LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Application of the Kohonen map analysis (KMA) on chromatographic datasets to achieve unsupervised classification of olive and non-olive oil samples: a novel approach

Photo by markuswinkler from unsplash

In the present work, a novel procedure that involves application of the Kohonen map analysis (KMA) algorithm on the chromatographic datasets is introduced for quality monitoring of olive oil samples.… Click to show full abstract

In the present work, a novel procedure that involves application of the Kohonen map analysis (KMA) algorithm on the chromatographic datasets is introduced for quality monitoring of olive oil samples. The proposed procedure is tested using the chromatographic datasets acquired for 118 oil samples belonging to the class of olive, non-olive and blended oil samples. The obtained results clearly indicate that the KMA algorithm is highly sensitive, specific and precise in classifying the samples. In summary, the proposed KMA-chromatographic combination provides a simple, unbiased and sensitive procedure to perform quality monitoring of the olive oils.

Keywords: application kohonen; kma; chromatographic datasets; kohonen map; oil samples; oil

Journal Title: Analytical Methods
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.