Calibration transfer of near-infrared spectra for extraction of informative components from spectra with canonical correlation analysis

2014 ◽  
Vol 28 (10) ◽  
pp. 773-784 ◽  
Author(s):  
Kaiyi Zheng ◽  
Xuan Zhang ◽  
Jibran Iqbal ◽  
Wei Fan ◽  
Ting Wu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Infrared Spectra ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Calibration Transfer ◽  
Near Infrared Spectra

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.

Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

2019 ◽  
Vol 52 (14) ◽  
pp. 2188-2202 ◽  
Author(s):  
Junxing Yang ◽  
Xiaoping Lou ◽  
Hongqi Yang ◽  
Huibing Yang ◽  
Chaoying Liu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Calibration Transfer

Calibration transfer of near-infrared spectroscopy by canonical correlation analysis coupled with wavelet transform

The Analyst ◽  
2017 ◽  
Vol 142 (12) ◽  
pp. 2229-2238 ◽  
Author(s):  
Jun Bin ◽  
Xin Li ◽  
Wei Fan ◽  
Ji-heng Zhou ◽  
Cheng-wei Wang
Keyword(s):  
Infrared Spectroscopy ◽  
Wavelet Transform ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Near Infrared Spectroscopy ◽  
Canonical Correlation ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Transfer

A novel calibration transfer approach, WTCCA, combining the advantages of WT and CTCCA has been proposed for NIRS multivariate calibration.

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.

Variable selection by double competitive adaptive reweighted sampling for calibration transfer of near infrared spectra

2019 ◽  
Vol 191 ◽  
pp. 109-117 ◽  
Author(s):  
Kaiyi Zheng ◽  
Tao Feng ◽  
Wen Zhang ◽  
Xiaowei Huang ◽  
Zhihua Li ◽  
...  
Keyword(s):  
Variable Selection ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Calibration Transfer ◽  
Near Infrared Spectra
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