Structural Investigations and Monitoring of Polymerisation by NIR Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 299-306 ◽  
Author(s):  
Gilbert Lachenal
Keyword(s):  
Hydrogen Bonding ◽  
Water Content ◽  
Physical State ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Relevant Information ◽  
Degree Of Cure ◽  
Structural Investigations ◽  
Mechanism Of Reaction ◽  
Cure Mechanism

Near infrared (NIR) spectra can give relevant information on the chemical and physical state of polymers and polymeric composites. Degree of cure, mechanism of reaction, crystallinity, orientation, water content and hydrogen bonding can be studied using NIR spectra without any sophisticated mathematical treatments. Some NIR applications are reviewed.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Beyond Prediction: Extracting Relevant Information from near Infrared Spectra

1996 ◽  
Vol 4 (1) ◽  
pp. 75-84 ◽  
Author(s):  
P. Robert ◽  
M.-F. Devaux ◽  
D. Bertrand
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Relevant Information ◽  
Correlated Data ◽  
Spectral Interpretation ◽  
Highly Correlated ◽  
Prediction Techniques ◽  
Spectral Patterns

With the increase of near infrared (NIR) applications, numerous chemometric methods have been developed. Among the mathematical treatments available, principal comoponent analysis (PCA) is certainly the most well-known when considering highly correlated data. In the field of near infrared spectroscopy, it allows the study of spectra without deleting wavelengths and without making any preliminary assumptions on the data. One advantage of PCA lies in the graphical displays obtained and, more precisely, on the similarity maps and spectral patterns. While the maps reveal clusters of the samples, the spectral patterns make a spectral interpretation possible. The present paper reviews our contribution to the development and application of PCA to NIR spectroscopy. It shows that PCA is the core of various mathematical treatments such as principal component regression (PCR), factorial discriminant analysis (FDA) and canonical correlation analysis (CCA). One advantage of using PCA in the prediction techniques lies in the use of all the wavelengths in the predictive model. The extraction of relevant and comprehensive wavelengths can be guided by CCA which allows the description of the samples by taking both mid- and near infrared data into account. Besides a comprehensive presentation of the mathematical treatements, examples are given.

Quantitative Analysis of Hydrogen Bonding in Poly(4-Vinylphenol) Blends Using near Infrared Spectroscopy

2003 ◽  
Vol 11 (3) ◽  
pp. 183-191 ◽  
Author(s):  
Haijun Cai ◽  
Josée Brisson
Keyword(s):  
Hydrogen Bonding ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Weight Fraction ◽  
Hydroxyl Groups ◽  
Absorption Coefficients ◽  
Oh Groups ◽  
Refinement Method ◽  
Poly Ethylene ◽  
Vinyl Phenol

Quantitative evaluation of hydrogen bonding of poly(4-vinyl phenol)/poly(ethylene oxide) PVPh/PEO blends was conducted using Fourier transform near infrared (FT-NIR) spectroscopy. Absorption coefficients of the free ( aF), intra- (PVPh–PVPh) ( aA) and inter-associated (PVPh–PEO) hydroxyl groups ( aI) were estimated. Two sets of approximations were tested, including adopting a least-squares refinement method to calculate absorption coefficients from all NIR spectra or using a literature value for aF. Each set of absorption coefficients thus estimated were used to determine hydroxyl concentration for the free and hydrogen-bonded hydroxyl overtone bands in the blends. A comparison is made among the resultant concentrations of the free, intra- and inter-associated hydroxyl groups. The concentration of free hydroxyl groups markedly decreases with PEO percentage, and that of intra-associated hydroxyl remains almost constant. Concentration for the inter-associated hydroxyl groups in the blends increases very slowly above 0.2 PEO weight fraction. When concentration of OH groups is reported per PVPh chain, FT-NIR measurements show a broad maximum in the number of interchain hydrogen bonds. This result can be used to explain partially previous orientation behaviour observed for PVPh/PEO blends.

Qualitative Analysis of Oil Sand Slurries Using On-Line NIR Spectroscopy

1996 ◽  
Vol 50 (12) ◽  
pp. 1535-1540 ◽  
Author(s):  
Waldemar I. Friesen
Keyword(s):  
Steady State ◽  
Water Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Fiber Optic Probe ◽  
Oil Sand ◽  
Plant Environment ◽  
On Line ◽  
Optic Probe

The development of a reliable on-line method to monitor process streams is important for improved process control in oil sand extraction plants. The suitability of diffuse reflectance near-infrared (NIR) spectroscopy for this purpose has been tested in a pilot plant environment. Spectra of a feed slurry flowing through a pipe were measured with the use of an on-line fiber-optic probe. Data were collected throughout a nine-hour period during which ore type and slurry water content were varied. The feasibility of monitoring feed stream conditions is demonstrated by principal component analysis of the measured spectra. Clustering of these spectra according to ore type and water content enables the detection of deviations from and transitions between steady-state conditions of the process. Estimates are given of characteristic times for the process to reach a steady state after a change in condition has been initiated. The use of artificial neural networks for classifying spectra on the basis of ore type is also illustrated.

Real-time release monitoring for water content and mean particle size of granules in lab-sized fluid-bed granulator by near-infrared spectroscopy

RSC Advances ◽  
2014 ◽  
Vol 4 (34) ◽  
pp. 17461-17468 ◽  
Author(s):  
Makoto Otsuka ◽  
Akira Koyama ◽  
Yusuke Hattori
Keyword(s):  
Particle Size ◽  
Infrared Spectroscopy ◽  
Water Content ◽  
Real Time ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Powder Bed ◽  
Mean Particle Size ◽  
Fluid Bed ◽  
Granular Properties

Simultaneous real-time monitoring of water content and mean particle size in the powder bed of a fluidized-bed granulator was performed by near-infrared (NIR) spectroscopy through a window, and the findings were used to evaluate the granular properties.

scholarly journals A Novel Approach to Determine Water Content in DMSO for a Compound Collection Repository

2005 ◽  
Vol 10 (6) ◽  
pp. 568-572 ◽  
Author(s):  
David J. Semin ◽  
Tim J. Malone ◽  
Matthew T. Paley ◽  
Peter W. Woods
Keyword(s):  
Water Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Storage Conditions ◽  
Thaw Cycle ◽  
Novel Approach ◽  
Accuracy And Precision ◽  
Compound Collection ◽  
Freeze Thaw Cycle ◽  
Stock Solutions

Mail Stop 29-M-B Thousand Oaks, CA 91320 The quality of a corporate compound collection can be significantly affected by a complex combination of storage and operational processing factors. Water content in DMSO solutions is one factor that is of great interest as it can affect solubility, degradation, and freeze-thaw cycle parameters. To the authors’ knowledge, this is the first report of using near-infrared (NIR) spectroscopy to assess water content in DMSO compound stock solutions within the common storage vessel format of polypropylene microtubes. The precision and accuracy of the NIR technique was benchmarked against a Karl Fisher titration method, and a correlation coefficient was determined to be 0.985 over a range of 1% to 10% water in DMSO by weight. The advantages of the NIR technique include accuracy, precision, speed, nondestructiveness, and the capability of assessing compounds under in situ storage conditions within microtubes. In this report, the authors demonstrate the accuracy and precision of using NIR to assess water content in DMSO solutions and present a case study to demonstrate the utility of the technique to aid in assessing a pharmaceutical compound collection.

scholarly journals Nondestructive Detection of Nitrogen in Chinese Cabbage Leaves Using VIS–NIR Spectroscopy

HortScience ◽  
2006 ◽  
Vol 41 (1) ◽  
pp. 162-166 ◽  
Author(s):  
Min Min ◽  
Won Suk Lee ◽  
Yong Hyeon Kim ◽  
Ray A. Bucklin
Keyword(s):  
Water Content ◽  
Chinese Cabbage ◽  
Near Infrared ◽  
Nutrient Management ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Validation Data ◽  
Data Set ◽  
Group I ◽  
Significant Wavelength

Proper nutrient management is essential to increase yield, quality and profit. This study was conducted to estimate the N concentrations of chinese cabbage (Brassica campestris L. ssp. pekinensis `Norangbom') plug seedlings using visible and near infrared spectroscopy for nondestructive N detection. Chinese cabbage seeds were sown and raised in three 200-cell plug trays filled with growing mixture in a plant growth chamber with three different levels (40%, 80%, and 100%) of required N. Reflectance for leaves of chinese cabbage seedlings was measured with a spectrophotometer 15 days after the experiment started. Reflectance was measured in the 400 to 2500 nm wavelength range at 1.1-nm increments. The leaves were dried afterwards to measure their water content and were analyzed for their actual N contents. The experiment was repeated twice (group I and II). Correlation coefficient spectrum, standard deviation spectrum, stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine wavelengths for N prediction models. Performances of SMLR and PLS were similar. For the validation data set (group II), SMLR produced an r2 of 0.846 and PLS yielded r2 of 0.840. The most significant wavelength 710 nm, which was identified by all methods, was correlated to chlorophyll. Water content positively correlated with N concentration (r = 0.76). Wavelengths of 1467, 1910, and 1938 nm selected by SMLR from both groups also showed that water had a strong effect on N prediction. Wavelengths near 2136 nm indicated that protein had potential use in N prediction. Wavelengths near 550 and 840 nm could also contribute to N prediction.

Monitoring the Quality of Oils for Biodiesel Production Using Multivariate near Infrared Spectroscopy Models

2008 ◽  
Vol 16 (5) ◽  
pp. 445-454 ◽  
Author(s):  
Patrícia Baptista ◽  
Pedro Felizardo ◽  
José C. Menezes ◽  
M. Joana Neiva Correia
Keyword(s):  
Quality Control ◽  
Water Content ◽  
Vegetable Oils ◽  
Iodine Value ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Acid Number ◽  
Biodiesel Production ◽  
Least Squares Regression

Biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol in the presence of a catalyst. The quality of the oils used for biodiesel production strongly influences the final properties of biodiesel, namely its compliance to the European Standard. This work reports the use of near infrared (NIR) spectroscopy in the quality control of several oil properties, such as the iodine value, the water content and the acid number but, more importantly, the weight–weight percentages (wt%) of soybean, palm and rapeseed oil in mixtures. Principal component analysis was used to perform a qualitative analysis of the spectra, whereas partial least squares regression allowed the development of calibration models between analytical reference data and NIR spectra. The calibration ranges were 60–126 g I2 100 g−1 for the iodine value, 478–2500 mg kg−1 for the water content and 0.13-6.56 mg KOH g−1 for the acid number, whereas the validation errors were around 3.1 g I2 100 g−1, 111 mg kg−1 and 0.22 mg KOH g−1, respectively. The results obtained show that NIR spectroscopy is a promising technique to carry out the quality control of the commonly used vegetable oils for biodiesel production, namely the quality assurance and authenticity. Furthermore, it is of great value to have a simple, fast and reliable method to identify the composition of an oil mixture and/or some of its quality parameters, prior to storage or upon admission of a new lot of oil.

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