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

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Application of the Kohonen map analysis (KMA) on chromatographic datasets to achieve unsupervised classification of olive and non-olive oil samples: a novel approach
Analytical Methods ( IF 2.7 ) Pub Date : 2017-10-19 00:00:00 , DOI: 10.1039/c7ay01963g
Keshav Kumar _{1,

2,

3,

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Affiliation

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.

中文翻译：

Kohonen图谱分析（KMA）在色谱数据集上的应用，以实现对橄榄油和非橄榄油样品的无监督分类：一种新颖的方法

在当前的工作中，引入了一种新颖的方法，该方法涉及在色谱数据集上应用Kohonen图分析（KMA）算法，以对橄榄油样品进行质量监控。使用从属于橄榄，非橄榄油和混合油样品类别的118个油样品中获得的色谱数据集，对建议的程序进行了测试。所获得的结果清楚地表明，KMA算法在对样本进行分类时具有很高的敏感性，特异性和精确性。总而言之，所提出的KMA色谱组合提供了一种简单，无偏且敏感的程序来进行橄榄油的质量监控。

更新日期：2017-11-23

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