This work was aimed at the investigation of the use of near-infrared spectroscopy (NIRS) for the identification of counterfeit drugs. The identification is based on the comparison of the NIR spectrum of a sample with typical spectra of the authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to observe the diversity of physico-chemical characteristics found among commercial products. The parameters which could affect the spectra of a given drug (especially if presented in solid form) were investigated and the results showed that the first derivative can minimise spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The power of NIRS in distinguishing among similar pharmaceuticals was demonstrated and a protocol is proposed to construct a multivariate model and to include it in a library allowing testing for drug authenticity. The methodology was evaluated with real samples of counterfeit drugs and was able to recognise all those presenting changes in composition as false. The results show unequivocally the potential of NIRS for rapid, on-site and non-destructive identification of counterfeit pharmaceuticals.
Near infrared (NIR) spectroscopy has been employed for the determination of hydrogen peroxide in antiseptic pharmaceutical preparations where it is found in concentrations of about 3.0% (w/v). Standard solutions containing hydrogen peroxide in the range 0–10% (w/v) were prepared and split into two groups, which were used for partial least squares (PLS1) modelling and for initial evaluation of the model performance. Absorbance spectra, in the wavelength range from 850 to 1800 nm, were obtained using a transflectance probe with optical path of 1.0 mm. The first derivative of the absorption spectra were employed for PLS1 modelling, which requires two factors, resulting in a root mean square error of cross-validation of 0.07% obtained by full cross-validation. The model was evaluated by using prepared and commercial samples of pharmaceuticals containing H2O2 in the range from 1 to 20% resulting in a root mean standard error of prediction of 0.05%.
A simple device is described to couple a fast-scanning acoustooptic tunable filter-based NIR spectrophotometer to a distillation apparatus for monitoring the condensed vapor in real time. The device consists of a small funnel whose glass neck (2-mm diameter) is bent into an "U" format to produce a flow cell of approximately 150-microL inner volume. A pair of optical fibers is used to deliver the monochromatic light and to collect the fraction passing through the glass tube. The end of the condenser of the distillation head touches the wall of the small funnel. The condensed liquid flows uncoupled from pressure changes in the interior of the distillation head. Absorbance spectra were obtained, during the distillation, as averages of 50 scans (4 s) every 5 s in the spectral range 950-1800 nm with nominal resolution of 2.0 nm. In the first experiments, the distillations were performed at constant power supplied to the sample (25 mL) in a microdistillation apparatus working without any type of reflux column. The usefulness of the real-time monitoring of distillation is demonstrated using some prepared binary mixtures and by comparing the distillation behavior of adulterated and regular gasoline samples. Data analysis and interpretation are facilitated by employing principal component analysis. The system accesses the composition of the condensate, which can separate and concentrate one or more compounds present in the original sample.
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