Absorption spectrophotometry signal de-noising using invariant wavelets

Diseases such as cancer, hepatitis and AIDS cause body fluid concentration and amount to become modified; their measurement can thus be useful as a diagnostic technique. Spectroscopy is one of the most widely used techniques for biological substance detection and quantification. The presence of unwanted signals is the main limiting factor for sensitivity and quality; this is called noise. Noise has different backgrounds which range from physical assumptions to environmental influence. Eliminating or reducing noise in spectroscopy has thus been studied for many years and the applicability of wavelet transform has been demonstrated in recent decades. This paper presents invariant wavelet transform for increasing signal to noise ratio in spectrophotometer signals and thus improve the quality of spectrophotometric analysis and biological substance quantification. The proposed technique was applied to artificially-generated signals and signals from two spectrometers, one having a continuum source and another with a laser radiation source. The results obtained with this technique were compared to those obtained from traditional filters: Gaussian, Wiener and orthogonal wavelets. This technique's main advantages are a substantial increase in signal to noise ratio and preservation of spectral peak location and width. These advantages increase biological substance detection and quantification quality and accuracy and allow automatic analysis of the spectrum. They can also lead to better understanding of experimental limitations and allow a quantitative study of the influence of changes in substance concentration in related diseases.