1 research outputs found
Evaluation of Non-linearity in MIR Spectroscopic Data for Compressed Learning
Mid-Infrared (MIR) spectroscopy has emerged as
the most economically viable technology to determine milk
values as well as to identify a set of animal phenotypes
related to health, feeding, well-being and environment.
However, Fourier transform-MIR spectra incurs a significant
amount of redundant data. This creates critical issues such
as increased learning complexity while performing Fog and
Cloud based data analytics in smart farming. These issues
can be resolved through data compression using unsupervisory
techniques like PCA, and perform analytics in the
compressed-domain i.e. without de-compressing. Compression
algorithms should preserve non-linearity of MIRS data
(if exists), since emerging advanced learning algorithms
can improve their prediction accuracy. This study has investigated
the non-linearity between the feature variables
in the measurement-domain as well as in two compressed
domains using standard Linear PCA and Kernel PCA. Also
the non-linearity between the feature variables and the
commonly used target milk quality parameters (Protein,
Lactose, Fat) has been analyzed. The study evaluates the
prediction accuracy using PLS and LS-SVM respectively as
linear and non-linear predictive models