutritional profiles of key food crops: cassava (Manihot esculenta), tomato (Solanum lycopersicum), bambara groundnut (Vigna subterranea), and fonio (Digitaria exilis), following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and examining studies from the past 20 years. After a comprehensive search of four databases, 5 studies on cassava, 9 on tomato, 6 on bambara groundnut, and 1 on fonio were selected. The findings reveal notable nutritional variations based on genotype: cassava genotypes exhibited high range hydrogen cyanide levels and starch content; tomato genotypes varied moderately in lycopene levels, and bambara groundnut accessions showed moderate protein levels and polyunsaturated fatty acids (PUFA) content up to 51.6 %. Although fonio exhibited limited genetic diversity, it maintained unique nutritional properties. Overall, the review indicates that utilising genetic diversity can aid in breeding nutrient-rich, resilient crops, enhancing food and nutrition security in resource-limited areas.
{"title":"Genetic diversity and nutritional variation in food crops in Ghana: A systematic review","authors":"Eunice Afia Amponsah , Amma Larbi , Moses Etsey , Gifty Yeboah , Linda Nana Esi Aduku , Charles Apprey , Herman Erick Lutterodt , Reginald Adjetey Annan","doi":"10.1016/j.jfca.2026.108878","DOIUrl":"10.1016/j.jfca.2026.108878","url":null,"abstract":"<div><div>Genetic diversity, which encompasses variations in gene sequences within a species, significantly impacts traits such as yield, nutrient content, resilience, and adaptability. This systematic review focuses on the nutritional profiles of key food crops: cassava (<em>Manihot esculenta</em>), tomato (<em>Solanum lycopersicum)</em>, bambara groundnut (<em>Vigna subterranea</em>), and fonio (<em>Digitaria exilis</em>), following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and examining studies from the past 20 years. After a comprehensive search of four databases, 5 studies on cassava, 9 on tomato, 6 on bambara groundnut, and 1 on fonio were selected. The findings reveal notable nutritional variations based on genotype: cassava genotypes exhibited high range hydrogen cyanide levels and starch content; tomato genotypes varied moderately in lycopene levels, and bambara groundnut accessions showed moderate protein levels and polyunsaturated fatty acids (PUFA) content up to 51.6 %. Although fonio exhibited limited genetic diversity, it maintained unique nutritional properties. Overall, the review indicates that utilising genetic diversity can aid in breeding nutrient-rich, resilient crops, enhancing food and nutrition security in resource-limited areas.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108878"},"PeriodicalIF":4.6,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we designed and synthesized nitrogen, iron, cobalt and nickel trimetallic loaded carbon nanosheets to detect chloramphenicol (CAP). Melamine was used as the carbon source and cobalt acetate tetrahydrate, nickel nitrate hexahydrate and potassium iron oxalate as the metal sources. Under hydrothermal reaction, melamine reacted with cobalt acetate tetrahydrate and nickel nitrate hexahydrate to form melamine cobalt-nickel complex, and then under high temperature calcination, the carbon nanosheets were carbonized, the metal ions were reduced to metal nanoparticles, and at the same time iron and nitrogen were co-doped, and finally, a new type of metal nanomaterials with three-metal (Fe, Co, Ni) loaded carbon nanosheets was obtained. The electrochemical detection results showed that Co Ni Fe NPs@CNS showed good electrocatalytic performance for CAP, exhibiting a wide detection range (0.1 µM-1000 µM), low detection limit (0.008 µM) and high sensitivity (0.3342 µA/µM). The recovery rate range for honey samples is 97.20 %-101.87 %, the recovery rate range for milk samples is 96.9 %-102.60 %. It can be used for the detection of actual samples with strong anti-interference ability. The electrocatalyst we studied has significant advantages in food detection due to its low detection limit, low cost, and good selectivity, and it demonstrates broad application prospects in the field of food analysis and detection.
本文设计并合成了氮、铁、钴和镍三金属负载的碳纳米片，用于检测氯霉素（CAP）。以三聚氰胺为碳源，四水合乙酸钴、六水合硝酸镍和草酸铁钾为金属源。在水热反应下，三聚氰胺与四水合乙酸钴和六水合硝酸镍反应形成三聚氰胺钴镍配合物，然后在高温煅烧下对碳纳米片进行碳化，将金属离子还原为金属纳米粒子，同时对铁和氮进行共掺杂，最终得到了一种新型的三金属（Fe、Co、Ni）负载碳纳米片的金属纳米材料。电化学检测结果表明，Co Ni Fe NPs@CNS对CAP具有良好的电催化性能，检测范围宽（0.1µM-1000µM），检出限低（0.008 µM），灵敏度高（0.3342 µa /µM）。蜂蜜样品的回收率范围为97.20 % ~ 101.87 %，牛奶样品的回收率范围为96.9 % ~ 102.60 %。可用于实际样品的检测，抗干扰能力强。所研究的电催化剂检出限低、成本低、选择性好，在食品分析检测领域具有显著优势，在食品分析检测领域具有广阔的应用前景。
{"title":"Detection of Chloramphenicol in milk and honey by electrochemical sensors based on iron, cobalt, and nickel trimetals/carbon nanosheets","authors":"Fangxun Liu, Yanrui Li, Shuang Liu, Pinyi Zhao, Xin Yang, Xin Li, Jinpeng Liu, Zheng Zhang, Genggeng Zhang, Peigang Han, Xianling Wang, Xinjian Yang, Huan Wang","doi":"10.1016/j.jfca.2026.108879","DOIUrl":"10.1016/j.jfca.2026.108879","url":null,"abstract":"<div><div>In this paper, we designed and synthesized nitrogen, iron, cobalt and nickel trimetallic loaded carbon nanosheets to detect chloramphenicol (CAP). Melamine was used as the carbon source and cobalt acetate tetrahydrate, nickel nitrate hexahydrate and potassium iron oxalate as the metal sources. Under hydrothermal reaction, melamine reacted with cobalt acetate tetrahydrate and nickel nitrate hexahydrate to form melamine cobalt-nickel complex, and then under high temperature calcination, the carbon nanosheets were carbonized, the metal ions were reduced to metal nanoparticles, and at the same time iron and nitrogen were co-doped, and finally, a new type of metal nanomaterials with three-metal (Fe, Co, Ni) loaded carbon nanosheets was obtained. The electrochemical detection results showed that Co Ni Fe NPs@CNS showed good electrocatalytic performance for CAP, exhibiting a wide detection range (0.1 µM-1000 µM), low detection limit (0.008 µM) and high sensitivity (0.3342 µA/µM). The recovery rate range for honey samples is 97.20 %-101.87 %, the recovery rate range for milk samples is 96.9 %-102.60 %. It can be used for the detection of actual samples with strong anti-interference ability. The electrocatalyst we studied has significant advantages in food detection due to its low detection limit, low cost, and good selectivity, and it demonstrates broad application prospects in the field of food analysis and detection.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108879"},"PeriodicalIF":4.6,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978060","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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