ANALYTICAL LETTERS
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2023
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IF 2
Xinlei Zhang,Wenbao Jia,Xinru Tang,Qing Shan,Qiyan Chen,Can Cheng,Jinfa Shao,Yongsheng Ling,Daqian Hei
Teas of the same type in a small growing area are generally similar, and identification of their detailed geographic origin is challenging and interesting. In this study, a convenient method for distinguishing Pu-erh teas from nearby locations was developed by combining two spectroscopic techniques with chemometric methods. The distinction was based on the difference in the mineral elements and organic metabolites from tea infusions. First, Pu-erh tea was infused with water at 90 °C for 30 min. Total reflection X-ray fluorescence and ultraviolet-visible (UV-Vis) spectroscopies were used to determine the elements (P, S, K, Ca, Mn, and Ni) and organic metabolites (caffeine and polyphenols), respectively. Chemometric methods were applied to classify the origin based on the component. The proposed method was used to test samples collected from 10 mutually adjacent high-altitude villages in Mengku, Lincang, Yunnan, China. The developed user-friendly method was successfully used for the rapid evaluation of the composition, enabling the identification of the geographic origins of Pu-erh tea. Mn, caffeine, and polyphenols were found to be the key origin markers. Compared to principal component analysis, linear discriminant analysis models exhibited high discrimination performance with 100% predictive ability to distinguish the geographic origins of Pu-erh teas over a small production area. This rapid and simple method may be used to solve classification problems in tea quality control and management.
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
高影响因子
JOURNAL OF CHROMATOGRAPHY A
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2024
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IF 3.8
Wenhui Lu,Chunli Ding,Mingshuo Zhu
The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.
研究领域:
法医学
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分析化学
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数据科学
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DOI
PubMed
高影响因子
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
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2024
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IF 4
Dingkui Xiang,Yue Ma,Jiaxu Yao,Minjie Zhao,Haijiao Li,Eric Marchioni,Li Zhou
Shiitake mushroom ( Lentinus edodes ), as a prestigious edible mushroom and one of the most famous medicinal mushrooms, is cultivated in several regions of China. In this study, the total lipids of shiitake mushroom from different origins were characterized by ultra-high-performance liquid chromatography-quadrupole-Exactive-Orbitrap MS/MS (UHPLC-QE Orbitrap/MS/MS). 20 lipid classes and 174 lipid molecular species were determined by non-targeted lipodomics analysis. The multivariate analysis showed that 19 lipids were screened as potential markers for distinguishing shiitake mushroom of different origins, for instance, phosphatidic acid (18:2/18:2) (PA 18:2/18:2) and phosphatidylglycerol (16:0/18:2) (PG 16:0/18:2). And the Partial Least Squares-Discriminant Analysis (PLS-DA) model reconstructed was used to distinguish shiitake mushroom from different regions with a success rate of 100 %. This work comprehensively provided the lipid information of shiitake mushroom and demonstrated the great potential of lipidomics for tracing the authenticity of shiitake mushrooms from different origins.
研究领域:
食品科学
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分析化学
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生物化学
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DOI
高影响因子
FOOD CHEMISTRY
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2022
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IF 9.231
Zhiqing Liu,Mantong Zhao,Xinwen Wang,Chuan Li,Zhongyuan Liu,Xuanri Shen,Dayong Zhou
The present study sought to analyze the lipid profiles of oyster Crassostrea gigas from Yellow Sea (YS), East China Sea, and South China Sea (SCS) through the untargeted lipidomics strategy based on UPLC-Q-Exactive Orbitrap mass spectrometry and multivariate statistics. The results elucidated that geographical differences had profound effects on the lipid content, composition, and lipid molecular profiles. Notably, oysters from the YS group contained the highest lipid content, including triacylglycerol, diacylglycerols, and the majority of phospholipid molecule species, while oysters from the ECS group contained most of the phosphatidylcholine species and the SCS group contained most of the sphingolipid species. Totally, 1155 lipid molecular species belonging to 21 subclasses were identified; of them, 45 lipid molecular species could serve as differential marker for lipid of oysters from different sea areas. Overall, lipidomics could be a potential approach for discrimination of lipid characters between marine shellfishes for geographical origin traceability.
研究领域:
海洋生物学
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分析化学
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食品科学
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DOI
高影响因子
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
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2023
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IF 4.3
Li Zhou,Yuting Guan,Jiaxu Yao,Minjie Zhao,Haiyan Fu,Jikai Liu,Eric Marchioni
Lilium brownii var. viridulum Baker is an economically important plant, and its bulbs are popular among consumers for their high nutritional value. Due to the influence of environment, the chemical composition of lily from different origin is different, which leads to the different medicinal and nutritional values. Thus, it is very significant and necessary to discriminate the origins of lily. In this study, lipid classes and molecular species of lily samples from eight origins were identified and quantified by UHPLC-QE-Orbitrap/MS/MS and combined with chemometrics for modeling and analysis. The results showed that a total of 16 lipid classes and 88 lipid molecules were identified and quantified. The partial least squares discriminant analysis (PLS-DA) showed that there were significant differences in lipid metabolites between LH (Daodi) and other origins. And 39 characteristic compounds such as TAG 16:0/18:0/18:2, TAG 18:0/18:1/18:2, and LDGCC 15:2 could be used as the markers to identify the geographical origin of lily. This study established a new method to identify the origin traceability of lily in different production areas, and provided a theoretical basis to evaluate the nutritional value of lilies from different origins.
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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产品关联
DOI
高影响因子
Scientific Reports
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2021
·Vol. 11
·期 1
·1-9
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IF 4.38
Huang Xiao-Ping,Lei Lei,Lei Shun-Xin,Zhu Wei-Wei,Yan Jun
Siraitia grosvenorii (LHG) is widely used as a medicinal and edible material around the world. The objective of this study was to develop an effective method for the authentication of the geographical origin of LHG in its main producing area Guangxi, China, which is identified as Chinese Protected Designation of Origin product, against other producing regions in China. The content of 14 elements (K, Na, Ca, P, Mg, Al, B, Ba, Cu, Fe, Mn, Ni, Zn, and Sr) of 114 LHG samples was determined by inductively coupled plasma optical emission spectrometry. Multivariate analysis was then performed to classify the geographical origin of LHG samples. The contents of multielement display an obvious trend of clustering according to the geographical origin of LHG samples based on radar plot and principal component analysis. Finally, three supervised statistical techniques, including linear discriminant analysis (LDA), k -nearest neighbours (k-NN), and support vector machine (SVM), were applied to develop classification models. Finally, 40 unknown LHG samples were used to evaluate the predictive ability of model and discrimination rate of 100%, 97.5% and 100% were obtained for LDA, k -NN, and SVM, respectively. This study indicated that it is feasible to attribute unknown LHG samples to its geographical origin based on its multielement content coupled with chemometric techniques.
研究领域:
植物学
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食品科学
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分析化学
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DOI
高影响因子
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
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2025
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IF 3.1
Xinyi Jiao,Rongrong Li,Simon Sani Ocholi,Haitao Wang,Tongcan Cui,Biying Chen,Liming Wang,Zhifei Fu,Erwei Liu,Fengchao Wang,Lifeng Han
Huanglian (HL) is a member of the Ranunculaceae family, including Coptis chinensis Franch., Coptis deltoidea C. Y. Cheng et Hsiao, or Coptis teeta Wall. The dried rhizomes are highly esteemed herbal medicine in Chinese pharmacopeia. However, the composition of HL is complex, and current identification technologies are insufficient for conducting a comprehensive analysis of HL, leading to major obstacles in quality control. Therefore, an in-depth exploration of the influence of species diversity and geographic provenance on the chemical profile of HL is imperative for its rational application and quality assurance. To comprehensively analyze compounds in HL samples from diverse geographical regions, this study employed an integrated approach combining offline two-dimensional ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry (2D-LC-MS/MS) with online 2D-LC-MS/MS. This dual-platform strategy enabled detailed characterization of complex compound profiles. Additionally, the retention time prediction (RT-Ensemble Pred) models were utilized to predict and identify the retention times of unknown compounds, which particularly facilitated the differentiation of isomers. The comprehensive research resulted in the identification of 150 chemical constituents in HL, including 72 isomers. Furthermore, the compounds were analyzed and categorized according to mathematical classification models, allowing for distinction between various geographical origins. Based on unexposed data, the model demonstrated robust predictive capability, enabling the selection of 20 distinctive characteristic compounds with prominent features for use in geographical origin discrimination. Overall, this multidimensional investigation significantly enhanced our understanding of the chemical composition and inherent variability of HL plant resources, providing crucial technical underpinnings and methodological insights for the comprehensive exploitation and utilization of HL in biomedical and pharmaceutical applications.
研究领域:
中药学
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分析化学
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植物化学
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2025
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IF 9.8
Minrui Zhou,Ting Li,Lingling Shen,Qisheng Zhong,Taohong Huang,Ting Zhou
This study introduces a method for authenticating organic milk and determining its geographic region using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Both lipids and peptides were analyzed, and their combined dataset significantly improved classification accuracy when integrated with machine learning models. To improve method reproducibility, a binary matrix composed of α-cyano-4-hydroxycinnamic acid and 2,5-dihydroxybenzoic acid, combined with a vacuum drying strategy, was implemented. This approach reduced the relative standard deviation by approximately 25 % compared to traditional matrices. Multivariate statistical analysis identified key lipids and peptides as markers for discriminating organic from conventional milk and for geographic region classification. Integrating the lipid-peptide dataset with machine learning achieved up to 100 % accuracy, outperforming models based on single-dataset. Additionally, two-way ANOVA revealed that farming system, geographic region and their interactions significantly influenced milk composition. This method demonstrates the potential of combining MALDI-TOF MS with machine learning to prevent organic milk fraud.
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
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
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2023
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IF 4.3
Liu Zhi,Gao Xianmei,Yang Jian,Zhao Duoyong,Li Bin,Zhang Zihong,Chen Piao,Wang Dongguang
Saffron spice, as a high value-added but not high-tech processed agricultural product, is easily faked and mislabeled its quality grade and origin for higher market profits and competitiveness. In this work, an analytical strategy combining chemical composition analysis and stable isotope ratio analysis of saffron spices was explored for verifying their quality and origin authenticity in China market. The crocins contents and stable isotope ratios of 70 saffron spice samples collected from five provinces of China and 95 imported products from Iran and Spain were determined together. The contents of crocins may be effective as the indexes of their quality evaluation but not for origin traceability. Relatively, regional difference of stable isotope ratios is significant (p < 0.05) between different origins. Partial least-squares discriminant analysis (PLS-DA) based on chemical compositions and stable isotope ratios of 165 saffron spice samples can identify imported products from Iran and Spain with the accuracies of 98.5 % and 100 %, but still may mis-discriminate samples from Henan and Xinjiang province of China as Iran products, which was further solved by PLS-DA of products from these origins. This strategy is promising as an effective tool for combating commercial frauds and ensuring the authenticity of saffron spice in China market.
研究领域:
食品科学
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分析化学
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植物学
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产品关联
DOI
高影响因子
FOOD CHEMISTRY
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2022
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IF 9.231
Dai-xin Yu,Xia Zhang,Sheng Guo,Hui Yan,Jie-mei Wang,Jia-qi Zhou,Jian Yang,Jin-Ao Duan
Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC–MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC–MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.
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
MICROCHEMICAL JOURNAL
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2025
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IF 5.1
Rao Fu,Peng Chen,Wenhao Dong,Jia Qiao,Yu Li,Mingxuan Li,Li Zeng,Chunqin Mao,Tulin Lu,Chenghao Fei
The evaluation of the herbal medicine quality often involves time-consuming and labor-intensive high-precision instrument testing. This study used Salvia miltiorrhiza as a case study, with 120 samples collected from various regions. Hyperspectral data, Fourier Transform Near-Infrared data, heavy metals and harmful elements data, and active ingredient data were systematically gathered. Using multivariate statistical analysis, multimodal information integration, and deep learning algorithms, a geographical origin tracing model was developed, achieving an accuracy rate of 100%. Additionally, spectral-ingredient prediction models for five heavy metals and harmful elements and four active components were established, allowing the heavy metals and harmful elements and active components of Salvia miltiorrhiza to be rapidly and accurately assessed. This study compared the performance of the hyperspectral and FT-NIR techniques in herbal medicine quality control. The results indicated that hyperspectral demonstrated broader applicability and superior performance in predicting heavy metals and harmful elements contents, whereas FT-NIR was more effective in analyzing chemical constituents. This research provides a scientific basis for Salvia miltiorrhiza origin tracing and quality prediction while also confirming the potential of multidimensional spectra techniques in the quality evaluation.
研究领域:
中药学
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光谱学
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数据分析
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产品关联
DOI
高影响因子
FOOD RESEARCH INTERNATIONAL
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2024
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IF 7
Haolei Han,Xiangxin Lin,Yuxuan Wu,Houru Li,Qi Jia,Changmin Hu,Yixuan Lin,Guangqin Liao,Yongzhong Qian,Jing Qiu
The Large Yellow Croaker (LYC) with rich lipids offers numerous health benefits, yet its lipid profile remains underexplored. Therefore, the lipids of LYC were comprehensively profiled and compared based lipidomics. Higher nutritional value of LYC was identified based on lipid and fatty acid assessment, particularly ovary and brain. A total of 963 lipid species belonging to 47 lipid subclasses were identified, exhibiting higher levels of glycerophospholipids (GPs) and sphingolipids (SPs) in the brain, as well as high levels of glycolipids (GLs) in the muscle. Furthermore, unique lipid subclasses were detected in the brain (Acylcarnitine (CAR), Alpha-hydroxy-N-stearoyl phytosphingosine (Cer-AP)) and ovary (N-arachidonoyl glycine (NAGly)). Notably, 8 lipid subclasses were selected as potential contributors for four tissues differentiation. Additionally, identification of LYC from various origins was achieved through lipidomics for the first time. LYC from Zhejiang and Fujian provinces could be distinguished by 100 lipid biomarkers. Most different lipids significantly negatively correlated with seawater pH and dissolved oxygen, but positively correlated with netting density, depth, environmental temperature and salinity. This comprehensive analysis provided valuable insights into LYC’s nutritional values and origin identified.
研究领域:
水产科学
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生物化学
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食品科学
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DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）