Bitter-Pungent Flavor Identification Based on Ingredient Information Similarity of Chinese Herbal Medicines.

BACKGROUND: The flavor theory of Chinese herbal medicines (CHMs) is one of the core theories of traditional Chinese medicine (TCM). Accurate flavor identification of CHMs is essential to guide the clinical application of CHMs. OBJECTIVE: To develop a new method for flavor identification of CHMs according to the ingredient information for CHMs. METHODS: It was found that the chemical basis of medicinal flavors was CHM ingredients. We developed a bitter-pungent flavor identification scheme to build a relationship between medicinal flavors and CHM ingredients. We firstly proposed a scientific hypothesis that "CHMs with similar flavors should have a similar chemical basis". To test this scientific hypothesis, we then explored an intelligent algorithm for bitter-pungent flavor identification of CHMs based on the information similarity of CHM ingredients. GC was used to separate the chemical ingredients of CHMs and analyze the ingredient information of CHMs. A distance metric learning algorithm was built to measure the similarity of GC chemical fingerprints. A bitter-pungent flavor identification scheme (BPFI) was proposed to predict the bitter-pungent flavor of CHMs. Finally, a number of experiments were performed to evaluate the identification performance of our scheme. RESULTS: Compared to classical algorithms, our proposed BPFI scheme has better flavor prediction performance. The total identification accuracy of our BPFI scheme reached 0.843. The area under ROC (receiver operating characteristic curve) curve (AUC) was 0.899. CONCLUSION: The experimental results confirmed our inference that the chemical basis of CHM flavors was CHM ingredients, and implied that CHMs with similar flavors had similar composition. The BPFI model proved to be effective and feasible. HIGHLIGHTS: Verification hypothesis: CHMs with similar flavors should have similar chemical basis.

Cold-hot nature identification of Chinese herbal medicines based on the similarity of HPLC fingerprints.

The nature theory of Chinese herbal medicines (CHMs) is the core theory of traditional Chinese medicine (TCM). Cold-hot nature is an important part of CHM nature. It is found that the material basis of cold-hot nature is CHM ingredients. To test the scientific hypothesis that "CHMs with similar cold-hot nature should have similar material basis," we explored an intelligent method for cold-hot nature identification of CHMs based on the feature similarity of CHM ingredients in this work. Sixty one CHMs were selected for cold-hot nature identification. High performance liquid chromatography (HPLC) was used to separate the chemical ingredients of CHMs and extract the feature information of CHM ingredients. A distance metric learning algorithm was then learned to measure the similarity of HPLC fingerprints. With the learned distance metric, cold-hot nature identification scheme (CHNIS) was proposed to build an identification model to evaluate the cold-hot nature of CHMs. A number of experiments were designed to verify the effectiveness and feasibility of the proposed CHNIS model. The total identification accuracy rate of 61 CHMs is 80.3%. The performance of the proposed CHNIS algorithm outperformed that of the compared classical algorithms. The experimental results confirmed our inference that CHMs with similar cold-hot nature had similar composition of substances. The CHNIS model was proved to be effective and feasible.

Nature Identification of Chinese Herbal Medicine Compounds Based on Molecular Descriptors.

BACKGROUND: The nature of Chinese herbal medicines (CHMs) is a bridge between traditional Chinese medicine and clinical application. Accurate nature identification of CHMs is essential for guiding the clinical application of CHMs. OBJECTIVE: To develop a new method for nature identification of CHMs according to compounds in CHMs. METHODS: The nature of a CHM is a comprehensive manifestation of the properties of various compounds in the CHM. In this study, 2012 CHM compounds were extracted to construct a compound data set. Molecular descriptors were utilized to build an identification model for classification of the cold-hot-neutral nature of CHM compounds. RESULTS: The predictive accuracy and confusion matrix were validated using the assembled data set. The best model produced accuracies of 96.5 ± 0.5% and 86.5 ± 1.5% on training set and test set, respectively. Furthermore, the identification model is robust in predicting the cold-hot-neutral nature of CHM compounds. CONCLUSION: This work shows how a classification model for medical nature identification can be developed. The derived model can be utilized for the application of CHMs. HIGHLIGHTS: To construct a nature identification model for analysis of the cold-hot-neutral nature of CHMs according to the compounds in CHMs.

Cold-hot nature identification based on GC similarity analysis of Chinese herbal medicine ingredients.

The theory of cold-hot nature of Chinese herbal medicines (CHMs) is the core theory of CHM. It has been found that the volatile oil ingredients in CHMs are closely related to their cold-hot nature. Guided by the scientific hypothesis that "CHMs with similar component substances should have similar medicinal natures", exploration of the intelligent identification of the cold-hot nature of CHMs based on the similarity of their volatile oil ingredients has become a research focus. Gas chromatography (GC) chemical fingerprints have been widely used in the separation of volatile oil ingredients to analyze the cold-hot nature of CHMs. To verify the above hypothesis, in this work, we study the quantification of the similarity of the volatile oil ingredients of CHMs to their fingerprint similarity and explore the relationship between the volatile oil ingredients of CHMs and their cold-hot nature. In this study, we utilize GC technology to analyze the chemical ingredients of 61 CHMs that have a clear cold-hot nature (including 30 'cold' CHMs and 31 'hot' CHMs). Using the constructed fingerprint dataset of CHMs, a distance metric learning algorithm is applied to measure the similarity of the GC fingerprints. Furthermore, an improved k-nearest neighbor (kNN) algorithm is proposed to build a predictive identification model to identify the cold-hot nature of CHMs. The experimental results prove our inference that CHMs with similar component substances should have similar medicinal natures. Compared with existing classical models, the proposed identification scheme has better predictive performance. The proposed prediction model is proved to be effective and feasible.

Multisolvent Similarity Measure of Chinese Herbal Medicine Ingredients for Cold-Hot Nature Identification.

Cold-hot nature theory is the core basic theory of traditional Chinese medicine (TCM). "Treating the hot syndrome with cold nature medicine and treating cold syndrome with hot nature medicine" indicates that correct classification of medical properties (cold or hot nature) of Chinese herbal medicines (CHMs) is an important basis for TCM treatment. In this study, we propose a novel multisolvent similarity measure retrieval scheme (MSSMRS) for discriminating CHMs as cold or hot. We explore a multisolvent distance metric learning algorithm to calculate similarity measure of CHM ingredients, and a retrieval scheme for nature identification. First, four solvents (chloroform, distilled water, absolute ethanol, and petroleum ether) are applied to extract ultraviolet (UV) spectrum data of CHM ingredients. Second, we study quantifying the similarity of CHM ingredients to fingerprint similarity. We explore a multisolvent distance metric learning (MSDML) algorithm to measure the similarity of CHM ingredients. MSDML can discover complementary characteristics of different solvent data sets through an optimization algorithm. Finally, a retrieval scheme is designed to analyze the relationship between the CHM ingredients and cold-hot nature. Extensive experimental results demonstrate that CHMs with similar compositions of substances have similar medicinal natures. Experimental evaluations based on the proposed retrieval scheme suggest the effectiveness of MSDML in the identification of the nature of CHMs.