Fernanda S. L. Costa

Non-destructive detection of adulterated tablets of glibenclamide using NIR and solid-phase fluorescence spectroscopy and chemometric methods.

This study describes a method for non-destructive detection of adulterated glibenclamide tablets. This method uses near infrared spectroscopy (NIRS) and fluorescence spectroscopy along with chemometric tools such as Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Unfolded Partial Least Squares with Discriminant Analysis (UPLS-DA). Both brand name (Daonil) and generic glibenclamide tablets were used for analysis. The levels of glibenclamide in each type of tablet were evaluated by derivative spectrophotometry in the ultraviolet region. The results obtained from the NIR and fluorescence spectroscopy along with those obtained from multivariate data classification show that this combined technique is an effective way to detect adulteration in drugs for the treatment of diabetes. In the future, this method may be extended to detect different types of counterfeit medications.

Variable selection with a support vector machine for discriminating Cryptococcus fungal species based on ATR-FTIR spectroscopy

Variable selection with supervised classification is currently an important tool for discriminating biological samples. In this paper, 15 supervised classification algorithms based on a support vector machine (SVM) were applied to discriminate Cryptococcus neoformans and Cryptococcus gattii fungal species using ATR-FTIR spectroscopy. These two fungal species of the Cryptococcus genus are the etiological agents of Cryptococcosis, which is an opportunistic or primary fungal infection with global distribution. This disease is potentially fatal, especially for immunocompromised patients, like those suffering from AIDS. The multivariate classification algorithms tested were based on principal component analysis (PCA), successive projections algorithm (SPA) and genetic algorithm (GA) as data reduction and variable selection methods, being coupled to a SVM with different kernel functions (linear, quadratic, 3rd order polynomial, radial basis function, and multilayer perceptron). Some of these algorithms achieved very successful classification rates for discriminating fungal species, with accuracy, sensitivity, and specificity equal to 100% using both SPA-SVM-polynomial and GA-SVM-polynomial algorithms. These results show the potential of such techniques coupled to ATR-FTIR spectroscopy as a rapid and non-destructive tool for classifying these fungal species.

Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy as a new technology for discrimination between Cryptococcus neoformans and Cryptococcus gattii

Systemic fungal infections are among the most difficult diseases to manage in humans, especially when the recognition of the correct species is required for a precise and successful treatment. This is the case for Cryptococcus species and its genotypes, which are the main cause of meningitides in immunocompromised patients. Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy with discriminant analysis was employed to distinguish between the pathogenic fungal species Cryptococcus neoformans and Cryptococcus gattii by determining which wavenumber–absorbance/intensity relationships might reveal biochemical differences. Cryptococcus inactivated colonies were applied to an ATR crystal, and vibrational spectra were obtained in the ATR mode. Twenty-eight Cryptococcus isolates, fourteen C. neoformans and fourteen C. gattii were investigated. Spectral categories were analyzed using principal component analysis (PCA), successive projection algorithm (SPA) and genetic algorithm (GA) followed by linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). Multivariate classification accuracy results were estimated based on sensitivity, specificity, positive (or precision) and negative predictive values, Youden index, and positive and negative likelihood ratios. Sensitivity for C. neoformans and C. gattii categories were 84.4% and 89.3%, respectively, using a QDA-LDA model with 17 wavenumbers with respect to their “fingerprints”. Compared to classical methods for differentiation of Cryptococcus species, this new technology could represent an alternative and innovative tool for faster and cheaper fungal identification for routine diagnostic laboratories.

Multivariate Control Charts for Simultaneous Quality Monitoring of Isoniazid and Rifampicin in a Pharmaceutical Formulation Using a Portable Near Infrared Spectrometer

One of the keys to maintain high manufacturing quality improvement is the use of control charts. In this work, multivariate control charts based on significant principal component (PC) scores and net analytical signal (NAS) were developed to simultaneously monitor the quality of two active pharmaceutical ingredients (API) (isoniazid and rifampicin) in pharmaceutical formulation (laboratory samples and production samples) using a portable near infrared spectrometer. The limits for both multivariate charts were estimated using the quality specifications from the pharmaceutical formulation (± 5% of the nominal content of each API). The use of these multivariate control charts has provided a simple and powerful tool to evaluate the content of pharmaceutical formulations based on isoniazid and rifampicin capsules combined with near infrared spectroscopy. The procedure is rapid and adjustable for monitoring the production of the pharmaceutical preparations produced at UFRN/Brazil toward the process analytical technology (PAT) for the treatment of pulmonary tuberculosis.

The Use of Near Infrared Spectroscopy and Multivariate Calibration for Determining the Active Principle of Olanzapine in a Pharmaceutical Formulation

The aim of this study was to quantitatively determine the olanzapine in a pharmaceutical formulation for assessing the potentiality of near infrared spectroscopy (NIR) combined with partial least squares (PLS) regression. The method was developed with samples based on a commercial formulation containing olanzapine and seven excipients. Laboratory and commercial samples (n = 27 and 18, respectively) were used by defining the calibration and prediction sets. The method was validated in the range from 1.0 to 12.5 of olanzapine per 100 mg of powder (average mass 210 mg), by accuracy, precision, linearity, analytical sensitivity, limit of detection and quantification. The multivariate model developed for olanzapine was based on PLS and the determination coefficient (rc and rp), with the root mean square error of calibration and prediction being 0.95, 0.93, 3.2 × 10-3 and 4.0 × 10-3% m/m, respectively. The proposed NIR method is an effective alternative for quantification of olanzapine in the pharmaceutical industry.

Comparison of multivariate classification algorithms using EEM fluorescence data to distinguish Cryptococcus neoformans and Cryptococcus gattii pathogenic fungi

Cryptococcus neoformans and Cryptococcus gattii are the etiologic agents of cryptococcosis, whose suitable treatment depends on rapid and correct detection and differentiation of the Cryptococcus species. Currently, this identification is made by classical and molecular techniques; however most of them are considered laborious and expensive. As an alternative method to discriminate C. gattii and C. neoformans, excitation-emission matrix (EEM) fluorescence spectroscopy combined with multivariate classification methods, Unfolded Partial Least Squares Discriminant Analysis (UPLS-DA), multiway-Partial Least Squares Discriminant Analysis (nPLS-DA), Parallel Factor Analysis (PARAFAC), Principal Component Analysis (PCA), Successive Projection Algorithm (SPA) and Genetic Algorithm (GA), followed by Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) was herein investigated. This technique showed to be an innovative and low cost methodology which requires a small sample volume. Among the methods, the most successful model was UGA-LDA, which showed a sensitivity of 88.9% within only 5 selected wavelengths in calibration and 100.0% prediction for both classes of C. neoformans and C. gattii, equaling or surpassing some of the biological tests that are usually carried out to differentiate these fungi.

On the synergy between silver nanoparticles and doxycycline towards the inhibition of Staphylococcus aureus growth

In a previous paper (RSC Adv., 2015, 5, 66886–66893), we showed that the combination of silver nanoparticles (NanoAg) with doxycycline (DO) culminated in an increased bactericidal activity towards E. coli. Herein we further investigated the metabolic changes that occurred on Staphylococcus aureus upon exposure to NanoAg with the help of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) coupled with multivariate data analysis. It has been discovered that the combination of DO with NanoAg produced metabolic changes in S. aureus that were not simply the overlap of the treatments with DO and NanoAg separately. Our results suggest that DO and NanoAg act synergistically to impede protein synthesis by the bacteria.

A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation

Background Multivariate transfer techniques have become a widely accepted concept over the past few years, since they avoid full recalibration procedures when instruments are changed to analyze a specific sample. Objective This paper reports a multivariate control chart transfer approach between two near infrared (NIR) spectrometers for simultaneous determination of rifampicin and isoniazid in pharmaceutical formu-lation using Direct Standardization (DS). Method The control charts are based on the calculation of Net Analyte Signal (NAS) models and the transfer samples are selected by the Kennard-Stone (KS) algorithm. Three control charts (NAS, interfer-ence and residual) transferred on both the master and slave instruments were measured. Results As a result, a classification model for rifampicin and isoniazid developed on a primary instrument has been successfully transferred to a secondary instrument. The spectral differences after the standardiza-tion procedure were considerably reduced and errors values found in the charts for both analytes were comparable with the errors obtained for the original chart models. Conclusion The proposed approach appears to be a valid alternative to the commonly used transfer of multivariate calibration models in simultaneous determination of isoniazid and rifampicin in pharmaceuti-cal formulation.Background: Multivariate transfer techniques have become a widely accepted concept over the past few years, since they avoid full recalibration procedures when instruments are changed to analyze a specific sample. Objective: This paper reports a multivariate control chart transfer approach between two near infrared (NIR) spectrometers for simultaneous determination of rifampicin and isoniazid in pharmaceutical formu-lation using Direct Standardization (DS). Method: The control charts are based on the calculation of Net Analyte Signal (NAS) models and the transfer samples are selected by the Kennard-Stone (KS) algorithm. Three control charts (NAS, interfer-ence and residual) transferred on both the master and slave instruments were measured. Results: As a result, a classification model for rifampicin and isoniazid developed on a primary instrument has been successfully transferred to a secondary instrument. The spectral differences after the standardiza-tion procedure were considerably reduced and errors values found in the charts for both analytes were comparable with the errors obtained for the original chart models. Conclusion: The proposed approach appears to be a valid alternative to the commonly used transfer of multivariate calibration models in simultaneous determination of isoniazid and rifampicin in pharmaceuti-cal formulation