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Monosegmented flow analysis (MSFA) has been used as a flow-batch system to produce a simple, robust, and mechanized titrator that enables true titrations to be performed without the use of standards. This paper also introduces the use of coulometry with monosegmented titration by proposing a versatile flow cell. Coulometric generation of the titrand is attractive for titrations performed in monosegmented systems, because the reagent can be added without increasing the volume of sample injected. Also, biamperomeric and potentiometric detection of titration end-points can increase the versatility of the monosegmented titrator. The cell integrates coulometric generation of the titrand with detection of end-point by potentiometry or biamperometry. The resulting titrator is a flow-batch system in which the liquid monosegment, constrained by the interfaces of the gaseous carrier stream, plays the role of a sample of known volume to be titrated. The system has been used for determination of ascorbic acid, by coulometric generation of I2 with biamperometric detection, and for determination of Fe(II), by coulometric generation of Ce(IV) with potentiometric detection of the end-point, both in feed supplements. [ABSTRACT FROM AUTHOR]

• NIRS determines the quality of sanitizers helping to face the CoVID-19 outbreak. • Most common ethanol-based hand sanitizers are analyzed by near-infrared. • Quality of alcohol gel monitored by near-infrared spectroscopy. • Low-cost near-infrared spectroscopy for quality control of sanitizers. 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. [ABSTRACT FROM AUTHOR]

Cheeses vary in chemical composition as they have different applications according to the technological features; some hard cheeses are consumed fresh while melting is a desirable characteristic for others. The conventional reference methods for determination of cheese composition and quality are accurate, but include physical-chemical analyses that are time-consuming and require use of chemical reagents. The objective of this work was to apply near-infrared hyperspectral imaging (NIR-HSI) as a fast method for prediction and visualization of spatial distribution of chemical composition of different types of cheeses (mozzarela, requeijão do norte, minas meia cura, coalho, prato). Moisture, fat, protein, and free oil were measured, and used as reference for prediction models, while spectral information extracted from images in the range of 928–2524 nm were used as predictors. Partial Least Squares (PLS) regression models were built with full spectra and using only few selected regions with higher importance, which were applied to the images to build chemical maps and visualize the spatial variation of the cheese composition. The regression models were able to predict moisture (R2 P = 0.92, RMSEP = 1.1%); fat (R2 P = 0.84, RMSEP = 1.5%); protein (R2 P = 0.80, RMSEP = 1.8%); and free oil (R2 P =0.64, RMSEP = 0.5%) for an external set of samples, while requiring a small number of latent variables (LVs) (between 3 and 4). The chemical maps allowed visualization of the spatial distribution for each attribute, demonstrating that the composition may vary within sample. The study demonstrated the potential of NIR-HSI for the determination of cheese composition and possible application to cheese quality control. [Display omitted] • Near infrared hyperspectral imaging was used for prediction of cheese composition. • Chemometrics were applied to build prediction models using hyperspectral images. • The chemical maps show spatial distribution of major components of cheeses. [ABSTRACT FROM AUTHOR]

Abstract 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. Graphical abstract Image 1 Highlights • NIRS and LIBS are practical analytical techniques commonly used for analysis of plant samples. • NIRS can be used to compensate for matrix variability in elemental analysis by LIBS. • NIRS + LIBS data fusion is a comprehensive, direct and green approach for quality control of forages. [ABSTRACT FROM AUTHOR]

Abstract A new procedure is described for the development of photometers that operate in the NIR spectral region. The instrument was based on a set of LEDs as radiation source and associate interference filters (IF) to narrow the selected wavelength. The procedure involves obtaining the spectra using a FT-NIR spectrophotometer from a sample set that has spectral variations of a given analyte concentration. Based on the combined LED/IF spectral bandwidths, the high resolution FT-NIR spectra were processed to obtain a deresolved data set with lower resolution. Multiple Linear Regression combined with a variable selection method were used to select the most significant variables, among those present in the deresolved spectral data, to quantify a desired analyte. The optimized calibration results employing these selected variables guided the choice of how many LEDs and which IFs should be used in the photometer. In order to demonstrate the proposed procedure feasibility, a photometer was built to quantify biodiesel content in diesel. Seven pairs of LED/IF were selected for the photometer, which were chosen according to the selected variables by the proposed procedure. In addition, lenses were used to conduct the LED/IF pulsed radiation to a single photodiode detector. The pulsed LED protocol allows the use of Sequential and Hadamard matrix-based multiplexing methods for spectral data acquisition. The results obtained for biodiesel content ranging from 0.50% to 20.00% blended with diesel and vegetable oil presented a RMSEP of 0.53% v/v, LOD range of 0.23%–0.55% v/v, and R2 of 0.99. These results show that the selected variables are not affected by the vegetable oil, a compound with very similar chemical (and spectral) characteristics to the fatty acid esters (biodiesel). The performance reached with the photometer was in accordance with that recommended by the National Agency of Petroleum, Natural Gas and Biofuels (ANP), which regulates and monitors fuel quality in Brazil. Highlights • Instrument based on a set of LEDs as radiation source and interference filters • MLR and variable selection method were used to select significant variables. • Sequential and Hadamard matrix methods for spectral data acquisition • A photometer was built to quantify biodiesel content in diesel. • Developed photometer is simple, cheap and portable. [ABSTRACT FROM AUTHOR]

Near infrared emission spectroscopy (NIRES) has been investigated to yield a new analytical method for determination of CaO, SiO 2 , Al 2 O 3 , Fe 2 O 3 , MgO, and SO 3 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), r 2  = 0.63] was found for Fe 2 O 3 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 C 4 AF 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. [ABSTRACT FROM AUTHOR]

The performance of a portable near infrared spectrophotometer to determine biodiesel content in diesel/biodiesel blends, as well as to quantify adulteration by vegetable oil, was evaluated. The NIR transmittance/absorbance spectra of 118 samples in the range 950–1650 nm were acquired using an ultra-compact spectrophotometer (MicroNIR), and also by using a Fourier transform benchtop instrument (FT-NIR) from 800 to 2500 nm. In a preliminary step, partial least squares (PLS) models aimed at the determination of biodiesel quality parameters were developed by using full spectral information. Thereafter, the variables showing significant regression coefficients were selected and the PLS models were re-processed. Successive Projections Algorithm (SPA) was used to screen for the most orthogonal variables to be used in a model constructed by using Multiple Linear Regression (MLR). For determining the contents of both biodiesel and vegetable oil in diesel/biodiesel blends, using MicroNIR and FT-NIR, the results indicated MLR-SPA as the best modelling strategy. RMSEP values of 0.22% v/v (MicroNIR) and 0.13% v/v (FT-NIR) for biodiesel and 0.34% v/v (MicroNIR) and 0.22% (FT-NIR) for vegetable oil contents were obtained. LOD values of 0.31% (MicroNIR) and 0.22% (FT-NIR) for biodiesel and 0.40% (MicroNIR) and 0.34% (FT-NIR) for vegetable oil determination were achieved. The results revealed comparable performances between the portable and the benchtop instruments, demonstrating the feasibility of monitoring in situ the quality characteristics of diesel/biodiesel blends according to biodiesel content and for detection of adulteration with vegetable oils by using the portable spectrophotometer. [ABSTRACT FROM AUTHOR]

[Display omitted] • The use of miniaturized near-infrared instruments is comprehensively reviewed. • Forensic analytical science fosters by miniaturized near-infrared instruments. • Near-infrared based on miniaturized instruments for in-field forensic analysis. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Near-infrared spectroscopy identifies and quantifies ecstasy seized samples. • Two level SIMCA classification minimizes the risk of forensic classification of illicit drugs. • MDMA and MDA are identified in ecstasy seized samples. • Total actives, MDMA, and MDA are quantified in seized 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. [ABSTRACT FROM AUTHOR]

Abstract: A new polarimetric instrument and measurement method is described based on the use of diode lasers as radiation source (532, 650 and 1064nm) and birefringent prisms, such as Glan-Laser and Wollaston, as analyzers. The laser radiation is passed through a dichroic polarizer film for further orientation of its polarization plane at 45° in relation to the polarization plane of the analyzer. The polarized beam, oriented in that way, passes the sample cell, impinges the prism surface, and the intensities of the two emerged beams are detected by two twin silicon detectors. Ideally, in the absence of any optically active substances, the crystals produces two orthogonally polarized refracted beams of equal intensity. In the presence of an optically active substance, the arctangent of the square root of the beam intensities ratio is equal to the new polarization angle (β) of the laser beam. The rotation angle imposed for any optically active substance present in the sample cell is then given by: α =(45– β)°. Because the rotation is obtained by the ratio of the intensities of two beams, it is independent of the laser intensity, which can vary up to ±15% with no significant effect on the accuracy of the polarimetric measurement. The instrument has been evaluated for measurement of optically active substances such as sucrose and fructose. The instrument employs low cost components, is capable of attaining a repeatability of ±0.003° and can measure the rotation angle, over a ±45° range, in less than 2s. Because it does not present any moving parts it can be easily adapted for in/on-line process monitoring of optically active substances. [Copyright &y& Elsevier]

Abstract: In this work, a Partial Least Squares (PLS) regression model using Near-Infrared (NIR) spectroscopy was developed to monitor the progress of the catalyzed transesterification reactions of soybean oil that produce biodiesel. The NIR spectra were collected during the transesterification reaction with a lab made spectrophotometric flow cell. Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy was employed for determining the conversion percentage of glycerides to methyl esters during the transesterification reaction and used as reference to build the PLS calibration model employing NIR spectroscopy data. The model, constructed with selected spectral range has not been tried before and allows the monitoring of the transesterification reaction in terms of conversion ratio for different temperatures. The model was validated and the values of Root Mean Square Error of Prediction (RMSEP) found for two different temperatures were 0.74% and 1.27% (of conversion) for reactions carried out at 20±0.2°C and 55±0.2°C, respectively. [Copyright &y& Elsevier]

A new approach to near-infrared (NW) spectropolarimetry is described, in which the properties of a noncollinear acousto-optical tunable filter (AOTF) made of an anisotropic crystal of TeO2 is employed to produce a novel instrument that precludes or reduces significantly the use of mechanical parts and simplifies the acquisition of optical rotation spectra of absorbing species in the NIR region. In essence, the instrument is based on the measurement of the relative beam intensities produced when a 45° plane polarized beam of radiation passes through a sample cell and is directed to the entrance window of the AOTF. Because of the crystal anisotropy, two planar and orthogonally polarized beams will leave the AOTF, angularly split from the nondiffracted beam, after the acousto-optical interaction has occurred. If an optically nonactive sample is present in the cell, equal intensities for both diffracted beams should be observed. On the other hand, the presence of an optically active sample will cause the polarization plane to rotate and a consequent difference in the intensities of the AOTF diffracted beams will be registered as a function of the optical activity of the sample. The instrument has been evaluated with aqueous solutions of sucrose, glucose, and fructose and for three forms of camphor (D, L, DL). [ABSTRACT FROM AUTHOR]

Abstract: A monosegmented volumetric Karl Fischer titrator is described to mechanize the determination of water content in organic solvents. The system is based on the flow-batch characteristics of the monosegmented analysis concept and employs biamperometry to monitor the progress of the titration. The system shows accuracy and precision that are highly independent of the flow rate, does not require calibration, and is carried out in a closed system capable of minimizing contact of the sample and reagents with ambient moisture. Sample volumes in the range of 40–300μL are employed, depending on the water concentration. An automatic dilution is provided to deal with concentrated samples. The consumption of Karl Fischer reagent depends on the water content of the sample but is not larger than 100μL. The system was evaluated for determination of water in ethanol and methanol in the range 0.02–0.5% (w/w). The average relative precision estimated in that range (9–3%) is comparable to that obtained with a larger volume commercial system and no significant difference was observed between the results obtained for the two systems at the 95% confidence level. A complete titration can be performed in less than 5min employing the proposed system. [Copyright &y& Elsevier]

• Omega-3 supplements are characterized using near-infrared spectroscopy. • Portable NIR spectrophotometer for quality screening of omega-3 supplements. • New regression approach to construct PLSR models. • Detection of compositional frauds in omega-3 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. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A NIR-based simulator to improve the compounding process of automotive gasoline. • An innovative linear co-adding approach on hydrocarbon streams spectra is presented. • The method enables computer aided design of gasoline with desired octane properties. • Simulations using spectra from portable NIR and FT-NIR provided equivalent results. • An easy-to-use algorithm enabled the simulator practical usage in industrial units. Abstract This work addresses the industrial process of compounding automotive gasoline by a controlled blending of feedstock hydrocarbon streams deriving from different stages of the cracking and refining process of naphtha. In this process, gasoline quality and profitability are directly affected by both the composition of each stream and the ratio of the streams to each other in the gasoline. Indeed, factors such as the process conditions, feedstock supply, production costs and origin of the naphtha frequently vary in both composition and ratio. As a result, the gasoline blending recipes must change constantly over time to keep up with the quality and economic characteristics of the product, with octane rating as a critical operational parameter in these processes. Near Infrared (NIR) spectroscopy is able to detect changes in composition and it can be considered during the design of a gasoline. Moreover, the use of NIR spectroscopy makes it possible to reduce significantly the time needed for analyzing the parameters of interest in the process, compared to the analytical methods that are commonly used. The main purpose of this work was to develop an off-line simulator of octane number in automotive gasoline based on NIR spectroscopy and chemometrics, aiming to improve the compounding process. The simulator was developed on the basis of the mathematical reproduction of gasoline spectra by a virtual controlled mixture of the near infrared (NIR) spectra of the hydrocarbon streams at appropriate ratio. Spectra were recorded with two instruments, a benchtop FT-NIR and a handheld instrument. Partial Least Squares (PLS) regression models were developed and validated to estimate the octane number from the simulated NIR spectra for potential gasolines. The comparison among the octane number predictions from the simulated spectra, the real NIR spectra and, also, the reference values, at different times over 2 years of production, representing a critical step in the development of the simulator, showed that the simulated spectra were able to predict values reliably. The developed octane simulator has an excellent potential for improving efficiency in the compounding process of automotive gasoline. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

[Display omitted] • New approach to screen commercial samples is described. • Screening analysis performed without support of reference analysis. • Near-infrared spectroscopy screens for material authenticity and adulteration. • Authenticity and adulteration of bulk sweeteners are determined. • Compact near-infrared Fourier spectrophotometer used in screening analysis. A novel analytical approach based on near-infrared (NIR) Fourier transform miniaturized spectrophotometer and chemometrics is proposed for screening commercial products. A guide for analysis by the proposed approach is provided and validated. The approach is more environmentally friendly and cost-effective, reducing the cost of implementation by avoiding the need for large data sets of samples analyzed by costly and laborious reference analytical methods. The combination of multivariate techniques (principal components analysis, PCA, alternating least-squares multivariate curve resolution, MCR-ALS, and partial least squares regression, PLS) with visual spectra inspection and spectral correlation yielded consistent qualitative and quantitative results. As a case study, the approach was applied to commercial bulk sweeteners commonly used in low carbohydrate diet (erythritol and xylitol) and non-conformities detection was feasible using few reference samples which were not analyzed by conventional analytical methods. The effect of particle size of commercial products and their mixtures on the NIR spectrum as well as the non-linear spectral response observed for binary mixtures of sweeteners are discussed. Mixtures of sweeteners, simulating product adulterations, were analyzed using PCA, non-linear MCR-ALS, and PLS, to evidence non-conformity of commercial products and to estimate the level of adulteration. [ABSTRACT FROM AUTHOR]

Sweet sorghum is a very robust crop which has the potential to be used in ethanol production due to its high fermentable sugar content present in its stem juice, very similar to sugarcane. Therefore, for breeding purposes it is relevant to analyze sugar composition in the juice to characterize sweet sorghum genotypes and their period of industrial utilization within different environments for maximum ethanol yield. In this work we developed a rapid, low cost and efficient method to determine the profile of sugars (sucrose, glucose and fructose) in sorghum juice by near infrared spectroscopy and partial least square regression, and validation of the method was performed according to the high-performance liquid chromatography method. Developed models provided root mean square error of prediction of 4, 1 and 0.6 mg·mL − 1 and ratio performance deviations of 8, 5 and 5 for sucrose, glucose and fructose, respectively. Relative standard deviations of three sweet sorghum juice samples were reported with content variation (low, medium and high) 0.2, 0.3, 0.8% for sucrose; 1, 2, 2% for glucose; 1, 2, 3% for fructose. Sugar profile is an asset for crop breeders to take decisions for the development of more productive cultivars and higher sugar content. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In forensic examination of questioned documents a type of casework often encountered are the frauds that occur by mean of addition and adulteration of parts of text or numbers on document. The goal of this work is to evaluate the performance of hyperspectral images (HSI) in the near (NIR) and middle (MIR) regions, combined with the unsupervised pattern recognition techniques Principal Component Analysis (PCA) and Projection Pursuit (PP) for a rapid, reliable and non-destructive identification of document falsifications by means of alterations and additions. Blind tests were conducted for this purpose. Sixteen black ink pens from different brands, models and ink types were employed to prepare the samples in two different ways: (i) for initial discrimination and method validation, straight lines of approximately 2 cm long were produced in white paper; (ii) for blind testing, three collaborators used any of the sixteen pens available and prepared genuine or altered/added numbers (in total 30 samples) in white paper and in bank check paper. Overall, PP analysis showed better results than PCA to discriminate the 120 pairs of ink lines in white paper using HSI-MIR (97.5% and 87.5%, respectively). It is important to mention that the 10.0% of pairs that were not discriminated by PCA analysis were discriminated by PP, which highlights the importance of the combined use of the two chemometric techniques. HSI-NIR in combination with PCA and PP analysis was able to solve 76.7% and 83.3% of the blind testing samples, respectively. When HSI-MIR was used in a complementary way to HSI-NIR, discrimination of blind test samples increased to 90%. Therefore, HSI-NIR and HSI-MIR combined with PCA and PP show great discrimination potential and provide objective examination of suspected fraudulent documents. [ABSTRACT FROM AUTHOR]

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 12.2 N 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. [ABSTRACT FROM AUTHOR]

In this work, multivariate empirical models using conventional near infrared (NIR) spectroscopy and hyperspectral images were employed, respectively, to determine and map the diethyleneglycol (DEG) content and the number of carboxylic end groups (TCG) in poly(ethylene terephthalate) fiber samples. NIR spectroscopy is a fast, clean and efficient technique. NIR spectral data were obtained using two conventional spectrometers and a hyperspectral camera. The regression models were built using partial least squares. The models showed satisfactory results for the evaluated parameters, with Root Mean Squared Error of Prediction (RMSEP) values equivalent to repeatability of the reference methods. The distribution maps of the DEG and TCG content in the fibers samples were obtained by using the models built with the average spectra of the images to estimate the pixel content of each species, allowing us to identify areas with high content of these parameters. [ABSTRACT FROM AUTHOR]