[Establishment of a fast discriminant model with electronic nose for Polygonati Rhizoma mildew based on odor variation].

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].

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.

[Effects of nitrogen and sulfur combined application on nutritional components and active components of Isatis indigotica at seedling stage].

Using split plot design, a pot experiment with sand culture was conducted to investigate the effects ofnitrogen and sulfur combined application on nutritional components and active component of Isatis indigotica at seedling stage under different N (5，15，25 mmol·L⁻¹)and S(0.00，1.25，2.50，5.00，7.50 mmol·L⁻¹) levels. The results showed thatthe two elements had obvious effects and the leaf and root dry weights of I. indigotica seedlings increased greatly at N2 level. Under the same nitrogen concentration, the leaf and root dry weights increased firstly and decreased with the rising of sulfur concentrations in which S2 was conducive to the growth and biomass accumulation. Soluble sugar, soluble protein, soluble amino acids contents were the highest in N1, N2 and N3 treatments, respectively. The influence of sulfur concentrations on nutritional components was same as biomass, but the peak of different nutritional components was diversity in different nitrogen levels. The effects on secondary metabolites (total flavones, indigo, indriubin, epigotrin contents) were not obvious significantly, in which these indexes by N1S3，N1S2，N3S0，N3S1were the highest, respectively. In conclusion, the combination of nitrogen and sulfur of N2S2(N 15 mmol·L⁻¹ and S 2.5 mmol·L⁻¹) was beneficial to the growth and secondary metabolites accumulation of I. indigotica. These results could provide a theoretical basis for rational fertilization and cultivation of I. indigotica seedling.