[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.

[CiteSpace knowledge map of research hotspots and frontiers of traditional Chinese medicine intervention in psoriasis in recent ten years].

This study aimed to investigate the development status of traditional Chinese medicine(TCM) intervention in psoriasis in recent ten years, analyze the research hotspots, and summarize the development trends to provide reference materials for scholars in this field. Taking the available literature related to the field of TCM intervention in psoriasis as the research object, the trends, contents, and source publications were statistically analyzed based on bibliometrics. The research cooperation and co-occurrence of keywords in this field were studied by the knowledge map analysis method based on CiteSpace. The total number of Chinese papers was 2 993 and English papers 285. In terms of publication trend, the annual publication of English papers was low but showed an obvious upward trend, while the increase in Chinese papers fluctuated and tended to be flat. In terms of the content of Chinese papers published, TCM ranked first according to the discipline(2 415). In English papers, the number of publications in pharmacology and pharmaceutical science was the highest(87). Literature source analysis showed that the Chinese and English journals with the most publications were China Journal of Traditional Chinese Medicine and Pharmacy and Evidence Based Complementary and Alternative Medicine, respectively. Beijing University of Chinese Medicine published the most dissertations in China(99). The authors with the most publications in Chinese and English were LI Bin(Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Shanghai University of Traditional Chinese Medicine) and LU Chuan-jian(Guangdong Hospital of Traditional Chinese Medicine). As revealed by the CiteSpace analysis of the research cooperation network, there were four mature and stable core teams in this field, but the cooperation intensity between different teams was weak. According to the keywords co-occurrence knowledge graph constructed by CiteSpace, the current hot keywords in this field are as follows: psoriasis, blood-heat syndrome, blood-stasis syndrome, fire needle, blood-dryness type, imiquimod, TCM bath, etiology and pathogenesis, cytokines, cupping therapy, etc. In summary, Chinese scholars have conducted active exploration and research in the field of TCM intervention in psoriasis in recent ten years. The overall development trend is good, and the breadth and depth of the research are constantly extending. It is suggested that relevant research should be free from discipline restrictions and strive for interdisciplinary integration.

[Comparison between macroscopic identification and DNA barcoding identification of Amomi Fructus].

This study aims to explore the consistency between macroscopic identification and DNA barcoding identification of Amomi Fructus. With the DNA barcoding identification results, we evaluated the reliability of identifying Amomi Fructus quality by combining macroscopic traits with main volatile chemical components. Thirteen batches of Amomi Fructus samples were collected for identification. Firstly, the morphological and sensory characteristics of each sample were observed and recorded according to the standard in Chinese Pharmacopoeia(2020 edition). The 100-fruit weight, longitudinal diameter, transverse diameter, and longitudinal diameter-to-transverse diameter ratio were measured, which correspond to large, solid, and full kernel representing good quality in the sensory evaluation. The odor value detected by electronic nose and major volatile components(borneol, camphor, limonene, and borneol acetate) correspond to the sensory evaluation of strong odor representing good quality. Secondly, DNA barcoding was employed to identify the 13 batches of samples. Finally, clustering analysis was performed for the main volatile components and macroscopic traits, and the identification results were compared with those of DNA barcoding. Except two batches of samples(No.6 and No.10), the macroscopic identification showed the results consistent with those of DNA barcoding, with an identification rate of 84.62%. The clustering results of the content of four volatile chemical components and macroscopic traits were also consistent with the DNA barcoding identification results. DNA barcoding can verify the results of macroscopic identification and provide a scientific basis for the inheritance and development of macroscopic identification. Moreover, the combination of macroscopic traits and chemical components demonstrates higher accuracy in the quality evaluation of Chinese medicinal materials.

Identification of Nutmeg With Different Mildew Degree Based on HPLC Fingerprint, GC-MS, and E-Nose.

Nutmeg (Myristicae Semen), the so-called Rou-Dou-Kou in Chinese, is one kind of Chinese herbal medicines (CHMs) as well as a globally popular spice. Hence, its stable quality and safe application attract more attention. However, it is highly prone to mildew during storage due to its rich volatile components and fatty oil. Therefore, in this study, an electronic nose (E-nose) was introduced to attempt to reliably and rapidly identify nutmeg samples with different degrees of mildew. Meanwhile, the chemical composition and volatile oil were analyzed using HPLC fingerprint and GC-MS, respectively, which could support and validate the result of E-nose. The results showed that the cluster results of HPLC fingerprint and GC-MS were generally consistent with E-nose, and they all clustered into two categories. Additionally, a discriminant model was established, which divided the samples into three categories: mildew-free, mildew-slight, and mildew, and a high DPR was obtained, which indicates that the E-nose could be a novel and promising approach for the establishment of a quality evaluation system to identify CHMs with different degrees of mildew rapidly, especially to identify early mildew.

[High-speed identification of odor changes and substance basis of Myristicae Semen mildew by electronic nose and HS-GC-MS].

This study adopted headspace-gas chromatography-mass spectrometry(HS-GC-MS) and electronic nose to detect volatile components from Myristicae Semen samples with varying degrees of mildew, aiming at rapidly identifying odor changes and substance basis of Myristicae Semen mildew. The experimental data were analyzed by electronic nose and principal component analysis(PCA). The results showed that Myristicae Semen samples were divided into the following three categories by electronic nose and PCA: mildew-free samples, slightly mildewy samples, and mildewy samples. Myristicae Semen samples with different degrees of mildew greatly varied in volatile components. The volatile components in the samples were qualitatively and quantitatively detected by HS-GC-MS, and 59 compounds were obtained. There were significant differences in the composition and content in Myristicae Semen samples with different degrees of mildew. The PCA results were the same as those by electronic nose. Among them, 3-crene, D-limonene, and other terpenes were important indicators for the identification of mildew. Bicyclo[3.1.0]hexane, 4-methylene-1-(1-methylethyl)-, terpinen-4-ol, and other alcohols were key substances to distinguish the degree of mildew. In the later stage of mildew, Myristicae Semen produced a small amount of hydroxyl and aldehyde compounds such as acetaldehyde, 2-methyl-propionaldehyde, 2-methyl-butyraldehyde, and formic acid, which were deduced as the material basis of the mildew. The results are expected to provide a basis for the rapid identification of Myristicae Semen with different degrees of mildew, odor changes, and the substance basis of mildew.

Quality grade classification of China commercial moxa floss using electronic nose: A supervised learning approach.

Moxa floss is the primary material used in moxibustion, an important traditional Chinese medicine therapy that uses ignited moxa floss to apply heat to the body for disease treatment. Till date, there is no available data regarding quality control of different grades of moxa floss. The objectives of this study were to explore the probative value of the electronic nose (e-nose) in differentiating different quality grades of commercial moxa floss sold in China, and to investigate if data mining techniques could be used to optimize the sensor array while retaining classification accuracy of the samples. The e-nose with 12 metal oxide semiconductor type sensors was used to analyze the odor profiles of 15 commercial moxa floss samples of different quality grades. Feature selection algorithms using principal component analysis (PCA) and BestFirst (BC) coupled with correlation-based feature subset selection (CfsSubsetEval) method were used to obtain the most efficient feature subsets. Results for the BC feature selection method identified 3 optimized sensors (S2, S6, and S11), suggesting that aromatic compounds relate more to the identification of the samples. Radial basis function (RBF), multilayer perceptron (MLP), and random forests (RF) performed well in discriminating the samples, retaining prediction accuracies above 85%, which achieved cost-effectiveness and operational simplicity, while retaining prediction accuracy. The e-nose could be a rapid and nondestructive method for objective preliminary classification of quality grades of moxa floss and may be used for future studies related to moxa products safety and quality.

[Change pattern and correlation analysis of macroscopic characteristics, active components and rancidness degrees of Armeniacae Semen Amarum in deterioration process].

To discuss the effect of deterioration on the quality of Armeniacae Semen Amarum by observing the changes of macroscopic characteristics, active components and rancidness degrees of Armeniacae Semen Amarum in deterioration process. The traditional macroscopic identification was used to observe, identify and classify the morphologic and organleptic characteristics of Armeniacae Semen Amarum. The contents of amygdalin and fatty oil(two representatives of active components) were detected by HPLC and general rule 0713 in Chinese Pharmacopoeia, respectively. Acid value and peroxide value of the samples were selected as the representative indices of different rancidness degrees, and the general rule 2303 was adopted as the method for quantitative analysis. Then principal component analysis(PCA), partial least square analysis discrimination analysis(PLS-DA) were further utilized to establish the discriminative models of samples with different rancidness degrees, and also to screen out the largest contribution factors. In sensory evaluation, Armeniacae Semen Amarum samples were divided into three groups: non-rancid, slightly-rancid, and noticeably-rancid. The color of seed coat, cotyledon and surface of noticeably-rancid samples was deepened, and the odor differed much from non-rancid samples. Average content of amygdalin and fatty oil in non-rancid samples was 4.12% and 67.77%, respectively, both meeting the requirements of Chinese Pharmacopoeia; and decreased to some extent in slightly-rancid samples. However, the content of amygdalin sharply dropped to 0.074% in noticeably-rancid samples. The acid value and peroxide value were increased significantly with the intensifying of the rancidness degree, from only 1.363 and 0.016 74 in non-rancid samples to 1.865 and 0.023 70 in slightly-rancid samples, even doubled in noticeably-rancid samples(2.167 and 0.033 82). The discriminative models established by PCA and PLS-DA could complete the task of distinguishing the non-rancid samples from noticeably-rancid ones. The contribution degree of amygdalin content as one of the input attributes of discriminative model was higher than 1. Rancidness affected the quality of Armeniacae Semen Amarum, resulting in appearance changes, decrease in content of active components, and increase in acid value and peroxide value. Obviously, noticeably-rancid samples were non-conforming to Chinese Pharmacopoeia and no longer suitable for medicinal use. Rancidness can significantly reduce the quality of Armeniacae Semen Amarum, and even could possibly produce toxicity, which should attach more attention.

[Discrimination of Armeniacae Semen Amarum from different processed products and various rancidness degrees by electronic nose and support vector machine].

This study was aimed to develop a simple, rapid and reliable method for identifying Armeniacae Semen Amarum from different processed products and various rancidness degrees. The objective odor information of Armeniacae Semen Amarum was obtained by electronic nose. 105 batches of Armeniacae Semen Amarum samples were studied, including three processed products of Armeniacae Semen Amarum, fried Armeniacae Semen Amarum and peeled Armeniacae Semen Amarum, as well as the samples with various rancidness degrees: without rancidness, slight rancidness, and rancidness. The discriminant models of different processed products and rancidness degrees of Armeniacae Semen Amarum were established by Support Vector Machine(SVM), respectively, and the models were verified based on back estimation of blind samples. The results showed that there were differences in the characteristic response radar patterns of the sensor array of different processed products and the samples with different rancidness degrees. The initial identification rate was 95.90% and 92.45%, whilst validation recognition rate was 95.38% and 91.08% in SVM identification models. In conclusion, differentiation in odor of different processed and rancidness degree Armeniacae Semen Amarum was performed by the electronic nose technology, and different processed and rancidness degrees Armeniacae Semen Amarum were successfully discriminated by combining with SVM. This research provides ideas and methods for objective identification of odor of traditional Chinese medicine, conducive to the inheritance and development of traditional experience in odor identification.

[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.

Organoleptic Evaluation of Amomi Fructus and Its Further Background Verified via Morphological Measurement and GC Coupled with E-Nose.

The present study investigated the maneuverability and reasonability of sensory analysis, which has been applied in TCM identification for a long time. Ten assessors were trained and generated the human panel to carry out the organoleptic evaluation of twenty-five batches of Sha-Ren samples. Accordingly, samples were scored from 0 (lowest) to 10 (highest) for sensory attributes. Based on this, samples were divided into three classes: high class (Yang-Chun-Sha from Guang-Dong), moderate class (Yang-Chun-Sha samples from Yun-Nan and Guang-Xi), and low class (Lv-Qiao-Sha from marketplaces). For further background, three instrumental approaches were employed: morphological measurement with three indices (longitudinal diameter, transverse diameter, and 100-fruit weight), GC for determination of bornyl acetate contents, and E-nose for aromatic fingerprint. It is demonstrated in the results that GC and E-nose analyses were in great agreement with organoleptic evaluation. It gives insights into further studies on searching better morphological indicators and improving discriminant model of E-nose.

Influence of storage duration and processing on chromatic attributes and flavonoid content of moxa floss.

OBJECTIVE: Moxibustion is an important traditional Chinese medicine therapy using heat from ignited moxa floss for disease treatment. The purpose of the present study is to establish a reproducible method to assess the color of moxa floss, discriminate the samples based on chromatic coordinates and explore the relationship between chromatic coordinates and total flavonoid content (TFC). METHODS: Moxa floss samples of different storage years and production ratios were obtained from a moxa production factory in Henan Province, China. Chromatic coordinates (L*, a* and b*) were analyzed with an ultraviolet-visible spectrophotometer and the chroma (C*) and hue angle (h°) values were calculated. TFC was determined by a colorimetric method. Data were analyzed with correlation, principal component analysis (PCA). RESULTS: Significant differences in the chromatic values and TFC were observed among samples of different storage years and production ratios. Samples of higher production ratio displayed higher chromatic characteristics and lower TFC. Samples of longer storage years contained higher TFC. Preliminary separation of moxa floss production ratio was obtained by means of color feature maps developed using L*-a* or L*-b* as coordinates. PCA allowed the separation of the samples from their storage years and production ratios based on their chromatic characteristics and TFC. CONCLUSION: The use of a colorimetric technique and CIELAB coordinates coupled with chemometrics can be practical and objective for discriminating moxa floss of different storage years and production ratios. The development of color feature maps could be used as a model for classifying the color grading of moxa floss.

Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms?

Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose) response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose.

Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose.

Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers' safety and efficacy. In recent decades, electronic nose (E-nose) has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model. Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model.

Identification of chinese herbal medicines from zingiberaceae family using feature extraction and cascade classifier based on response signals from e-nose.

Identification of Chinese herbal medicines (CHMs) by human experience is often inaccurate because individual ability and external factors may influence the outcome. However, it might be promising to employ an electronic nose (E-nose) to identify them. This paper presents a rapid and reliable method for identification of ten different species of CHMs from Zingiberaceae family based on their response signals from E-nose. Ten Zingiberaceae CHMs were measured and their maximum response values were analyzed by principal component analysis (PCA). Result shows that E Zhu (Curcuma phaeocaulis Val.) and Yi Zhi (Alpinia oxyphylla Miq.) could not be distinguished completely by PCA. Two solutions were proposed: (i) using BestFirst+CfsSubsetEval (BC) method to extract more discriminative features to select sensors with higher contribution rate and remove the redundant signals; (ii) employing a novel cascade classifier with two stages to enhance the distinguishing-positive rate (DPR). Based on these strategies, six features were extracted and used in different stages of the cascade classifier with higher DPRs.

[Research on correlation between odor and chemical compounds of Lonicera japonica].

This study aims to investigate the relationship between odor and contents of the chemical compounds in Lonicera japonica, including chlorogenic acid, galuteolin and polyphenols. High performance liquid chromatography (HPLC) was applied to determine the contents of chlorogenic acid and galuteolin in L. japonica. The ponptent of polyphenols was determined by UV-Vis Spectrophotometry. Electronic nose was used to extract and measure the odor of L. japonica. Then SPSS 17.0 software was employed for data processing. There is a significant positive correlation between the comprehensive index value of aroma and the contents of chlorogenic acid and polyphenols. The regression equations have been established. However, the relationship between the comprehensive index value and the content of galuteolin is not obvious. This is proof that the odor of L. japonica has close connection with the chemical compounds. Therefore, this research offered a new method for initially determine or predict the content of the chemical composition in L. japonica,

[Optimization method of MOS sensor array for identification of traditional Chinese medicine based on electronic nose].

Optimization of sensor array is a significant topic in the application of electronic nose (EN). Stepwise discriminant analysis and cluster analysis combining with screening of typical index were employed to optimize the original array in the classification of 100 samples from 10 kinds of traditional Chinese medicine based on alpha-FOX3000 EN. And the identification ability was evaluated by three algorithm including principle component analysis, Fisher discriminant analysis and random forest. The results showed that the identification ability of EN was improved since not only the effective information was maintained but also the redundant one was eliminated by the optimized array. The optimized method was eventually established, it was accurate and efficient. And the optimized array was built up, that is, S1, S2, S5, S6, S8, S12.