Regression models based on new local strategies for near infrared spectroscopic data

2016 ◽  
Vol 933 ◽  
pp. 50-58 ◽  
Author(s):  
F. Allegrini ◽  
J.A. Fernández Pierna ◽  
W.D. Fragoso ◽  
A.C. Olivieri ◽  
V. Baeten ◽  
...  
2018 ◽  
Vol 133 ◽  
pp. 9-15 ◽  
Author(s):  
Torbjörn A. Lestander ◽  
Linda Sandström ◽  
Henrik Wiinikka ◽  
Olov G.W. Öhrman ◽  
Mikael Thyrel

2020 ◽  
Vol 1108 ◽  
pp. 1-9 ◽  
Author(s):  
Jari Torniainen ◽  
Isaac O. Afara ◽  
Mithilesh Prakash ◽  
Jaakko K. Sarin ◽  
Lauri Stenroth ◽  
...  

The Analyst ◽  
2017 ◽  
Vol 142 (8) ◽  
pp. 1320-1332 ◽  
Author(s):  
Arash Hanifi ◽  
Uday Palukuru ◽  
Cushla McGoverin ◽  
Michael Shockley ◽  
Eliot Frank ◽  
...  

Non-destructive near infrared spectroscopic data can be utilized for assessment of compositional and mechanical properties of engineered cartilage.


2003 ◽  
Vol 11 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Tom Fearn ◽  
Anthony M.C. Davies

The advent of spectral imaging and recent trends towards the compilation of large spectral databases have caused renewed interest in the compression of near infrared spectra for purposes of storage. A comparison of approaches using Fourier and wavelet transforms shows that wavelets are generally, though not always, more efficient than Fourier at compressing near infrared spectra. The Daubechies extremal phase wavelet of order 4 is a good choice for this purpose.


2010 ◽  
Vol 107 (3) ◽  
pp. 271-276 ◽  
Author(s):  
M. Monroy ◽  
S. Prasher ◽  
M.O. Ngadi ◽  
N. Wang ◽  
Y. Karimi

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