Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid-infrared spectral data using five different discrimination and classification methods

2016 ◽  
Vol 30 (11) ◽  
pp. 663-681 ◽  
Author(s):  
Siyuan Hou
Keyword(s):  
Spectral Data ◽  
Joint Fluid ◽  
Classification Methods ◽  
Mid Infrared ◽  
Diagnostic Models ◽  
Infrared Spectral ◽  
Canine Osteoarthritis

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

2011 ◽  
Vol 5 (3) ◽  
pp. 381-387 ◽  
Author(s):  
Daniel Cozzolino ◽  
Wies Cynkar ◽  
Nevil Shah ◽  
Paul Smith
Keyword(s):  
Spectral Data ◽  
Grape Juice ◽  
Mid Infrared ◽  
Infrared Spectral ◽  
Varietal Differentiation

scholarly journals Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning

2020 ◽  
Vol 103 (10) ◽  
pp. 9355-9367
Author(s):  
S.J. Denholm ◽  
W. Brand ◽  
A.P. Mitchell ◽  
A.T. Wells ◽  
T. Krzyzelewski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Spectral Data ◽  
Bovine Tuberculosis ◽  
Mid Infrared ◽  
Infrared Spectral

Readily dispersible clay in soils from different Brazilian regions by visible, near, and mid-infrared spectral data

2021 ◽  
Vol 42 (18) ◽  
pp. 6945-6962
Author(s):  
Isabela Mello Silva ◽  
Danilo Jefferson Romero ◽  
Clécia Cristina Barbosa Guimarães ◽  
Marcelo Rodrigo Alves ◽  
Lucas Prado Osco ◽  
...  
Keyword(s):  
Spectral Data ◽  
Mid Infrared ◽  
Infrared Spectral ◽  
Dispersible Clay

Simultaneous Determination of Sugars by Multivariate Analysis Applied to Mid-Infrared Spectra of Biological Samples

1997 ◽  
Vol 51 (3) ◽  
pp. 369-375 ◽  
Author(s):  
Frédéric Cadet ◽  
Christine Robert ◽  
Bernard Offmann
Keyword(s):  
Spectral Data ◽  
Biological Samples ◽  
Reducing Sugars ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Mid Infrared ◽  
Total Sugars ◽  
Infrared Spectral ◽  
Ir Bands

We have investigated the use of principal component analysis (PCA) to describe and assess mid-infrared spectral data obtained from complex biological samples containing sucrose, fructose, and glucose. The correlation coefficients between spectral data and chemical values of each variable (sucrose, glucose, fructose, total sugars, and reducing sugars) showed that in each case, axes 1, 3, 4, and 5 had the highest values. These values also indicated which axes each variable was mostly correlated with. The results also showed that the samples were distributed according to their sucrose concentrations (or total sugars) along a concentration gradient in the projection plan formed between axes 1 and 3. No clear discrimination according to concentration was observed with other factorial maps. Prediction equations that linked sucrose, fructose, glucose, total sugar, and reducing sugars concentrations to the spectral data were established by regression on the principal component. Very high correlation coefficients values between the first 10 axes and the chemical values were obtained (between 0.9757 and 0.998). From such aqueous biological samples containing a ternary mixture of sucrose, fructose, and glucose, it was possible to (1) identify the characteristic IR bands of these different sugars (and their combination: reducing sugars/total sugars) and (2) to specifically measure their concentrations with a relatively good accuracy.

A Bayesian Approach to Binary Classification of Mid-Infrared Spectral Data With Noisy Sensors

2020 ◽  
Vol 20 (13) ◽  
pp. 6964-6970
Author(s):  
Bernardo Aquino ◽  
David Jerome Benirschke ◽  
Vijay Gupta ◽  
Scott Howard
Keyword(s):  
Spectral Data ◽  
Bayesian Approach ◽  
Binary Classification ◽  
Mid Infrared ◽  
Infrared Spectral
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