Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

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

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    Statistical 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.
    Original languageEnglish
    Title of host publicationProceedings : IWSSIP 2012, 11-13 April 2012, Vienna, Austria
    Publication date2012
    ISBN (Print)978-3-200-02588-2
    Publication statusPublished - 2012
    Event19th International Conference on Systems, Signals and Image Processing (IWSSIP 2012) - Vienna, Austria
    Duration: 11 Apr 201213 Apr 2012


    Conference19th International Conference on Systems, Signals and Image Processing (IWSSIP 2012)
    Internet address

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