Nested Genetic Algorithm for Resolving Overlapped Spectral Bands

Resolvingoverlappedpeaksisamainproblemencounteredinal1alyzingspectraanddataprocessing.OptimizationmethodshavebeenusedforresoIvingoverlappedbands.Geneticalgorithrns(GAs)arenumericaloptin1izationmethodsbasedontheconceptsofgeneticsandnaturalselection.Therefore,GAspossessebetternonlinearparalleIandseIfadaptiveproperties'.Inthiswork,anestedgenetica1gorithm(NGA),includinggeneticparameterleveIandgeneticimplementedleveI,wasdescribedandappliedforresolvingsimulatedunresolvedbands.Figurelshowsthelo…     

径向基函数神经网络用于重叠色谱峰解析

 在高斯基径向基函数神经网络 (RBFNN)学习算法中引入了鲁棒性和随机全局寻优的两阶段遗传算法 :结构学习和参数优化。通过两阶段学习算法的交替使用 ,使网络具有结构自学习和参数优化的能力 ,而后将网络应用于组分数未知的重叠色谱峰解析。该方法具有不需人为干预 ,可自动确定网络结构即组分数的优点 ;并且解析精度较高 ,适用于多组分重叠色谱峰的解析 ;对完全重叠色谱峰也具有良好的解析能力。     

改进遗传算法用于未知组分数的重叠色谱峰的解析

按照指数修正的Gauss卷积色谱峰模型，构造了结合模拟退火及变长染色体的改进遗传算法，并结合爬山法，可以方便快捷地进行数目未知的重叠色谱峰的精确解析；此法成功地应用于大鼠脑微透析液氨基酸定量分析中，解析结果良好。     

Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS)

Analytical instruments that can measure small amounts of chemicals in complicated biological samples are often useful as diagnostic tools. However, it can be challenging to optimize these sensors using actual clinical samples, given the heterogeneous background and composition of the test materials. Here we use gas chromatography-differential mobility spectrometry (GC/DMS) to analyze the chemical content of human exhaled breath condensate (EBC). Ultimately, this system can be used for non-invasive disease diagnostics. Many parameters can be adjusted within this instrument system, and we implemented a factorial design-of-experiments to systematically test several combinations of parameter settings while concurrently analyzing effects and interactions.We examined four parameters that affect sensitivity and detection for our instrument, requiring a 2⁴ factorial design. We optimized sensor function using EBC samples spiked with acetone, a known clinical biomarker in breath. Two outputs were recorded for each experiment combination: number of chemicals detected, and the amplitude of acetone signal. Our goal is to find the best parameter combination that yields the highest acetone peak while also preserving the largest number of other chemical peaks in the spectra. By optimizing the system, we can conduct further clinical experiments with our sensor more efficiently and accurately.     

Rapid Diagnosis of Type II Diabetes Using Fourier Transform Mid-Infrared Attenuated Total Reflection Spectroscopy Combined with Support Vector Machine

Type II diabetes was diagnosed by Fourier transform mid-infrared (FTMIR) attenuated total reflection (ATR) spectroscopy in combination with support vector machine (SVM). Spectra of serum samples from 65 patients with clinical confirmed type II diabetes mellitus and 55 healthy volunteers were acquired using ATR-FTMIR and were first pretreated by three pretreatments (Savitzky–Golay smoothing, multiple scattering correction, and wavelet transforms algorithms) to reduce the interfering information before establishing the SVM models. The parameters of SVM (penalty factor C and kernel function parameter gamma) were optimized to improve the generalization abilities of the models. A grid search method (GS), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were used to find out the optimal parameter values. The results showed that the maximum accuracies were 95.74, 97.87, and 89.36% for the optimized GS, GA, and PSO algorithms. The maximum sensitivities were 96, 100, and 92, and the maximum specificity were 95.45, 95.45, and 86.36%, respectively. The results indicated that the accuracy of type II diabetes was improved using the GS, GA, and PSO algorithms for optimizing the SVM parameters. The GA was found to be slightly better than the GS and PSO. The results of the experiment confirmed that the combination of the ATR-FTMIR spectroscopy and SVM was able to rapidly and accurately diagnose type II diabetes without reagents.     

Resolving batch chromatographic overlapping peaks of flavoring essence using stepwise key spectrum selection

Stepwise key spectrum selection (SKSS) was introduced to resolve batch overlapping peaks from gas chromatography-mass spectrometry (GC-MS) analysis of ten batch tobacco flavoring samples in different storage times. Resolution was implemented on a software platform that embedded the SKSS method. The data from GC-MS analysis of the samples were saved and prepared in ASCII files and then were inputted into the software platform for visual inspections. The data segment with overlapping peaks was precut for subsequent analysis. Spectral background in the data was removed using a linear fitting of the baseline. Four components in the overlapping peaks were automatically detected by the SKSS method. The resolution of the concentration profiles and spectra of the four components was conducted by setting only one parameter, the negative area ratio, as 0.01. The fixed size moving window evolving factor analysis and evolving factor analysis were applied to validate the resolved concentration profiles. The resolved mass spectra were validated by the searched standard through library search at the pure component regions revealed by the resolved concentration profiles. The results showed that the SKSS method could be a simple but powerful tool in resolving batch chromatographic overlapping peaks.     

Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap

While DNA detection using capillary electrophoresis has enabled improvements in both resolution and throughput, the use of CE – particularly with multiple dye channels – can introduce artifacts that can complicate analyses. Undetected pull‐up artifacts can pose a challenge to investigators, especially in low‐level samples, while partial pull‐up peaks can distort peak height balance within a locus and impact the downstream likelihood ratio. Current methods for addressing pull‐up are typically manually implemented. This study presents an effective alternative: a series of mathematical models, created using symbolic regression achieved through genetic programming. The models estimate the amount of pull‐up expected in a peak from a true allele for a given dye‐dye relationship and instrument type. This leads to the removal of artifactual pull‐up peaks and peak height corrections when pull‐up is present within true alleles. When models are used in conjunction with a dynamic threshold, pull‐up peaks were automatically detected and removed with an accuracy rate of 96.1%. The removal of partial pull‐up from true allele peaks led to a more accurate heterozygote balance for the affected locus. These models have been optimized for use with any analytical threshold and can be implemented by any lab using a 3100 or 3500 instrument series.     

Analysis of quaternary carbon resonances of vinylidene chloride/methyl acrylate copolymers

Analysis of the quaternary carbon resonance signals of vinylidene chloride in vinylidene chloride (V)/methyl acrylate (M) copolymers at pentad level of compositional sensitivity is presented in this paper. The analysis has been done by resolving overlapped and complex resonance signals using an approach based on the intensities of resonances, chemical shift prediction and spectral simulation. Intensities of the resonance signals were calculated using the reactivity ratios optimized from the dyad and triad fractions, obtained from the ¹³C ¹H NMR data, by applying genetic algorithm. Joint confidence interval was obtained for the optimized reactivity ratios. The chemical shift modeling of the quaternary carbon resonance signals in terms of empirical additive parameters was done. The chemical shifts of overlapping pentad resonances were predicted from the empirical additive parameters optimized using genetic algorithm. Comparison of the intensities of pentad resonances assigned by chemical shift modeling and experimental intensities of resonances has been done to ascertain the assignments made. Comparison between simulated and experimental spectra at pentad level of sensitivity has been done.     

Identification of Africanized honeybees

Gas chromatography and pattern recognition methods were used to develop a potential method for differentiating European honeybees from Africanized honeybees. The test data consisted of 237 gas chromatograms of hydrocarbon extracts obtained from the wax glands, cuticle, and exocrine glands of European and Africanized honeybees. Each gas chromatogram contained 65 peaks corresponding to a set of standardized retention time windows. A genetic algorithm (GA) for pattern recognition was used to identify features in the gas chromatograms characteristic of the genotype. The pattern recognition GA searched for features in the chromatograms that optimized the separation of the European and Africanized honeybees in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the peaks identified by the pattern recognition GA primarily contained information about differences between gas chromatograms of European and Africanized honeybees. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acted as an information filter, significantly reducing the size of the search space since it restricted the search to feature sets whose principal component plots showed clustering on the basis of the bees' genotype. In addition, the algorithm focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a neural network. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one-pass procedure for feature selection and classification.     

Resolution of Overlapping Chromatographic Peaks Using a Genetic Algorithm

ABSTRACT

A genetic algorithm for resolution of overlapping chromatographic peaks (GAROCP) using real-number coding, non-uniform mutation and arithmetical crossover methods is described in this paper. It was applied to resolution of highly overlapped multicomponent high-performance liquid chromatographic peaks by fitting experimental chromatogram to the exponentially modified Gaussian (EMG) model. The genetic algorithm was used to find the minimum of fitting error to optimize the parameters in the EMG functions which determine the shape and area of each peak. The applicability of the method was investigated with both simulated signals calculated by EMG functions and experimental multicomponent overlapping chromatograms.     

An adaptive single-well stochastic resonance algorithm applied to trace analysis of clenbuterol in human urine

Based on the theory of stochastic resonance, an adaptive single-well stochastic resonance (ASSR) coupled with genetic algorithm was developed to enhance the signal-to-noise ratio of weak chromatographic signals. In conventional stochastic resonance algorithm, there are two or more parameters needed to be optimized and the proper parameters values were obtained by a universal searching within a given range. In the developed ASSR, the optimization of system parameter was simplified and automatic implemented. The ASSR was applied to the trace analysis of clenbuterol in human urine and it helped to significantly improve the limit of detection and limit of quantification of clenbuterol. Good linearity, precision and accuracy of the proposed method ensure that it could be an effective tool for trace analysis and the improvement of detective sensibility of current detectors.     

Kinetic simulation of oxidative coupling of methane over perovskite catalyst by genetic algorithm： Mechanistic aspects

The reaction kinetics of oxidative coupling of methane catalyzed by perovskite was studied in a fixed bed flow reactor. At atmospheric pressure, the reactions were carried out at 725, 750 and 775 ℃, inlet methane to oxygen ratios of 2 to 4.5 and gas hourly space velocity (GHSV) of 100 min-1. Correlation of the kinetic data has been performed with the proposed mechanisms. The selected equations have been regressed with experimental data accompanied by genetic algorithm (GA) in order to obtain optimized parameters. After investigation the Langmuir-Hinshelwood mechanism was selected as the best mechanism, and Arrhenius and adsorption parameters of this model were obtained by linear regression. In this research the Marquardt algorithm was also used and its results were compared with those of genetic algorithm. It should be noted that the Marquardt algorithm is sensitive to the selection of initial values and there is possibility to trap in a local minimum.     

基于积分渐进展开的气相色谱重叠峰快速解析法

论文提出用积分渐进展开解析气相色谱重叠峰,该方法有3个主要步骤：首先将谷峰或肩峰分成两个积分区域,得到一个子区域的积分方程和一个重叠峰面积的代数方程;然后用数值积分求出这两个方程计算中所需要的峰面积,再用积分渐进公式将积分方程展开成代数方程;最后,将这两个方程与峰高约束方程联立后,得到一个非线性代数方程组,用Gauss-Seidel迭代可以快速求解方程组,方程收敛的最大迭代次数不超过20次。仿真和实验结果表明,解析的峰高和峰面积误差均很小,峰面积最大误差低于6.44%,峰高的最大误差约为6.80%。由于该算法精度高,效率高,所以这个方法可以用于气相色谱重叠峰和一般色谱峰的实时在线解析。     

PLUMS: a program for the rapid optimization of focused libraries

PLUMS is a new method to perform rational monomer selection for combinatorial chemistry libraries. The algorithm has been developed to optimize focused libraries with specific two-dimensional and/or three-dimensional properties. A preliminary step is the identification of those molecules in the initial virtual library which satisfy the imposed property constraints; we define these molecules as the virtual hits. From the virtual hits, PLUMS generates a starting library, which is the true combinatorial library that includes all the virtual hits. Monomers are then removed in an iterative fashion, thus reducing the size of the library. At each iteration, the worst monomer is removed. Each sublibrary is selected using a global scoring function, which balances effectiveness and efficiency. The iterative process continues until one is left with a library that consists entirely of virtual hits. The optimal library, which is the best compromise between effectiveness and efficiency, can then be selected according to the score. During the iterative process, equivalent solutions may well occur and are taken into account by the algorithm, according to a user-defined parameter. The number of monomers for each substitution site and the size of the library are parameters that can be either optimized or used to constrain the selection. The results obtained on two test libraries are presented. PLUMS was compared with genetic algorithms (GA) and monomer frequency analysis (MFA), which are widely used for monomer selection. For the two test libraries, PLUMS and GA gave equivalent results. MFA is the fastest method, but it can give misleading solutions. Possible advantages and disadvantages of the different methods are discussed.     

The stochastic resonance algorithm with the direct current signal as external force and its application to the detection of weak chromatographic peaks

As a potential tool for amplifying weak chromatographic peaks, the stochastic resonance algorithm was developed based upon a counterintuitive physical phenomenon. Therefore, the essential step, parameter optimization, was perplexing and difficult for analysts. In order to avoid optimizing the system parameters on a case‐by‐case basis, an improved algorithm was proposed by introducing a constant or direct current signal into the signal to be measured as the external force. The weak chromatographic peak can be amplified and detected by the new algorithm using the same set of parameters. Two sets of our previous experimental data were reanalyzed by using the developed algorithm and the results were satisfactory. A generalized solution was expected to come into being on account of the new algorithm.     

Kinetic simulation of oxidative coupling of methane over perovskite catalyst by genetic algorithm:Mechanistic aspects

The reaction kinetics of oxidative coupling of methane catalyzed by perovskite was studied in a fixed bed flow reactor.At atmospheric pressure,the reactions were carried out at 725,750 and 775 ?C,inlet methane to oxygen ratios of 2 to 4.5 and gas hourly space velocity (GHSV) of 100 min?1.Correlation of the kinetic data has been performed with the proposed mechanisms.The selected equations have been regressed with experimental data accompanied by genetic algorithm (GA) in order to obtain optimized parameters.After investigation the Langmuir-Hinshelwood mechanism was selected as the best mechanism,and Arrhenius and adsorption parameters of this model were obtained by linear regression.In this research the Marquardt algorithm was also used and its results were compared with those of genetic algorithm.It should be noted that the Marquardt algorithm is sensitive to the selection of initial values and there is possibility to trap in a local minimum.     

基于遗传算法的色谱指纹峰配对识别方法

指纹峰配对识别是色谱指纹图谱分析中的关键环节之一，本文提出一种基于遗传算法的色谱指纹峰配对识别方法。该法根据对照色谱指纹图谱的峰分布特性初选出若干标定蜂，将其存入一个候选标定蜂库；同时根据这些候选标定峰从待测指纹图谱中选出相应的候选标定峰，也存入候选标定峰库；再用遗传算法从库中选取一组标定峰用于校正待测指纹图谱中各峰的峰位，并自动识别出与对照色谱指纹图谱相对应的各指纹峰。仿真实验及实际分析实验结果均表明，该法识别指纹峰准确可靠，可用于色谱指纹图谱相似度的快速自动计算。     

SODOCK: swarm optimization for highly flexible protein-ligand docking

Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A.     

一阶和二阶导数相综合的色谱峰检测法

本文提出了一种介于一阶和二阶导数之间的色谱峰检测新方法，它能排除基线漂移的影响，具有抗噪声干扰能力，能判别重叠峰，还能够得色谱峰上各拐点，的位置并发现肩峰型重叠峰。该方法只需要设置一个起动参数即可自动进行峰检测，具有很强的自适应性和健壮性，极大地方便了用户。