Indoor Air Quality and Bioaerosols in Spanish University Classrooms.

A comprehensive study assessed indoor air quality parameters, focusing on relevant air pollutants such as particulate matter (PM10 and PM2.5), gaseous compounds (CO, CO2, formaldehyde, NO2) and volatile/semi-volatile organic chemicals, as well as respiratory viruses (including SARS-CoV-2), fungi and bacteria in Spanish university classrooms. Non-target screening strategies evaluated the presence of organic pollutants inside and outside the classrooms. Saliva samples from teachers and students were collected to explore correlations between respiratory viruses in the air and biological samples. Indoor results revealed the punctual exceedance of recommended guidelines for CO2, formaldehyde (HCHO), volatile organic compounds (TVOCs) and PM in the least naturally ventilated classrooms. Significant differences occurred between the classes, with the least ventilated one showing higher average concentrations of CO2, HCHO, NO2, PM10 and PM2.5. A respiratory virus (rhinovirus/enterovirus) was detected in the medium naturally ventilated classroom, although saliva samples tested negative. Suspect screening tentatively identified 65 substances indoors and over 200 outdoors, with approximately half reporting a high toxicological risk based on the Cramer rules. The study provides a comprehensive overview of indoor air quality, respiratory viruses and organic pollutants in university classrooms, highlighting the variations and potential health risks associated with ventilation differences.

Quantification of phenolic acids by partial least squares Fourier-transform infrared (PLS-FTIR) in extracts of medicinal plants.

INTRODUCTION: Phenolic compounds are ubiquitous compounds found in all plants as their secondary metabolites. Phenols are becoming increasingly important particularly because of their beneficial effects on health. OBJECTIVE: To provide a faithful calibration model for the simultaneous determination and quantification of phenolic acids, as salicylic, vanillic, p-hydroxybenzoic acids, eugenol and thymol in different extracts of medicinal plants, a comparative study was made between two methods of infrared measurements based on attenuated total reflectance (ATR) and transmission. METHODS: Characteristic absorbance peak heights of mid-infrared spectra of individual phenolic acids were measured for the compounds. For partial least squares regression (PLS-R) calibration mixtures of phenolic acids, wavenumber ranges, spectra pretreatment and number of latent variables, were assayed to improve the prediction capability of models using different spectral preprocessing techniques after mean centring of infrared data. Plant extracts were prepared by using water/methanol and ethanolic extraction solvents followed by Fourier-transform infrared (FTIR)-spectrometry analysis. The concentrations of phenolic compounds contained in the extracts were obtained by using the best models selected of the PLS calibration. RESULTS: PLS-ATR-mid-infrared (MIR) measurement provided the most accurate results and offers a good methodology for the determination of phenolic acids. The analysis showed that the rate of phenolic acids and monoterpenic phenols in extracts of medicinal plants is in the same range obtained with the Folin-Ciocalteu method, which confirm that the developed method using PLS is therefore, highly specific and selective. CONCLUSION: The simultaneous direct quantification of various phenolic acids in different plant extracts was possible with a fast and simple methodology based on PLS-ATR-FTIR analysis.

Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy.

Near-infrared (NIR) spectroscopy was evaluated as a rapid method for the determination of oleic, palmitic, linoleic and linolenic acids as well as omega-3, omega-6, and to predict polyunsaturated, monounsaturated and saturated fatty acids, together with triacylglycerides, diglycerides, free fatty acids and ergosterol in salmon oil. To do it, Partial Least Squares (PLS) regression models were applied to correlate NIR spectra with aforementioned fatty acids and lipid classes. Results obtained were validated in front of reference procedures based on high performance thin layer and gas chromatography. PLS-NIR has a good predictive capability with relative root mean square error of prediction (RRMSEP) values below or equal to 1.8% and provides rapid analysis without the use of any chemicals making it an environmentally friendly methodology.

A green analytical chemistry approach for lipid extraction: computation methods in the selection of green solvents as alternative to hexane.

There is a great interest in finding alternatives and green solvents in extraction processes to replace petroleum based solvents. In order to investigate these possibilities, computational methods, as Hansen solubility parameters (HSP) and conductor-like screening model for real solvent (COSMO-RS), were used in this work to predict the solvation power of a series of solvents in salmon fish lipids. Additionally, experimental studies were used to evaluate the performance in lipids extraction using 2-methyltetrahydrofurane, cyclopentyl methyl ether, dimethyl carbonate, isopropanol, ethanol, ethyl acetate, p-cymene and d-limonene compared with hexane. Lipid classes of extracts were obtained by using high performance thin-layer chromatography (HPTLC), whereas gas chromatography with a flame ionization detector (GC/FID) technique was employed to obtain fatty acid profiles. Some differences between theoretical and experimental results were observed, especially regarding the behavior of p-cymene and d-limonene, which separate from the predicted capability. Results obtained from HPTLC indicated that p-cymene and d-limonene extract triglycerides (TAGs) and diglycerides (DAGs) at levels of 73 and 19%, respectively, whereas the other studied extracts contain between 75 and 76% of TAGs and between 16 and 17% of DAGs. Fatty acid profiles, obtained by using GC-FID, indicated that saturated fatty acids (SFAs) between 19.5 and 19.9% of extracted oil, monounsaturated fatty acids (MUFAs) in the range between 43.5 and 44.9%, and PUFAs between 31.2 and 34.6% were extracted. p-Cymene and limonene extracts contained lower percentages than the other studied solvents of some PUFAs due probably to the fact that these unsaturated fatty acids are more susceptible to oxidative degradation than MUFAs. Ethyl acetate has been found to be the best alternative solvent to hexane for the extraction of salmon oil lipids. Graphical Abstract ᅟ.

Identification and determination of synthetic cannabinoids in herbal products by dry film attenuated total reflectance-infrared spectroscopy.

A new procedure has been developed for the identification and quantitative determination of synthetic cannabinoids in illicit herbal preparations. The methodology is based on the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) measurement of sample extracts with 2-propanol drying 5µL of the extracts onto the ATR crystal. The qualitative identification was carried out on the 2-propanol extract after identification of the herbal matrix, followed by its subtraction and using a cut-off criterion of 75%. Quantitative determination was made by univariate calibration using the absorbance of the band located at 1520cm-1 of the spectrum. Four different cannabinoids, RCS-4, JWH-210, UR-144 and JWH-081 were used as test analytes and the ATR-FTIR method provided limits of quantification from 14 till 79mgL-1. Sized blank market samples were successfully identified and UR-144 quantified.

Prediction of alkaline earth elements in bone remains by near infrared spectroscopy.

An innovative methodological approach has been developed for the prediction of the mineral element composition of bone remains. It is based on the use of Fourier Transform Near Infrared (FT-NIR) diffuse reflectance measurements. The method permits a fast, cheap and green analytical way, to understand post-mortem degradation of bones caused by the environment conditions on different skeletal parts and to select the best preserved bone samples. Samples, from the Late Roman Necropolis of Virgen de la Misericordia street and En Gil street located in Valencia (Spain), were employed to test the proposed approach being determined calcium, magnesium and strontium in bone remains and sediments. Coefficients of determination obtained between predicted values and reference ones for Ca, Mg and Sr were 90.4, 97.3 and 97.4, with residual predictive deviation of 3.2, 5.3 and 2.3, respectively, and relative root mean square error of prediction between 10% and 37%. Results obtained evidenced that NIR spectra combined with statistical analysis can help to predict bone mineral profiles suitable to evaluate bone diagenesis.

Determination of 3,4-methylenedioxypyrovalerone (MDPV) in oral and nasal fluids by ion mobility spectrometry.

A fast and sensitive methodology has been developed for the evaluation of the 3,4-methylenedioxypyrovalerone (MDPV) consumed. Based on ion mobility spectrometry (IMS), MDPV was directly determined in nasal fluids with a limit of detection (LOD) in the order of 22 ng mL(-1), which corresponds to an absolute amount of 33 ng of MDPV per swab. MDPV was also determined after liquid-liquid microextraction (LLME) in oral fluids to avoid matrix effects, obtaining a LOD value of 4.4 ng mL(-1) in oral fluid samples. The IMS spectrum for MDPV exhibited a peak with K0 = 1.210 ± 0.005 cm(2)V(-1) s(-1) at a drift time of 14.62 ms, the total analysis time being 4.5 min per oral fluid and 1.5 min per nasal fluid sample. Samples must be analyzed within 24 h following collection and dissolution in 2-propanol, based on the complementary stability studies.

Green direct determination of mineral elements in artichokes by infrared spectroscopy and X-ray fluorescence.

Near infrared (NIR) and X-ray fluorescence (XRF) spectroscopy were investigated to predict the concentration of calcium, potassium, iron, magnesium, manganese and zinc in artichoke samples. Sixty artichokes were purchased from different Spanish areas (Benicarló, Valencia and Murcia). NIR and XRF spectra, combined with partial least squares (PLS) data treatment, were used to develop chemometric models for the prediction of mineral concentration. To obtain reference data, samples were mineralised and analysed by inductively coupled plasma optical emission spectrometry (ICP-OES). Coefficients of determination obtained for the regression between predicted values and reference ones for calcium, potassium, magnesium, iron, manganese and zinc were 0.61, 0.79, 0.53, 0.77, 0.54 and 0.60 for NIR and 0.96, 0.93, 0.80, 0.79, 0.76 and 0.90 for XRF, respectively. Both assayed methodologies, offer green alternatives to classical mineral analysis, but XRF provided the best results in order to be used as a quantitative screening method.

Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine.

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2% w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.

Detection of tetrahydrocannabinol residues on hands by ion-mobility spectrometry (IMS). Correlation of IMS data with saliva analysis.

Ion-mobility spectroscopy (IMS) was evaluated as a high-throughput, cheap, and efficient analytical tool for detecting residues of tetrahydrocannabinol (THC) on hands. Regarding the usefulness of hand residues as potential samples for determining THC handling and abuse, we studied the correlation between data obtained from cannabis consumers who were classified as positive after saliva analysis and from those who were classified as positive on the basis of the information from hand-residue analysis. Sampling consisted of wiping the hands with borosilicate glass microfiber filters and introducing these directly into the IMS after thermal desorption. The possibility of false positive responses, resulting from the presence of other compounds with a similar drift time to THC, was evaluated and minimised by applying the truncated negative second-derivative algorithm. The possibility of false negative responses, mainly caused by competitive ionisation resulting from nicotine, was also studied. Graphical abstract THC residues: from hands to analytical signals.

Analysis of ecstasy in oral fluid by ion mobility spectrometry and infrared spectroscopy after liquid-liquid extraction.

We developed and evaluated two different strategies for determining abuse drugs based on (i) the analysis of saliva by ion mobility spectrometry (IMS) after thermal desorption and (ii) the joint use of IMS and infrared (IR) spectroscopy after liquid-liquid microextraction (LLME) to enable the sensitivity-enhanced detection and double confirmation of ecstasy (MDMA) abuse. Both strategies proved effective for the intended purpose. Analysing saliva by IMS after thermal desorption, which provides a limit of detection (LOD) of 160µgL(-1), requires adding 0.2M acetic acid to the sample and using the truncated negative second derivative of the ion mobility spectrum. The joint use of IMS and IR spectroscopy after LLME provides an LOD of 11µgL(-1) with the former technique and 800µgL(-1) with the latter, in addition to a limit of confirmation (LOC) of 1.5mgL(-1). Using IMS after thermal desorption simplifies the operational procedure, and using it jointly with IR spectroscopy after LLME allows double confirmation of MDMA abuse with two techniques based on different principles (viz., IMS drift times and IR spectra). Also, it affords on-site analyses, albeit at a lower throughput.

Direct determination of major components in human diets and baby foods.

A fast method has been developed for the determination of fat, proteins, carbohydrates, and energy value in baby food, infant fast food, and canteen menus, using near-infrared (NIR) and mid-infrared (MIR) spectroscopy measurements and multivariate calibration methods based on partial least square regression. Reference standard methods were employed to build and validate the infrared methods for direct determination of nutrients. Coefficients of determination obtained between predicted values and reference ones for total fat, proteins, carbohydrates, and energy value were 96.7, 98.1, 98.9, and 96.5 for NIR and 91.0, 93.0, 92.0, and 84.1 for MIR, respectively, with relative root mean square error of prediction (RRMSEP) below or equal to 9 % for NIR and 16 % for MIR. Results obtained indicate that both NIR and MIR techniques have good predictive capabilities, with the NIR method being the most accurate and simple.

Assessment of the statistical significance of classifications in infrared spectroscopy based diagnostic models.

Fourier transform infrared (IR) spectroscopy in combination with multivariate data analysis is a versatile tool that can be applied to disease diagnosis. However, a rigorous validation of the obtained models is necessary in order to obtain robust results. This work evaluates the advantages of the use of permutation testing for determining the statistical significance of the misclassification errors obtained from IR based diagnostic models through cross validation (CV). The model performance, estimated by CV, is compared to a distribution of CV-performance values obtained using randomly permuted class labels. The distribution of 'random CV-values' is considered as a null distribution and used to establish the significance of the model estimators obtained using real class labels. ATR-FTIR spectra of serum samples were classified using random forest (RF) classifiers according to two criteria, the tag number (a randomly assigned pseudo class membership) and the level of urea (real class). CV errors obtained were compared to the null distribution of CV errors from a permutation test and an independent validation set. The procedure was evaluated testing typical conditions leading to overoptimistic estimations provided by the CV like e.g. the size of subsamples used during CV, variable selection and the use of replicates. Results show that for the tag number (pseudo class), CV indicated classification errors between 23 and 33% depending on the subsample size employed. Those values were even lower when variable selection or replicates were used. However, permutation testing indicated that those CV errors were non-significant. In contrast, for sample classification according to their levels of urea, all cross validation errors were found to be significant. Although the proposed method is computationally intensive, it provides a simple way of calculating an empirical p-value of the CV-estimator, thus establishing the statistical significance and providing a feasibility indicator especially useful for studies where the number of samples is limited.

Detection and characterization of emerging psychoactive substances by ion mobility spectrometry.

Rapid detection and identification of novel psychoactive substances (NPS) continues to present significant analytical challenges to forensic and analytical chemists. Ion mobility spectrometry (IMS) has been traditionally considered as the analytical technique of choice to detect illicit drugs in security points in airports, borderlines and customs. Databases of the reduced mobility (K0 ) values of illicit drugs are available in the scientific literature and they should be completed with data of emerging designer drugs. In this paper, we have evaluated the effect of different measurement conditions and determined the K0 values of an important number of NPS including different families; such as phenethylamines, cathinones, synthetic cannabinoids and tryptamines among others to be incorporated to the existing data to provide a rapid detection and identification of this emerging threat.

Determination of the mineral composition of foods by infrared spectroscopy: a review of a green alternative.

The determination of mineral composition of foods involves, in most cases, the use of long and tedious sample preparation, which consumes acids and reagents and sometimes requires the use of expensive instrumentation. This is the main reason for the search for direct analytical procedures, based on the use of infrared sample spectra and chemometrics, to model the signals in order to determine the presence of essential and trace toxic elements in foods. The state-of-the-art of the research in this field has been established in the present review article from the critical evaluation of articles available in the literature. Chemometric methods employed and their validation, together with a discussion about the different techniques used for signal acquisition, were evaluated for their ability to predict new sample composition.

Towards the determination of isoprene in human breath using substrate-integrated hollow waveguide mid-infrared sensors.

Selected volatile organic compounds (VOCs) in breath may be considered biomarkers if they are indicative of distinct diseases or disease states. Given the inherent molecular selectivity of vibrational spectroscopy, infrared sensing technologies appear ideally suitable for the determination of endogenous VOCs in breath. The aim of this study was to determine that mid-infrared (MIR; 3-20 µm) gas phase sensing is capable of determining isoprene in exhaled breath as an exemplary medically relevant VOC by hyphenating novel substrate-integrated hollow waveguides (iHWG) with a likewise miniaturized preconcentration system. A compact preconcentrator column for sampling isoprene from exhaled breath was coupled to an iHWG serving simultaneously as highly miniaturized gas cell and light conduit in combination with a compact Fourier transform infrared spectrometer. A gas mixing system enabled extensive system calibration using isoprene standards. After system optimization, a calibration function obtaining a limit of quantification of 106 ppb was achieved. According to the literature, the obtained sensitivity is sufficient for quantifying middle to high isoprene concentrations occurring in exhaled breath. Finally, a volunteer breath sample was analysed proving comparable values of isoprene in a real-world scenario. Despite its fundamental utility, the proposed methodology contains some limitations in terms of sensitivity and temporal resolution in comparison with the readily available measurement techniques that should be addressed during future optimization of the system. Nonetheless, this study presents the first determination of endogenous VOCs in breath via advanced hollow waveguide MIR sensor technology, clearly demonstrating its potential for the analysis of volatile biomarkers in exhaled breath.

A green method for the determination of cocaine in illicit samples.

Direct determination of cocaine in untreated seized samples has been made based on diffuse reflectance measurements of the near infrared (NIR) radiation through samples contained inside standard glass vials. The method used a series of previously analyzed samples, by the reference gas chromatography method, to build a partial least squares calibration model which was validated using an independent set of samples. The use of a general model for samples containing from 11.38% till 86.44% (w/w) cocaine was based on the use of spectral ranges from 12500.7 to 10128.6, 9339.8 to 6967.7 and 5388.3 to 4597.6cm(-1) with previous first derivative and vector normalization data pre-processing and provided a root mean square error of prediction (RMSEP) of 4.0% (w/w) with a residual prediction deviation (RPD) of 3.9% (w/w), based on the use of 8 latent variables, 34 samples for calibration and an independent set of 44 samples for validation. The aforementioned results could be improved on considering two separate models, one for high concentrated bulk samples and another for samples diluted with cutting agents. Additionally a new set of batch samples with cocaine concentrations from 60% till 84% was evaluated by using the developed method.

Chemometric determination of lipidic parameters in serum using ATR measurements of dry films of solvent extracts.

Attenuated total reflectance (ATR) infrared spectroscopy of dried organic extracts of serum samples has been evaluated as a fast method for the determination of triglycerides, cholesterol, high density lipoprotein (HDL) and low density lipoprotein (LDL). After careful selection of solvents based on green parameters, serum samples were extracted using hexane-isopropanol and ethyl acetate-ethanol mixtures. Microscopy studies and comparison with standard spectra were performed in order to investigate whether the proposed methodology is suitable for the quantification of lipids in serum samples. The results of these preliminary studies confirmed that the variations in the IR spectra of sample extracts could be related quantitatively to variations in the concentrations of the target analytes. Then, ATR spectra of the dried sample extracts were obtained and direct measurement of the spectra were carried out and modelled using partial least squares (PLS) and reference concentrations. PLS models obtained from the extracts of the two mixtures were compared with those obtained from direct measurement of sera samples. The prediction errors obtained using the proposed approach were considerably (between 27 and 72%) better than those obtained by the direct measurements of sera. For triglycerides and cholesterol relative errors below 9% and 12% respectively were obtained with this method, which are comparable to the tolerance for the errors of the control analysis established at the hospital. For HDL and LDL, the errors found were between 18 and 20%. The incorporation of a preprocessing extraction step, involves time and solvent consumption. However, the results obtained provide evidence that the proposed method provides, in a few minutes and using simple instrumentation and with minimum cost, important information about the lipidic profile of patients sera at a good screening confidence level.

Atmospheric compensation in Fourier transform infrared (FT-IR) spectra of clinical samples.

A new method is proposed for the elimination of the spectral contribution of two atmospheric gases (CO2 and H2O) in Fourier transform infrared (FT-IR) spectra of clinical samples. The algorithm is based on the measurement of reference spectra of H2O followed by an automatic calculation of the spectral contribution of the above-mentioned gases to the sample spectra. Then this contribution is compensated by spectral subtraction. Attenuated total reflectance FT-IR spectra of serum and urine samples in the presence of atmospheric gases were corrected and compared with spectra obtained with an N2 purge. Visual inspection of the spectra as well as calculated noise levels confirmed that the method compensated for the contribution of CO2 and H2O to serum and urine spectra to a great extent. As compared with the correction performed by a commercial software package, the proposed method clearly improved the correction of CO2 and provided similar results for H2O compensation. Correlation values between corrected spectra and those obtained under N2-purged conditions in the fingerprint region were higher than 0.995, and noise values in regions where samples did not absorb confirmed the suitability of the correction. The proposed method is fast, easy, and extensible to other gaseous interferences, devices, and acquisition conditions.

Modified locally weighted--partial least squares regression improving clinical predictions from infrared spectra of human serum samples.

Locally weighted partial least squares regression (LW-PLSR) has been applied to the determination of four clinical parameters in human serum samples (total protein, triglyceride, glucose and urea contents) by Fourier transform infrared (FTIR) spectroscopy. Classical LW-PLSR models were constructed using different spectral regions. For the selection of parameters by LW-PLSR modeling, a multi-parametric study was carried out employing the minimum root-mean square error of cross validation (RMSCV) as objective function. In order to overcome the effect of strong matrix interferences on the predictive accuracy of LW-PLSR models, this work focuses on sample selection. Accordingly, a novel strategy for the development of local models is proposed. It was based on the use of: (i) principal component analysis (PCA) performed on an analyte specific spectral region for identifying most similar sample spectra and (ii) partial least squares regression (PLSR) constructed using the whole spectrum. Results found by using this strategy were compared to those provided by PLSR using the same spectral intervals as for LW-PLSR. Prediction errors found by both, classical and modified LW-PLSR improved those obtained by PLSR. Hence, both proposed approaches were useful for the determination of analytes present in a complex matrix as in the case of human serum samples.