The present work focuses on the development of a fast and cost effective method based on Laser Induced Breakdown Spectroscopy (LIBS) to the quality control, traceability and detection of adulteration in milk. Two adulteration cases have been studied; a qualitative analysis for the discrimination between different milk blends and quantification of melamine in adulterated toddler milk powder. Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze LIBS spectra obtaining a correct classification rate of 98% with a 100% of robustness. For the quantification of melamine, two methodologies have been developed; univariate analysis using CN emission band and multivariate calibration NN model obtaining correlation coefficient (R2) values of 0.982 and 0.999 respectively. The results of the use of LIBS technique coupled with chemometric analysis are discussed in terms of its potential use in the food industry to perform the quality control of this dairy product.

Paleoclimate studies play a crucial role in understanding past and future climates and their environmental impacts. Current methodologies for performing highly sensitive elemental analysis at micrometre spatial resolutions are restricted to the use of complex and/or not easily applied techniques, such as synchrotron radiation X-ray fluorescence micro-analysis (μ-SRXRF), nano secondary ion mass spectrometry (nano-SIMS) or laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Moreover, the analysis of large samples (>few cm²) with any of these methods remains very challenging due to their relatively low acquisition speed (~1–10 Hz), and because they must be operated in vacuum or controlled atmosphere. In this work, we proposed an imaging methodology based on laser-induced breakdown spectroscopy, to perform fast multi-elemental scanning of large geological samples with high performance in terms of sensitivity (ppm-level), lateral resolution (up to 10 μm) and operating speed (100 Hz). This method was successfully applied to obtain the first megapixel images of large geological samples and yielded new information, not accessible using other techniques. These results open a new perspective into the use of laser spectroscopy in a variety of geochemical applications.

The present work reports the distribution of pollutants in the Madrid city and province from 22 monitoring stations during 2010 to 2017. Statistical tools were used to interpret and model air pollution data. The data include the annual average concentrations of nitrogen oxides, ozone, and particulate matter (PM₁₀), collected in Madrid and its suburbs, which is one of the largest metropolitan places in Europe, and its air quality has not been studied sufficiently. A mapping of the distribution of these pollutants was done, in order to reveal the relationship between them and also with the demography of the region. The multivariate analysis employing correlation analysis, principal component analysis (PCA), and cluster analysis (CA) resulted in establishing a correlation between different pollutants. The results obtained allowed classification of different monitoring stations on the basis of each of the four pollutants, revealing information about their sources and mechanisms, visualizing their spatial distribution, and monitoring their levels according to the average annual limits established in the legislation. The elaboration of contour maps by the geostatistical method, ordinary kriging, also supported the interpretation derived from the multivariate analysis demonstrating the levels of NO₂ exceeding the annual limit in the centre, south, and east of the Madrid province.