Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

Yang, Yue; Wu, Yongjiang; Li, Weili; Liu, Xuesong; Zheng, Jiyu; Zhang, Wentao; Chen, Yong

doi:10.1016/j.saa.2017.10.019

Cited by 57 publications

(21 citation statements)

References 40 publications

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“…As one of the most robust and accurate data mining algorithms, SVM has been implemented in many programming languages, including R, MATLAB, and so on, which has led SVM to be adopted by a much wider audience. In recent years, SVM has successfully been applied to a number of applications, such as classification of species or geographical origin traceability of food [53,68,69]. It is important to note that SVM can achieve high classification accuracy whlie using a small number of training samples [56,67].…”

Section: Discussionmentioning

confidence: 99%

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Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Shen

Wang

2020

Molecules

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Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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Section: Discussionmentioning

confidence: 99%

“…It is important to note that SVM can achieve high classification accuracy whlie using a small number of training samples [56,67]. Additionally, it is also a suitable classifier for high-dimensional data [53,69].…”

Section: Discussionmentioning

confidence: 99%

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Shen

Wang

2020

Molecules

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“…have great influences on the growth quality of the medicinal plants, some chemical constituents of the same crude drug from different geographical origins vary in content. Therefore, NIR spectroscopy has been also used to determine the geographical origins of TCM, such as Radix Pseudostellariae [21], Herba Epimedii [22], and Gastrodiae Rhizoma [23] in recent years. However, there has not been any reports until now on the use of NIR spectroscopy for the discrimination of Trichosanthis Fructus from different geographical origins, and the discrimination between crude drugs and prepared slices.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

Wen

et al. 2019

Molecules

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Near infrared (NIR) spectroscopy with chemometric techniques was applied to discriminate the geographical origins of crude drugs (i.e., dried ripe fruits of Trichosanthes kirilowii) and prepared slices of Trichosanthis Fructus in this work. The crude drug samples (120 batches) from four growing regions (i.e., Shandong, Shanxi, Hebei, and Henan Provinces) were collected, dried, and used and the prepared slice samples (30 batches) were purchased from different drug stores. The raw NIR spectra were acquired and preprocessed with multiplicative scatter correction (MSC). Principal component analysis (PCA) was used to extract relevant information from the spectral data and gave visible cluster trends. Four different classification models, namely K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and support vector machine-discriminant analysis (SVM-DA), were constructed and their performances were compared. The corresponding classification model parameters were optimized by cross-validation (CV). Among the four classification models, SVM-DA model was superior over the other models with a classification accuracy up to 100% for both the calibration set and the prediction set. The optimal SVM-DA model was achieved when C =100, γ = 0.00316, and the number of principal components (PCs) = 6. While PLS-DA model had the classification accuracy of 95% for the calibration set and 98% for the prediction set. The KNN model had a classification accuracy of 92% for the calibration set and 94% for prediction set. The non-linear classification method was superior to the linear ones. Generally, the results demonstrated that the crude drugs from different geographical origins and the crude drugs and prepared slices of Trichosanthis Fructus could be distinguished by NIR spectroscopy coupled with SVM-DA model rapidly, nondestructively, and reliably.

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“…14 When NIRs combined with chemometrics was used for quality control of TCM, speci¯c models are usually constructed for di®erent herbs and chemical components. [15][16][17] However, there are similar or even common components in di®erent TCM. The nonglobal models between di®erent TCM make it necessary to construct and switch the model according to speci¯c TCM, which is ine±cient and laborious.…”

Section: Introductionmentioning

confidence: 99%

Rapid and simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy and chemometrics

Zhang

Chen

et al. 2019

J. Innov. Opt. Health Sci.

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Coptidis Rhizoma (Chinese: Huanglian) and Phellodendri Chinensis Cortex (Chinese: Huangbo) are widely used Traditional Chinese Medicine, and often used in combination because of their similar pharmacological effects in clinical practice. However, the quality control methods of the two drugs are different and complicated, which is time consuming and laborious in practical application. In this paper, rapid and simultaneous determination of moisture and berberine in Coptidis Rhizoma (CR) and Phellodendri Chinensis Cortex (PC) was realized by near-infrared spectroscopy (NIRs) combined with global models. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) method were applied for variable selection. Principal component analysis (PCA) and partial least squares regression method (PLSR) were applied for qualitative and quantitative analysis, respectively. The characteristic variables of berberine showed similarity and consistency in distribution, providing basis for the global models. For moisture content, the global model had relative standard error of prediction set (RSEP) value of 3.04% and 2.53% for CR and PC, respectively. For berberine content, the global model had RSEP value of 5.41% and 3.97% for CR and PC, respectively. These results indicated the global models based on CARS-PLS method achieved satisfactory prediction for moisture and berberine content, improving the determination efficiency. Furthermore, the greater range and larger number of samples enhanced the reliance of the global model. The NIRs combined with global models could be a powerful tool for quality control of CR and PC.

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Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

Cited by 57 publications

References 40 publications

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

Rapid and simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy and chemometrics

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