Introduction

Smoking is a practice in which a person most commonly uses tobacco, which is burned and smoke is inhaled⁵. Smoking greatly affects person's physical and mental health like it can cause wrinkles, cervical cancer, miscarriage and still birth in pregnant females, and also can cause problem with erection in males⁶.Hence there is an increasing need in awareness on smoking and molecular level variation which happens due to smoking should be understood by the smoker for effective awareness⁷.

FTIR is Fourier Transform Infrared Spectroscopy. FTIR is the most powerful tool for identifying chemicals that are either organic or inorganic in nature⁸. It can be utilized for identifying components of unknown mixture. It can also be applied to analyze solids, liquids, and gases. In this method IR radiation is passed through the sample, some of the IR radiation is absorbed by the sample and some of it is allowed to pass through. The resulting spectrum represents the molecular absorption and transmission, creating a molecular fingerprinting of sample. FTIR Technique is applied in the field of biology for tracking the chemical changes in a live cell, studying the light induced reactions in photo biochemical systems such as rhodopsin, bacteriorhodopsin and photosynthetic reaction centres⁹. It is also applied to the in-vitro characterization of biomaterials. FTIR micro-spectroscopy has been used effectively to diagnose and characterize various types of disorders. Encouraging results were obtained previously in an attempt to diagnose leukemia, Gastric Inflammation, Alzheimers disease and kidney stone analysis. Applications of FTIR Spectroscopy over microbiology can give successful information in determining the strain and species difference between various microorganisms¹⁰, detection of virus infected cell and fungi in wood. Besides that, FTIR’s role in diagnostic aspects involving body fluids has been of growing importance in the last few years.

In our present study, we had examined the saliva samples obtained from 23 non-smokers, 18 samples of active smokers taken 15 minutes before smoking, 18 immediately after smoking, 18 thirty minutes after smoking, 18 one hour after smoking, 17 past smokers who had left smoking two months before and 27 past smokers who had left smoking six months before. FTIR Spectroscopy was employed to detect stage wise diagnosis with specific Biomarkers pattern discrimination between non-smokers, active smokers and past smokers. The results showed several spectral peaks, which are useful biomarkers for detection of an active smoker and to find out time taken by him to reach to the normal state in the spectral line pattern.

Materials and Methods

Saliva samples collected from of 68 individuals, the samples had been classified as 23 non-smokers, 18 samples of active smokers and 27 samples of past smokers. Samples of active smokers were collected based on time intervals 15 minutes before smoking, immediately after smoking, 30 minutes after smoking and one hour after smoking. To bring more clarity to the data, 27 past smokers were included in our study out of which 13 had stopped smoking before 2 months from our analysis and 14 samples of past smokers, who stopped smoking before 6 months. The samples were taken from individuals who were free from any infectious diseases and the body mass indexes of the 68 individuals were found to be normal. Participants gave written informed consent for the collection of samples. 2ml of saliva samples were obtained in an Eppendorf tube from each individual and freshly centrifuged for 5 min at 14,000 rpm in Eppendorf centrifuge. The supernatant was removed and then used for the spectral analysis.

Sample layer Preparation

FTIR Data Analysis

Results & Discussion

Careful inspection of the obtained spectra from the FTIR data analysis gives an exclusive spectral variation among non-smokers, active smokers and past smokers with dissimilarity in the absorbance bands at 737cm^-1 ,1659 cm^-1, ,2146 cm^-1, 3414 cm^-1 of spectral region. The spectral line pattern using FTIR analysis for non-smoker was found to be having the common peaks at conventional places (Fig.1)

Figure 1

Fig.1. FTIR analysis of non-smokers

The spectral analyses were performed to the active smoker’s samples and it was found that one hour time duration is required for regaining the original spectral position. The spectral position of an active smoker 15 minutes before smoking is found to have minimum variation as far as peaks are concerned (Fig.2). The FTIR analysis was also done immediately after smoking (Fig.3), 30 minutes after smoking (Fig.4) and 1 hour after smoking (Fig.5). The clear stable variation has been identified by comparing non-smokers with past smokers. The results for past smokers (Fig.6, 7) suggested that the spectral variation had been retained for a period of 6 months.

Figure 2

Fig.2. FTIR analysis of an active smoker 15 minutes prior to smoking

Figure 3

Fig.3. FTIR analysis of an active smoker immediately after smoking

Figure 4

Fig.4. FTIR analysis of an active smoker 30 minutes after smoking

Figure 5

Fig.5. FTIR analysis of an active smoker 1 hour after smoking

Figure 6

Fig.6. FTIR analysis of a past smoker (2 Months)

Figure 7

Fig.7. FTIR analysis of a past smoker (6 Months)

Analysis of variance (ANOVA) was performed to validate the statistical significance of the work. The results suggested that the peaks show variability in the active smoking and past smoking cases when compared with the non-smokers. It was established as statistically significant with p < 0.001. Hence these results are biologically and statistically significant and they can be considered as a valid platform to detect the active, past and non-smokers.

Statistical Analysis

Analysis of variance was performed to identify the spectral variations in smoking candidates who were statistically significant. Cluster analysis is one of simplest and most rapid procedure for classification of biological data. In the present study, these two techniques were used to classify certain regions of the FTIR spectra of the examined healthy and patient samples. Statistical analysis was performed by Gene spring GX7.3 Micro array software PCA for entire spectrum from 500 to 4000 cm^-1 show the full successful classification to distinguish healthy and Parkinson's patient samples. Cluster analysis were performed for the selected region 1543 cm^-1 with similarity measurement of Pearson correlation and average linkage algorithm¹⁴. The results indicated excellent classification for the region 1543 cm^-1 (Fig.8) for classical classification of stages.

Figure 8

Fig.8. Overall graph for statistical analysis

Conclusion

In this present stage, diagnosis of Parkinson's disease is very crucial and no diagnosis method is available from blood yet¹⁵. We examined the salivary samples of 129 subjects by FTIR spectro-microscopy technique for detecting and early diagnosis of Parkinson's disease by rapid and simple methodology. Although the results obtained in this study could be considered only as preliminary results, but still several interesting and consistent spectral differences between patient and healthy spectra may be considered as a promising basis for a future study including large number of samples. These spectral differences could be useful as biomarkers for Parkinson's disease diagnosis. Furthermore, this technique is more feasible, it required small amount of plasma (1μl), which can be easily obtained from any patient, and the final results could be obtained during very short time and ANN can be further implemented as the tool for the rapid diagnosis of Parkinson's disease.

Acknowledgments