Non-destructive Spectroscopy combined with chemometrics as a tool for Green Chemical Analysis of environmental samples: A review

Spatial distribution of polycyclic aromatic hydrocarbons in sediment deposits in a Seine estuary tributary by hyperspectral imaging

Water bodies allow the storage of sediments from their catchment areas, including sediments containing persistent contaminants. This study used visible and near-infrared hyperspectral imaging to characterize the composition of sediment deposits collected in Martot Pond (France) and to reconstruct the volume of polycyclic aromatic hydrocarbon (PAH) contaminated sediments in the pond. Additionally, combining this method with polychlorinated biphenyl (PCB) analysis enhanced the age model associated with these sediments. To achieve this, indicators of oxides and chlorophyll a (and its derivatives) were employed to correlate various sediment cores, and to propose a sedimentary filling mode for the pond. Furthermore, one sedimentary unit, which appears homogeneous but of variable size within the pond, exhibited repetitive alternations associated with tidal cycles due to a defect in the Martot dam, corresponding to 34 +/- 3 days. A chemometric approach was used to model PAHs with near-infrared hyperspectral imaging data (validation determination coefficient of 0.85, Root Mean Squared Error of Prediction of 1.64 mg/kg). This model was then applied to other cores, coupled with the sedimentary filling mode in the pond, allowing the reconstruction of the volume of PAH contamination. Thus, this study demonstrates that hyperspectral imaging is a powerful tool for estimating various contaminants in sediments: not only is it much faster than conventional chromatographic methods, it also provides a more detailed understanding of a sample, and even of a site through the correlation of multiple core samples.

Phenotypic profiling based on body fluid traces discovered at the scene of crime: Raman spectroscopy of urine stains for race differentiation

Modern criminal investigations heavily rely on trace bodily fluid evidence as a rich source of DNA. DNA profiling of such evidence can result in the identification of an individual if a matching DNA profile is available. Alternatively, phenotypic profiling based on the analysis of body fluid traces can significantly narrow down the pool of suspects in a criminal investigation. Urine stain is a frequently encountered specimen at the scene of crime. Raman spectroscopy offers great potential as a universal confirmatory method for the identification of all main body fluids, including urine. In this proof-of-concept study, Raman spectroscopy combined with advanced statistics was used for race differentiation based on the analysis of urine stains. Specifically, a Random Forest (RF) model was built, which allowed for differentiating Caucasian (CA) and African American (AA) descent donors with 90% accuracy based on Raman spectra of dried urine samples. Raman spectra were collected from samples of 28 donors varying in age and sex. This novel technology offers great potential as a universal forensic tool for phenotypic profiling of a potential suspect immediately at the scene of a crime, providing invaluable information for a criminal investigation.

PCA and PLS Analysis of Lanthanides Using Absorbance and Single-Beam Visible Spectra

During process monitoring applications, referenced optical spectroscopy, such as absorbance spectroscopy, can suffer from environmental and instrumental fluctuations that alter the intensity of irradiance reaching the spectrometer's detector at each detected frequency. Temperature, vibration, light source aging, instrument damage, detector aging, detector registry shifts, sampling cell degradation, and similar perturbations create situations in which a previously collected reference spectrum may no longer be valid for the current state of the system. This can lead to the calculation of poor-quality absorbance spectra that are unsuitable for qualitative or quantitative analysis based on prior calibration models. The use of single-beam spectra in the creation of multivariate calibration models circumvents the need for collecting and maintaining a stable reference spectrum throughout an ongoing chemical process. However, unlike absorbance spectra, which typically have a zero baseline, single-beam spectra contain a high background signal relative to an analyte signal, and they may also contain intense peaks from the light source. Here, multivariate principal component analysis (PCA) and partial least squares (PLS) regression models are built using single-beam and absorbance spectra to compare the efficacy of both types of spectra for qualitative and quantitative analyses of lanthanide solutions. A multileg fiber optic UV-visible spectrometer is utilized to collect samples under three distinct wavelength registries in three unique sampling cells and under lighting conditions spanning 0.2 to 2.0 relative transmittance. Under these conditions, single-beam spectral PCA models produced enhanced discrimination between sampling conditions, allowing spectra to be grouped by the instrumental conditions under which they were collected. Absorbance and single-beam PLS models produced equivalent quantitations of the lanthanide concentrations.

A simple strategy based on ATR-FTIR difference spectroscopy to monitor substrate intake and metabolite release by growing bacteria

Attenuated total reflectance Fourier transform infrared (ATR-FTIR) difference spectroscopy has been employed for a variety of applications spanning from reaction mechanisms analysis to interface phenomena assessment. This technique is based on the detection of spectral changes induced by the chemical modification of the original sample. In the present study, we highlight the potential of the ATR-FTIR difference approach in the field of microbial biochemistry and biotechnology, reporting on the identification of main soluble species consumed and released by growing bacteria during the biohydrogen production process. Specifically, the mid-infrared spectrum of a model culture broth, composed of glucose, malt extract and yeast extract, was used as background to acquire the FTIR difference spectrum of the same broth as modified by Enterobacter aerogenes metabolism. The analysis of difference signals revealed that only glucose is degraded during hydrogen evolution in anaerobic conditions, while ethanol and 2,3-butanediol are the main soluble metabolites released with H2. This fast and easy analytical approach can therefore represent a sustainable strategy to screen different bacterial strains and to select raw and waste materials to be employed in the field of biofuel production.

Soybean sorting based on protein content using X-ray fluorescence spectrometry

The purpose of this research was to evaluate performance of an energy-dispersive X-ray fluorescence (XRF) sensor to classify soybean based on protein content. The hypothesis was that sulfur signals and other XRF spectral features can be used as proxies to infer soybean protein content. Sample preparation and equipment settings to optimize detection of S and other specific emission lines were tested for this application. A logistic regression model for classifying soybean as high- or low-protein was developed based on XRF spectra and protein contents. Additionally, the model was validated with an independent set of samples. Global accuracies of the method were 0.83 (training set) and 0.81 (test set) and the corresponding kappa indices were 0.66 and 0.61, respectively. These numbers indicated satisfactory performance of the sensor, suggesting that XRF spectral features can be applied for screening protein content in soybean.

Quantitative Determination of Diosmin in Tablets by Infrared and Raman Spectroscopy

Diosmin is widely used in the treatment of chronic venous diseases and hemorrhoids. Based on Raman and infrared reflection spectra of powdered tablets in the mid- and near-infrared regions and results of reference high-performance liquid chromatographic analysis, partial least squares models that enable fast and reliable quantification of the studied active ingredient in tablets, without the need for extraction, were elaborated. Eight commercial preparations containing diosmin in the 66-92% (w/w) range were analyzed. In order to assess and compare the quality of the developed chemometric models, the relative standard errors of prediction for calibration and validation sets were calculated. We found these errors to be in the 1.0-2.4% range for the three spectroscopic techniques used. Diosmin content in the analyzed preparations was obtained with recoveries in the 99.5-100.5% range.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

A novel spatially resolved interactance spectroscopy system to estimate degree of red coloration in red-fleshed apple

Reliable information about degree of red coloration in fruit flesh is essential for grading and sorting of red-fleshed apples. We propose a spatially resolved interactance spectroscopy approach as a new rapid and non-destructive technique to estimate degree of red coloration in the flesh of a red-fleshed apple cultivar 'Kurenainoyume'. A novel measurement system was developed to obtain spatially resolved interactance spectra (190-1070 nm) for apple fruits at eight different light source-detector separation (SDS) distances on fruit surface. Anthocyanins in apple were extracted using a solvent extraction technique, and their contents were quantified with a spectrophotometer. Partial least squares (PLS) regression analyses were performed to develop estimation models for anthocyanin content from spatially resolved interactance spectra. Results showed that the PLS models based on interactance spectra obtained at different SDS distances achieved different predictive accuracy. Further, the system demonstrated the possibility to detect the degree of red coloration in the flesh at specific depths by identifying an optimal SDS distance. This might contribute to provide a detailed profile of the red coloration (anthocyanins) that is unevenly distributed among different depths of the flesh. This new approach may be potentially applied to grading and sorting systems for red-fleshed apples in fruit industry.

Development of a Near Infrared Spectroscopy method for the in-line quantitative bilastine drug determination during pharmaceutical powders blending

The Food and Drug Administration (FDA)'s guidelines and the Process Analytical Technology (PAT) approach conceptualize the idea of real time monitoring of a process, with the primary objective of improvement of quality and also of time and resources saving. New instruments are needed to perform an efficient PAT process control and Near Infrared Spectroscopy (NIRS), thanks to its rapid and drastic development of last years, could be a very good choice, in virtue of its high versatility, speed of analysis, non-destructiveness and absence of sample chemical treatment. This work was aimed to develop a NIR analytical method for bilastine assay in powder mixtures for direct compression. In particular, the use of NIR instrumentation should allow to control the bilastine concentration and the whole blending process, assuring the achievement of a homogeneous blend. The commercial tablet formulation of bilastine was particularly suitable for this purpose, due to its simple composition (four excipients) and direct compression manufacturing process. Calibration and validation set were prepared according to a Placket-Burman experimental design and acquired with a miniaturized NIR in-line instrument (MicroNIR by Viavi Solution Inc.). Chemometric was applied to optimize information extraction from spectra, by subjecting them to a Standard Normal Variate (SNV) and a Savitzky-Golay second derivative pre-treatment. This spectra pre-treatment, combined with the most suitable wavelength interval (resulted between 1087 and 1217 nm), enabled to obtain a Partial Least Square (PLS) model with a good predictive ability. The selected model, tried on laboratory and production batches, provided in both cases good assay predictions. Results were confirmed by traditional HPLC (High Performance Liquid Chromatography) API (Active Pharmaceutical Ingredient) content uniformity test on the final product.

Interactions between mercury and environmental factors: A chemometric assessment in seafood from an eutrophic estuary in southeastern Brazil

Guanabara Bay (GB) is an estuary in Brazil, constantly the target of pollutants, such as mercury (Hg). Thus, our study aimed to evaluate (i) total mercury (THg) content in shrimp and squid species from GB; (ii) associate THg content to contamination in swimming crabs; (iii) explore potential differences between species, and size; (iv) correlate abiotic water data to the determined THg contents; (v) verify if Hg concentrations are below acceptable limits. Swimming crabs showed greater Hg contamination compared to other species. For shrimp only biometric variables are related to Hg, while for squid, only abiotic. Only squids did not show a correlation between Hg and animal size. Finally, the detected Hg values are below the tolerable limits established by legislations. Our results indicate that the dynamics of Hg contamination differs between groups and that further studies are needed to elucidate the mechanisms that affect bioaccumulation in different species.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Prediction of Soil Clay Content and Cation Exchange Capacity Using Visible Near-Infrared Spectroscopy, Portable X-ray Fluorescence, and X-ray Diffraction Techniques

This article investigates a novel data fusion method to predict clay content and cation exchange capacity using visible near-infrared (visNIR) spectroscopy, portable X-ray fluorescence (pXRF), and X-ray diffraction (XRD) techniques. A total of 367 soil samples from two study areas in regional Australia were analyzed and intra- and interarea calibration options were explored. Cubist models were constructed using information from each device independently and in combination. pXRF produced the most accurate predictions of any individual device. Models based on fused data significantly improved the accuracy of predictions compared with those based on individual devices. The combination of pXRF and visNIR had the greatest performance. Overall, the relative increase in Lin's concordance correlation coefficient ranged from 1% to 12% and the corresponding decrease in root-mean-square error (RMSE) ranged from 10% to 46%. Provision of XRD data resulted in a decrease in observed RMSE values, although differences were not significant. Validation metrics were less promising when models were calibrated in one study area and then transferred to the other. Observed RMSE values were ∼2 to 3 times larger under this model transfer scenario and independent use of XRD was found to have the best overall performance.

Spectroscopic Estimation of N Concentration in Wheat Organs for Assessing N Remobilization Under Different Irrigation Regimes

Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. Here, the remobilization of N from anthesis to maturity in two wheat cultivars under three irrigation regimes was measured and its relationship to organ N concentration was examined. Based on spectral data of organ powder samples, partial least squares regression (PLSR) models were calibrated to estimate N concentration (N _mass) and validated against laboratory-based measurements. Although spectral reflectance could accurately estimate N _mass, the PLSR-based N _mass-spectra predictive model was found to be organ-specific, organs at the top canopy (chaff and top three leaves) received the best predictions (R ² > 0.88). In addition, N remobilization efficiency (NRE) in the top two leaves and top third internode was highly correlated with its corresponding N concentration change (ΔN _mass) with an R ² of 0.90. ΔN _mass of the top first internode (TIN1) explained 78% variation of the whole-plant NRE. This study provides a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

Detection of Lipid Oxidation in Infant Formulas: Application of Infrared Spectroscopy to Complex Food Systems

 Fish- or algal oils have become a common component of infant formula products for their high docosahexaenoic acid (DHA) content. DHA is widely recognized to contribute to the normal development of the infant, and the European Commission recently regulated the DHA content in infant formulas. For many manufacturers of first-age early life nutrition products, a higher inclusion level of DHA poses various challenges. Long-chain polyunsaturated fatty acids (LC-PUFAs) such as DHA are very prone to oxidation, which can alter the organoleptic property and nutritional value of the final product. Traditional methods for the assessment of oxidation in complex systems require solvent extraction of the included fat, which can involve harmful reagents and may alter the oxidation status of the system. A rapid, efficient, non-toxic real-time method to monitor lipid oxidation in complex systems such as infant formula emulsions would be desirable. In this study, infrared spectroscopy was therefore chosen to monitor iron-induced oxidation in liquid infant formula, with conjugated dienes and headspace volatiles measured with GC-MS as reference methods. Infrared spectra of infant formula were recorded directly in mid- and near-infrared regions using attenuated total reflectance Fourier-transform (ATR-FTIR) and near-infrared (NIRS) spectrophotometers. Overall, good correlation coefficients (R2 > 0.9) were acquired between volatiles content and infrared spectroscopy. Despite the complex composition of infant formula containing proteins and sugars, infrared spectroscopy was still able to detect spectral changes unique to lipid oxidation. By comparison, near-infrared spectroscopy (NIRS) presented better results than ATR-FTIR: prediction error ATR-FTIR 18% > prediction error NIRS 9%. Consequently, NIRS demonstrates great potential to be adopted as an in-line or on-line, non-destructive, and sustainable method for dairy and especially infant formula manufacturers.

Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms

A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented. Batches with high and low vigor soybean seeds were analyzed. Support vector machine (SVM), K-nearest neighbors (KNN), and discriminant analysis were applied to the raw spectral and reduced-dimensionality data from PCA (principal component analysis). Proteins, fatty acids, and amides were identified as the main molecules responsible for the discrimination of the batches. The cross-validation tests pointed out that high vigor soybean seeds were successfully discriminated from low vigor ones with an accuracy of 100%. These findings indicate FTIR spectroscopy associated with multivariate analysis as a new alternative approach to discriminate seed vigor.

Determination of Cadmium (II) in Aqueous Solutions by In Situ MID-FTIR-PLS Analysis Using a Polymer Inclusion Membrane-Based Sensor: First Considerations

Environmental monitoring is one of the most dynamically developing branches of chemical analysis. In this area, the use of multidimensional techniques and methods is encouraged to allow reliable determinations of metal ions with portable equipment for in-field applications. In this regard, this study presents, for the first time, the capabilities of a polymer inclusion membrane (PIM) sensor to perform cadmium (II) determination in aqueous solutions by in situ visible (VIS) and Mid- Fourier transform infrared spectroscopy (MID-FTIR) analyses of the polymeric films, using a partial least squares (PLS) chemometric approach. The influence of pH and metal content on cadmium (II) extraction, the characterization of its extraction in terms of the adsorption isotherm, enrichment factor and extraction equilibrium were studied. The PLS chemometric algorithm was applied to the spectral data to establish the relationship between cadmium (II) content in the membrane and the absorption spectra. Furthermore, the developed MID-FTIR method was validated through the determination of the figures of merit (accuracy, linearity, sensitivity, analytical sensitivity, minimum discernible concentration difference, mean selectivity, and limits of detection and quantitation). Results showed reliable calibration curves denoting systems' potentiality. Comparable results were obtained in the analysis of real samples (tap, bottle, and pier water) between the new MID-FTIR-PLS PIM based-sensor and F-AAS.

Fast, direct and in situ monitoring of lipid oxidation in an oil-in-water emulsion by near infrared spectroscopy

Lipid oxidation has implications on food, cosmetics and other fat containing products. Fatty acid autoxidation alters both the quality and safety of these products. Efficient and fast methods are needed to track lipid oxidation in complex systems. In this study, an oil-in-water emulsion (20% v/v of fish oil stabilized with high oleic sunflower lecithin) was subjected to iron-initiated oxidation. Conjugated dienes (CDs) were measured after fat extraction using a standardized method. Near infrared spectroscopy (NIRS) has been used to record chemical changes occurring during oxidation directly in the emulsion. Variations were noticed in different spectral regions. Partial least squares regression (PLSR) revealed correlations between conjugated diene values and NIRS spectra. High coefficients of determination (R2 = 0.967 and 0.996) were found for calibration and prediction respectively. The CD value was predicted from NIRS spectra with an error of 7.26 mmol eq. LH kg-1 oil (7.8% error). Limits of detection (LOD) and quantification (LOQ) of 4.65 and 15.5 mmol eq. LH kg-1 oil were estimated. NIRS is a rapid and simple method for measuring lipid oxidation (CD value) in an emulsion without prior fat extraction. NIRS can replace the reference methods that use hazardous solvents and consume time. Therefore, NIRS enables in-line monitoring for process and quality control.

An efficient variable selection method based on random frog for the multivariate calibration of NIR spectra

Variable selection is a critical step for spectrum modeling. In this study, a new method of variable interval selection based on random frog (RF), known as Interval Selection based on Random Frog (ISRF), is developed. In the ISRF algorithm, RF is used to search the most likely informative variables and then, a local search is applied to expand the interval width of the informative variables. Through multiple runs and visualization of the results, the best informative interval variables are obtained. This method was tested on three near infrared (NIR) datasets. Four variable selection methods, namely, genetic algorithm PLS (GA-PLS), random frog, interval random frog (iRF) and interval variable iterative space shrinkage approach (iVISSA) were used for comparison. The results show that the proposed method is very efficient to find the best interval variables and improve the model's prediction performance and interpretation.

Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms

The research on meteorites from hot and cold deserts is gaining advantages from the recent improvements of portable technologies such as X-ray fluorescence spectroscopy (XRF). The main advantages of portable instruments include the fast recognition of meteorites through their classification in macro-groups and discrimination from materials such as industrial slags, desert varnish covered rocks and iron oxides, named "meteor-wrongs". In this study, 18 meteorite samples of different nature and origin were discriminated and preliminarily classified into characteristic macro-groups: iron meteorites, stony meteorites and meteor-wrongs, combining a portable energy dispersive XRF instrument (pED-XRF), principal component analysis (PCA) and some machine learning algorithms applied to the XRF spectra. The results showed that 100% accuracy in sample classification was obtained by applying the cubic support vector machine (CSVM), fine kernel nearest neighbor (FKNN), subspace discriminant-ensemble classifiers (SD-EC) and subspace discriminant KNN-EC (SKNN-EC) algorithms on standardized spectra.

Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w⁻¹ followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R² ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.

Applications of Green Chemistry Approaches in Environmental Analysis

Background:

Green chemistry is the application of methodologies and techniques to reduce the use of hazardous substances, minimize waste generation and apply benign and cheap applications.

Methods:

In this article, the following issues were considered: greener solvents and reagents, miniaturization of analytical instrumentation, reagent-free methodologies, greening with automation, greener sample preparation methods, and greener detection systems. Moreover, the tables along with the investigated topics including environmental analysis were included. The future aspects and the challenges in green analytical chemistry were also discussed.

Results:

The prevention of waste generation, atomic economy, use of less hazardous materials for chemical synthesis and design, use of safer solvents, auxiliaries and renewable raw materials, reduction of unnecessary derivatization, design degradation products, prevention of accidents and development of real-time analytical methods are important for the development of greener methodologies.

Conclusion:

Efforts should also be given for the evaluation of novel solid phases, new solvents, and sustainable reagents to reduce the risks associated with the environment. Moreover, greener methodologies enable energy efficient, safe and faster that reduce the use of reagents, solvents and preservatives which are hazardous to both environment and human health.

Prediction of quality parameters in straw wine by means of FT-IR spectroscopy combined with multivariate data processing

This study represents the first attempt to combine mid infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars content and total acidity in straw wine. 302 Italian samples, representing different vintages, production regions and grape varieties, were analysed using FT-MIR spectroscopy and reference methods. New regression functions based on a combination of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of quality parameters: this approach allows overcoming the issue of matrix complexity, reducing spectral interferences and enhancing the information embodied in fingerprinting data. The models proposed are characterised by an excellent reliability, with low error in prediction (alcohol: 0.28%; sugars: 9.9 g/L; acidity: 0.29 g/L) comparable both to reference methods and table wine models. Results demonstrate that vibrational spectroscopy, combined with a proper multivariate data strategy, represents a suitable strategy for the quick and non-destructive assessment of quality parameters of straw wine.

Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy

The determination of chemical composition of lignocellulose biomass by wet chemistry analysis is labor-intensive, expensive, and time consuming. Near infrared (NIR) spectroscopy coupled with multivariate calibration offers a rapid and no-destructive alternative method. The objective of this work is to develop a NIR calibration model for olive tree lignocellulosic biomass as a rapid tool and alternative method for chemical characterization of olive tree pruning over current wet methods. In this study, 79 milled olive tree pruning samples were analyzed for extractives, lignin, cellulose, hemicellulose, and ash content. These samples were scanned by reflectance diffuse near infrared techniques and a predictive model based on partial least squares (PLS) multivariate calibration method was developed. Five parameters were calibrated: Lignin, cellulose, hemicellulose, ash, and extractives. NIR models obtained were able to predict main components composition with R2cv values over 0.5, except for lignin which showed lowest prediction accuracy.

Original plant traceability of Dendrobium species using multi-spectroscopy fusion and mathematical models

Dendrobium is the largest genus of orchids most of which have excellent medicinal properties. Fresh stems of some species have been consumed in daily life by Asians for thousands of years. However, there are differences in flavour and clinical efficacy among different species. Therefore, it is necessary for a detector to establish an effective and rapid method controlling botanical origins of these crude materials. In our study, three spectroscopies including mid-infrared (MIR) (transmission and reflection mode) and near-infrared (NIR) spectra were investigated for authentication of 12 Dendrobium species. Generally, two fusion strategies, reflection MIR and NIR spectra, were combined with three mathematical models (random forest, support vector machine with grid search (SVM-GS) and partial least-squares discrimination analysis (PLS-DA)) for discrimination analysis. In conclusion, a low-level fusion strategy comprising two spectra after pretreated by the second derivative and multiplicative scatter correction was recommended for discrimination analysis because of its excellent performance in three models. Compared with MIR spectra, NIR spectra were more responsible for the discrimination according to a bi-plot analysis of PLS-DA. Moreover, SVM-GS and PLS-DA were suitable for accurate discrimination (100% accuracy rates) of calibration and validation sets. The protocol combined with low-level fusion strategy and chemometrics provides a rapid and effective reference for control of botanical origins in crude Dendrobium materials.

Phenotype Profiling for Forensic Purposes: Determining Donor Sex Based on Fourier Transform Infrared Spectroscopy of Urine Traces

Forensic science is an important field of analytical chemistry where vibrational spectroscopy, in particular Fourier transform infrared spectroscopy and Raman spectroscopy, present advantages as they have a nondestructive nature, high selectivity, and no need for sample preparation. Herein, we demonstrate a method for determination of donor sex, based on attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy of dry urine traces. Trace body fluid evidence is of special importance to the modern criminal investigation as a source of individualizing DNA evidence. However, individual identification of a urine donor is generally difficult because of the small amount of DNA. Therefore, the development of an innovative method to provide phenotype information about the urine donor-including sex-is highly desirable. In this study, we developed a multivariate discriminant model for the ATR FT-IR spectra of dry urine to identify the donor sex. Rigorous selection of significant wavenumbers on the spectrum using a genetic algorithm enabled superb discrimination performance for the model and conclusively indicated a chemical origin for donor sex differences, which was supported by physiological knowledge. Although further investigations need to be conducted, this proof-of-concept study demonstrates the great potential of the developed methodology for phenotype profiling based on the analysis of urine traces.

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.

DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis

Learning patterns from spectra is critical for the development of chemometric analysis of spectroscopic data. Conventional two-stage calibration approaches consist of data preprocessing and modeling analysis. Misuse of preprocessing may introduce artifacts or remove useful patterns and result in worse model performance. An end-to-end deep learning approach incorporated Inception module, named DeepSpectra, is presented to learn patterns from raw data to improve the model performance. DeepSpectra model is compared to three CNN models on the raw data, and 16 preprocessing approaches are included to evaluate the preprocessing impact by testing four open accessed visible and near infrared spectroscopic datasets (corn, tablets, wheat, and soil). DeepSpectra model outperforms the other three convolutional neural network models on four datasets and obtains better results on raw data than in preprocessed data for most scenarios. The model is compared with linear partial least square (PLS) and nonlinear artificial neural network (ANN) methods and support vector machine (SVR) on raw and preprocessed data. The results show that DeepSpectra approach provides improved results than conventional linear and nonlinear calibration approaches in most scenarios. The increased training samples can improve the model repeatability and accuracy.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm^-1 region.

Trends in analytical techniques applied to particulate matter characterization: A critical review of fundaments and applications

Epidemiological studies have shown the association of airborne particulate matter (PM) size and chemical composition with health problems affecting the cardiorespiratory and central nervous systems. PM also act as cloud condensation nuclei (CNN) or ice nuclei (IN), taking part in the clouds formation process, and therefore can impact the climate. There are several works using different analytical techniques in PM chemical and physical characterization to supply information to source apportionment models that help environmental agencies to assess damages accountability. Despite the numerous analytical techniques described in the literature available for PM characterization, laboratories are normally limited to the in-house available techniques, which raises the question if a given technique is suitable for the purpose of a specific experimental work. The aim of this work consists of summarizing the main available technologies for PM characterization, serving as a guide for readers to find the most appropriate technique(s) for their investigation. Elemental analysis techniques like atomic spectrometry based and X-ray based techniques, organic and carbonaceous techniques and surface analysis techniques are discussed, illustrating their main features as well as their advantages and drawbacks. We also discuss the trends in analytical techniques used over the last two decades. The choice among all techniques is a function of a number of parameters such as: the relevant particles physical properties, sampling and measuring time, access to available facilities and the costs associated to equipment acquisition, among other considerations. An analytical guide map is presented as a guideline for choosing the most appropriated technique for a given analytical information required.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS)

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg^-1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww^-1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg^-1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.

Spectral Mining for Discriminating Blood Origins in the Presence of Substrate Interference via Attenuated Total Reflection Fourier Transform Infrared Spectroscopy: Postmortem or Antemortem Blood?

Often in criminal investigations, discrimination of types of body fluid evidence is crucially important to ascertain how a crime was committed. Compared to current methods using biochemical techniques, vibrational spectroscopic approaches can provide versatile applicability to identify various body fluid types without sample invasion. However, their applicability is limited to pure body fluid samples because important signals from body fluids incorporated in a substrate are affected strongly by interference from substrate signals. Herein, we describe a novel approach to recover body fluid signals that are embedded in strong substrate interferences using attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy and an innovative multivariate spectral processing. This technique supported detection of covert features of body fluid signals, and then identified origins of body fluid stains on substrates. We discriminated between ATR FT-IR spectra of postmortem blood (PB) and those of antemortem blood (AB) by creating a multivariate statistics model. From ATR FT-IR spectra of PB and AB stains on interfering substrates (polyester, cotton, and denim), blood-originated signals were extracted by a weighted linear regression approach we developed originally using principal components of both blood and substrate spectra. The blood-originated signals were finally classified by the discriminant model, demonstrating high discriminant accuracy. The present method can identify body fluid evidence independently of the substrate type, which is expected to promote the application of vibrational spectroscopic techniques in forensic body fluid analysis.