Development of methods based on NIR and Raman spectroscopies together with chemometric tools for the qualitative and quantitative analysis of gasoline

Gasoline quality control is essential for SI engines performance and to reduce environmental impacts by generation of undesirable pollutants. Methods established by the American Society for Test and Materials (ASTM) are the most employed for determining physicochemical quality parameters of motor gasoline, however, these methods present some disadvantages such as time-consuming analysis and need of large amount of sample. For this purpose, near-infrared (NIR) and Raman spectroscopies could be promising alternatives, since they are nondestructive techniques which require little or no sample preparation, a small amount of sample, short analysis time, and also present the possibility of simultaneous determination of many parameters. Although, the use of chemometric tools is often needed in order to extract maximum of useful information from the NIR and Raman spectra related to the parameter being studied. In this work, the qualitative classification of commercial gasoline samples related to their ethanol contents and antiknock indexes was reached by using principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) models. The values for the misclassification error obtained for the classification of these parameters by both NIR and Raman spectroscopies were less than 3.0%. The multivariate calibration technique, partial least squares (PLS), was used for both NIR and Raman data to obtain predictive models for the quantification of eight physicochemical quality parameters of gasoline such as relative density, motor octane number, research octane number, antiknock index, and gasoline composition by aromatics, benzene, olefins, and paraffins. The accuracy of these PLS models was evaluated by applying the elliptical joint confidence region (EJCR) test, and the ideal theoretical point (slope=1, intercept=0) was involved by the ellipses of all obtained models by both NIR and Raman data demonstrating that BIAS is absent in a confidence interval of 95%. The results obtained in this study demonstrated that both spectroscopic techniques together with chemometric tools provided an excellent performance, thus, being good alternatives to the conventional methods to be used for the quality control of motor gasoline.Master's Thesis in Quality in the Analytical LaboratoryQAL399BJMAMN-QALJMAMN-QAL

Designing A Calibration Set in Spectral Space for Efficient Development of An NIR Method For Tablet Analysis

Designing a calibration set is the first step in developing a spectroscopic calibration method for quantitative analysis of pharmaceutical tablets. This step is critical because successful model development depends on the suitability of the calibration data. For spectroscopic-based methods, traditional concentration based techniques for designing calibration sets are prone to have redundant information while simultaneously lacking necessary information for a successful calibration model. The traditional method also follows the same design approach for different spectroscopic techniques and different formulations, thereby lacks the optimizing capability to be technique and formulation specific. A method for designing a calibration set in the Near Infrared (NIR) spectral space was developed for quantitative analysis of tablets. The pure component NIR spectra of a tablet formulation were used to define the spectral space of that formulation. This method minimizes sample requirements to provide an efficient means for developing multivariate spectroscopic calibration. Multiple comparative studies were conducted between commonly employed experimental design approaches to calibration development and the newly developed spectral space based technique. The comparisons were conducted on single API (Active Pharmaceutical Ingredient) and multiple API formulation to quantify model drugs using NIR spectroscopy. Partial least squares (PLS) models were developed from respective calibration designs. Model performance was comprehensively assessed based on the ability to predict API concentrations in independent prediction sets. Similar prediction performance was achieved using the smaller calibration set designed in spectral space, compared to the traditionally designed large calibration sets. An improved prediction performance was observed for the spectrally designed calibration sets compared to the traditionally designed calibration sets of equal sizes. Spectral space was also used to incorporate physico-chemical information into the calibration design to provide an efficient means of developing robust calibration model. Robust calibration model is critical to ensure consistent model performance during model lifecycle. A weight coefficient based technique was developed for selecting loading vector in PLS model to aid in building robust calibration model. It was also demonstrated that the optimal structures of calibration sets are different between NIR and Raman spectroscopy for the same tablet formulation. The optimum calibration structures are also different between two APIs for the same spectroscopic technique, indicating the criticality of the calibration design to be formulation and technique specific. This study demonstrates that a calibration set designed in spectral space provides an efficient means of developing spectroscopic multivariate calibration for tablet analysis. This study also provides opportunity to design formulation and technique specific calibration sets to optimize calibration capability

Quality-by-design approach for the development of lipid-based nanosystems for anti-mycobacterial therapy

In this work, we rationally developed a lipid-based nanotechnological platform for hydrophobic anti-mycobacterial drugs. For this purpose, Artificial Intelligence tools were employed to assist formulation development, from the initial design to its conversion into a solid dosage form. Reproducible nanocarriers exhibiting suitable properties were achieved through a simple and robust procedure. Furthermore, the analysis of their in vitro performance revealed promising results in terms of permeability, cell uptake and selective intracellular release. Thus demonstrating the potential of these nanosystems to treat intestinal intracellular infections, increasingly related with Crohn´s disease development

Investigation of Volatile Organic Compounds (VOCs) released as a result of spoilage in whole broccoli, carrots, onions and potatoes with HS-SPME and GC-MS

Vegetable spoilage renders a product undesirable due to changes in sensory characteristics. The aim of this study was to investigate the change in the fingerprint of VOC composition that occur as a result of spoilage in broccoli, carrots, onions and potatoes. SPME and GC-MS techniques were used to identify and determine the relative abundance of VOC associated with both fresh and spoilt vegetables. Although a number of similar compounds were detected in varying quantities in the headspace of fresh and spoilt samples, certain compounds which were detected in the headspace of spoilt vegetables were however absent in fresh samples. Analysis of the headspace of fresh vegetables indicated the presence of a variety of alkanes, alkenes and terpenes. Among VOCs identified in the spoilt samples were dimethyl disulphide and dimethyl sulphide in broccoli; Ethyl propanoate and Butyl acetate in carrots; 1-Propanethioland 2-Hexyl-5-methyl-3(2H)-furanone in onions; and 2, 3-Butanediol in potatoes. The overall results of this study indicate the presence of VOCs that can serve as potential biomarkers for early detection of quality deterioration and in turn enhance operational and quality control decisions in the vegetable industry