FOOD CHEMISTRY
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2024
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IF 8.5
Tianyue Liu,Yining Li,He Cheng,Ruixuan An,Xinglin Hou,Yasong Zhang,Jieyu Zhao,Hao Zhang,Jingsheng Tian,Zhiyi Yao
Metal ions (MIs) identification is essential for the safety assessment, traceability and authentication of food. Most current approaches for detecting MIs are difficult to reconcile the simplicity, sensitivity and stability simultaneously. In this work, we proposed a novel strategy for discriminating MIs based on fluorescent supramolecular nanoaggregates (SNAs). In the presence of MI, perylene diimide derivatives (PDI)-based SNAs could be formed through the multiple non-covalent interactions between them including electrostatic, coordination and π-π interactions. With the assistance of discriminant analysis (LDA), different MIs (Hg 2+ , Ag + , Cd 2+ , Fe 3+ , Cr 3+ , Fe 2+ , Zn 2+ , Cu 2+ and Pb 2+ ) were successfully identified at three different concentration levels. It featured good quantitative sensing abilities in buffer solutions and practical samples. Furthermore, a water-quality evaluation model was successfully constructed for the distinction of different sources of drinking water, and the fluorescence array sensor technology was applied for the first time to the geographical traceability of apples.
研究领域:
分析化学
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环境科学
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食品科学
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材料科学
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DOI
PubMed
高影响因子
FOOD RESEARCH INTERNATIONAL
·
2025
·
IF 8
Xinyao Lin,Chunling Yin,Leqian Hu,Liuchuang Zhao,Mengyao Chen,Xia Hua,Zhimin Liu,Peng Li
Fluorescent sensor arrays are becoming a hot topic in many fields because they can simultaneously detect multiple targets in complex research systems. However, most researches have only collected two-dimensional fluorescence spectral data from fluorescent sensor array interacting with target analytes. In contrast, three-dimensional fluorescence spectra can provide richer information than two-dimensional fluorescence spectra. Based on the hypothesis that collecting three-dimensional fluorescence spectra can obtain more abundant information of green tea samples from different regions, which can improve the accuracy and reliability of origin identification. This study aimed to explore the feasibility of using three-dimensional fluorescent sensor array combined with multi-way pattern recognition methods for the origin discrimination of green tea based on the differences in the contents and types of metal ions in green tea. To investigate this, we first designed a fluorescent sensor array based on amino acid-derived carbon dots and examined its ability to recognize common metal ions in green tea. Excitation-emission matrix spectra of green tea extracts from different geographical origins after interaction with the fluorescent sensor array were collected. Several multi-way pattern recognition methods were used to analyze the three-dimensional fluorescent array data of 100 green tea samples from five origins. The overall classification results of green tea from the five geographical origins were satisfactory, with the best prediction accuracy reaching 96.88%. In comparison, multilinear partial least squares discriminant analysis could make full use of the information of three-dimensional fluorescence data. And its correct identification results for green tea were superior to those of unfold partial least squares discriminant analysis. These results sufficiently demonstrated that the fluorescent sensor array integrated with multi-way pattern recognition, has promising potential for tracing the origin of Chinese green tea.
研究领域:
食品科学
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分析化学
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产品关联
DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
·
2025
·Vol. 26
·期 15
·7293
·
IF 4.9
Yiqing Luo,Min Guo,Lei Hu,Jiaxin Yang,Junyu Xu,Muhammad Rafiq,Ying Wang,Chunsong Cheng,Shaohua Zeng
Viticis Fructus (VF), a fruit known for its unique flavor profile and various health benefits, demonstrates substantial quality variations depending on its area of production. Traditional methods of production area verification based on internal compound analysis are hampered by a number of technical limitations. This investigation systematically characterized the cuticular wax composition of VF sample from a diverse variety of production areas. Quantitative analyses were conducted to evaluate the spatial distribution patterns of the wax constituents. Significant regional variations were observed: Anhui sample exhibited the highest total wax content (21.39 μg/cm 2 ), with n-alkanes dominating at 76.67%. High-latitude regions showed elevated triterpenoid acid levels, with maslinic acid (0.53 μg/cm 2 ) and ursolic acid (0.34 μg/cm 2 ) concentrations exceeding those of their low-latitude counterparts by four- and three-fold, respectively. Altitudinal influence manifested in long-chain alcohol accumulation, as triacontanol reached 0.87 μg/cm 2 in high-altitude sample. Five key biomarkers demonstrated direct quality correlations: eicosanoic acid, n-triacontane, dotriacontanol, β-amyrin, and α-amyrin. This study established three novel origin identification protocols: single-component quantification, multi-component wax profiling, and wax ratio analysis. This work not only reveals the latitudinal dependence of VF wax composition, but also provides a scientific framework for geographical authentication. Our findings advance wax-based quality evaluation methodologies for fruit products, offering practical solutions for production area verification challenges in food raw materials.
研究领域:
食品科学
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分析化学
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植物生物化学
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地球化学
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农业科学
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产品关联
DOI
PubMed
高影响因子
Food Chemistry-X
·
2025
·
IF 8.2
Chongxin Liu,Simona Grasso,Nigel Patrick Brunton,Qi Yang,Shaobo Li,Li Chen,Dequan Zhang
The origin traceability of lamb is a longstanding concern for consumers. Despite the widespread application of untargeted metabolomics in meat origin traceability, challenges remain in achieving rapid and accurate identification of biomarkers. This study utilized untargeted metabolomics to analyse five breeds of geographical indication lamb, obtaining profile data comprising a total of 4139 metabolites. Using random forest recursive feature elimination, 29 potential biomarkers were initially identified, which showed significant breed-specific and production environment-related variations. Upon further assessment, a refined panel of 14 metabolic biomarkers demonstrated optimal accuracy and robustness in tracing lamb origin. When combined with the Naive Bayes algorithm, these biomarkers yielded the highest classification accuracy among all evaluated machine learning methods. The random forest recursive feature elimination presents a practical approach for handling high-dimensional metabolomics datasets compared to previous analytical methods. These findings also provide valuable insights into the development of machine learning-based biomarker panels, greatly enhancing the breed-specific traceability of lamb.
研究领域:
食品科学
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分析化学
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代谢组学
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机器学习
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畜牧学
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食品真实性鉴定
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产品关联
DOI
PubMed
高影响因子
Foods
·
2025
·Vol. 14
·期 19
·3381
·
IF 5.1
Jiaqi Liu,Hao Duan,Li Wang,Rui Qin,Jiao Liu,Hong Liu,Shuyuan Bao,Wenjie Yan
This study aimed to systematically characterize the volatile organic compound (VOC) profiles of safflower (Carthamus tinctoriusL.) from eight major production regions, providing a scientific basis for quality evaluation and geographical traceability. VOC profiling was conducted using gas chromatography–ion mobility spectrometry (GC–IMS), and regional differences were assessed through multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS–DA), Euclidean distance, and hierarchical clustering. Key differential compounds were identified by variable importance in projection (VIP) and relative odor activity value (ROAV) analyses, with aldehydes and esters emerging as the primary contributors to the discrimination of samples across regions. VOC fingerprints of safflower were further established, and a combined VIP–ROAV strategy was proposed for the screening of characteristic compounds. These findings provide a reliable reference for safflower quality control and offer practical guidance for its geographical authentication in the food industry.
研究领域:
植物化学
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代谢组学
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食品科学
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分析化学
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天然产物化学
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质量控制
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地理标志产品
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产品关联
DOI
PubMed
高影响因子
FOOD CONTROL
·
2024
·
IF 5.6
Yuean Chu,Mingyue Yu,Yunxing Li,Suoxian Yang,Hongyan Bi
Tracement of the origin of seafoods and aquatic products is an important issue for avoiding the falsification of production regions of aquatic products. Herein, by analyzing black tiger shrimp ( Penaeus monodon ) as a model of aquatic products, we demonstrated a method to trace the origin of aquatic products by analyzing the gut microbiome of black tiger shrimp ( Penaeus monodon ) collected from three commercial farms in China, via matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. Multivariate analysis was executed on the obtained mass spectrometric datasets of bacteria extracted and collected from shrimp samples with different production regions. The gut microorganisms of black tiger shrimps can be identified by scoring the gut microbiota of black tiger shrimps collected from three producing areas. The black tiger shrimp from three production regions were found with distinguished characteristics of bacterial prevalence in their digestive gland microbiota. The dominant bacteria genus in digestive gland microbiota from black tiger shrimp samples from the three production regions are completely different. Single-blind shrimp samples with unknown production region was successfully screened by comparing the detected MS data in different ways. This strategy can trace the production region of the analyzed shrimp samples in a quick and efficient mode. The application of the present protocol was further demonstrated by analyzing the biometric species from gill tissue of Chinese mitten crab ( Eriocheir sinensis ) samples collected from crab-producing areas in Jiangsu Province (China), that is, Taihu Lake, Gaoyou Lake, and Hongze Lake for tracing their production area. It is expected to extend the presently developed method to provide clues for the origin traceability of other aquatic products.
研究领域:
水产养殖
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食品科学
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微生物学
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质谱分析
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产品关联
DOI
高影响因子
EUROPEAN FOOD RESEARCH AND TECHNOLOGY
·
2025
·
IF 3.2
Liu Nan-nan,Ren Qing-dan,Li Long,Wang Li-na,Shi Guang-liang,Qin Shou-tao
Apis cerana honey from Changbai Mountain (CBS honey), produced in northeastern China, is a high-quality specialty honey that has long been recognized as a natural health product with medicinal properties. In this study, UPLC-MS/MS metabolomics and ultra-fast proteomics technologies were employed to analyze the differences in metabolites and proteins among Apis cerana honey samples collected from various Chinese regions, including Jilin, Sichuan, Hainan, Hubei, and Tibet. Through an integrated multi-omics analysis, we identified candidate characteristic compounds, including 12 potential characteristic metabolites and 13 characteristic proteins, to differentiate CBS honey from Apis cerana honeys produced in other regions. These findings provide foundational reference for the authentication and geographical origin traceability of CBS honey.
研究领域:
食品科学
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代谢组学
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蛋白质组学
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分析化学
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产地溯源
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产品关联
DOI
高影响因子
TALANTA
·
2025
·
IF 6.1
Zixuan Zhang,Xiaogang Lu,Meng Jin,Runli Gao,Chengxin Pei,Hongmei Wang
Organophosphorus nerve agents (OPNAs), including G-agents, EGA (ethyltabun, phosphonamidic acid, P -cyano- N , N -diethyl-, ethyl ester) and V-agents, VM ( O -ethyl S -(2-diethylaminoethyl) phosphonothiolate), are highly toxic chemical warfare agents (CWAs) with severe risks to human health and environmental security. This study proposes a chemometric-driven framework for forensic tracing of their synthetic pathways using high-resolution GC × GC-TOFMS. By integrating advanced statistical analysis, we identified 160 synthesis-associated chemical attribution signatures (CAS) for EGA and 138 process-specific CAS for VM, with 11 overlapping markers, including ethoxyphosphates and diethylaminoethylamine derivatives. Key distinguishing features include sulfur-containing derivatives (e.g., phosphorothioates) for VM and cyanide byproducts for EGA. These data were visualized through innovative chord diagrams and heatmaps, offering unprecedented insights into compound-sample relationships critical for forensic traceability. Multivariate classification using Hierarchical Cluster Analysis (HCA) and Partial Least Squares Discriminant Analysis (PLS-DA) achieved exceptional accuracy—97.2 % for EGA and 100 % for VM via HCA, and 100 % for both agents via PLS-DA. Robust k-fold cross-validation (k = 5 and 10) further confirmed model reliability, with R 2 values approaching unity. This work establishes a comprehensive CAS database and a scalable analytical framework, advancing forensic capabilities from reactive detection to proactive, intelligence-driven prevention.
研究领域:
分析化学
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法庭科学
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化学计量学
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环境毒理学
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化学战剂
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数据科学
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产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）