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 click the icon from the toolbar.</li>
		<li>On the dialog that opens, click the Optical Bench tab.</li>
		<li>Check whether the spectrometer settings are ok. If yes, close the dialog. If no, continue with step 4.</li>
		<li>From the Configuration drop-down list, select the particular spectrometer type.</li>
		<li>Enter the spectrometer&#8217;s IP address into the Optical Bench URL entry field.</li>
		<li>Click the Connect button.</li>
	</ol>
	</li>
	<li>Setting up measurement parameters
	<ol>
		<li>On the Measure menu, select the Measurement command, or click the icon from the toolbar.</li>
		<li>On the dialog that opens, define the measurement parameters on the different tabs. 
		NOTE: Details on the individual measurement parameters are described in the OPUS Reference Manual.</li>
		<li>Click the Accept &#38; Exit button.</li>
	</ol>
	</li>
	<li>Storing experiment file
	<ol>
		<li>On the Measure menu, select the Advanced Measurement command. Then, click the Advanced tab.</li>
		<li>On the dialog that opens, define the resolution as 4 cm-1.</li>
		<li>Define the number of scans as 16 scans in the Sample/Background Scan Time entry fields.</li>
		<li>Define the path to automatically store the measuring data from 4,000 cm-1-12,500 cm-1.</li>
		<li>Determine the data type for the result spectrum as Absorbance.</li>
		<li>Click the Save button.</li>
		<li>On the dialog that opens, define a name for the experiment file and save this name.</li>
	</ol>
	</li>
	<li>Measuring background spectrum
	<ol>
		<li>On the Measure menu, select the Advanced Measurement command.</li>
		<li>Click the Optic tab.</li>
		<li>On the dialog that opens, click the Aperture setting drop-down list and select the same value used to acquire a sample spectrum.</li>
		<li>Click the Basic tab.</li>
		<li>On the dialog that opens, click the Background Single Channel button.</li>
	</ol>
	</li>
	<li>Measuring sample spectrum
	<ol>
		<li>Place the sample into the optical path of the spectrometer. The way in which this is done depends on the spectrometer configuration.</li>
		<li>On the Measure menu, select the Advanced Measurement command.</li>
		<li>Click the Basic tab.</li>
		<li>On the dialog that opens, define the sample description and sample form in the particular entry field. This information is stored together with the spectrum.</li>
		<li>Click the Sample Single Channel button to start online measurement. And save the NIR spectrum of each scan as OPUS file.</li>
	</ol>
	</li>
	<li>Collect the polyphenylene samples every 6 h and test the o-cresol concentration with liquid chromatography in the laboratory of industry to obtain a chemical reference value. 
	NOTE: Laboratory staff of industry field take each polyphenyl ether sample from the outlet of the liquid phase polyphenyl ether. The o-cresol content in each sample was measured three times by liquid chromatography. Then, the mean value of the results of the three times analysis was taken as the reference value of the o-cresol content to reduce the accidental error.</li>
	<li>Obtain 600 chemical reference values of o-cresol concentration in the laboratory. The calibration range of o-cresol concentration is from 42.1063 mg/1 g polyphenyl ether product to 51.6763 mg/1 g polyphenyl ether product.</li>
	<li>Combine the NIR spectra at the given test times with the chemical reference values of the o-cresol concentration.</li>
	<li>Use the software OPUS to read the original spectral set as shown in Figure 1.
	<ol>
		<li>On the File menu, click the Load File command.</li>
		<li>On the dialog that opens, select the particular spectrum file.</li>
		<li>Click the Open button. The spectrum is displayed in the spectrum window.</li>
	</ol>
	</li>
</ol>
2. NIR spectroscopy data pre-processing
<ol>
	<li>With the spectral preprocessing function in, obtain spectral dataset preprocessed with first-order derivative.
	<ol>
		<li>Open The Unscrambler which is a multivariate data analysis and experimental design software, select the Import command under File. Import the OPUS file as original NIR spectral dataset.</li>
		<li>Select Transform command under Modify. And select the Savitzky Golay Derivatives under Derivatives.</li>
		<li>Define the Samples and Variables as All Samples and All Variables in Scope. And define the number of Smoothing points as 13 and the Derivative as 1st derivative in Parameters.</li>
		<li>Click OK to start the derivative. 
		CAUTION: The increase of smoothness can reduce the sharp fluctuations of the curve, reduce the noise effect but also weaken the characteristics of the curve and make the curve distorted. Therefore, the appropriate smoothness selected according to the observation of the actual fluctuation intensity of the curve and the effect after processing.</li>
	</ol>
	</li>
	<li>Perform vector normalization on the sample spectra to normalize the value of the absorbance.
	<ol>
		<li>Select the Normalization command under Modify.</li>
		<li>Define the Samples and Variables as All Samples and All Variables in Scope.</li>
		<li>Select Vector normalization in the Type.</li>
		<li>Click OK to perform vector normalization.</li>
	</ol>
	</li>
</ol>
3. Establishment of PLSR model
<ol>
	<li>Creation of the NIR spectral data set 
	<ol>
		<li>Open Uncrambler.exe, select Export under File with the Matlab files to export the preprocessed spectral data set into .mat File and to obtain the spectral data set X automatically with 2203 variables.</li>
		<li>Obtain a complete NIR spectral dataset X (a matrix of 600 rows and 2203 columns) and the corresponding chemical reference values Y (a vector of 600 rows) in the form of .mat file for subsequent analysis and modeling.</li>
	</ol>
	</li>
	<li>Selection of the appropriate number of principal components
	<ol>
		<li>Open Matlab and import the .mat file containing the preprocessed near-infrared spectral data into the workspace by dragging the .mat file to the workspace. 
		NOTE: The .mat file stores the near-infrared spectral data X as an independent variable and the o-cresol content of the product as a dependent variable in the form of two matrices.</li>
		<li>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.</li>
		<li>Extract 15 principal components according to the optimization objective of Equation 1 and the OLSR model between the extracted principal components and the predicted values of the o-cresol concentration with the program containing the command plsregress() in Matlab. 
		[XL, YL, XS, YS, BETA, PCTVAR, MSE] = plsregress(X,Y,ncomp,&#8217;CV&#8217;,k); 
		Consult the MATLAB help document to get the usage details and the return value. 
		NOTE: <img alt="Equation 3" src="/files/ftp_upload/59077/59077eq3.jpg" /> Equation 1<img alt="Equation 4" src="/files/ftp_upload/59077/59077eq4.jpg" /> 
		<img alt="Equation 5" src="/files/ftp_upload/59077/59077eq5.jpg" /> , and <img alt="Equation 6" src="/files/ftp_upload/59077/59077eq6.jpg" /> is the ith principal components of the NIR spectral data; 
		<img alt="Equation 7" src="/files/ftp_upload/59077/59077eq7.jpg" /> is the projection of the ith principal components of the NIR spectral data; 
		<img alt="Equation 8" src="/files/ftp_upload/59077/59077eq8.jpg" /> is the Pearson correlation coefficient for the ith principal components and the o-cresol concentration.</li>
		<li>Obtain the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> value of the NIR spectral data and the predicted values for the different principal components using Equation 2. 
		NOTE: <img alt="Equation 9" src="/files/ftp_upload/59077/59077eq9.jpg" /> Equation 2 
		<img alt="Equation 10" src="/files/ftp_upload/59077/59077eq10.jpg" /> is the sum of squares due to error and is defined as <img alt="Equation 11" src="/files/ftp_upload/59077/59077eq11.jpg" /> ; 
		<img alt="Equation 12" src="/files/ftp_upload/59077/59077eq12.jpg" /> is the total sum of squares and is defined as <img alt="Equation 13" src="/files/ftp_upload/59077/59077eq13.jpg" />; 
		<img alt="Equation 14" src="/files/ftp_upload/59077/59077eq14.jpg" /> is the reference value of the o-cresol concentration of test dataset; 
		<img alt="Equation 15" src="/files/ftp_upload/59077/59077eq15.jpg" /> is the predicted value of the o-cresol concentration of test dataset; 
		<img alt="Equation 16" src="/files/ftp_upload/59077/59077eq16.jpg" /> is the mean value of reference value of the o-cresol concentration of test dataset; 
		<img alt="Equation 17" src="/files/ftp_upload/59077/59077eq17.jpg" /> is the number of samples of test dataset.</li>
		<li>Determine the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> values and the trend with increasing number of principal components as shown in Figure 2. Select 10 as the appropriate number of principal components with the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> value of 0.9917. 
		NOTE:<img alt="Equation 18" src="/files/ftp_upload/59077/59077eq18.jpg" /> value is the proportion of the variance in the dependent variable that is predictable by the independent variables. The higher the <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" /> value is, the higher the goodness-of-fit is and vice versa.</li>
	</ol>
	</li>
	<li>Validation of the goodness-of-fit and accuracy of the PLSR model with 10 principal components by using the command plsregress().
	<ol>
		<li>Repeat the modeling process with 10 principal components as steps 3.2.1-3.2.5 with 10 principal components.</li>
		<li>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 MSPECV.</li>
		<li>Plot the percent variance explained in NIR spectral data, the residuals, and the MSPECV as Figures 3, 4, and 5.</li>
		<li>Tabulate the evaluation indicators of <img alt="Equation 1" src="/files/ftp_upload/59077/59077eq1.jpg" />,<img alt="Equation 2" src="/files/ftp_upload/59077/59077eq2.jpg" />, and MSPE of 10-fold cross validation for the PLSR model for a quantitative analysis as shown in Table 1. 
		NOTE: The equations of <img alt="Equation 2" src="/files/ftp_upload/59077/59077eq2.jpg" /> and MSPE are shown as Equation 3 and Equation 4. 
		<img alt="Equation 19" src="/files/ftp_upload/59077/59077eq19.jpg" /> Equation 3 
		<img alt="Equation 20" src="/files/ftp_upload/59077/59077eq20.jpg" /> Equation 4 
		<img alt="Equation 21" src="/files/ftp_upload/59077/59077eq21.jpg" /> is the covariance of reference value and predicted value of o-cresol concentration; <img alt="Equation 22" src="/files/ftp_upload/59077/59077eq22.jpg" /> is the standard deviation of reference value of o-cresol concentration; 
		<img alt="Equation 23" src="/files/ftp_upload/59077/59077eq23.jpg" /> is the standard deviation of predicted value of o-cresol concentration.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

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><tbody><tr><td>MPA II Multi Purpose FT-NIR Analyzer</td><td>Bruker</td><td>1</td><td></td></tr><tr><td>Fiber Optic Probes(Liquid phase)</td><td>Bruker</td><td>1</td><td></td></tr><tr><td>Liquid chromatography analyzer&amp;nbsp;</td><td>/</td><td>1</td><td></td></tr><tr><td>Laboratory Equipment and Supplies(e.g. test tube, etc.)</td><td>/</td><td></td><td></td></tr><tr><td>MATLAB</td><td>MathWork</td><td>1</td><td></td></tr><tr><td>OPUS</td><td>Bruker</td><td>1</td><td></td></tr><tr><td>Principal computer</td><td>DELL</td><td>1</td><td></td></tr><tr><td>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 ...

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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 ...

o-cresol-concentration-online-measurement-based-on-near-infrared

Research

JoVE Journal

Engineering

6.5K Views.  Jiangnan University. De betekenis van dit experimentele manuscript ligt in de toepassingen van nabij-infrarood spectroscopie en de data mining algoritmen voor de online monitoring van de werkelijke procesindustrie. De grootste voordelen van deze technologie liggen in de snelheid. De niet-destructieve eigenschap van de nabij-infrarood detectietechnologie en de praktijk van gedeeltelijke minst vierkanten, of PLS, algoritme.Sluit om te beginnen de spectrometer zoals beschreven in het tekstprotocol. Gebruik de...

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

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared spectroscopy is used to predict nondestructively total sugar, total organic acid, and total anthocyanin content in each blueberry. The technique is expected to enable the selection of only delicious blueberries from all harvested ones. The near-infrared absorption spectra of blueberries are measured with the diffuse reflectance mode at the positions not on the calyx. The ingredient contents of a blueberry determined by high-performance liquid chromatography are used to construct models to predict the ingredient contents from observed spectra. Partial least squares regression is used for the construction of the models. It is necessary to properly select the pretreatments for the observed spectra and the wavelength regions of the spectra used for analyses. Validations are necessary for the constructed models to confirm that the ingredient contents are predicted with practical accuracies. Here we present a protocol to construct and validate the models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy.

We present here a protocol to construct and validate models for nondestructive prediction of total sugar, total organic acid, and total anthocyanin content in individual blueberries by near-infrared spectroscopy.

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared ...

Chemistry

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted virtual 3D environments, track a participants' behavioral performance within the virtual environment and synchronize with external devices for physiological and neuroimaging measures, including electroencephalogram and eye tracking.
Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables continuous, noninvasive, and portable monitoring of changes in cerebral blood oxygenation related to human brain functions2-7. Over the last decade fNIR is used to effectively monitor cognitive tasks such as attention, working memory and problem solving7-11. fNIR can be implemented in the form of a wearable and minimally intrusive device; it has the capacity to monitor brain activity in ecologically valid environments. 
Cognitive functions assessed through task performance involve patterns of brain activation of the prefrontal cortex (PFC) that vary from the initial novel task performance, after practice and during retention12. Using positron emission tomography (PET), Van Horn and colleagues found that regional cerebral blood flow was activated in the right frontal lobe during the encoding (i.e., initial na&iuml;ve performance) of spatial navigation of virtual mazes while there was little to no activation of the frontal regions after practice and during retention tests. Furthermore, the effects of contextual interference, a learning phenomenon related to organization of practice, are evident when individuals acquire multiple tasks under different practice schedules13,14. High contextual interference (random practice schedule) is created when the tasks to be learned are presented in a non-sequential, unpredictable order. Low contextual interference (blocked practice schedule) is created when the tasks to be learned are presented in a predictable order.
Our goal here is twofold: first to illustrate the experimental protocol design process and the use of MazeSuite, and second, to demonstrate the setup and deployment of the fNIR brain activity monitoring system using Cognitive Optical Brain Imaging (COBI) Studio software15. To illustrate our goals, a subsample from a study is reported to show the use of both MazeSuite and COBI Studio in a single experiment. The study involves the assessment of cognitive activity of the PFC during the acquisition and learning of computer maze tasks for blocked and random orders. Two right-handed adults (one male, one female) performed 315 acquisition, 30 retention and 20 transfer trials across four days. Design, implementation, data acquisition and analysis phases of the study were explained with the intention to provide a guideline for future studies.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments. Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables noninvasive and portable monitoring of cerebral blood oxygenation changes. This paper summarizes collective use of MazeSuite and fNIR within a cognitive processing learning paradigm.

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted ...

Neuroscience

Raman and IR Spectroelectrochemical Methods as Tools to Analyze Conjugated Organic Compounds

In the presented work, two spectroelectrochemical techniques are discussed as tools for the analysis of the structural changes occurring in the molecule on the vibrational level of energy. Raman and IR spectroelectrochemistry can be used for advanced characterization of the structural changes in the organic electroactive compounds. Here, the step-by-step analysis by means of Raman and IR spectroelectrochemistry is shown. Raman and IR spectroelectrochemical techniques provide complementary information about structural changes occurring during an electrochemical process, i.e. allows for the investigation of redox processes and their products. The examples of IR and Raman spectroelectrochemical analysis are presented, in which the products of the redox reactions, both in solution and solid state, are identified.

A protocol of step-by-step Raman and IR spectroelectrochemical analysis is presented.

In the presented work, two spectroelectrochemical techniques are discussed as tools for the analysis of the structural changes occurring in the molecule ...

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon (EC). This is commonly accomplished using thermal-optical OC/EC analyzers (TOAs), which enable measurement via controlled thermal pyrolysis and oxidation under specific temperature protocols and within constrained atmospheres. Several commercial TOAs exist, including a semi-continuous instrument that enables on-line analyses in the field. This instrument employs an in-test calibration procedure that requires relatively frequent calibration. This article details a calibration protocol for this semi-continuous TOA and presents an open-source software tool for data analysis and rigorous Monte Carlo quantification of uncertainties. Notably, the software tool includes novel means to correct for instrument drift and identify and quantify the uncertainty in the OC/EC split point. This is a significant improvement on the uncertainty estimation in the manufacturer&#8217;s software, which ignores split point uncertainty and otherwise uses fixed equations for relative and absolute errors (generally leading to under-estimated uncertainties and often yielding non-physical results as demonstrated in several example data sets). The demonstrated calibration protocol and new software tool enabling accurate quantification of combined uncertainties from calibration, repeatability, and OC/EC split point are shared with the intent of assisting other researchers in achieving better measurements of OC, EC, and total carbon mass in aerosol samples.

This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon analyzer.

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon ...

Environment

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in human subjects. This information is very important for skin-related diseases and characterizing skin care product performance. This protocol illustrates a method for confocal Raman spectra collection and the subsequent analysis of the spectral dataset leveraging chemometrics. The goal of this method is to establish a standard protocol for data collection and provide general guidance for data analysis. Preprocessing (e.g., removal of outlier spectra) is a critical step when processing large datasets from clinical studies. As an example, we provide guidance based on prior knowledge of a dataset to identify the types of outliers and develop specific strategies to remove them. A principal component analysis is performed, and the loading spectra are compared with spectra from reference materials to select the number of components used in the final multivariate curve resolution (MCR) analysis. This approach is successful for extracting meaningful information from a large spectral dataset.

Here, we present a protocol for collection of confocal Raman spectra from human subjects in clinical studies combined with chemometric approaches for spectral outlier removal and the subsequent extraction of key features.

Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in ...

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems

Femtosecond time-resolved stimulated Raman spectroscopy is a promising method of observing the structural dynamics of short-lived transients with near infrared (near-IR) transitions, because it can overcome the low sensitivity of spontaneous Raman spectrometers in the near-IR region. Here, we describe technical details of a femtosecond time-resolved near-IR multiplex stimulated Raman spectrometer that we have recently developed. A description of signal generation and optimization, measurement, data acquisition, and calibration and correction of recorded data is provided as well. We present an application of our spectrometer to analyze the excited-state dynamics of &#946;-carotene in toluene solution. A C=C stretch band of &#946;-carotene in the second lowest excited singlet (S2) state and the lowest excited singlet (S1) state is clearly observed in the recorded time-resolved stimulated Raman spectra. The femtosecond time-resolved near-IR stimulated Raman spectrometer is applicable to the structural dynamics of &#960;-conjugate systems from simple molecules to complex materials.

Details of signal generation and optimization, measurement, data acquisition, and data handling for a femtosecond time-resolved near-IR stimulated Raman spectrometer are described. A near infrared stimulated Raman study on the excited-state dynamics of &#946;-carotene in toluene is shown as a representative application.

Femtosecond time-resolved stimulated Raman spectroscopy is a promising method of observing the structural dynamics of short-lived transients with near ...

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding of the neurobiological underpinnings of social interactions, and possibly interpersonal relationships. Functional near-infrared spectroscopy (fNIRS) is well suited for conducting hyperscanning experiments because it measures local hemodynamic effects with a high sampling rate and, importantly, it can be applied in natural settings, not requiring strict motion restrictions. In this article, we present a protocol for conducting fNIRS hyperscanning experiments with parent-child dyads and for analyzing brain-to-brain synchrony. Furthermore, we discuss critical issues and future directions, regarding the experimental design, spatial registration of the fNIRS channels, physiological influences and data analysis methods. The described protocol is not specific to parent-child dyads, but can be applied to a variety of different dyadic constellations, such as adult strangers, romantic partners or siblings. To conclude, fNIRS hyperscanning has the potential to yield new insights into the dynamics of the ongoing social interaction, which possibly go beyond what can be studied by examining the activities of individual brains.

The present protocol describes how to conduct fNIRS hyperscanning experiments and analyze brain-to-brain synchrony. Further, we discuss challenges and possible solutions.

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding ...

Cerebrovascular Reactivity Measurement with Functional Near Infrared Spectroscopy

Cerebrovascular reactivity (CVR) is the capacity of blood vessels in the brain to alter cerebral blood flow (either with dilation or constriction) in response to chemical or physical stimuli. The amount of reactivity in the cerebral microvasculature depends on the integrity of the capacitance vasculature and is the primary function of endothelial cells. CVR is, therefore, an indicator of the microvasculature&#8217;s physiology and overall health. Imaging methods that can measure CVR are available but can be costly, and require magnetic resonance imaging centers and technical expertise. In this study, we used fNIRS technology to monitor changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the cerebral microvasculature to assess the CVR of 15 healthy controls (HC) in response to a vasoactive stimulus (inhaled 5% carbon dioxide or CO2). Our results suggest that this is a promising imaging technology that offers a non-invasive, accurate, portable, and cost-effective method of mapping cortical CVR and associated microvasculature function, resulting from a traumatic brain injury or other conditions associated with cerebral microvasculopathy.

Presented here is a protocol for imaging and measurement of cerebrovascular reactivity in humans with functional Near Infrared Spectroscopy (fNIRS). fNIRS is a novel imaging modality that captures the concentration changes of hemoglobin species in the brain&#8217;s outermost cortex under specific stimuli.

Cerebrovascular reactivity (CVR) is the capacity of blood vessels in the brain to alter cerebral blood flow (either with dilation or constriction) in ...

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical treatment. Among the many neuroimaging technologies used to measure brain activity, functional near-infrared spectroscopy (fNIRS) enables the evaluation of dynamic cortical activities by measuring the local hemoglobin levels similar to functional magnetic resonance imaging (fMRI). Also, due to lesser physical restriction in fNIRS, multiple variants of sensorimotor tasks can be evaluated. Many laboratories have developed several methods for fNIRS data analysis; however, despite the fact that the general principles are the same, there is no universally standardized method. Here, we present the qualitative and comparative analytic methods of data obtained from a multi-channel fNIRS experiment using a block design. For qualitative analysis, we used a software for NIRS as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results for each session by visualizing the activated area during the task. In addition, the non-invasive three-dimensional digitizer can be used to estimate the fNIRS channel locations relative to the brain. To corroborate the NIRS-SPM findings, the amplitude of the changes in hemoglobin levels induced by the sensorimotor task can be statistically analyzed by comparing the data obtained from two different sessions (before and after intervention) of the same study subject using a multi-channel hierarchical mixed model. Our methods can be used to measure the pre- vs. post-intervention analysis in a variety of neurological disorders such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders.

We describe the analysis of continuous-wave functional near-infrared spectroscopy experiment using a block design with a sensorimotor task. To increase the reliability of the data analysis, we used the qualitative general linear model-based statistical parametric mapping and the comparative hierarchical mixed models for multi-channels.

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical ...

fNIRS Hyperscanning Protocol to Analyze Inter-Brain Synchrony in Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy (fNIRS) hyperscanning is an innovative technique that enables real-time monitoring of brain activity among multiple individuals engaged in social interactions. Researchers in this field quantify concurrent brain activities through the index of inter-brain synchrony (IBS). In psychological counseling research, the use of fNIRS to measure IBS has garnered attention for its potential to illuminate the dynamics of counselor-client interactions. Nevertheless, the field currently lacks a standardized protocol for precisely measuring IBS between counselors and clients, which would facilitate the revelation of real-time interaction patterns during counseling sessions. To address this need, this paper proposes a detailed standardized protocol, outlining the procedural steps for conducting fNIRS hyperscanning in psychological counseling settings, focusing on the acquisition of brain signals, calculation of IBS between counselors and clients, and analysis of lead-lag patterns of IBS throughout counseling sessions. Implementing this standardized fNIRS hyperscanning pipeline not only enhances the reproducibility and reliability of IBS measurements in psychological counseling research but also facilitates deeper insights into the neural mechanisms underlying working alliance. By integrating fNIRS hyperscanning into naturalistic counseling environments, researchers can advance understanding of how IBS correlates with counseling outcomes, potentially informing personalized approaches to mental health treatment.

The protocol aims to provide a detailed description of the methodology for conducting an fNIRS hyperscanning study in psychological counseling. This includes the preparations for the experiment, the procedure for collecting data, and the subsequent data analysis process.

Functional Near-Infrared Spectroscopy (fNIRS) hyperscanning is an innovative technique that enables real-time monitoring of brain activity among multiple ...

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

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