Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

One of the major challenges of masking the bitter taste in medications: an overview of quantitative methods for bitterness

Objective

The aim of the present study was to carry out a systematic research on bitterness quantification to provide a reference for scholars and pharmaceutical developers to carry out drug taste masking research. Significance: The bitterness of medications poses a significant concern for clinicians and patients. Scientifically measuring the intensity of drug bitterness is pivotal for enhancing drug palatability and broadening their clinical utility.

Methods

The current study was carried out by conducting a systematic literature review that identified relevant papers from indexed databases. Numerous studies and research are cited and quoted in this article to summarize the features, strengths, and applicability of quantitative bitterness assessment methods.

Results

In our research, we systematically outlined the classification and key advancements in quantitative research methods for assessing drug bitterness, including in vivo quantification techniques such as traditional human taste panel methods, as well as in vitro quantification methods such as electronic tongue analysis. It focused on the quantitative methods and difficulties of bitterness of natural drugs with complex system characteristics and their difficulties in quantification, and proposes possible future research directions.

Conclusion

The quantitative methods of bitterness were summarized, which laid an important foundation for the construction of a comprehensive bitterness quantification standard system and the formulation of accurate, efficient and rich taste masking strategies.

Recent Advances in Bitterness-Sensing Systems

Bitterness is one of the basic tastes, and sensing bitterness plays a significant role in mammals recognizing toxic substances. The bitter taste of food and oral medicines may decrease consumer compliance. As a result, many efforts have been made to mask or decrease the bitterness in food and oral pharmaceutical products. The detection of bitterness is critical to evaluate how successful the taste-masking technology is, and many novel taste-sensing systems have been developed on the basis of various interaction mechanisms. In this review, we summarize the progress of bitterness response mechanisms and the development of novel sensors in detecting bitterness ranging from commercial electronic devices based on modified electrodes to micro-type sensors functionalized with taste cells, polymeric membranes, and other materials in the last two decades. The challenges and potential solutions to improve the taste sensor quality are also discussed.

The Flavor Profiles of Highland Barley Fermented with Different Mushroom Mycelium

Highland barley was fermented with Cordyceps militaris, Stropharia rugoso-annulata, Morchella esculenta, Schizophyllum commune and Tremella sanguinea. The flavor profiles were investigated by electronic nose (E-nose), headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and sensory evaluation by train panel. Fermentation with mushroom mycelium was able to change the aroma profile of highland barley. The original strong grassy taste was reduced due to a decrease in hexanal, decanal and 2-pentylfuran, and new aromatic flavors (floral, sweet and mushroom fragrance) were acquired after fermentation. The overall flavor of the fermented highland barley varied with mushroom strains. Schizophyllum commune gave a heavier sour taste to the fermented highland barley. However, fermentation with T. sanguinea increased the content of methyl 4-methoxybenzoate making the sample difficult to accepted. Fermentation with C. militaris, M. esculenta, and S. rugoso-annulata increased the volatile contents. The high levels of 1-octen-3-ol and esters gave a strong mushroom, oily and fruity flavor. Morchella esculenta showed the best performance and the highest acceptance in the fermented highland barley. Our results suggest that fermentation with mushroom mycelium can improve the flavor of highland barley, which provides an innovative utilization of highland barley.

No ambiguity: Chemosensory-based ayurvedic classification of medicinal plants can be fingerprinted using E-tongue coupled with multivariate statistical analysis

Background: Ayurveda, the indigenous medical system of India, has chemosensory property (rasa) as one of its major pharmacological metric. Medicinal plants have been classified in Ayurveda under six rasas/tastes—sweet, sour, saline, pungent, bitter and astringent. This study has explored for the first time, the use of Electronic tongue for studies of rasa-based classification of medicinal plants.

Methods: Seventy-eight medicinal plants, belonging to five taste categories (sweet, sour, pungent, bitter, astringent) were studied along with the reference taste standards (citric acid, hydrochloric acid, caffeine, quinine, L-alanine, glycine, β-glucose, sucrose, D-galactose, cellobiose, arabinose, maltose, mannose, lactose, xylose). The studies were carried out with the potentiometry-based Electronic tongue and the data was analysed using Principle Component Analysis, Discriminant Function Analysis, Taste Discrimination Analysis and Soft Independent Modeling of Class Analogy.

Results: Chemosensory similarities were observed between taste standards and the plant samples–citric acid with sour group plants, sweet category plants with sucrose, glycine, β-glucose and D-galactose. The multivariate analyses could discriminate the sweet and sour, sweet and bitter, sweet and pungent, sour and pungent plant groups. Chemosensory category of plant (classified as unknown) could also be identified.

Conclusion: This preliminary study has indicated the possibility of fingerprinting the chemosensory-based ayurvedic classification of medicinal plants using E-tongue coupled with multivariate statistical analysis.

Identification of antidiabetic components in Uncariae Rammulus Cum Uncis based on phytochemical isolation and spectrum-effect relationship analysis

OBJECTIVES

Uncariae Rammulus Cum Uncis (URCU) is a commonly used herbal medicine to treat diabetes. This work is aimed to discover and identify the antidiabetic components from URCU extract.

METHODS

Column chromatography and recrystallisation were used to separate individual compounds from URCU extract, and the obtained individual compounds were used for determination of α-glucosidase inhibitory activity. Molecular docking was applied to predict the molecular interactions. High-performance liquid chromatography (HPLC) was used for fingerprint analysis of 12 batches of URCU. HPLC fingerprints were assessed by the similarity analysis (SA) and hierarchical clustering analysis (HCA). The spectrum-effect relationship analysis of URCU was assessed by orthogonal partial least squares (OPLS) and bivariate correlation analysis (BCA).

RESULTS

A total of 10 potential bioactive compounds were isolated and six of them showed potent α-glucosidase inhibitory activity (IC₅₀  = 4.21-166.10 μM). The molecular docking results revealed that the binding energy was consistent with the results of α-glucosidase inhibition activity analysis (-8.55 to -4.84 kcal/mol). The ethanol extracts of the 12 batches of URCU showed inhibitory effect on α-glucosidase in a dose-dependent manner, and the IC₅₀ values ranged from 0.94 μg/mL to 12.57 μg/mL. The spectrum-effect relationship analysis results indicated that 13 peaks might be potential antidiabetic compounds in URCU, including 18 (hyperoside) and 19 (rutin).

CONCLUSION

A comprehensive connection between URCU chemical components and α-glucosidase inhibitory activity was established for the first time by using a spectrum-effect relationship model, which might be applicable to the quality control of URCU.

The qualitative and quantitative assessment of xiaochaihu granules based on e-eye, e-nose, e-tongue and chemometrics

Xiaochaihu granules (XCHG), a famous Chinese patent medicine with high sales, have more than 100 approved number by China Food and Drug Administration (CFDA). Therefore, it is important to evaluate the quality of XCHG from different pharmaceutical companies. The data fusion of electronic eye (e-eye), electronic nose (e-nose) and electronic tongue (e-tongue) combined with chemometrics were applied for qualitative identification and quantitative prediction of XCHG quality. Firstly, main chemical constituents, such as saikosaponin b2, baicalin and glycyrrhizin were quantified with ultra-high-performance liquid chromatography (UHPLC). Secondly, the characteristic features of odor, color, and taste of XCHG were measured by e-nose, e-eye and e-tongue, and the Pearson correlation between constituents and e-signals was analyzed. Thirdly, partial least squares discrimination analysis (PLS-DA) of e-eye, e-nose and e-tongue were classified by the hierarchical clustering analysis (HCA) results of the main constituents of XCHG separately. Finally, partial least-squares regression (PLSR) was used to build the prediction model between components and data fusion of e-eye, e-nose and e-tongue. The results showed that saikosaponin b2, baicalin and glycyrrhizin were the three main components in XCHG samples. in which saikosaponin b2 ranged from 0.280 to 2.186 mg (relative standard deviation (RSD), 62.10 %), baicalin range from 25.883 mg to 49.108 mg (RSD, 16.64 %), and glycyrrhizin ranged from 0.897 mg to 6.052 mg (RSD, 40.32 %) of 31 batches of XCHG in each bag. Pearson correlation results showed that the main constituents were related to the core e-signals of XCHG, such as Eab, bitterness and R2 (odor sensitive to nitrogen oxide). Data fusion of e-eye, e-nose and e-tongue with main constitutes of XCHG using the PLSR model showed that the root mean square error (RMSE) values were 0.320 and 0.090 for saikosaponin b2 and licoricesaponin G2 (P < 0.000). The saikosaponin b2 and licoricesaponin G2 contents in XCHG could be predicted with integrated data of e-nose, e-eye, and e-tongue using the PLSR model.

Similarity Evaluation on the Compound TCM Formulation "Huoling Shengji Granule" and Its Placebo by Intelligent Sensory Evaluation Technologies and the Human Sensory Evaluation Method Based on Critical Quality Attributes

To evaluate the similarity of Huoling Shengji granule (HLG) and its placebo at both granules and solution status, the innovative methods that consist of intelligent sensory evaluation technologies and human sensory evaluation methods were developed based on critical quality attributes (CQAs) of granule. The CQAs for traditional Chinese medicine (TCM) placebo granule were mainly divided into three categories: formulation attributes, visual attributes, and attributes of taste and smell. In this investigation, the novel intelligent sensory evaluation technologies including the physical property testing apparatus, computer vision system, color card, and electronic tongue (E-tongue) were employed for characterization of CQAs of HLG and its placebo. Meanwhile, human sensory evaluation by test panels was used to description the HLG and its placebo in terms of appearance, color, taste, and smell. On that basis, the similarity of placebo to CQAs of HLG was assessed by calculating the angle cosine values. The intelligent and human sensory evaluation results showed that the similarity values of HLG and its placebo about the CQAs at granule and solution status were all close to 1, which means that the two preparations have high similarities. In this study, the established similarity evaluation methods based on the CQAs were convenient and reliable, which can be utilized to evaluate the similarity of TCM granule and their placebo at granule and solution status, and demonstrated to be well applied in placebo-controlled trials.

Challenges in using Electronic tongue to study rasa of plants: II. Impact of solvent and concentration on sensor response and taste ranking

Background

Although Electronic tongue is used in pharmaceutical, food and beverage industries for objective evaluation of taste, its use in medicinal plants from an ayurvedic perspective is novel. Control experiments are therefore necessary to standardise and optimise parameters.

Objective

The aim is to optimise the use of solvent and standardise sample concentration for study of plants from an ayurvedic standpoint of rasa. The major objectives are two-fold: (i) evaluate sensor response to different types of solvent water (ii) explore use of E-tongue in taste ranking of medicinal plants used in ayurveda.

Materials and methods

Single, double and triple distilled, reverse osmosis and milliQ waters were evaluated separately and as a medium for preparing plant extracts. For taste ranking, standard addition method using d-glucose as sweet taste standard was used for different brands of mango juices (case in point study) and eight medicinal plants from sweet category. The effect of sample concentration and taste standard on taste ranking were evaluated.

Results

MQ and TD water demonstrated similar organoleptic properties whereas plant extracts prepared in DD and MQ water showed maximum taste-based differentiation. The mango juices were taste discriminated by E-tongue and ranked based on their sweetness scores. The relative ranking of plant samples showed concentration dependence and also varied with the concentration range of taste standard.

Conclusion

Milli-Q and double distilled water can be used for E-tongue studies of medicinal plants. While the results open up the possibility of taste ranking of medicinal plants, they also demonstrate the importance of standardising and optimising the concentration of samples and taste standards in the context of ayurvedic rasa based studies.

The use of electronic tongue and sensory panel on taste evaluation of pediatric medicines: a systematic review

The palatability of medications is an essential factor for children's adherence to drug treatment. Several methods for drug taste assessment have been developed. The aim of this review is to explore the literature reports of the main methods for the evaluation of medicines taste, named electronic tongue (e-tongue, in vitro) and human sensory panel. A systematic search was performed up to March 2020 and a total of 88 articles were selected. The e-tongue (57.5%) has been more frequently described than the sensory panel (10.3%), while some articles (32.2%) used both techniques. 74.7% of the articles mentioned 'pediatric', 'paediatric' or 'children' in the text, but only 19.5% developed formulations targeting pediatric audience and sensory testing in children is rarely seen. The e-tongue has predominance of use in the taste evaluation of pediatric medicines probably since it is fast, easy to perform and risk free, besides presenting less imprecise data and no fatigue. The human panel is more realistic, despite its intrinsic variability. In this sense, it is proposed the use of e-tongue as a fast way to select the most promising sample(s) and, after that, the sensory panel should be applied in order to confirm the taste masking.

Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.

Bitterness and antibacterial activities of constituents from Evodia rutaecarpa

Background

Bitter herbs are important in Traditional Chinese Medicine and the Electronic Tongue (e-Tongue) is an instrument that can be trained to evaluate bitterness of bitter herbs and their constituents. The aim of this research was to evaluate bitterness of limonoids and alkaloids from Evodia rutaecarpa to demonstrate that they are main bitter material basic of E. rutaecarpa.

Methods

Nine compounds, including limonoids, indoloquinazoline alkaloids and quinolone alkaloids, were isolated, identified and analyzed by the e-Tongue. Additionally, the antibacterial activities of the nine compounds were evaluated against E. coli and S. aureus.

Results

All the nine compounds had bitter taste and antibacterial activities to some extent. Among them, limonoids, which were the bitterest compounds, had greater antibacterial activities than alkaloids. And there is a positive correlation between bitter taste and antibacterial activities.

Conclusions

It was confirmed in our study that limonoids, indoloquinazoline alkaloids and quinolone alkaloids are main bitter material basic of E. rutaecarpa based on two evaluation methods of e-Tongue and antibacterial experiment. In addition, the e-Tongue technique is a suitable new method to measure bitter degree in herbs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12906-017-1701-8) contains supplementary material, which is available to authorized users.

Optimization and validation of the protocol used to analyze the taste of traditional Chinese medicines using an electronic tongue

Tools to define the active ingredients and flavors of Traditional Chinese Medicines (TCMs) are limited by long analysis times, complex sample preparation and a lack of multiplexed analysis. The aim of the present study was to optimize and validate an electronic tongue (E-tongue) methodology to analyze the bitterness of TCMs. To test the protocol, 35 different TCM concoctions were measured using an E-tongue, and seven replicate measurements of each sample were taken to evaluate reproducibility and precision. E-tongue sensor information was identified and classified using analysis approaches including least squares support vector machine (LS-SVM), support vector machine (SVM), discriminant analysis (DA) and partial least squares (PLS). A benefit of this analytical protocol was that the analysis of a single sample took <15 min for all seven sensors. The results identified that the LS-SVM approach provided the best bitterness classification accuracy (binary classification accuracy, 100%; ternary classification accuracy, 89.66%). The E-tongue protocol developed showed good reproducibility and high precision within a 6 h measurement cycle. To the best of our knowledge, this is the first study of an E-tongue being applied to assay the bitterness of TCMs. This approach could be applied in the classification of the taste of TCMs, and serve important roles in other fields, including foods and beverages.