Sensory quality control for food certification: A case study on wine. Panel training and qualification, method validation and monitoring

Etaio, Iñaki; Albisu, M.; Ojeda, M.; Gil, Pilar; Egea, Jesús Salmerón; Elortondo, Francisco José Pérez

doi:10.1016/j.foodcont.2009.08.011

Cited by 32 publications

(23 citation statements)

References 14 publications

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“…The trained panel proved accurate in differentiating samples with the rancid flavor defect according to the combined methods applied. In another study Etaio et al(2010) started a selection for a wine trained panel with 31 assessors, finishing with 13 in the training, ending with seven experts as in the present study, but using only percentages of success; no ANOVA was applied. This proves the difficulty in obtaining a large number of expert assessors.…”

Section: Discussionmentioning

confidence: 99%

“…However, consumer preferences, acceptance and feedback are very important to the market (Ares et al, 2015), which demonstrates the importance of sensory analysis in many areas, from the development of products to quality control (Ares et al, 2015;Latreille et al, 2006). Therefore, selection and training are required to assess reliable measurements from individual reactions (Latreille et al, 2006;Etaio et al, 2010). To accredit a trained panel, the assessors must present repeatability (consensus), reproducibility, discrimination ability; and they must be able to notice differences that might seem small to consumers, with all technical competence acquired remaining over time (Etaio et al, 2010;González et al, 2007;López-Aguilar et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, selection and training are required to assess reliable measurements from individual reactions (Latreille et al, 2006;Etaio et al, 2010). To accredit a trained panel, the assessors must present repeatability (consensus), reproducibility, discrimination ability; and they must be able to notice differences that might seem small to consumers, with all technical competence acquired remaining over time (Etaio et al, 2010;González et al, 2007;López-Aguilar et al, 2007). A number of five to eight trained assessors are sufficient for a reliable evaluation (Dutcosky, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The steps to be followed to achieve a reliable sensory trained panel are basically: assessor selection, basic, and specific training, assessor qualification and method validation (Etaio et al, 2010). However, this entire process is known to be time consuming, expensive and not practical due to several aspects (Kamruzzaman et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…However, references for selecting and training demonstrate the details of the process, including the temperature and time of rancidity, and are very limited in the literature (Latreille et al, 2006;Elortondo et al, 2007;Etaio et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Selecting and training a panel to evaluate the rancid defect in soybean oil and fish hamburgers

et al. 2017

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SUMMARY:Due to its composition of unsaturated and polyunsaturated fatty acids, oils and fats are very susceptible to oxidation, with rancidity being one of the main defects. Among the several existing methodologies to monitor oxidation in foods, sensory analysis stands out because of the sensitivity of responses. Accordingly, this study aimed to select and train a panel of expert assessors to identify the rancid flavor, showing the statistical steps in the process. Assessors were selected according to their individual performance, statistically analyzed by ANOVA and Tukey's mean comparison, Wald Sequential Analysis and chi-square test. The validation of the trained panel was carried out with the sensory analysis of fish burgers and soybean oil. F Value and box-plot graphic methods were effective for better visualization of results when used along with the mean and standard deviation tables. The final trained panel consisted of seven assessors, who have been able to identify and differentiate rancid taste in both samples used for validation. KEYWORDS: Bioactive Phytochemicals; Fish; Flavors; Food Quality; Sensory AnalysisRESUMEN: Selección y entrenamiento de un panel para evaluar el defecto rancio en aceite de soja y hamburguesas de pescado. Debido a su composición en ácidos grasos insaturados y poliinsaturados, los aceites y grasas son muy susceptibles a la oxidación, siendo la rancidez uno de los principales defectos. Entre las diversas metodologías existentes para seguir la oxidación en los alimentos, el análisis sensorial destaca por la sensibilidad de las respuestas. Este estudio tuvo como objetivo seleccionar y capacitar a un panel de catadores entrenados para identificar el sabor rancio y se muestra estadisticamente los pasos del proceso. Los catadores fueron seleccionados de acuerdo a su capacidad individual y estadísticamente analizados por ANOVA comparando las medias mediante Tukey, análisis secuencial de Wald y prueba de chi-cuadrado. La validación del panel entrenado se realizó con el análisis sensorial de hamburguesas de pescado y aceite de soja. El valor F y la representación gráfica boxplot fueron eficaces para una mejor visualización de los resultados cuando se utilizan junto con las tablas de desviación media y estándar. El panel final estuvo formado por siete catadores que fueron capaces de identificar y diferenciar el sabor rancio en ambas muestras utilizadas para su validación.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selecting and training a panel to evaluate the rancid defect in soybean oil and fish hamburgers

et al. 2017

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Sustainability Challenges in Food Tourism

Kuang

Bhat

2017

Sustainability Challenges in the Agrofood Sector

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Measurement uncertainty with nested mixed effect models

Deldossi

Zappa

2011

Quality & Reliability Eng

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Measurement uncertainty in experiments is receiving increasing interest among practitioners both for quantitative and qualitative evaluations. Applications may be found, for example, to compare experiments run in different laboratories, to certify the precision of gauges used in the masurement process or to measure uncertainty in a classification process, i.e. when objects are qualitatively evaluated by appraisers. In this paper, after having briefly presented how to measure uncertainty using gauge repeatability and reproducibility studies, we focus on a problem quite relevant for practitioners: how to deal with the presence of correlation among 'replications'. This issue is common to many food/agriculture experiments. By a modification of a nested mixed effect design, we describe the impact of dependencies among replications on measurement capability.

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Sensory quality control for food certification: A case study on wine. Panel training and qualification, method validation and monitoring

Cited by 32 publications

References 14 publications

Selecting and training a panel to evaluate the rancid defect in soybean oil and fish hamburgers

Selecting and training a panel to evaluate the rancid defect in soybean oil and fish hamburgers

Sustainability Challenges in Food Tourism

Measurement uncertainty with nested mixed effect models

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