Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

Objective: Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. Background data: Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. Methods: Skin biopsy fragments of *2 mm 2 from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. Results: We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p < 0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. Conclusions: This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

PCA and PLS could discriminate Raman spectra of skin tissues, opening the way for an in vivo optical diagnosis.

& The present study aims at the use of Raman spectroscopy in the quantification of unsaturated fats in fat-containing foods, compared to the information available in the Nutritional Table, to obtain a non-destructive optical quantification of unsaturation. Raman spectra of edible oil, margarine, mayonnaise, hydrogenated fat, and butter were obtained with a near-infrared Raman spectrometer (830 nm). By analyzing selected bands in the regions of 1750, 1660, 1440, 1300, and 1260 cm À1 , the amount of total and unsaturated fat of samples of oil, margarine, and mayonnaise were correlated with the information displayed in the Nutritional Table. The amount of unsaturated trans fat in selected samples was correlated to the Raman shift of 1660 cm À1 . Dispersive Raman spectroscopy was shown to be effective in quantifying the unsaturated fats in oil, margarine, and mayonnaise, and trans fat in hydrogenated oils and butter.

This work aims the detection of the histopathologic alterations of in vitro human gastric mucosa using spectral informations from laser-induced fluorescence spectroscopy (LIFS) technique with excitation at 488 nm (argon laser). A total of 108 biopsies with endoscopic diagnosis of gastritis and gastric cancer were obtained at the antral gastric region, from 35 patients with dyspeptic digestive complaints. The biopsies were collected during the endoscopic examination. On each biopsy fragment the autofluorescence spectrum was collected in two random points, through a fiber-optic catheter coupled to the excitation laser. The fluorescence emission spectra collected by the fibers were directed to the spectrograph and detected by the CCD camera. The spectra were then separated in groups (N, normal; LI, light inflammation; MI, moderated inflammation; CA, adenocarcinoma), based on the histopathology. The ratio between the emission wavelengths 550 and 600 nm was used as a diagnostic parameter. Analysis of fluorescence spectra was able to identify the normal tissue from adenocarcinoma lesions with 100% of sensibility and specificity. The ratio intensities between inflammation (light and moderated), although presented significantly statistical differences when compared to the normal mucosa, do not furnish enough sensibility and specificity for use as an identification method due to high variations. LIFS, with excitation of 488 nm, could be used in the differentiation of normal tissue and neoplasic lesions, assisting a less invasive diagnosis.