Orthogonal Signal Correction Algorithm Research Articles

Quality-related batch process monitoring based on multi-way orthogonal signal correction enhanced total principal component regression

Batch process quality-related fault detection is necessary for keeping operation safety and quality consistency. However, the process variables have a weak ability to explain the quality variables makes the batch process quality-related fault detection a difficult task. In this work, a multi-way orthogonal signal correction enhanced total principal component regression (MOSC-ETPCR) is proposed to achieve the nonlinear quality-related fault detection of the batch process. First, after batch process data expansion, the orthogonal signal correction algorithm is used to filter out the quality-irrelevant information in process variables and avoid the influence of quality-irrelevant data on process modeling. Secondly, the nonlinear characteristics of the process are extracted by the maximum information coefficient matrix, and the quality-related nonlinear regression model is constructed to ensure the maximum correlation between the extracted features and quality variables. Thirdly, the statistics and corresponding control limits are established based on the obtained regression model. Finally, the effectiveness of the MOSC-ETPCR algorithm was verified by numerical simulation and the penicillin fermentation process.

Intensified Partial Least Squares Fault Diagnosis Method based on CARS

Partial Least Squares (PLS) is a fault diagnosis model used in statistical process monitoring. PLS decomposes the data space into a quality correlated subspace and an uncorrelated subspace, and monitors the state of both subspaces by setting the appropriate statistics. However, post-processing methods such as modified-PLS (MPLS) and efficient projection to latent structures (EPLS) gradually lose their ability to detect faults when low-intensity faults that do not affect quality. In order to eliminate the drawbacks of post-processing methods and improve the stability of algorithms under different conditions, this paper introduces the competitive and adaptive reweighted sampling method (CARS) into the field of fault diagnosis and proposes a fault filtering method combining CARS and orthogonal signal correction algorithm (OSC). First, an initial screening of the variable space was completed using CARS method. OSC algorithm is then used to remove quality- uncorrelated components. Next, the variable space is completely decomposed into a quality-correlated and an irrelevant subspace using intensified partial least squares method (IPLS). Finally, the validity of the model was verified using the simulations.

Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning

External factors including moisture content negatively affect the prediction accuracy of soil organic carbon (SOC) using on-line visible and near-infrared (vis-NIR) spectroscopy. This study compared the performances of four algorithms to remove the moisture content effect [direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction (OSC)] against non-corrected (NC) spectral models developed with partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and M5Rules regression. An on-line soil sensing platform coupled with a vis-NIR spectrophotometer (305–1700 nm) was used to scan twelve agricultural fields in Belgium and France. A total of 372 soil samples collected during the on-line measurement were divided into a calibration (260) and a prediction (112) dataset, using the Kennard-Stone algorithm. The latter set together with identical laboratory-measured 112 dry soil spectra formed a transfer dataset to develop EPO, DS and PDS correction matrices. Results showed that models after EPO, PDS and OSC corrections resulted in improved accuracy [coefficient of determination (R2) = 0.60–0.82, root mean square error (RMSE) = 16.1–5.7 g kg−1)], compared to the NC models (R2 = 0.58–0.73, RMSE = 16.5–6.8 g kg−1), whereas the DS (R2 = −0.10 to 0.26, RMSE = 26.8–21.9 g kg−1) provided deteriorated prediction accuracy. The EPO and OSC models provided better prediction accuracy than that of the PDS corrected models. The OSC-M5Rules (R2 = 0.82, RMSE = 5.7 g kg−1) obtained the highest accuracy followed by EPO-M5Rules (R2 = 0.74, RMSE = 6.7 g kg−1) and NC-M5Rules (R2 = 0.73, RMSE = 6.8 g kg−1), which outperformed all PLSR, RF and SVM models. Therefore, on-line vis-NIR spectra should be corrected with the OSC algorithm before calibrating a machine learning model for accurate prediction of SOC.

Orthogonal signal correction assisted PLS analysis of EEMF spectroscopic data sets: fluorimetric analysis of polycyclic aromatic hydrocarbon mixtures

Excitation-emission matrix fluorescence (EEMF) spectroscopy was combined with orthogonal signal correction (OSC) assisted partial least square (PLS) analysis to achieve simultaneous quantification of aqueous mixtures of carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs). The application of OSC algorithm on EEMF data sets prior to PLS analysis was found to significantly improve the performance of calibration model towards the PAHs quantification.

A correlated information removing based interference suppression technique in electronic nose for detection of bacteria

A sensor array with 30 gas sensors is used in the electronic nose (e-nose) for bacteria detection in wound infection. However, the interference is an urgent problem in e-nose, since it would impact on the detection of target due to the cross-sensitivity of gas sensors, especially the background interference caused by carrier gas. The related methods to suppress the background interference are independent component analysis and orthogonal signal correction algorithm which are unreasonable, because it is difficult to obtain the so-called reference vector in complex real-world scenario. Consider that the sampling process of pump suction is divided into three parts: baseline collecting, sample collecting and system purging. In the case of stabilized carrier gas, the information in baseline can be fully used to suppress the interference in sampling stage. Thus a novel and effective correlated information removing based interference suppression (CIRIS) method is proposed. Specifically, the principle of this method is to suppress the interference of the sampling stage by removing the information correlated with baseline samples. Experimental results show that the proposed method (CIRIS with principal component analysis used to calculate the projection matrix) is significantly effective for interference suppression in e-nose.

A novel pattern mismatch based interference elimination technique in E-nose

Metal oxide semiconductor (MOS) sensor array with cross-sensitivity to target gases is often used in electronic nose (e-nose) for monitoring indoor air quality. However, MOS sensors have their own defects of high susceptibility to some interferences which would seriously impact on the detection of target gases. Therefore it is urgent to solve the problem of interferences elimination as e-nose composed of MOS sensors cannot be used when there are interferences. A closely related method tends to discriminate the interference gases and target gases, and it depends on the type of interference gases. However, there are numerous interferences in real-world application scenario, which is impossible to be sampled in laboratory experiments. Considering that target gases detected by an e-nose can be fixed as invariant information, a novel and effective Pattern Mismatch based Interference Elimination (PMIE) method is proposed in this paper. It contains two parts: interference discrimination (i.e. pattern mismatch) and correction (i.e. interference elimination). Specifically, the principle of interference discrimination is whether a new pattern violates the rules established on the invariant target gases information (i.e. the case of interference gas appearing) or not (i.e. the case of only target gas appearing). If the current pattern of the sensor array is of interference, orthogonal signal correction algorithm (OSC) is used for interference correction. Experimental results prove that the proposed PMIE method is significantly effective for interference elimination in e-nose.

Quantitative Analysis of Overlapping X-Ray Fluorescence Spectra for Ni, Cu, Zn in Soil by Orthogonal Signal Correction and Partial Least Squares Algorithm

In this paper, the combination method viz. orthogonal signal correction (OSC) and partial least squares (PLS) algorithm is applied to analyze overlapping X-ray fluorescence spectra. The experiment is carried out by using 16 prepared soil samples which are divided into 4 groups according to their different combinations. Prediction results of these 4 groups are respectively calculated using OSC and PLS algorithm. Influence factors of the model are discussed according to specific samples. The experiment result shows that correction factor of OSC can be increased more than one and OSC-PLS algorithm can effectively eliminate the influence of overlapping XRF spectra and improve the predictive accuracy.

Using the orthogonal projections methods for predicting rice (Oryza sativa L.) yield with canopy reflectance data

Predicting crop yield in-season over large areas before harvest is an important topic in agricultural decision-making. This study compares the performance of partial least squares regression (PLSR) for predicting rice yield (Oryza sativa L.) using different signal correction methods on canopy reflectance spectral data. These signal correction methods include the standard normal variate (SNV) transformation, multiplicative scatter correction (MSC), orthogonal signal correction algorithm with leave-one-out cross-validation (OSC(CV)), and orthogonal projections to latent structures (O-PLS). Data were acquired over a wavelength range of 350–1100 nm. However, the influence of the intra-variance based on measured dates appeared in the original spectra. Using these pre-processing methods effectively reduced the influence of noise and increased the performance of the final PLSR model. Although SNV and MSC had good predictive ability, they could not clearly identify intra-variance effects. Conversely, the PLSR models with OSC and O-PLS were based on only one component, and could be interpreted in terms of crop parameters. Moreover, the Y-orthogonal component of O-PLS clearly identified intra-variance based on measured dates and provided superior modelling ability. The results of this study show that the O-PLS method is a useful tool for correction and interpretation when constructing a PLSR model for predicting rice yield in-season using canopy reflectance data.

Quantitative Analysis of Near-Infrared Spectra by Wavelet-based Interferences Removal and Least Squares Support Vector Regression

A new hybrid algorithm named EODT-LS-SVR based on least squares support vector regression (LS-SVR) with wavelet-based EODT algorithm as preprocessed tools is proposed for removing the interferences and developing the quantitative models with high precision in near-infrared (NIR) spectra. EODT-LS-SVR algorithm is composed of two steps. In the first step, the preprocessing algorithm named EODT, which combines the ideas of wavelet packet transform (WPT), orthogonal signal correction (OSC) and information theory, is employed for the characteristic extraction of analyte information through multi-scale analysis. Entropy-based baseline signal removing (EBSR) algorithm is applied to remove the baseline of the spectra based on information theory with WPT-based analysis, and then the information orthogonal to the concentrations of analyte is removed by OSC algorithm in each frequency band of spectra. In the second step, LS-SVR method coupled with grid search and particle swarm optimization (PSO) technique for parameters optimization is used to enhancing the quality of regression models. EODT-LS-SVR algorithm was validated by two NIR spectral datasets, one used for measuring the fat concentration of milk and the other used for measuring the oil content of corn. The comparison of prediction results demonstrated that the performance of calibration models developed by EODT-LS-SVR algorithm is better than that developed by other conventional algorithms, showing the high efficiency and the high quality for quantitative model development in NIR spectra of complex samples.

고차원 스펙트라 데이터 분석을 위한 Adjusted Direct Orthogonal Signal Correction 기법

Modeling and analysis of high-dimensional spectral data provide an opportunity to uncover inherent patterns in various information-rich data. Orthogonal signal correction (OSC) a preprocessing technique has been widely used to remove unwanted variations of spectral data that do not contribute to prediction or classification. In the present study we propose a novel OSC algorithm called adjusted direct OSC to improve visualization and the ability of classification. Experimental results with real mass spectral data from condom lubricants demonstrate the effectiveness of the proposed approach.

On-line concentration measurement for anti-solvent crystallization of β-artemether using UV–vis fiber spectroscopy

Concentration monitoring is essential for quality control of crystallizations. This work establishes the technical feasibility of Ultraviolet (UV) fiber spectroscopy for on-line concentration measurement of anti-solvent crystallization of β-artemether, where ethanol was selected as solvent and water was used as anti-solvent. The orthogonal signal correction (OSC) algorithm was selected to preprocess the UV spectra, and the results showed that the wavelength shift of UV maximum absorbance of β-artemether in ethanol+water solvent mixtures can be effectively eliminated by OSC algorithm. Then models for prediction of β-artemether concentration based on Lambert–Beer law were developed, and the models were verified by comparison between the training set and the validation set, as well as its directly application to the anti-solvent crystallization process of β-artemether. The results show that the model is suitable for on-line concentration measurement of anti-solvent crystallization of β-artemether, with reasonable accuracy and precision.

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.

The relationship between net analyte signal/preprocessing and orthogonal signal correction algorithms

A comparison is made between two versions of net analyte signal/processing (NAS/NAP) algorithms and two different orthogonal signal correction (OSC) algorithms. Although the NAP and OSC algorithms preprocess spectra differently, we show by comparison of the external prediction errors (RMSEP) of these algorithms based on terpolymer and synthetic data that these two types of algorithms have the same predictive performance. Under some conditions of overlap and noise, the algorithms can be made to show a slight difference in performance: in those cases, the performance of Lorber's NAS algorithm and Fearn's OSC algorithm track closely and that Olivieri's NAP algorithm tracks with the performance of a modified form of Fearn's OSC. The relationship between these two NAP preprocessing methods is also explored to show that the set of NAS spectra generated by Olivieri's NAP is a subset of that generated by Lorber's, since Lorber's NAP imposes a precisely orthogonal constraint on the target property and Olivieri's NAP relaxes that restriction. These two NAP algorithms can be applied in a piecewise manner for removing local irrelevant information. We also explore the relationship between local versions of these NAP and OSC algorithms. Piecewise implementations of Olivieri's NAP and the modified version of Fearn's OSC are applied to filter a set of spectra. Results from modeling show that these algorithms also have the same performance. Discontinuities occur in spectra preprocessed either by piecewise versions of Fearn's OSC or Lorber's NAP algorithms.

Suppressing the Temperature Effect in near Infrared Spectroscopy by Using Orthogonal Signal Correction

Temperature changes alter the near infrared spectra for liquid samples and substantially diminish the predictive ability of calibration models constructed from spectra that are not recorded under strictly controlled temperature conditions. The effect is especially significant in compounds forming hydrogen bonds, which exhibit shifts and intensity variations in their spectral absorption maxima and provide non-linear relationships between variables that require more complex models. In this work, we used the variable filtering method known as orthogonal signal correction (OSC) with a view to eliminate the spectral variability introduced by temperature change that is unrelated to the concentrations of the ingredients of an esterification reaction. Once the spurious spectral information was removed by the OSC algorithm, calibration models were constructed from spectra recorded over a broad temperature range in order to determine each individual component (PLS1) and the four reaction ingredients simultaneously (PLS2) in liquid samples. The OSC spectral treatment was found to provide more simple and accurate ( RSEP < 5%) predictive models that those obtained in its absence.

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.

A Novel Preprocessing Algorithm for Three-Way HPLC Data

Orthogonal signal correction (OSC) was a data preprocessing algorithm. It ensured that the filtered information was irrelevant to concentration data while using it to filter the noise from the original data. This paper extended the OSC application range from two-way data to three-way data. Two drug data sets, Enoxacin, Norfloxacin, Ciprofloxacin and Betamethasone, cortisone acetate, prednisone acetate, showed that the application of the OSC algorithm to three-way HPLC data was feasible and needed further research.

Improved piecewise orthogonal signal correction algorithm.

Piecewise orthogonal signal correction (POSC), an algorithm that performs local orthogonal filtering, was recently developed to process spectral signals. POSC was shown to improve partial leastsquares regression models over models built with conventional OSC. However, rank deficiencies within the POSC algorithm lead to artifacts in the filtered spectra when removing two or more POSC components. Thus, an updated OSC algorithm for use with the piecewise procedure is reported. It will be demonstrated how the mathematics of this updated OSC algorithm were derived from the previous version and why some OSC versions may not be as appropriate to use with the piecewise modeling procedure as the algorithm reported here.

Factor selection strategies for orthogonal signal correction applied to calibration of near-infrared spectra

Orthogonal signal correction (OSC) is a preprocessing technique used for correction of instrumental drift, bias and scatter in near-infrared spectra. OSC separates the variation into orthogonal factors, where the factors contain the variation within the X matrix data that is not correlated with the y matrix vector data. The aim of this study is to investigate different orthogonal factor selection methods, which will enhance the performance of the OSC routine for quantitative analysis of near-infrared spectra. In order for factor selection methods to be applied to OSC, an implementation of an existing OSC algorithm is used; this method computes OSC factors in a principal component manner. A binarized weighting matrix is then applied to the OSC factors for the purpose of OSC factor subset selection/nonselection. The optimization strategies used for subset selection of OSC factors were a genetic algorithm and stepwise selection. Three data sets were formed: (1) no preprocessing, (2) preprocessing by removal of sequential OSC factors and (3) preprocessing by removal of an OSC factor subset. Combinations of spectral predictors were selected from these data sets by hill climbing, feature selection, genetic algorithm and full-spectrum modeling. Partial least squares regression was undertaken to form calibration models. It was found that selection of OSC factor subsets produced better standard errors of prediction relative to data preprocessed by sequential selection of OSC factors.

Orthogonal signal correction: algorithmic aspects and properties

AbstractThe objective of this paper is to present a modified algorithm for the orthogonal signal correction (OSC) filter based on the approaches proposed by Wold, Antti, Lindgren and Öhman (Chemometrics Intell. Lab. Syst. 1998; 44: 175–185) and Fearn (Chemometrics Intell. Lab. Syst. 2000; 50: 47–52). An OSC filter consists of a trio of building blocks: weights, components and loadings, $\font\mathbfit=cmmib10\def\bfit#1{\hbox{\mathbfit #1}} {\bf \{w_{{\bfit j}}, t_{{\bfit j}}, p_{{\bfit j}}\}_{{\bfit j}=1}^{{\bfit A}}}$. The original OSC filter of Wold et al. was based on the framework of the non‐linear iterative partial least squares (NIPALS) algorithm. Adopting this approach enabled the mathematical justification for the selection of the loading vectors p j and components t j, but there was no theoretical foundation for the selection of w j. In contrast, the approach of Fearn described an objective function for the selection of the weight vectors w j, but in this case there is no theoretical justification for either p j or t j. Combining both approaches, within a NIPALS framework, enables a clear theoretical basis for the selection of all three building blocks to be established. A number of orthogonal and optimal properties of the NIPALS‐based OSC algorithm, although previously reported, are also theoretically proven. Finally, it is shown that the modified OSC algorithm is equivalent to Fearn's OSC but is interpretable as a consequence of it being presented from a NIPALS perspective. This enables the possible extension of OSC to dynamic and non‐linear systems. Copyright © 2002 John Wiley & Sons, Ltd.