Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy

2007 ◽  
Vol 96 (3) ◽  
pp. 427-433 ◽  
Author(s):  
M.R. Maleki ◽  
A.M. Mouazen ◽  
H. Ramon ◽  
J. De Baerdemaeker
2021 ◽  
pp. 096703352110065
Author(s):  
Sylvain Treguier ◽  
Kevin Jacq ◽  
Christel Couderc ◽  
Hicham Ferhout ◽  
Helene Tormo ◽  
...  

Fast diagnostic tools such as near infrared spectroscopy have recently gained interest for bacterial identification. To avoid a process involving microbial pellet or suspension preparation from Petri dishes for NIR analysis, direct screening from agar in Petri dishes was explored. This two-step study proposes a new procedure for bacterial screening directly on agar plates with minimal nutrient medium bias. Firstly, principal component analyses showed optimal discrimination between the genera Lactobacillus, Pseudomonas and Brochothrix on different culture media, in transmission mode and with the bottom of Petri dishes facing the light source. The repeatability of spectra in these conditions was assessed with an average coefficient of variation inferior to 5% in the 12,500–3680 cm−1 range. Secondly, 40 strains of Lactococcus and Enterococcus species were grown on Bennett agar and measured over a series of five assays. Principal component analyses highlighted better clustering according to genera and species and lower external bias while retaining the 8790–3680 cm−1 spectral range and applying an extended multiplicative scatter correction with an average agar spectrum as a reference, in comparison to raw data and standard multiplicative scatter correction.


2012 ◽  
Vol 110 ◽  
pp. 314-320 ◽  
Author(s):  
Cheng He ◽  
Longjian Chen ◽  
Zengling Yang ◽  
Guangqun Huang ◽  
Na Liao ◽  
...  

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.


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