JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
·
2013
·
IF 2.947
Chen Li,Feng Xu,Chen Cao,Ming-Ying Shang,Cui-Ying Zhang,Jie Yu,Guang-Xue Liu,Xuan Wang,Shao-Qing Cai
Traditional Chinese medicines (TCM) can be identified by experts according to their odors. However, the identification of these medicines is subjective and requires long-term experience. In this paper, electronic nose, headspace gas chromatography–mass spectrometry (GC–MS) and chemometrics methods were applied to differentiate two species of Asari Radix et Rhizoma by their odors. The samples used were the dried roots and rhizomes of Asarum heterotropoides var. mandshuricum (AH) and Asarum sieboldii (AS). The electronic nose was used to determine the odors of the samples and enabled rapid differentiation of AH and AS when coupled with principal component analysis. Headspace GC–MS was utilized to reveal the differences between the volatile constituents of AH and AS. In all, 54 volatile constituents were identified, and 9 major constituents (eucalyptol, eucarvone, 3,5-dimethoxytoluene, 3,4,5-trimethoxytoluene, methyleugenol, 2,3,5-trimethoxytoluene, croweacin, pentadecane and asaricin) could be used as chemical markers to distinguish these two species. AH contained higher relative contents of eucarvone (1.79–16.76%), 3,5-dimethoxytoluene (6.64–26.52%), 3,4,5-trimethoxytoluene/methyleugenol (6.43–31.67%) and 2,3,5-trimethoxytoluene (1.64–6.66%), whereas AS had higher relative contents of eucalyptol (14.06–24.95%), croweacin (5.64–13.55%), pentadecane (8.44–20.82%) and asaricin (7.03–13.45%). Moreover, AH and AS could be distinguished according to the contents of either all 54 identified volatile constituents or only the 9 major constituents by employing cluster analysis. The proposed method is rapid, simple, eco-friendly and can successfully differentiate these two species of Asari Radix et Rhizoma by their odors.
研究领域:
中药鉴定
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分析化学
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天然药物化学
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代谢组学
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药用植物学
✓ 已验证
DOI
高影响因子
RSC Advances
·
2017
·Vol. 7
·期 73
·46461-46471
·
IF 3.108
Shui Jiang,Jun Wang,Yubing Sun
Chinese pecans (Carya cathayensis) continuously deteriorate during storage because of their high fatty acid contents. In this study, an electronic nose (E-nose) was introduced to characterize Chinese pecans with different storage times. Chemometric methods (principal component analysis (PCA), partial least squares regression (PLSR), and back propagation neural networks (BPNNs)) were employed to analyze E-nose data. For qualitative analysis, PCA could visualize the discrimination between different pecans based on the E-nose data. For quantitative analysis, the results indicated that BPNN models performed better both in predicting storage times and fatty acid contents than the PLSR models. In addition, a multi-target BPNN regression model was built to simultaneously predict the contents of the six main fatty acids, and the results (R2 > 0.95 in calibration sets and R2 > 0.88 in validation sets) were satisfactory. This study provides a potentially viable method for determining the storage times and fatty acid profiles of nut products.
研究领域:
食品科学
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分析化学
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农产品贮藏
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营养化学
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食品安全
✓ 已验证
DOI
高影响因子
FOOD CHEMISTRY
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2017
·
IF 4.529
Ting Shi,MengTing Zhu,Yi Chen,XiaoLi Yan,Qian Chen,XiaoLin Wu,Jiangnan Lin,Mingyong Xie
Proton nuclear magnetic resonance ( 1 H NMR) and chemometrics were employed to detect the adulteration of camellia oil (CAO) with 3 different cheap vegetable oils. With the intensity of 15 selected 1 H NMR signals as input variables, principal component analysis (PCA) showed good group clustering results for pure and nonpure CAO, but unsatisfied identification accuracy for the adulterated oil types, indicating relatively small difference among those oils. Whereas these difference could be revealed by orthogonal projection to latent structures discriminant analysis (OPLS-DA), with identification accuracy higher than 90%. Partial least squares (PLS) was further applied for the prediction of adulteration level in CAO. With less than 6 variables screened out by variable importance in the projection (VIP) scores as potential key markers, the developed PLS models showed better accuracy. The prediction results for 10 hold-out samples also confirmed that this method was accurate and fast for the detection of CAO adulteration.
研究领域:
食品科学
·
分析化学
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食品安全
·
代谢组学
·
油脂化学
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）