14 resultados para Near-infrared spectroscopy
em Universidade Federal do Rio Grande do Norte(UFRN)
Resumo:
This thesis aimed to assess the increase in solubility of simvastatin (SINV) with solid dispersions using techniques such as kneading (MA), co-solvent evaporation (ES), melting carrier (FC) and spray dryer (SD). Soluplus (SOL), PEG 6000 (PEG), PVP K-30 (PVP) e sodium lauryl sulphate (LSS) were used as carriers. The solid dispersions containing PEG [PEG-2(SD)], Soluplus [SOL-2(MA)] and sodium lauryl sulphate [LSS-2(ES)] were presented with a greater increase in solubility (5.02, 5.60 and 5.43 times respectively); analyses by ANOVA between the three groups did not present significant difference (p<0.05). In the phase solubility study, the calculation of the Gibbs free energy (ΔG) revealed that the spontaneity of solubilisation of SINV occurred in the order SOL>PEG >PVP 75%>LSS, always 80%. The phase diagrams of PEG and LSS presented solubilization stoichiometry of type 1:1 (type AL). The diagrams with PVP and SOL tend to 1:2 stoichiometry (type AL + AP). The stability coefficients (Ks) of the phase diagrams revealed that the most stable reactions occurred with LSS and PVP. The solid dispersions were characterized by Fourier transform infrared (FTIR), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), particle size distribution (PSD), near-infrared spectroscopy imaging (NIR-CI) and X-ray diffraction of the powder using the Topas software (PDRX-TOPAS). The solid dispersion PEG-2(SD) presented the greatest homogeneity and the lowest degree of crystallinity (18.2%). The accelerated stability study revealed that the solid dispersions are less stable than SINV, with PEG-2(SD) being the least stable, confirmed by FTIR and DSC. The analyses by PDRX-TOPAS revealed the amorphous character of the dispersions and the mechanism of increasing solubility
Resumo:
A chemical process optimization and control is strongly correlated with the quantity of information can be obtained from the system. In biotechnological processes, where the transforming agent is a cell, many variables can interfere in the process, leading to changes in the microorganism metabolism and affecting the quantity and quality of final product. Therefore, the continuously monitoring of the variables that interfere in the bioprocess, is crucial to be able to act on certain variables of the system, keeping it under desirable operational conditions and control. In general, during a fermentation process, the analysis of important parameters such as substrate, product and cells concentration, is done off-line, requiring sampling, pretreatment and analytical procedures. Therefore, this steps require a significant run time and the use of high purity chemical reagents to be done. In order to implement a real time monitoring system for a benchtop bioreactor, these study was conducted in two steps: (i) The development of a software that presents a communication interface between bioreactor and computer based on data acquisition and process variables data recording, that are pH, temperature, dissolved oxygen, level, foam level, agitation frequency and the input setpoints of the operational parameters of the bioreactor control unit; (ii) The development of an analytical method using near-infrared spectroscopy (NIRS) in order to enable substrate, products and cells concentration monitoring during a fermentation process for ethanol production using the yeast Saccharomyces cerevisiae. Three fermentation runs were conducted (F1, F2 and F3) that were monitored by NIRS and subsequent sampling for analytical characterization. The data obtained were used for calibration and validation, where pre-treatments combined or not with smoothing filters were applied to spectrum data. The most satisfactory results were obtained when the calibration models were constructed from real samples of culture medium removed from the fermentation assays F1, F2 and F3, showing that the analytical method based on NIRS can be used as a fast and effective method to quantify cells, substrate and products concentration what enables the implementation of insitu real time monitoring of fermentation processes
Resumo:
In this work calibration models were constructed to determine the content of total lipids and moisture in powdered milk samples. For this, used the near-infrared spectroscopy by diffuse reflectance, combined with multivariate calibration. Initially, the spectral data were submitted to correction of multiplicative light scattering (MSC) and Savitzsky-Golay smoothing. Then, the samples were divided into subgroups by application of hierarchical clustering analysis of the classes (HCA) and Ward Linkage criterion. Thus, it became possible to build regression models by partial least squares (PLS) that allowed the calibration and prediction of the content total lipid and moisture, based on the values obtained by the reference methods of Soxhlet and 105 ° C, respectively . Therefore, conclude that the NIR had a good performance for the quantification of samples of powdered milk, mainly by minimizing the analysis time, not destruction of the samples and not waste. Prediction models for determination of total lipids correlated (R) of 0.9955, RMSEP of 0.8952, therefore the average error between the Soxhlet and NIR was ± 0.70%, while the model prediction to content moisture correlated (R) of 0.9184, RMSEP, 0.3778 and error of ± 0.76%
Resumo:
This work is combined with the potential of the technique of near infrared spectroscopy - NIR and chemometrics order to determine the content of diclofenac tablets, without destruction of the sample, to which was used as the reference method, ultraviolet spectroscopy, which is one of the official methods. In the construction of multivariate calibration models has been studied several types of pre-processing of NIR spectral data, such as scatter correction, first derivative. The regression method used in the construction of calibration models is the PLS (partial least squares) using NIR spectroscopic data of a set of 90 tablets were divided into two sets (calibration and prediction). 54 were used in the calibration samples and the prediction was used 36, since the calibration method used was crossvalidation method (full cross-validation) that eliminates the need for a validation set. The evaluation of the models was done by observing the values of correlation coefficient R 2 and RMSEC mean square error (calibration error) and RMSEP (forecast error). As the forecast values estimated for the remaining 36 samples, which the results were consistent with the values obtained by UV spectroscopy
Resumo:
In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma
Resumo:
This paper investigates the potential of near infrared spectroscopy (NIR) for forensic analysis of human hair samples in order to differentiate smokers from nonsmokers, using chemometric modeling as an analytical tool. We obtained a total of 19 hair samples, 9 smokers and 10 nonsmokers varying gender, hair color, age and duration of smoking, all collected directly from the head of the same great Natal-RN. From the NIR spectra obtained without any pretreatment of the samples was performed an exploratory multivariate chemical data by applying spectral pretreatments followed by principal component analysis (PCA). After chemometric modeling of the data was achieved without any experimental data beyond the NIR spectra, differentiate smokers from nonsmokers, by demonstrating the significant influence of tabacco on the chemical composition of hair as well as the potential of the methodology in forensic identification
Resumo:
Intelligent and functional Textile Materials have been widely developed and researched with the purpose of being used in several areas of science and technology. These fibrous materials require different chemical and physical properties to obtain a multifunctional material. With the advent of nanotechnology, the techniques developed, being used as essential tools to characterize these new materials qualitatively. Lately the application of micro and nanomaterials in textile substrates has been the objective of many studies, but many of these nanomaterials have not been optimized for their application, which has resulted in increased costs and environmental pollution, because there is still no satisfactory effluent treatment available for these nanomaterials. Soybean fiber has low adsorption for thermosensitive micro and nanocapsules due to their incompatibility of their surface charges. For this reason, in this work initially chitosan was synthesized to functionalise soybean fibres. Chitosan is a natural polyelectrolyte with a high density of positive charges, these fibres have negative charges as well as the micro/nanocápsules, for this reason the chitosan acts as auxiliary agent to cationize in order to fix the thermosensitive microcapsules in the textile substrate. Polyelectrolyte was characterized using particle size analyses and the measurement of zeta potential. For the morphological analysis scanning Electron Microscopy (SEM) and x-Ray Diffraction (XRD) and to study the thermal properties, thermogravimetric analysis (TGA), Differential Scanning Calorimetry (DSC), Near Infrared Spectroscopy analysis in the Region of the Fourier Transform Infrared (FTIR), colourimetry using UV-VIS spectrum were simultaneously performed on the substrate. From the measurement of zeta potential and in the determination of the particle size, stability of electrostatic chitosan was observed around 31.55mV and 291.0 nm respectively. The result obtained with (GD) for chitosan extracted from shrimp was 70 %, which according to the literature survey can be considered as chitosan. To optimize the dyeing process a statistical software, Design expert was used. The surface functionalisation of textile substrate with 2% chitosan showed the best result of K/S, being the parameter used for the experimental design, in which this showed the best response of dyeing absorbance in the range of 2.624. It was noted that soy knitting dyed with the thermosensitive micro andnanocapsules property showed excellent washing solidity, which was observed after 25 home washes, and significant K/S values.
Resumo:
This work was developed with the objective of proposing a simple, fast and versatile methodological routine using near-infrared spectroscopy (NIR) combined with multivariate analysis for the determination of ash content, moisture, protein and total lipids present in the gray shrimp (Litopenaeus vannamei ) which is conventionally performed gravimetrically after ashing at 550 ° C gravimetrically after drying at 105 ° C for the determination of moisture gravimetrically after a Soxhlet extraction using volumetric and after digestion and distillation Kjedhal respectively. Was first collected the spectra of 63 samples processed boiled shrimp Litopenaeus vannamei species. Then, the determinations by conventional standard methods were carried out. The spectra centered average underwent multiplicative scattering correction of light, smoothing Saviztky-Golay 15 points and first derivative, eliminated the noisy region, the working range was from 1100,36 to 2502,37 nm. Thus, the PLS models for predicting ash showed R 0,9471; 0,1017 and RMSEP RMSEC 0,1548; Moisture R was 0,9241; 2,5483 and RMSEP RMSEC 4,1979; R protein to 0,9201; 1,9391 and RMSEP RMSEC 2,7066; for lipids R 0,8801; 0,2827 and RMSEP RMSEC 0,2329 So that the results showed that the relative errors found between the reference method and the NIR were small and satisfactory. These results are an excellent indication that you can use the NIR to these analyzes, which is quite advantageous, since conventional techniques are time consuming, they spend a lot of reagents and involve a number of professionals, which requires a reasonable runtime while after the validation of the methodology execution using NIR reduces all this time to a few minutes, saving reagents, time and without waste generation, and that this is a non-destructive technique.
Resumo:
In this study were conducted experimental procedures for determination of variation of the expandability of rigid polyurethane foam (PUR) from a natural oil polyol (NOP), specifically the Castor oil plant, Ricinus communis, pure and additions of the vermiculite in phase dispersed in different percentage within a range from 0% to 20%, mass replacement. From the information acquired, were defined the parameters for production of bodies of test, plates obtained through controlled expansion, with the final volume fixed. Initially, the plates were subjected to thermal performance tests and evaluated the temperature profiles, to later be extracted samples duly prepared in accordance with the conditions required for each test. Was proceeded then the measurement of the coefficient of thermal conductivity, volumetric capacity heat and thermal diffusivity. The findings values were compared with the results obtained in the tests of thermal performance, contributing to validation of the same. Ultimately, it was investigated the influence that changes in physical-chemical structure of the material had exerted on the variation of thermophysical quantities through gas pycnometry, scanning electron microscopy (SEM) combined with energy dispersive X-ray fluorescence spectroscopy (EDXRF), infrared spectroscopy using Fourier transform (FTIR), thermogravimetric analysis (TGA) and differential thermal analysis (DTA). Based on the results obtained was possible to demonstrate that all load percentage analyzed promoted an increase in the potential expansion (PE) of the resin. In production of the plates, the composites with density near at the free expansion presented high contraction during the cure, being the of higher density adopted as definitive standard. In the thermal performance tests, the heating and cooling curves of the different composites had presented symmetry and values very close for lines of the temperature. The results obtained for the thermophysical properties of composites, showed little difference in respect of pure foam. The percentage of open pores and irregularities in the morphology of the composites were proportionate to the increment of vermiculite. In the interaction between the matrix and dispersed phase, there were no chemical transformations in the region of interface and new compounds were not generated. The composites of PUR-NOP and vermiculite presented thermal insulating properties near the foam pure and percentage significantly less plastic in its composition, to the formulation with 10% of load
Resumo:
In this work we used chemometric tools to classify and quantify the protein content in samples of milk powder. We applied the NIR diffuse reflectance spectroscopy combined with multivariate techniques. First, we carried out an exploratory method of samples by principal component analysis (PCA), then the classification of independent modeling of class analogy (SIMCA). Thus it became possible to classify the samples that were grouped by similarities in their composition. Finally, the techniques of partial least squares regression (PLS) and principal components regression (PCR) allowed the quantification of protein content in samples of milk powder, compared with the Kjeldahl reference method. A total of 53 samples of milk powder sold in the metropolitan areas of Natal, Salvador and Rio de Janeiro were acquired for analysis, in which after pre-treatment data, there were four models, which were employed for classification and quantification of samples. The methods employed after being assessed and validated showed good performance, good accuracy and reliability of the results, showing that the NIR technique can be a non invasive technique, since it produces no waste and saves time in analyzing the samples
Resumo:
The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums
Resumo:
Aiming to consumer s safety the presence of pathogenic contaminants in foods must be monitored because they are responsible for foodborne outbreaks that depending on the level of contamination can ultimately cause the death of those who consume them. In industry is necessary that this identification be fast and profitable. This study shows the utility and application of near-infrared (NIR) transflectance spectroscopy as an alternative method for the identification and classification of Escherichia coli and Salmonella Enteritidis in commercial fruit pulp (pineapple). Principal Component Analysis (PCA), Independent Modeling of Class Analogy (SIMCA) and Discriminant Analysis Partial Least Squares (PLS-DA) were used in the analysis. It was not possible to obtain total separation between samples using PCA and SIMCA. The PLS-DA showed good performance in prediction capacity reaching 87.5% for E. coli and 88.3% for S. Enteritides, respectively. The best models were obtained for the PLS-DA with second derivative spectra treated with a sensitivity and specificity of 0.87 and 0.83, respectively. These results suggest that the NIR spectroscopy and PLS-DA can be used to discriminate and detect bacteria in the fruit pulp
Resumo:
The Benzylpenicillin (PENG) have been as the active ingredient in veterinary medicinal products, to increase productivity, due to its therapeutic properties. However, one of unfortunate quality and used indiscriminately, resulting in residues in foods exposed to human consumption, especially in milk that is essential to the diet of children and the ageing. Thus, it is indispensable to develop new methods able to detect this waste food, at levels that are toxic to human health, in order to contribute to the food security of consumers and collaborate with regulatory agencies in an efficient inspection. In this work, were developed methods for the quality control of veterinary drugs based on Benzylpenicillin (PENG) that are used in livestock production. Additionally, were validated methodologies for identifying and quantifying the antibiotic residues in milk bovine and caprine. For this, the analytical control was performed two steps. At first, the groups of samples of medicinal products I, II, III, IV and V, individually, were characterized by medium infrared spectroscopy (4000 – 600 cm-1). Besides, 37 samples, distributed in these groups, were analyzed by spectroscopy in the ultraviolet and near infrared region (UV VIS NIR) and Ultra Fast Liquid Chromatograph coupled to linear arrangement photodiodes (UFLC-DAD). The results of the characterization indicated similarities, between PENG and reference standard samples, primarily in regions of 1818 to 1724 cm-1 of ν C=O that shows primary amides features of PENG. The method by UFLC-DAD presented R on 0.9991. LOD of 7.384 × 10-4 μg mL-1. LOQ of 2.049 × 10-3 μg mL-1. The analysis shows that 62.16% the samples presented purity ≥ 81.21%. The method by spectroscopy in the UV VIS NIR presented medium error ≤ 8 – 12% between the reference and experimental criteria, indicating is a secure choice for rapid determination of PENG. In the second stage, was acquiring a method for the extraction and isolation of PENG by the addition of buffer McIlvaine, used for precipitation of proteins total, at pH 4.0. The results showed excellent recovery values PENG, being close to 92.05% of samples of bovine milk (method 1). While samples of milk goats (method 2) the recovery of PENG were 95.83%. The methods for UFLC-DAD have been validated in accordance with the maximum residue limit (LMR) of 4 μg Kg-1 standardized by CAC/GL16. Validation of the method 1 indicated R by 0.9975. LOD of 7.246 × 10-4 μg mL-1. LOQ de 2.196 × 10-3 μg mL-1. The application of the method 1 showed that 12% the samples presented concentration of residues of PENG > LMR. The method 2 indicated R by 0.9995. LOD 8.251 × 10-4 μg mL-1. LOQ de 2.5270 × 10-3 μg mL-1. The application of the method showed that 15% of the samples were above the tolerable. The comparative analysis between the methods pointed better validation for LCP samples, because the reduction of the matrix effect, on this account the tcalculs < ttable, caused by the increase of recovery of the PENG. In this mode, all the operations developed to deliver simplicity, speed, selectivity, reduced analysis time and reagent use and toxic solvents, particularly if compared to the established methodologies.
Resumo:
The Benzylpenicillin (PENG) have been as the active ingredient in veterinary medicinal products, to increase productivity, due to its therapeutic properties. However, one of unfortunate quality and used indiscriminately, resulting in residues in foods exposed to human consumption, especially in milk that is essential to the diet of children and the ageing. Thus, it is indispensable to develop new methods able to detect this waste food, at levels that are toxic to human health, in order to contribute to the food security of consumers and collaborate with regulatory agencies in an efficient inspection. In this work, were developed methods for the quality control of veterinary drugs based on Benzylpenicillin (PENG) that are used in livestock production. Additionally, were validated methodologies for identifying and quantifying the antibiotic residues in milk bovine and caprine. For this, the analytical control was performed two steps. At first, the groups of samples of medicinal products I, II, III, IV and V, individually, were characterized by medium infrared spectroscopy (4000 – 600 cm-1). Besides, 37 samples, distributed in these groups, were analyzed by spectroscopy in the ultraviolet and near infrared region (UV VIS NIR) and Ultra Fast Liquid Chromatograph coupled to linear arrangement photodiodes (UFLC-DAD). The results of the characterization indicated similarities, between PENG and reference standard samples, primarily in regions of 1818 to 1724 cm-1 of ν C=O that shows primary amides features of PENG. The method by UFLC-DAD presented R on 0.9991. LOD of 7.384 × 10-4 μg mL-1. LOQ of 2.049 × 10-3 μg mL-1. The analysis shows that 62.16% the samples presented purity ≥ 81.21%. The method by spectroscopy in the UV VIS NIR presented medium error ≤ 8 – 12% between the reference and experimental criteria, indicating is a secure choice for rapid determination of PENG. In the second stage, was acquiring a method for the extraction and isolation of PENG by the addition of buffer McIlvaine, used for precipitation of proteins total, at pH 4.0. The results showed excellent recovery values PENG, being close to 92.05% of samples of bovine milk (method 1). While samples of milk goats (method 2) the recovery of PENG were 95.83%. The methods for UFLC-DAD have been validated in accordance with the maximum residue limit (LMR) of 4 μg Kg-1 standardized by CAC/GL16. Validation of the method 1 indicated R by 0.9975. LOD of 7.246 × 10-4 μg mL-1. LOQ de 2.196 × 10-3 μg mL-1. The application of the method 1 showed that 12% the samples presented concentration of residues of PENG > LMR. The method 2 indicated R by 0.9995. LOD 8.251 × 10-4 μg mL-1. LOQ de 2.5270 × 10-3 μg mL-1. The application of the method showed that 15% of the samples were above the tolerable. The comparative analysis between the methods pointed better validation for LCP samples, because the reduction of the matrix effect, on this account the tcalculs < ttable, caused by the increase of recovery of the PENG. In this mode, all the operations developed to deliver simplicity, speed, selectivity, reduced analysis time and reagent use and toxic solvents, particularly if compared to the established methodologies.