Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the e ectiveness of rapidly di erentiating between di erent soil types and detecting the presence of petroleum traces in di erent soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum

Mid-Infrared Laser Spectroscopy Applications I: Detection of Traces of High Explosives on Reflective and Matte Substrates

Mid-infrared (MIR) lasers have revolutionized infrared vibrational spectroscopy, converting an already dominant spectroscopic analysis technique into an even more powerful, easier to use, and quicker turn-around cadre of versatile spectroscopic tools. A selection of applications, revisited under the umbrella of MIR laser-based properties, very high brightness, collimated beams, polarized sources, highly monochromatic tunable sources, and coherent sources, is included. Applications discussed concern enhanced detection, discrimination, and quantification of high explosives (HEs). From reflectance measurements of chemical residues on highly reflective metallic substrates to reflectance measurements of HEs deposited on non-reflective, matte substrates is discussed. Coupling with multivariate analyses (MVA) techniques of Chemometrics allowed near trace detection of HEs, with sharp discrimination from highly MIR absorbing substrates

Mid-Infrared laser spectroscopy detection and quantification of explosives in soils using multivariate analysis and artificial intelligence

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils

Mid-Infrared Laser Spectroscopy Applications in Process Analytical Technology: Cleaning Validation, Microorganisms, and Active Pharmaceutical Ingredients in Formulations

Mid-infrared (MIR) lasers are very high-brightness energy sources that are replacing conventional thermal sources (globars) in many infrared spectroscopy (IRS) techniques. Although not all laser properties have been exploited in depth, properties such as collimation, polarization, high brightness, and very high resolution have contributed to recast IRS tools. Applications of MIR laser spectroscopy to process analytical technology (PAT) are numerous and important. As an example, a compact grazing angle probe mount has allowed coupling to a MIR quantum cascade laser (QCL), enabling reflectance-absorbance infrared spectroscopy (RAIRS) measurements. This methodology, coupled to powerful multivariable analysis (MVA) routines of chemometrics and fast Fourier transform (FFT) preprocessing of the data resulted in very low limits of detection of active pharmaceutical ingredients (APIs) and high explosives (HEs) reaching trace levels. This methodology can be used to measure concentrations of surface contaminants for validation of cleanliness of pharmaceutical and biotechnology processing batch reactors and other manufacturing vessels. Another application discussed concerns the enhanced detection of microorganisms that can be encountered in pharmaceutical and biotechnology plants as contaminants and that could also be used as weapons of mass destruction in biological warfare. In the last application discussed, the concentration of APIs in formulations was determined by MIR laser spectroscopy and was cross validated with high-performance liquid chromatography

Eco-epidemiological analysis of rickettsial seropositivity in rural areas of Colombia: A multilevel approach

ABSTARCT: Rickettsiosis is a re-emergent infectious disease without epidemiological surveillance in Colombia. This disease is generally undiagnosed and several deadly outbreaks have been reported in the country in the last decade. The aim of this study is to analyze the eco-epidemiological aspects of rickettsial seropositivity in rural areas of Colombia where outbreaks of the disease were previously reported. A cross-sectional study, which included 597 people living in 246 households from nine hamlets in two municipalities of Colombia, was conducted from November 2015 to January 2016. The survey was conducted to collect sociodemographic and household characteristics (exposure) data. Blood samples were collected to determine the rickettsial seropositivity in humans, horses and dogs (IFA, cut-off = 1/128). In addition, infections by rickettsiae were detected in ticks from humans and animals by real-time PCR targeting gltA and ompA genes. Data was analyzed by weighted multilevel clog-log regression model using three levels (person, household and hamlets) and rickettsial seropositivity in humans was the main outcome. Overall prevalence of rickettsial seropositivity in humans was 25.62% (95%CI 22.11-29.12). Age in years (PR = 1.01 95%CI 1.01-1.02) and male sex (PR = 1.65 95%CI 1.43-1.90) were risk markers for rickettsial seropositivity. Working outdoors (PR = 1.20 95%CI 1.02-1.41), deforestation and forest fragmentation for agriculture use (PR = 1.75 95%CI 1.51-2.02), opossum in peridomiciliary area (PR = 1.56 95%CI 1.37-1.79) and a high proportion of seropositive domestic animals in the home (PR20-40% vs 40% vs <20% = 3.14 95%CI 2.43-4.04) were associated with rickettsial seropositivity in humans. This study showed the presence of Rickettsia antibodies in human populations and domestic animals. In addition, different species of rickettsiae were detected in ticks collected from humans and animals. Our results highlighted the role of domestic animals as sentinels of rickettsial infection to identify areas at risk of transmission, and the importance of preventive measures aimed at curtailing deforestation and the fragmentation of forests as a way of reducing the risk of transmission of emergent and re-emergent pathogens

4to. Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad. Memoria académica

Este volumen acoge la memoria académica de la Cuarta edición del Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad, CITIS 2017, desarrollado entre el 29 de noviembre y el 1 de diciembre de 2017 y organizado por la Universidad Politécnica Salesiana (UPS) en su sede de Guayaquil. El Congreso ofreció un espacio para la presentación, difusión e intercambio de importantes investigaciones nacionales e internacionales ante la comunidad universitaria que se dio cita en el encuentro. El uso de herramientas tecnológicas para la gestión de los trabajos de investigación como la plataforma Open Conference Systems y la web de presentación del Congreso http://citis.blog.ups.edu.ec/, hicieron de CITIS 2017 un verdadero referente entre los congresos que se desarrollaron en el país. La preocupación de nuestra Universidad, de presentar espacios que ayuden a generar nuevos y mejores cambios en la dimensión humana y social de nuestro entorno, hace que se persiga en cada edición del evento la presentación de trabajos con calidad creciente en cuanto a su producción científica. Quienes estuvimos al frente de la organización, dejamos plasmado en estas memorias académicas el intenso y prolífico trabajo de los días de realización del Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad al alcance de todos y todas

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI 2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining https://researchonline.ljmu.ac.uk/images/research_banner_face_lab_290.jpgunderweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesity

Surface persistence of trace level deposits of highly energetic materials

In the fields of Security and Defense, explosive traces must be analyzed at the sites of the terrorist events. The persistence on surfaces of these traces depends on the sublimation processes and the interactions with the surfaces. This study presents evidence that the sublimation process of these traces on stainless steel (SS) surfaces is very different than in bulk quantities. The enthalpies of sublimation of traces of four highly energetic materials: triacetone triperoxide (TATP), 2,4-dinitrotoluene (DNT), 2,4,6-trinitrotoluene (TNT), and 1,3,5- trinitrohexahydro-s-triazine (RDX) deposited on SS substrates were determined by optical fiber coupled-grazing angle probe Fourier Transform Infrared (FTIR) Spectroscopy. These were compared with enthalpies of sublimation determined by thermal gravimetric analysis for bulk amounts and differences between them were found. The sublimation enthalpy of RDX was very different for traces than for bulk quantities, attributed to two main factors. First, the beta-RDX phase was present at trace levels, unlike the case of bulk amounts which consisted only of the alpha-RDX phase. Second, an interaction between the RDX and SS was found. This interaction energy was determined using grazing angle FTIR microscopy. In the case of DNT and TNT, bulk and traces enthalpies were statistically similar, but it is evidenced that at the level of traces a metastable phase was observed. Finally, for TATP the enthalpies were statistically identical, but a non-linear behavior and a change of heat capacity values different from zero was found for both trace and bulk phases

Surface persistence of trace level deposits of highly energetic materials

In the fields of Security and Defense, explosive traces must be analyzed at the sites of the terrorist events. The persistence on surfaces of these traces depends on the sublimation processes and the interactions with the surfaces. This study presents evidence that the sublimation process of these traces on stainless steel (SS) surfaces is very different than in bulk quantities. The enthalpies of sublimation of traces of four highly energetic materials: triacetone triperoxide (TATP), 2,4-dinitrotoluene (DNT), 2,4,6-trinitrotoluene (TNT), and 1,3,5- trinitrohexahydro-s-triazine (RDX) deposited on SS substrates were determined by optical fiber coupled-grazing angle probe Fourier Transform Infrared (FTIR) Spectroscopy. These were compared with enthalpies of sublimation determined by thermal gravimetric analysis for bulk amounts and differences between them were found. The sublimation enthalpy of RDX was very different for traces than for bulk quantities, attributed to two main factors. First, the beta-RDX phase was present at trace levels, unlike the case of bulk amounts which consisted only of the alpha-RDX phase. Second, an interaction between the RDX and SS was found. This interaction energy was determined using grazing angle FTIR microscopy. In the case of DNT and TNT, bulk and traces enthalpies were statistically similar, but it is evidenced that at the level of traces a metastable phase was observed. Finally, for TATP the enthalpies were statistically identical, but a non-linear behavior and a change of heat capacity values different from zero was found for both trace and bulk phases

Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics

Mid-infrared (MIR) laser spectroscopy was used to detect the presence of residues of high explosives (HEs) on fabrics. The discrimination of the vibrational signals of HEs from a highly MIR-absorbing substrate was achieved by a simple and fast spectral evaluation without preparation of standards using the classical least squares (CLS) algorithm. Classical least squares focuses on minimizing the differences between the spectral features of the actual spectra acquired using MIR spectroscopy and the spectral features of calculated spectra modeled from linear combinations of the spectra of neat components: HEs, fabrics, and bias. Samples in several combinations of cotton fabrics/HEs were used to validate the methodology. Several experiments were performed focusing on binary, ternary, and quaternary mixtures of TNT, RDX, PETN, and fabrics. The parameters obtained from linear combinations of the calculated spectra were used to perform discrimination analyses and to determine the sensitivity and selectivity of HEs with respect to the substrates and to each other. However, discrimination analysis was not necessary to achieve successful detection of HEs on cotton fabric substrates. The RDX signals (mRDX>0.02 mg) on cotton were used to calculate the limit of detection (LOD). The signalto- noise ratios (S/N) calculated from the spectra of cotton dosed with decreasing masses of RDX until S/N&3 resulted in a LOD of 15–33 mg, depending on the vibrational band used. Linear fits generated by comparing the mass dosed RDX with the fraction predicted were also used to calculate the LOD based on the uncertainty of the blank and the slope. This procedure resulted in a LOD of 58 mg. Probably the most representative value of the method LOD was calculated using an interpolation of a threshold determined using the predicted average value for the blank plus 3.28 times the standard deviations (p-value threshold) for low surface dosages of RDX (LOD¼40 mg). The contribution demonstrates that to achieve HE detection on fabrics using the proposed algorithm, i.e., determining the presence/absence of HEs on the substrates, the library must contain the spectra of HEs, substrates, and potential interferents or that these spectra be added to the models in the field. If the model does not contain the spectra of the fabric components, there is a high probability of finding false positives for clean samples (no HEs) and a low probability for failed detection in samples with HEs. More work will be required to demonstrate that these new approaches to HE detection work on real-world samples and when contaminating materials are present in the samples