scholarly journals Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohd Yusop Nurida ◽  
Dolmat Norfadilah ◽  
Mohd Rozaiddin Siti Aishah ◽  
Chan Zhe Phak ◽  
Syafiqa M. Saleh
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Spectrometry ◽  
Calibration Model ◽  
Co2 Absorption ◽  
Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

scholarly journals Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 232
Author(s):  
Hanim Z. Amanah ◽  
Salma Sultana Tunny ◽  
Rudiati Evi Masithoh ◽  
Myoung-Gun Choung ◽  
Kyung-Hwan Kim ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Prediction Models ◽  
Soybean Seed ◽  
Fourier Transform Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Spectroscopic Techniques ◽  
Ft Ir

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.

scholarly journals   Near infrared spectroscopy for deoxynivalenol content estimation in intact wheat grain

2012 ◽  
Vol 58 (No. 4) ◽  
pp. 196-203 ◽  
Author(s):  
V. Dvořáček ◽  
A. Prohasková ◽  
J. Chrpová ◽  
L. Štočková
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Resistance Breeding ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Errors ◽  
Wheat Grain ◽  
Wide Range

Non-invasive determination of deoxynivalenol (DON) still presents a challenging problem. Therefore, the present study was aimed at a rapid determination of DON in whole wheat grain by means of FT-NIR spectroscopy, with a wide range of concentrations for potential applications in breeding programs and common systems of quality management using partial least square calibration (PLS) and discriminant analysis technique (DA). Using a set of artificially infected wheat samples with a known content of DON, four PLS models with different concentration range were created. The broadest model predicting DON in the concentration range of 0&ndash;90 mg/kg possessed the highest correlation coefficients of calibration and cross validation (0.94 and 0.88); but also possessed the highest prediction errors (SEP = 6.23 mg/kg). Thus the subsequent combination of DA as the wide range predictive model and the low-range PLS model was used. This technique gave more precise results in the samples with lower DON concentrations &ndash; below 30 mg/kg (SEP = 2.35 mg/kg), when compared to the most wide-range PLS model (SEP = 5.95 mg/kg).<br />Such technique enables to estimate DON content in collections of artificially infected wheat plants in Fusarium resistance breeding experiments. &nbsp;

Near-Infrared Spectroscopic Measurement of Organic Matter in Soil

2012 ◽  
Vol 499 ◽  
pp. 414-418
Author(s):  
Tao Pan ◽  
Zhen Tao Wu ◽  
Jie Mei Chen
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Modeling Prediction ◽  
Validation Set ◽  
Selection Of

Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

scholarly journals Near Infrared (NIR) Spectroscopy to Predict Physical Properties of Acacia mangium at Three Different Age Classes

2017 ◽  
Vol 4 (1) ◽  
pp. 7-12
Author(s):  
Lina Karlinasari ◽  
Merry Sabed
Keyword(s):  
Moisture Content ◽  
Physical Properties ◽  
Wood Density ◽  
Near Infrared ◽  
Acacia Mangium ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model

Near Infrared (NIR) spectroscopy has been used to predict several properties of wood. This is one of the nondestructive testing (NDT) methods providing fast and reliable wood characterization analysis which can be applied in various manufacture industry, included forest sector, in control and process monitoring task. Moisture content and wood density are important properties related to strength properties. The aim of this study was to evaluate NIR technique in obtaining calibration models for determining moisture content and wood density of Acacia mangium in the age of 5, 6, 7 years-old. Spectra were measured in both solid and ground wood samples. Laboratory testing of physical properties were determined by volumetric and gravimetric methods. The laboratory values were correlated with the NIR spectra using multivariate analysis statistic of Partial Least Square (PLS). The calibration-validation model of this relationship was evaluated by using the coefficient of determination (R2), root means square error of calibration (RMSEC) and cross-validation (RMSECV) values. Generally, a better accuracy was obtained by using calibration model of ground wood compared to that of solid wood samples. At age of 7 years-old, the R2 allowed the use of NIR spectra of solid samples to develop calibration and validation model, especially for wood density. Based on ratio of performance to deviation (RPD) and RMSE, ground samples demonstrated a higher value of RPD, RMSEC, and RMSECV compared to solid wood for all properties.

Application of near Infrared Spectroscopy for the Quantification of Quality Parameters in Selected Vegetables and Essential Oil Plants

1998 ◽  
Vol 6 (A) ◽  
pp. A125-A130 ◽  
Author(s):  
H. Schulz ◽  
H.-H. Drews ◽  
R. Quilitzsch ◽  
H. Krüger
Keyword(s):  
Essential Oil ◽  
Oil Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Total Carotenoids ◽  
Good Calibration

The use of near-infrared (NIR) spectroscopy for the prediction of the essential oil content and composition in various umbelliferae genotypes was investigated. Furthermore an NIR method was developed for the quantification of total carotenoids and sugars present in different carrot varieties. Applying partial least square algorithm very good calibration statistics ( SECV, R2) were obtained for the prediction of the essential oil content in fennel (0.47, 0.83), caraway (0.29, 0.93), dill (0.30, 0.96) and coriander (0.29, 0.93). Satisfactory calibration results were received for the NIR determination of total carotenoids (1.54, 0.80) and of saccharose(0.74, 0.76) in carrots. The performed study demonstrates that NIR can be used to rapidly and accurately predict secondary metabolites such as carotenoids, anethole, fenchone, estragole, limonene and carvone occurring in vegetables and in fruits of various essential oil plants.

Reagentless near-Infrared Determination of Cholesterol in Undiluted Human Serum Using Interval Partial Least Square

2011 ◽  
Vol 345 ◽  
pp. 128-133
Author(s):  
Hong Zhi Gao ◽  
Qi Peng Lu ◽  
Fu Rong Huang
Keyword(s):  
Human Serum ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Sample Set ◽  
Cholesterol Measurement

In order to determination of cholesterol in human serum with no reagent using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of cholesterol (1688-1760nm), in which the PLS model has a low RMSEP with 0.241mmol/L and a high R with 0.975, is selected with 23 intervals. The results indicate that, the informative region of cholesterol can be obtained by iPLS and applied to design the simpler reagentless NIR instruments for inexpensive cholesterol measurement in future.

Determination of resin and moisture content in melamine-formaldehyde paper using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 311-323 ◽  
Author(s):  
Ana Henriques ◽  
Miguel Gonçalves ◽  
Nádia Paiva ◽  
João Ferra ◽  
Jorge Martins ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Resin Content ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Criterion ◽  
Calibration Model ◽  
Melamine Formaldehyde

This paper describes the use of near infrared spectroscopy as a tool for the determination of moisture and resin content on papers impregnated with melamine-formaldehyde resins for high-pressure laminate production. The papers had different colours and grammages. The near infrared analysis range comprised wavelengths between 12,000 cm−1 and 4000 cm−1. Several multivariate calibration procedures and pre-processing techniques were tested for selection of the best spectral interval, including interval partial least-square, forward interval partial least-square and synergy interval partial least-square. The performance of calibration models was evaluated computing the root mean-squared error of cross-validations and the coefficient of determination (R2). An external validation procedure was done using different decorative papers (red, pearl, yellow, violet and pale green). The performances of the best models were compared using the statistical criterion root mean square error of prediction. It was shown that the developed models can be applied in the determination of resin content independently of the grammage and colour of the papers. However, regarding the volatile content, the models seemed to be affected by external factors, such as the presence of dyes and pigments, and were only applicable to papers having spectra similar to those used in the calibration model.

Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method

2009 ◽  
Vol 17 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Yoshifumi Mohri ◽  
Yukoh Sakata ◽  
Makoto Otsuka
Keyword(s):  
Acid Content ◽  
Glycyrrhizic Acid ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Wave Number ◽  
Coefficient Of Determination ◽  
Calibration Model

The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.

Quality Determination of Glass/Epoxy Prepreg by NIR Spectroscopy

2010 ◽  
Vol 152-153 ◽  
pp. 77-80
Author(s):  
Wei Li ◽  
Wei Jia Gao ◽  
Ping Chen ◽  
Bao Lei Sun
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Volatile Content ◽  
Calibration Models ◽  
Significant Difference ◽  
Nondestructive Quality ◽  
The Mean

A near-infrared spectroscopy (NIR) technique has been applied for rapid and nondestructive quality determination of glass/epoxy prepreg. Abundant information related with resin and volatile was observed in the NIR spectra of the prepreg cloth. The partial least square (PLS) regression was used to develop the calibration models by utilizing several spectral pretreatments combined with different spectra ranges. Some unknown samples were analyzed by the NIR method. The mean absolute predicted errors were 0.32% and 0.214% for the resin content and the volatile content respectively. The results of the paired t-test revealed that there was no significant difference between the NIR method and standard method. The NIR method can be used to predict the resin and volatile content simultaneously within 30s. The study indicates that the NIR method is sufficiently for quality determination of glass/epoxy prepreg cloth.

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