REGRESSION AND SPARSE REGRESSION METHODS FOR VISCOSITY ESTIMATION OF ACID MILK FROM IT’S SLS FEATURES

Sara Sharifzadeh, Jacob Lerche Skytte, Otto Højager Attermann Nielsen, Bjarne Kjær Ersbøll, Line Harder Clemmensen

AbstractStatistical solutions find wide spread use in food and medicine
quality control. We investigate the effect of different regression
and sparse regression methods for a viscosity estimation
problem using the spectro-temporal features from new Sub-
Surface Laser Scattering (SLS) vision system. From this investigation,
we propose the optimal solution for regression
estimation in case of noisy and inconsistent optical measurements,
which is the case in many practical measurement systems.
The principal component regression (PLS), partial least
squares (PCR) and least angle regression (LAR) methods are
compared with sparse LAR, lasso and Elastic Net (EN) sparse
regression methods. Due to the inconsistent measurement
condition, Locally Weighted Scatter plot Smoothing (Loess)
has been employed to alleviate the undesired variation in the
estimated viscosity. The experimental results of applying different
methods show that, the sparse regression lasso outperforms
other methods. In addition, the use of local smoothing
has improved the results considerably for all regression methods.
Due to the sparsity of lasso, this result would assist to
design a simpler vision system with less spectral bands.
KeywordsRegression, Sparse Regression, Smoothing, Sub-Surface Laser Scattering (SLS)
TypeConference paper [With referee]
Conference19th International Conference on Systems, Signals and Image Processing, Vienna, Austria
Year2012    Month April
AddressVienna-Austria
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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