Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

Abstract

Raman spectroscopy has proven to be a valuable tool for analyzing various types of forensic evidence such as traces of body fluids. In this work, Raman spectroscopy was employed as a nondestructive technique for the analysis of dry traces of oral fluid to differentiate between smoker and nonsmoker donors with the aid of advanced statistical tools. A total of 32 oral fluid samples were collected from donors of differing gender, age and race and were subjected to Raman spectroscopic analysis. A genetic algorithm was used to determine eight spectral regions that contribute the most to the differentiation of smokers and nonsmokers. Thereafter, a classification model was developed based on the artificial neural network that showed 100% accuracy after external validation. The developed approach demonstrates great potential for the differentiation of smokers and nonsmokers based on the analysis of dry traces of oral fluid.

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

“…32 ANNs are another machine learning approach that has successfully been applied on spectral data for classification purposes. 33,34 ANN often proved superior compared to SVM and PLS-DA approaches. 29,33 An example of ANNs applied on NIR data includes cellulose pulp dryness determination in industrial processing.…”

“…Jiménez‐Romero et al demonstrated the classification potential of both PLS‐DA and kNN on 1000–2500 nm NIR spectra to compare the production batches of pharmaceuticals 32 . ANNs are another machine learning approach that has successfully been applied on spectral data for classification purposes 33,34 . ANN often proved superior compared to SVM and PLS‐DA approaches 29,33 .…”

Methods in Raman spectroscopy for saliva studies – a review

“…This is a sensible choice given the effect that the smoking phenotype, independent of the cancer status, can have on the salivary Raman spectra. [146,207,216,[239][240][241][242] The authors perform a baseline check by ELISA for possible inherent morphine traces in the hospitalized cohort. Hern andez-Arteaga [200] et al further classify patients as with "no systemic disease" other than the disease being studied, i.e., breast cancer, as well as with "no oral complaints" due to the nonspecificity of sialic Salemmilani (2018).…”

“…Purchased saliva sample studies include Shende (Lee Biosolutions), [141] Muro (Lee Biosolutions and Bioreclamation), [154] Muro (Biological Specialty Company, Lee Biosolutions, Bioreclamation), [155] D'Elia (Bioreclamation), [105] Eom (Lee Biosolutions), [197] and Al-Hetani (Bioreclamation). [239] Obtaining saliva from a commercial source may simplify subsequent steps in saliva storage and measurement and negate problems regarding collection and questions on optimal preprocesses e.g., centrifugation.…”

Atomic force microscopy and Raman spectra profile of blood components associated with exposure to cigarette smoking

“…Raman spectroscopy of body uids is an alternative method that minimizes invasion in the analysis compared with others. [61][62][63][64] Blood plasma contains several biomolecules as proteins, lipids, glucose, vitamins, hormones, and inorganic materials, as well as waste products of the metabolism; the changes in these biomolecules can be very good indicators of the health status of a person. 65,66 Although blood plasma Raman spectrum it is complex to obtain without Surface-Enhanced Raman Spectroscopy (SERS), there are some successful examples for it.…”