RESUMEN

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Asunto(s)

RESUMEN

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Asunto(s)

RESUMEN

This paper presents a computational method based on non-classical logic dedicated to routing management and information stream control in communication networks. Paraconsistent logic (PL) was used to create an algorithmic structure whose main property is to accept contradiction. Moreover, a computational structure, the denominated paraconsistent data analyzer (PDAPAL2v), was constructed to perform routing management in communication networks. Direct comparisons of PDAPAL2v with a classical logic system that simulates routing conditions were made in the laboratory. In the conventional system, the paraconsistent algorithms were considered as binary logic gates, and in the tests, the same adjustment limits of PDAPAL2v were applied. Using a database with controlled insertion of noise, we obtained an efficacy of 97% in the detection of deteriorated packets with PDAPAL2v and 72% with the conventional simulation system. Functional tests were carried out, showing that PDAPAL2v is able to assess the conditions and degradation of links and perform the analysis and correlation of various inputs and variables, even if the signals have contradictory values. From practical tests in the laboratory, the proposed method represents a new way of managing and controlling communication network routes with good performance.

Asunto(s)

RESUMEN

Automotive engine lubricating oils are not only intended to reduce friction between parts, but also act on the cooling of motor components and protection of metals against corrosion. To improve its properties and efficiency, additives are added to the base oil for different goals. However, over time of use, external factors modify its properties, such as the engine operating temperature, the frictional force between parts, the mixture of this oil with fuel before burning and with combustion products, causing loss of their efficiency. This work aimed to evaluate, with Raman spectroscopy technique, the temperature-induced changes related to degradation of mineral, semi-synthetic and synthetic automotive lubricating oils. Samples being subject to periodic heating cycle were kept to average temperature of 133 ℃, considering 8 h per day, for six days, until complete 48 h of heating. By analyzing the Raman spectra, it was possible to identify common peaks between the three types of oils and changes caused by heating cycles. Principal components analysis showed that the synthetic oil degraded in less extent than the semi-synthetic one, and this one degraded less than the mineral oil. Spectral models to predict the heating time based on the spectral variations identified using principal components analysis and the regression done using partial least squares, using the heating time as independent variable and the spectral features as dependent variables, was able to predict the heating time for each of oil types with high correlation and prediction error (r > 0.97 and error <4.0 h) for both principal components analysis and partial least squares regression models. Raman technique was able to identify chemical changes resulting from the heating of lubricant oils and to correlate these changes with the heating time, thus becoming a technique of interest for the preventive maintenance area.

RESUMEN

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.

Asunto(s)

RESUMEN

High-level sport requires analysis of athletes' metabolic conditions in order to improve the training. Raman spectroscopy can be used to assess urinary composition advantageously when compared to conventional methods of urinalysis. In this work, Raman spectroscopy has been employed to detect creatine in urine of professional swimmers before and after training compared to sedentaries. It has been collected urine samples from five swimmers before and immediately after 150 min of swimming and submitted to Raman spectroscopy (830 nm excitation, 350 mW laser power, 20 s integration time) and compared to the urine from a control group (14 sedentary subjects). The Raman spectra of urine from four swimmers after training showed peaks related to creatine at 829, 915, 1049, and 1397 cm-1, besides peaks referred to urea, creatinine, ketone bodies, and phosphate. A spectral model estimated the concentration of creatine to be from 0.26 to 0.72 g/dL in the urine of these athletes. The presence of this metabolic biomarker in the urine of some swimmers suggests a metabolic profile influenced by the diet, supplementation, individual metabolism, and the self-response to the training. Raman spectroscopy allows a rapid and reliable detection of creatine excreted in the urine of swimming athletes, which may be used to adjust the nutrition/supplementation of each individual as well as the individual response and energy consumption depending on the type and duration of the training.

Asunto(s)

RESUMEN

The differences in the biochemistry of normal and cancerous tissue could be better exploited by Raman spectroscopy when the spectral information from normal tissue is subtracted from the abnormal tissues. In this study, we evaluated the use of the normal-subtracted spectra to evidence the biochemical differences in the pre-cancerous and cancerous skin tissues compared with normal skin, and to discriminate the groups with altered tissues with respect to the normal sites. Raman spectra from skin tissues [normal (Normal), benign (dermatitis-BEN), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and actinic keratosis (KER)] were obtained in vivo (Silveira et al., 2015, doi: https://doi.org/10.1002/lsm.22318) and used to develop the spectral model. The mean spectrum of the normal sites (circumjacent to each lesion) from each subject was calculated and subtracted from each individual spectrum of that particular subject independently of the group (Normal, BEN, BCC, SCC, KERAT). The mean spectra of each altered group and the mean spectra of the differences were firstly evaluated in terms of biochemical contribution or differentiation comparing the normal site. Then, the normal-subtracted spectra were submitted to discriminant models based on partial least squares and principal components regression (PLS-DA and PCR-DA), and the discrimination were compared with the model using non-subtracted spectra. Results showed that the peaks of nucleic acids, lipids (triolein) and proteins (elastin and collagens I, III, and IV) were significantly different in the lesions, higher for the pre- and neoplastic lesions compared with normal and benign. The PLS-DA showed that the groups could be discriminated with 90.3% accuracy when the mean-subtracted spectra were used, contrasting with 75.1% accuracy when the non-subtracted spectra were used. Also, when discriminating non-neoplastic tissue (Normal + BEN) from pre- and neoplastic sites (BCC + SCC + KERAT), the accuracy increases to 92.5% for the normal-subtracted compared with 85.3% for the non-subtracted. The subtraction of the mean normal spectrum from the subject obtained circumjacent to each lesion could significantly increase the diagnostic capability of the Raman-based discrimination algorithm.

Asunto(s)

RESUMEN

We present the use of Raman spectroscopy for determination of functional characteristics of insect repellents and sunscreens by identifying the active ingredients of these products applied topically to the skin. Commercial formulations of insect repellents and sunscreens (SPF 15 and 30) were obtained, and Raman spectra were obtained from the formulations and from volunteers' skins with topical applications of such products compared to controls. The results indicated that, for insect repellents, the peaks at 527 and 1003 cm - 1 were markers of the presence of the active ingredient diethyl toluamide in the skin, while for sunscreens, the peaks at 1177, 1288, and 1611 cm - 1, associated to octinoxate, benzophenone-3, and avobenzone, were markers of the presence of solar filters in the skin. The results suggested reliability in the use of Raman spectroscopy to identify the active ingredients of insect repellents and sunscreens topically applied on the skin; the applied methodology can be used to determine the functional characteristics of topical products with similar characteristics.

Asunto(s)

RESUMEN

Raman spectroscopy (RS) is a vibrational technique that is suitable for performing biochemical analyses in human tissues and fluids. This work has investigated the identification of biochemical markers due to physical performance in the urine of swimming athletes. This was achieved by means of the Raman features that were found before and after the swimming training compared to the sedentary control subjects. These particular biochemical marker identifications refer to and infer the physiological status of individuals. The urine samples (single stream) were collected before and after the training (velocity, middle distance and distance) of professional swimmers, as well as from sedentary subjects (control). The urine samples were submitted to RS (830 nm excitation, 350 mW, 400-1800 cm-1 spectral range, 4 cm-1 resolution) and the spectra after the training were compared to the spectra before training, and subsequently, to the control subjects. The principal component analysis (PCA) was employed in order to identify the biochemicals that were responsible for the spectral differences. The Raman features of the urine samples after training showed peaks that were related to common urine metabolites, such as urea and creatinine. PCA analysis also revealed Raman features that were attributed to other metabolites, such as creatine, ketone bodies, phosphate and nitrogenous compounds in the swimmers after training. RS was a rapid and reliable technique for the evaluation of urine metabolites that were related to the physical performance of high-level swimmers, which then allowed for an accurate assessment and a control of their physiological efficiencies.

Asunto(s)

RESUMEN

Raman spectroscopy is a rapid and non-destructive technique suitable for biological fluids analysis. In this work, dispersive Raman spectroscopy has been employed as a rapid and nondestructive technique to detect the metabolites in urine of physically active subjects before and after vigorous 30min pedaling or running compared to sedentary subjects. For so, urine samples from 9 subjects were obtained before and immediately after physical activities and submitted to Raman spectroscopy (830nm excitation, 250mW laser power, 20s integration time) and compared to urine from 5 sedentary subjects. The Raman spectra of urine from sedentary showed peaks related to urea, creatinine, ketone bodies, phosphate and other nitrogenous compounds. These metabolic biomarkers presented peaks with different intensities in the urine of physically active individuals after exercises compared to before, measured by the intensity of selected peaks the Raman spectra, which means different concentrations after training. These peaks presented different intensity values for each subject before physical activity, also behaving differently compared to the post-training: some subjects presented increase while others decrease the intensity. Raman spectroscopy may allow the development of a rapid and non-destructive test for metabolic evaluation of the physical training in active and trained subjects using urine samples, allowing nutrition adjustment with the sport's performance.

Asunto(s)

RESUMEN

OBJECTIVE: One of the limiting factors of employing laser radiation on dental therapies is the potential of causing thermal injuries to pulp tissues. The purpose of this study was to evaluate intra-chamber temperature increase on extracted human teeth exposed to 670 nm wavelength InGaAlP laser diode radiation. MATERIALS AND METHODS: In vitro intra-chamber temperature measurements of 12 standardized human teeth (incisors, canines, premolars, and molars; n = 3) were taken both before and after laser radiation. A type-K thermocouple fast-response thermocouple wire with a 0.5 mm diameter probe was inserted into the tooth pulp chamber (ICEL-Manaus-brand). The laser device Lasotronic-brand InGaAlP laser diode was used to irradiate tooth enamel, perpendicularly to the external surface for 30 sec, with power of levels of 340, 272, 204, 136, and 68 mW. The measurements were taken at three time points: 0, 30 sec, and 3 min after the laser irradiation. Measurements were repeated 24 h after removal and reinsertion of the probe in the pulp chamber. The temperature gradient (ΔT in °C) was calculated (ΔT = final temperature-initial temperature) for each group. Data of ΔT were statistically analyzed by two-way analysis of variance (ANOVA) at the 95% confidence level and compared by Tukey post hoc test (α = 0.05). RESULTS: ANOVA showed statistically significant differences to the interaction of factors (p < 0.05). The highest ΔT values obtained were observed to incisors with 340 mW, 272 mW; 204 mW of power (respectively 4.7°C, 4.2°C, and 3.1°C); and canines presented the lowest ΔT (0.8°C-0.3°C) with no influence of power output. CONCLUSIONS: Since the thermal increase was observed in this study, especially in incisors, attention should be paid to avoid pulpal damage.

Asunto(s)

RESUMEN

Raman spectroscopy has been employed in the quantitative analysis of biochemical components in human serum. This study aimed to develop a spectral model to estimate the concentration of glucose and lipid fractions in human serum, thus evaluating the feasibility of Raman spectroscopy technique for diagnostic purposes. A total of 44 samples of blood serum were collected from volunteers submitted to routine blood biochemical assay analysis. The biochemical concentrations of glucose, triglycerides, cholesterol, and high-density and low-density lipoproteins (HDL and LDL) were obtained by colorimetric method. Serum samples (200 µL) were submitted to Raman spectroscopy (830 nm, 250 mW, 50-s accumulation). The spectra of sera present peaks related to the main constituents, particularly proteins and lipids. A quantitative model based on partial least squares (PLS) regression has been developed to estimate the concentration of these compounds, taking the biochemical concentrations assayed by the colorimetric method as sample's actual concentrations. The PLS model based on leave-one-out cross-validation approach estimated the concentration of triglycerides and cholesterol with r = 0.98 and 0.96, and root mean square error of 35.4 and 15.9 mg/dL, respectively. For the other biochemicals, the r was ranging from 0.75 to 0.86. These results evidenced the possibility of performing biochemical assay in blood serum samples by Raman spectroscopy and PLS regression and may be employed as a means of diagnosis in routine clinical analysis.

Asunto(s)

RESUMEN

This work aimed the assessment of biochemical changes induced by laser or LED irradiation during mineralization of a bone defect in an animal model using a spectral model based on Raman spectroscopy. Six groups were studied: clot, laser (λ = 780 nm; 70 mW), LED (λ = 850 ± 10 nm; 150 mW), biomaterial (biphasic synthetic micro-granular hydroxyapatite (HA) + ß-tricalcium phosphate), biomaterial + laser, and biomaterial + LED. When indicated, defects were further irradiated at a 48-h interval during 2 weeks (20 J/cm2 per session). At the 15th and 30th days, femurs were dissected and spectra of the defects were collected. Raman spectra were submitted to a model to estimate the relative amount of collagen, phosphate HA, and carbonate HA by using the spectra of pure collagen and biomaterials composed of phosphate and carbonate HA, respectively. The use of the biomaterial associated to phototherapy did not change the collagen formation at both 15 and 30 days. The amount of carbonate HA was not different in all groups at the 15th day. However, at the 30th day, there was a significant difference (ANOVA, p = 0.01), with lower carbonate HA for the group biomaterial + LED compared to biomaterial (p < 0.05). The phosphate HA was higher in the groups that received biomaterial grafts at the 15th day compared to clot (significant for the biomaterial; p < 0.01). At the 30th day, the phosphate HA was higher for the group biomaterial + laser, while this was lower for all the other groups. These results indicated that the use of laser phototherapy improved the repair of bone defects grafted with the biomaterial by increasing the deposition of phosphate HA.

Asunto(s)

RESUMEN

Paraconsistent logic (PL) is a type of non-classical logic that accepts contradiction as a fundamental concept and has produced valuable results in the analysis of uncertainties. In this work, algorithms based on a type of PL-paraconsistent annotated logic of two values (PAL2v)-are interconnected into a network of paraconsistent analysis (PANnet). PANnet was applied to a dataset comprising 146 Raman spectra of skin tissue biopsy fragments of which 30 spectra were determined to represent normal skin tissue (N), 96 were determined to represent tissue with basal cell carcinoma, and 19 were determined to be tissue with melanoma (MEL). In this database, paraconsistent analysis was able to correctly discriminate 136 out of a total of 145 fragments, obtaining a 93.793 % correct diagnostic accuracy. The application of PAL2v in the analysis of Raman spectroscopy signals produces better discrimination of cells than conventional statistical processes and presents a good graphical overview through its associated lattice structure. The technique of PAL2v-based data processing can be fundamental in the development of a computational tool dedicated to support the diagnosis of skin cancer using Raman spectroscopy.

Asunto(s)

RESUMEN

Identification of cocaine and subsequent quantification immediately after seizure are problems for the police in developing countries such as Brazil. This work proposes a comparison between the Raman and FT-IR techniques as methods to identify cocaine, the adulterants used to increase volume, and possible degradation products in samples seized by the police. Near-infrared Raman spectra (785 nm excitation, 10 sec exposure time) and FT-IR-ATR spectra were obtained from different samples of street cocaine and some substances commonly used as adulterants. Freebase powder, hydrochloride powder, and crack rock can be distinguished by both Raman and FT-IR spectroscopies, revealing differences in their chemical structure. Most of the samples showed characteristic peaks of degradation products such as benzoylecgonine and benzoic acid, and some presented evidence of adulteration with aluminum sulfate and sodium carbonate. Raman spectroscopy is better than FT-IR for identifying benzoic acid and inorganic adulterants in cocaine.

Asunto(s)

RESUMEN

Osteoporosis is a disease with great importance in current public health due to the associated risk of fracture; therefore, a rapid and accurate diagnosis becomes increasingly important. Recent literature has described a possible relationship between the changes in the organic phase of bone and the changes in nail keratin measured through Raman spectroscopy, aiming at the development of a standard for measuring bone quality and fracture risk both rapid and accurately. This work evaluated the correlation between the bone mineral density (BMD) scores of women with and without osteoporotic disease with the changes in the Raman spectra of the nail keratin, by assessing the intensity of the peak at 510 cm(-1) (S-S bridge) and the scores of principal component analysis (PCA), correlated with the values of BMD measured at the lumbar and hip. Raman spectra of ex vivo fingernails of 213 women were obtained by means of a dispersive Raman spectrometer (830 nm, 300 mW, in the spectral range between 400 and 1,800 cm(-1)). Peak intensities at ∼510 cm(-1) (assigned to the keratin S-S bridge) were measured, and the scores of first principal component loading vectors were calculated. Results showed no differences in the mean Raman spectra of nails of groups with and without osteoporosis. No correlation was found between the BMD scores and both the intensities of the 510 cm(-1) peak and the scores of the first four principal component vectors. Results suggest that BMD and fracture risk could not be assessed by the nail keratin features.

Asunto(s)

RESUMEN

Raman spectroscopy has been proposed for detecting biochemical alterations in prostate cancer (PrCa) compared to benign prostate tissue in in vitro fragments from surgery for diagnostic purposes. Freezer-stored fragments of human prostate tissues were unfrozen and submitted to Raman spectroscopy with a dispersive spectrometer (830-nm and 200-mW laser parameters, 30-s exposure time). Fragments were fixed and submitted to histopathology to grade PrCa according to Gleason score. A total of 160 spectra were taken from 32 samples (16 benign tissues and 16 PrCa tissues). The relative concentrations of selected biochemicals were estimated using a least-squares fitting model applied to the spectra of pure compounds and the tissue spectrum. A discrimination model was developed employing the most statistically relevant compounds with capability of separating PrCa from benign tissues. The fitting model revealed that actin, hemoglobin, elastin, phosphatidylcholine, and water are the most important biochemicals to discriminate prostate depending on the Gleason score. A discrimination based on Euclidean distance using the relative concentrations of phosphatidylcholine and water showed the higher accuracy of 74 % to discriminate PrCa from benign tissue. Raman spectroscopy is an analytical technique with possibility for identifying biochemical constitution of prostate and could be used for diagnostic purposes.

Asunto(s)

RESUMEN

BACKGROUND AND OBJECTIVE: Because of their aggressiveness, brain tumors can lead to death within a short time after diagnosis. Optical techniques such as Raman spectroscopy may be a technique of choice for in situ tumor diagnosis, with potential use in determining tumor margins during surgery because of its ability to identify biochemical changes between normal and tumor brain tissues quickly and without tissue destruction. METHODS: In this work, fragments of brain tumor (glioblastoma, medulloblastoma, and meningioma) and normal tissues (cerebellum and meninges) were obtained from excisional intracranial surgery and from autopsies, respectively. Raman spectra (dispersive spectrometer, 830 nm 350 mW, 50 sec accumulation, total 172 spectra) were obtained in vitro on these fragments. It has been developed as a model to discriminate between the spectra of normal tissue and tumors based on the scores of principal component analysis (PCA) and Euclidean distance. RESULTS: ANOVA indicated that the scores of PC2 and PC3 show differences between normal and tumor groups (p<0.05) which could be employed in a discrimination model. PC2 was able to discriminate glioblastoma from the other tumors and from normal tissues, showing featured peaks of lipids/phospholipids and cholesterol. PC3 discriminated medulloblastoma and meningioma from normal tissues, with the most intense spectral features of proteins. PC3 also discriminated normal tissues (meninges and cerebellum) by the presence of cholesterol peaks. Results indicated a sensitivity and specificity of 97.4% and 100%, respectively, for this in vitro diagnosis of brain tumor. CONCLUSIONS: The PCA/Euclidean distance model was effective in differentiating tumor from normal spectra, regardless of the type of tissue (meninges or cerebellum).

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)