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

Identification of Bletilla striata and related decoction pieces: a data fusion method combining electronic nose, electronic tongue, electronic eye, and high-performance liquid chromatography data

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr). Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared. Results and Discussion: Gas chromatography-ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

Application of Data Fusion in Traditional Chinese Medicine: A Review

Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder

Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its counterfeit. Electronic sensory technologies are the inheritance and development of traditional empirical identification. Considering that each drug has its own specific odor and taste characteristics, electronic tongue (E-tongue), electronic nose (E-nose) and GC-MS were used to evaluate the aroma and taste of BBP and its common counterfeit. Two active components of BBP, namely tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) were measured and linked with the electronic sensory data. The results showed that bitterness was the main flavor of TUDCA in BBP, saltiness and umami were the main flavor of TCDCA. The volatiles detected by E-nose and GC-MS were mainly aldehydes, ketones, alcohols, hydrocarbons, carboxylic acids, heterocyclic, lipids, and amines, mainly earthy, musty, coffee, bitter almond, burnt, pungent odor descriptions. Four different machine learning algorithms (backpropagation neural network, support vector machine, K-nearest neighbor, and random forest) were used to identify BBP and its counterfeit, and the regression performance of these four algorithms was also evaluated. For qualitative identification, the algorithm of random forest has shown the best performance, with 100% accuracy, precision, recall and F1-score. Also, the random forest algorithm has the best R² and the lowest RMSE in terms of quantitative prediction.

Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

Characterization of phenolics and discovery of α-glucosidase inhibitors in Artemisia argyi leaves based on ultra-performance liquid chromatography-tandem mass spectrometry and relevance analysis

Artemisia argyi leaves (AAL) has been widely used as herbal medicine and food supplement and in China and other Asian countries. The aim of this work is to qualitative and quantitative characterization of phenolic compounds in AAL and screening of natural product inhibitors of α-glucosidase from AAL. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was employed to rapid and comprehensive identification of phenolic compounds in AAL, and a total of thirty-three phenolic compounds were identified. High performance liquid chromatography with diode array detection (HPLC-DAD) was established and validated to simultaneously determinate ten main bioactive phenolics compounds in different batches of AAL samples. Meanwhile, the inhibitory capacities of different batches of AAL samples on α-glucosidase were evaluated. Then, relevance analysis, including grey relational analysis and Pearson correlation analysis were employed to investigate the correlations between the contents of phenolic compounds and α-glucosidase inhibitory activities, and discover the α-glucosidase inhibitors in AAL. The relevance analysis results indicated that three phenolic compounds, 3-caffeoylquinic acid, 3,4-dicaffeoylquinic acid and 3,5-dicaffeoylquinic acid could be potential α-glucosidase inhibitors in AAL. Moreover, the α-glucosidase inhibitory activities of the three phenolic compounds were validated by in vitro and in vivo experiments. The possible inhibiting effect of the three phenolic compounds on α-glucosidase was also explored by molecular docking analysis, and the results indicated that the binding of the three α-glucosidase inhibitors to α-glucosidase mainly by hydrogen bonds, hydrophobic forces and ionic bonds. The present research provided a deep insight into phenolic compounds and α-glucosidase inhibitory activities of AAL, and discovered the α-glucosidase inhibitors in AAL.

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.

The Effect of Hot-air Dried Lentinula edodes on the Quality and Organoleptic Properties of Rolled-dumplings

The effect of hot-air dried Lentinula edodes pileus (DLE) on the quality and organoleptic properties of rolled-dumplings was evaluated. DLE was prepared by drying at 60°C for 24 h and added (Non, 7%, and 9%) to rolled-dumplings. The proximate composition, pH, color (CIE L*, a*, b*), and cooking yield were analyzed. Texture profile analysis, electronic-nose (e-nose), electronic-tongue (e-tongue), and organoleptic evaluation were also conducted. The cooking yield of dumplings with 9% DLE was significantly lower than that of the congeners without DLE, whereas 7% DLE did not lead to significant differences compared without DLE. With increasing DLE addition, the pH and lightness of the dumplings decreased significantly, whereas the redness tended to increase. The texture profile was significantly higher for the dumplings with DLE compared to those without DLE. E-nose analysis confirmed that DLE addition led to the positive odors (methanethiol: meaty, sulfurous; 3-methylbutanal: malty, toasted) and the negative odors (trimethylamine: ammoniacal; acetic acid: acidic, sour). E-tongue analysis showed that DLE addition decreased the intensity of the sourness and increased the intensity of the saltiness and umami of rolled-dumplings. DLE addition improved the overall organoleptic properties, but 9% DLE can be recognized as a foreign substance in organoleptic acceptance. Consequently, DLE has the potential to serve as a flavor and odor enhancer for rolled-dumplings, and the addition of DLE can positively improve consumer acceptance by improving the quality and organoleptic properties.