In this paper, a feature extracting method based on wavelets for horizontal attenuated total reflectance Fourier transform
infrared spectroscopy (HATR-FTIR) cancer data analysis and classification using artificial neural network trained with
back-propagation algorithm is presented. 168 Spectra were collected from 84 pairs of fresh normal and abnormal lung
tissue's samples. After preprocessing, 12 features were extracted with continuous wavelet analysis. Based on BPNN
classification, all spectra were classified into two categories : normal or abnormal. The accuracy of identifying normal,
early carcinoma, and advanced carcinoma were 100%, 90% and 100% respectively. This result indicated that FTIR with
continuous wavelet transform (CWT) and the back-propagation neural network (BPNN) could effectively and easily
diagnose lung cancer in its early stages.
By experimenting with Fourier transform infrared spectroscopy (FTIR), it was found that FTIR yielded much better results and faster determination of normal and malignant gastric tissues when accompanied with OMNI-sampler. The results showed that there were obvious and regularity differences between FTIR spectra of them in spectral parameters such as frequency, intensity and shape of the bands etc. They indicated significant differences of content, structure and conformation of proteins, nucleic acids and lipids in normal and malignant gastric tissues. The probability found by the results of goodness-of-fit tests of frequency of the bands in the second derivative FTIR, between each of the normal and malignant gastric tissues was less than 0.01. This result was found to be significant. The present results suggested that FTIR could show the properties of normal and malignant gastric tissues in the molecular level. It contains the ability to supply rich and reliable information to investigation of normal and malignant gastric tissues and can be used as a convenient and reliable diagnostic tool for tumors.
In recent years, FTIR has been found its wider application in analysis processes of pharmaceutical solids. This is, in part, thanks to the development of powerful multivariate quantitative techniques, such as partial least-squares (PLS) modeling software and the emergence of some new reflectance sampling techniques which allow direct measurement of the IR spectrum of solids in their native states. Horizontal Attenuated Total Reflectance (HATR) is a quite popular sampling technique in recent years. In this work, the feasibility of the Horizontal Attenuated Total Reflectance Infrared Spectroscopy (HATR-FTIR) to the quantitative and quantitative analysis of nimodipine tablets is investigated. Quantitative analysis of nimodipine is carried out by using a classical least-squares for areas procedure. Obtained from validated samples of nimodipine, quantitative results demonstrate clearly the strong potential of HATR-FTIR technique through using quantitative analysis of nimodipine content of pharmaceutical tablets.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.