Variety identification of brown sugar using short-wave near infrared spectroscopy and multivariate calibration

2007 ◽  
Author(s):  
Haiqing Yang ◽  
Di Wu ◽  
Yong He
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Short Wave ◽  
Variety Identification ◽  
Brown Sugar

scholarly journals Monitoring Urban Wastewaters’ Characteristics by Visible and Short Wave Near-Infrared Spectroscopy

Water ◽  
2013 ◽  
Vol 5 (4) ◽  
pp. 2026-2036 ◽  
Author(s):  
Ignacio Melendez-Pastor ◽  
M. Almendro-Candel ◽  
Jose Navarro-Pedreño ◽  
Ignacio Gómez ◽  
Mariano Lillo ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Short Wave

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

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