Determination of the characteristic sensory profiles of Aloreña table-olive

Soldevilla, H. Galán; Pérez‐Cacho, Pilar Ruiz; Campuzano, J. A. Hernández

doi:10.3989/gya.132312

Cited by 18 publications

(19 citation statements)

References 24 publications

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“…Furthermore, a repeated K-fold-CV procedure was also applied for comparison purposes with the LOO-CV technique results, to ensure that there were no overfitting issues, since satisfactory results were obtained in both cross-validation techniques. It should be remarked that although the number of different independent samples studied is similar to those usually used in the literature regarding table olive analysis (Casale et al 2010;Cano-Lamadrid et al 2017;Cortés-Delgado et al 2016;Galán-Soldevilla et al 2013;Lanza and Amoruso 2016;Marx et al 2017a, b;Panagou et al 2008;Ramírez et al 2016;Sousa et al 2011), it does not allow the establishment of a representative external dataset, which justifies use of this cross-validation variant.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Table Olives’ Organoleptic Defect Intensities Based on the Potentiometric Fingerprint Recorded by an Electronic Tongue

Marx

Rodrigues

Dias

et al. 2017

Food Bioprocess Technol

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Table olives are prone to the appearance of sensory defects that decrease their quality and in some cases result in olives unsuitable for consumption. The evaluation of the type and intensity of the sensory negative attributes of table olives is recommended by the International Olive Council, although not being legally required for commercialization. However, the accomplishment of this task requires the training and implementation of sensory panels according to strict directives, turning out in a time-consuming and expensive procedure that involves a degree of subjectivity. In this work, an electronic tongue is proposed as a taste sensor device for evaluating the intensity of sensory defects of table olives. The potentiometric signal profiles gathered allowed establishing multiple linear regression models, based on the most informative subsets of signals (from 24 to 29 recorded during the analysis of olive aqueous pastes and brine solutions) selected using a simulated annealing meta-heuristic algorithm. The models enabled the prediction of the median intensities (R 2 ≥ 0.942 and RMSE ≤ 0.356, for leave-one-out or repeated K-fold cross-validation procedures) of butyric, musty, putrid, winey-vinegary, and zapateria negative sensations being, in general, the predicted intensities within the range of intensities perceived by the sensory panel. Indeed, based on the predicted mean intensities of the sensory defects, the electroche micalchemometric approach developed could correctly classify 86.4% of the table olive samples according to their trade category based on a sensory panel evaluation and following the International Olive Council regulations (i.e., extra, 1st choice, 2nd choice, and olives that may not be sold as table olives). So, the satisfactory overall predictions achieved demonstrate that the electronic tongue could be a complementary tool for assessing table olive defects, reducing the effort of trained panelists and minimizing the risk of subjective evaluations.

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Section: Discussionmentioning

confidence: 99%

Assessment of Table Olives’ Organoleptic Defect Intensities Based on the Potentiometric Fingerprint Recorded by an Electronic Tongue

Marx

Rodrigues

Dias

et al. 2017

Food Bioprocess Technol

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“…Eight trained tasters (three men and five women, aged between twenty-seven and sixty) from the panel at the C ordoba University Sensory Laboratory took part in this study. These tasters are selected and trained in accordance with international regulations (ISO 5492:2008, ISO 8586:2012, ISO 13299:2016, for 400 h in general aspects of sensory analysis, and in the descriptive and quantitative analysis of many products Ruiz Perez-Cacho et al, 2005, 2008Rodriguez et al, 2010;Gal an-Soldevilla et al, 2013;Rodr ıguez et al, 2015). The tasters also receive a specific 15-h period of training in cheese (ISO 22935-1:2009a, ISO 22935-2:2009b.…”

Section: Trained Panelmentioning

confidence: 99%

Linear regression models for estimating the effect of technological factors on the sensory characteristics of goat cheeses

Pérez‐Cacho

Ruiz

Dios-Palomares

et al. 2019

Int J of Food Sci Tech

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Summary Linear regression models were applied to predict the effect of the breed and technological factors on the sensory characteristic of cheeses. Seventy goat cheeses from forty local producers were analysed. They represented the totality of the Andalusian cheese‐making sector (different goat breeds, type of coagulant, ripening time and heat treatment of milk). Sensory attributes of cheeses were influenced by all the factors studied. Ripening affects all sensory attributes while the breed and the heat treatment are the most influential factors on the flavour and the type of coagulant on the texture attributes of these cheeses. Linear regression models were found to be suitable for studying which factors had an independent influence on the sensory attributes of these cheeses. This work could help researchers and producers to a better sensory characterisation of their products.

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“…The samples were stored in the original packaging, in dark at ambient temperature (~20°C) until analysis or after opened in the refrigerator at 4°C (being all of them analyzed before the end of the expiration date). It should be remarked that the number of different independent samples studied is of the same order of magnitude as those usually used in the literature regarding table olives analysis [17][18][19][20]22,[28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Sensory classification of table olives using an electronic tongue: Analysis of aqueous pastes and brines

Marx

Rodrigues

Dias

et al. 2017

Talanta

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A B S T R A C T Table olives are highly appreciated and consumed worldwide. Different aspects are used for trade category classification being the sensory assessment of negative defects present in the olives and brines one of the most important. The trade category quality classification must follow the International Olive Council directives, requiring the organoleptic assessment of defects by a trained sensory panel. However, the training process is a hard, complex and sometimes subjective task, being the low number of samples that can be evaluated per day a major drawback considering the real needs of the olive industry. In this context, the development of electronic tongues as taste sensors for defects' sensory evaluation is of utmost relevance. So, an electronic tongue was used for table olives classification according to the presence and intensity of negative defects. Linear discrimination models were established based on sub-sets of sensor signals selected by a simulated annealing algorithm. The predictive potential of the novel approach was first demonstrated for standard solutions of chemical compounds that mimic butyric, putrid and zapateria defects (≥93% for cross-validation procedures). Then its applicability was verified; using reference table olives/brine solutions samples identified with a single intense negative attribute, namely butyric, musty, putrid, zapateria or winey-vinegary defects (≥93% cross-validation procedures). Finally, the E-tongue coupled with the same chemometric approach was applied to classify table olive samples according to the trade commercial categories (extra, 1 st choice, 2 nd choice and unsuitable for consumption) and an additional quality category (extra free of defects), established based on sensory analysis data. Despite the heterogeneity of the samples studied and number of different sensory defects perceived, the predictive linear discriminant model established showed sensitivities greater than 86%. So, the overall performance achieved showed that the electrochemical device could be used as a taste sensor for table olives organoleptic trade successful classification, allowing a preliminary quality assessment, which could facilitate, in the future, the complex task of sensory panelists.

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Determination of the characteristic sensory profiles of Aloreña table-olive

Cited by 18 publications

References 24 publications

Assessment of Table Olives’ Organoleptic Defect Intensities Based on the Potentiometric Fingerprint Recorded by an Electronic Tongue

Assessment of Table Olives’ Organoleptic Defect Intensities Based on the Potentiometric Fingerprint Recorded by an Electronic Tongue

Linear regression models for estimating the effect of technological factors on the sensory characteristics of goat cheeses

Sensory classification of table olives using an electronic tongue: Analysis of aqueous pastes and brines

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