An application of cluster analysis and multivariate classification methods to spring water monitoring data

2007 ◽  
Vol 87 (2) ◽  
pp. 119-127 ◽  
Author(s):  
Gaetano Ragno ◽  
Michele De Luca ◽  
Giuseppina Ioele
2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.


PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0227540
Author(s):  
Kelly Hibbeler Albus ◽  
Ruthanne Thompson ◽  
Forrest Mitchell ◽  
James Kennedy ◽  
Alexandra G. Ponette-González

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