Rapid discrimination of Chinese red ginseng and Korean ginseng using an electronic nose coupled with chemometrics

Discrimination between raw and ginger juice processed Fructus Gardeniae based on UHPLC-Q-TOF-MS and Heracles NEO ultra-fast gas phase electronic nose

INTRODUCTION

Fructus Gardeniae (ZZ), a traditional Chinese herb, has been used in treating patients with jaundice, inflammation, etc. When mixed with ginger juice and stir-baked, ginger juice-processed Fructus Gardeniae (JZZ) is produced, and the chemical compositions in ZZ would be changed by adding the ginger juice.

OBJECTIVE

To illuminate the differential components between ZZ and JZZ.

METHODS

HPLC, UHPLC-Q-TOF-MS, and Heracles NEO ultra-fast gas phase electronic nose were applied to identify the differential components between ZZ and JZZ.

RESULTS

HPLC fingerprints of ZZ and JZZ were established, and 24 common peaks were found. The content determination results showed that the contents of shanzhiside, geniposidic acid, genipin-1-β-D-gentiobioside and geniposide increased, while the contents of crocin I and crocin II decreased in JZZ. By UHPLC-Q-TOF-MS, twenty-six possible common components were inferred, among which 11 components were different. In further investigation, eight components were identified as the possible distinctive non-volatile compounds between ZZ and JZZ. By Heracles NEO ultra-fast gas phase electronic nose, four substances were inferred as the possible distinctive volatile compounds in JZZ.

CONCLUSION

Shanzhiside, caffeic acid, genipin-1-β-D-gentiobioside, geniposide, rutin, crocin I, crocin II, and 4-Sinapoyl-5-caffeoylquinic acid were identified as the possible differential non-volatile components between ZZ and JZZ. Aniline, 3-methyl-3-sulfanylbutanol-1-ol, E-3-octen-2-one, and decyl propaonate were inferred as the possible distinctive volatile compounds in JZZ. This experiment explored a simple approach with objective and stable results, which would provide new ideas for studying decoction pieces with similar morphological appearance, especially those with different odors.

Identification of anti-inflammatory components in Panax ginseng of Sijunzi Decoction based on spectrum-effect relationship

Objective

This study aimed to identify the main medicinal active components of Panax ginseng (P. ginseng) in the compatibility environment of clinical application. For this purpose, the anti-inflammatory ingredients of P. ginseng were investigated based on its therapeutic effect in Sijunzi Decoction (SJD) which is a widely used traditional Chinese formula.

Methods

The fingerprints of 10 batches of SJD consisting of different sources of P. ginseng were established by UPLC technique to investigate the chemical components. At the same time, the anti-inflammatory effects of these components were evaluated by dextran sulfate sodium-induced ulcerative colitis mouse model. Grey relational analysis was applied to explore the correlation degree between fingerprints and anti-inflammatory effects in SJD. Lipopolysaccharide-stimulated RAW264.7 murine macrophages were established to evaluate the anti-inflammatory action of the screened effective substances of P. ginseng.

Results

According to grey relational analysis, notoginsenoside R₁, ginsenoside Rg₂ and ginsenoside Rb₃ of P. ginseng were the major anti-inflammatory contributions in SJD. They had been proven to be closely associated with the anti-inflammatory process of SJD and displayed a close effect compared with SJD by LPS-stimulated RAW264.7 murine macrophages.

Conclusion

Our work provides a general strategy for exploring the pharmacological ingredients of P. ginseng in traditional Chinese formulas which is beneficial for establishing the quality standards of traditional herbs in traditional Chinese medicine prescription based on their clinical therapeutic effect.

Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods

Crataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.

Characterization and Analysis of Okoume and Aiele Essential Oils from Gabon by GC-MS, Electronic Nose, and Their Antibacterial Activity Assessment

Essential oil resins of Aucoumea klaineana (Okoume) and Canarium schweinfurthii (Aiele) species, of the Burseraceae family, were studied to investigate their bioactive constituents and their antibacterial activities. Aiele resin had a higher yield (6.86%) of essential oil than Okoume (3.62%). Twenty-one compounds for Okoume and eighteen for Aiele essential oil were identified using a gas chromatography-mass spectrometry (Gp-C-MS) technique. The main compounds identified in Okoume essential oil were benzenemethanol, α, α,4-trimethyl (28.85%), (+)-3-carene (3,7,7-trimethyl bicyclo[4.1.0]hept-3-ene) (17.93%), D-Limonene ((4R)-1-methyl-4-prop-1-en-2-ylcyclohexene) (19.36%). With regard to the Aiele essential oil, we identified (1R,4S)-1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.64%), and 1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.83%). Two strains of bacteria, Escherichia coli and Staphylococcus aureus, were used in antibacterial tests. S. aureus was found to be more sensitive to Okoume and Aiele essential oils, with a high inhibition zone ranging from 20 to 16 mm. In comparison, the inhibition zone ranged from 6 to 12 mm for E. coli. An electronic nose (e-nose) combined with pattern analysis methods such as principal component analysis (PCA), discriminant function analysis (DFA), and hierarchical cluster analysis (HCA) were used to discriminate the essential oil samples. In summary, the e-nose and GC-MS allowed the identification of bioactive compounds in the essential oil samples, which have a strong antimicrobial activity, with satisfactory results.

Detection of ripeness grades of berries using an electronic nose

The estimation of ripeness is a significant section of quality determination since maturity at harvest can affect sensory and storage properties of fruits. A possible tactic for defining the grade of ripeness is sensing the aromatic volatiles released by fruit using electronic nose (e-nose). For detection of the five ripeness grades of berries (whiteberry and blackberry), the e-nose machine was designed and fabricated. Artificial neural networks (ANN), principal components analysis (PCA), and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10-11-5) can classify the samples in five classes in ANN analysis with a precision of 100% and 88.3% for blackberry and whiteberry, respectively. Also, PCA analysis characterized 97% and 93% variance in the blackberry and whiteberry, respectively. The least correct classification for whiteberry was observed in the LDA method.

A chemometric modeling-free near infrared barcode strategy for smart authentication and geographical origin discrimination of Chinese ginseng

With the growing interest in alternative medicine, handy identification and differentiation of herbal medicines are becoming increasingly important. Here we report a chemometric modeling-free near infrared (NIR) barcode strategy for the smart identification and geographical origin discrimination of Chinese ginseng. The novel strategy demands the transformation of Chinese ginseng (standard and sample) NIR spectra into a barcode representation through assigning zero intensity to every NIR peak except the peaks having intensities greater than average peak intensity. Meanwhile, for Chinese ginseng standard NIR barcode, barcoding condition such as padding size was carefully optimized. It has been demonstrated that the padding size for each bar in the barcode is 8 cm^-1. By comparing the percentage of nonzero overlap between Chinese ginseng standard barcode and sample barcodes, eight batches of samples (including Chinese ginseng, American ginseng and counterfeit) were successfully identified with 100% accuracy, respectively. Interestingly, the discrimination of the origin of ginsengs from three provinces (Jilin, Liaoning and Heilongjiang) of Northeastern China was achieved utilizing NIR barcode method. Two characteristic bars at 7750 and 8250 cm^-1 were inspected in the ginseng sample from Jilin province, two specific bars at 6780 and 7015 cm^-1 were displayed in the ginseng sample from Liaoning province and three distinct bars at 6560, 6910 and 7995 cm^-1 were monitored in the ginseng sample from Heilongjiang province. The results indicate that the proposed method will be greatly expanded and applied as an inspecting platform for the on-site analysis and valid identification of Chinese ginseng in herbal markets by a handheld spectrometer or barcode scanner.

Placebo design in WHO-registered trials of Chinese herbal medicine need improvements

BACKGROUND

Physical identical and pharmacological inert are the basic requirements for placebo design, which are essential in clinical trials to evaluate the efficacy of an intervention. However, it is difficult to makeup a placebo of Chinese herbal medicine (CHM) because of special color, taste and smell, etc. Currently, there is no specific requirements and standards for the creation of a CHM-placebo. The purpose of this study is to review the characteristics of the CHM-placebo design and application in registered clinical trials with CHM interventions and identify the common problems, if any.

METHODS

The World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) was systematically searched for CHM interventional trials with placebo-controlled design up to 31 December 2017. Registered information of each included trial was collected from specific registries involved in ICTRP through hyperlinks. Descriptive statistics were used to analyze the characteristics of placebo design in CHM trial registrations.

RESULTS

A total of 889 CHM interventional trials were registered from 1999 to 2017, and 40.8% (363) of them included CHM-placebo control design. The common ways of their design were: placebo as sole control (191, 52.6%); placebo as add-on control with baseline treatment (84, 23.1%); and placebo as double-dummy control (57, 15.7%). Among 363 included trials, 46 (12.7%) reported the compositions of placebos, including CHM ingredients (17 trials), excipients and other agents (29 trials). 2 (0.6%) reported pharmacological inert testing, and 52 (14.3%) descripted their placebos to be physically identical with the CHMs. 14 (3.9%) reported quality control of placebos, and 2 (0.6%) provided blinding assessment of placebos.

CONCLUSIONS

The placebos included in most CHM trial registrations is not optimal in terms of placebo design, application, evaluation and reporting. Specific guidelines or standards of CHM-placebo design, including usage requirements, preparation specifications, quality assessments and reporting guidelines should be developed thus to improve their quality.

Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.

Effects of maltose and lysine treatment on coffee aroma by flash gas chromatography electronic nose and gas chromatography-mass spectrometry

BACKGROUND

Arabica coffee is a sub-tropical agricultural product in China. Coffee undergoes a series of thermal reactions to form abundant volatile profiles after roasting, so it loses a lot of reducing sugars and amino acids. Adding carbonyl compounds with amino acids before roasting could ensure the nutrition and flavour of coffee. The technology is versatile for the development of coffee roasting process. This investigation evaluates the effects of combining maltose and lysine (Lys) to modify coffee aroma and the possibly related mechanisms. Arabica coffee was pretreated with a series of solvent ratios of maltose and Lys with an identical concentration (0.25 mol L^-1 ) before microwave heating.

RESULTS

It was found that the combination of maltose and Lys significantly (P ≤ 0.05) influenced quality indices of coffee (pH and browning degree). Ninety-six aromatic volatiles have been isolated and identified. Twelve volatile profiles revealed the relationship between fragrance difference and compound content in coffee. Moreover, coffee aroma was modified by a large number of volatiles with different chemical classes and character.

CONCLUSION

Thus, our results suggest that the combination of reagents changed overall aroma quality through a series of complex thermal reactions, especially the ratio of Lys/maltose over 2:1. © 2017 Society of Chemical Industry.

Differentiation of cumin seeds using a metal-oxide based gas sensor array in tandem with chemometric tools

Cumin is a plant of the Apiaceae family (umbelliferae) which has been used since ancient times as a medicinal plant and as a spice. The difference in the percentage of aromatic compounds in cumin obtained from different locations has led to differentiation of some species of cumin from other species. The quality and price of cumin vary according to the specie and may be an incentive for the adulteration of high value samples with low quality cultivars. An electronic nose simulates the human olfactory sense by using an array of sensors to distinguish complex smells. This makes it an alternative for the identification and classification of cumin species. The data, however, may have a complex structure, difficult to interpret. Given this, chemometric tools can be used to manipulate data with two-dimensional structure (sensor responses in time) obtained by using electronic nose sensors. In this study, an electronic nose based on eight metal oxide semiconductor sensors (MOS) and 2D-LDA (two-dimensional linear discriminant analysis), U-PLS-DA (Partial least square discriminant analysis applied to the unfolded data) and PARAFAC-LDA (Parallel factor analysis with linear discriminant analysis) algorithms were used in order to identify and classify different varieties of both cultivated and wild black caraway and cumin. The proposed methodology presented a correct classification rate of 87.1% for PARAFAC-LDA and 100% for 2D-LDA and U-PLS-DA, indicating a promising strategy for the classification different varieties of cumin, caraway and other seeds.

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.

Discrimination of American ginseng and Asian ginseng using electronic nose and gas chromatography-mass spectrometry coupled with chemometrics

BACKGROUND

American ginseng (Panax quinquefolius L.) and Asian ginseng (Panax ginseng Meyer) products, such as slices, have a similar appearance, but they have significantly different prices, leading to widespread adulteration in the commercial market. Their aroma characteristics are attracting increasing attention and are supposed to be effective and nondestructive markers to determine adulteration.

METHODS

The aroma characteristics of American and Asian ginseng were investigated using gas chromatography-mass spectrometry(GC-MS) and an electronic nose (E-nose). Their volatile organic compounds were separated, classified, compared, and analyzed with different pattern recognition.

RESULTS

The E-nose showed a good performance in grouping with a principle component analysis explaining 94.45% of variance. A total of 69 aroma components were identified by GC-MS, with 35.6% common components and 64.6% special ingredients between the two ginsengs. It was observed that the components and the number of terpenes and alcohols were markedly different, indicating possible reasons for their difference. The results of pattern recognition confirmed that the E-nose processing result is similar to that of GC-MS. The interrelation between aroma constituents and sensors indicated that special sensors were highly related to some terpenes and alcohols. Accordingly, the contents of selected constituents were accurately predicted by corresponding sensors with most R2 reaching 90%.

CONCLUSION

Combined with advanced chemometrics, the E-nose is capable of discriminating between American and Asian ginseng in both qualitative and quantitative angles, presenting an accurate, rapid, and nondestructive reference approach.

Current application of chemometrics in traditional Chinese herbal medicine research

Traditional Chinese herbal medicines (TCHMs) are promising approach for the treatment of various diseases which have attracted increasing attention all over the world. Chemometrics in quality control of TCHMs are great useful tools that harnessing mathematics, statistics and other methods to acquire information maximally from the data obtained from various analytical approaches. This feature article focuses on the recent studies which evaluating the pharmacological efficacy and quality of TCHMs by determining, identifying and discriminating the bioactive or marker components in different samples with the help of chemometric techniques. In this work, the application of chemometric techniques in the classification of TCHMs based on their efficacy and usage was introduced. The recent advances of chemometrics applied in the chemical analysis of TCHMs were reviewed in detail.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

A rapid discrimination of authentic and unauthentic Radix Angelicae Sinensis growth regions by electronic nose coupled with multivariate statistical analyses

Radix Angelicae Sinensis, known as Danggui in China, is an effective and wide applied material in Traditional Chinese Medicine (TCM) and it is used in more than 80 composite formulae. Danggui from Minxian County, Gansu Province is the best in quality. To rapidly and nondestructively discriminate Danggui from the authentic region of origin from that from an unauthentic region, an electronic nose coupled with multivariate statistical analyses was developed. Two different feature extraction methods were used to ensure the authentic region and unauthentic region of Danggui origin could be discriminated. One feature extraction method is to capture the average value of the maximum response of the electronic nose sensors (feature extraction method 1). The other one is to combine the maximum response of the sensors with their inter-ratios (feature extraction method 2). Multivariate statistical analyses, including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and hierarchical clustering analysis (HCA) were employed. Nineteen samples were analyzed by PCA, SIMCA and HCA. Then the remaining samples (GZM1, SH) were projected onto the SIMCA model to validate the models. The results indicated that, in the use of feature extraction method 2, Danggui from Yunnan Province and Danggui from Gansu Province could be successfully discriminated using the electronic nose coupled with PCA, SIMCA and HCA, which suggested that the electronic-nose system could be used as a simple and rapid technique for the discrimination of Danggui between authentic and unauthentic region of origin.

Discrimination of chicken seasonings and beef seasonings using electronic nose and sensory evaluation

This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes.

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.

Chemical characteristics combined with bioactivity for comprehensive evaluation of Panax ginseng C.A. Meyer in different ages and seasons based on HPLC-DAD and chemometric methods

Panax ginseng C.A. Meyer has been known as a valuable traditional Chinese medicines for thousands years of history. Ginsenosides, the main active constituents, exhibit prominent immunoregulation effect. The present study first describes a holistic method based on chemical characteristic and lymphocyte proliferative capacity to evaluate systematically the quality of P. ginseng in thirty samples from different seasons during 2-6 years. The HPLC fingerprints were evaluated using principle component analysis (PCA) and hierarchical clustering analysis (HCA). The spectrum-efficacy model between HPLC fingerprints and T-lymphocyte proliferative activities was investigated by principal component regression (PCR) and partial least squares (PLS). The results indicated that the growth of the ginsenosides could be grouped into three periods and from August of the fifth year, P. ginseng appeared significant lymphocyte proliferative capacity. Close correlation existed between the spectrum-efficacy relationship and ginsenosides Rb1, Ro, Rc, Rb2 and Re were the main contributive components to the lymphocyte proliferative capacity. This comprehensive strategy, providing reliable and adequate scientific evidence, could be applied to other TCMs to ameliorate their quality control.

Comparative analysis of two species of Asari Radix et Rhizoma by electronic nose, headspace GC-MS and chemometrics

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.