Research on Comparison and Evaluation Studies of Several Smoothing Denoising Method Based on γ-ray Spectrum Detector

The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average, center of gravity, least squares of polynomial, slide converter of discrete funcion convolution etc. The process of spectrum data is realized, and the results are assessed in H/FWHM（ Peak High/Full Width at Half Maximum） and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion, but the Gaussian function theory in discrete function convolution slide method is used to filter the complex y-spectrum on Embedded system nlatform, and the statistical fluctuation of y-snectrum filtered wall.

Advances in intelligent mass spectrometry data processing technology for in vivo analysis of natural medicines

Natural medicines(NMs)are crucial for treating human diseases.Efficiently characterizing their bioactive components in vivo has been a key focus and challenge in NM research.High-performance liquid chromatography-high-resolution mass spectrometry(HPLC-HRMS)systems offer high sensitivity,resolution,and precision for conducting in vivo analysis of NMs.However,due to the complexity of NMs,conventional data acquisition,mining,and processing techniques often fail to meet the practical needs of in vivo NM analysis.Over the past two decades,intelligent spectral data-processing techniques based on various principles and algorithms have been developed and applied for in vivo NM analysis.Consequently,improvements have been achieved in the overall analytical performance by relying on these techniques without the need to change the instrument hardware.These improvements include enhanced instrument analysis sensitivity,expanded compound analysis coverage,intelligent identification,and characterization of nontargeted in vivo compounds,providing powerful technical means for studying the in vivo metabolism of NMs and screening for pharmacologically active components.This review summarizes the research progress on in vivo analysis strategies for NMs using intelligent MS data processing techniques reported over the past two decades.It discusses differences in compound structures,variations among biological samples,and the application of artificial intelligence(AI)neural network algorithms.Additionally,the review offers insights into the potential of in vivo tracking of NMs,including the screening of bioactive components and the identification of pharmacokinetic markers.The aim is to provide a reference for the integration and development of new technologies and strategies for future in vivo analysis of NMs.

Mining plant metabolomes:Methods,applications,and perspectives

Plants produce a variety of metabolites that are essential for plant growth and human health.To fully understand the diversity of metabolites in certain plants,lots of methods have been developed for metabolites detection and data processing.In the data-processing procedure,how to effectively reduce false-positive peaks,analyze large-scale metabolic data,and annotate plant metabolites remains challenging.In this review,we introduce and discuss some prominent methods that could be exploited to solve these problems,including a five-step filtering method for reducing false-positive signals in LC-MS analysis,QPMASS for analyzing ultra-large GC-MS data,and MetDNA for annotating metabolites.The main applications of plant metabolomics in species discrimination,metabolic pathway dissection,population genetic studies,and some other aspects are also highlighted.To further promote the development of plant metabolomics,more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed.With the improvement of these technologies and the development of genomics and transcriptomics,plant metabolomics will be widely used in many fields.