A modification on the vector cosine algorithm of Similarity Analysis for improved discriminative capacity and its application to the quality control of Magnoliae Flos

Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment

Most of the fingerprint matching algorithms were proposed for large area fingerprints, which can hardly work effectively in small-area fingerprints. In this work, an improved ORB algorithm is proposed for small-area fingerprint matching in embedded mobile devices. In feature descriptor design, we analyzed the characters of the fingerprint in the embedded mobile devices and discard the multi-scale feature process to reduce the amount of operations. Moreover, we proposed a fusion descriptor combing LBP and rBRIEF descriptor. In the key point matching process, we proposed a two-step (coarse and fine) matching method by using Hamming distance and cosine similarity, respectively. The experimental results show that the proposed method has a rejection rate of 6.4%, a false recognition rate of 0.1%, and an average matching time of 58ms. It can effectively improve the performance of small-area fingerprint matching and meet the application requirements of embedded mobile device authentication.

Comprehensive evaluation integrating omics strategy and machine learning algorithms for consistency of calculus bovis from different sources

In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.

Integrated quality assessment of Tieshuang Anshen prescription by multiple fingerprint profiles combined with quantitative analysis and chemometric methods

Chinese herbal medicine compound prescription (CHM-CP) contains more complex active compounds than single herbal medicine owing to its compatibility.

Development of a comprehensive quality control method for the quantitative analysis of volatiles and lignans in Magnolia biondii Pamp. by near infrared spectroscopy

The quality of drug is vital to its curative effect, thus it is important to develop a comprehensive quality control method for commonly used drugs. In this study, we developed a Gas chromatography-mass spectrometry separation method for the qualitative and quantitative analysis of volatiles, together with a High-performance liquid chromatography-mass spectrometry separation method for lignans in Magnolia biondii Pamp.. 79 volatiles and 11 lignans were identified via comparing their chromatographic behavior and mass spectra data with those in the literature. The methods were then used to determine the contents of volatiles (1, 8-cineole, d-Limonene, α-terpineol, linalool, L-camphor brain and bornyl acetate) and lignans (epieudesmin, magnolin, epi-magnolin A and fargesin) in Magnolia biondii Pamp.. Subsequently, 13 qualitative models including volatiles (1, 8-cineole, d-Limonene, α-terpineol, linalool, L-camphor brain and bornyl acetate), water-soluble extractive, lignans (pinoresinol dimethyl ether, magnolin, epi-magnolin A and fargesin) and moisture were developed by Near-Infrared Spectroscopy based on partial least square regression herein. The reference values were obtained by High-performance liquid chromatography, Gas chromatography and etc., while the predicted values were attained from the NIR spectrum. Compared with the traditional detection methods, NIR technique methodology significantly improved the ability to evaluate the quality of Magnolia biondii Pamp., which had the advantages of convenience, celerity, highly efficiency, low cost, no harm to samples, no reagent consumption, and no pollution to the environment. Moreover, the systematic analysis method combined pharmaceutical analysis with pharmacochemistry was proposed to prepare volatiles, water-soluble extractive and lignans parts from the same sample. This way could extract more index components to be beneficial in the quality control of Magnolia biondii Pamp. roundly.

An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry

Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine. However, this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness. In this work, we proposed a multiple reaction monitoring (MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment. The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards, based on which the large-scale fingerprinting method was efficiently established. This approach was exemplified on QiShenYiQi Pill (QSYQ), a traditional Chinese medicine-derived drug product, and its robustness was systematically evaluated by four indices: clustering analysis by principal component analysis, similarity analysis by the congruence coefficient, the number of separated peaks, and the peak area proportion of separated peaks. Compared with conventional ultraviolet-based fingerprints, the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples, but also higher robustness under different chromatographic conditions (i.e., flow rate, apparent pH, column temperature, and column). The result also showed for such large-scale fingerprints including a large number of peaks, the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm. This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive, robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.

•

MRM fingerprints are proposed for quality assessment of traditional medicine.

•

MRM fingerprints show favorable robustness, coverage and discriminatory capacity.

•

Similarity analysis methods for such large-scale fingerprints are proposed.