Journal of Food Composition and Analysis最新文献

A fluorescent strategy for Escherichia coli detection in raw beef in combination with click chemistry 结合click化学的生牛肉大肠杆菌荧光检测策略

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-19 DOI: 10.1016/j.jfca.2025.107940

Abdulhakeem Alzahrani

{"title":"A fluorescent strategy for Escherichia coli detection in raw beef in combination with click chemistry","authors":"Abdulhakeem Alzahrani","doi":"10.1016/j.jfca.2025.107940","DOIUrl":"10.1016/j.jfca.2025.107940","url":null,"abstract":"<div><div>Foodborne bacterial infections are a growing concern for the public. The development of a rapid, economical and sensitive method for foodborne bacteria detection remains a paramount challenge. In this study, <em>Escherichia coli</em> (<em>E. coli</em>) bacteria-assisted click chemistry brings borophene quantum dots (B QDs) at the close proximity of Fe<sub>3</sub>O<sub>4</sub>@Au NPs that enabled magnetic separation as well as quantification of <em>E. Coli</em> in the presence of cupric ion (Cu<sup>2 +</sup>). At first, azide-modified B QDs and alkyne-modified gold coated iron oxide nanoparticles (Fe<sub>3</sub>O<sub>4</sub>@Au NPs) was synthesized through wet-chemical method. The size of Fe<sub>3</sub>O<sub>4</sub>@Au NPs and B QDs was 80 nm and ∼3 nm, respectively. The absorbance peak of the Fe<sub>3</sub>O<sub>4</sub>@Au NPs and the emission peak of B QDs were located at 540 nm and 530 nm, respectively. <em>E. Coli</em> bacterial metabolic product cuprous ion (Cu<sup>+</sup>) was utilized to trigger the click reaction between B QDs and Fe<sub>3</sub>O<sub>4</sub>@Au NPs. As a result, a self-assembled structure of B QDs and Fe<sub>3</sub>O<sub>4</sub>@Au NPs formed which enables magnetic separation and fluorescent quantification of <em>E. coli</em>. The calculated limits of detection (LODs) of <em>E. coli</em> in PBS and spiked raw meat samples were 3 colony-forming unit (CFU)/mL and 20 CFU/mL, respectively. The present <em>E. coli</em> assay was selective in the presence of other bacteria.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107940"},"PeriodicalIF":4.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335977","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

Novel competitive SERS/Electrochemical dual-channel biosensor for ultrasensitive quantitative detection of Pb2+ based on Fe3O4@Au@PAMAM and AuNs@4-MBA@Au 基于Fe3O4@Au@PAMAM和AuNs@4-MBA@Au的新型竞争SERS/电化学双通道生物传感器用于Pb2+的超灵敏定量检测

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-19 DOI: 10.1016/j.jfca.2025.107933

Xinran Yang , Zonglin Li , Junhui Du , Xueli Zhang , Haibin Liu , Xuechao Zhang , Lijun Li , Chuanjin Cui , Hongshuo Chen

{"title":"Novel competitive SERS/Electrochemical dual-channel biosensor for ultrasensitive quantitative detection of Pb2+ based on Fe3O4@Au@PAMAM and AuNs@4-MBA@Au","authors":"Xinran Yang , Zonglin Li , Junhui Du , Xueli Zhang , Haibin Liu , Xuechao Zhang , Lijun Li , Chuanjin Cui , Hongshuo Chen","doi":"10.1016/j.jfca.2025.107933","DOIUrl":"10.1016/j.jfca.2025.107933","url":null,"abstract":"<div><div>In this paper, a SERS/electrochemical dual-channel aptamer biosensor was established for ultrasensitive detection of Pb<sup>2+</sup>. Firstly, the capture probe Fe<sub>3</sub>O<sub>4</sub>@Au@PAMAM@Aptamer and the signal probe AuNs@4-MBA@Au@ssDNA were chemically synthesized step by step. Secondly, the carboxyl graphene oxide (GO) was electrodeposited onto the electrode surface to gain the GO/SPCE. Meanwhile, PAMAM@Au nanomaterials were successfully synthesized and thus prepared the Aptamer/PAMAM@Au/GO/SPCE. During the process, SEM, TEM, FTIR and XPS were applied to characterize the synthesized nanomaterials which confirmed the successful synthesis of the nanomaterials. Finaly, the performance of this biosensor was evaluated in a series of Pb<sup>2+</sup> aqueous solutions by surface enhanced Raman (SERS) and differential pulse voltammetry (DPV). The results showed that the lowest limit of detection (LOD) was 3.16 × 10<sup>−5</sup> nM, and the sample recovery was 99.50–103.67 %. Furthermore, the biosensor demonstrated good specificity, repeatability, and stability. Reliable results were also obtained in actual sample testing. Therefore, it can provide a viable method for trace detection of heavy metal ions.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107933"},"PeriodicalIF":4.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330118","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

Application of deep learning and explainable artificial intelligence (XAI) for detecting red chilli powder adulteration 深度学习和可解释人工智能（XAI）在红辣椒粉掺假检测中的应用

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-19 DOI: 10.1016/j.jfca.2025.107947

Dilpreet Singh Brar , Birmohan Singh , Vikas Nanda

{"title":"Application of deep learning and explainable artificial intelligence (XAI) for detecting red chilli powder adulteration","authors":"Dilpreet Singh Brar , Birmohan Singh , Vikas Nanda","doi":"10.1016/j.jfca.2025.107947","DOIUrl":"10.1016/j.jfca.2025.107947","url":null,"abstract":"<div><div>To tackle the challenge of Red Chilli Powder adulteration (RCP), an artificial intelligence (AI)-based framework was proposed using empirical analysis of eight pre-trained two-dimensional convolutional neural network (2D-CNN) models for RCP adulteration detection. Moreover, to enhance the convergence and performance of the proposed architecture, an optimiser AdamClr is integrated with minimum and maximin learning rate of 0.00005 and 0.01, respectively. The RCP is categorised into two classes; C1_PRcP includes pure RCP of Jodhpuri (JP) variety, and Class 2 (C2_ARcP) consists of various natural adulterants (i.e., wheat bran (WB), rice hull (RB), wood saw (WS), three low-grade varieties of RCP at the lowest concentration of 5 %). Additionally, the model which outperforms corresponding architectures is further evaluated using explainable artificial intelligence (XAI) technology. DenseNet_169, trained at BS 64, delivers 97.99 % accuracy for detecting natural adulterants (C2_ARcP) in high-grade RCP (C1_PRcP). The XAI model (Grad-CAM and LIME) explained the accurate adulteration prediction of the DensNet_169 2D-CNN model. The heat map obtained from both XAI models illustrated the significant areas that explained the model's decision-making. The proposed model effectively detects RCP adulteration and its applicability can be enhanced by increasing dataset diversity. Overall, the integrated 2D-CNN-XAI approach holds significant potential to revolutionise quality control and assurance in the food industry.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107947"},"PeriodicalIF":4.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322225","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

Machine learning and natural language processing models to predict the extent of food processing 机器学习和自然语言处理模型来预测食品加工的程度

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107938

Nalin Arora , Sumit Bhagat , Riya Dhama , Ganesh Bagler

{"title":"Machine learning and natural language processing models to predict the extent of food processing","authors":"Nalin Arora , Sumit Bhagat , Riya Dhama , Ganesh Bagler","doi":"10.1016/j.jfca.2025.107938","DOIUrl":"10.1016/j.jfca.2025.107938","url":null,"abstract":"<div><div>The dramatic increase in consumption of ultra-processed food has been associated with numerous adverse health effects. Given the public health consequences linked to ultra-processed food consumption, it is highly relevant to build computational models to predict the processing of food products. We created a range of machine learning, deep learning, and NLP models to predict the extent of food processing by integrating the FNDDS dataset of food products and their nutrient profiles with their reported NOVA processing level. Starting with the full nutritional panel of 102 features, we further implemented coarse-graining of features to 65 and 13 nutrients by dropping flavonoids and then by considering the 13-nutrient panel of FDA, respectively. LGBM Classifier and Random Forest emerged as the best model for 102 and 65 nutrients, respectively, with an F1-score of 0.9411 and 0.9345 and MCC of 0.8691 and 0.8543. For the 13-nutrient panel, Gradient Boost achieved the best F1-score of 0.9284 and MCC of 0.8425. We also implemented NLP based models, which exhibited state-of-the-art performance. Besides distilling nutrients critical for model performance, we present a user-friendly web server for predicting processing level based on the nutrient panel of a food product: <span><span>https://cosylab.iiitd.edu.in/food-processing/</span><svg><path></path></svg></span>.<span><span><sup>1</sup></span></span></div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107938"},"PeriodicalIF":4.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322224","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

Optical properties of multilayered tissues of different varieties of apples and inspection models of internal quality 不同品种苹果多层组织光学特性及内在品质检验模式

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107942

Zhiming Guo , Xuan Chen , Chanjun Sun , Usman Majeed , Chen Wang , Shuiquan Jiang , Xiaobo Zou

{"title":"Optical properties of multilayered tissues of different varieties of apples and inspection models of internal quality","authors":"Zhiming Guo , Xuan Chen , Chanjun Sun , Usman Majeed , Chen Wang , Shuiquan Jiang , Xiaobo Zou","doi":"10.1016/j.jfca.2025.107942","DOIUrl":"10.1016/j.jfca.2025.107942","url":null,"abstract":"<div><div>Apples being multilayered fruits possess optical properties which respond to spectroscopy-based quality inspection models. The variation in optical properties of apple tissues is crucial for efficient spectroscopic detection. Absorption coefficients (µ<sub>a</sub>) and the reduced scattering coefficients (µ'<sub>s</sub>) of three apple varieties had wavelength range from 475 nm to 1600 nm using a double integrating sphere technique. The quantitative prediction models for soluble solid content (SSC), firmness index (FI), and pH for synergistic interval (SI), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA) with partial least square (PLS) was accurate. Interestingly, CARS-PLS model using µ<sub>a</sub> provided the best quantitative predictions for SSC (Rp = 0.9833, RMSEP = 0.2630) and pH (Rp = 0.8429, RMSEP = 0.1229). Additionally, the GA-PLS model based on µ'<sub>s</sub> yield accurate prediction for FI (Rp = 0.9372, RMSEP = 0.1478). On the other hand, differences in apple peel color were also observed among the varieties. The qualitative discrimination model random forest (RF) and backpropagation (BP) for apple varieties color detection achieved highest accuracy (100 %). These findings confirmed the feasibility of combining optical properties with color detection to identify apple variety.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107942"},"PeriodicalIF":4.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330116","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

Detection of safflower adulteration in saffron by textural features 用质地特征检测藏红花中红花掺假

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107941

Amir Kazemi , Mostafa Khojastehnazhand , Seyyed Hossein Fattahi

{"title":"Detection of safflower adulteration in saffron by textural features","authors":"Amir Kazemi , Mostafa Khojastehnazhand , Seyyed Hossein Fattahi","doi":"10.1016/j.jfca.2025.107941","DOIUrl":"10.1016/j.jfca.2025.107941","url":null,"abstract":"<div><div>Saffron, a highly valuable spice in global trade, is often intentionally or unintentionally mixed with safflower stamens. In this study, a machine vision system was utilized to capture the images of saffron samples at different mixture proportions to explore the authentication. Then, three feature extraction algorithms including gray level co-occurrence matrix, gray-level run-length matrix, and Local Binary Pattern were applied to extract the textural features of data. Discriminant Analysis, Support Vector Machine, and Artificial Neural Network algorithms as supervised classification models were applied to classify datasets. The models were applied for 3 class and 6 class datasets to explore classification ability. The best outcome for the 6-class dataset was with the Support Vector Machine model and with all features with an accuracy of 80 %. For 3 class datasets, Discriminant Analysis model had the best result with all features and with the accuracy of 97.78 %. Then, to explore the importance of features, two Minimum Redundancy Maximum Relevance and Chi-Square Test algorithms were applied. For the gray level co-occurrence matrix extracted features, Chi-Square Test algorithm with 10 features had the best accuracy with a test accuracy of 76.94 %. Therefore, because of the adulteration of some profiteer sellers, the results of the proposed approach can be utilized in designing a system for exploring the authenticity of saffron and satisfaction of buyers.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107941"},"PeriodicalIF":4.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322223","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 liquid chromatographic time-of-flight tandem mass spectrometric method for the comparative study of propolis aqueous and cyclodextrin extracts employing target and suspect screening 目的筛选和可疑筛选蜂胶水溶液和环糊精提取物的液相色谱飞行时间串联质谱比较研究

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107930

Natasa P. Kalogiouri , Stamatia Christaki , Anastasia Loukri , Petros D. Mitsikaris , Ioannis Mourtzinos

{"title":"A liquid chromatographic time-of-flight tandem mass spectrometric method for the comparative study of propolis aqueous and cyclodextrin extracts employing target and suspect screening","authors":"Natasa P. Kalogiouri , Stamatia Christaki , Anastasia Loukri , Petros D. Mitsikaris , Ioannis Mourtzinos","doi":"10.1016/j.jfca.2025.107930","DOIUrl":"10.1016/j.jfca.2025.107930","url":null,"abstract":"<div><div>Green solvents are being studied for extracting bioactive compounds. In the present study, phenolic extracts of propolis were prepared employing water and aqueous β-cyclodextrin solution. A liquid chromatographic method coupled to time-of-flight (LC-QTOF-MS/MS) was developed and validated employing target and suspect screening. The method showed good linearity (R<sup>2</sup> > 0.9900) in the working range between 0.050 and 5 mg L<sup>–1</sup>. and presented low limits of quantification (LOQs) and detection (LODs) over the ranges 0.050–0.100 mg L<sup>−1</sup> and 0.015–0.030 mg L<sup>−1</sup>, respectively. The RSD values were lower than 6.8 % for intra-day and inter-day precision, demonstrating good precision. The matrix effect values were lower than 19.2 % for all the analytes indicating the low influence of the matrix in the determination. Overall, in the green extracts analyzed, 16 target bioactive compounds were determined and 24 suspects were tentatively identified. In terms of targeted analysis, the methanolic extract presented higher concentrations of phenolic compounds compared to the aqueous and β-cyclodextrin ones. Suspect screening revealed that marker compounds of poplar-type propolis were identified in all three extracts, highlighting the potential of green extraction solvents for the recovery of propolis bioactive compounds.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107930"},"PeriodicalIF":4.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322219","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 dual-site fluorescent probe for colorimetric detection of FA and its application in poultry products chicken feet 双位点荧光探针比色法检测FA及其在家禽产品鸡爪中的应用

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107917

Xiaoming Wu, Leyuan Ding, Shaoxiang Yang, Hongyu Tian, Baoguo Sun

引用次数: 0

Assessing the nutritional value of farmed and wild gilthead sea bream (Sparus aurata): Implications for local aquaculture in the central Algerian coast 评估养殖和野生金头鲷（Sparus aurata）的营养价值：对阿尔及利亚中部海岸当地水产养殖的影响

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107944

Elyes Kelai , Soufiane Bensalem , Bilal Boufekane , Adh’ya-eddine Hamitouche , Khaldoun Bachari , Kamel Harchouche , Nour el Islam Bachari

{"title":"Assessing the nutritional value of farmed and wild gilthead sea bream (Sparus aurata): Implications for local aquaculture in the central Algerian coast","authors":"Elyes Kelai , Soufiane Bensalem , Bilal Boufekane , Adh’ya-eddine Hamitouche , Khaldoun Bachari , Kamel Harchouche , Nour el Islam Bachari","doi":"10.1016/j.jfca.2025.107944","DOIUrl":"10.1016/j.jfca.2025.107944","url":null,"abstract":"<div><div>Aquaculture is a sustainable alternative to traditional fishing, especially in the Mediterranean, where wild fish populations are declining. Consumer concerns about the nutritional value of farmed fish versus wild fish impede aquaculture development in Algeria. This study compares the nutritional value of farmed gilthead sea bream (<em>Sparus aurata</em>) in Algeria to wild fish. Samples were taken from four natural fishing areas and two fish farms. Biometric analysis revealed that farmed fish had positive allometric growth while wild fish had isometric growth. According to proximate composition analysis, wild fish contained more water (75.64 % vs. 70,6 %) and less organic matter (23.5 % vs. 26.2 %) than farmed fish. Fatty acid profiles analyzed with GC-MS in SIM mode revealed that farmed fish contained more omega-6 fatty acids (mean: 22.89 mg/100 mg), whereas wild fish had higher omega-3 levels (mean: 8.78 mg/100 mg). Despite these differences, both farmed and wild gilthead sea bream had high nutritional quality, particularly in omega-3 fatty acids. These findings indicate that farmed fish can be a high-quality alternative to wild fish in the Mediterranean region as long as feed formulations are optimized to better mimic wild fish lipid profiles. This study addresses consumer concerns and promotes sustainable aquaculture in Algeria.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107944"},"PeriodicalIF":4.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313522","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

Fluorescence and colorimetric detection of ascorbic acid in beverages using manganese-doped carbon dots nanozyme 锰掺杂碳点纳米酶荧光比色法检测饮料中的抗坏血酸

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-06-18 DOI: 10.1016/j.jfca.2025.107934

Zhigang Ding , Maomao Zhang , Hongmei Gao , Wei Fan , Xianxiang Wang

{"title":"Fluorescence and colorimetric detection of ascorbic acid in beverages using manganese-doped carbon dots nanozyme","authors":"Zhigang Ding , Maomao Zhang , Hongmei Gao , Wei Fan , Xianxiang Wang","doi":"10.1016/j.jfca.2025.107934","DOIUrl":"10.1016/j.jfca.2025.107934","url":null,"abstract":"<div><div>This study aimed to present the synthesis of manganese-doped carbon dots (Mn-CQDs) via a straightforward hydrothermal procedure, facilitated by histidine. These Mn-CQDs exhibit enhanced fluorescence and peroxidase-like activities. Utilizing these properties, we developed a dual-mode fluorescence and colorimetric assay for detecting ascorbic acid (AA) in beverages. The addition of AA enhances the fluorescence and reduces the blue oxidized 3,3′,5,5′-tetramethylbenzidine (oxTMB) to a colorless form. The assay demonstrated a robust linear relationship between the changes in fluorescence intensity and absorbance with AA concentration. The limits of detection for AA were 1.54 µM using the fluorescence method and 0.69 µM using the colorimetric method, across detection ranges of 0.5–150 µM and 0.5–70 µM, respectively. The method’s significantly lower LOD compared to existing sensors allows for highly sensitive AA detection, which is critical for precise quantification. The assay was successfully validated in various beverage matrices, showcasing its rapid, efficient, and reliable performance with potential to enhance food safety monitoring.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"146 ","pages":"Article 107934"},"PeriodicalIF":4.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330117","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