Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

Variation in Platinum group metals determination using ICP OES induced by the effect of complex matrices and the correction method based on multivariate calibration

The presence of interelements due to sample complexity can produce significant effects on the quantitative determination of Platinum group metals (PGMs) by using inductively coupled plasma optical emission spectrometry (ICP...

Quantification of Carbon Dioxide (CO2), Methane (CH4), and Nitrous Oxide (N2O) Using Near Infrared Spectroscopy and Multivariate Calibration in High Humidity Levels

In this work we developed a promising analytical method combining Fourier transform nearinfrared (FT-NIR) spectroscopic technique and first-order multivariate calibration using partial least-squares (PLS) model to simultaneously quantify the main greenhouse gases (GHG’s): methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O) and water vapor (H2O). The models were built using 70 mixtures with different concentration of these gases, 0.25-32.0 ppm to CH4 and N2O, and 50-1100 ppm to CO2 and different values of relative humidity (52-85%, 20 ºC) in synthetic air. After preparing each of the mixtures, they were analyzed by using FT-NIR and a reference analytical technique based on gas chromatography with mass spectrometric detection (GC-MS). The FT-NIR spectrometer was coupled with a long optical path cell, with 105.6 meters of optical path. In sequence, the spectra of all mixtures and its concentration values for each gas were used to build the multivariate calibration models, using PLS regressions. For this, the mixtures were grouped with Kennard Stone algorithm, 50 samples to calibration set and 20 samples to prediction set. The values of RMSEP (root mean square error of prediction) obtained for each model are 0.66, 28.7 and 0.66 ppm, respectively, for CH4, CO2, and N2O. The limits of quantification (LOQ) for each PLS models are 0.26, 3.6, and 0.99 ppm, respectively, for CH4, CO2, and N2O. The results show the potentiality of application of this system to monitoring emission sources in which the concentration of these gases are relatively high, as urban centers, industrial areas, and landfills.

Multivariate analysis of images in spectrophotometric methods: Quantification of soil organic matter

Soil organic matter (SOM) is usually quantified by Walkley-Black titration method or using a spectrophotometric method. This study proposes an alternative method for quantification of SOM using digital image from scanner and mathematical algorithms to replace titration and spectrophotometry procedures. For this, after SOM oxidation by potassium dichromate, digital images were acquired. Posteriorly, extraction of RGB color histograms from images have occurred, followed by the use of multivariate calibration method: partial least squares (PLS). Six soil samples were analyzed. We used the Walkley-Black method as reference. SOM was estimated by images using the PLS tool. The new method, besides being a fast, low cost, and more operational alternative, presented statistically equal results in relation to the reference method, as assessed by the Student t-test and F-test at 95 % confidence.

SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION OF PHENYLEPHRINE HYDROCHLORIDE AND NAPHAZOLINE HYDROCHLORIDE IN EYE DROPS BY CHEMOMETRIC TECHNIQUES AND ARTIFICIAL NEURAL NETWORK

Three multivariate calibration-prediction techniques, partial least squares (PLS), principal component regression (PCR) and artifi cial neural networks (ANN), have been applied without separation in the spectrophotometric multi-component analysis of phenylephrine hydrochloride and naphazoline hydrochloride. A set of 25 synthetic mixtures of phenylephrine hydrochloride and naphazoline hydrochloride has been evaluated to determine the predictability of PLS, PCR and ANN. The absorbance data matrix was obtained by measuring zero-order absorbances between 230-300 nm at intervals of 3 nm. The suitability of the models was determined on the basis of root mean square error (RMSE), root mean squared cross validation error (RMSECV) and root mean squared prediction error (RMSEP) values of calibration and validation data. The results showed a very good correlation between true values and the predicted concentration values. Therefore, the methods developed can be used for routine drug analysis without chemical pre-treatment.