JOURNAL OF FOOD COMPOSITION AND ANALYSIS
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2024
·
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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产品关联
DOI
高影响因子
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
·
2023
·
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
·
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
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
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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产品关联
DOI
PubMed
高影响因子
FOOD RESEARCH INTERNATIONAL
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2023
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IF 8.1
Peng Chen,Yabo Shi,Xiaoyan Xiao,Rong Xue,Yu Li,Lin Li,Chunqin Mao,Tulin Lu,Conglong Xu
The geographical traceability of food products is seen as a distinctive feature of the future of food which is increasingly becoming a concern for consumers. In this research, differences in the lipid composition of Coix seed samples from four major Chinese origins were investigated using non-targeted lipidomics. By multivariate statistical analysis, unsupervised PCA and OPLS-DA based differentiation between the four origins of Coix seed samples could be achieved. The OPLS-DA VIP > 1 screened 72 lipids out of 1211 lipids as potential markers to distinguish Coix seeds from different origins. In addition, the potential markers (SPH(d16:0), Cer(d18:2/20:0 + O) and PC(8:0e/8:0) were combined with statistical analysis algorithms to construct a discriminant function for rapid differentiation of Coix seed samples from different origins and a specific function for different origins with 100% discrimination accuracy. In general, a rapid and accurate method combining multivariate chemometrics and algorithms was developed based on untargeted lipidomics to determine the geographical origin of Coix seed samples, which can also be applied to other agricultural products.
研究领域:
食品科学
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植物学
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生物化学
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2022
·
IF 9.231
Jing Nie,Rui Weng,Chunlin Li,Xiuhua Liu,Fang Wang,Karyne M. Rogers,Yongzhong Qian,Yongzhi Zhang,Yuwei Yuan
Geographical origin discrimination of agro-products is essential to guarantee food safety and fair trade. Garlic samples cultivated in six provinces or major production regions in China were characterized for stable isotopes ( δ 13 C, δ 2 H, δ 18 O, δ 15 N, and δ 34 S), bioelemental contents (% C, % N and % S), and sulfur-containing compounds (8 organosulfur components and 2 amino acids). Results showed that many of the 18 analyzed garlic variables had significant differences among production regions. Some sulfur-containing compounds found in garlic from different provinces had a strong correlation with sulfur isotopes, suggesting garlic sulfur isotopes were also affected by geographical origin. Two supervised pattern recognition models (PLS-DA and k -NN) were developed using stable isotopes, elemental contents, and sulfur-containing compounds, and had a discrimination accuracy of 93.4 % and 87.8 %, respectively. Chemometric classification models using multi-isotopes, elements and sulfur-containing compounds provides a useful method to authenticate Chinese garlic origins.
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
PubMed
高影响因子
FOOD RESEARCH INTERNATIONAL
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2025
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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
FOOD CONTROL
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2022
·
IF 6.652
Leqian Hu,Yan Zhang,Ying Ju,Xiangru Meng,Chunling Yin
In general, fluorescent probes are used to determine certain metal ions due to their high selectivity, whereas the weakly selective probe could produce different fluorescence spectra after interacting with numerous metal ions. Based on the different species and contents of metal ions in rice, the weakly selective fluorescence probe combined with chemometrics for rice origin traceability and adulteration identification was studied in this study. Excitation-emission matrix spectra (EEMs) of rice extracts from different geographical origins (including adulterated rice) combined with the weakly selective probe were collected. Considering the three-dimensional (3D) characteristics of the EEMs, multi-dimensional principal component analysis (M-PCA) and unfold partial least squares discriminant analysis (U-PLS-DA) pattern recognition methods were used to extract useful information from complex 3D fluorescence data. And the models were built to classify the origin and adulteration of rice. The results of the M-PCA analysis showed that rice from different origins could not be completely distinguished from each other, but there was a clustering trend. It was suggested that the 3D fluorescence data measured after the reaction of the weakly selective probe with rice extracts may be used for rice origin traceability combined with pattern recognition. The analysis of 3D fluorescence data based on U-PLS-DA showed that the classification accuracy of training sets was 100%, and the accuracy of predicted sets was 98%. The results of the M-PCA analysis also showed that rice with different adulteration ratios had a clustering trend. The accurate recognition rate of training sets and predicted sets after U-PLS-DA analysis of adulterated rice was 99% and 95%, respectively. The results showed that the weakly selective probe could be used for rice origin traceability and adulteration identification after the reaction with rice extracts, combined with pattern recognition methods. This study broadens the application range of the fluorescent probe, which can be used for origin traceability and adulteration recognition of foods that cannot produce fluorescence themselves.
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
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
·
分析化学
·
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
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
·
代谢组学
·
蛋白质组学
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分析化学
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产地溯源
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）