Chromatographic unsupervised classification of olive and non-olive oil samples with the aid of graph theory

2017 ◽  
Vol 9 (44) ◽  
pp. 6267-6272 ◽  
Author(s):  
Keshav Kumar
Keyword(s):  
Graph Theory ◽  
Olive Oil ◽  
Unsupervised Classification ◽  
Chemometric Approach

Graph theory is introduced as a novel chemometric approach for classifying the samples in an unsupervised manner.

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

2017 ◽  
Vol 9 (45) ◽  
pp. 6386-6393 ◽  
Author(s):  
Keshav Kumar
Keyword(s):  
Olive Oil ◽  
Unsupervised Classification ◽  
Quality Monitoring ◽  
Novel Approach ◽  
Kohonen Map ◽  
Map Analysis

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.

Unsupervised classification of street architectures based on InfoGAN

2019 ◽  
Author(s):  
Ning Wang ◽  
Xianhan Zeng ◽  
Renjie Xie ◽  
Zefei Gao ◽  
Yi Zheng ◽  
...  
Keyword(s):  
Unsupervised Classification

scholarly journals Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1241
Author(s):  
Nikolaos Gyftokostas ◽  
Eleni Nanou ◽  
Dimitrios Stefas ◽  
Vasileios Kokkinos ◽  
Christos Bouras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Olive Oil ◽  
Absorption Spectra ◽  
Absorption Spectroscopy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spectroscopic Techniques ◽  
Breakdown Spectroscopy ◽  
Olive Oils ◽  
Laser Induced Breakdown

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

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.

Sign in / Sign up

Export Citation Format

Share Document