In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis

2002 ◽  
Vol 56 (10) ◽  
pp. 1268-1274 ◽  
Author(s):  
Francesca Apruzzese ◽  
Ramin Reshadat ◽  
Stephen T. Balke
Keyword(s):  
Least Squares ◽  
Latent Variables ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Polymer Processing ◽  
Near Infrared Spectra ◽  
Scattering Effects ◽  
Spectral Data Analysis

The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.

scholarly journals NIR-chemometric approaches for evaluating carbonization characteristics of hydrothermally carbonized lignin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Un Taek Hwang ◽  
Kyeyoung Jo ◽  
Taekyeong Lee ◽  
Jinseok Park ◽  
...  
Keyword(s):  
Least Squares ◽  
Infrared Spectra ◽  
Regression Models ◽  
Near Infrared ◽  
Prediction Models ◽  
Hydrothermal Carbonization ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Near Infrared Spectra ◽  
Mean Square Errors

AbstractThe aim of this study is to establish prediction models for the non-destructive evaluation of the carbonization characteristics of lignin-derived hydrochars as a carbon material in real time. Hydrochars are produced via the hydrothermal carbonization of kraft lignins for 1–5 h in the temperature range of 175–250 °C, and as the reaction severity of hydrothermal carbonization increases, the hydrochar is converted to a more carbon-intensive structure. Principal component analysis using near-infrared spectra suggests that the spectral regions at 2132 and 2267 nm assigned to lignins and 1449 nm assigned to phenolic groups of lignins are informative bands that indicate the carbonization degree. Partial least squares regression models trained with near-infrared spectra accurately predicts the carbon content, oxygen/carbon, and hydrogen/carbon ratios with high coefficients of determination and low root mean square errors. The established models demonstrate better prediction than ordinary least squares regression models.

An Integrated Strategy to Qualitatively Differentiate Components of Raw and Processed Viticis Fructus Based on NIR, HPLC and UPLC-MS Analysis

Planta Medica ◽  
2018 ◽  
Vol 84 (17) ◽  
pp. 1280-1291 ◽  
Author(s):  
Jiayin Diao ◽  
Can Xu ◽  
Huiting Zheng ◽  
Siyi He ◽  
Shumei Wang
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Principal Component ◽  
Close Correlation ◽  
Chinese Herbs ◽  
Plot Analysis ◽  
Near Infrared Spectra ◽  
Processed Products

AbstractViticis Fructus is a traditional Chinese herbal drug processed by various methods to achieve different clinical purposes. Thermal treatment potentially alters chemical composition, which may impact on effectiveness and toxicity. In order to interpret the constituent discrepancies of raw versus processed (stir-fried) Viticis Fructus, a multivariate detection method (NIR, HPLC, and UPLC-MS) based on metabonomics and chemometrics was developed. Firstly, synergy interval partial least squares and partial least squares-discriminant analysis were employed to screen the distinctive wavebands (4319 – 5459 cm−1) based on preprocessed near-infrared spectra. Then, HPLC with principal component analysis was performed to characterize the distinction. Subsequently, a total of 49 compounds were identified by UPLC-MS, among which 42 compounds were eventually characterized as having a significant change during processing via the semiquantitative volcano plot analysis. Moreover, based on the partial least squares-discriminant analysis, 16 compounds were chosen as characteristic markers that could be in close correlation with the discriminatory near-infrared wavebands. Together, all of these characterization techniques effectively discriminated raw and processed products of Viticis Fructus. In general, our work provides an integrated way of classifying Viticis Fructus, and a strategy to explore discriminatory chemical markers for other traditional Chinese herbs, thus ensuring safety and efficacy for consumers.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance

1988 ◽  
Vol 42 (5) ◽  
pp. 722-728 ◽  
Author(s):  
J. L. Ilari ◽  
H. Martens ◽  
T. Isaksson
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Large Degree ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Analytical Technique ◽  
Scattering Effects ◽  
Reflectance Data

Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.

Foliar Damage Assessment and Selection of Cold Resistant Genotypes Using near Infrared Spectra of Eucalyptus Globulus Leaves

2009 ◽  
Vol 17 (4) ◽  
pp. 223-231 ◽  
Author(s):  
Rosario del P. Castillo ◽  
David Contreras ◽  
Matthias Otto ◽  
Jaime Baeza ◽  
Juanita Freer
Keyword(s):  
Eucalyptus Globulus ◽  
Near Infrared ◽  
Cold Resistance ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Near Infrared Spectra ◽  
Orthogonal Signal Correction ◽  
Foliar Damage ◽  
Reference Parameters

Near infrared (NIR) spectroscopy was used to predict cold resistance in Eucalyptus globulus genotypes after acclimatation treatments at low temperatures. Branches of the genotypes were maintained during 31 days in three cold chambers and the NIR spectra of milled leaves were obtained. The samples were subsequently exposed to artificial freezing (with some branches exposed to −2°C) and the foliar damage was assessed by visually estimating the necrotic area of each leaf. These values were used as reference parameters to evaluate cold resistance in the genotypes. A partial least squares (PLS) method was performed using the foliar damage and the NIR spectra of leaves. Spectra were treated with multiplicative scatter correction (MSC) and orthogonal signal correction (OSC). An excellent model was achieved which predicted foliar damage in the genotypes with a low standard error of prediction (3.5%), a high regression coefficient in cross-validation and external validation ( r > 0.9) and a high percentage of the variance explained by the spectra (95.4%). Furthermore, a pattern recognition method, using a regularised discriminant analysis (RDA) of the scores matrix obtained in PLS, in a denominated PLS/RDA on scores strategy, was applied directly to the spectra to classify each genotype as tolerant or sensitive; 100% of the genotypes were correctly assigned. These results demonstrate the advantages of using the NIR spectra of leaves as a rapid, nondestructive tool to evaluate cold resistance in genotypes.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

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