Experimento didático de quimiometria para calibração multivariada na determinação de paracetamol em comprimidos comerciais utilizando espectroscopia no infravermelho próximo: um tutorial, parte II

The aim of this manuscript was to show the basic concepts and practical application of Partial Least Squares (PLS) as a tutorial, using the Matlab computing environment for beginners, undergraduate and graduate students. As a practical example, the determination of the drug paracetamol in commercial tablets using Near-Infrared (NIR) spectroscopy and Partial Least Squares (PLS) regression was shown, an experiment that has been successfully carried out at the Chemical Institute of Campinas State University for chemistry undergraduate course students to introduce the basic concepts of multivariate calibration in a practical way.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Predicting rapeseed oil content with near-infrared spectroscopy

The objective of this work was to establish a calibration equation and to estimate the efficiency of near-infrared reflectance (NIR) spectroscopy for evaluating rapeseed oil content in Southern Brazil. Spectral data from 124 half-sib families were correlated with oil contents determined by the chemical method. The accuracy of the equation was verified by coefficient of determination (R²) of 0.92, error of calibration (SEC) of 0.78, and error of performance (SEP) of 1.22. The oil content of ten genotypes, which were not included in the calibration with NIR, was similar to the one obtained by the standard chemical method. NIR spectroscopy is adequate to differentiate oil content of rapeseed genotypes.

Discrimination of native wood charcoal by infrared spectroscopy

Brazil is one of the largest producers and consumers of charcoal in the world. About 50% of its charcoal comes from native forests, with a large part coming from unsustainable operations. The anatomic identification of charcoal is subjective; an instrumental technique would facilitate the monitoring of forests. This study aimed to verify the feasibility of using medium and near infrared reflectance spectroscopy to discriminate native (ipê) from plantation charcoals (eucalyptus). Principal Components Analysis, followed by Discriminant Factorial Analysis formed two different groups indicated by Mahalanobis distances of 40.6 and 80.3 for near and mid infrared, respectively. Validation of the model showed 100% efficacy.

High pressure near infrared study of the mutated light-harvesting complex LH2

The pressure sensitivities of the near infrared spectra of the light-harvesting (LH2) complex and a mutant complex with a simplified BChl-B850 binding pocket were compared. In the mutant an abrupt change in the spectral properties occurred at 250 MPa, which was not observed with the native sample. Increased disorder due to collapse of the chromophore pocket is suggested.

Resolução multivariada de curvas com mínimos quadrados alternantes: descrição, funcionamento e aplicações

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) is a resolution method that has been efficiently applied in many different fields, such as process analysis, environmental data and, more recently, hyperspectral image analysis. When applied to second order data (or to three-way data) arrays, recovery of the underlying basis vectors in both measurement orders (i.e. signal and concentration orders) from the data matrix can be achieved without ambiguities if the trilinear model constraint is considered during the ALS optimization. This work summarizes different protocols of MCR-ALS application, presenting a case study: near-infrared image spectroscopy.

Determination of the HMX and RDX content in synthesized energetic material by HPLC, FT-MIR, and FT-NIR spectroscopies

A new method has been developed for determining the content of mixtures of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) and hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), the HMX/RDX ratio, in explosive compositions by Fourier transform infrared spectroscopy (FT-IR), in the regions MIR (mid infrared) and NIR (near infrared) with reference values obtained by chromatographic analysis (HPLC). Plots of relative MIR (A917 / A783) or NIR absorbance values (A4412 / A4317) versus HMX/RDX ratio determined by HPLC analysis revealed good linear relationships.

LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR

Least-squares support vector machines (LS-SVM) were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

Previsão das propriedades físicas do papel kraft por espectroscopia no infravermelho próximo (NIR) e regressão por mínimos quadrados parciais (PLS)

The main objective of the present work is represented by the characterization of the physical properties of industrial kraft paper (i.e. transversal and longitudinal tear resistance, transversal traction resistance, bursting or crack resistance, longitudinal and transversal compression resistance (SCT (Compressive Strength Tester) and compression resistance (RCT-Ring Crush Test)) by near infrared spectroscopy associated to partial least squares regression. Several multivariate models were developed, many of them with high prevision capacity. In general, low prevision errors were observed and regression coefficients that are comparable with those provided by conventional standard methodologies.

Determinação não destrutiva do nitrogênio total em plantas por espectroscopia de reflectância difusa no infravermelho próximo

Diffuse reflectance near-infrared (DR-NIR) spectroscopy associated with partial least squares (PLS) multivariate calibration is proposed for a direct, non-destructive, determination of total nitrogen in wheat leaves. The procedure was developed for an Analytical Instrumental Analysis course, carried out at the Institute of Chemistry of the State University of Campinas. The DR-NIR results are in good agreement with those obtained by the Kjeldhal standard procedure, with a relative error of less than ± 3% and the method may be used for teaching purposes as well as for routine analysis.

Sensores ópticos com detecção no infravermelho próximo e médio

Optical chemical sensors with detection in the near and mid infrared region are reviewed. Fundamental concepts of infrared spectroscopy and optical chemical sensors are briefly described, before presenting some aspects on optical chemical sensors, such as synthesis of NIR and IR reagents, preparation of new materials as well as application in determinations of species of biological, industrial and environmental importance.

DISCRIMINAÇÃO DE MADEIRAS SIMILARES POR FLUORESCÊNCIA MOLECULAR E MÍNIMOS QUADRADOS PARCIAIS

Wood is an extremely complex biological material, which can show macroscopic similarities that make it difficult to discriminate between species. Discrimination between similar wood species can be achieved by either anatomic or instrumental methods, such as near infrared spectroscopy (NIR). Although different spectroscopy methods are currently available, few studies have applied them to discriminate between wood species. In this study, we applied a partial least squares-discriminant analysis (PLS-DA) model to evaluate the viability of using direct fluorescence measurements for discriminating between Eucalyptus grandis, Eucalyptus urograndis, and Cedrela odorata. The results show that molecular fluorescence is an efficient technique for discriminating between these visually similar wood species. With respect to calibration and the validation samples, we observed no misclassifications or outliers.

White mold detection in common beans through leaf reflectance spectroscopy

ABSTRACT This study aimed to identify wavelengths based on leaf reflectance (400-1050 nm) to estimate white mold severity in common beans at different seasons. Two experiments were carried out, one during fall and another in winter. Partial Least Squares (PLS) regression was used to establish a set of wavelengths that better estimates the disease severity at a specific date. Therefore, observations were previously divided in two sub-groups. The first one (calibration) was used for model building and the second subgroup for model testing. Error measurements and correlation between measured and predicted values of disease severity index were employed to provide the best wavelengths in both seasons. The average indexes of each experiment were of 5.8% and 7.4%, which is considered low. Spectral bands ranged between blue and green, green and red, and red and infrared, being most sensitive for disease estimation. Beyond the transition ranges, other spectral regions also presented wavelengths with potential to determine the disease severity, such as red, green, and near infrared.

Espectroscopia de infravermelho na determinação da textura do solo

A aplicação de técnicas espectroscópicas que utilizam a radiação infravermelha (NIRS-Near Infrared Spectroscopy e DRIFTS-Diffuse Reflectance Fourier Transformed Spectroscopy) na análise inorgânica do solo tem sido proposta desde a década de 1970, mas até os dias atuais são raros os métodos implementados rotineiramente no Brasil. Isso deve-se à dificuldade em construir modelos de calibração, por meio de métodos estatísticos multivariados, utilizando-se amostras reais de solo, de constituição complexa, que varia geograficamente e de acordo com o manejo. Por isso, os objetivos deste trabalho foram construir modelos de calibração em NIRS e DRIFTS para a quantificação das frações de argila e areia, em amostras de solos de classes diferentes - Latossolo Vermelho (predominante), Nitossolo, Argissolo Vermelho e Neossolo Quartzarênico - e avaliar qual dessas duas técnicas é mais adequada para essa finalidade, assim como a interferência do agrupamento de amostras e da seleção de variáveis espectrais na qualidade desses modelos. Para isso, valores de referência obtidos pelo método do densímetro, método largamente utilizado nos laboratórios de análise de solo, foram correlacionados com valores de absorbância em NIRS e DRIFTS pela ferramenta estatística PLS (Partial Least Squares), obtendo-se altos coeficientes de determinação (R²), de 0,95, 0,90 e 0,91 para argila, silte e areia, respectivamente, na validação externa. Isso confirma a aplicabilidade das técnicas espectroscópicas na análise granulométrica do solo para fins agrícolas. O agrupamento das amostras segundo a localização e a seleção de variáveis espectrais pouco influenciou na qualidade dos modelos. A técnica espectroscópica mais indicada para essa finalidade foi a DRIFTS.

Redes neurais e suas aplicações em calibração multivariada

Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.