Orthogonal Signal Correction Methods Research Articles

Loopy Orthogonal Signal Correction Scatter Correction in Non-Invasive Blood Glucose

Spectroscopy is the study of matter based on light, sound, or particles emitted, absorbed, or reflected as well as the study of methods for generating and analyzing spectra. The spectrum has systematic diversity, namely the presence of light scattering and differences in the size of objects. The spectroscopic output allows for scattering shifts, because the same object measured several times does not exactly produce the same spectrum. Problems found in the spectrum can be overcome by pre-processing the data, namely the scatter correction method. Scatter correction is used to reduce the physical properties in the spectrum so that the information obtained is relatively the same for each spectrum, produces good estimates, and can be interpreted well. One of the spectroscopic tools that utilize infrared light is a non-invasive blood glucose level measuring device. The output of the tool is the time domain and intensity spectrum. Each object from the resulting spectrum still has noise, so scatter correction can be applied to this data. The purpose of this study was to perform a loopy Orthogonal Signal Correction (OSC) scatter correction method on time domain spectrum data on intensity on a non-invasive blood glucose level measuring device. The OSC method uses the concept of orthogonality to the mean by drawing the intensity value, weighting it, calculating the vector loading and then making corrections to the initial intensity. Based on the analysis, the loopy OSC method is better than OSC because the convergence is more accurate, the mean difference is smaller, the variance is smaller and the value converges on all the values tested. Based on exploration and the average difference, the loopy OSC method is better able to form the same pattern for each replication. This also shows that an object that is measured repeatedly has been able to be identified as the same object.

Implication of phenol red in quantification of cultured cancerous cells using near-infrared spectroscopy and aquaphotomics

This study aims to evaluate the ability of absorbance spectra in the near-infrared (NIR) region to predict the number of cells for different cell lines. The cancerous cell lines were human cervix adenocarcinoma (HeLa) and human prostate carcinoma (DU145), and L929 was a normal mouse skin fibroblast. The number of cells varied from 50,000 to 275,000 with an interval of 25,000 for each cell line. Vis-NIR absorbance spectra (400–1100 nm) at each number of cells (50,000–275,000) for L929, DU145, and HeLa cell lines cultured in media with and without phenol red were recorded. Multiple linear regression (MLR) and partial least squares regression (PLSR) models were developed in the NIR region (680–1050 nm) to quantify the number of cells for the three cell lines. The outcomes showed that the quantification analysis of the number of cells using MLR and PLSR models produced high prediction accuracy with R2≥ 93%. The best results were obtained using PLSR for the preprocessed spectra using an orthogonal signal correction method. It was found that the presence of phenol red in the culture medium improved the prediction accuracy in the case of HeLa (SECV = 271 cells) and DU145 (SECV = 250 cells) cell lines, but the accuracy was higher when phenol red was not present for the L929 cell line (SECV = 24 cells). In addition, the existence of phenol red boosted the accuracy when the global PLSR model was built, irrespective of the cell type (SECV = 7,754 cells). The effect of phenol red was explained in the terms of its impact on the water molecular structure of the cells’ culture medium which influences the light scattering.

Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy

Highly toxic pesticides are forbidden because of threatening to human lives, property, and the environment. However, some of them are still used illegally by added into normal pesticides to enhance the effectiveness and lower the cost. Therefore, it is very important to detect the illegally added ingredients in pesticides, such as Isofenphos-methyl in Tolfenpyrad. In this research, a rapid and accurate method for determining the concentration of Isofenphos-methyl in Tolfenpyrad is studied by near infrared spectroscopy with only nine characteristic wavelengths. Different pre-treating methods of direct orthogonal signal correction and first-order derivative on raw spectra and analysis methods of back propagation neural network and partial least squares are investigated to establish quantitative analysis models for comparison. Among them, the back propagation neural network model pre-treated with the direct orthogonal signal correction method has good prediction accuracy and prevents the data from being over-corrected. The correlation coefficients of the model calibration set and prediction set are as high as 0.999 and 0.989, respectively. Root Mean Square Error of Prediction and Ratio of Prediction to Deviation are 0.27% and 9.17, respectively. The prediction concentration limit can be as low as 0.5%. The results show that the direct orthogonal signal correction pre-treating method combined with back propagation neural network can be used for precisely quantitative determination of pesticide doping concentration. It provides an effective approach for rapid and accurate determination of prohibited components in pesticides.

Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

Several orthogonal signal correction (OSC) methods coupled with partial least-squares (PLS) were applied to near-infrared spectra in a longitudinal study for the prediction of glucose, urea, and triacetin in liquid samples. The OSC methods included Wold’s OSC, Fearn’s OSC, orthogonal projection to latent structures (OPLS), and direct orthogonal signal correction (DOSC). These preprocessing methods are designed to simplify the spectra and remove information that is orthogonal to the analyte. Samples consisted of various concentrations of glucose, urea, and triacetin in the range from 1 to 19 mM. Calibration and prediction data were collected over 1.7 years to test how well each of the OSC methods helped to maintain analyte prediction performance over time. When comparing the OSC methods, it was found that all of the OSC methods improved the predictions in most data sets throughout the study but that DOSC clearly outperformed the others. On the basis of 18 prediction sets collected across 617 days after the collection of the calibration data, the average percent improvement in the standard error of prediction (SEP) when applying DOSC was 4.6%, 83.1%, and 43.0%, respectively, for glucose, urea, and triacetin. These comparisons were made relative to the SEP values obtained with unprocessed spectra.

Does the limited use of orthogonal signal correction pre-treatment approach to improve the prediction accuracy of soil organic carbon need attention?

Visible-Near-infrared (Vis-NIR) spectroscopy is a relatively modern method that can be used to predict soil properties such as soil organic carbon (SOC). Predictions of soil properties with Vis-NIR requires pre-processing algorithms. Applying a wrong type or applying too extreme pre-processing algorithms may result in the removal of valuable information or may even introduce unwanted variations. This can negatively affect the prediction accuracy of the property being studied. Orthogonal signal correction (OSC) pre-processing method has been used in other fields for visible and near infrared spectra improvement. However, the application of OSC application in soil science remains limited. The main idea behind OSC is the removal of only unwanted variation from the spectrum unlike some other pre-treatment methods which are believed to remove valuable information in the process of removing undesirable variation. This study verifies the effectiveness of the OSC against nine commonly used pre-treatment methods across three different agricultural fields for both lab-dry and in-field spectra. For the prediction, partial least square regression (PLSR) and support vector machine regression (SVMR) algorithms were used. In this study, the OSC method overall improved prediction accuracy the most (e.g. with OSC the best result was R2CV = 0.79, without OSC the best result was R2CV = 0.62) and is therefore a promising tool that should be included in further studies on different soils and other soil properties.

Improving forest burn severity estimations with partial least squares regression and orthogonal signal correction methods in Daxing’an Mountains, China

Several indices and simple empirical models and ratios of single band from pre- and post-fire Landsat images have been developed to estimate and/or map burn severity. However, these models and indices are usually site-, time- and vegetation-dependent and their applications are limited. The Daxing’an Mountains range has the largest forested area in China and is prone to wildfires. Whether or not the existing models can effectively characterize the burn severity over a large region is unclear. In this study, we used the orthogonal signal correction method based on partial least squares regression (PLSR) to select those variables that better interpret the variance of burn severity. A new index and other commonly used indices were used to construct a new, multivariate PLSR model which was compared with the popular single variable models, according to three assessment indices: relative root mean square error (RMSE%), relative bias (RE%) and Nash–Sutcliffe efficiency (NSE%). The results indicate that the multivariate PLSR model performed better than the other single variable models with higher NSE% (68.2% vs. 67.8%) and less RE% (3.7% vs. − 8.7%), while achieving almost the same RMSE%. We also discuss the spectral characteristics of the four selected variables for constructing the multivariate PLSR model and their correlation with the field burn severity data. The new model developed from this study should help to better understand the patterns of forest burn severity and assist in vegetation restoration efforts in the region.

Rapid and Nondestructive Analysis of Bacillus Calmette–Guerin Polysaccharide Nucleic Acid Injection by near-Infrared Spectroscopy with Chemometrics

ABSTRACTA novel quantitative analytical method using near-infrared (NIR) spectroscopy combined with chemometrics has been developed to determine the polysaccharides and nucleic acids for routine quality analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections. A Monte-Carlo method was used to detect and discard outliers and to improve the predictive ability of the model. Various other spectral preprocessing methods such as smoothing, derivative, multiplicative scattering correction, standard normal variables, and orthogonal signal correction methods were used to remove noise and other irrelevant information from the spectra. Sample-set partitioning based on joint x–y distance method was utilized to divide the sample measurements into calibration and validation datasets. The optimal wavelength variables were determined by competitive adaptive weighted sampling. The model was established and cross-validated using partial least square regression. The root mean square errors of cross-validation for polysaccharides and nucleic acids were determined to be 0.0382 and 5.218, and the root mean square errors of prediction were 0.0229 and 6.282. The overall results show that NIR spectroscopy combined with chemometry is effective for the quantitative analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections.

Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy

Soil organic carbon (SOC) is an essential property for soil function, fertility and sustainability of agricultural systems. It can be measured with visible and near-infrared reflectance (VIS-NIR) spectroscopy efficiently based on empirical equations and spectra data for air/oven-dried samples. However, the spectral signal is interfered with by soil moisture content (MC) under in situ conditions, which will affect the accuracy of measurements and calibration transfer among different areas. This study aimed to (1) quantify the influences of MC on SOC prediction by VIS-NIR spectroscopy; and (2) explore the potentials of orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) methods in the removal of moisture interference. Ninety-eight samples were collected from the Jianghan plain, China, and eight MCs were obtained for each sample by a rewetting process. The VIS-NIR spectra of the rewetted soil samples were measured in the laboratory. Partial least squares regression (PLSR) was used to develop SOC prediction models. Specifically, three validation strategies, namely moisture level validation, transferability validation and mixed-moisture validation, were designed to test the potentials of OSC and GLSW in removing the MC effect. Results showed that all of the PLSR models generated at different moisture levels (e.g., 50–100, 250–300 g·kg−1) were moderately successful in SOC predictions (r2pre = 0.58–0.85, RPD = 1.55–2.55). These models, however, could not be transferred to soil samples with different moisture levels. OSC and GLSW methods are useful filter transformations improving model transferability. The GLSW-PLSR model (mean of r2pre = 0.77, root mean square error for prediction (RMSEP) = 3.08 g·kg−1, and residual prediction deviations (RPD) = 2.09) outperforms the OSC-PLSR model (mean of r2pre = 0.67, RMSEP = 3.67 g·kg−1, and RPD = 1.76) when the moisture-mixed protocol is used. Results demonstrated the use of OSC and GLSW combined with PLSR models for efficient estimation of SOC using VIS-NIR under different soil MC conditions.

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

Abstract For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R 2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.

Analysis of Data Mining Method for Near Infrared Spectral Data of Dairy Products Based Orthogonality

There are advantages such as real time, quick, multi-constituents simultaneous measurement, green and no pollution, and so on, of using near infrared spectral method to detect concentration information of dairy products constituents. From time domain and frequency domain, near infrared spectral data mining methods based on orthogonality were separately researched to realize noise filtering and useful information extracting. For milk spectrum, methods in the time domain including Orthogonal Signal Correction and Direct Orthogonalization, Wavelet Transform de-noising method in the frequency domain, were separately explored and used in spectral preprocessing, PLS method was used to build calibration model, and processing effect of different data mining methods was compared with and analyzed. It is showed that in the time domain, using the orthogonality methods to make data mining for milk spectrum, the complex interfering signal and noise information uncorrelated with the measured constituents can be can reduced effectively, and the correlated information with the measured constituents is reserved in maximal limitation, prediction capability of calibration model is improved, furthermore, the Orthogonal Signal Correction method is better than the Direct Orthogonalization method, moreover, in the frequency domain, wavelet de-noising is better than the orthogonality methods within the time domain.

Automated Principal Component-Based Orthogonal Signal Correction Applied to Fused Near Infrared−Mid-Infrared Spectra of French Olive Oils

An approach for automating the determination of the number of components in orthogonal signal correction (OSC) has been devised. In addition, a novel principal component OSC (PC-OSC) is reported that builds softer models for removing background from signals and is much faster than the partial least-squares (PLS) based OSC algorithm. These signal correction methods were evaluated by classifying fused near- and mid-infrared spectra of French olive oils by geographic origin. Two classification methods, partial least-squares-discriminant analysis (PLS-DA) and a fuzzy rule-building expert system (FuRES), were used to evaluate the signal correction of the fused vibrational spectra from the olive oils. The number of components was determined by using bootstrap Latin partitions (BLPs) in the signal correction routine and maximizing the average projected difference resolution (PDR). The same approach was used to select the number of latent variables in the PLS-DA evaluation and perfect classification was obtained. Biased PLS-DA models were also evaluated that optimized the number of latent variables to yield the minimum prediction error. Fuzzy or soft classification systems benefit from background removal. The FuRES prediction results did not differ significantly from the results that were obtained using either the unbiased or biased PLS-DA methods, but was an order of magnitude faster in the evaluations when a sufficient number of PC-OSC components were selected. The importance of bootstrapping was demonstrated for the automated OSC and PC-OSC methods. In addition, the PLS-DA algorithms were also automated using BLPs and proved effective.

Rapid and accurate determination of VFAs and ethanol in the effluent of an anaerobic H 2-producing bioreactor using near-infrared spectroscopy

A rapid quantification method, based on Fourier transform near-infrared (FT-NIR) spectroscopy, was developed and validated to nondestructively quantify the concentrations of volatile fatty acids and ethanol in the effluent of an anaerobic H 2-producing bioreactor. The first-derivative spectra calculated by a simple numerical difference, combined with the orthogonal signal correction method, were used as spectral preprocessing options. A calibration model was established and validated using gas chromatography measurement results. The number of internal latent variables was optimized based on the lowest root-mean-square error of calibration. The calibration model established shows the satisfactory results for the lowest root-mean-square errors of prediction compared to other preprocess methods. The method developed in this work is demonstrated to be more flexible compared with other approaches to determine the concentrations of volatile fatty acids and ethanol in the anaerobic reactor effluents.

Mixture resolution according to the percentage of robusta variety in order to detect adulteration in roasted coffee by near infrared spectroscopy

Near infrared spectroscopy (NIRS), combined with multivariate calibration methods, has been used to quantify the robusta variety content of roasted coffee samples, as a means for controlling and avoiding coffee adulteration, which is a very important issue taking into account the great variability of the final sale price depending on coffee varietal origin. In pursuit of this aim, PLS regression and a wavelet-based pre-processing method that we have recently developed called OWAVEC were applied, in order to simultaneously operate two crucial pre-processing steps in multivariate calibration: signal correction and data compression. Several pre-processing methods (mean centering, first derivative and two orthogonal signal correction methods, OSC and DOSC) were additionally applied in order to find calibration models with as best a predictive ability as possible and to evaluate the performance of the OWAVEC method, comparing the respective quality of the different regression models constructed. The calibration model developed after pre-processing derivative spectra by OWAVEC provided high quality results (0.79% RMSEP), the percentage of robusta variety being predicted with a reliability notably better than that associated with the models constructed from raw spectra and also from data corrected by other orthogonal signal correction methods, and showing a higher model simplicity.

Ridge estimated orthogonal signal correction for data preprocessing prior to PLS modeling: QSAR studies of cyclooxygenase-2 inhibitors

A version of orthogonal signal correction (OSC), ridge estimated OSC (REOSC) algorithm has been proposed to improve the performance of the OSC. The REOSC preprocesses the original data according to a principle that only the irrelevant variation in OSC components disturbing the PLS modeling model should be removed and the useful information regarding the structure–activity relationship analysis should be retained. The problem of possible removal of useful information in OSC can be overcome in a more or less extent by using a ridge parameter in construction of the estimator in REOSC. Generalized cross-validation method was employed to select the ridge parameter and the number of OSC components. The proposed methodology has been tested in PLS modeling for QSAR studies of cyclooxygenase-2 inhibitors. A comparison in the modeling power of PLS is made between non-processed data set, ordinarily OSC processed data set and that processed by the proposed ridge estimated OSC method. It has been demonstrated that data preprocessing using ridge estimated OSC could improve PLS regression models by reducing both model complexity and prediction errors. The analysis of the cyclooxygenase-2 inhibitor data provides some insight in the effect of molecular structure on the activity of anti-inflammatory agents.

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.

Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.

Generalization of OWAVEC method for simultaneous noise suppression, data compression and orthogonal signal correction

This paper presents modifications to our recently introduced pre-processing method, orthogonal WAVElet correction (OWAVEC), based on the combination of wavelet analysis and an orthogonal correction algorithm, described in detail in a former paper [I. Esteban-Díez, J.M. González-Sáiz, C. Pizarro, OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration, Anal. Chim. Acta 515 (2004) 31–41], aimed at extending its applicability and at improving its performance; thanks to an additional use of OWAVEC as an effective data compression tool. The OWAVEC method uses the discrete wavelet transform (DWT) to decompose each individual signal into the wavelet domain, and then an orthogonalization algorithm is applied to the obtained wavelet coefficients matrix to remove the information not related to a considered response variable. Later, the corrected wavelet coefficients are ranked by their variance or by their correlation coefficient with the response variable, and the subset providing the most stable and reliable calibration model is finally selected (data compression). The new version of OWAVEC has been applied to two NIR data sets to test its performance. For both regression problems studied, high quality calibration models with very high compression ratios were obtained, providing improved predictive results and a considerably lower overfitting than other orthogonal signal correction methods. The generalized OWAVEC method presented here may be used as a global tool for simultaneous noise suppression, data compression and orthogonal correction of signals.

ATR-FTIR in monitoring of crystallization processes: comparison of indirect and direct OSC methods

A stable PLS calibration for solute concentration measurement in batch cooling crystallization processes is presented. Two example cases are included: Sulfathiazole dissolved in five different mixtures of water and 1-propanol and C15 dissolved in toluene. The data to be used in calibration are validated using Multivariate Statistical Process Control (MSPC) and sensitivity analyses criteria. Two Orthogonal Signal Correction (OSC) filters are compared as preprocessing techniques to enhance the model performance. The inherent criteria for selecting the best performing model was RMSEV of the test set. Models are validated with two different external test sets. One measured in conditions similar to calibration set and another measured in conditions close to true crystallization measurement conditions. The latter one tests the flexibility of the model to be used in future predictions. Results show that careful data and model validation steps clearly improve the modeling procedure. OSC filtering clearly simplified the model and also enhanced the performance of the PLS model. The models have successfully been used in several crystallization experiments.

Classification of Wine and Alcohol Vinegar Samples Based on Near-Infrared Spectroscopy. Feasibility Study on the Detection of Adulterated Vinegar Samples

Near-infrared (NIR) spectroscopy was used to discriminate between wine vinegar (red or white) and alcohol vinegar. One orthogonal signal correction method (OSC) was applied on a set of 73 vinegar NIR spectra from both origins and artificial blends made in the laboratory in order to remove information unrelated to a specific chemical response (tartaric acid), which was selected due to its high discriminant ability to differentiate between wine vinegar and alcohol vinegar samples. These corrected NIR spectra, as well as raw NIR spectra and 14 physicochemical variables, were used to develop separate classification models using the potential functions method as a class-modeling technique. The aforementioned models were compared to evaluate the suitability of NIR spectroscopy as a rapid method for discriminating between vinegar origins. The transformation of vinegar NIR spectra by means of an orthogonal signal correction method prompted a notable improvement in the specificity of the constructed classification models. The classification model developed was then applied to artificial vinegar blends made in the laboratory to test its capacity to recognize adulterated vinegar samples.

An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS

Two orthogonal signal correction methods (OSC and DOSC) were applied on a set of 83 roasted coffee NIR spectra from varied origins and varieties in order to remove information unrelated to a specific chemical response (caffeine), which was selected due to its high discriminant ability to differentiate between arabica and robusta coffee varieties. These corrected NIR spectra, as well as raw NIR spectra and three chemical quantities (caffeine, chlorogenic acids and total acidity), were used to develop separate classification models accordingly using the potential functions method as a class-modelling technique in order to evaluate their respective capacities to discriminate between coffee varieties and the influence of these pre-processing methods on the classification of the coffee samples into their corresponding variety class. The transformation of roasted coffee NIR spectra by means of an orthogonal signal correction method, taking into account in this correction a chemical response closely related to the sample origin, prompted a notable improvement in the specificity of the constructed classification models.