Early-stage cancer detection is challenging due to the lack of associated oral-tissue clinical features and absence of changes on conventional cellular-imaging, serological and histopathological exams. By using a molecular-sensitive optical technique such as Fourier-transform infrared (FT-IR) spectroscopy, disease-specific biochemical changes can be detected non-destructively, non-invasively and with small sample volumes. In this study, we have used FT-IR spectroscopy to analyze saliva samples of control, smoker, and occasional smoker groups in the fingerprint region (900cm-1 to 1800cm-1). Saliva-sample classification was performed with a neural network algorithm and leave-one-out validation. Correctly classified instances were 72.7% for the control group, 65.5% for occasional smokers and 75% for smokers.
Diabetes is a chronic disease that affects millions of people every year worldwide. Patients with diabetes have high levels of glucose in their blood, since their bodies cannot produce or adequately use the insulin produced. Identification of diabetes is usually performed by tests of glucose in the blood by means of colorimetric reactions, which are time consuming and use a considerable amount of reagents. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy has been used in clinical research as a potential tool to obtain spectrochemical information of biological materials. The infrared spectra can be used as source of information for classiciation models and biomarker extraction by using specific computational tools. In this paper, a semi-portable Bruker Alpha ATR-FTIR was employed to analyse urine samples of 7 patients (3 normal, 2 diabetics and 2 pre-diabetics) in order to distinguish these three groups based on their spectrochemical information. Cross-validated principal component analysis, coupled with linear discriminant analysis was applied to the spectral dataset, resulting in 94% total accuracy. Sensitivities were observed to be 95%, 96% and 100% for normal, pre-diabetics and diabetics patients, respectively, with specificities of 93%, 91% and 100%. These findings show the potential of ATR-FTIR as a new possible tool for identification of diabetics in clinical environments, whereby the diagnosis can be performed in a rapid, non-invasive and automated way.
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