Tobacco-induced alterations to the Fourier-transform infrared spectrum of serum

Abstract

Infrared (IR) spectroscopy can distinguish differences in the characteristics of diverse molecules by using infrared radiation to probe chemical bonds. Consequently, alterations to the molecular characteristics of tissues and body fluids that help define specific pathological processes and conditions can be identified by IR spectroscopy. This study analyzed the molecular spectrum of cotinine by IR spectroscopy and determined tobacco-induced alterations to the IR profile of serum to establish whether these alterations can differentiate smokers and nonsmokers. The IR spectra of serum samples obtained from 20 smokers and 25 nonsmokers were captured using a FTS-40 IR spectrometer. Linear discriminant analysis method was used to partition the samples into smoker and nonsmoker groups according to the discriminatory patterns in the data and into a validation set to test the accuracy of the trained algorithm in distinguishing smokers and nonsmokers. Cotinine molecules were shown to exhibit a characteristic IR absorption spectrum. Several differences in the sera spectra of the two groups were observed, including an overall shift in the secondary structure of serum proteins favoring increased beta-sheet content in smokers. The overall accuracy of the training and validation sets was 96.7%, and 82.8%, respectively. The identification of specific absorption peaks for tobacco-induced alterations to the IR molecular profile of serum permits the development of an IR spectroscopy technique that can be used to differentiate smokers from nonsmokers. This further extends the utility of IR spectroscopy as a rapidly emerging tool in the field of molecular biodiagnostics.

Diabetes-related molecular signatures in infrared spectra of human saliva

“…We generated combined (type 1 and type 2) diagnostic algorithms, essentially as we have reported previously [19,26,27]. The diagnosis of each saliva sample was provided prior to linear discriminant analysis (LDA) calculations.…”

“…Sensitivity, specificity and positive and negative predictive values were determined for the classification process through cross-validation. Approximately two thirds of the samples were designated the training set, the remaining one-third the test set, again, as we have previously described [19,26,27]. …”

Periodontitis-specific molecular signatures in gingival crevicular fluid

“…To properly assess the predictive value of this classification procedure, the spectra were split into a training set, which was used as the basis to identify discriminatory patterns, and an independent test set to assess the accuracy of the trained algorithm in classifying samples of unknown origin. Approximately two‐thirds of the samples were designated the training set and the remaining one‐third the test set, as we have previously described (25). The training set for periodontitis and control groups comprised 27 periodontitis and 35 control samples, and the test set comprised 16 periodontitis and 12 control samples.…”

Diabetes‐Associated Periodontitis Molecular Features in Infrared Spectra of Gingival Crevicular Fluid

“…Training and prediction were optimized by using the so‐called leave‐one‐out method of cross‐validation. In a second approach, the remaining one‐third was used as the test set and presented to the model masked, as described previously 36 . The selection sites for both sets were random.…”