A portable Raman sensor for the rapid discrimination of olives according to fruit quality

Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García‐Mesa, José A.

doi:10.1016/j.talanta.2012.01.053

Cited by 33 publications

(20 citation statements)

References 41 publications

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“…150 Similarly, in a subsequent study, these two kinds of coffee were discriminated by using FT-Raman. 152 Different from the other studies on olive oil, Gouvinhas et al, produced extra virgin olive oil by means of taking samples from three types of olive in different stages of their ripening periods. The discrimination of the coffees was performed by calculating the "spectral kahweol index" with the spectra obtained from the samples with different geographical origins.…”

Section: Coffeementioning

confidence: 99%

Dispersive and FT-Raman spectroscopic methods in food analysis

et al. 2015

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Raman spectroscopy is a powerful technique for molecular analysis of food samples. A fingerprint spectrum can be obtained for a target molecule by using Raman technology, as specific signals are obtained for chemical bonds in the target. In this way, food components, additives, processes and changes during shelf life, adulterations and numerous contaminants such as microorganisms, chemicals and toxins, can also be determined with or without the help of chemometric methods. The studies included in this review show that Raman spectroscopy has great potential for food analyses. In this review, we aim to bring together Raman studies on components, contaminants, raw materials, and adulterations of various food, and attempt to prepare a database of Raman bands obtained from food samples.

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Section: Coffeementioning

confidence: 99%

Dispersive and FT-Raman spectroscopic methods in food analysis

et al. 2015

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“…Discriminant analysis provided prediction abilities of 100% for sound, 79% for frostbite, 96% for ground, and 92% for fermented olives using cross-validation. A similar study was carried out by Guzman et al 58 in 2012 with a portable Raman sensor to classify slurries coming from sound and ground milled olives. They compare soft independent modelling of class analogies (SIMCA), partial least square discriminant analysis (PLS-DA) and k-nearest neighbours (k-NN) algorithms, and the last one was the better with 100% and 97% of success for sound and ground olives respectively.…”

Section: Determination Of the Oil And Water Content Of Olive Pomacementioning

confidence: 72%

“…The best results were obtained using the KNN method, with prediction abilities of 100% for 'sound' and 97% for 'ground' in an independent validation set 58 Real-time characterisation of olive fruit and the potential characteristics of the extracted oil in olive paste RMSEP values for extractability index: 0,04%; Oil content on FW: 56 achieved a standard error of prediction of 0.92% for moisture and 0.811% for fat content. Furthermore, in 2007 Bendini et al 36 obtained an estimation error of 1.29% and 0.67% for moisture and fat content, respectively.…”

Section: Determination Of the Oil And Water Content Of Olive Pomacementioning

confidence: 98%

Novel technologies for monitoring the in‐line quality of virgin olive oil during manufacturing and storage

Ortega

Gila

Puerto³

et al. 2016

J Sci Food Agric

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The quality of virgin olive oil is related to the agronomic conditions of the olive fruits and the process variables of the production process. Nowadays, food markets demand better products in terms of safety, health and organoleptic properties with competitive prices. Innovative techniques for process control, inspection and classification have been developed in order to to achieve these requirements. This paper presents a review of the most significant sensing technologies which are increasingly used in the olive oil industry to supervise and control the virgin olive oil production process. Throughout the present work, the main research studies in the literature that employ non-invasive technologies such as infrared spectroscopy, computer vision, machine olfaction technology, electronic tongues and dielectric spectroscopy are analysed and their main results and conclusions are presented. These technologies are used on olive fruit, olive slurry and olive oil to determine parameters such as acidity, peroxide indexes, ripening indexes, organoleptic properties and minor components, among others. © 2016 Society of Chemical Industry.

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“…Application fields such as genetics, combined with increasing computing power, have prompted some of these developments [14], [34], [36], [54]. The classification of plants has clearly played an important role in the fields of biology [31], [55], [56].…”

Section: Discussionmentioning

confidence: 99%

A New Avenue for Classification and Prediction of Olive Cultivars Using Supervised and Unsupervised Algorithms

Beiki

Saboor²,

Ebrahimi

2012

PLoS ONE

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Various methods have been used to identify cultivares of olive trees; herein we used different bioinformatics algorithms to propose new tools to classify 10 cultivares of olive based on RAPD and ISSR genetic markers datasets generated from PCR reactions. Five RAPD markers (OPA0a21, OPD16a, OP01a1, OPD16a1 and OPA0a8) and five ISSR markers (UBC841a4, UBC868a7, UBC841a14, U12BC807a and UBC810a13) selected as the most important markers by all attribute weighting models. K-Medoids unsupervised clustering run on SVM dataset was fully able to cluster each olive cultivar to the right classes. All trees (176) induced by decision tree models generated meaningful trees and UBC841a4 attribute clearly distinguished between foreign and domestic olive cultivars with 100% accuracy. Predictive machine learning algorithms (SVM and Naïve Bayes) were also able to predict the right class of olive cultivares with 100% accuracy. For the first time, our results showed data mining techniques can be effectively used to distinguish between plant cultivares and proposed machine learning based systems in this study can predict new olive cultivars with the best possible accuracy.

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A portable Raman sensor for the rapid discrimination of olives according to fruit quality

Cited by 33 publications

References 41 publications

Dispersive and FT-Raman spectroscopic methods in food analysis

Dispersive and FT-Raman spectroscopic methods in food analysis

Novel technologies for monitoring the in‐line quality of virgin olive oil during manufacturing and storage

A New Avenue for Classification and Prediction of Olive Cultivars Using Supervised and Unsupervised Algorithms

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