Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review.

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples.

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

Feasibility of near-infrared spectroscopy for species identification and parasitological diagnosis of freshwater snails of the genus Biomphalaria (Planorbidae).

Near Infrared Spectroscopy (NIRS) has been applied in epidemiological surveillance studies of insect vectors of parasitic diseases, such as the Dengue's mosquitoes. However, regarding mollusks, vectors of important worldwide helminth diseases such as schistosomiasis, fascioliasis and angiostrongyliasis, NIRS studies are rare. This work proposes to establish and standardize the procedure of data collection and analysis using NIRS applied to medical malacology, i.e., to mollusk vectors identifications. Biomphalaria shells and live snails were analyzed regarding several operational aspects, such as: moisture, shell side and position of the live animal for acquisition of NIR spectra. Representative spectra of Biomphalaria shells and live snails were collected using an average of 50 scans per sample and resolution of 16 cm-1. For shells, the sample should first be dried for a minimum of 15 days at an average temperature of 26±1°C, and then placed directly in the equipment measurement window with its left side facing the light beam. Live animals should be dried with absorbent paper; placed into a glass jar, and analyzed similarly to the shells. Once standardized, the technique was applied aiming at two objectives: identification of Biomphalaria using only the shells and parasitological diagnosis for Schistosoma mansoni infection. The discrimination of the three Biomphalaria species intermediate hosts of S. mansoni only by shell has technical limit due to the scarcity of organic material. Nevertheless, it was possible to differentiate B. straminea from B. tenagophila and B. glabrata with 96% accuracy. As for the parasitological diagnosis, it was possible to differentiate infected mollusks shedding S. mansoni cercariae from the non-infected ones with 82, 5% accuracy. In conclusion, the Near Infrared Spectroscopy (NIR's) technique has proven to be an innovative and sound tool to detect infection by S. mansoni in the different species of Biomphalaria intermediate hosts.

A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics.

Microplastic pollution is a global concern theme, and there is still the need for less laborious and faster analytical methods aiming at microplastics detection. This article describes a high throughput screening method based on near-infrared hyperspectral imaging (HSI-NIR) to identify microplastics in beach sand automatically with minimum sample preparation. The method operates directly in the entire sample or on its retained fraction (150 µm-5 mm) after sieving. Small colorless microplastics (<600 µm) that would probably be imperceptible as a microplastic by visual inspection, or missed during manual pick up, can be easily detected. No spectroscopic subsampling was performed due to the high-speed analysis of line-scan instrumentation, allowing multiple microplastics to be assessed simultaneously (video available). This characteristic is an advantage over conventional infrared (IR) spectrometers. A 75 cm2 scan area was probed in less than 1 min at a pixel size of 156 × 156 µm. An in-house comprehensive spectral dataset, including weathered microplastics, was used to build multivariate supervised soft independent modelling of class analogy (SIMCA) classification models. The chemometric models were validated for hundreds of microplastics (primary and secondary) collected in the environment. The effect of particle size, color and weathering are discussed. Models' sensitivity and specificity for polyethylene (PE), polypropylene (PP), polyamide-6 (PA), polyethylene terephthalate (PET) and polystyrene (PS) were over 99% at the defined statistical threshold. The method was applied to a sand sample, identifying 803 particles without prior visual sorting, showing automatic identification was robust and reliable even for weathered microplastics analyzed together with other matrix constituents. The HSI-NIR-SIMCA described is also applicable for microplastics extracted from other matrices after sample preparation. The HSI-NIR principals were compared to other common techniques used to microplastic chemical characterization. The results show the potential to use HSI-NIR combined with classification models as a comprehensive microplastic-type characterization screening.

Field Application of NIR Spectroscopy for the Discrimination of the Biomphalaria Species That Are Intermediate Hosts of Schistosoma mansoni in Brazil.

Near Infrared Spectroscopy (NIRS) is a spectroscopic technique that evaluates the vibrational energy levels of the chemical bonds of molecules within a wavelength range of 750-2,500 nm. This simple method acquires spectra that provide qualitative and quantitative data on the chemical components of the biomass of living organisms through the interaction between the electromagnetic waves and the sample. NIRS is an innovative, rapid, and non-destructive technique that can contribute to the differentiation of species based on their chemical phenotypes. Chemical profiles were obtained by NIRS from three snail species (Biomphalaria glabrata, Biomphalaria straminea, and Biomphalaria tenagophila) that are intermediate hosts of Schistosoma mansoni in Brazil. The correct identification of these species is important from an epidemiological viewpoint, given that each species has distinct biological and physiological characteristics. The present study aimed to develop a chemometric model for the interspecific and intra-specific classification of the three species, focusing on laboratory and field populations. The data were obtained from 271 live animals, including 150 snails recently collected from the field, with the remainder being raised in the laboratory. Populations were sampled at three localities in the Brazilian state of Rio de Janeiro, in the municipalities of Sumidouro (B. glabrata) and Paracambi (B. straminea), and the borough of Jacarepaguá in the Rio de Janeiro city (B. tenagophila). The chemometric analysis was run in the Unscrambler® software. The intra-specific classification of the field and laboratory populations obtained accuracy rates of 72.5% (B. tenagophila), 77.5% (B. straminea), and 85.0% (B. glabrata). The interspecific differentiation had a hit rate of 75% for the field populations and 80% for the laboratory populations. The results indicate chemical and metabolic differences between populations of the same species from the field and the laboratory. The chemical phenotype, which is closely related to the metabolic profile of the snails, varied between environments. Overall, the NIRS technique proved to be a potentially valuable tool for medical malacology, enabling the systematic discrimination of the Biomphalaria snails that are the intermediate hosts of S. mansoni in Brazil.

Monitoring the quality of ethanol-based hand sanitizers by low-cost near-infrared spectroscopy.

The use of near-infrared spectroscopy (NIRS) based on a low-cost portable instrument for monitoring the quality of the three major formulations of ethanol-based hand sanitizers used for prevention against CoVID-19 disease is described. The quality of the sanitizers was evaluated using two approaches. In the first, a qualitative method was developed to identify gross non-conformities, using NIR spectral data compression by principal components analysis and projection of the spectrum of the tested sample in the principal component space delimited by samples of sanitizers prepared in the laboratory. In the second, a quantitative method was designed to determine the active substance (ethanol) employing multivariate regression based on partial least squares. The results demonstrate that the first approach can be used to detect non-conformities in the sanitizer composition, mostly associated with incorrect ethanol content. The second explores the use of NIRS for determination of the ethanol content in the three formulations aiming the quality control of the sanitizer manufacturing process. The ethanol content can be determined with an absolute root mean square error of prediction (RMSEP) equal to 0.68% (m/m), 0.83% (m/m) and 1.0% (v/v) for the three formulations evaluated. The RMSEP was estimated as 1.3% (m/m) for the commercial products. The measurement protocol takes approximately 1 min and requires only about 120 µL of a sample. Besides, NIRS was employed to compare the rate of volatilization of the ethanol in the different formulations, an important parameter concerning the efficacy of ethanol-based sanitizers.

Feasibility of a portable, low-cost near-infrared spectrophotometer for the quality screening of omega-3 dietary supplements.

The quality of omega-3 supplements, commercialized at substantial high prices and supplied by several manufacturers, must be assessed. The existing reference methods to attest the quality of omega-3 supplements are based on chromatography, which requires expensive equipment, a cumbersome analytical protocol to determine the contents of the active components, and operates ex-situ. This work evaluates, comprehensively, the feasibility of a low-cost near-infrared spectrophotometer and simple chemometrics to achieve fast and robust characterization of omega-3 supplements. The necessary attention, very often neglected, to the performance of the low-cost portable equipment is highlighted, and an appropriate sample measurement protocol is established. A non-conventional way to construct multivariate regression models based on partial least square regression to evaluated the omega-3 content in the supplements was proposed using few references values and completing the data set with the label values selected using the X-Y relation outliers plots. The results showed that the simple, fast and inexpensive approach proposed could deliver in situ relevant information related to the quality of the supplements such as identification several raw materials employed in its fabrication, screening for the content of the active components, identify frauds/non-conformities, and report on unscrupulous marketing practices.

Determination of the isotopic composition of enriched materials using laser ablation molecular isotopic spectrometry: partial least squares and multivariate curve resolution for the determination of 15N content in enriched urea.

A quantitative analytical method based on laser ablation molecular isotopic spectrometry (LAMIS) and multivariate analysis was developed and evaluated for the determination of the isotopic composition of enriched materials. The method consists preparing a concentrated solution of the enriched material, using small quantities of a sample (125 mg), and ensuring the economic efficiency of the analysis. Standard solutions of known isotopic contents are prepared by employing mixtures of urea highly enriched in 15N and urea of natural isotopic ratio and analyzed by mass spectrometry. A small volume (30 µL) of these solutions is delivered to a filter paper disc (3 cm diameter). After drying, the disc, offering a homogeneously distributed analyte, is presented to a LAMIS equipment to acquire the vibronic emission spectra containing information about the isotopologues of interest. To illustrate the proposed method, the content of 15N is determined in enriched samples of urea. In this case, each spectrum is normalized by the intensity of emission of the CN isotopologues for the electronic (Δν = 0) emission band at 387.1 nm, ensuring better accuracy. Selected regions and single wavelengths of the vibronic emission spectrum (Δν = + 1 or - 1) related to CN species were employed to construct multivariate partial least squares (PLS) and univariate regression models to predict the isotopic content of new samples. Besides, the LAMIS data set was evaluated by multivariate curve resolution (MCR) algorithm. The best MCR and PLS models presented similar results regarding the accuracy to determine 15N content in enriched urea. MCR is capable of identifying spectral interferences and minimizing its effect. The results show that the proposed method based on LAMIS and PLS or MCR multivariate analysis can determine the 15N content in the range 5-50% with a root mean square error of prediction (RMSEP) respectively equal to 0.5 or 0.7% (m/m) in comparison with reference results obtained by mass spectrometry. Graphical abstract.

Evaluation of a low-cost portable near-infrared spectrophotometer for in situ cocaine profiling.

The potential of a low-cost (∼US$ 1000) portable near-infrared (NIR) spectrophotometer for in situ characterization of seized cocaine samples was evaluated. A set of 240 samples of cocaine seized in several regions and cities across Brazil by its federal police was employed in this study. These samples were previously analyzed by chromatography at the Forensic Chemistry Laboratory of the National Institute of Criminalistics in Brasília-DF for the contents of several constituents to chemically characterize the samples. A low-cost NanoNIR spectrophotometer (Texas instruments) was used to acquire the NIR spectra of the samples in the range 900-1700 nm. The spectra set was treated by the second derivative to construct and validate multivariate regression (Partial Least Square - PLS) and classification (software independent modeling of class analogy - SIMCA) models aiming to characterize the samples. Consequently, an informative toll for objective decision making could be used by the police agents to produce immediate answers to forensic questions raised at the point of seizing. Among those questions the most relevant are: does the seized sample contain cocaine? what is the cocaine form? what is its content? is the sample adulterated and/or diluted? what is the content of adulterant? is the sample significantly adulterated and/or diluted? what is the degree of oxidation of the cocaine? The results of this work allow to propose a NIR/chemometrics based analytical protocol providing fast answers to these questions with satisfactory confidence level for the purpose of reliably screen the seized samples.

Comparing laser induced breakdown spectroscopy, near infrared spectroscopy, and their integration for simultaneous multi-elemental determination of micro- and macronutrients in vegetable samples.

Laser-induced breakdown spectroscopy (LIBS) is an appealing analytical technique for simultaneous multi-elemental analysis. Near-infrared spectroscopy (NIRS) has also been suggested for the same purpose, mainly for vegetable samples. However, LIBS has failed to provide adequate results in many cases due to sample matrix complexity, and NIRS performance is harmed because of its lack of sensitivity and indirect correlation with inorganic elemental species. In this work, the performance of these two techniques are compared for the determination of micro- and macroelements in vegetable samples (Brachiaria forages) using multivariate regression. In addition, a data fusion scheme, in which spectral data sourced by NIRS is integrated with LIBS, is proposed to improve elemental content determination in those samples. The information of the molecular composition detected by NIR vibrational spectroscopy was consistently selected by recursive partial least squares to yield quantitative multivariate models for K, Ca, Mg, Mn and Fe in forage plants that are superior to models based on the use of individual NIRS and LIBS spectral information. While all data fusion models showed better predictive accuracy than any of the two individual techniques, best results were observed for Ca. This suggests that matrix composition affects each element determination by LIBS distinctively and supports the idea that a successful quantitative data fusion strategy for LIBS requires a technique such as NIRS which is sensitive to this variability.

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.

Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.

Last decade's advances and modern aspects of near infrared spectroscopy are critically examined and reviewed. Innovative instrumentation, highlighted by portable and imaging instruments, chemometrics data multivariate processing, and new and valuable applications are presented and discussed. Because of these advances, this mature analytical technique is continually experiencing renewed interest. The drawbacks and misuses of the technique and its supporting mathematical tools are also addressed. The principal achievements in the field are shown in a critical manner, in order to understand why the technique has found intensive application in the most diverse and modern areas of analytical importance during the last ten years.

Surface-enhanced Raman spectroscopy and MCR-ALS for the selective sensing of urinary adenosine on filter paper.

Adenosine is a purine nucleoside that is present in all human cells and is essential for regulating certain physiological activities in tissues and organs. Since adenosine is considered to be a potential cancer biomarker in urine, its determination may be crucial for the early diagnosis and non-invasive monitoring of cancer. Herein, we present a label-free method to quantify urinary adenosine using surface-enhanced Raman spectroscopy (SERS) and multivariate curve resolution-alternating least squares (MCR-ALS). Ring-oven preconcentration and direct deposition of monodisperse gold nanoparticles on filter paper were employed to improve the sampling efficiency. Further, MCR-ALS (assessed with and without a correlation constraint), the standard addition method and pH controls were combined to compensate for the matrix effect and to address overlapping bands in the analysis of human urine samples. As a result, the proposed method showed to be sensitive (LOD varying between 3.8 and 4.9 µmol L-1, S/R = 3), reproducible (RSD less than ±â€¯15%), and selective over other nucleosides (guanosine, cytidine, thymidine and uridine) and unknown interferences (second-order advantage). This is the first report of a SERS-chemometric method applied to urinary adenosine sensing at physiologically relevant concentrations, with minimal sample preparation, and has strong potential to be a valuable tool in cancer research.

Near infrared emission spectroscopy for rapid compositional analysis of Portland cements.

Near infrared emission spectroscopy (NIRES) has been investigated to yield a new analytical method for determination of CaO, SiO2, Al2O3, Fe2O3, MgO, and SO3 in Portland cement samples. The analyses were accomplished by correlation of the second derivative NIRES spectra with reference elemental analyses made by X-ray Fluorescence (XRF), using Partial Least Square (PLS) regression models. Four different types of cements (type II, III, IV and V) were included in the models. The results show RMSEP from 0.18 to 1.67% (m/m) (which represents 8.6 to 2.8% of the mean value parameters, respectively) and precision (repeatability) from 0.12 to 1.49% (m/m). The worse performance of prediction [RMSEP = 0.18% (m/m), r2 = 0.63] was found for Fe2O3 determination due its low variability in the samples employed in the calibration and validation set, usual low content in cements, and high correlation with the weak emitting C4AF phase. However, the paired t-test at 95% confidence level demonstrated that the analytical results obtained by NIRES multivariate models are equivalent to measurements performed by XRF for all oxides analyzed. The cost, speed and safety aspects are improved in comparison with the XRF method. The method based on NIRES requires no sample preparation and speed up the cement analysis (6 min) about 5 times when compared with the method based on XRF presently employed by cement industries.

Coupling of the ring-oven-based preconcentration technique and surface-enhanced Raman spectroscopy: Application for the determination of purine bases in DNA.

In this paper, we present the advantages and limitations of the coupling of a ring-oven-based preconcentration technique and surface-enhanced Raman spectroscopy (SERS). Three different methods to promote analyte adsorption on gold nanoparticles using crystal violet as a probe molecule were assessed. The results showed significant improvements in sampling process, selectivity, sensitivity, repeatability (less than ± 10%), and detection limits (nanomolar level) using a sample volume as small as 300 µL. Finally, the standard addition method was successfully applied to the quantitative SERS detection of adenine and guanine in calf thymus DNA after ring-oven preconcentration with a calculated value of (G + C)/(A + T) close to the literature value. This work could therefore pave the way to quantifying a wide variety of biologically relevant compounds in real-world samples via the use of a biodegradable, low-cost and disposable paper platform for SERS.

Multivariate quantification of mebendazole polymorphs by terahertz time domain spectroscopy (THZ-TDS).

This work presents an analytical method based on terahertz-time domain spectroscopy (THz-TDS) and partial least squares (PLS) regression models to quantify mebendazole (MBZ) polymorphs (forms A, B and C) in pharmaceutical raw material. Mebendazole polymorphs A, B and C were quantified with RMSEP values of 1.5% w/w, 1.2% w/w and 1.8% w/w, respectively. The limits of detection (LOD) ranges obtained with the best PLS regression models were 2.7-4.3% w/w, 2.9-4.0% w/w and 2.4-3.1% w/w, for polymorphs A, B and C, respectively. This analytical performance is better than those for the methods described in the literature using near (NIR) and middle (MIR) infrared spectroscopies. The main advantage of THz spectroscopy is its ability to access directly information related to crystal lattices. According to the results, the developed method is a powerful technique for the quantification of MBZ polymorphs in raw material. This methodology can be implemented as a Process Analytical Technology (PAT) tool for quality control of pharmaceutical feedstock.

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.

iHEART: a miniaturized near-infrared in-line gas sensor using heart-shaped substrate-integrated hollow waveguides.

A novel heart-shaped substrate-integrated hollow waveguide (hiHWG) was integrated with a near-infrared micro-spectrometer (µNIR) for sensing natural gases, resulting in an ultra-compact near-infrared gas sensing system - iHEART. The iHEART system was evaluated using two different µNIR spectrometers, and the performance was compared with a laboratory NIR spectrometer for gas analysis based on an acousto-optic tunable filter (AOTF). The spectral data were pre-processed using the 1(st) derivative Savitzky-Golay algorithm, and then used for establishing multivariate regression models based on partial least squares (PLS). The root mean square errors of prediction (RMSEPs) obtained for major components of natural gas with both iHEART systems were similar to those associated with the AOTF spectrophotometer combined with a conventional long-path measurement cell. It was demonstrated that the iHEART system has significant potential for the development of compact in-line gas sensing systems, thus facilitating monitoring of (petro)chemically relevant processes and products. However, the flexibility and modularity of the system also allows tailoring iHEART to a wide range of other relevant analytical measurement scenarios requiring short response times and minute gas sample volumes.

Rapid and non-destructive determination of quality parameters in the 'Tommy Atkins' mango using a novel handheld near infrared spectrometer.

The objective of this study was to evaluate the potential of a new handheld ultra-compact near infrared (NIR) spectrometer, based on the linear-variable filter (LVF) technology for rapid and non-destructive quality control analysis of the 'Tommy Atkins' mango. Multivariate calibration models were built using the Partial Least Squares (PLS) regression method to determine soluble solids (SS), dry matter (DM), titratable acidity (TA) and pulp firmness (PF). Different spectral pre-processing techniques were tested. Coefficient of determination and root mean square errors of prediction (RMSEP) values were, respectively: 0.92 and 0.55°Brix for SS, 0.67 and 0.51% for DM, 0.50 and 0.17% citric acid for TA, 0.72 and 12.2N for PF. The predictive models allowed monitoring physico-chemical changes in each fruit during ripening. The results show the feasibility of using the new NIR handheld spectrometer to determine quality parameters in the 'Tommy Atkins' mango.