The detection of ignitable liquids (ILs) can be crucial when it comes to determining arson cases. Such identification of ILs is a challenging task that may be affected by a number of factors. ...Microbial degradation is considered one of three major processes that can alter the composition of IL residues. Since biodegradation is a time related phenomenon, it should be studied at different stages of development. This article presents a method based on ion mobility spectroscopy (IMS) which has been used as an electronic nose. In particular, ion mobility sum spectrum (IMSS) in combination with chemometric techniques (hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA)) has been applied for the characterization of different biodegraded ILs. This method intends to use IMSS to identify a range of ILs regardless of their degree of biodegradation. Three ILs (diesel, gasoline and kerosene) from three different commercial brands were evaluated after remaining in a soil substrate for several lengths of time (0, 2, 5, 13 and 38 days). The HCA results showed the samples’ trend to fall into categories characterized by ILs type and biodegradation time. The LDAs allowed a 99% successful classification of the samples according to the IL type. This is the first time that an HS-IMS technique has been used to detect ILs that have undergone biodegradation processes. The results show that IMS may be a promising alternative to the current standard method based on gas-chromatography for the analysis of biodegraded ILs. Furthermore, no pretreatment of the samples nor the use of a solvent is required.
Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid ...residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials' guidelines (ASTM E1618) based on gas chromatography-mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace-mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates-four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.
Display omitted
•Automated characterization of petroleum wax blends from Vis-NIR data.•Boruta algorithm improves the performance of the regression models.•PLSR, SVR and RF enable the blending ...percentage to be determined.•Boruta-PLS model showed the best performance with a R2 of 0.9908 for the test set.•Interactive web with the best model was developed for automated data processing.
Petroleum waxes are products derived from lubricating oils with a wide spectrum of industrial and consumer applications that depend on their composition. In addition, the intended applications of this product are also subject to the practice of blending petroleum waxes with different chemical characteristics (e.g., paraffin waxes and microwaxes) to achieve the appropriate physicochemical properties. This study introduces a novel method based on visible and near-infrared spectroscopy (Vis-NIR) combined with machine learning (ML) for the characterization of blends of the two types of commonly marketed petroleum waxes (paraffin waxes and microwaxes). With spectroscopic data, Partial Least Squared Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF) Regression-based regression ML models have been developed, obtaining satisfactory results for the characterization of the percentage of blending in petroleum waxes. Moreover, strategies using wrapper variable selection methods like the Boruta algorithm and Genetic Algorithm (GA) have been implemented to assess if fewer predictors enhance model performance. Particularly, the application of wrapper variable selection methods, specifically the Boruta algorithm, has led to an improvement in the performance of the models obtained. Results obtained by the Boruta-PLS model showed the best performance with an RMSE of 2.972 and an R2 of 0.9925 for the test set and an RMSE of 1.814 and an R2 of 0.9977 for the external validation set. Additionally, this model allowed for establishing the relative importance of the variables in the characterization of the waxes mixture, pointing out that the hydrocarbon content ratio is critical in the determination of this value. An interactive web application was developed using the best model developed for easy processing of the data by the users.
Patients with endometrial cancer differ in terms of the extent of T-cell infiltration; however, the association between T-cell subpopulations and patient outcomes remains unexplored. We characterized ...285 early-stage endometrial carcinoma samples for T-cell infiltrates in a tissue microarray format using multiplex fluorescent immunohistochemistry. The proportion of T cells and their subpopulations were associated with clinicopathological features and relapse-free survival outcomes. CD3+ CD4+ infiltrates were more abundant in the patients with higher grade or non-endometrioid histology. Cytotoxic T cells (CD25+, PD-1+, and PD-L1+) were strongly associated with longer relapse-free survival. Moreover, CD3+ PD-1+ stromal cells were independent of other immune T-cell populations and clinicopathological factors in predicting relapses. Patients with high stromal T-cell fraction of CD3+ PD-1+ cells were associated with a 5-year relapse-free survival rate of 93.7% compared to 79.0% in patients with low CD3+ PD-1+ fraction. Moreover, in patients classically linked to a favorable outcome (such as endometrioid subtype and low-grade tumors), the stromal CD3+ PD-1+ T-cell fraction remained prognostically significant. This study supports that T-cell infiltrates play a significant prognostic role in early-stage endometrial carcinoma. Specifically, CD3+ PD-1+ stromal cells emerge as a promising novel prognostic biomarker.
Highlights • Quinpirole alleviates cold and tactile allodynia in a rat model of neuropathic pain. • Spinal mechanisms might be involved in the anti-allodynic effect of quinpirole. • However, ...supraspinal mechanisms probably account for most of the analgesic activity. • Pain relief is gradually achieved when quinpirole in chronically administered. • The study shows the potential of D2R agonists for the management of neuropathic pain.
Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the ...combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.
A methodology for the design of a thermal distortion compensation system is presented which will assist the manufacturing processes to reach higher levels of accuracy when working with large machines ...in common shop floor environments. A parametric state-space representation was selected as model architecture, providing multiple inputs and outputs capability and a compact formulation that takes into account previous thermal states of the machine. Inputs for the model are spindle speed and temperatures of main motor gearbox and room air. Outputs are the estimations of the thermal drift of the machine tool centre point along the three axes in different positions within the working volume. Model parameters were numerically identified with initial experimental tests performed in a large gantry-type milling machine, measuring mentioned variables and also thermal distortion values using a reference artifact along with non-contact proximity sensors. Proposed model was finally verified with a new validation measurement in the machine. Obtained results revealed 80% of error reduction in the vertical axis which comprised 70% of total thermal effects and 50% in the longitudinal X axis which comprised 25% of total thermal effects. Also it was concluded that the model benefits from using valuable information about the machine state from previous spindle speed register instead of using only temperature values. Proposed methodology benefits from providing a feasible implementation in real shop floor conditions without the necessity of including additional temperature sensors or probing systems in the machine.
•A new methodology for thermal drift compensation has been developed.•A mathematical model is used based in state-space representation with Kalman filter.•Model inputs are room air and gearbox temperatures and machine spindle speed.•Thermal drift in different TCP points is estimated and compensated online.•Validation was done in a large milling machine with results of up to 80% error reduction.
Objective
To determine, using the Delphi method, standardized recommendations for the follow‐up of patients undergoing an interventional procedure for the treatment of chronic pain in Spain.
Methods
...First, a systematic literature review was performed to identify the literature on the management of patients with chronic pain undergoing interventional techniques; subsequently, a two‐round Delphi survey with 108 questions was conducted. The questionnaire was validated by a Scientific Committee (5 experts) and sent to 47 experts specialized in chronic pain. “Consensus” or “intermediate consensus” was determined when ≥ 75% or < 75% to ≥ 65% of the experts selected the same answer for each item, respectively. Then, a face‐to‐face deliberation process was held with the Scientific Committee to analyze and discuss the results.
Results
The questionnaire was completed by 24 panelists (51%). Consensus was reached on 88.4% of the questions. The panelists identified pain, drug consumption, and quality of life as essential variables in the follow‐up of patients with chronic pain. Consensus was reached on most of the scales/questionnaires to be used in measuring outcomes during follow‐up, except for psychological status. Regarding the follow‐up frequency, in radicular spinal chronic pain, a consensus was reached on the first visit 1–2 months after the intervention, during the first year, at 1, 3, 6, and 12 months, and then every 6 months thereafter. For non‐radicular spinal chronic pain, the first visit 1–2 months after surgery was agreed upon, however, there was no consensus on follow‐up during the first year. For non‐spinal chronic pain, consensus was reached regarding the first visit at 1–2 months after surgery and during the first year at 1, 3, 6, and 12 months. No consensus was reached on follow‐up frequency for oncological chronic pain. After receiving a permanent neurostimulator implant for chronic pain, the first visit was agreed upon at 1–3 weeks, during the first year, at 2 weeks, 1, 3, 6, and 12 months, and after, every 6 months. For intrathecal infusion, it was agreed that the first visit should occur during the first month, and thereafter whenever the pump requires a refill.
Conclusions
These findings provide recommendations in relation to the frequency of follow‐up and the scales to be used with chronic pain patients undergoing interventional techniques in Spain.
The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents ...an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box-Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%. Analytical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), efficiently categorized samples by odor intensity. The Gaussian support vector machine (SVM), random forest, partial least squares regression, and support vector regression (SVR) algorithms were evaluated for their efficacy in odor grade classification and quantification. Gaussian SVM emerged as superior in classification tasks, achieving 100% accuracy, while Gaussian SVR excelled in quantifying odor levels, with a coefficient of determination (R
) of 0.9667 and a root mean square error (RMSE) of 6.789. This approach offers a fast, reliable, robust, objective, and reproducible alternative to the current ASTM sensory panel assessments, leveraging the analytical capabilities of HS-GC/MS and the predictive power of ML for quality control in the petrochemical sector's food-grade paraffin waxes.
Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality ...controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications.
PDF
