Journal of Food Composition and Analysis最新文献

{"title":"Maintaining national food databases to enhance precision: A report from the preconference workshop 1 of the 2025 international diet and activity methods conference, Toronto, Canada, April 27, 2025","authors":"Anja Pia Biltoft-Jensen ,&nbsp;Tue Christensen ,&nbsp;Anders Poulsen ,&nbsp;Renee Sobolewski ,&nbsp;Ilana Nogueira Bezerra ,&nbsp;Dirce Maria Marchioni ,&nbsp;Eliana Bistriche Giuntini ,&nbsp;Mai Matsumoto ,&nbsp;Hidemi Takimoto ,&nbsp;Mieko Nakamura ,&nbsp;Cho-il Kim ,&nbsp;Sung Ok Kwon ,&nbsp;Caroline T.M. van Rossum ,&nbsp;Marga C. Ocké ,&nbsp;Birdem Amoutzopoulos ,&nbsp;Polly Page","doi":"10.1016/j.jfca.2026.108970","DOIUrl":"10.1016/j.jfca.2026.108970","url":null,"abstract":"<div><div>National food composition databases are essential for data quality and reliability in dietary monitoring, nutrition research, and policy. Nevertheless, they face growing challenges from rapidly evolving food markets, new data demands, and limited analytical resources. This paper summarizes outcomes of a preconference workshop at ICDAM 2025 that brought together experts from seven countries to share experiences in developing and maintaining databases. Presentations highlighted strategies to keep databases fit for purpose, including standardized and harmonized food descriptions, inclusion of biodiversity and regional foods; maintaining strong analytical anchors with continuous updates; systematic handling of missing values (recipe calculations, preparation factors, and imputation), and AI-based linking of branded with generic foods to address nutrient gaps and capture market changes. These developments also underline the value of food composition databases: supporting national dietary surveys, monitoring reformulation, informing front-of-pack labeling, and enabling evaluation of nutrition policies. Breakout discussions emphasized challenges such as resource constraints and reliance on secondary data, as well as demands for new data types beyond traditional nutrients, alongside opportunities in branded data, automation, FAIR data standards, and harmonization. The workshop concluded that sustained investment, modernized infrastructures, and collaborative governance are critical to ensure food composition databases remain robust, policy-relevant, and globally interoperable.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"151 ","pages":"Article 108970"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171117","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}

{"title":"A novel hydrophobic solidified deep eutectic solvents for rapid and accurate determination of Brilliant Blue in food and beverage","authors":"Marzieh Fallahinezhad,&nbsp;Niayesh Farahian,&nbsp;Hossein Salar Amoli","doi":"10.1016/j.jfca.2026.108978","DOIUrl":"10.1016/j.jfca.2026.108978","url":null,"abstract":"<div><div>In this study, a novel hydrophobic solidified deep eutectic solvent (SFDES) was prepared and applied for the extraction and determination of Brilliant Blue (BB) in food and beverage matrices. The SFDES, prepared from benzyltriethylammonium chloride and fatty acids, is considered a greener alternative to conventional organic solvents, and excellent solidification properties, enabling efficient phase separation and analyte enrichment. Its structural and functional properties were confirmed by attenuated total reflectance Fourier-transform infrared spectroscopy, demonstrating successful formation of the eutectic system. Coupling the prepared SFDES with an ultrasonic-assisted dispersive liquid–liquid microextraction procedure allowed highly efficient extraction under optimized conditions, achieving a linear range of 0.1–30.0 mg L⁻¹, a low limit of detection of 1.49 µg L⁻¹, and a high enrichment factor of 65.9. The method was validated on real food samples, including jelly, candy, and soft drinks, yielding recoveries of 98.3–101.5 % and intraday RSDs below 1.8 %. The interday RSD values were 3.2 %. Greenness evaluation using the GAPI and AGREE tools confirmed the approach’s environmental sustainability. These findings demonstrate that the newly made SFDES is a promising green extraction medium for trace-level determination of BB in complex food matrices.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"151 ","pages":"Article 108978"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171118","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}

{"title":"Understanding the physicochemical profile of honey: A comprehensive review of quality and authenticity parameters","authors":"Chandra Kiran,&nbsp;Murtaza Riyaz","doi":"10.1016/j.jfca.2026.108993","DOIUrl":"10.1016/j.jfca.2026.108993","url":null,"abstract":"<div><div>Honey, produced by different bee species is a complex matrix comprising ∼80–85 % sugars, 15–17 % water, and minor yet bioactive constituents including organic acids, enzymes, amino acids, vitamins, minerals, and polyphenols. Its quality and market value are increasingly compromised by adulteration and contamination, making physicochemical profiling essential for authentication and safety assurance. This review consolidates current advances in evaluating key parameters—colour (Pfund values ranging from light amber to dark amber depending on floral source), moisture content (&lt;20 % in premium samples to ensure microbial stability), pH (3.2–4.5) and free acidity (&lt;50 meq/kg as per Codex standards), electrical conductivity (0.1–1.5 mS/cm distinguishing nectar from honeydew honeys), mineral content (K often exceeding 1000 mg/kg), and carbohydrate profile (fructose 31–44 %, glucose 23–38 %, F/G ratio &gt;1 in slow-crystallising honeys). By integrating compositional benchmarks with advanced analytical approaches, physicochemical characterization not only facilitates botanical and geographical traceability but also provides a robust framework for detecting sugar-syrup adulteration, preserving honey’s authenticity, therapeutic value, and consumer trust.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"151 ","pages":"Article 108993"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171058","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}

{"title":"Influence of aroma-enhancing yeast nutrients on the volatile profile of important Austrian white wine grape varieties","authors":"Reinhard Eder ,&nbsp;Sezer Sari ,&nbsp;Phillip Eder ,&nbsp;Peter Derkits ,&nbsp;Georg Leindl ,&nbsp;Christian Philipp","doi":"10.1016/j.jfca.2026.108990","DOIUrl":"10.1016/j.jfca.2026.108990","url":null,"abstract":"<div><div>As consumer demands for grape wines increase, it is becoming important for producers to optimise the aroma. Yeast nutrient salts can help to achieve this. While the influence of classic yeast nutrient salts (e.g. diammonium phosphate (DAP)) has been well investigated, there are only a few studies on organic yeast nutrients. The aim of the study is to compare four concepts of yeast nutrition for fermentation: 1) without nutrient; 2) salt nutrition with DAP and thiamine; 3) full nutrition (combined products of yeast autolysates, DAP, and VitB1); and 4) complex nutrition (yeast autolysates with promoted additional function plus DAP) for four important Austrian grape varieties. Around 70 relevant minor and major volatile compounds were analysed. It was shown that the addition of nutrients generally has a positive influence on the volatile composition of the wines. Thiols responded most consistently to nutrient addition, with 3-sulfanylhexan-1-ol increasing by 20–60 % compared with the control. In contrast, full and special nutrient concepts did not outperform simple salt nutrition; their effects on monoterpenes and esters were inconsistent and strongly dependent on the grape–yeast combination. The hypothesis that full or even more expensive special nutrition would consistently yield superior results could not be confirmed. Although three of the four variety groups showed similar behaviour of fu with sp and co with sa, this pattern was not uniformly expressed across all grape–yeast combinations. Future studies with different fermentation conditions (NOPA initial value, fermentation temperature, different yeasts), including an in-depth sensory study, cell growth analysis, a screening of undesired compounds such as biogenic amines and a more comprehensive study including international grape varieties would be useful.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"151 ","pages":"Article 108990"},"PeriodicalIF":4.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421709","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}

{"title":"Analysis of volatile components in Pheretima and its extracts using HS-GC-IMS and HS-SPME-GC-MS combined with ROAV","authors":"Tongtong Yang ,&nbsp;Bo Tang ,&nbsp;Junyi Tang ,&nbsp;Chenqi Xu ,&nbsp;Yanlong Hong ,&nbsp;Fei Wu ,&nbsp;Xiao Lin","doi":"10.1016/j.jfca.2025.108835","DOIUrl":"10.1016/j.jfca.2025.108835","url":null,"abstract":"<div><div>Pheretima products are widely used, but the stenchy odor is a constraint to their application. In this study, 86 and 673 volatile components (VOCs) were identified in Pheretima and its extracts using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), respectively. An increase in acid and amine contents after processing was found, which may explain the increased stenchy odor. Through orthogonal partial least squares discriminant analysis (OPLS-DA), 14 differential markers between aqueous and alcoholic extracts were screened, and 3-methylbutanal, pentanal, and trimethylamine were identified as the key differential odor components. Combined with relative odor activity value (ROAV) analysis, 10 key odor components were identified, including: 3-methylbutanal, 1-octen-3-ol, dimethyl trisulfide, (2<em>E</em>,6<em>Z</em>)-nona-2,6-dienal, methyl mercaptan, guaiacol, isobutyraldehyde, 1,8-cineole, 2-pentylfuran, and pentanal. This study provides theoretical support for the optimization of the odor of Pheretima-containing products and promotes their application.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108835"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A time-resolved fluorescent signal tag-mediated immunochromatographic assay for the rapid and sensitive detection of fluoxetine","authors":"Wenjia Yang ,&nbsp;Lu Zeng ,&nbsp;Xinyu Zhang ,&nbsp;Xiangrui Li ,&nbsp;Qiujin Zhu","doi":"10.1016/j.jfca.2026.108884","DOIUrl":"10.1016/j.jfca.2026.108884","url":null,"abstract":"<div><div>The illegal addition of fluoxetine (FLX) to weight-loss products poses significant health risks to consumers. In this study, computer-aided simulation technology was utilized to design and screen FLX hapten. By optimizing the alkyl linker strategy, while preserving the structural similarity with the parent molecule, the hydrophobic interaction was enhanced, thereby significantly improving antibody performance. Based on this strategy, we successfully developed a highly sensitive monoclonal antibody (mAb). Leveraging this, we established an immunochromatographic assay (ICA) utilizing time-resolved fluorescent microspheres (TRFMs) for detecting FLX in capsule, tablet, and beverage samples, with limits of detection (LODs) of 3.010 and 1.549 µg/kg, and 0.886 ng/mL, respectively. Notably, the proposed ICA demonstrated acceptable selectivity, stability, and reproducibility. The recovery rates for FLX in the aforementioned matrices were 97.9 %-101.8 %, 96.9 %-103.4 %, and 99.1 %-101.7 %, respectively. These results were consistent with those obtained via LC-MS/MS. The TRFMs-based ICA provides a reliable, rapid, high-sensitivity, and real-time method for monitoring FLX levels. Moreover, this assay provides a promising strategy for the rapid detection of unauthorized non-food additives in food products.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108884"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927840","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}

{"title":"Advanced black tea fermentation grade identification: Terahertz time-domain spectroscopy coupled with self-attention mechanism guided deep learning","authors":"Meiyuan Chen ,&nbsp;Mengjiao Xue ,&nbsp;Zhusheng liu ,&nbsp;Ting Chen ,&nbsp;Yuanpeng Li ,&nbsp;Min Li ,&nbsp;Wenchang Huang ,&nbsp;Lingli Liu ,&nbsp;Yuna Chen ,&nbsp;Yan Chen ,&nbsp;Chunyu Guo ,&nbsp;Shan Tu ,&nbsp;Jian Tang ,&nbsp;Jun Liu ,&nbsp;Junhui Hu","doi":"10.1016/j.jfca.2026.108872","DOIUrl":"10.1016/j.jfca.2026.108872","url":null,"abstract":"<div><div>Fermentation is essential in black tea production, markedly influencing its flavor profile. In this study, terahertz time-domain spectroscopy (THz-TDS) was integrated with deep learning methodologies, specifically a convolutional neural network coupled with self-attention mechanism (CNN-SAM), to enable precise classification of black tea fermentation grades. Principal component analysis (PCA) revealed distinct spatial clustering of samples across different fermentation levels within three-dimensional feature space, albeit with partial inter-class overlap. The variance contributions of the first three principal components were 46.13 % (PC1), 23.67 % (PC2), and 8.27 % (PC3), respectively. The CNN-SAM architecture achieved 100 % classification accuracy, substantially outperforming conventional deep learning models (CNN, LSTM, and CNN-LSTM). Through the self-attention mechanism, the model identified 0.70–0.74 THz and 1.51–1.53 THz as discriminative spectral regions, thereby conferring enhanced interpretability. This THz-TDS–deep learning paradigm establishes a robust, non-destructive analytical framework for black tea fermentation quality assessment, offering significant potential for industrial deployment and processing standardization.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108872"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927839","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}

{"title":"Review of advances in hyper-spectral imaging-based non-destructive testing methods in food application","authors":"Sha Wu ,&nbsp;Lianfei Huang ,&nbsp;Renshuai Huang","doi":"10.1016/j.jfca.2026.108860","DOIUrl":"10.1016/j.jfca.2026.108860","url":null,"abstract":"<div><div>Ensuring food quality and safety is a global priority, and non-destructive testing (NDT) plays a critical role in monitoring products throughout the supply chain. Hyperspectral imaging (HSI), originally developed for remote sensing, has emerged over the past two decades as a powerful NDT tool for food analysis by integrating two-dimensional imaging with spectroscopy to acquire both spatial and spectral information at the pixel level. This review provides a comprehensive and application-oriented overview of HSI-based NDT methods in food science and food quality and safety control. First, the fundamental principles, sensing and acquisition modes, and typical hardware configurations of HSI systems are summarized. Second, key steps in hyperspectral data processing—including image calibration, spectral preprocessing, feature extraction, and model construction—are discussed, with particular emphasis on recent algorithmic advances such as deep learning-based feature learning, spectral–spatial modeling, and multisource data fusion. Subsequently, representative applications of HSI are critically reviewed in three major domains: food composition analysis (moisture, protein, fat, carbohydrates, and functional components), quality assessment (appearance, texture, freshness, and storage stability), and food safety (pesticide residues, heavy metals, mycotoxins, microbial spoilage, adulteration, and traceability). Finally, current challenges and future perspectives are outlined, including the need for standardised acquisition and validation protocols, miniaturised and low-cost HSI devices, integration with Internet of Things (IoT) technologies for supply chain monitoring, and open benchmark datasets to support robust deep learning models. By synthesising methodological advances and recent applications, this review aims to clarify how HSI can contribute to reliable, efficient, and non-destructive monitoring of food quality and safety.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108860"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927836","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}

{"title":"Nutrient profile-based food categorization and group-wise missing data imputation for commercial food composition database","authors":"Hyemi Shin,&nbsp;Hyein Seo","doi":"10.1016/j.jfca.2025.108828","DOIUrl":"10.1016/j.jfca.2025.108828","url":null,"abstract":"<div><div>Food composition databases (FCDBs) are essential resources for personalized nutrition, product development, and food substitution analysis. However, their applicability is often limited by missing data and heterogeneity among food classification systems. In this study, we propose a data-driven framework for nutrient-based food categorization and missing data imputation. First, we apply clustering algorithms to group food items based on nutritional similarity, providing a complementary approach to conventional food classification systems. This nutrition profile-based food group supports interoperability and reduces the manual effort required for mapping heterogeneous food categories. Second, we introduce a group-wise imputation strategy that incorporates food group information to improve the estimation of missing nutrient values. Using commercial data from two different FCDBs, we show that incorporating nutrient-based food group information into the imputation process yields more reliable results than conventional methods that do not exploit such structure. Furthermore, we evaluate the effectiveness of deep learning-based imputations that capture complex nutrient relationships, even in the absence of explicit group information. The proposed methodology offers a scalable and low-cost solution for improving food category systems and nutrient data coverage, contributing to more coherent and analytically useful FCDBs.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"150 ","pages":"Article 108828"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927755","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}

{"title":"Genetic diversity and nutritional variation in food crops in Ghana: A systematic review","authors":"Eunice Afia Amponsah ,&nbsp;Amma Larbi ,&nbsp;Moses Etsey ,&nbsp;Gifty Yeboah ,&nbsp;Linda Nana Esi Aduku ,&nbsp;Charles Apprey ,&nbsp;Herman Erick Lutterodt ,&nbsp;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":"2026-02-01","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}