Can odors of TCM be captured by electronic nose? The novel quality control method for musk by electronic nose coupled with chemometrics

Comparative Analysis of Acanthopanacis Cortex and Periplocae Cortex Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis

Chinese Herbal Medicines (CHMs) can be identified by experts according to their odors. However, the identification of these medicines is subjective and requires long-term experience. The samples of Acanthopanacis Cortex and Periplocae Cortex used were dried cortexes, which are often confused in the market due to their similar appearance, but their chemical composition and odor are different. The clinical use of the two herbs is different, but the phenomenon of being confused with each other often occurs. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two species for fast and robust discrimination, in order to provide a scientific basis for avoiding confusion and misuse in the process of production, circulation and clinical use. In this study, the odor and volatile components of these two medicinal materials were detected by the E-nose and by gas chromatography-mass spectrometry (GC-MS), respectively. An E-nose combined with pattern analysis methods such as principal component analysis (PCA) and partial least squares (PLS) was used to discriminate the cortex samples. The E-nose was used to determine the odors of the samples and enable rapid differentiation of Acanthopanacis Cortex and Periplocae Cortex. GC-MS was utilized to reveal the differences between the volatile constituents of Acanthopanacis Cortex and Periplocae Cortex. In all, 82 components including 9 co-contained components were extracted by chromatographic peak integration and matching, and 24 constituents could be used as chemical markers to distinguish these two species. The E-nose detection technology is able to discriminate between Acanthopanacis Cortex and Periplocae Cortex, with GC-MS providing support to determine the material basis of the E-nose sensors' response. The proposed method is rapid, simple, eco-friendly and can successfully differentiate these two medicinal materials by their odors. It can be applied to quality control links such as online detection, and also provide reference for the establishment of other rapid detection methods. The further development and utilization of this technology is conducive to the further supervision of the quality of CHMs and the healthy development of the industry.

Determination of the geographical origin of Tetrastigma hemsleyanum Diels & Gilg using an electronic nose technique with multiple algorithms

Tetrastigma hemsleyanum Diels & Gilg, an herbal medicinal plant, is planted widely in bamboo forests in southern China to promote economic benefits. Volatile compounds (VOCs) of T. hemsleyanum from different geographical regions are difficult to identify in field forests. In this study, VOCs from leaf samples of different geographical origins were analyzed using an electronic nose with 10 different sensors. Principal component analysis (PCA), partial least-squares regression (PLS), hierarchical cluster analysis (HCA), and radial basis function (RBF) neural networks were used to determine differences among different local samples. The results demonstrated that PCA achieved an accurate discrimination percentage of 91.31% for different samples and HCA separated the samples into different groups. The RBF neural network was successfully applied to predict samples with no specified localities. T. hemsleyanum samples from geographically close regions tended to group together, whereas those from distant geographical regions showed obvious differences. These results indicate that an electronic nose is an effective tool for detecting VOCs and discriminating the geographical origins of T. hemsleyanum. This study provides insights for further studies on the fast detection of VOCs from plants and effect of forests and plant herbal medicines on improving air quality.

A novel strategy for discriminating different cultivation and screening odor and taste flavor compounds in Xinhui tangerine peel using E-nose, E-tongue, and chemometrics

A rapid strategy for discriminating Quanzhi (QZ) and Bozhi (BZ) of different cultivation of Xinhui tangerine peel was established by combining electronic nose, electronic tongue and chemometrics, which can be used as tool in the market to identify food fraud. 30 volatiles and 34 low molecular weight compounds of characteristic fingerprints of Xinhui tangerine peel of 108 samples were identified using GC-MS and UHPLC-Q-TOF-MS. Key compounds of BZ and QZ were screened and further compared by chemometrics. We discriminated odor and taste of BZ and QZ using electronic nose and electronic tongue, respectively. Our studies showed that β-myrcene, limonene, β-trans-Ocimene, γ-terpinene and terpinolene, etc, were screened the chief volatile flavor compounds by Spearman's rank correlation. Hydroxymethyl furfural, hesperitin, nobiletin and tangeretin, etc, were screened the key taste flavor compounds based gray relational analysis and partial least squares regression. Our study provides further insight for quality evaluation of Xinhui tangerine peel.

Zoology, chemical composition, pharmacology, quality control and future perspective of Musk (Moschus): a review

Musk, the dried secretion from the musk sac gland which is located between the navel and genitals of mature male musk deer, is utilized as oriental medicine in east Asia. It has been utilized to treat conditions such as stroke, coma, neurasthenia, convulsions, and heart diseases in China since ancient times. This paper aims to provide a comprehensive overview of musk in zoology, chemical composition, pharmacology, clinical applications, and quality control according to the up-to-date literature. Studies found that musk mainly contains macrocyclic ketones, pyridine, steroids, fatty acids, amino acids, peptides, and proteins, whilst the main active ingredient is muscone. Modern pharmacological studies have proven that musk possesses potent anti-inflammatory effects, neuroprotective effects, anti-cancer effects, antioxidant effects, etc. Moreover, muscone, the main active ingredient, possesses anti-inflammatory, neuroprotective, antioxidant, and other pharmacological effects. In the quality control of musk, muscone is usually the main detection indicator, and the common analytical method is GC, and researchers have established novel and convenient methods such as HPLC-RI, RP-UPLC-ELSD, and Single-Sweep Polarography. In addition, quality evaluation methods based on steroids and the bioactivity of musk have been established. As for the identification of musk, due to various objective factors such as the availability of synthetic Muscone, it is not sufficient to rely on muscone alone as an identification index. To date, some novel technologies have also been introduced into the identification of musk, such as the electronic nose and DNA barcoding technology. In future research, more in vivo experiments and clinical studies are encouraged to fully explain the pharmacological effects and toxicity of musk, and more comprehensive methods are needed to evaluate and control the quality of musk.

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.

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.

A mid-level data fusion approach for evaluating the internal and external changes determined by FT-NIR, electronic nose and colorimeter in Curcumae Rhizoma processing

The aim of this study was to establish a robust and rapid method for identification of the changes of Curcumae Rhizoma (CR) in its physical properties and the components after processing. Color and odor used as the indexes for physical property evaluation in this study were quantified by non-targeted bionic detectors colorimeter and e-nose according to Chinese pharmacopoeia, and all chemical changes of the samples were observed by NIR. The technique of mid-level data fusion was adopted to simulate the recognition mode of human, and the prediction accuracy was 100%. A modified NIR band extraction method was used for feature selection after removing the redundant data which otherwise may compromise the accuracy of analysis, with a result showing bisdemethoxycurcumin and curcumol as the key factors most correlated with the changes during processing. The fused matrix was analyzed to evaluate the weight of different sensors and precisely describe the external changes of the samples, followed by a pairwise correlation analysis to investigate the role of colorimeter, e-nose and NIR in identifying the changes caused by processing as well as their correlation with each other. The results of data fusion revealed that aromatic derivatives produced during processing were closely associated with the changes in external characteristics, i.e., color and smell of the samples, while the changes in proteins did not cause significant differences. Correlation analysis demonstrated that bionic sensors could be classified into two groups: a*, WW and WS sensors, which were related to NIR band at about 6500-6700 cm^-1 formed by NH vibration of amide and protein degradation, were sensitive to the processed CR; while L*, b* and WC sensors were found to be correlated to NIR band around 8000 cm^-1 which was caused by first overtone of C-H combination of aromatic derivatives, thus could be employed as the detectors for raw CR.

An electronic tongue as a classifier tool for assessing perfume olfactory family and storage time-period

The identification of more than three perfumes is difficult and no analytical tool can completely replace the human olfactory system for fragrance classification. Indeed, no analytical system can mimic the human fragrance perception, being the recognition of perfume aroma patterns by conventional or sensor-based analytical tools a challenging task. For the perfume sector, the possibility of applying fast, cost-effective and green analytical devices for perfume analysis would represent a huge economic revenue. Since the perfume aroma pattern will depend on the composition of the liquid phase and on the diffusion properties of their volatile components, this work aimed to apply a potentiometric electronic tongue, comprising non-specific cross-sensitive lipid polymeric membranes, combined with chemometric techniques, as a novel perfume classifier. The multisensors device allowed establishing perfumes' unique fingerprints, which were successfully used to discriminate men from women perfumes, to identify the perfume aroma family (Citric-Aromatic, Floral, Floral-Fruity, Floral-Oriental, Floral-Woody, Woody-Oriental and Woody-Spicy) and, assessing the perfume storage time-period (≤ 9 months; 9-24 months; and, ≥ 24 months). The established linear discriminant models were based on single-run potentiometric profiles gathered by sub-sets of sensors selected using the simulated annealing algorithm, which enabled achieving correct classification rates of 93-100% (for leave-one-out cross-validation procedure). The satisfactory performance of the electronic tongue demonstrates the versatility of the proposed approach as a practical perfume preliminary classifier sensor device, which industrial application may be foreseen in a near future, contributing to a green-sustained economic growth of the perfume industry.

Discrimination of Two Cultivars of Alpinia Officinarum Hance Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Chemometrics

Background: Alpinia officinarum Hance is both an herbal medicine and a condiment, and generally has different cultivars such as Zhutou galangal and Fengwo galangal. The appearance of these A. officinarum cultivars is similar, but their chemical composition and quality are different. It is therefore important to discriminate between different A. officinarum plants to ensure the consistency of the efficacy of the medicine. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two cultivars for fast and robust discrimination. Methods: Odor and volatile components of all A. officinarum samples were detected by the E-nose and gas chromatography-mass spectrometry (GC-MS), respectively. The E-nose sensors and GC-MS data were analyzed respectively by principal component analysis (PCA), the correlation between E-nose sensors and GC-MS data were analyzed by partial least squares (PLS). Results: It was found that Zhutou galangal and Fengwo galangal can be discriminated by combining the E-nose with PCA, and the E-nose sensors S2, S6, S7, S9 were important sensors for distinguishing different cultivars of A. officinarum. A total of 56 volatile components of A. officinarum were identified by the GC-MS analysis, and the composition and content of the volatile components from the two different A. officinarum cultivars were different, in particular the relative contents of 1,8-cineole and α-farnesene. The classification result by PCA analysis based on GC-MS data was consistent with the E-nose results. The PLS analysis demonstrated that the volatile terpene, alcohol and ester components primarily interacted with the sensors S2 and S7, indicating that particular E-nose sensors were highly correlated with some aroma constituents. Conclusions: Combined with advanced chemometrics, the E-nose detection technology can discriminate two cultivars of A. officinarum, with GC-MS providing support to determine the material basis of the E-nose sensors’ response.

A New Strategy for Deleting Animal drugs from Traditional Chinese Medicines based on Modified Yimusake Formula

Traditional Chinese medicine (TCM), such as Uyghur Medicine (UM) has been used in clinical treatment for many years. TCM is featured as multiple targets and complex mechanisms of action, which is normally a combination of medicinal herbs and sometimes even contains certain rare animal medicinal ingredients. A question arises as to whether these animal materials can be removed replaced from TCM applications due to their valuable rare resources or animal ethics. Here, we select a classical UM Yimusake formula, which contains 3 animal drugs and other 8 herbs, and has got wealthy experience and remarkable achievements in treating erectile dysfunction (ED) in China. The active components, drug targets and therapeutic mechanisms have been comprehensively analyzed by systems-pharmacology methods. Additionally, to validate the inhibitory effects of all candidate compounds on their related targets, in vitro experiments, computational analysis and molecular dynamics simulations were performed. The results show that the modified, original and three animal materials display very similar mechanisms for an effective treatment of ED, indicating that it is quite possible to remove these three animal drugs from the original formula while still keep its efficiency. This work provides a new attempt for deleting animal materials from TCM, which should be important for optimization of traditional medicines.

A novel method for the discrimination of Hawthorn and its processed products using an intelligent sensory system and artificial neural networks

Hawthorn (CFS) has commonly been applied as an important traditional Chinese medicine and food for thousands of years. The raw material of CFS is commonly processed by stir-frying to obtain yellow (CFY), dark brown (CFD), and carbon dark (CFC) colored products, which are used for different clinical uses. In this study, an intelligent sensory system (ISS) was used to obtain the color, gas, and flavor samples data, which were further employed to develop a novel and accurate method for the identification of CFS and its processed products using principal component analysis. Moreover, this research developed a model of an artificial neural network, which could be used to predict the total organic acid, total flavonoids, citric acid, hyperin, and 5-hydroxymethyl furfural via determination of the color, odor, and taste of a sample. In conclusion, the ISS and the artificial neural network are useful tools for rapid, accurate, and effective discrimination of CFS and its processed products.

A Novel Method for the Discrimination of Semen Arecae and Its Processed Products by Using Computer Vision, Electronic Nose, and Electronic Tongue

Areca nut, commonly known locally as Semen Arecae (SA) in China, has been used as an important Chinese herbal medicine for thousands of years. The raw SA (RAW) is commonly processed by stir-baking to yellow (SBY), stir-baking to dark brown (SBD), and stir-baking to carbon dark (SBC) for different clinical uses. In our present investigation, intelligent sensory technologies consisting of computer vision (CV), electronic nose (E-nose), and electronic tongue (E-tongue) were employed in order to develop a novel and accurate method for discrimination of SA and its processed products. Firstly, the color parameters and electronic sensory responses of E-nose and E-tongue of the samples were determined, respectively. Then, indicative components including 5-hydroxymethyl furfural (5-HMF) and arecoline (ARE) were determined by HPLC. Finally, principal component analysis (PCA) and discriminant factor analysis (DFA) were performed. The results demonstrated that these three instruments can effectively discriminate SA and its processed products. 5-HMF and ARE can reflect the stir-baking degree of SA. Interestingly, the two components showed close correlations to the color parameters and sensory responses of E-nose and E-tongue. In conclusion, this novel method based on CV, E-nose, and E-tongue can be successfully used to discriminate SA and its processed products.

Standardisation of Moxibustion: Challenges and Future Development

International organisations such as WHO and the International Organization for Standardization are increasingly committed to the development of traditional Chinese medicine (TCM). Moxibustion is an integral modality of acupuncture that has been in use for thousands of years. It stands out as a health choice due to its potential effects in disease prevention, health promotion and maintenance, as well as affordability and accessibility. As the use of moxibustion increases, concerns are being raised regarding its safety and quality. The need to establish standards to protect patient safety is paramount in all medical fields. As a form of medical practice, it is essential to develop moxibustion standards in the areas of terminology, moxibustion products, treatment rooms and practice to ensure its harmonisation and safe usage. The evidence base guiding policy and decision making has to be based on evidence from basic and clinical research. Promoting strategic basic and clinical research on the safety and effectiveness of moxibustion will answer some of the fundamental questions surrounding moxibustion, create a climate of awareness and acceptance and, in turn, drive its standardisation.

Geoherbalism evaluation of Radix Angelica sinensis based on electronic nose

Radix Angelica sinensis (Danggui, DG), derived from the dry root of Angelicae sinensis, is popularly used for its antioxidant, hematinic and immuno-enhancement. However, DG from different origins possess different quality, and difficult to identity. In this study, we used electronic nose technique to investigate DG from different producing areas for monitoring the correlation of origin and quality. The electronic nose was employed to establish classification model of DG originated from four main producing areas of Gansu, Yunnan, Sichuan and Hubei in China. Principal component analysis (PCA) and discriminant function analysis (DFA) were performed to differentiate DG samples from four main producing areas. The content of phthalides of DG were determined to confirm the quality changes and investigate its correlation with the odor response values by Gas Chromatography-Mass Spectrometer (GC-MS). The results of PCA and DFA analysis showed that the electronic nose could accurately distinguish DG from four main producing areas. The method of electronic nose for identification could be verified by GC-MS technology, and the main ingredient content was consistent with its odor of DG. In conclusion, electronic nose could effectively identify different origins of DG, and could be applied for rapid identification and quality control of genuine Angelica herbs.

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.

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.

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.

Evaluation of the odour of Aminoleban® EN, taste-masked with flavoured powders, by human and electronic noses

OBJECTIVES

Aminoleban® EN is a bitter total enteral nutrient product with an unpleasant odour. The purpose of this study was to evaluate the odour of Aminoleban® EN, taste-masked with various flavoured powders, by human testing and by using an electronic nose system (Alpha M.O.S.).

METHODS

Initially, the odour of Aminoleban® EN alone at various concentrations was evaluated. Secondly, the odour of Aminoleban® EN with the addition of various flavoured powders was evaluated; the evaluation included data on odour intensity, preference levels and evoked taste image.

KEY FINDINGS

With the human nose, a concentration of 0.43 g/100 ml seemed to be the taste threshold for Aminoleban® EN. In the electronic nose system, principal component analysis revealed that concentrations over 0.39 g/100 ml Aminoleban® EN could be distinguished from water. It was clear from both human testing and using the electronic nose system that the unpleasant odour of Aminoleban® EN could be masked by pineapple- and plum-flavoured powders, both of which contain not only sweet but also sour components.

CONCLUSIONS

The electronic nose system may be useful for detecting odours at or below the level detectable by the human olfactory system and for classifying the quality of different odours, thus reducing the amount of human testing required.