Raman imaging spectroscopy is a relatively new technique that has raised interest in several areas of knowledge due to the possibility of evaluating the spatial distribution of compounds based on their spectra. This paper presents a brief history of the development of this important technique, a theoretical description of the Raman effect and the preprocessing and processing (both univariate and multivariate) tools for Raman data. We also describe the current state of applications for major areas such as agriculture/biomass, environment, biology, medicine, pharmaceutical, geology, foodstuff, artwork, archaeology, material characterization and forensic.

This paper presents methodologies for monitoring the quality of methyl cotton biodiesel in biodiesel/diesel blends using mid-infrared spectroscopy (MIR) and chemometrics tools. The first method relates to the construction of multivariate control charts with the aim of qualitatively monitoring the samples according to the Brazilian specification for biodiesel in the biodiesel/diesel blends (7.00 ± 0.5% v/v of biodiesel). The second concerns the construction of partial least squares (PLS) to determine the content of the biodiesel in the biodiesel/diesel blend. The PLS model was validated from multivariate figures of merit according to the guidelines of ASTM E1655-05 and IUPAC. The results from both methods were satisfactory for both qualitative and quantitative monitoring. Therefore, the proposed methodologies for monitoring the quality of biodiesel in biodiesel/diesel blends are fast, practical, economical and efficient and can be used by industries and service stations.

This work aimed at employing partial least square discriminant analysis (PLS2-DA), allied to mid-infrared (MIR) spectroscopy as an analytical method for simultaneous classification of biodiesels from different oils (soybean and used frying oil) and routes (methylic and ethylic). The evaluation of the model was verified through values of sensitivity and specificity for each parameter, in the interest class. PLS2-DA model showed 100% correct classification in the discrimination of types of biodiesels. Therefore, the proposed methodology is fast, because it allows simultaneous classification of different types of biodiesels. Consequently, it can be used in quality control of this type of biofuel.