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

Optimized fuzzy C-means clustering algorithm for the interpretation of the near-infrared spectra of rocks

2017 ◽  
Vol 50 (5) ◽  
pp. 270-274 ◽  
Author(s):  
Wu Fang ◽  
Wei Liang-shu ◽  
Huang Jun-jie ◽  
Liu Gui-ling ◽  
Jiang Xi-ping
Keyword(s):  
Infrared Spectra ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Fuzzy C Means ◽  
Near Infrared Spectra ◽  
Fuzzy C Means Clustering

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.

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.

scholarly journals Discrimination of mineral waters using near infrared spectroscopy and aquaphotomics

2014 ◽  
Vol 68 (2) ◽  
pp. 257-264 ◽  
Author(s):  
Jelena Muncan ◽  
Lidija Matija ◽  
Jovana Simic-Krstic ◽  
Srecko Nijemcevic ◽  
Djuro Koruga
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Dynamic Properties ◽  
Mineral Waters ◽  
Molecular Organization ◽  
Human Organism ◽  
Water Molecules ◽  
Near Infrared Spectra ◽  
Healthy Functioning

Despite that water is one of the most studied materials today its dynamic properties are still not well understood. Water state in human organism is of high importance for normal healthy functioning of human body. Different kinds of water are usually classified according to its present solutes, and concentrations of these solutes, but though it is known that water molecules can form clusters around present solutes, classification of waters based on types of water molecular organization and present clusters is not present in current literature. In this study we used multivariate analysis for classification of commercial mineral waters based on their near infrared spectra (NIR). Further, we applied Aquaphotomics, a new approach for interpretation of near infrared spectra of water, which gives insight into organization of water molecules in each of these waters.

Sign in / Sign up

Export Citation Format

Share Document