Journal of Food Composition and Analysis最新文献_第4页

Assessment of tin and lead in commercial canned foods and associated health risk indicators 商业罐头食品中锡和铅的评估及相关健康风险指标

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-03 DOI: 10.1016/j.jfca.2025.108400

Farah Anisa Abd Rahim , Aemi Syazwani Abdul Keyon , Amirah Farhan Kamaruddin

{"title":"Assessment of tin and lead in commercial canned foods and associated health risk indicators","authors":"Farah Anisa Abd Rahim , Aemi Syazwani Abdul Keyon , Amirah Farhan Kamaruddin","doi":"10.1016/j.jfca.2025.108400","DOIUrl":"10.1016/j.jfca.2025.108400","url":null,"abstract":"<div><div>This study assesses the levels and health risks of inorganic tin (Sn) and total lead (Pb) in commercial canned food samples, including pineapple chunks, baked beans, green peas, and sweetened corn. Four canned food products from two brands each were analyzed in triplicate. Samples analysed via inductively coupled plasma–optical emission spectroscopy (ICP-OES). Sn was detected only in acidic foods, with the highest concentrations found in pineapple chunks (Brand A: 73.10 ± 0.92 mg/kg; Brand B: 44.00 ± 0.30 mg/kg), suggesting pH-driven migration from packaging. Pb was present in all samples, ranging from 0.15 to 0.21 mg/kg, indicating broader environmental or agricultural sources. Sn levels were below the maximum permissible limit set by Malaysian Food Act 1983 (Act 281). Health risk was evaluated using Estimated Daily Intake (EDI) and Hazard Quotient (HQ). The highest EDI values were 0.03 mg/kg/day for Sn and 5.57 × 10<sup>−5</sup> mg/kg/day for Pb. HQ values for Sn were below 1, suggesting no significant non-carcinogenic risk at current intake levels. Despite low risk, detection of both metals especially in commonly consumed canned goods warrants closer inspection of food safety thresholds and manufacturing practices. This study provides current, Malaysia-specific data that can support regulatory monitoring and public awareness.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108400"},"PeriodicalIF":4.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264813","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

Enzymatic characteristics and origin tracing of nucleotidase and adenosine deaminase in Cordyceps sinensis based on HPLC 冬虫夏草中核苷酸酶和腺苷脱氨酶的酶学特征及来源溯源

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-03 DOI: 10.1016/j.jfca.2025.108407

Wenqing Li, Baoqing Wen, Kunxia Lin, Wenjia Li, Zhengming Qian

{"title":"Enzymatic characteristics and origin tracing of nucleotidase and adenosine deaminase in Cordyceps sinensis based on HPLC","authors":"Wenqing Li, Baoqing Wen, Kunxia Lin, Wenjia Li, Zhengming Qian","doi":"10.1016/j.jfca.2025.108407","DOIUrl":"10.1016/j.jfca.2025.108407","url":null,"abstract":"<div><div>Adenosine is a key quality marker for the valuable fungus <em>Cordyceps sinensis</em> (<em>C. sinensis</em>). This study presents the first HPLC-based method for quantifying the activities and kinetic parameters of two adenosine-metabolizing enzymes—nucleotidase and adenosine deaminase—in <em>C. sinensis</em>. The enzyme extraction protocols, reaction systems, and assay procedures were optimized. Water was selected as the extraction solvent to preserve native enzymatic activity. Nucleotidase exhibited optimal activity at pH 6 and 35°C, while adenosine deaminase showed optimal activity at pH 8 and 45°C, revealing distinct catalytic profiles. Adenosine deaminase exhibited superior thermostability compared to nucleotidase. Kinetic analysis revealed that nucleotidase had a Michaelis constant of 0.24 µmol·mL<sup>−1</sup> and a maximum reaction rate of 7.92 µmol·L<sup>−1</sup>·min<sup>−1</sup>, while adenosine deaminase showed corresponding values of 0.14 µmol·mL⁻¹ and 0.61 µmol·L⁻¹ ·min⁻¹ . Method validation confirmed the reliability of the analytical system. Source-tracing analysis revealed that nucleotidase originated primarily from the fungal component, whereas adenosine deaminase was exclusively derived from the host larva and detected only in insect tissues. Therefore, adenosine deaminase activity serves as a reliable marker for distinguishing insect-free fungus from fungus-insect complex. These findings provide a scientific basis for optimizing adenosine accumulation and improving the quality control and management of <em>C. sinensis</em>.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108407"},"PeriodicalIF":4.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265434","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

Rapid anatomical classification and lead contamination analysis in edible legumes using novel LIBS–deep learning frameworks 使用新型libs -深度学习框架的食用豆类快速解剖分类和铅污染分析

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-02 DOI: 10.1016/j.jfca.2025.108394

Asiri Iroshan , Nuerbiye Aizezi , Yuzhu Liu

{"title":"Rapid anatomical classification and lead contamination analysis in edible legumes using novel LIBS–deep learning frameworks","authors":"Asiri Iroshan , Nuerbiye Aizezi , Yuzhu Liu","doi":"10.1016/j.jfca.2025.108394","DOIUrl":"10.1016/j.jfca.2025.108394","url":null,"abstract":"<div><div>This study presents a novel analytical approach combining Laser-Induced Breakdown Spectroscopy (LIBS) with two advanced deep learning frameworks, DLIBS-FFNet and PLSNetL, for anatomical classification and heavy metal quantification in edible legumes. The elemental composition of six bean varieties was analyzed across three anatomical components (coat, hilum, and cotyledon), revealing consistent mineral profiles rich in essential nutrients such as Ca, K, and Mg. The DLIBS-FFNet model, which integrates Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Autoencoders (AE) for feature fusion, achieved high classification accuracy of up to 96.12 % for non-contaminated beans and 99.83 % for Pb-contaminated samples. Concurrently, PLSNetL, a Partial Least Squares regression-based neural network with dynamic peak selection and adaptive feature extraction, accurately predicted lead (Pb) concentrations across the anatomical components, with R² values of 0.9924, 0.9022, and 0.8462. The combined use of LIBS with these frameworks offers a rapid, non-destructive, and robust method for compositional profiling and contaminant analysis in legumes, contributing valuable insights to food safety assessment and food composition research.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108394"},"PeriodicalIF":4.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264809","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

Health risk assessment of heavy metals in drinking water reservoirs of Yasuj Iran using Monte Carlo simulation and sensitivity analysis 基于蒙特卡罗模拟和敏感性分析的伊朗Yasuj饮用水水库重金属健康风险评价

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-02 DOI: 10.1016/j.jfca.2025.108398

Saeid Fallahizadeh , Seyed Nouredin Hosseini gousheh , Amin Hossaini motlagh , Mohammadreza Zarei , Negin Rahimi , Seyed Abdolmohammad Sadat

{"title":"Health risk assessment of heavy metals in drinking water reservoirs of Yasuj Iran using Monte Carlo simulation and sensitivity analysis","authors":"Saeid Fallahizadeh , Seyed Nouredin Hosseini gousheh , Amin Hossaini motlagh , Mohammadreza Zarei , Negin Rahimi , Seyed Abdolmohammad Sadat","doi":"10.1016/j.jfca.2025.108398","DOIUrl":"10.1016/j.jfca.2025.108398","url":null,"abstract":"<div><div>Heavy metals (HMs) in drinking water are a health hazard to humans as they induce a variety of diseases. In this study, the concentrations of HMs (As, Cd, Pb, Hg, Cu, and Zn) in 24 water samples collected from eight reservoirs of Yasuj (Iran) were analyzed using an inductively coupled plasma mass spectrometry. Potential carcinogenic and non-carcinogenic health risks due to the ingestion of HMs in the water samples were also assessed estimating hazard quotient (HQ), hazard index (HI) and cancer risk (CR). The average concentrations (μg/L) of HMs were found to be 2.53 (As), 0.33 (Cd), 13.94 (Cu), 2.97 (Pb), 0.99 (Hg), and 258.65 (Zn). All HMs had HQ values under 1 for all age categories, but the average value of HI estimated for infants was above safety limits at 1.21. The average values of CRs were estimated as 2.18 × 10<sup>−4</sup> (As) and 1.13 × 10<sup>−4</sup> (Cd). Monte Carlo simulation showed that HM concentrations are mostly acceptable, but that infants are in considerable health jeopardy. The study thus calls for continuous monitoring and risk mitigation measures to protect these vulnerable sections against the prospective long-haul impacts of heavy metal exposure via drinking water.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108398"},"PeriodicalIF":4.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264801","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 Co-ZnO@mMWCNTs nanocomposite for micro solid phase extraction of Cd²⁺ and Pb²⁺ from water and food samples 一种新型Co-ZnO@mMWCNTs纳米复合材料用于从水和食品样品中微固相萃取Cd 2 +和Pb 2 +

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-02 DOI: 10.1016/j.jfca.2025.108401

Mustafa Soylak , Abdirashid Adam Isak , Ozgur Ozalp

引用次数: 0

A novel SERS aptasensor based on Au-Mn-Cu nanozyme induced catalytic precipitation reactions combining with catalytic hairpin assembly strategy for sensitive detection of chloramphenicol 基于Au-Mn-Cu纳米酶诱导的催化沉淀反应结合催化发夹组装策略的新型SERS适体传感器用于氯霉素的灵敏检测

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-02 DOI: 10.1016/j.jfca.2025.108402

Xiaoyu Yang , Youlin Tang , Runzi Zhang , Yao Liu , Mengjun Wang , Juan Zhang , Yi He

{"title":"A novel SERS aptasensor based on Au-Mn-Cu nanozyme induced catalytic precipitation reactions combining with catalytic hairpin assembly strategy for sensitive detection of chloramphenicol","authors":"Xiaoyu Yang , Youlin Tang , Runzi Zhang , Yao Liu , Mengjun Wang , Juan Zhang , Yi He","doi":"10.1016/j.jfca.2025.108402","DOIUrl":"10.1016/j.jfca.2025.108402","url":null,"abstract":"<div><div>Chloramphenicol (CAP) is an antibiotic that has the potential to induce irreversible aplastic anemia. Therefore, developing sensitive detection methods for CAP has become crucial. In this study, an aptasensor for the detection of CAP was developed by integrating catalytic precipitation reactions using a gold (Au)-manganese (Mn)-copper (Cu) nanozyme with surface enhanced Raman spectroscopy (SERS). First, a trimetallic nanozyme composed of Au, Mn, and Cu was synthesized, exhibiting horseradish peroxidase-like activity. Subsequently, target CAP induced catalytic hairpin assembly (CHA) cycle can open a large number of hairpin H1, enabling the binding of nanozyme@H2 to the SERS substrate Fe<sub>3</sub>O<sub>4</sub>@SiO<sub>2</sub>@Ag@4-chloro-1-naphthol (4-CN). The trimetallic nanozyme can catalyze the conversion of 4-CN into benzo-4-chloro-hexadienone (4-CD), which further initiates catalytic precipitation reactions (NCP), resulting in a decrease in the Raman signal of 4-CN. By this way, the aptasensor could achieve a sensitivity at 3.52 × 10<sup>-13</sup> mol/L, offering a novel strategy for the detection of CAP in food products.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108402"},"PeriodicalIF":4.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220078","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

Discrimination of easily confused tea leaves with similar appearance (Gougu tea vs. Gonglao tea) via an integrated method of electronic tongue, HPLC-QTOF-MS-VirtualTaste, electronic nose, electrochemical fingerprinting and machine learning 利用电子舌、HPLC-QTOF-MS-VirtualTaste、电子鼻、电化学指纹和机器学习等综合方法对外观相似的易混淆茶叶（沟沟茶和公老茶）进行鉴别

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-02 DOI: 10.1016/j.jfca.2025.108404

Rui-Bo Sun , Yue-Hua Chen , Xin-Ru Zhang , Fang-Tong Liu , Wen-Yu Wang , Jia-Nuo Zhang , Yi-Fan Wang , Hui Zhang , Ming Xie , Gui-Zhong Xin , Hui-Peng Song

{"title":"Discrimination of easily confused tea leaves with similar appearance (Gougu tea vs. Gonglao tea) via an integrated method of electronic tongue, HPLC-QTOF-MS-VirtualTaste, electronic nose, electrochemical fingerprinting and machine learning","authors":"Rui-Bo Sun , Yue-Hua Chen , Xin-Ru Zhang , Fang-Tong Liu , Wen-Yu Wang , Jia-Nuo Zhang , Yi-Fan Wang , Hui Zhang , Ming Xie , Gui-Zhong Xin , Hui-Peng Song","doi":"10.1016/j.jfca.2025.108404","DOIUrl":"10.1016/j.jfca.2025.108404","url":null,"abstract":"<div><div>Gougu tea (GG) and Gonglao tea (GL) were historically misclassified in tea markets for centuries due to their highly similar appearance. To resolve this long-standing challenge, our study focused on two objectives: elucidating the necessity for differentiating them, and constructing an efficient method for their discrimination. For the first time, E-tongue, HPLC-QTOF-MS-VirtualTaste, E-nose, electrochemical fingerprinting, and machine learning were integrated to comprehensively analyze their differences in flavor and composition. E-tongue analysis confirmed bitterness as a shared sensory attribute in GG and GL, while HPLC-QTOF-MS-VirtualTaste revealed their distinct bitter components. Organic acids and triterpenes predominated among the 85 taste components in GG, while alkaloids predominated among the 60 taste components in GL. Quantitative analysis showed that the average chlorogenic acid content (GG's primary bitter component) was 6.4787 mg/g, whereas berberine (GL's main bitter component) reached 17.0383 mg/g. E-nose analysis detected 51 and 38 volatile components in GG and GL, respectively. Eleven common components primarily exhibited fruity and sweet sensory characteristics. Furthermore, electrochemical fingerprinting combined with the random forest algorithm was established, achieving 99.85 % discrimination accuracy. Moreover, this approach possessed the advantages of low cost and simplicity. Our research contributes to addressing the centuries-old challenge of market confusion between GG and GL.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108404"},"PeriodicalIF":4.6,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265838","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

Fine-grained food image recognition using a convolutional neural network and swin transformer hybrid model 基于卷积神经网络和swin变压器混合模型的细粒度食物图像识别

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-01 DOI: 10.1016/j.jfca.2025.108395

Zhiyong Xiao, Guang Diao, Chaoliang Liu, Zhaohong Deng

{"title":"Fine-grained food image recognition using a convolutional neural network and swin transformer hybrid model","authors":"Zhiyong Xiao, Guang Diao, Chaoliang Liu, Zhaohong Deng","doi":"10.1016/j.jfca.2025.108395","DOIUrl":"10.1016/j.jfca.2025.108395","url":null,"abstract":"<div><div>With increasing public emphasis on dietary monitoring and quality of life, fine-grained food image recognition has become an important research area in computer vision. However, distinguishing visually similar food items remains challenging, as traditional classification methods often fail to achieve satisfactory accuracy. To address this, this paper proposes a novel CNN-Transformer-based model that integrates convolutional neural networks (CNNs) with attention mechanisms. Specifically, the model introduces a Global Attention and Local Covariance Convolutional Feature Fusion module into the Swin Transformer framework. This module combines a deep convolutional network, a multi-layer perceptron, and a feature fusion component, enabling better capture of fine-grained details while integrating global context. Extensive experiments conducted on two public fine-grained food image datasets, FoodX-251 and UEC Food-256, demonstrate the superior performance of the proposed model. It achieves accuracy rates of 81.47 % and 83.44 %, respectively, outperforming most existing methods under the same experimental conditions.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108395"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265837","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 potentiometric sweetness sensor for sugars using lipid/polymer membranes containing boronic acid 一种含硼酸的脂质/聚合物膜糖的电位式甜味传感器

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-01 DOI: 10.1016/j.jfca.2025.108396

Xiao Wu , Shintaro Soeda , Taichi Nomoto , Toshihiro Nose , Yuanchang Liu , Shunsuke Kimura , Takeshi Onodera , Hidekazu Ikezaki , Kiyoshi Toko

{"title":"A potentiometric sweetness sensor for sugars using lipid/polymer membranes containing boronic acid","authors":"Xiao Wu , Shintaro Soeda , Taichi Nomoto , Toshihiro Nose , Yuanchang Liu , Shunsuke Kimura , Takeshi Onodera , Hidekazu Ikezaki , Kiyoshi Toko","doi":"10.1016/j.jfca.2025.108396","DOIUrl":"10.1016/j.jfca.2025.108396","url":null,"abstract":"<div><div>A novel potentiometric sweetness sensor was developed using a lipid/polymer membrane incorporating 3-nitrophenylboronic acid (3NPBA). Although sugars are non-ionic, the sensor exhibited positive potential responses to various sugars under mildly acidic conditions (pH 5.8). Regression analysis revealed that the sensor response is governed by a combination of three key factors: (1) the binding of boronic acid to cis-diol groups of sugars, (2) the formation of complexes between sugars and hydrated potassium ions, and (3) the proximity of sugar molecules to the membrane surface. The sensor exhibited strong concentration-dependent response for carbohydrate-based sweeteners such as fructose, lactose, and maltitol in the range of 1–1000 mM, as well as for stevioside in the range of 0.01–10 mM, demonstrating good sensitivity. Additionally, under pH-controlled conditions, the sensor demonstrated good selectivity for sweetness, making it a promising tool for taste analysis in food, pharmaceutical, and functional product applications.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108396"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220076","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

The impact of agricultural practices on food composition – A systematic review 农业实践对食品成分的影响——系统综述

IF 4.6 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-10-01 DOI: 10.1016/j.jfca.2025.108388

Amos O. Anim , Herman Erick Lutterodt , Caleb William Ofori , Ivy Oduro-Boateng , Justina Achiaa Bonsu , Gloria M. Ankar-Brewoo , Linda Nana Esi Aduku , Charles Apprey , Reginald Adjetey Annan

{"title":"The impact of agricultural practices on food composition – A systematic review","authors":"Amos O. Anim , Herman Erick Lutterodt , Caleb William Ofori , Ivy Oduro-Boateng , Justina Achiaa Bonsu , Gloria M. Ankar-Brewoo , Linda Nana Esi Aduku , Charles Apprey , Reginald Adjetey Annan","doi":"10.1016/j.jfca.2025.108388","DOIUrl":"10.1016/j.jfca.2025.108388","url":null,"abstract":"<div><div>Agricultural practices play a crucial role in shaping food composition, directly influencing nutritional quality and the ability to address food and health challenges. This review, conducted following PRISMA guidelines, evaluated the effects of agricultural interventions on crop composition. From 2271 articles identified across Scopus, Taylor & Francis, and CABI Direct, 190 studies were included. Fertilizer application was the most reported intervention, followed by bio-stimulants, irrigation strategies, and harvesting timing. Organic amendments and deficit irrigation increased phenolics and other bioactive compounds in fruits and vegetables. Macro- and micronutrient fertilizers enhanced protein, mineral, and antioxidant levels but, when misapplied, often led to nutrient dilution, antagonism, or reduced accumulation of other beneficial compounds. Foliar and soil amendments proved adequate zinc, iron, and selenium biofortification strategies in grains. Amino acid applications reduced heavy metal uptake in cereals grown in contaminated soils, lowering toxic exposure risks. Modern agricultural practices demonstrate strong potential to enhance crop nutrition and mitigate nutrient deficiencies, but these practices must be carefully managed to avoid unintended trade-offs that compromise food quality. These findings highlight the need for evidence-based, context-specific agricultural policies and farmer training to maximize nutritional gains while safeguarding long-term food security.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"148 ","pages":"Article 108388"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220185","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