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

Multimodal sensory reliance in the nocturnal homing of the amblypygid \u3ci\u3ePhrynus pseudoparvulus\u3c/i\u3e (Class Arachnida, Order Amblypygi)?

Like many other nocturnal arthropods, the amblypygid Phrynus pseudoparvulus is capable of homing. The environment through which these predators navigate is a dense and heterogeneous tropical forest understory and the mechanism(s) underlying their putatively complex navigational abilities are presently unknown. This study explores the sensory inputs that might facilitate nocturnal navigation in the amblypygid P. pseudoparvulus. Specifically, we use sensory system manipulations in conjunction with field displacements to examine the potential involvement of multimodal—olfactory and visual—stimuli in P. pseudoparvulus’ homing behavior. In a first experiment, we deprived individuals of their olfactory capacity and displaced them to the opposite side of their home trees (\u3c5 m). We found that olfaction-intact individuals were more likely to be re-sighted in their home refuges than olfaction-deprived individuals. In a second experiment, we independently manipulated both olfactory and visual sensory capacities in conjunction with longer-distance displacements (8 m) from home trees. We found that sensory-intact individuals tended to be re-sighted on their home tree more often than sensory-deprived individuals, with a stronger effect of olfactory deprivation than visual deprivation. Comparing across sensory modality manipulations, olfaction-manipulated individuals took longer to return to their home trees than vision-manipulated individuals. Together, our results indicate that olfaction is important in the nocturnal navigation of P. pseudoparvulus and suggest that vision may also play a more minor role

Prevalence of anemia and deficiency of iron, folic acid, and zinc in children younger than 2 years of age who use the health services provided by the Mexican Social Security Institute

<p>Abstract</p> <p>Background</p> <p>In Mexico, as in other developing countries, micronutrient deficiencies are common in infants between 6 and 24 months of age and are an important public health problem. The objective of this study was to determine the prevalence of anemia and of iron, folic acid, and zinc deficiencies in Mexican children under 2 years of age who use the health care services provided by the Mexican Institute for Social Security (IMSS).</p> <p>Methods</p> <p>A nationwide survey was conducted with a representative sample of children younger than 2 years of age, beneficiaries, and users of health care services provided by IMSS through its regular regimen (located in urban populations) and its Oportunidades program (services offered in rural areas). A subsample of 4,955 clinically healthy children was studied to determine their micronutrient status. A venous blood sample was drawn to determine hemoglobin, serum ferritin, percent of transferrin saturation, zinc, and folic acid. Descriptive statistics include point estimates and 95% confidence intervals for the sample and projections for the larger population from which the sample was drawn.</p> <p>Results</p> <p>Twenty percent of children younger than 2 years of age had anemia, and 27.8% (rural) to 32.6% (urban) had iron deficiency; more than 50% of anemia was not associated with low ferritin concentrations. Iron stores were more depleted as age increased. Low serum zinc and folic acid deficiencies were 28% and 10%, respectively, in the urban areas, and 13% and 8%, respectively, in rural areas. The prevalence of simultaneous iron and zinc deficiencies was 9.2% and 2.7% in urban and rural areas. Children with anemia have higher percentages of folic acid deficiency than children with normal iron status.</p> <p>Conclusion</p> <p>Iron and zinc deficiencies constitute the principal micronutrient deficiencies in Mexican children younger than 2 years old who use the health care services provided by IMSS. Anemia not associated with low ferritin values was more prevalent than iron-deficiency anemia. The presence of micronutrient deficiencies at this early age calls for effective preventive public nutrition programs to address them.</p