Reliable Classification of Olive Oil Origin Based on Minor Component Profile Using 1 H‐NMR and Multivariate Analysis

2019 ◽  
Vol 121 (12) ◽  
pp. 1900027 ◽  
Author(s):  
Ole Winkelmann ◽  
Torben Küchler
Keyword(s):  
Multivariate Analysis ◽  
Olive Oil ◽  
Minor Component ◽  
H Nmr ◽  
Reliable Classification

scholarly journals Alignment and Proficiency of Virgin Olive Oil Sensory Panels: The OLEUM Approach

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 355 ◽  
Author(s):  
Sara Barbieri ◽  
Karolina Brkić Bubola ◽  
Alessandra Bendini ◽  
Milena Bučar-Miklavčič ◽  
Florence Lacoste ◽  
...  
Keyword(s):  
Quality Control ◽  
Decision Tree ◽  
Olive Oil ◽  
Reference Materials ◽  
Virgin Olive Oil ◽  
Sensory Data ◽  
Physical Chemical ◽  
Control Procedures ◽  
Reliable Classification

A set of 334 commercial virgin olive oil (VOO) samples were evaluated by six sensory panels during the H2020 OLEUM project. Sensory data were elaborated with two main objectives: (i) to classify and characterize samples in order to use them for possible correlations with physical–chemical data and (ii) to monitor and improve the performance of panels. After revision of the IOC guidelines in 2018, this work represents the first published attempt to verify some of the recommended quality control tools to increase harmonization among panels. Specifically, a new “decision tree” scheme was developed, and some IOC quality control procedures were applied. The adoption of these tools allowed for reliable classification of 289 of 334 VOOs; for the remaining 45, misalignments between panels of first (on the category, 21 cases) or second type (on the main perceived defect, 24 cases) occurred. In these cases, a “formative reassessment” was necessary. At the end, 329 of 334 VOOs (98.5%) were classified, thus confirming the effectiveness of this approach to achieve a better proficiency. The panels showed good performance, but the need to adopt new reference materials that are stable and reproducible to improve the panel’s skills and agreement also emerged.

scholarly journals Multivariate analysis applied to Raman mapping of dye-functionalized carbon nanotubes: a novel approach to support the rational design of functional nanostructures

The Analyst ◽  
2015 ◽  
Vol 140 (16) ◽  
pp. 5754-5763 ◽  
Author(s):  
Sonja Visentin ◽  
Nadia Barbero ◽  
Francesca Romana Bertani ◽  
Mariangela Cestelli Guidi ◽  
Giuseppe Ermondi ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Multivariate Analysis ◽  
Rational Design ◽  
Raman Mapping ◽  
Routine Test ◽  
Functionalized Carbon Nanotubes ◽  
Functional Nanostructures ◽  
Novel Approach ◽  
Reliable Classification

A powerful routine test proposed for the rational design of functional nanostructures allows fast and reliable classification of differently treated CNTs.

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

2010 ◽  
Vol 399 (6) ◽  
pp. 2093-2103 ◽  
Author(s):  
Cristina Ruiz-Samblás ◽  
Luis Cuadros-Rodríguez ◽  
Antonio González-Casado ◽  
Francisco de Paula Rodríguez García ◽  
Paulina de la Mata-Espinosa ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Olive Oil ◽  
Chromatographic Profiles

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.

scholarly journals A study of statistical classification of aphasia: multivariate analysis of SLTA.

1984 ◽  
Vol 4 (2) ◽  
pp. 620-628 ◽  
Author(s):  
Hiroichi Tasaki ◽  
Shunzo Watanabe ◽  
Kei Hojo ◽  
Kazue Chishima ◽  
Hirobumi Metoki
Keyword(s):  
Multivariate Analysis ◽  
Statistical Classification

Classification of prostatic diseases by means of multivariate analysis on in vivo proton MRSI and DCE-MRI data

2009 ◽  
Vol 22 (10) ◽  
pp. 1036-1046 ◽  
Author(s):  
Mariacristina Valerio ◽  
Valeria Panebianco ◽  
Alessandro Sciarra ◽  
Marcello Osimani ◽  
Stefano Salsiccia ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Dce Mri ◽  
Prostatic Diseases

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.

Classification of unifloral honeys using multivariate analysis

2015 ◽  
Vol 27 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Mircea Oroian ◽  
Sonia Amariei ◽  
Alice Rosu ◽  
Gheorghe Gutt
Keyword(s):  
Multivariate Analysis
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