Alexander Kai-man Leung

A review on applications of wavelet transform techniques in chemical analysis: 1989–1997

Abstract Starting from 1989, a new mathematical technique known as wavelet transform (WT) has been applied successfully for signal processing in chemistry. The number of publications related to the application of WT to manipulate chemical data has increased rapidly in the last 2 years from one paper being published in 1989 to 18 papers in 1996 and 41 papers in 1997. More than 70 papers were published within the period from 1989 to 1997. In these published works, WT was mainly employed for noise removal and data compression in different fields of analytical chemistry that include flow injection analysis, high performance liquid chromatography, infrared spectrometry, mass spectrometry, nuclear magnetic resonance spectrometry, ultraviolet–visible spectrometry and voltammetry. It has been employed to solve certain problems in quantum chemistry and chemical physics. In this paper, applications of the wavelet transform and its derivative wavelet packet transform (WPT) are reviewed. Research works on WT by Chinese researchers in China are also included.

Wavelet Transform: A Method for Derivative Calculation in Analytical Chemistry

A novel method based on wavelet transform is proposed in this work for approximate derivative calculation. An approximate first derivative of an analytical signal can be expressed as the difference between the two scale coefficients C1, which were generated from any two Daubechies wavelet functions. The optimal results for both synthetic and experimental data were obtained with the use of the Daubechies wavelet functions D8 and D18. Our work demonstrated that the new method can enhance the signal-to-noise ratio at higher order derivative calculation and retain all major properties of the conventional methods.

APPLICATION OF WAVELET TRANSFORM IN INFRARED SPECTROMETRY : SPECTRAL COMPRESSION AND LIBRARY SEARCH

Abstract In recent years, a new mathematical technique called wavelet transform (WT) has been developed and adopted for signal processing in analytical chemistry owing to its efficiency, more number of basis functions available, and higher speed in data treatment compared to fast Fourier transform (FFT). In this paper, the fast wavelet transform (FWT) and its derivative, wavelet packet transform (WPT), were applied to compress infrared (IR) spectrum for storage and spectral searching. In WT treatment, the number of data to be processed has to be 2 P with P being any integer. In this work, we proposed the coefficient position retaining (CPR) method to handle data with length of odd number. The performance of the two proposed WT methods in data compression and spectral library searching are compared with that of the FFT method. The results indicated that our proposed WT methods works better than FFT in compression of IR spectra and spectral library searching.

Analysis of the Water Soluble Constituents of Cordyceps Sinensis With Heuristic Evolving Latent Projections

ABSTRACT A chemometric technique, heuristic evolving latent projection (HELP), was applied to analyze the data obtained from high performance liquid chromatography coupled with the diode array detection (HPLC-DAD) for a traditional Chinese medicinal herb, Cordyceps sinensis. Based on this method, the water soluble components, nucleosides in Cordyceps sinensis were identified. Ten and eleven volatile components were identified from the fungal and larvae parts of Cordyceps sinensis sample, respectively. As compared with the conventional method, HELP was found to be suitable for quantitative analysis of complex real systems such as Chinese medicinal products.

A roughness penalty approach and its application to noisy hyphenated chromatographic two-way data

In order to improve the signal detection and resolution of chemical components with very low concentrations in hyphenated chromatographic two‐way data, the effect of measurement noise from the instruments on these two aspects is first investigated in the present paper. A new smoothing technique called the roughness penalty method is introduced to reduce the influence of this measurement noise. Our results show that the proposed method can enhance the detection ability significantly. In addition, the resolved spectra after the roughness penalty smoothing are found to be significantly improved. The performance of the method was assessed using simulated and real hyphenated two‐way data. Copyright

Chapter 9 - Application of Wavelet Transform in Processing Chromatographic Data

This chapter describes application of wavelet transform in processing chromatographic data. Chromatography is used widely in analytical chemistry for the separation of compounds in sample mixtures. By adopting different chemical and physical properties, various chromatographic techniques and instruments are developed for chemical analysis. Such techniques include paper chromatography, thin layer chromatography (TLC), gas chromatography (GC), and many more. There was a tendency to combine different analytical techniques or instruments with chromatography for separation and characterization. In chromatographic data analysis and signal processing, analytical chemists always face problems such as noise suppression, signal enhancement,peak detection, resolution enhancement, and multivariate signal resolution. Various chemometric methods are proposed for tackling these problems. Transformation techniques, such as the Fourier transform, Laplace transform, and Hartley transform are utilized in chromatography for data processing. Recently, the new mathematical technique wavelet transform (WT) is introduced to help solve various problems.

Compression of ultraviolet–visible spectrum with recurrent neural network

Abstract Data compression method based on the recurrent neural network (RNN) of the dynamical system approach was proposed and applied to ultraviolet–visible (UV–VIS) spectra. RNN schemes with different network size were studied and their performance was evaluated by using both synthetic and experimental data. It was found that the storage space of the spectral information under study could be reduced significantly by using the proposed RNN method with quality spectra regenerated from the compressed data. Furthermore, the method was found to perform as good as the wavelet transform (WT) in data compression and in some cases, even better.

Three-Dimensional Model Of The ATP-Binding Domain Of Pyridoxal Kinase

With a combination of multiple sequence alignment, secondary structure prediction and fold recognition techniques, a molecular model of the ATP-binding domain of pyridoxal kinase was constructed. The overall fold of the model consists of a central β-sheet of five parallel strands sandwiched between six helices. It shows high resemblance with the typical Rossmann fold in nucleotide binding. The quality of this model was assessed with the program PROCHECK using conformation criteria. PROCHECK locates over 90% of the residues of the current model in the “most favored” and “additional allowed” regions in Ramachandran plot.

Applications of Wavelet Transform in Spectroscopic Studies

This chapter presents some specific applications of wavelet transform (WT) in analytical chemistry. It focuses on three major areas in analytical chemistry that include spectroscopy, chromatography, and electrochemical studies. The spectroscopic techniques, ultraviolet-visible (UV-VIS) spectroscopy, infrared (IR) spectroscopy, mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, and photoacoustic (PA) spectroscopy, are widely used in analytical chemistry for both qualitative and quantitative analysis. In spectroscopic measurement, the raw spectral data X are a combination of the true readings and noise in the discrete format. To extract the true readings from the raw data, a digital processing method such as filtering is commonly employed. In addition, infrared spectroscopy plays an important role in the identification and characterization of chemicals and is used widely in modern laboratories. The chapter describes applications of wavelet transform in ultraviolet visible spectroscopy. Ultraviolet-visible spectroscopy is another technique that is used extensively in analytical chemistry for characterization, identification, and quantitative analysis.

Chapter 11 - Applications of Wavelet Transform in Spectroscopic Studies

This chapter presents some specific applications of wavelet transform (WT) in analytical chemistry. It focuses on three major areas in analytical chemistry that include spectroscopy, chromatography, and electrochemical studies. The spectroscopic techniques, ultraviolet-visible (UV-VIS) spectroscopy, infrared (IR) spectroscopy, mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, and photoacoustic (PA) spectroscopy, are widely used in analytical chemistry for both qualitative and quantitative analysis. In spectroscopic measurement, the raw spectral data X are a combination of the true readings and noise in the discrete format. To extract the true readings from the raw data, a digital processing method such as filtering is commonly employed. In addition, infrared spectroscopy plays an important role in the identification and characterization of chemicals and is used widely in modern laboratories. The chapter describes applications of wavelet transform in ultraviolet visible spectroscopy. Ultraviolet-visible spectroscopy is another technique that is used extensively in analytical chemistry for characterization, identification, and quantitative analysis.