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A variable importance criterion for variable selection in near-infrared spectral analysis
摘要： Variable selection is a universal problem in building multivariate calibration models, such as quantitative structure-activity relationship (QSAR) and quantitative relationships between quantity or property and spectral data. Significant improvement in the prediction ability of the models can be achieved by reducing the bias induced by the uninformative variables. A new criterion, named as C, is proposed in this study to evaluate the importance of the variables in a model. The value of C is defined as the average contribution of a variable to the model, which is calculated by the statistics of the models built with different combinations of the variables. In the calculation, a large number of partial least squares (PLS) models are built using a subset of variables selected by randomly re-sampling. Then, a vector of the prediction errors, in terms of root mean squared error of cross validation (RMSECV), and a matrix composed of 1 and 0 indicating the selected and unselected variables can be obtained. If multiple linear regression (MLR) is employed to model the relationship between the RMSECVs and the matrix, the coefficients of the MLR model can be used as a criterion to evaluate the contribution of a variable to the RMSECV. To enhance the efficiency of the method, a multi-step shrinkage strategy was used. Comparison with Monte Carlo-uninformative variables elimination (MC-UVE), randomization test (RT) and competitive adaptive reweighted sampling (CARS) was conducted using three NIR benchmark datasets. The results show that the proposed criterion is effective for selecting the informative variables from the spectra to improve the prediction ability of models.
关键词： multivariate calibration,multi-step strategy,variable selection,near-infrared spectroscopy
更新于2025-09-23 15:23:52
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Simultaneous Determination of Clarithromycin, Tinidazole and Omeprazole in Helicure Tablets Using Reflectance Near-Infrared Spectroscopy with the Aid of Chemometry
摘要： A near infrared spectroscopic method for the simultaneous determination of the active principles clarithromycin, tinidazole and omeprazole in a pharmaceutical preparation was developed. The three active principles are quantified using partial least-squares regression methods. The proposed method is applicable over a wide analyte concentration range (80–120%) of labeled content, so it requires careful selection of the calibration set and to ensure thorough homogenization of the product. The method was validated in accordance with the ICH standard validation guidelines for NIR spectroscopy by determining its selectivity, linearity, accuracy, precision and stability. Based on the results, it is an effective alternative to the existing choice (HPLC) for the same purpose.
关键词： Partial least squares,Clarithromycin,Helicure,Near Infrared Spectroscopy,Preprocessing,Genetic algorithm,Multivariate calibration
更新于2025-09-23 15:23:52
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Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data
摘要： In soil proximal sensing with visible and near-infrared spectroscopy, the currently available hyperspectral snapshot camera technique allows a rapid image data acquisition in a portable mode. This study describes how readings of a hyperspectral camera in the 450–950 nm region could be utilised for estimating soil parameters, which were soil organic carbon (OC), hot-water extractable-C, total nitrogen and clay content; readings were performed in the lab for raw samples without any crushing. As multivariate methods, we used PLSR with full spectra (FS) and also combined with two conceptually different methods of spectral variable selection (CARS, “competitive adaptive reweighted sampling” and IRIV, “iteratively retaining informative variables”). For the accuracy of obtained estimates, it was beneficial to use segmented images instead of image mean spectra, for which we applied a regular decomposing in sub-images all of the same size and k-means clustering. Based on FS-PLSR with image mean spectra, obtained estimates were not useful with RPD values less than 1.50 and R2 values being 0.51 in the best case. With segmented images, improvements were marked for all soil properties; RPD reached values ≥ 1.68 and R2 ≥ 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR.
关键词： spectral variable selection,hyperspectral snapshot camera,partial least squares regression,multivariate calibration,hyperspectral imaging,proximal soil sensing
更新于2025-09-23 15:22:29
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Calibration strategies for the direct determination of rare earth elements in hard disk magnets using laser-induced breakdown spectroscopy
摘要： This study is dedicated to the direct determination of base (B and Fe) and some rare earth elements (REE; Dy, Gd, Nd, Pr, Sm and Tb) in hard disk magnets. Five calibration strategies were tested and compared. Two of them are related to multivariate calibration: multiple linear regression (MLR) and partial least squares (PLS). Both presented adequate trueness values within a range of 80–120% for almost all analytes. The only exception was Tb, which was probably due to matrix effects. The use of MLR and PLS permits the testing of calibration models in the presence of interference, but matrix effects are not corrected. Because of this, three other univariate calibration methods were also tested and compared: multi-energy calibration (MEC), one-point gravimetric standard addition (OP GSA) and two-point calibration transfer (TP CT). These three calibration approaches permit matrix effects corrections, but an appropriate selection of the blank and standard is mandatory. The standard error obtained ranged from 0.01 to 6% using these univariate calibration methods.
关键词： Multivariate calibration,Two-point calibration transfer,Multi-energy calibration,One-point gravimetric standard addition,E-waste,Matrix effects
更新于2025-09-19 17:13:59