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
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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DOI
PubMed
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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DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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2024
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IF 4.3
Fang Li,Chunling Yin,Kaidi Lv,Wenbo Chen,Liuchuang Zhao,Zhimin Liu,Leqian Hu
Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.
研究领域:
中药学
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分析化学
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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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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
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
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2019
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IF 2.994
Yan Liu,Zhenzhen Xia,Liyun Yao,Yang Wu,Yan Li,Shan Zeng,Hao Li
Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the sesame oils from different Chinese provinces and determinate the lignans (sesamin and sesamolin) in these sesame oils. The geographic discriminant model was constructed by principal component analysis (PCA) combining with linear discriminant analysis (LDA), and the quantitative analysis models of lignans were built using partial least squares (PLS) regression. Multiplicative scatter correction (MSC) and competitive adaptive reweighted sampling (CARS) were adopted to optimize the regression models. It was found that the discriminant model could recognize the sesame oils from different Chinese provinces correctly, and the contents of sesamin and sesamolin calculated from the optimized models and high-performance liquid chromatography (HPLC) analysis are rather close. Reasonable results proved the feasibility of near-infrared spectroscopy (NIRS) combined with chemometric methods for geographic origin of sesame oils and quantitate analysis of lignans in sesame oils.
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
MICROCHEMICAL JOURNAL
·
2024
·
IF 4.9
Fen Wang,Junfeng Fan,Yabin An,Guoliang Meng,Bingyu Ji,Yi Li,Caihong Dong
Ophiocordyceps sinensis (OS) is a unique and precious medicinal fungus endemic to the Tibetan Plateau, growing underground for the majority of its lifecycle. The mineral composition of OS is a distinctive feature, yet there is currently a lack of comprehensive mineral profiles. The concerns regarding excessive heavy metals and associated health risks significantly impact the development of the OS industry. Furthermore, OS samples from various production areas exhibit distinct qualities, resulting in significant price differences. Mineral profiles hold great potential for origin tracing. Consequently, there is an urgent need for quality evaluation, health risk assessment, and geographic origin tracing of OS by mineral element analysis. Here, 96 OS samples were collected from the representative producing areas in China to investigate their mineral profiles by inductively coupled plasma-optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS). Potassium (K) was found to be the most abundant mineral in the OS samples, with a mean content of 10.73 g/kg. High levels of phosphor (P), sulfur (S) and magnesium (Mg) were also detected with concentrations exceeding 1 g/kg. The mean concentrations of total arsenic in all OS samples were determined to be 8.33 mg/kg. The OS from Nagqu exhibited a relatively high quality with lower levels of lead (Pb), cadmium (Cd) and arsenic. Health risk assessment based on the total target hazard quotient (THQ) value of heavy metals of Zn, Mn, Fe, copper (Cu), nickel (Ni), chromium (Cr), Pb and Cd indicated that consumption of OS from China did not pose an obvious risk to human health at the ingestion rate declared by the Chinese Pharmacopeia. Finally, three orthogonal partial least squares discriminant analysis (OPLS-DA) models were constructed using 75 samples to trace the geographic origins. These models successfully authenticated OS samples from six producing areas and confirmed by 21 blind test samples with 90.48 % accuracy. These results affirmed that OS from different regions possessed distinct metal profiles, which could be utilized to authenticate the geographic origins of O. sinensis .
研究领域:
药用真菌学
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环境科学
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公共健康
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DOI
高影响因子
Food Chemistry-X
·
2025
·
IF 8.2
Yiqing Luo,Lei Hu,Kai Chen,Junyu Xu,Mengting Xiao,Shimeng Li,Yiying Yang,Ying Wang,Zhaoqi Xie,Shaohua Zeng,Chunsong Cheng
Schisandra sphenanthera fruit, a popular wild berry widely used for beverages industrial material in northeast Asian regions, urgently requires origin tracing technologies for adapting to the market rule of ‘better quality, better price’. This study first employed gas chromatography–mass spectrometry to analyze the cuticular wax of S. sphenanthera fruits from three main ecologically distinct production areas in China. The results showed that high levels of fatty acids (FA) and long-chain primary alcohols (LPA, C ≥ 26) were detected in the southwestern region (FA: 2.66 μg/cm 2 and LPA: 2.02 μg/cm 2 ); The highest aldehyde (2.12 μg/cm 2 ) and lowest sterol (0.50 μg/cm 2 ) content were detected from the central region; The lowest total wax (14.52 μg/cm 2 ) and higher sterol content (0.16 μg/cm 2 ) were detected in the eastern region. This study provided five indicative wax tracers firstly that serve as geographical indicators, enhancing knowledge of their ecological adaptability while establishing traceable chemical markers for geographical origin tracing.
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高影响因子
危险品化学品经营许可证（带存储）