Rapid identification and differential markers of Curcumae Radix decoction pieces of different sources based on Heracles Neo ultra-fast gas phase electronic nose / 中国中药杂志

Since Curcumae Radix decoction pieces have multiple sources, it is difficult to distinguish depending on traditional cha-racters, and the mixed use of multi-source Curcumae Radix will affect its clinical efficacy. Heracles Neo ultra-fast gas phase electronic nose was used in this study to quickly identify and analyze the odor components of 40 batches of Curcumae Radix samples from Sichuan, Zhejiang, and Guangxi. Based on the odor fingerprints established for Curcumae Radix decoction pieces of multiple sources, the odor components was identified and analyzed, and the chromatographic peaks were processed and analyzed to establish a rapid identification method. Principal component analysis(PCA), discriminant factor analysis(DFA), and soft independent modeling cluster analysis(SIMCA) were constructed for verification. At the same time, one-way analysis of variance(ANOVA) combined with variable importance in projection(VIP) was employed to screen out the odor components with P<0.05 and VIP>1, and 13 odor components such as β-caryophyllene and limonene were hypothesized as the odor differential markers of Curcumae Radix decoction pieces of diffe-rent sources. The results showed that Heracles Neo ultra-fast gas phase electronic nose can well analyze the odor characteristics and rapidly and accurately discriminate Curcumae Radix decoction pieces of different sources. It can be applied to the quality control(e.g., online detection) in the production of Curcumae Radix decoction pieces. This study provides a new method and idea for the rapid identification and quality control of Curcumae Radix decoction pieces.

Establishment of a fast discriminant model with electronic nose for Polygonati Rhizoma mildew based on odor variation / 中国中药杂志

The odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees was analyzed and the relationship between the odor variation and the mildewing degree was explored. A fast discriminant model was established according to the response intensity of electronic nose. The α-FOX3000 electronic nose was applied to analyze the odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees and the radar map was used to analyze the main contributors among the volatile organic compounds. The feature data were processed and analyzed by partial least squares discriminant analysis(PLS-DA), K-nearest neighbor(KNN), sequential minimal optimization(SMO), random forest(RF) and naive Bayes(NB), respectively. According to the radar map of the electronic nose, the response values of three sensors, namely T70/2, T30/1, and P10/2, increased with the mildewing, indicating that the Pollygonati Rhizoma produced alkanes and aromatic compounds after the mildewing. According to PLS-DA model, Pollygonati Rhizoma samples of three mildewing degrees could be well distinguished in three areas. Afterwards, the variable importance analysis of the sensors was carried out and then five sensors that contributed a lot to the classification were screened out: T70/2, T30/1, PA/2, P10/1 and P40/1. The classification accuracy of all the four models(KNN, SMO, RF, and NB) was above 90%, and KNN was most accurate(accuracy: 97.2%). Different volatile organic compounds were produced after the mildewing of Pollygonati Rhizoma, and they could be detected by electronic nose, which laid a foundation for the establishment of a rapid discrimination model for mildewed Pollygonati Rhizoma. This paper shed lights on further research on change pattern and quick detection of volatile organic compounds in moldy Chinese herbal medicines.

Identification of Curcuma herbs using XGBoost algorithm in electronic nose odor fingerprint / 中国中药杂志

This article aims to identify four commonly applied herbs from Curcuma genus of Zingiberaceae family,namely Curcumae Radix( Yujin),Curcumae Rhizoma( Ezhu),Curcumae Longae Rhizoma( Jianghuang) and Wenyujin Rhizoma Concisum( Pianjianghuang). The odor fingerprints of those four herbal medicines were collected by electronic nose,respectively. Meanwhile,XGBoost algorithm was introduced to data analysis and discriminant model establishment,with four indexes for performance evaluation,including accuracy,precision,recall,and F-measure. The discriminant model was established by XGBoost with positive rate of returning to 166 samples in the training set and 69 samples in the test set were 99. 39% and 95. 65%,respectively. The top four of the contribution to the discriminant model were LY2/g CT,P40/1,LY2/Gh and LY2/LG,the least contributing sensor was T70/2. Compared with support vector machine,random forest and artificial neural network,XGBoost algorithms shows better identification capacity with higher recognition efficiency. The accuracy,precision,recall and F-measure of the XGBoost discriminant model forecast set were 95. 65%,95. 25%,93. 07%,93. 75%,respectively. The superiority of XGBoost in the identification of Curcuma herbs was verified. Obviously,this new method could not only be suitable for digitization and objectification of traditional Chinese medicine( TCM) odor indicators,but also achieve the identification of different TCM based on their odor fingerprint in electronic nose system. The introduction of XGBoost algorithm and more excellent algorithms provide more ideas for the application of electronic nose in data mining for TCM studies.

Qualitative identification of Aucklandiae Radix with LLE + SMA classification models based on electronic nose / 中草药

Objective: To establish an odor fingerprint with electronic nose technology to qualitatively identify Aucklandiae Radix odor from different producing areas. Methods: Eight batches of Aucklandiae Radix samples from eight different producing areas were collected. The odor information of each sample was obtained by electronic nose. The LDA algorithm based on Fisher’s identification criterion and the nonlinear dimensionality reduction LLE + SMA algorithm were used to distinguish the Aucklandiae Radix odor of different origins. Results: It was found that the LDA algorithm based on Fisher’s discriminant criterion could not distinguish the Aucklandiae Radix scent of different producing areas. Some of the samples in the place of origin had a lot of overlap, and the LLE + SMA algorithm could distinguish the odor very well. It can completely distinguish eight batches of Aucklandiae Radix samples from eight different producing areas. Conclusion: It is feasible to apply the electronic nose technology to the odor differentiation of Aucklandiae Radix from different producing areas, and provide new ideas and methods for the quality evaluation of Aucklandiae Radix.

Identification of Aurantii Fructus from different growing areas based on bionic olfaction technology / 中草药

Objective To establish odor fingerprint determination method of Aurantii Fructus from different growing areas based on bionic olfaction technology, and provide the reference for the quality control of Aurantii Fructus. Methods The bionic olfactory system (electronic nose, PEN3) was used to measure the odor of Aurantii Fructus from different growing areas; The LDA (Linear discriminant analysis) method was used to determine its odor fingerprint, and the simulation for identification was realized based on MATLAB 2013. Results Although these odor fingerprints of Aurantii Fructus from different growing areas were similar, there were significant differences in peaks' value from each other. The established odor fingerprints can achieve the 96% identification accuracy between known and unknown samples. Conclusion The method is simple, accurate and repeatable, which can be used for quality control of Aurantii Fructus from different growing areas.