Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

Signal processing and detection limits for graphite furnace atomic absorption with Zeeman background correction

The signal handling requirements for graphite furnace atomic absorption are much more demanding than those for flame atomic absorption. Graphite furnace signals change rapidly, background levels are higher, and signal interpretation needs are more extensive.We have identified a number of signal generation and processing factors that are important for success in graphite furnace analyses. These include: use of the transverse, a.c. Zeeman technique with the magnet on the analyte for background correction; production of a series of signal integrals at line frequency to accurately represent the shape of the furnace peak; use of interpolation techniques to better correct for rapidly changing background levels; use of integrated peak absorbance (A.s) signals rather than peak height absorbance for quantitative measurements; use of baseline correction to improve the accuracy of integrated peak absorbance signals; and use of graphical techniques to facilitate data interpretation and methods development.Examples are presented that illustrate the contribution of these factors to precision and detection limit performance. It is possible to improve detection limits over those previously reported by choosing appropriate signal handling parameters.     

Signal and data processing for atomic absorption spectrophotometry

The state-of-the-art of signal and data processing techniques for atomic absorption spectrophotometry is described and discussed. Aspects of optical and atomizer design of greatest importance for providing the best signal to noise S/N ratio and minimum curvature are summarized. In background corrected systems, amplifier gain and time constants must be carefully matched, especially for transient signals. A method is given for calculating the sampling time of peak search systems. Methods of signal averaging are described and the importance of precision calculations is stressed. The correct sequence of readings for calibration is discussed. The causes of curvature are shown for simple and complex curves and methods of correction are compared. Other desirable functions are calculation of sensitivity and detection limit, error warnings and external data output facilities.     

New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data

Two new algorithms for automated processing of liquid chromatography/mass spectrometry (LC/MS) data are presented. These algorithms were developed from an analysis of the noise and artifact distribution in such data. The noise distribution was analyzed by preparing histograms of the signal intensity in LC/MS data. These histograms are well fit by a sum of two normal distributions in the log scale. One new algorithm, median filtering, provides increased performance compared to averaging adjacent scans in removing noise that is not normally distributed in the linear scale. Another new algorithm, vectorized peak detection, provides increased robustness with respect to variation in the noise and artifact distribution compared to methods based on determining an intensity threshold for the entire dataset. Vectorized peak detection also permits the incorporation of existing algorithms for peak detection in ion chromatograms and/or mass spectra. The application of these methods to LC/MS spectra of complex biological samples is described.     

Thermal marks as a signal processing aid for a portable capillary electropherograph

The interpretation of raw signals in capillary CE can be challenging if there are unknown peaks, or the signal is corrupt due to baseline fluctuations, EOF velocity drift, etc. Signal processing could be required before results can be interpreted. A suite of signal processing algorithms has been developed for CE data analysis, specifically for use in field experiments for the detection of nerve agents using portable CE instruments. Everything from baseline correction and electropherogram alignment to peak matching and identification is included in these programs. Baseline correction is achieved by interpolating a new baseline according to points found using all local extremes, by applying an appropriate outliers test. Irreproducible migration times are corrected by compensating for EOF drift, measured with the aid of thermal marks. Thermal marks are small disturbances in the capillary created by punctual heating that move with the velocity of EOF. Peaks in the sample electropherogram are identified using a fuzzy matching algorithm, by comparing peaks from the sample electropherogram to peaks from a reference electropherogram.     

基于临近比较的快速拉曼光谱基线校正方法

拉曼光谱成像技术是基于拉曼散射效应所开发的一项现代检测技术,在现代生产、科学研究过程中使用非常广泛。拉曼光谱信号受荧光效应和仪器等方面的影响,往往会产生基线漂移,严重影响对信号特征的进一步提取。因此,必须对拉曼光谱信号进行基线校正。传统的基线校正方法,只针对单一光谱信号,计算量较大,在处理由大量拉曼信号组成的成像数据时,耗时较长且效果不佳。该文提出一种基于临近比较的快速基线校正方法,根据在相同背景下采集的光谱之间的相关性,实现快速基线校正,提高了拉曼成像数据的处理速度。     

Enhancing chemical analysis with signal derivatization using simple available software packages

Derivative techniques for analytical signal processing are useful for solving some noise and signal resolution problems in various fields of study such as titrimetry, spectrophotometry, chromatography and electrochemistry. The broad use of these techniques, however, is often limited by costly inflexible built-in software packages in commercial analytical instruments. We propose here the application of commercial simple software packages such as Microsoft® Excel and Microcal Origin for signal smoothing and fitting, and for obtaining derivative analytical signals in batch and flow-based analyses, including potentiometric titration, spectrophotometry, chromatography, voltammetry and sequential injection analysis (SIA). The worldwide (especially Excel) software packages are easy-to-use for less experienced users and have also capabilities for advanced users, and therefore employing such packages can result in expansion of useful derivative techniques. We demonstrate application of the available package-aided derivative capabilities for enhancing some chemical analyses, including potentiometric acid–base titration, Bradford assay of protein, chromatographic separation of ajmaline and reserpine and anodic stripping voltammetry of copper. The derivative signals from smoothed and fitted curves offer better accuracy and precision, even for non-resolving peaks and tailing peaks. In some cases, the optimization of experimental conditions is not further required, which can lead to fast method development.     

Studies of spline wavelet least square in processing electrochemical signals

The application of spline wavelet least square (SWLS) in analytical chemistry signals is presented in this paper. As a new technique in signal processing, to extract useful signals from high noise, the influences of different parameters on the results of processing is discussed in details. If the suitable parameters are selected, useful signals can be filtered from the noise of S/N=0.5. The relative error of peak current is less than 3.0%, and that of peak potential is less than 10%. Comparison of this method with wavelet multifrequency channel decomposition (WMCD) and spline least square (SLS) has also been made and it indicates that SWLS can solve some problems in WMCD and SLS. The experimental results are also satisfactory.     

Spline wavelet analysis for voltammetric signals

Application of wavelet multifrequency channel decomposition (WMCD) in electroanalytical chemistry is presented in this paper. A new approach on this digital processing technique, to extract useful information from high noise signals in voltammetry, is described in detail. The method of constructing a model of B-spline WMCD and its application with 2nd, 3rd and 4th order B-spline in the linear scan voltammetry are thoroughly discussed and several results obtained. If the suitable optimal wavelet basis and frequency scale value are selected, the absolute values of the peak relative errors are less than 2% when the signal-to-noise ratio (S/N) is greater than 0.2, and the absolute values of the peak potential relative errors are less than 12% when S/N is greater than 0.3. The processed results of experimental data with high noise are also satisfactory. The whole computation is simple and needs shorter time than other signal processing methods.     

Optical imaging with dynamic contrast agents

Biological imaging applications often employ molecular probes or nanoparticles for enhanced contrast. However, resolution and detection are still often limited by the intrinsic heterogeneity of the sample, which can produce high levels of background that obscure the signals of interest. Herein, we describe approaches to overcome this obstacle based on the concept of dynamic contrast: a strategy for elucidating signals by the suppression or removal of background noise. Dynamic contrast mechanisms can greatly reduce the loading requirement of contrast agents, and may be especially useful for single-probe imaging. Dynamic contrast modalities are also platform-independent, and can enhance the performance of sophisticated biomedical imaging systems or simple optical microscopes alike. Dynamic contrast is performed in two stages: 1) a signal modulation scheme to introduce time-dependent changes in amplitude or phase, and 2) a demodulation step for signal recovery. Optical signals can be coupled with magnetic nanoparticles, photoswitchable probes, or plasmon-resonant nanostructures for modulation by magnetomotive, photonic, or photothermal mechanisms, respectively. With respect to image demodulation, many of the strategies developed for signal processing in electronics and communication technologies can also be applied toward the editing of digital images. The image-processing step can be as simple as differential imaging, or may involve multiple reference points for deconvolution by using cross-correlation algorithms. Periodic signals are particularly amenable to image demodulation strategies based on Fourier transform; the contrast of the demodulated signal increases with acquisition time, and modulation frequencies in the kHz range are possible. Dynamic contrast is an emerging topic with considerable room for development, both with respect to molecular or nanoscale probes for signal modulation, and also to methods for more efficient image processing and editing.     

基于伪随机二进制序列的阻抗谱快速重构及其在电化学能源领域的应用

阻抗谱的应用范围越来越广,其传统测试方法耗时长的问题也日益突出. 提高阻抗谱测量速度的各种尝试中,合成宽带激励信号和设计高效率估计算法被认为是最具潜力的解决方案,由于伪随机二进制序列（pseudo-random binary sequence,PRBS）具有功率谱平坦和易生成等优点,它在阻抗谱快速测试中具有独特优势. 本文综述了快速阻抗谱测试中三个核心问题：PRBS信号类型、不同快速算法及其在电化学能源领域的典型应用. 对于PRBS信号类型,即最大长度序列信号、混合PRBS、离散区间二进制序列和正交PRBS,本文讨论了它们各自的特点和应用范围;对于不同的PRBS激励信号的快速算法,即离散傅里叶变换/快速傅里叶变换、小波变换、快速m序列变换、基于系统辨识的参数估计算法以及这些算法各自的特点和应用范围,本文进行了深入的分析;对于PRBS阻抗谱快速测量的应用,本文以铅酸电池、锂离子电池、质子交换膜燃料电池和超级电容器等电化学能源为例,验证了其应用的可行性. 为促进技术的进一步完善,本文总结和分析了PRBS阻抗谱快速测量存在的挑战,并提出了克服这些挑战所必需的未来研究方略.     

Photocount distribution of photons emitted from three sites of a human body

Spontaneous photon emission from 30 sites on the skin of a live human subject is measured at different times and on different days. Signals from three representative sites of low, intermediate and high intensities are selected for further analysis. Fluctuations in these signals are measured by the probabilities of detecting different numbers of photons in a bin. The probabilities have non-classical features and are well described by the signal in a quantum squeezed state of photons. Measurements with bins of three sizes yield same values of three parameters of the squeezed state. A procedure for making correction due to background noise is developed. The correction changes the parameters of the quantum state. The new state appears more like a coherent state of photons.     

A software package for the evaluation of peak parameters in an analytical signal based on a non-linear regression method

Computer-based data handling of analytical signals to extract commonly used analytical parameters often produces poor results if the signals are affected by noise and a drifting baseline. Only a minor part of the information present in the signal is used for correction. A computer package is presented, in which the total information of the signal is used to give accurate evaluation of analytical parameters. The program uses a non-linear regression method to deconvolute analytical signals into a number of peaks and a baseline. To describe the peak shape any mathematical model can be used. In the computer package, a Gaussian curve-related model is used, with variable asymmetry. The baseline is described with a polynomial of variable order. The method is simultaneously a filter procedure Deconvolution of poorly separated peaks is possible. The software is developed on a minicomputer; however, test results of this study indicate the feasibility of implementation on a microcomputer without extremely time-consuming runs of the program.     

Recent progress on nanopore electrochemistry and advanced data processing

Nanopore-based technology offers nanoscale chemical environments with intriguing confinement effects, which isolates individual analytes from the bulk solution. This confined space combines mass transportation and electrochemical measurement, providing new insight into single entity sensing. In this mini-review, we highlight the exciting progress on nanopore electrochemistry. Starting with a concise summary of nanopore-based electrodes, we introduce the fabrication methods and characterizations of the various nanopore electrodes. Then, the special attention focuses on the application of nanopore electrochemistry in single nanoparticle analyzing and intracellular electrochemical sensing. The advanced data analysis tools and Machine Learning algorithms for rapid encoding single-molecule characteristic sets are also covered, which promotes the sensitivity of nanopore electrochemistry and opens a new possibility for revealing single-entity heterogeneity.     

近红外分析中光谱预处理及波长选择方法进展与应用

光谱预处理和波长选取方法在近红外光谱分析技术中相当重要。本文综述了常用的NIR预处理和波长选取方法及这一领域的最新进展,详细介绍正交信号校正(OSC)、净分析信号(NAS)和小波变换(WT)等新光谱预处理方法以及无信息变量消除(UVE)和遗传算法(GA)等波长选取方法,并给出了这些方法的具体算法和一些应用实例。     

A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction

Despite many advantages of laser sampling in analytical atomic spectrometry, the signal intensity of these hyphenated methods suffers from the specific influences resulting from the laser ablation process of a sample. The problems that arise affect the accuracy and precision of analytical results. Different techniques of analytical signal normalization which improve figures-of-merit for atomic spectrometry with laser sampling are considered in this review. The characteristics and application of these approaches including the use of internal standards, single or several reference signals and multivariate correction of analytical signal are discussed.     

Orthogonal Wavelets Analysis of Electroanalytical Signals

ABSTRACT

A signal processing technique based on orthogonal wavelet analysis is applied to process various simulated electroanalytical signals. The results indicate that if the scale parameters are selected, the orthogonal wavelet processing method (OWPM) can remove high-frequency noise. Experimental signal was recorded by computer and used to test the OWPM procedure. After processing with OWPM, the processed data was used to analyze the mechanism of the electrode reactions. Processed results of the experimental data are also satisfactory.     

Design and operation of microelectrochemical gates and integrated circuits

Here we report a simple design philosophy, based on the principles of bipolar electrochemistry, for the operation of microelectrochemical integrated circuits. The inputs for these systems are simple voltage sources, but because they do not require much power they could be activated by chemical or biological reactions. Device output is an optical signal arising from electrogenerated chemiluminescence. Individual microelectrochemical logic gates are described first, and then multiple logic circuits are integrated into a single microfluidic channel to yield an integrated circuit that can perform parallel logic functions. AND, OR, NOR, and NAND gates are described. Eventually, systems such as those described here could provide on-chip data processing functions for lab-on-a-chip devices.     

Wavelet transform as a new approach to the enhancement of signal-to-noise ratio in anodic stripping voltammetry

De-noising signals is a frequent aim achieved by signal processing in analytical chemistry. The purpose is to enable the detection of trace concentrations of analytes. The limit of detection is defined as the lowest amount of analyte that still causes signals greater than the background noise. Appropriate de-noising decreases only the noise and maintains the measurement signal, so that signal-to-noise ratios are enhanced. One adequate mean of signal processing for this purpose is wavelet transform, which still is not a common tool in analytical chemistry. In this paper, the ability of de-noising by wavelet transform is shown for measurements in anodic stripping voltammetry using a hanging mercury drop electrode. The calculation of limits of detection and signal-to-noise ratios on the basis of peak-to-peak noise is exercised to quantify the performance of de-noising. Furthermore, signal shape with regard of easing the application of base lines is discussed. Different wavelet functions are used, and the results are compared also to Fourier transform. Coiflet2 was found out to reduce noise by the factor of 330 and is proposed as the adequate wavelet function for voltammetric and similar signals.