Prediction of melanin content of Fonsecaea pedrosoi using Fourier transform infrared spectroscopy (FTIR) and chemometrics.

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Hyperspectral imaging in forensic science: An overview of major application areas.

Analysis of evidence is a challenge. Crime scene materials are complex, diverse, sometimes of an unknown nature. Forensic science provides the most critical applications for their examination. Chemical tests, analytical methods, and techniques to process the evidence must be carefully selected by the forensic scientist. Ideally, it may be interpreted, analyzed, and judged in the original context of the crime scene. In this sense, hyperspectral imaging (HSI) has been employed as an analytical tool that maintains the integrity of the samples/objects for multiple and sequential analysis and for counter-proof exams. This paper is an overview of forensic science trends for the application of HSI techniques in the last ten years (2011-2021). The examination of documents was the main area of exploration, followed by bloodstain analysis aging process; trace analysis of explosives and gunshot residue. Chemometric tools were also addressed since they are crucial to obtain the most important information from the samples. There are great challenges in applying HSI in forensic science, but there have been clear technological and scientific advances, and a solid foundation has been built for the use of HSI in real-life cases.

Overview of cocaine identification by vibrational spectroscopy and chemometrics.

The use of non-destructive forensic methods for cocaine identification is of outstanding importance, given the amount of samples seized. Techniques such as ATR-FTIR, Raman, and NIR spectroscopy have become alternatives to circumvent this problem, as they allow fast, cheap analysis, and enable the reanalysis of samples. When combined with chemometrics, these spectroscopic methods can be used to determine and quantify cocaine samples, meaning that the limitations of existing techniques can be overcome. This review article covers spectroscopic techniques for identifying cocaine in different forms and matrices, such as food and textiles, which are materials used for smuggling. The chemometric identification of cocaine in oral fluid and water is also discussed. In addition, vibrational spectroscopy techniques using portable equipment are described. This work seeks to evaluate the main chemometric applications of spectroscopic data and to find new perspectives on the identification of cocaine using chemometrics.

Grape juice reduces the effects of amyloid ß aggregation phenotype and extends the longevity in Caenorhabditis elegans.

OBJECTIVES: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the presence of aggregated amyloid-ß (Aß) peptides. Several natural compounds have been proposed against this disease and grape products are among these. However, little is known about grape juice potential. Transgenic Caenorhabditis elegans (C. elegans) strains that express human Aß have been used as an in vivo model for AD. METHODS: In this study, we have exposed CL2006 worms to nine different juices obtained from different cultivars. RESULTS: Cora, Bordo, Isabel, Isabel Precoce, BRS-Magna, BRS-Rubea and BRS-Violeta juices improved the behavioral phenotype (paralysis) that is caused by Aß aggregation in the transgenic animals at the concentrations tested and no toxic effects were found. Some juices were also able to increase the worm's lifespan. We could not attribute lifespan increase and paralysis reduction with any specific compound found in the phytochemical analysis. DISCUSSION: Our data indicate that the rich constitution of the juices is responsible for attenuating the phenotype caused by Aß aggregation in C. elegans.

Curve fitting and linearization of UV-Vis spectrophotometric measurements to estimate yeast in inoculum preparation.

The counting of microorganisms is essential in the area of microbiology, especially in the preparation of inoculum. The main methods for obtaining inoculum are McFarland standard, Neubauer chamber, and plate count. However, the visual comparison is subjective while the counting in the chamber and the plating are technically time-consuming. For this reason, our article aims to correlate the absorbance of the spectrophotometer in the visible ultraviolet region (UV-Vis) with the cell counting in the Neubauer chamber. This study used suspensions of Candida spp. measured at three wavelengths (530, 600, and 700 nm) and counting in a Neubauer chamber. In the next step, curves were adjusted with different polynomials using absorbances and counts. The two best polynomial curve fittings were the Saturation Growth Rate (SGR) and Morgan-Mercer-Flodin (MMF). Therefore, the polynomials were linearized and a direct correlation between absorbance and the number of cells was made. The proposed method proved to be more accurate (5 ± 0.5 × 106) than the comparison with the McFarland turbidity (1-5 x 106) and more practical than plate counting. Predicting the number of cells by UV-Vis is an alternative that reduces the uncertainty of the cell count interval for inoculum preparation.

Rapid classification of chromoblastomycosis agents genera by infrared spectroscopy and chemometrics supervised by sequencing of rDNA regions.

Chromoblastomycosis (CBM) is a skin and subcutaneous infection caused by species of seven fungal genera. Identification of CBM species is performed by DNA sequencing of one or more genes, which becomes a time-consuming work. Fourier Transform Infrared Spectroscopy (FTIR) has been used for the identification of other microorganisms, however, only one CBM genus was evaluated by FTIR analysis to date. Therefore, the study is aimed to differentiate the CBM agents for identification at genera level using FTIR supervised by Internal Transcribed Spacer (ITS) rDNA region. Seventy-seven isolates of the main five CBM genera were prepared for Attenuated Total Reflection FTIR (ATR-FTIR) with a new methodology using slices of dry fungus in glass fixing-modeling proposed in this study. The algorithm Hierarchical Cluster Analysis (HCA) was used to analyze the differences and similarities between species through the spectra. Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) allowed to correctly classify all samples of five CBM genera. The ATR-FTIR/OPLS-DA models highlighted important contributions of regions attributed to NH and OH stretching, amide I of proteins, polysaccharides bands and fingerprint region for the complete differentiation of the genera investigated. Thus, FTIR can be a fast and inexpensive alternative for identification of CBM agents.

Use of digital images to count colonies of biodiesel deteriogenic microorganisms.

This work presents a novel, robust procedure for the semi-automated counting of colony-forming units of Bacillus pumilus (a bacterium) and Meyerozyma guilliermondii (a yeast) both isolated from diesel oil. The counting is performed from digital images of Petri dishes containing the samples by a developed Python code, and the images are acquired from a low-cost scanning apparatus. The counting algorithm is based on the similar morphological characteristics of the bacterium and the yeast colonies. It was compared with classical counting methodology, and the results showed calibration and validation curves with a coefficient of determination (R2) of 0.99 and 0.98, respectively. The developed methodology is a valuable alternative to estimate the microbial contamination of biofuels.

Comparison between counterfeit and authentic medicines: A novel approach using differential scanning calorimetry and hierarchical cluster analysis.

Erectile dysfunction medicines such as Cialis and Viagra are very popular worldwide and are between the most prevalent counterfeit medicines in Brazil. A range of analytical methods has been used to analyze Cialis and Viagra, such as ATR-FTIR, GCMS and UPLC-MS. Until now, there are no data available of DSC methods for analysis of counterfeit medicines of Cialis and Viagra. DSC is a thermal analysis that provides useful information of physico-chemical events, and however is almost not used for forensic purposes. In this study, thermal analysis of 25 counterfeit Viagra and Cialis seized by Brazilian Federal Police were performed by DSC and compared to their authentic medicines and analytical standards, along with chemometric tools. Authentic samples of Viagra displayed a similar thermal profile with the API, while Cialis were different with additional endothermic peaks, that could be related to excipients interference. Thermograms of Viagra counterfeit samples were similar to authentic samples, while Cialis showed an enlargement and displacement of endothermic peaks. Also, some Cialis counterfeit samples showed melting peaks attributed to sildenafil, the API of Viagra, instead tadalafil, confirming previous results obtained by UPLC-MS. Multivariate analysis with application of Hierarchical Cluster Analysis classified different groups of samples, including a cluster with counterfeit Cialis and Viagra, indicating the use of same API for both counterfeit medicines and possibly the same illicit production; and a cluster with authentic Viagra and counterfeit Cialis, confirming the addition of sildenafil instead tadalafil to Cialis counterfeit samples. Here for the first time we described the use of DSC for chemical profiling of Cialis and Viagra and showed that even when applied to a small group of samples, DSC along with chemometric tools can be considered as a good auxiliary method in forensic casework samples. DSC provided useful data to perform the identification of counterfeit and authentic medicines, with low cost and a simple method.

An LC-ESI-MS/MS method for residues of fluoroquinolones, sulfonamides, tetracyclines and trimethoprim in feedingstuffs: validation and surveillance.

An increasing concern about food safety has been observed over the years. The presence of drugs residues in food is one of the major subjects of research in food safety. Feedingstuffs can be responsible for carryover into the food chain of residues of several drugs. This paper describes the development, validation and application of a fast and simple method for analysis of 24 antibiotic residues in feedingstuffs for cattle, pigs and poultry. Analytes include compounds from different antimicrobials classes, such as sulfonamides (sulfadiazine, sulfamethazine, sulfamethoxazole, sulfaquinoxaline, sulfachlorpyridazine, sulfadoxine, sulfadimethoxine, sulfizoxazole, sulfamerazine and sulfathiazole), fluoroquinolones (ciprofloxacin, enrofloxacin, norfloxacin, danofloxacin, difloxacin, sarafloxacin, flumequine, nalidixic acid and oxolinic acid), tetracyclines (tetracycline, doxycycline, oxytetracycline and chlortetracycline) and trimethoprim. Samples were extracted with methanol:water (70:30) 0.1% formic acid, followed by clean-up steps using centrifugation, low-temperature purification (LTP) and ultracentrifugation. Instrumental analysis was performed using liquid chromatography coupled to tandem mass spectrometry. Chromatographic separation was achieved using a C18 column and a mobile phase composed of acetonitrile and water, both with 0.1% formic acid. Validation parameters such as limit of detection (LOD), limit of quantification (LOQ), selectivity, linearity, accuracy, precision, decision limit (CCα) and detection capability (CCß) were determined and meet the adopted criteria. LOD and LOQ were set to 30 and 75 µg kg-1, respectively. Inter-day precision were in the range from 4.0 to 11.1%, and linearity provides values of r2 above 0.95 for all analytes. The optimised method was applied to the analysis of more than 1500 real samples within the period 2012-2017. Non-compliant results were discussed and classified in terms of analytes, feed types and target species. Multivariate analysis of the data was performed using principal component analysis.

Pseudallescheria boydii and Meyerozyma guilliermondii: behavior of deteriogenic fungi during simulated storage of diesel, biodiesel, and B10 blend in Brazil.

Due to their renewable and sustainable nature, biodiesel blends boost studies predicting their stability during storage. Besides chemical degradation, biodiesel is more susceptible to biodegradation due to its raw composition. The aim of this work was to evaluate the deteriogenic potential (growth and degradation) of Pseudallescheria boydii and Meyerozyma guilliermondii in degrading pure diesel (B0), pure biodiesel (B100), and a B10 blend in mineral medium during storage. The biodeterioration susceptibility at different fuel ratios and in BH minimal mineral medium were evaluated. The biomass measurements of P. boydii during 45 days indicated higher biomass production in the B10 blend. The growth curve of M. guilliermondii showed similar growth in B10 and B100. Although there was no significant production of biosurfactant, lipase production was detected in the tributyrin agar medium of both microorganisms. The main compounds identified in the aqueous phase by GC-MS were alcohols, esters, acids, sulfur, ketones, and phenols. The results showed that P. boydii grew at the expense of fuels, degrading biodiesel esters, and diesel hydrocarbons. M. guilliermondii grew in B100 and B10; however, degradation was not detected.

Comparison of Cocaine/Crack Biomarkers Concentrations in Oral Fluid, Urine and Plasma Simultaneously Collected From Drug Users.

The use of oral fluid (OF) as an alternative specimen for drug analysis has become very popular in forensic toxicology. Many clinical studies have evaluated the correlations between concentrations of cocaine and its metabolites in OF and other matrices, but results have shown high variability. In addition, there are no data available regarding the correlations between biomarkers of crack-cocaine use in different matrices. This study evaluated the relationship between concentrations of cocaine/crack-cocaine biomarkers in OF, urine and plasma samples collected from cocaine users. All samples were analyzed for the presence of cocaine (COC), benzoylecgonine (BZE) and anhydroecgonine (AEC) by a validated liquid chromatography-mass spectrometry method. Median COC, BZE and AEC concentrations ranged from 4.20 to 33.26 ng/mL, from 13.03 to 3,615.86 ng/mL and from 7.40 to 1,892.5 ng/mL across matrices, respectively. The relationship between drug concentrations in OF versus plasma (OF/P) and OF versus urine (OF/U) was evaluated by their coefficients of determination (R2). Least-squares regression analyses demonstrated significant correlations between OF/P and OF/U for cocaine and BE (P < 0.05), with R2 = 0.17, 0.07 for cocaine and R2 = 0.73, 0.45 for BE, respectively. The correlation coefficients (r) found for BZE, COC and AEC in OF/P and OF/U were 0.85 and 0.67 (P < 0.05); 0.41 and 0.26 (P < 0.05); and 0.30 and -0.37 (P > 0.05), respectively. Many factors contribute to the variability of drug correlation ratios in studies involving random samples, including uncertainty about the time of last administration and dosage. Overall, we found significant R2 values for COC and BZE in OF/P and OF/U, but not for AEC. Despite the good correlations found in some cases, especially for BZE, the large variation in drug concentrations seen in this work suggests that OF concentrations should not be used to estimate concentrations of COC, BZE or AEC in plasma and/or urine.

Near infrared spectroscopy combined with chemometrics for growth stage classification of cannabis cultivated in a greenhouse from seized seeds.

Cannabis sativa L. (cannabis, Cannabaceae), popularly called marijuana, is one of the oldest plants known to man and it is the illicit drug most used worldwide. It also has been the subject of increasing discussions from the scientific and political points of view due to its medicinal properties. In recent years in Brazil, the form of cannabis drug trafficking has been changing and the Brazilian Federal Police has exponentially increased the number of seizures of cannabis seeds sent by the mail. This new form of trafficking encouraged the study of cannabis seeds seized germinated in a greenhouse through NIR spectroscopy combined with chemometrics. The plants were cultivated in a homemade greenhouse under controlled conditions. In three different growth periods (5.5weeks, 7.5weeks and 10weeks), they were harvested, dried, ground and directly analyzed. The iPCA was used to select the best NIR spectral range (4000-4375cm-1) in order to develop unsupervised and supervised methods. The PCA and HCA showed a good separation between the three groups of cannabis samples at different growth stages. The PLS-DA and SVM-DA classified the samples with good results in terms of sensitivity and specificity. The sensitivity and specificity for SVM-DA classification were equal to unity. This separation may be due to the correlation of cannabinoids and volatile compounds concentration during the growth of the cannabis plant. Therefore, the growth stage of cannabis can be predicted by NIR spectroscopy and chemometric tools in the early stages of indoor cannabis cultivation.

Seized cannabis seeds cultivated in greenhouse: A chemical study by gas chromatography-mass spectrometry and chemometric analysis.

Cannabis sativa L. is cultivated in most regions of the world. In 2013, the Brazilian Federal Police (BFP) reported 220 tons of marijuana seized and about 800,000 cannabis plants eradicated. Efforts to eradicate cannabis production may have contributed to the development of a new form of international drug trafficking in Brazil: the sending of cannabis seeds in small amounts to urban centers by logistics postal. This new and increasing panorama of cannabis trafficking in Brazil, encouraged the chemical study of cannabis seeds cultivated in greenhouses by gas-chromatography coupled with mass spectrometry (GC-MS) associated with exploratory and discriminant analysis. Fifty cannabis seeds of different varieties and brands, seized by the BFP were cultivated under predefined conditions for a period of 4.5 weeks, 5.5 weeks, 7.5 weeks, 10 weeks and 12 weeks. Aerial parts were analyzed and cannabigerol, cannabinol, cannabidiol, cannabichromene Δ9-tetrahydrocannabinol (THC) and other terpenoids were detected. The chromatographic chemical profiles of the samples were significantly different, probably due to different variety, light exposition and age. THC content increased with the age of the plant, however, for other cannabinoids, this correlation was not observed. The chromatograms were plotted in a matrix with 50 rows (samples) and 3886 columns (abundance in a retention time) and submitted to PCA, HCA and PLS-DA after pretreatment (normalization, first derivative and autoscale). The PCA and HCA showed age separation between samples however it was not possible to verify the separation by varieties and brands. The PLS-DA classification provides a satisfactory prediction of plant age.

Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis.

Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties.

Toxic and nutrient elements in yerba mate (Ilex paraguariensis).

Toxic and nutrient elements were investigated in yerba mate (Ilex paraguariensis) from South America. Fifty-four brands of commercialised yerba mate from Argentina, Brazil, Paraguay and Uruguay were analysed for Al, Ba, Ca, Cu, Fe, K, Mg, Mn, P, Sr, and Zn, using inductively coupled plasma optical emission spectrometry (ICP-OES), and Li, Be, Ti, V, Cr, Ni, Co, As, Se, Rb, Mo, Ag, Cd, Sb, La, Ce, Pb, Bi and U using inductively coupled plasma mass spectrometry (ICP-MS). Antimony, Se, Ag and Bi were not detected in any sample whereas the limits of detection (LODs) of these elements were 0.19, 0.40, 0.003 and 0.001 µg g(-1), respectively. Analysis of variance (ANOVA) revealed that the concentrations of Cd, Ti, Ni, As, Mo, U, Li and Be in yerba mate were not statistically different with regard to the country of origin, while those of the other investigated elements differed.

Multivariate optimization for extraction of pyrethroids in milk and validation for GC-ECD and CG-MS/MS analysis.

A simple and inexpensive method based on solvent extraction followed by low temperature clean-up was applied for determination of seven pyrethroids residues in bovine raw milk using gas chromatography coupled to tandem mass spectrometry (GC-MS/MS) and gas chromatography with electron-capture detector (GC-ECD). Sample extraction procedure was established through the evaluation of seven different extraction protocols, evaluated in terms of analyte recovery and cleanup efficiency. Sample preparation optimization was based on Doehlert design using fifteen runs with three different variables. Response surface methodologies and polynomial analysis were used to define the best extraction conditions. Method validation was carried out based on SANCO guide parameters and assessed by multivariate analysis. Method performance was considered satisfactory since mean recoveries were between 87% and 101% for three distinct concentrations. Accuracy and precision were lower than ±20%, and led to no significant differences (p < 0.05) between results obtained by GC-ECD and GC-MS/MS techniques. The method has been applied to routine analysis for determination of pyrethroid residues in bovine raw milk in the Brazilian National Residue Control Plan since 2013, in which a total of 50 samples were analyzed.

Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources--canola, cotton, corn, palm, sunflower and soybeans--were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

Simultaneous diffuse reflectance infrared determination of clavulanic acid and amoxicillin using multivariate calibration techniques.

A method for simultaneous determination of clavulanic acid (CA) and amoxicillin (AMO) in commercial tablets was developed using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and multivariate calibration. Twenty-five samples (10 commercial and 15 synthetic) were used as a calibration set and 15 samples (10 commercial and 5 synthetic) were used for a prediction set. Calibration models were developed using partial least squares (PLS), interval PLS (iPLS), and synergy interval PLS (siPLS) algorithms. The best algorithm for CA determination was siPLS model with spectra divided in 30 intervals and combinations of 2 intervals. This model showed a root mean square error of prediction (RMSEP) of 5.1 mg g(-1). For AMO determination, the best siPLS model was obtained with spectra divided in 10 intervals and combinations of 4 intervals. This model showed a RMSEP of 22.3 mg g(-1). The proposed method was considered as a suitable for the simultaneous determination of CA and AMO in commercial pharmaceuticals products.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods.

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm(-1)). This model produced a RMSECV of 400 mg kg(-1) S and RMSEP of 420 mg kg(-1) S, showing a correlation coefficient of 0.990.

Lipolytic activity of chromoblastomycosis agents measured by infrared spectroscopy and chemometric methods.

The lipase activity of nine strains of six chromoblastomycosis agents (Fonsecaea pedrosoi, Phialophora verrucosa, Cladophialophora bantianum, Cladophialophora carrionii, Rhinocladiela aquaspersa and Exophiala jeanselmei) grown on solid medium was investigated using Fourier transform infrared spectroscopy and hierarchical clustering analysis. The data was quantified by p-nitrophenyl palmitate assay using partial least squares (PLS) regression. These methods allowed the correlation of six genera and species within the 1230-1650 and 2800+3000 cm(-1) spectral ranges among strains grown for 14 days from their respective lipolytic activity with RMSEV=0.048 and R2val=0.95 and ten latent variables. The lipolytic activity also was predicted using PLS models with 1230-1650 and 2800-3000 cm(-1) and 900-1450 cm(-1) spectral ranges for strains grown for 21 days. The separate analysis of F. pedrosoi strains yielded a prediction model for biomass at 21 days with RMSEV=0.065 and R2val=0.95 with eight latent variables using (1100-1300)+(1330-1460)+(1550-1650) cm(-1) spectral regions The best model obtained with the clustering of P. verrucosa, C. bantianum, C. carrionii, R. aquaspersa and E. jeanselmei strains was constructed with the same spectral ranges, but with RMSEV=0.074 and R2val=0.94 and ten latent variables. Infrared spectroscopy is suitable for the quantitation of extracellular lipase activity linked to the biomass of chromoblastomycosis agents.