Raman spectroscopy is a non-destructive analytical technique that is used to identify chemical bonds in a sample by measuring the energy shifts of vibrational, rotational, and other low-frequency modes in a system. It is based on the scattering of monochromatic light, usually from a laser, when it interacts with a molecule. The resulting Raman spectrum is a characteristic fingerprint of the sample that can be used to identify the presence of specific chemical bonds and functional groups.
Raman spectroscopy has a number of advantages over other analytical techniques, including the ability to analyze samples in their native state, the ability to measure the chemical composition of samples through thick layers or containers, and the ability to analyze samples at a molecular level. This makes it particularly useful for a variety of applications, including:
Materials science: Raman spectroscopy is used to identify and characterize materials, including minerals, polymers, and semiconductors. It can be used to study the structural and chemical properties of materials, including their crystallinity, phase transitions, and defects.
Pharmaceuticals: Raman spectroscopy is used to identify and quantify active ingredients in pharmaceutical formulations, to ensure quality and purity. It can also be used to study the stability and degradation of drugs, and to evaluate the interactions between drugs and excipients.
Biology and medicine: Raman spectroscopy is used to study the structure and function of biological tissues and cells, including their chemical composition, structure, and metabolism. It has been used to study a variety of diseases, including cancer, and is particularly useful for non-invasive diagnosis and monitoring.
“The following is a research with the data obtained by PTT Raman Spectrometers. For more details please reach the original document and click here.”
Optical Diagnosis of Oral Cancer Detection
The abstract we have provided describes the use of Raman spectroscopy for the diagnosis of oral cancer. A total of 80 samples (44 tumor and 36 normal) were collected from three different sub-sites (tongue, buccal mucosa, and gingiva) and analyzed using Raman spectroscopy. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples, and the classifications were validated using leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The results showed that the PCA-QDA classifier model had the best performance, with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The main biomolecular difference markers for detecting oral cancer were found to be protein, amino acid, and beta-carotene variations.
Journal: J. Clin. Med., 2019
Article: Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection