Name: o-cresol concentration online measurement based on near infrared spectroscopy via partial least square regression
Uploaded: 2019-11-08T12:30:00
Description: Watch this Scientific Journal Video about O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression at JoVE.com

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61722306 and 61473137) and National First-class Discipline Program of Light Industry Technology and Engineering (LITE2018-025).

1. NIR spectrum data acquisition with Fourier transform (FT)-NIR process spectrometer
<ol>
	<li>Install the liquid phase optical fiber probe of the near-infrared spectrometer at the outlet of the polyphenyl ether product. And open the OPUS software on the upper computer connected to the instrument and start to configure the measurement.</li>
	<li>Connecting to spectrometer
	<ol>
		<li>On the Measure menu, select the Optic Setup and Service command, or ...

<ol>
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This protocol describes the process of performing the PLSR on the measurement of the o-cresol concentration remaining in the liquid product of polyphenylene ether with NIRS.
The two critical steps in this process are the pre-processing of the original NIR spectral data and the variables selection of the high-dimensional NIR spectral data.
Generally, the non-systematic background interference leads to the non-systematic scattering deviation or baseline drift of NIR s...

Near infrared (NIR) spectroscopy (NIRS) has gained wide acceptance as a fast, efficient, non-destructive, and non-polluting modern analytical technology; the method has been used during the past several years for product quality detection and analysis and chemical component measurement in industrial processes. The most essential specialty of the method is its ability to record spectra for solid and liquid samples without any pre-processing, making NIRS especially suitable for the direct and rapid detection and analysis of natural and synthetic products1,2. Unlike traditional sensors that measure process variables (e.g., temperature, pressure, liquid level, etc.) at a macroscopic scale and inevitably suffer the external noise and background interference, NIRS detects the structural information of the chemical composition at microscopic and molecular scales. Thus, essential information can be measured more accurately and effectively than with other methods3,4.
Polyphenyl ether, as one of the engineering plastics, are widely used due to its heat resistance, flame retardant, insulation, electrical properties, dimensional stability, impact resistance, creep resistance, mechanical strength and other properties5. More importantly, it is non-toxic and harmless compared to other engineering plastics. At present, 2,6-xylenol is one of the basic raw materials for the synthesis of polyphenylene ether, and it is usually prepared by catalyzed alkylation of phenol with methanol method6. There are two main products of this preparation method, o-cresol and 2,6-xylenol. After a series of separation and extraction steps, 2,6 xylenol is used to produce polyphenylene ether. However, trace amounts of o-cresol remain in 2,6-xylenol. O-cresol does not participate in the synthesis of polyphenylene ether and will remain in the polyphenylene ether product, resulting in a decrease in product quality or even the substandard. At present, most companies still analyze the compositions of complex organic mixtures such as liquid phase polyphenyl ether products containing impurities (e.g., o-cresol) by physical or chemical separation analysis such as chromatography7,8. The separation principle of chromatography is the use of the mixture of compositions in the fixed phase and the flow phase in the dissolution, analysis, adsorption, desorption or other affinity of the minor differences in the performance. When the two phases move relative to each other, the compositions are separated by the above actions repeatedly in the two phases. Depending on the object, it usually takes a few minutes to a few tens of minutes to complete a complex material separation operation. It can be seen that the measurement efficiency is low.
Nowadays, the measurement of product quality and the advanced control technology based on this analysis for the modern fine process chemical materials industry is the key direction to further improve product quality. In the process industry of polyphenyl ether production, real-time measurement of o-cresol content in polyphenylene ether product is of great development significance. Chromatographic analysis clearly cannot meet the requirements of advanced control technology for real-time measurement of substances and signal feedback. Therefore, we propose the partial least squares regression (PLSR) method to establish a linear model between the NIRS data and the o-cresol concentration, which realize the online measurement of o-cresol content in the liquid polyphenylene ether product of outlet.
The pre-processing for NIRS plays the most important role prior to multivariate statistical modeling. NIRS wavenumbers in the NIR spectrum and the particle sizes of biological samples are comparable, so it is known for unexpected scatter effects that has influence on the recorded sample spectra. By performing appropriate pre-processing methods, these effects are easy to be eliminated largely9. The most commonly used pre-processing techniques in NIRS are categorized as scatter correction and spectral derivative methods. First group of methods includes multiplicative scatter correction, detrending, standard normal variate transformations, and normalization. The spectral derivation methods include the use of the first and second derivatives.
Prior to developing a quantitative regression model, it is important to remove the unsystematic scatter variations from the NIRS data because they have a significant influence on the accuracy of the predictive model, its complexity and parsimony. The selection of a suitable pre-processing method should always depend on the subsequent modeling step. Here, if the NIR spectral dataset does not follow the Lambert-Beer law, then other factors tend to compensate for the non-ideal behavior of the prediction for predicted components. The disadvantage of the existence of such needless factors leads to the increase of model complexity, even most likely, a reduction in the robustness. Thus, the application of spectral derivatives and a conventional normalization to the spectral data is an essential part of the method.
After spectral preprocessing, the NIRS data with a high signal-to-noise ratio and low background interference are obtained. Modern NIRS analysis provides the rapid acquisition of large amounts of absorbance over an appropriate spectral range. The chemical composition of the sample is then predicted by extracting the relevant variables using the information contained in the spectral curve. Generally, NIRS is combined with multivariate analysis techniques for qualitative or quantitative analyses10. A multivariate linear regression (MLR) analysis is commonly used for developing and mining the mathematical relationship between the data and the components in industrial processes and has been widely used in NIRS analysis.
However, there are two fundamental problems when implementing an MLR for preprocessed NIRS data. One problem is the variable redundancy. The high dimensionality of the NIRS data often renders the prediction of a dependent variable unreliable because variables are included that have no correlation with the components. These redundant variables reduce the information efficiency of the spectral data and affect the accuracy of the model. In order to eliminate the variable redundancy, it is essential to develop and maximize the correlation between the NIRS data and the predicted components.
Another problem is the issue of multicollinearity in the NIRS data. One of the important assumptions of multiple linear regression models is that there is no linear relationship between any of the explanatory variables of the regression model. If this linear relationship exists, it is proved that there is multicollinearity in the linear regression model and the assumption is violated. In multiple linear regressions, such as an ordinary least squares regression (OLSR), multiple correlations between the variables affect the parameter estimation, increase the model error, and affect the stability of the model. To eliminate the multilinear correlation between the NIR spectral data, we use variable selection methods that maximize the inherent variability of the samples.
Here, we propose to use the PLSR, which is a generalization of multiple linear regression that has been widely used in the field of NIRS11,12. The PLSR integrates the basic functions of the MLR, canonical correlation analysis (CCA), and principal component analysis (PCA) and combines the forecasting analysis with a non-model data connotation analysis. The PLSR can be divided into two parts. The first part selects the components of the characteristic variables and the predicted components by partial least squares analysis (PLS). PLS maximizes the inherent variability of principal components by making the covariance of the principal components and predicted components as large as possible when extracting the principal components. Next, the OLSR model of o-cresol concentration is established for the principal components selected. PLSR is suitable for the analysis of noisy data with numerous independent variables that are strongly collinear and highly correlated and for the simultaneous modeling of several response variables. Also, PLSR extracts the effective information of the sample spectra, overcomes the problem of multicollinearity, and has the advantages of strong stability and high prediction accuracy13,14.
The following protocol describes the process of using the PLSR model for measuring the o-cresol concentration using NIR spectral data. The reliability and accuracy of the model are evaluated quantitatively by using the determination coefficient (<img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" />), the prediction correlation coefficient (<img alt="Equation 2" src="/files/ftp_upload/59077/59077eq2.jpg" />) and the mean square prediction error of cross-validation (MSPECV). Moreover, to intuitively show the advantages of the PLSR, the evaluation indicators are visualized in several plots for a qualitative analysis. Finally, evaluation indicators of an experiment are presented in table format to quantitatively illustrate the reliability and precision of the PLSR model.

<table border="0" cellpadding="0" cellspacing="0" style="width:536px;" width="535">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:319px;">MPA II Multi Purpose FT-NIR Analyzer</td>
			<td style="width:67px;">Bruker</td>
			<td style="width:88px;">1</td>
			<td style="width:63px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fiber Optic Probes(Liquid phase)</td>
			<td>Bruker</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Liquid chromatography analyzer&#160;</td>
			<td>/</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Laboratory Equipment and Supplies(e.g. test tube, etc.)</td>
			<td>/</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MATLAB</td>
			<td>MathWork</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">OPUS</td>
			<td>Bruker</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Principal computer</td>
			<td>DELL</td>
			<td>1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">The Unscrambler</td>
			<td>CAMO</td>
			<td>1</td>
			<td></td>
		</tr>
	</tbody>
</table>

o-cresol concentration online measurement based on near infrared spectroscopy via partial least square regression

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the prediction of the components in industrial processes. The ability to record spectra for solid and liquid samples without any pretreatment is advantageous and the method is widely used. However, the disadvantages of analyzing high-dimensional near-infrared spectral data include information redundancy and multicollinearity of the spectral data. Thus, we propose to use partial least squares regression method, which has traditionally been used to reduce the data dimensionality and eliminate the collinearity between the original features. We implement the method for predicting the o-cresol concentration during the production of polyphenylene ether. The proposed approach offers the following advantages over component regression prediction methods: 1) partial least squares regression solves the multicollinearity problem of the independent variables and effectively avoids overfitting, which occurs in a regression analysis due to the high correlation between the independent variables; 2) the use of the near-infrared spectra results in high accuracy because it is a non-destructive and non-polluting method to obtain information at microscopic and molecular scales.

The predicted value of o-cresol Impurity in polyphenyl ether products is obtained by PLSR-based near-infrared spectroscopy. Figure 2 and Figure 3 respectively show the reliability of the method in the feature selection stage from the curve of the decision coefficient and the error interpretation percentage increasing with the number of principal components.
Specifically, please note that in the ...

Watch this Scientific Journal Video about O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression at JoVE.com

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

The protocol describes a method of predicting o-cresol concentration during the production of polyphenylene ether using near-infrared spectroscopy and partial least squares regression. To describe the process more clearly and completely, an example of predicting the o-cresol concentration during the production of polyphenylene is used to clarify the steps.

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the ...

The significance of this experimental manuscript lies in the applications of near-infrared spectroscopy and the data mining algorithms to the online monitoring of actual process industry. The biggest advantages of this technology lie in its rapidity. The nondestructive property of its near-infrared detection technology and the practice of partial least squares, or PLS, algorithm.To begin, connect the spectrometer as described in the text protocol. To set up measurement parameters, use the OPUS software. Navigate to the Measure menu, and select the Advanced Measurement command.On the dialog that opens, define the measurement parameters on the different tabs. To store the experiment file, click the Advanced tab. Define the resolution as four inverse centimeters.Define the number of scans as 16 scans in the sample background scan time entry fields. Define the path to automatically store the measuring data from 4, 000 inverse centimeters to 12, 500 inverse centimeters. Determine the data type for the result spectrum as absorbance, and save the experiment file.Now click the Optic tab. Click the aperture setting dropdown list, and select the same value used to acquire a sample spectrum. Then click the Basic tab.Click the Background Single Channel button, and place the sample into the optical path of the spectrometer to measure the sample spectrum. Define the sample description and sample form in the particular entry field. This information is stored together with the spectrum.Now click the Sample Single Channel button to start the online measurement. Save the NIR spectrum of each scan as an OPUS file. Use the software OPUS to read the original spectral set.On the File menu, click the Load File command. On the dialog that opens, select the particular spectrum file. Click the Open button.The spectrum is displayed in the spectrum window. With the spectral pre-processing function in, obtain spectral data set pre-processed with the first-order derivative. First open the Unscrambler, which is a multivariate data analysis and experimental design software.Then select the Import command under File. Import the OPUS file as an original NIR spectral dataset. Select the Transform command under Modify.Then select the Savitzky-Golay derivatives under Derivatives. Define the samples and variables as all samples and all variables in Scope. Also define the number of smoothing points as 13 and the derivative as first derivative in Parameters.Click OK to start the derivative. Perform vector normalization on the sample spectra to normalize the value of the absorbance. Select the normalization command under Modify.Define the samples and variables as all samples and all variables in Scope. Select vector normalization in the type. Click OK to perform vector normalization.To select the appropriate number of principal components, open MATLAB and import the MAT file containing the preprocessed near-infrared spectral data by dragging the MAT file to the work space. Open the programmed M-file in the editor. Click Open under the Editor option, select the compiled M-file in the file storage directory and then click Confirm.Work in MATLAB to extract 15 principal components according to the optimization objective and the OLSR model between the extracted principal components and the predicted values of the O-cresol concentration. Determine the R-squared values and the trend with increasing number of principal components. Select 10 as the appropriate number of principal components with the R-squared value of 0.9917.To validate the goodness of fit and accuracy of the PLSR model, repeat the modeling process with 10 principal components. Evaluate the model based on a 10-fold cross validation using the plots of the percent variance explained in the NIR spectral data, the residuals, and the mean square prediction error of cross validation or MSPECV. Plotted here are the residuals, which refer to the difference between the O-cresol content reference value and the PLSR model estimate.The plotted data shows that PLSR for the measurement of the O-cresol content based on the NIR spectral data has high accuracy. The cross validation mean square error is a measure of the degree of difference between the reference and the predicted O-cresol content. The smaller the value, the better the accuracy of the predictive model describing O-cresol content.Mean square prediction error of cross validation for the O-cresol concentration measurement based on the PLSR decreases as the number of principal components increases. The error reaches an acceptable minimum at 10 principal components. This proves that the PLSR results in high stability for the measurement of O-cresol concentration using NIRS.While attempting this procedure, the most important step is to accurately obtain the reference values of compositions because this is the basis for all the preprocessing and the modeling performed in the later stage. In addition to PLS, some current popular machine learning algorithms such as deep learning and decision tree can be used in this procedure, too. Combined with NIRS detection technology, we believe that the proposed data mining is a meaningful template for application in the process of industrial automation to intelligent transformation in modern industry.

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