Journal of Food Composition and Analysis最新文献

英文中文

State-of-the-art review of morel: From chemistry to nutrition and health benefits 羊肚菌的最新研究成果：从化学到营养和健康益处

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107351

Chengxuan Xu , Lijuan Qian , Qi Meng , Yujun Sun

Morchella esculenta (L.) Pers. (M. esculenta), commonly known as the morel, is a highly esteemed edible and medicinal mushroom renowned for its distinctive flavor and diverse health benefits. This species is rich in essential nutrients and bioactive compounds, including proteins, carbohydrates, vitamins, and minerals, which contribute to its high nutritional value and unique flavor profile. Notably, morels contain an array of bioactive constituents such as polysaccharides, polyphenols, alkaloids, saponins, terpenoids, quinones, lignocellulosic enzymes, and lipoxygenase. These compounds underpin the diverse bioactivities attributed to morels, including immunomodulation, antioxidation, organ protection, lipid and glucose homeostasis regulation, anti-cancer effects, and mitigation of chemotherapy-induced toxicity. This review comprehensively summarizes the key nutrients and bioactive compounds present in morels, detailing their extraction methods and subsequent analyses. The insights provided aim to support potential industrial applications of morels, particularly in the development of functional foods. Furthermore, this review explores the various bioactivities of morels and their underlying molecular mechanisms, contributing to a deeper understanding of this valuable fungal resource.

{"title":"State-of-the-art review of morel: From chemistry to nutrition and health benefits","authors":"Chengxuan Xu , Lijuan Qian , Qi Meng , Yujun Sun","doi":"10.1016/j.jfca.2025.107351","DOIUrl":"10.1016/j.jfca.2025.107351","url":null,"abstract":"<div><div><em>Morchella esculenta</em> (L.) Pers. (M. <em>esculenta</em>), commonly known as the morel, is a highly esteemed edible and medicinal mushroom renowned for its distinctive flavor and diverse health benefits. This species is rich in essential nutrients and bioactive compounds, including proteins, carbohydrates, vitamins, and minerals, which contribute to its high nutritional value and unique flavor profile. Notably, morels contain an array of bioactive constituents such as polysaccharides, polyphenols, alkaloids, saponins, terpenoids, quinones, lignocellulosic enzymes, and lipoxygenase. These compounds underpin the diverse bioactivities attributed to morels, including immunomodulation, antioxidation, organ protection, lipid and glucose homeostasis regulation, anti-cancer effects, and mitigation of chemotherapy-induced toxicity. This review comprehensively summarizes the key nutrients and bioactive compounds present in morels, detailing their extraction methods and subsequent analyses. The insights provided aim to support potential industrial applications of morels, particularly in the development of functional foods. Furthermore, this review explores the various bioactivities of morels and their underlying molecular mechanisms, contributing to a deeper understanding of this valuable fungal resource.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107351"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394534","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}

引用次数: 0

A cumulative learning method for pixel-level hyperspectral detection of aflatoxins on peanuts using convolutional neural network

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107356

Yifan Zhao , Hongfei Zhu , Limiao Deng , Zhongzhi Han

This study introduces a novel cumulative learning method to overcome the limitations of hyperspectral data in aflatoxin detection in peanuts. By aggregating spectral characteristics from remote sensing and near-infrared spectral images, the method enhances detection accuracy. We validate its effectiveness through comparative model analysis, utilizing superimposed images of similar materials to address data heterogeneity and low resolution. The results demonstrate that the cumulative learning model's performance is significantly improved, with all six methods achieving accuracies above 0.97, surpassing the original 1D-CNN and traditional transfer learning models. Additionally, compared to advanced semi-supervised models, the cumulative learning method exhibits superior performance, with accuracies exceeding 0.95. This approach not only reduces model complexity and data collection costs but also effectively enhances classification accuracy in peanut aflatoxin detection, thereby facilitating efficient online monitoring.

引用次数: 0

Beef quality dual-label classification incorporating texture and multihead map attention mechanisms 结合纹理和多头图关注机制的牛肉质量双标签分类法

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107360

Runzhe Zhang, Weiming Shi, Yueyang Pan, Yifan Zhao, Zhenlu Hua, Yanshen Zhao, Qiang Pan, Zhongzhi Han

With the increasing consumer demand for high-quality and safe beef, there is an urgent need for advanced scientific testing methods to ensure product quality. This study introduces a rapid and accurate dual-label classification system for beef cuts and marbling grades, combining a multi-head attention mechanism and contrast features extracted from the Gray-Level Co-occurrence Matrix (GLCM), significantly improving classification accuracy. A comparison of different ResNet models revealed that ResNet50 achieved the highest classification accuracies, 85.65 % and 85.03 %, respectively. Building on the ResNet50 model, GLCM was used to extract contrast features in four directions from beef images, followed by feature fusion. Compared to SE and non-corresponding multi-head Graph Attention Networks (GAT), a multi-head GAT focusing on GLCM texture features in each direction was selected, with these features fused one-to-one with the fully connected layer features of RGB images and incorporating a feature fusion mechanism. Our model achieved 93.5 % accuracy in cut classification and 92.25 % in grade classification. Additionally, an app based on the optimal model was developed, enabling users to perform real-time testing and obtain results. The goal of this study was to develop the fastest and most accurate dual-label classification system, significantly improving the speed and reliability of obtaining product information during marbling selection.

{"title":"Beef quality dual-label classification incorporating texture and multihead map attention mechanisms","authors":"Runzhe Zhang, Weiming Shi, Yueyang Pan, Yifan Zhao, Zhenlu Hua, Yanshen Zhao, Qiang Pan, Zhongzhi Han","doi":"10.1016/j.jfca.2025.107360","DOIUrl":"10.1016/j.jfca.2025.107360","url":null,"abstract":"<div><div>With the increasing consumer demand for high-quality and safe beef, there is an urgent need for advanced scientific testing methods to ensure product quality. This study introduces a rapid and accurate dual-label classification system for beef cuts and marbling grades, combining a multi-head attention mechanism and contrast features extracted from the Gray-Level Co-occurrence Matrix (GLCM), significantly improving classification accuracy. A comparison of different ResNet models revealed that ResNet50 achieved the highest classification accuracies, 85.65 % and 85.03 %, respectively. Building on the ResNet50 model, GLCM was used to extract contrast features in four directions from beef images, followed by feature fusion. Compared to SE and non-corresponding multi-head Graph Attention Networks (GAT), a multi-head GAT focusing on GLCM texture features in each direction was selected, with these features fused one-to-one with the fully connected layer features of RGB images and incorporating a feature fusion mechanism. Our model achieved 93.5 % accuracy in cut classification and 92.25 % in grade classification. Additionally, an app based on the optimal model was developed, enabling users to perform real-time testing and obtain results. The goal of this study was to develop the fastest and most accurate dual-label classification system, significantly improving the speed and reliability of obtaining product information during marbling selection.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107360"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419020","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}

引用次数: 0

A novel application of ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry for the rapid quantification of acrylamide in coffee 超高效液相色谱串联三重四极杆质谱法在快速定量咖啡中丙烯酰胺中的新应用

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107357

Peng Wang , Nianhua Zhang , Zhengyan Hu , Pinggu Wu , Jiawen Mao , Jingshun Zhang , Zengxuan Cai , Zhiyuan Wang , Junlin Wang

We developed a simple, rapid, accurate, and sensitive method for the determination of acrylamide (AA) in coffee based on ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry (UPLC-MS/MS). The AA in coffee was simply extracted with water and then defatted with dichloromethane. After that, concentration and purification were achieved through solid-phase extraction techniques. The calibration curve for AA showed excellent linearity over the concentration range of 0.5–500 ng/mL, with a correlation coefficient (r) in excess of 0.999. Furthermore, our method exhibited high precision, as evidenced by intra- and inter-day coefficients of variation of less than 5.7 % and 6.3 %, respectively. The analytical accuracy for AA quantification ranged from 98.0 % to 105.2 %, and the limit of detection and limit of quantification for AA in coffee were determined to be 1.5 μg/kg and 5.0 μg/kg, respectively. In addition, the applicability of the proposed method was assessed through its implementation in the quantification of AA in both roasted and instant coffee. The results prove that the method is simple, rapid, accurate, and sensitive, making it as an excellent option for the detection of AA in coffee.

{"title":"A novel application of ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry for the rapid quantification of acrylamide in coffee","authors":"Peng Wang , Nianhua Zhang , Zhengyan Hu , Pinggu Wu , Jiawen Mao , Jingshun Zhang , Zengxuan Cai , Zhiyuan Wang , Junlin Wang","doi":"10.1016/j.jfca.2025.107357","DOIUrl":"10.1016/j.jfca.2025.107357","url":null,"abstract":"<div><div>We developed a simple, rapid, accurate, and sensitive method for the determination of acrylamide (AA) in coffee based on ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry (UPLC-MS/MS). The AA in coffee was simply extracted with water and then defatted with dichloromethane. After that, concentration and purification were achieved through solid-phase extraction techniques. The calibration curve for AA showed excellent linearity over the concentration range of 0.5–500 ng/mL, with a correlation coefficient (<em>r</em>) in excess of 0.999. Furthermore, our method exhibited high precision, as evidenced by intra- and inter-day coefficients of variation of less than 5.7 % and 6.3 %, respectively. The analytical accuracy for AA quantification ranged from 98.0 % to 105.2 %, and the limit of detection and limit of quantification for AA in coffee were determined to be 1.5 μg/kg and 5.0 μg/kg, respectively. In addition, the applicability of the proposed method was assessed through its implementation in the quantification of AA in both roasted and instant coffee. The results prove that the method is simple, rapid, accurate, and sensitive, making it as an excellent option for the detection of AA in coffee.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107357"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419018","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}

引用次数: 0

Accurate prediction of piperine content in black pepper using combined CNN and regression modelling with PDMAM@G electrode and cyclic voltammetry

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107355

Sanjoy Banerjee , Santanu Ghorai , Milan Dhara , Hemanta Naskar , Sk Babar Ali , Nityananda Das , Pradip Saha , Bhimsen Tudu , Arpitam Chatterjee , Rajib Bandyopadhyay , Bipan Tudu

A novel graphite electrode with molecular imprints was developed for the selective and sensitive detection of piperine in black pepper. The electrode incorporates molecularly imprinted polymer (MIP) layers synthesized using poly (N,N-dimethylacrylamide) (PDMAM) as the monomer, ethylene glycol dimethacrylate (EGDMA) as the cross-linker, and piperine as the template, enabling specific recognition and quantification of piperine. Cyclic voltammetry (CV) was employed for electrochemical measurements, and the sensor was validated on black pepper samples from four different brands, demonstrating its practical applicability. To enhance prediction accuracy, convolutional neural network (CNN)-based feature extraction was combined with regression models for the analysis of CV signals. This hybrid approach, integrating CNN-extracted features with regression techniques such as K-nearest neighbour regressor (KNNR), gradient boost regressor (GBR), and random forest regressor (RFR), exhibited significant improvements in accuracy compared to the CNN model alone. Comprehensive experimental evaluations revealed that the CNN-KNNR model achieved a mean absolute percentage error of 0.034 and an R² value of 0.9999 when compared to reference values obtained through reverse-phase high-performance liquid chromatography (RP-HPLC).

{"title":"Accurate prediction of piperine content in black pepper using combined CNN and regression modelling with PDMAM@G electrode and cyclic voltammetry","authors":"Sanjoy Banerjee , Santanu Ghorai , Milan Dhara , Hemanta Naskar , Sk Babar Ali , Nityananda Das , Pradip Saha , Bhimsen Tudu , Arpitam Chatterjee , Rajib Bandyopadhyay , Bipan Tudu","doi":"10.1016/j.jfca.2025.107355","DOIUrl":"10.1016/j.jfca.2025.107355","url":null,"abstract":"<div><div>A novel graphite electrode with molecular imprints was developed for the selective and sensitive detection of piperine in black pepper. The electrode incorporates molecularly imprinted polymer (MIP) layers synthesized using poly (N,N-dimethylacrylamide) (PDMAM) as the monomer, ethylene glycol dimethacrylate (EGDMA) as the cross-linker, and piperine as the template, enabling specific recognition and quantification of piperine. Cyclic voltammetry (CV) was employed for electrochemical measurements, and the sensor was validated on black pepper samples from four different brands, demonstrating its practical applicability. To enhance prediction accuracy, convolutional neural network (CNN)-based feature extraction was combined with regression models for the analysis of CV signals. This hybrid approach, integrating CNN-extracted features with regression techniques such as K-nearest neighbour regressor (KNNR), gradient boost regressor (GBR), and random forest regressor (RFR), exhibited significant improvements in accuracy compared to the CNN model alone. Comprehensive experimental evaluations revealed that the CNN-KNNR model achieved a mean absolute percentage error of 0.034 and an R² value of 0.9999 when compared to reference values obtained through reverse-phase high-performance liquid chromatography (RP-HPLC).</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107355"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403014","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}

引用次数: 0

A molecularly imprinted electrochemical sensor based on rGO@rGNR modification for zearalenone determination

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107361

Xiaoqi Zheng, Xuan Yang, Hao Xie, Yuan Li, Xinyi Li, Binbin Zhou

The research on electrochemical sensors has made great progress in recent years, but they still face challenges in detecting trace harmful substances in complex matrices. In this comprehensive investigation, quasi-one-dimensional reduced graphene nanoribbons (rGNR) and two-dimensional reduced graphene oxide (rGO) were jointly functionalized on the surface of a glassy carbon electrode, yielding a sophisticated three-dimensional rGO@rGNR hybrid material. The intrinsic synergistic effect of the two carbon materials on the structure of rGO@rGNR improved the comparative specific surface area and the total conductivity. Subsequently, by leveraging the specificity of molecularly imprinted polymers (MIP), an electrochemical sensor has been developed to detect zearalenone (ZEA). After fine-tuning the experimental parameters, the sensor exhibited an impressive linear range of 0.5–500 ng·mL^–1, a low detection limit of 0.19 ng·mL^–1, and outstanding selectivity. Moreover, the recovery rate of ZEA in corn meal samples is good. Compared to previously reported sensors for ZEA detection, this sensor boasts simplicity in operation, economy in cost, exceptional sensitivity, and superior selectivity.

{"title":"A molecularly imprinted electrochemical sensor based on rGO@rGNR modification for zearalenone determination","authors":"Xiaoqi Zheng, Xuan Yang, Hao Xie, Yuan Li, Xinyi Li, Binbin Zhou","doi":"10.1016/j.jfca.2025.107361","DOIUrl":"10.1016/j.jfca.2025.107361","url":null,"abstract":"<div><div>The research on electrochemical sensors has made great progress in recent years, but they still face challenges in detecting trace harmful substances in complex matrices. In this comprehensive investigation, quasi-one-dimensional reduced graphene nanoribbons (rGNR) and two-dimensional reduced graphene oxide (rGO) were jointly functionalized on the surface of a glassy carbon electrode, yielding a sophisticated three-dimensional rGO@rGNR hybrid material. The intrinsic synergistic effect of the two carbon materials on the structure of rGO@rGNR improved the comparative specific surface area and the total conductivity. Subsequently, by leveraging the specificity of molecularly imprinted polymers (MIP), an electrochemical sensor has been developed to detect zearalenone (ZEA). After fine-tuning the experimental parameters, the sensor exhibited an impressive linear range of 0.5–500 ng·mL<sup>–1</sup>, a low detection limit of 0.19 ng·mL<sup>–1</sup>, and outstanding selectivity. Moreover, the recovery rate of ZEA in corn meal samples is good. Compared to previously reported sensors for ZEA detection, this sensor boasts simplicity in operation, economy in cost, exceptional sensitivity, and superior selectivity.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107361"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394535","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}

引用次数: 0

Research on millet origin identification model based on improved parrot optimizer optimized regularized extreme learning machine

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107354

Peng Gao, Na Wang, Yang Lu, Jinming Liu, Guannan Wang, Rui Hou

To achieve nondestructive identification of millet origins, near-infrared spectroscopy technology was employed to obtain the original spectral data of millet. By integrating the Parrot Optimizer (PO) with the Regularized Extreme Learning Machine (RELM), the model achieved an accuracy of 83.67 % in millet origin identification. To further enhance model performance, this study incorporated strategies such as chaotic mapping and adaptivity into PO, resulting in the Improved Parrot Optimizer (IPO). The IPO was then combined with RELM to construct the IPO-RELM model, which significantly improved the model's generalization capability and robustness. Experimental results demonstrated that the IPO-RELM model outperformed the RELM model, achieving an accuracy of 98.33 %, a precision of 98.49 %, a recall of 98.33 %, an F1 score of 98.41 %, and a Kappa coefficient of 98 %, representing respective improvements of 11.32 %, 7.92 %, 11.32 %, 9.62 %, and 13.90 % over the traditional RELM model. Furthermore, the performance of the IPO-RELM model was validated using two publicly available datasets, confirming its superiority over the conventional RELM model. Compared to the PO algorithm, the IPO algorithm exhibited enhanced global search and local optimization capabilities with faster convergence speed. The IPO-RELM model accurately and efficiently identified millet origin information, providing robust support for ensuring millet quality and safety, while also contributing to the protection of the uniqueness and market value of geographically indicated agricultural products.

{"title":"Research on millet origin identification model based on improved parrot optimizer optimized regularized extreme learning machine","authors":"Peng Gao, Na Wang, Yang Lu, Jinming Liu, Guannan Wang, Rui Hou","doi":"10.1016/j.jfca.2025.107354","DOIUrl":"10.1016/j.jfca.2025.107354","url":null,"abstract":"<div><div>To achieve nondestructive identification of millet origins, near-infrared spectroscopy technology was employed to obtain the original spectral data of millet. By integrating the Parrot Optimizer (PO) with the Regularized Extreme Learning Machine (RELM), the model achieved an accuracy of 83.67 % in millet origin identification. To further enhance model performance, this study incorporated strategies such as chaotic mapping and adaptivity into PO, resulting in the Improved Parrot Optimizer (IPO). The IPO was then combined with RELM to construct the IPO-RELM model, which significantly improved the model's generalization capability and robustness. Experimental results demonstrated that the IPO-RELM model outperformed the RELM model, achieving an accuracy of 98.33 %, a precision of 98.49 %, a recall of 98.33 %, an F1 score of 98.41 %, and a Kappa coefficient of 98 %, representing respective improvements of 11.32 %, 7.92 %, 11.32 %, 9.62 %, and 13.90 % over the traditional RELM model. Furthermore, the performance of the IPO-RELM model was validated using two publicly available datasets, confirming its superiority over the conventional RELM model. Compared to the PO algorithm, the IPO algorithm exhibited enhanced global search and local optimization capabilities with faster convergence speed. The IPO-RELM model accurately and efficiently identified millet origin information, providing robust support for ensuring millet quality and safety, while also contributing to the protection of the uniqueness and market value of geographically indicated agricultural products.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107354"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403103","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}

引用次数: 0

Ni-Co MOF/ Zn-NTA nanoflowers as adsorbent for dispersive solid phase microextraction of triazole fungicides in water and fruit juice samples before their HPLC-DAD detection

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-10 DOI: 10.1016/j.jfca.2025.107349

Ozgur Ozalp , Mustafa Soylak

A dispersive solid phase microextraction (d-SPME) method was developed for the trace level analysis of Difenoconazole (DFC), Hexaconazole (HXC) and Triticonazole (TRT), which are the triazole fungicides most commonly used by vegetable and fruit producers. Ni-Co MOF/Zn-NTA nanoparticles synthesized for precise and precise extraction of three different triazole fungicides without matrix interference before developing the method were successfully characterized by FT-IR, XRD, FE-SEM, SEM-EDX. pH (5), amount of adsorbent (20 mg), adsorption time (6 min) and desorption time (6 min), sample volume (30 mL) and type of eluent (methanol/ammonia mixture (95/5, v/v) were optimized. Enrichment of three different triazole fungicides were done under optimal conditions for real samples. The preconcentration factor was 30. The results of LOD and LOQ values for DFC, HXC and TRT were determined as 1.5–2.5 ng mL⁻¹ and 5.3–8.0 ng mL⁻¹, respectively. In the developed d-SPME method, RSD values were determined to be below 5 %.

{"title":"Ni-Co MOF/ Zn-NTA nanoflowers as adsorbent for dispersive solid phase microextraction of triazole fungicides in water and fruit juice samples before their HPLC-DAD detection","authors":"Ozgur Ozalp , Mustafa Soylak","doi":"10.1016/j.jfca.2025.107349","DOIUrl":"10.1016/j.jfca.2025.107349","url":null,"abstract":"<div><div>A dispersive solid phase microextraction (d-SPME) method was developed for the trace level analysis of Difenoconazole (DFC), Hexaconazole (HXC) and Triticonazole (TRT), which are the triazole fungicides most commonly used by vegetable and fruit producers. Ni-Co MOF/Zn-NTA nanoparticles synthesized for precise and precise extraction of three different triazole fungicides without matrix interference before developing the method were successfully characterized by FT-IR, XRD, FE-SEM, SEM-EDX. pH (5), amount of adsorbent (20 mg), adsorption time (6 min) and desorption time (6 min), sample volume (30 mL) and type of eluent (methanol/ammonia mixture (95/5, v/v) were optimized. Enrichment of three different triazole fungicides were done under optimal conditions for real samples. The preconcentration factor was 30. The results of LOD and LOQ values for DFC, HXC and TRT were determined as 1.5–2.5 ng mL<sup>−1</sup> and 5.3–8.0 ng mL<sup>−1</sup>, respectively. In the developed d-SPME method, RSD values were determined to be below 5 %.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107349"},"PeriodicalIF":4.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387600","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}

引用次数: 0

An updated picture of the food supply in Spain using the branded food composition database TABULA™

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-10 DOI: 10.1016/j.jfca.2025.107347

Marina Redruello-Requejo , María de Lourdes Samaniego-Vaesken , Julia Almazán-Catalán , María González-Rodríguez , Carmen Morais-Moreno , Alejandra Carretero-Krug , Ana M. Puga , Ana Montero-Bravo , Teresa Partearroyo , Gregorio Varela-Moreiras

Food reformulation strategies can contribute to the promotion of better food environments and healthier dietary choices. Such strategies involve modifying the processing methods and/or the composition of food and beverage products to improve their nutritional proﬁle or to reduce their content of ingredients or nutrients of health concern. TABULA™ is a new branded food composition database created with the aim of being periodically updated to reflect the Spanish food landscape. Based on the first version of the TABULA™ dataset, we provide a descriptive analysis of the composition of n = 6500 pre-packaged branded food and beverages marketed in Spain between January 2022 and November 2023. Information was collected from the package labelling of food and beverage products of manufacturer and distributor brands available through major retailer online shopping platforms. Products were categorized as defined in the World Health Organization 2023 nutrient profile model for the European Region. Analysis was performed for nutrients of mandatory declaration. For most categories, median content of the nutrients targeted for reformulation (namely sugars, salt, and saturated fats) was below the high-content thresholds defined by the Spanish Agency for Food Safety and Nutrition (AESAN). Due to their concurrent high contents of sugar and saturated fats as per the AESAN thresholds, “chocolate and sugar confectionery, energy bars, sweet toppings and desserts” and “edible ices” emerged as categories with a less favourable nutritional profile. We conclude that it is essential to monitor the nutritional quality and consumption of marketed food products to ensure the success of reformulation policies aimed at improving public health. The dynamic nature of food markets, characterized by frequent product introductions and discontinuations, highlights the need for branded food composition databases like TABULA™, which are helpful tools for monitoring product reformulation.

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引用次数: 0

Mechanisms underlying astaxanthin alterations during on-site processing of Antarctic krill (Euphausia superba)

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis

Pub Date : 2025-02-09 DOI: 10.1016/j.jfca.2025.107339

Xixi Wang , Xinyu Ma , Yating Zhang , Dong Su , Xiaofang Liu , Yuan Yu , Junkui Miao , Kailiang Leng

Astaxanthin is a crucial natural carotenoid present in Antarctic krill (Euphausia superba); however, there are no reports of possible compositional changes in Antarctic krill during on-site processing in Antarctica. In this study, Antarctic krill underwent serious color deterioration during cold storage, accompanied by an overall upward trend in its total carotenoids, thiobarbituric acid reactive substance (TBARS) value and lipid oxidation (LPO). The texture profile also changed significantly: softer texture, reduced elasticity, decreased chewiness and cohesiveness. Asta-C16:0, Asta-C18:1, Asta-C14:0, Asta-C20:5, Asta-C14:0/C14:0, Asta-C14:0/C16:0, Asta-C16:0/C16:0 and Asta-C16:0/C18:1 were found in astaxanthin monoesters and diesters. Overall, the arrangement of the contents of astaxanthin molecules in Antarctic krill was largely consistent with previous literatures. Additionally, Asta-C18:1/C18:4 was not detected in any of the samples, a discrepancy potentially linked to seasonal variations across studies. Transcriptome analysis revealed that differentially expressed genes (DEGs) related to protein hydrolysis and oxidative stress were identified through metabolic pathway enrichment analysis. Molecular docking suggested that pancreatic lipase likely plays a key role in astaxanthin degradation triggered by the strong autolysis environment of Antarctic krill. This research provides a basis for further avoiding the degradation and efficient utilization of astaxanthin in Antarctic krill during the processing and storage.

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引用次数: 0

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