Mid and near Infrared Study of Carbohydrates by Canonical Correlation Analysis

1993 ◽  
Vol 1 (2) ◽  
pp. 99-108 ◽  
Author(s):  
P. Robert ◽  
M.F. Devaux ◽  
A. Qannari ◽  
M. Safar
Keyword(s):  
Correlation Analysis ◽  
Infrared Spectra ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Sugar Content ◽  
Infrared Region ◽  
Total Sugar ◽  
Mid Infrared ◽  
Total Sugar Content ◽  
Near Infrared Spectra

Multivariate data treatments were applied to mid and near infrared spectra of glucose, fructose and sucrose solutions in order to specify near infrared frequencies that characterise each carbohydrate. As a first step, the mid and near infrared regions were separately studied by performing Principal Component Analyses. While glucose, fructose and sucrose could be clearly identified on the similarity maps derived from the mid infrared spectra, only the total sugar content of the solutions was observed when using the near infrared region. Characteristic wavelengths of the total sugar content were found at 2118, 2270 and 2324 nm. In a second step, the mid and near infrared regions were jointly studied by a Canonical Correlation Analysis. As the assignments of frequencies are generally well known in the mid infrared region, it should be useful to study the relationships between the two infrared regions. Thus, the canonical patterns obtained from the near infrared spectra revealed wavelengths that characterised each carbohydrate. The OH and CH combination bands were observed at: 2088 and 2332 nm for glucose, 2134 and 2252 nm for fructose, 2058 and 2278 nm for sucrose. Although a precise assignment of the near infrared bands to chemical groups within the molecules was not possible, the present work showed that near infrared spectra of carbohydrates presented specific features.

Canonical Correlation Analysis of Mid- and Near-Infrared Oil Spectra

1993 ◽  
Vol 47 (7) ◽  
pp. 1024-1029 ◽  
Author(s):  
M. F. Devaux ◽  
P. Robert ◽  
A. Qannari ◽  
M. Safar ◽  
E. Vigneau
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Infrared Spectra ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Principal Component ◽  
Canonical Variate ◽  
Mid Infrared ◽  
Highly Correlated ◽  
Spectral Patterns

A mathematical procedure based on Canonical Correlation Analysis (CCA) was used in order to assign the wavelengths of the near-infrared spectra through knowledge of the mid-infrared spectra. The relevance of the treatment was tested on commercial oils that mainly differ in their level of unsaturation. Initially, two separated Principal Component Analyses (PCAs) were performed on the near- and mid-infrared data to overcome the high intercorrelations across the wavelengths. CCA was then applied to the resulting principal components. Near- and mid-infrared canonical variates were assessed so that they achieved maximum correlation. The procedure makes it possible to draw CCA spectral patterns that exhibit significant positive and negative peaks. The first near-infrared canonical variate was highly correlated with the first mid-infrared canonical variate ( r2 = 0.97). The corresponding near- and mid-infrared CCA spectral patterns were therefore given the same interpretation. The mid-infrared pattern opposed negative peaks characteristic of CH2 groups to the positive peaks of CH3 and =CH groups. Consequently, in the near-infrared pattern, the positive peaks at 1708, 2140, 2170, and 2480 nm were assigned to CH3 or =CH groups, and the negative peaks at 2304, 2344, and 2445 nm were assigned to CH2 groups. A more precise interpretation was obtained by comparing the wavelengths observed to theoretical values and to previous assignments.

Quantum mechanical simulations of near-infrared spectra of biomolecules – Long-chain fatty acids

NIR news ◽  
2018 ◽  
Vol 29 (6) ◽  
pp. 13-19 ◽  
Author(s):  
Krzysztof B Beć ◽  
Justyna Grabska
Keyword(s):  
Fatty Acids ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Infrared Region ◽  
Quantum Mechanical ◽  
Long Chain Fatty Acids ◽  
Near Infrared Spectra ◽  
Technical Article ◽  
Long Chain ◽  
Chain Fatty Acids

Exact and in-depth interpretation of near-infrared spectra has often appeared problematic in any case stepping beyond the simplest molecules. The inherent complexity of near-infrared spectra due to the abundance of combination modes and the resulting extensive band overlay frequently limits our comprehension of the spectral bands to vague wavenumber regions in which certain modes likely appear. Coincidently, quantum mechanical simulation of spectra which could offer momentous support in solving such problems has rather been rare in the case of near-infrared region due to practical limitations. Recent years have seen a trending development of accurate and affordable methods of near-infrared spectra simulation. A trend in modelling increasingly complex molecules can be noticed reaching even fairly large biomolecules. In this technical article we overview the most recent accomplishments in the field on the example of long-chain fatty acids and their cyclic dimers, which extend beyond 100 atoms.

Determination of Protein in Ground Wheat Samples by Mid-Infrared Diffuse Reflectance Spectroscopy

1997 ◽  
Vol 51 (8) ◽  
pp. 1200-1204 ◽  
Author(s):  
James B. Reeves ◽  
Stephen R. Delwiche
Keyword(s):  
Protein Content ◽  
Infrared Spectra ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared ◽  
Near Infrared Spectra ◽  
Hard Red Winter Wheat ◽  
Ground Wheat

The objective of this study was to determine whether mid-infrared diffuse reflectance spectroscopy could be used in the same manner as near-infrared diffuse reflectance spectroscopy to quantitatively determine the protein content of ground wheat samples. One hundred and thirty hard red winter wheat samples were assayed for protein by combustion and scanned in the near- and mid-infrared. Samples (UDY ground) were scanned neat in the near-infrared from 1100 nm (9091 cm−1) to 2498 nm (4003 cm−1) on a scanning monochromator and in the mid-infrared from 4000 cm−1 (2500 nm) to 400 cm−1 (25,000 nm) on a Fourier transform spectrometer at 4-and 16-cm−1 resolutions. Protein content varied from a low of 8.98% to a high of 18.70% (average of 12.86% with a standard deviation of 1.66%). Calibrations developed with the use of partial least-squares gave an R2 and bias-corrected standard error of performance of 0.999 and 0.054 for the near-infrared and 0.997 and 0.085 for the mid-infrared (4 cm−1 resolution). Calibration results based on mid-infrared spectra, while not as good as those for near-infrared spectra, were nevertheless quite good. These results demonstrate that it is possible to develop satisfactory calibrations for protein in ground wheat with the use of mid-infrared spectra without the need for sample dilution with KBr.

Predicting Mid-Infrared Spectra from Near-infrared Spectra

1995 ◽  
Vol 1 (3) ◽  
pp. 231-243
Author(s):  
Raymond Lew ◽  
Anwer Khan ◽  
Stephen T. Balke
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mid Infrared ◽  
Near Infrared Spectra

Variable selection in visible and near-infrared spectra: Application to on-line determination of sugar content in pears

2012 ◽  
Vol 109 (1) ◽  
pp. 142-147 ◽  
Author(s):  
Huirong Xu ◽  
Bing Qi ◽  
Tong Sun ◽  
Xiaping Fu ◽  
Yibin Ying
Keyword(s):  
Variable Selection ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Sugar Content ◽  
Near Infrared Spectra ◽  
On Line

Classification of lignocellulosic biomass by weighted-covariance factor fuzzy C-means clustering of mid-infrared and near-infrared spectra

2017 ◽  
Vol 31 (2) ◽  
pp. e2865 ◽  
Author(s):  
Abbas Rammal ◽  
Eric Perrin ◽  
Valeriu Vrabie ◽  
Isabelle Bertrand ◽  
Brigitte Chabbert
Keyword(s):  
Lignocellulosic Biomass ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Mid Infrared ◽  
Fuzzy C Means ◽  
Near Infrared Spectra ◽  
Fuzzy C Means Clustering
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