Classification of bloodstains deposited at different times on floor tiles using hierarchical modelling and a handheld NIR spectrometer.

Bloodstains are commonly encountered at crime scenes, especially on floor tiles, and can be deposited over different periods and intervals. Therefore, it is crucial to develop techniques that can accurately identify bloodstains deposited at different times. This study builds upon a previous investigation and aims to enhance the performance of three distinct hierarchical models (HMs) designed to differentiate and identify stains of human blood (HB), animal blood (AB), and common false positives (CFPs) on nine different types of floor tiles. Soft Independent Modeling Class Analogies (SIMCA), and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed as decision rules in this process. The originally published model was constructed using a training set that included samples with a known time of deposit of six days. This model was then tested to predict samples with various deposition times, including human blood samples aged for 0, 1, 9, 20, 30, and 162 days, as well as animal blood samples aged for 0, 1, 10, 13, 20, 29, 105, and 176 days. To improve the identification of human blood, the models were modified by adding zero-day and one-day-old bloodstains to the original training set. All models showed improvement when fresher samples were included in the training set. The best results were achieved with the hierarchical model that used partial least squares-discriminant analysis as the second decision rule and incorporated one-day-old samples in the training set. This model yielded sensitivity values above 0.92 and specificity values above 0.7 for samples aged between zero and 30 days.

Chemical profile, antimicrobial activity, and leaf anatomy of Adenophyllum porophyllum var. cancellatum.

Adenophyllum porophyllum var. cancellatum, known as "árnica del monte" in Mexico, is an aromatic annual plant belonging to the Asteraceae family that grows from southern Arizona to central Mexico. The aerial parts of the plant are used in traditional medicine to treat skin diseases such as irritations, infections, and wounds. In this study, the essential oil of this plant was characterized, and its antimicrobial activity was evaluated. This species has large glands in its leaves; therefore, for quality control purposes, an anatomical study of the leaves was performed. The essential oil was isolated from the aerial parts of the plant through hydro-distillation and analyzed using a gas chromatography/mass spectrometry (GC/MS) system. Its anti-yeast activity was evaluated against three Candida species and ten bacterial strains using the disk diffusion technique. The minimum inhibitory concentration (MIC), minimum fungicidal concentration (MFC), and minimum bactericidal concentration (MBC) were determined using broth microdilution. Anatomical study was performed on the middle part of the leaf. A yield of 0.5% of the essential oil was obtained from the herb, and Eighteen compounds in the essential oil were identified, within them trans pinocamphone (29.5%), limonene (24.7%), pinocarvone (21.8%), and cis pinocamphone (8.0%) were the main components. The inhibition zones were between 10 mm and 20 mm, and the MIC and MFC against the three Candida species ranged from 60 to 500 µg/ml. The leaf anatomy showed anisocytic stomata, simple and glandular trichomes of different types, and large and elliptical-shaped lysigenous glands, which can be used for taxonomic identification. The A. porophyllum var. cancellatum essential oil can serve as an alternative source of natural antimicrobial agents as an affordable approach to control infectious diseases. This is the first study that reports the chemical composition and antimicrobial activity of the essential oil, as well as the leaf anatomy of this species.

Genotyping of rabies positive samples isolated from animals in Mato Grosso and Rondônia - Brazil.

Lyssavirus is a genus that causes infectious disease transmit by bats transmit, which results in economic losses in livestock and public health problems. From 2005 to 2019, more than 49 thousand cases of the disease were registered in animals in Brazil, with 3418 registered in Mato Grosso (MT). The lack of information on the genetic diversity and distribution of the rabies virus in MT was the motivation for carrying out this study. A total of 117 samples of brain tissue from cattle, horses, donkeys, mules and sheep from 29 municipalities in the state of MT and one municipality in Rondônia were used. Direct immunofluorescence and/or biological tests performed from 2014 to 2021 indicated that all samples were positive for the disease. RNA was extracted and molecular analysis was performed using RT-PCR for the N gene. Of the 117 samples analyzed, 50 were amplified by RT-PCR, purified and sequenced. The samples showed 93.13%-100% identity with the rabies virus. The sequences were submitted to phylogenetic analysis that resulted in a tree of four clades; these were genetically grouped into distinct regions within the Desmodus rotundus lineage. The results of the geolocation of clades will be useful to guide monitoring, control and health surveillance programs in MT.

Calibration with virtual standards for monitoring biodiesel production using a miniature NIR spectrometer.

This work describes the use of virtual standards as calibration samples in an innovative multivariate calibration approach for the on-line monitoring of alkyl-esters content during biodiesel production process using a miniature near infrared (NIR) spectrometer. For comparison purposes, a partial least squares (PLS) model was built using synthetic blends prepared in laboratory with different concentrations of oil, glycerol, biodiesel, and ethanol and resulted in a satisfactory predictive ability (root mean square error of prediction, RMSEP, of 1.51% w/w). When compared to conventional methods, calibration with synthetic blends has the advantage of simplifying the experimental procedure and reducing the need for reference analysis. Nevertheless, it still requires the preparation of a considerable number of blends in laboratory. To overcome this limitation, this study proposed an innovative approach where a PLS model was constructed based on virtual standards: representative calibration spectra were created by mathematically mixing spectra from pure components and performing an adjustment using the Piecewise Direct Standardization (PDS) method. This significantly reduced the need for calibration synthetic blends and led to similar results (RMSEP of 1.75% w/w), compared to the previous approach. This work also demonstrates the use of the constructed models to predict the concentration profiles of alkyl-esters during the batch transesterification process.

Handheld near infrared spectrometer and machine learning methods applied to the monitoring of multiple process stages in industrial sugar production.

This work aimed to evaluate the performance of a handheld NIR spectrometer in developing calibration models to quantify brix and pol at various stages of an industrial sugar production process. Because of sample variability, collected over two harvesting seasons, NIR measurements were acquired either in transmittance or diffuse reflectance. For modelling purpose, partial least squares (PLS), also combined with variable selection techniques, and support vector machine regression (SVR) were investigated. SVR was applied to handle non-linearities within the data. In general, results illustrated the best performance of SVR, that yielded lower root mean square error of prediction (RMSEP) values for brix and pol for spectra acquisition in transmittance (0.59 and 0.69%w/w) and using diffuse reflectance (1.44 and 2.44%w/w), respectively. Results from models using spectra collected in transmittance were comparable to those reported in other works where benchtop instruments were used, highlighting the cheaper and simpler employment of the portable spectrometer.

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles.

One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.

A green analytical method for the analysis of polycyclic aromatic hydrocarbons in oral fluids from crack smokers.

Aim: Develop and validate a method of solid-phase microextraction (SPME) and liquid chromatography to investigate three major polycyclic aromatic hydrocarbons (PAHs) in oral fluid. Results/Methodology: The extraction phase was exposed to 1.5 ml of diluted oral fluid under stirring at 1000 rpm for 60 min, at 70°C. Then, it was immersed in 200 µl of acetonitrile for 10 min at 25°C for desorption of the analytes. Linearity, absolute recovery, and inter- and intra-assay relative standard deviations and relative errors were 50-300 ng.ml-1, ≥24% and ≤15% for all analytes, respectively. A full factorial design was used to SPME optimization. Discussion/Conclusion: The method is suitable for the exploratory analysis of some PAHs in the oral fluid of crack smokers.

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study.

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.

Quality by design applied to olanzapine and quetiapine LC-MS/MS bioanalysis.

One major challenge in quantifying drugs in biological matrices is to manage interfering compounds. A technique such liquid chromatography coupled to mass spectrometry in tandem (LC-MS/MS) is especially suitable for this application due to its high sensitivity and selectivity in detecting low concentrations of analytes in a complex system. Due to the complexity of LC-MS/MS systems, a number of experimental parameters must be optimized to provide an adequate separation and detection of the analyte. In the present work, a design of experiments approach was developed to optimize an LC-MS/MS-based bioanalytical method to extract olanzapine (OLZ) and quetiapine (QTP) from human plasma. Three steps for the optimization process were conducted: central composite face-centered design to optimize chromatographic parameters (Step 1), ionization in mass spectrometry (Step 2) and a full 32 factorial design to optimize analyte extraction conditions (Step 3). After the optimization process, resolutions and QTP and OLZ retention time (2.3 and 4, respectively) were optimum with pH of 4.7 and 85.5% of acetonitrile for the chromatographic step. Mass spectrometry optimization step provided an increase of (±50%) in the average peak area with high signal-to-noise relationship for the analytes studied. The proposed extraction method was 70% more efficient than the initial method for all drugs analyzed.

Identification of Luminescent Markers for Gunshot Residues: Fluorescence, Raman Spectroscopy, and Chemometrics.

Gunshot residue (GSR) is an evidence of major importance in firearm-related crimes. The recent introduction of nontoxic ammunition has made impossible the characterization of GSR particles by the current methods employed by forensic experts. To overcome this drawback, the introduction of luminescent markers was proposed, allowing on-site visual detection of luminescent gunshot residue (LGSR) at the crime scene. Three different luminescent markers coordinated with europium for specific and selective encoding of ammunition have been proposed. To promote a variety of versatile tools for GSR analysis, spectroscopic techniques combined with chemometric methods can be applied to achieve a reliable, fast, and nondestructive means to identify LGSR and discriminate among the different markers. Luminescence (emission and excitation), normal, and resonance Raman spectroscopies associated with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were evaluated. The classification model using the complementary information on emission and excitation spectra, a.k.a. data fusion, provided a 100% correct classification for all markers. A comprehensive study has been developed to show that the insertion of luminescent markers enables not only the easy localization of GSR residues but also the possibility of ammunition encoding through the use of multivariate classification methods.

Raman hyperspectral imaging and a novel approach for objective determination of the order of crossing ink lines.

The determination of the chronological order of crossing ink lines is a difficult problem in the examination of Questioned Documents. Modern methodologies are based on non-destructive chemical and physical analysis. However, the determination of the chronological sequence of crossing ink lines remains visual and subjective. This manuscript describes a study where Raman hyperspectral imaging is associated with different chemometric techniques and a novel method is applied for the objective determination of the chronological order of gel pen ink crossings. Eight blue and black gel pens of different brands were used to prepare 56 crossings on white paper. Raman mapping analyzed the entire crossing and the hyperspectral images were treated with k-means, Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) and Partial Least Squares - Discriminant Analysis (PLS-DA). The order of crossings was determined visually and using a novel method that consisted in calculating the relative quantity of pixels of each ink in the crossing region that were equal or higher than an established threshold. Different thresholds were tested using mean and standard deviation. The MCR-ALS and the thresholds mean minus 1,96 of the standard deviation and the mean minus 2575 of the standard deviation determined correctly more than 70% of the crossings. This method proved to be more efficient than microscopy examination and more objective since no inconclusive determinations were obtained. The methodology is promising for entirely objective and highly effective determination of the chronological sequence of crossing ink lines.

Evaluating the potential of near infrared hyperspectral imaging associated with multivariate data analysis for examining crossing ink lines.

The determination of the chronological sequence of crossing ink lines is a recurrent issue in the forensic examination of questioned documents. This manuscript intends to evaluate the potential of hyperspectral imaging in the near infrared range (HSI-NIR) combined with multivariate data analysis for rapid, objective and nondestructive analysis of crossing ink lines made with black pens. Twenty-one black gel and ballpoint pens from different brands and models were employed to prepare crossing combinations in both orders in white office paper. An initial pre-selection and extraction step using the Principal Components Analysis (PCA) scores plot arranged as histograms was necessary for extracting the inks spectra. Then, Partial Least Squares-Discriminant Analysis (PLS-DA) was applied for selection of the most important variables. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in the augmented form was performed using both the raw and the pre-processed spectra. However, the pre-processing of spectra resulted in incorrect identification of pixels in the inks distribution maps. The MCR-ALS results obtained using the raw spectra allowed the correct determination of the order of crossings in only one pair of gel-gel pen crossings. The remainder were either impossible to discriminate ink from paper or the skipping of one pen ink line led to incorrect determinations in one of the orders. In spite of the practical advantages of the HSI-NIR for document examination and the different chemometric approaches considered for surpassing some of the spectral limitations, the results obtained demonstrate the difficulties of using this technology for application in real forensic cases.

Chemometric approaches for document dating: Handling paper variability.

A non-destructive methodology based on Fourier Transformed Infrared Spectroscopy (FTIR) is proposed in this research to estimate the age of documents of different ages. Due the variability in the samples caused by their different chemical compositions, chemometric approaches were proposed to build one unique regression model able to determine the age of the paper regardless of its composition. PLS models were built employing Generalized Least Squares Weighting (GLSW) and Orthogonal Least Squares (OLS) filters to reduce the variability of samples from the same year. Afterwards, sparse PLS, which is an extension of the PLS model including a variable selection step, was applied to compare its performance with the preprocessing filters. All techniques proposed were compared to the initial PLS models, showing the potential of the chemometric approaches applied to FTIR data to estimate the age of unknown documents.

Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms.

The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits.

A Simple Device for Lens-to-Sample Distance Adjustment in Laser-Induced Breakdown Spectroscopy (LIBS).

A simple device based on two commercial laser pointers is described to assist in the analysis of samples that present uneven surfaces and/or irregular shapes using laser-induced breakdown spectroscopy (LIBS). The device allows for easy positioning of the sample surface at a reproducible distance from the focusing lens that conveys the laser pulse to generate the micro-plasma in a LIBS system, with reproducibility better than ±0.2 mm. In this way, fluctuations in the fluence (J cm-2) are minimized and the LIBS analytical signals can be obtained with a better precision even when samples with irregular surfaces are probed.

Standardization from a benchtop to a handheld NIR spectrometer using mathematically mixed NIR spectra to determine fuel quality parameters.

The interest in performing in field measures using portable instruments is growing increasingly. Calibration transfer techniques can be used to enable models, predicted values or spectra obtained in a benchtop instrument be used in portable instrument, saving money and time required for a complete recalibration. Most of the calibration transfer methods require a set of transfer samples which spectra have to be acquired in both spectrometers. The present work evaluates the use of virtual standards as transfer samples in the reverse standardization (RS) method in order to standardize very dissimilar spectral responses of fuel samples (gasoline and biodiesel blends) from a high-resolution benchtop Frontier FT-NIR (PerkinElmer) spectrometer and a handheld MicroNIR™1700 (JDSU). These virtual standards can be created by mathematically mixing spectra from the pure solvents present in gasoline or diesel/biodiesel (D/B) blends, to avoid volatilization and changes in the composition of the compounds during storage and/or transportation of the real transfer fuel samples. Virtual standards were created using ten and five pure solvents for gasoline and D/B blends, respectively. Partial least squares regression (PLS) models were built for five quality parameters of gasoline (distillation temperatures at 10%, 50%, 90% and final boiling point (FBP) volume recovered and density) and one of D/B blends (biodiesel content). The RMSEP values obtained after the standardization approaches were equivalent to the reproducibility of the reference methods, except for density and biodiesel content parameters obtained for the virtual samples standardization approach. RS procedure provided promising results showing that it is possible to transfer gasoline or D/B blend spectra acquired with a high-resolution benchtop instrument to the handheld MicroNIR using virtual standards as transfer samples.

Comprehensive near infrared study of Jatropha oil esterification with ethanol for biodiesel production.

This work presents a comprehensive near infrared study for in-line monitoring of the esterification reaction of high acid oils, such as Jatropha curcas oil, using ethanol. Parallel reactions involved in the process were carried out to select a spectral region that characterizes the evolution of the esterification reaction. Using absorbance intensities at 5176cm(-1), the conversion and kinetic behaviors of the esterification reaction were estimated. This method was applied to evaluate the influence of temperature and catalyst concentration on the estimates of initial reaction rate and ester conversion as responses to a 2(2) factorial experimental design. Employment of an alcohol/oil ratio of 16:1, catalyst concentration of 1.5% w/w, and temperatures at 65°C or 75°C, made it possible to reduce the initial acidity from 18% to 1.3% w/w, which is suitable for transesterification of high free fatty acid oils for biodiesel production. Using the proposed analytical method in the esterification reaction of raw materials with high free fatty acid content for biodiesel makes the monitoring process inexpensive, fast, simple, and practical.

Dispersive liquid-liquid microextraction based on solidification of floating organic drop and high-performance liquid chromatography to the analysis of cocaine's major adulterants in human urine.

A simple method has been proposed for the determination of cocaine's major adulterants (caffeine, levamisole, lidocaine, phenacetin, diltiazem, and hydroxyzine) in human urine by dispersive liquid-liquid microextraction based on solidification of floating organic drop (DLLME-SFO) in combination with high-performance liquid chromatography - photodiode array detector (HPLC-PDA). The reversed-phase chromatographic separation was obtained with a column C18 extended (250×4.6mm; 5µm; 80Å) in gradient elution mode using acetonitrile-trifluoroacetic acid 0.026% (v,v) (pH=2.5) at 1mLmin-1 as mobile phase, at 25°C, and detection at 235nm. The analysis time was 25min. This condition had the best resolution factors (>1.15), retention factors (>0.68), number of plates (>2094.9), and separation factors (>1.05) for all targets, indicating a good separation. The kind of extraction and dispersive solvent were investigated for unifactorial design. The buffer pH, the volume of extraction and disperser solvent, and the amount of salt were optimized for full factorial design. Under optimum conditions, human urine samples were alkalized with 0.5M sodium phosphate buffer (pH 10) and added to sodium chloride (20%m/v). Acetonitrile (150µL) and 1-dodecanol (30µL) were used as dispersive and extraction solvent, respectively. The method presented linear range of 312.5-3125ngmL-1 to caffeine and levamisole and 187.5-1875ngmL-1 to lidocaine, phenacetin, diltiazem, and hydroxyzine. The limit of quantification was 187.5ngmL-1 to lidocaine, phenacetin, diltiazem, and hydroxyzine and 312.5ngmL-1 for caffeine and levamisole. The recovery mean values were between 6.0 and 42.6%. The method showed good precision and accuracy, with within- and between-run relative standard deviation and relative error less than 15%. The samples were stable after freeze-thaw cycle and short-term room temperature stability tests. Besides, this method was satisfactorily applied in urine of cocaine users. It is expected that this method, which was the first to combine the use of DLLME-SFO and HPLC-PDA for the determination of cocaine's major adulterants in human urine, will contribute to the accuracy in the diagnosis of acute intoxication, the proper planning of therapeutic measures, as well as to the favorable prognostic of cocaine intoxicated patients.

NIR hyperspectral imaging to evaluate degradation in captopril commercial tablets.

Pharmaceutical quality control is important for improving the effectiveness, purity and safety of drugs, as well as for the prevention or control of drug degradation. In the present work, near infrared hyperspectral images (HSI-NIR) of tablets with different expiration dates were employed to evaluate the degradation of captopril into captopril disulfide in different layers, on the top and on the bottom surfaces of the tablets. Multivariate curve resolution (MCR) models were used to extract the concentration distribution maps from the hyperspectral images. Afterward, multivariate image techniques were applied to the concentration distribution maps (CDMs), to extract features and build models relating the main characteristics of the images to their corresponding manufacturing dates. Resolution methods followed by extracting features were able to estimate the tablet manufacture date with a prediction error of 120days. The model developed could be useful to evaluate whether a sample shows a degradation pattern consistent with the date of manufacturing or to detect abnormal behaviors in the natural degradation process of the sample. The information provided by the HIS-NIR is important for the development of the process (QbD), looking inside the formulation, revealing the behavior of the active pharmaceutical ingredient (API) during the product's shelf life.