Aging of polypropylene probed by near infrared spectroscopy

2021 ◽  
pp. 096703352199911
Author(s):  
Hideyuki Shinzawa ◽  
Ryota Watanabe ◽  
Shogo Yamane ◽  
Maito Koga ◽  
Hideaki Hagihara ◽  
...  
Keyword(s):  
Regression Models ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Aging Treatment ◽  
Pls Regression ◽  
Two Dimensional ◽  
Compositional Change ◽  
Working Mechanism ◽  
Thermally Induced ◽  
Amorphous Structures

This paper describes the first in-depth attempt to characterize thermally induced aging of polypropylene (PP) samples by near infrared (NIR) spectroscopy. Significant levels of variation in NIR bands associated with short (amorphous-dominated) and long (crystalline-dominated) helices was readily captured when PP samples were subjected to thermal aging treatment. Partial least squares (PLS) regression models derived from the NIR spectra indicated significant level of correlation between the actual and predicted elongations of the samples. Analysis of PLS scores and two-dimensional (2 D) correlation spectra derived from the aged PP samples revealed inner working mechanism of the regression model. Namely, the aging treatment essentially induces compositional change in crystalline and amorphous structures of the PP samples, which eventually affect the variation of the PLS scores. Thus, by utilizing the scores, it becomes possible to predict the change in the elongation property of the aged PP sample.

Critical evaluation of spectral information of benchtop vs. portable near-infrared spectrometers: quantum chemistry and two-dimensional correlation spectroscopy for a better understanding of PLS regression models of the rosmarinic acid content in Rosmarini folium

The Analyst ◽  
2017 ◽  
Vol 142 (3) ◽  
pp. 455-464 ◽  
Author(s):  
Christian G. Kirchler ◽  
Cornelia K. Pezzei ◽  
Krzysztof B. Beć ◽  
Sophia Mayr ◽  
Mika Ishigaki ◽  
...  
Keyword(s):  
Quantum Chemistry ◽  
Acid Content ◽  
Rosmarinic Acid ◽  
Regression Models ◽  
Near Infrared ◽  
Critical Evaluation ◽  
Correlation Spectroscopy ◽  
Spectral Information ◽  
Pls Regression ◽  
Two Dimensional

We show the importance of monitoring the performances of available NIR-spectrometers in every analytical area.

True Accuracy of near Infrared Spectroscopy and its Dependence on Precision of Reference Data

2002 ◽  
Vol 10 (1) ◽  
pp. 15-25 ◽  
Author(s):  
L.K. Sørensen
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reference Data ◽  
Nir Spectroscopy ◽  
Limited Resources ◽  
Standard Errors ◽  
Calibration Data ◽  
Pls Regression ◽  
Calibration Equations ◽  
Very High

A more precise estimate of the accuracy of near infrared (NIR) spectroscopy is obtained when the measured standard errors of cross validation ( SECV) and prediction ( SEP) are corrected for imprecision of the reference data. The significance of correction increases with increasing imprecision of reference data. Very high precision of reference data obtained through replicate analyses under reproducibility conditions may not be the optimal goal for the development of calibration equations. In a situation of limited resources, the precision of the reference data should be related to the obtainable accuracy of the spectroscopic system. Investigation of several routine applications based on the partial least-squares (PLS) regression technique showed that increased precision of calibration data only resulted in marginal improvements in true accuracy if the total standard error of reference results from the beginning was less than the estimated true accuracy of the corresponding NIR calibration.

Two-Dimensional Vibration Spectroscopy. V: Correlation of Mid- and near Infrared of Hard Red Winter and Spring Wheats

1996 ◽  
Vol 4 (1) ◽  
pp. 139-152 ◽  
Author(s):  
F.E. Barton ◽  
D.S. Himmelsbach ◽  
D.D. Archibald
Keyword(s):  
Raman Spectra ◽  
Spring Wheat ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Correlation Spectroscopy ◽  
Analytical Models ◽  
Electromagnetic Spectrum ◽  
Two Dimensional ◽  
Stretching Vibrations ◽  
Agricultural Materials

Two-dimensional correlation spectroscopy across the near infrared (NIR) and mid-infrared (MIR) regions have been used to explain the NIR spectra of hard red winter and spring wheat and provide additional confidence in analytical models developed with empirical data. Recent studies have shown that the major C–H stretching vibrations and some of the aromatic C–H and ring stretching vibrations and the minor vibrations in the “fingerprint” region are correlated also. The technique has been expanded to include Raman spectra. The Raman spectra were enhanced with Maximum Likelihood methods to improve signal-to-noise (S/N) while maintaining resolution. This was necessary to eliminate the effects of fluorescence which degrades S/N. The use of NIR lasers at 1.1 μm generally eliminates fluorescence as a problem, but it is still quite prevalent in agricultural materials. The original study did not show any significant correlations to aromatic functionality. However, the band at 1552 nm correlates to the Raman and not to the MIR. This band has shown up in NIR spectroscopy models for the determination of lignin, but is not readily observed in the MIR. Thus it correlates to a Raman active rather than a MIR active band. The same phenomena are observed for the amide I, II and III bands for wheat. The interesting features from NIR and MIR are that there are correlations that distinguish winter from spring wheat. These, and the Raman spectra of wheat, will be shown. These studies show that multiple regions of the electromagnetic spectrum can be, and in deed need to be, used to interpret adequately the spectral and statistical results we have traditionally obtained in the NIR.

Two-Dimensional Infrared and Near-Infrared Correlation Spectroscopy: Applications to Studies of Temperature-Dependent Spectral Variations of Self-Associated Molecules

1997 ◽  
Vol 51 (4) ◽  
pp. 526-535 ◽  
Author(s):  
Yukihiro Ozaki ◽  
Yongliang Liu ◽  
Isao Noda
Keyword(s):  
Near Infrared ◽  
Oleyl Alcohol ◽  
Correlation Spectroscopy ◽  
Intensity Change ◽  
Pure Liquid ◽  
Two Dimensional ◽  
Temperature Dependent ◽  
Thermally Induced ◽  
2D Ir ◽  
Almost All

This paper demonstrates the potential of generalized two-dimensional (2D) Fourier transform (FT) infrared (IR) and near-infrared (NIR) correlation spectroscopy in the studies of temperature-dependent spectral variations of self-associated molecules. Three examples of the 2D correlation analysis are discussed in this paper. The first two are concerned with the temperature-dependent IR and NIR spectral changes of N-methylacetamide (NMA) in the pure liquid state. The 2D IR correlation approach revealed that almost all the peaks of NMA in the 3400–1100-cm−1 region consist of two to four separate bands. For example, the amide I band contains contributions from at least four distinct bands at 1685, 1665, 1650, and 1635 cm−1. The analysis of the asynchronous 2D IR spectrum in the amide I region showed that the sequence of spectral intensity change in the ascending order of temperature was given by 1635<1650<1665<1685 cm−1. These bands at 1635, 1650, 1665, and 1685 cm−1 were assigned to the amide I modes of chain oligomers of various sizes and dimer of NMA; the longer the chain, the lower the frequency. The closeup view of 2D NIR correlation spectra of NMA obtained at narrow spectral and temperature windows enabled us to propose not only band assignments in the 6800–6050-cm−1 region but also a detailed mechanistic picture of the thermally induced dissociation of NMA for each temperature range. We also applied the generalized 2D correlation approach to the analysis of a set of FT NIR spectra of oleyl alcohol under temperature variations. The 2D NIR analysis enhanced the spectral resolution and simplified the spectra with overlapped bands. For example, it was found that a band at 7090 cm−1 arising from the first overtone of an OH stretching mode of the monomeric alcohol consists of two bands due to the rotational isomerism of the free OH group. An intriguing possibility of correlating various overtone and fundamental bands to establish unambiguous assignments was also suggested from the 2D NIR study on oleyl alcohol.

A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re-weighted sampling

2021 ◽  
pp. 096703352098236
Author(s):  
Zhaoqiong Jiang ◽  
Yiping Du ◽  
Fangping Cheng ◽  
Feiyu Zhang ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Pls Regression ◽  
Variable Selection Method ◽  
Solids Content ◽  
Pomegranate Arils ◽  
Mlr Model

The objective of this study was to develop a multiple linear regression (MLR) model using near infrared (NIR) spectroscopy combined with chemometric techniques for soluble solids content (SSC) in pomegranate samples at different storage periods. A total of 135 NIR diffuse reflectance spectra with the wavelength range of 950-1650 nm were acquired from pomegranate arils. Based upon sampling error profile analysis (SEPA), outlier diagnosis was conducted to improve the stability of the model, and four outliers were removed. Several pretreatment and variable selection methods were compared using partial least squares (PLS) regression models. The overall results demonstrated that the pretreatment method of the first derivative (1D) was very effective and the variable selection method of stability competitive adaptive re-weighted sampling (SCARS) was powerful for extracting feature variables. The equilibrium performance of 1D-SCARS-PLS regression model for ten times was similar to 1D-PLS regression model, so that the advantage of wavelength selection was inconspicuous in PLS regression model. However, the number of variables selected by 1D-SCARS was less to 9, which was enough to establish a simple MLR model. The performance of MLR model for SSC of pomegranate arils based on 1D-SCARS was receivable with the root-mean-square error of calibration set (RMSEC) of 0.29% and prediction set (RMSEP) of 0.31%. This strategy combining variable selection method with MLR may have a broad prospect in the application of NIR spectroscopy due to its simplicity and robustness.

Near infrared estimation of concentration of ginsenosides in Asian ginseng

2018 ◽  
Vol 27 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Tetsuya Inagaki ◽  
Norihisa Katayama ◽  
Rae-Kwang Cho ◽  
Xijun Chen ◽  
Satoru Tsuchikawa
Keyword(s):  
Regression Models ◽  
Quality Evaluation ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Red Ginseng ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Powder Samples ◽  
White Ginseng ◽  
Ginsenoside Content

In this study, the feasibility of near infrared reflectance spectroscopy for the quality evaluation of the main bioactive compounds, ginsenosides, in Panax ginseng was examined. Second derivative NIR spectra of standard reagents of ginsenoside Rg1, Re, Rb1, Rc, Rb2 and Rd were used for analysis. Characteristic bands were observed at around 5250 cm−1 in the spectra of ginsenoside Rg1 group (including Rg1 and Re); however, this was not to be observed on the spectra of ginsenoside Rb1 group (including Rb1, Rc, Rb2 and Rd). PLS regression models were constructed of air-dry ginseng powder samples and ginsenoside content in ginsengs was determined by HPLC methods. The calibration models covered various types of ginseng (white ginseng, red ginseng and bleached ginseng) from various cultivated areas (Japan, China and Korea) and were well established for each kind of ginsenoside. It was shown that NIR spectroscopy can be used for the accurate prediction of ginsenoside.

Comparison of Near-Infrared, Infrared, and Raman Spectroscopy for the Analysis of Heavy Petroleum Products

2000 ◽  
Vol 54 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Hoeil Chung ◽  
Min-Sik Ku
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Petroleum Products ◽  
American Petroleum Institute ◽  
Visible Range ◽  
Pls Regression ◽  
Absorption Bands ◽  
Heavy Petroleum

Near-infrared (NIR) spectroscopy has been successfully applied to the determination of API (American Petroleum Institute) gravity of atmospheric residue (AR), which is the heaviest fraction in crude oil. This fraction is completely dark and very viscous. Preliminary studies involving Raman and infrared (IR) spectroscopies were also evaluated along with NIR spectroscopy. The Raman spectrum of AR was completely dominated by strong fluorescence from polycyclic aromatic hydrocarbons, called asphaltenes. IR spectroscopy provided reasonable spectral features; however, its spectral reproducibility was poorer and noisier than that of NIR. Although absorption bands in the NIR region were broad and less characterized, NIR provided better spectral reproducibility with higher signal-to-noise ratio (which is one of the most important parameters in quantitative calibration in comparison to Raman and IR spectroscopies). Partial least-squares (PLS) regression was utilized to develop calibration models. NIR spectra of AR samples were broad, and baselines were varying due to the strong absorption in the visible range. However, the necessary information was successfully extracted and correlated to the reference API gravity with the use of PLS regression. API gravities in the prediction set were accurately predicted with an SEP (standard error of prediction) of 0.22. Additionally NIR showed approximately three times better repeatability compared to the ASTM reference method, which directly influences the process control performance.

Rheo-Optical Near-Infrared (NIR) Characterization of Hydroxyl-Functionalized Polypropylene (PPOH)-Mesoporous Silica Nanocomposites Using Two-Trace Two-Dimensional (2T2D) Correlation Analysis

2019 ◽  
pp. 000370281986156 ◽  
Author(s):  
Ryota Watanabe ◽  
Hideaki Hagihara ◽  
Hiroaki Sato ◽  
Junji Mizukado ◽  
Hideyuki Shinzawa
Keyword(s):  
Mesoporous Silica ◽  
Near Infrared ◽  
Tensile Deformation ◽  
Nir Spectroscopy ◽  
Amorphous Region ◽  
Amorphous Structure ◽  
Correlation Spectroscopy ◽  
Two Dimensional ◽  
Correlation Peak ◽  
Thickness Change

A rheo-optical characterization technique based on the combination of near-infrared (NIR) spectroscopy and mechanical analysis was applied to the nanocomposite consisting of hydroxyl-functionalized polypropylene (PPOH) and mesoporous silica (MPS) to probe the deformation behavior. Substantial levels of spectral changes of NIR spectral features were captured when the polymer samples underwent tensile deformation. Sets of spectra were subjected to projection treatment to remove the effect of baseline fluctuations and thickness change inevitably caused by the tensile deformation of the sample. Then, two-trace two-dimensional (2T2D) correlation spectroscopy was applied to the pretreated spectra to elucidate spectroscopic signature associated with the difference between the initial and deformed samples. An asynchronous correlation peak appears between the bands at 1720 and 1700 nm respectively reflecting the contributions of predominantly amorphous and crystalline component of the PPOH, indicating the predominant variation of amorphous structure followed by that of crystalline structure. In addition, the predominant spectral change related to the amorphous band becomes even more acute by including the MPS with large pores. It is hence likely that the larger pore size of the MPS confines the more amorphous structure, which, in turn, causes simultaneous reorientation of the polymer chains in the amorphous region during the elastic deformation. Consequently, the incorporation of the MPS selectively restricts the deformation of the amorphous structure which eventually provides the obvious increase in the mechanical property of the PPOH polymer.

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