Color of Salmon Fillets By Computer Vision and Sensory Panel

Quevedo, Roberto; Aguilera, José Miguel; Pedreschi, Franco

doi:10.1007/s11947-008-0106-6

Cited by 147 publications

(77 citation statements)

References 30 publications

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“…On the contrary, when the lighting is not standard, an empirical conversion must be performed, as it is stated in previous works regarding the measurement of the colour of the solid foods. [21][22][23][24][25][26] These empirical models must be fitted by a calibration procedure using samples with known colour values. In this work, the following empirical conversion model was used, with linear and quadratic relations, as well as interactions between the RGB values:…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

Characterisation of liquid food colour from digital images

Segura

Salvadori

Goñi

2017

International Journal of Food Properties

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The characteristic colour of translucent liquid foods is defined as the colour values at infinite depth, where the liquid depth and the background effects are surpassed. A measurement cell with variable depth was built, and the L*a*b* colour was measured from digital images. Colour versus depth was fitted to an exponential equation, from which the characteristic colour was obtained. Thirteen different liquids were tested with this methodology and compared with spectrophotometer measurements. The average total colour difference between approaches was 50.85 ± 18.89. Image analysis led to more realistic predictions, and a high correlation between Hue angle values was obtained for most samples, with the exception of only one. ARTICLE HISTORY

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Section: Image Acquisition and Processingmentioning

confidence: 99%

Characterisation of liquid food colour from digital images

Segura

Salvadori

Goñi

2017

International Journal of Food Properties

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“…Based on techniques from other studies investigating the relationship between changes in color of food products and their consumer evaluations (Abbott et al, 2004;Zhou et al, 2004;Pérez-Gago et al, 2006;Pristijono et al, 2006;Quevedo et al, 2008), the shelf-life of each apple slice was determined as the time required for browning to develop to an unacceptable level. Browning of the cut surfaces was evaluated by three referees according to a visual color scale of 1 -5, where 1 = fresh without any browning, 2 = slight browning of the cut surface, 3 = modérate browning, 4 = severe brown discoloration, 5 = complete discoloration.…”

Section: Sensory Evaluationmentioning

confidence: 99%

“…Recently, León et al (2006) demonstrated a computer visión system (CVS) for measuring color in L*a*b* coordinates from RGB space. Some studies have been undertaken to apply that approximation to food (Pedreschi et al, 2007;Quevedo et al, 2008). During the description of browning kinetics using color information, an L* mean valué is generally assumed.…”

Section: Introductionmentioning

confidence: 99%

A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

Lunadei

Galleguillos

Diezma

et al. 2011

Postharvest Biology and Technology

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Keywords:Fresh-cut apples Enzymatic browning CIÉ L*a*f>* color space Multispectral images Image analysisThe main objective of this study was to develop a visión system that is able to classify fresh-cut apple slices according to the development of enzymatic browning. The experiment was carried out on 'Granny Smith' apple slices stored at 7.5 °C for 9 days (n = 120). Twenty-four samples were analyzed per day: at zero time and after storage for 1, 3, 7 and 9 days, which corresponds to treatments to, ti, h, ti and t% respectively. Multispectral images were acquired from the samples by employing a 3-CCD camera centered at the infrared (IR, 800nm), red (R, 680nm) and blue (B, 450nm) wavelengths. Apple slices were evaluated visually according to a visual color scale of 1 -5 (where 1 corresponds to fresh samples without any browning and 5 to samples with severe discoloration), to obtain a sensory evaluation Índex (¡SE) for each sample. Finally, for each sample and for each treatment, visible (VIS) relative reflectance spectra (360-740 nm) were obtained. In order to identify the most related wavelengths to enzymatic browning evolution, unsupervised pattern recognition analysis of VIS reflectance spectra was performed by principal components analysis (PCA) on the autoscaled data. Máximum loading valúes corresponding to the B and R áreas were observed. Therefore, a classification procedure was applied to the relative histograms of the following monochromatic images (virtual images), which were computed pixel by pixel: (R-B)/(R + B),R-B and B/R. Inall cases,a non-supervised classification procedure was able to genérate three image-based browningreference classes (BRC): Cluster A (corresponding to the to samples), ClusterB (ti andt3 samples) and Cluster C(t7 and tg samples). Aninternal and anexternal validation (n = 120) were carried out, and the best classifications were obtained with the (R -B)/(R + B) and B/R image histograms (internal validation: 99.2% of samples correctly classified for both virtual images; external validation: 84% with (R -B)/(R + B) and 81% with B/R). The camera classification was evaluated according to the colorimetric measurements, which were usually utilized to evalúate enzymatic browning development (CIÉ L'a'b* color parameters and browning Índex, BI) and according to ¡SE-For both validation phases a*, b*, BI and ¡SE increased while I* valúes decreased with image-based class number, thereby reflecting their browning state.

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“…Subsequently, the roes were drained in sieve and then separated by color according to the hexadecimal code and RGB model for color (Quevedo, Aguilera, & Pedreschi, 2010). Generally the gonads of sea urchin are divided into three color categories: yellow or bright orange, represents intact and firm texture or otherwise less intense yellow, indicates intact and firm texture also, and brown color, may be ruptured and features smooth and creamy texture (Stamatis & Vafidis, 2009).…”

Section: Methodsmentioning

confidence: 99%

<b>Rock-boring urchin roe from Brazilian subtropical coastal zone for human consumption

et al. 2017

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ABSTRACT. The portion of food interest of sea urchin is located in the gonads, a dish increasingly appreciated in the international cuisine. The aims of this study were evaluate the process of fresh roes from rock-boring urchin sea Echinometra lucunter for human consumption, check the acceptance and preference of the product, and consumer purchase intent. Studies on processing of roes from E. lucunter are nonexistent, thus, this study is a pioneer in Brazil. The roes were processed following the steps: wash in a solution of potassium alum, drainage, separation by color, packaging and storage under refrigeration (0-4°C). Following, batches of the roes were characterized by physical, chemical and microbiological analyses, and subjected to sensory analysis to check acceptance and preference of potential consumers. The methodology of potassium alum used was efficient for E. lucunter roes and the color attribute was more relevant in the acceptance, preference and purchase intent from processed fresh roes. The extraction process might be improved in order to minimize rupture of the gonads. For managers of fisheries and sea food quality we recommend the implementation of public policies that result in the sustainability of seaurchin species and food safety for the consumers.Keywords: Echinometra lucunter, adding value, nama uni, purchase intent.Ovas de ouriço-do-mar de zona costeira subtropical brasileira para consumo humano RESUMO. A porção de interesse alimentar do ouriço-do-mar se concentra nas gônadas, alimento cada vez mais apreciado pela cozinha internacional. Os objetivos deste estudo foram avaliar o processamento de ovas frescas do ouriço Echinometra lucunter para consumo humano, verificar a aceitação e a preferência desse produto, bem como a intenção de compra pelo consumidor. Estudos sobre o processamento das ovas de E. lucunter são inexistentes; assim, este estudo é pioneiro no Brasil. As ovas foram processadas, seguindo as etapas: lavagem em solução de alúmen de potássio, drenagem, separação por cor, embalagem e armazenamento sob refrigeração (0 a 4ºC). Posteriormente, lotes do produto foram caracterizados por análises físicas, químicas e microbiológicas, e submetidos à análise sensorial para verificação da aceitação e da preferência pelos potenciais consumidores. A metodologia com alúmen de potássio utilizada foi eficiente para ovas do E. lucunter, e o atributo cor foi mais relevante na aceitação, na preferência e na intenção de compra de ovas frescas processadas. O processo de extração pode ser melhorado, a fim de minimizar ruptura das gônadas. Para os gestores pesqueiros e da qualidade de frutos do mar, recomendamos a implementação de políticas públicas que resultem na sustentabilidade das espécies de ouriço-do-mar e segurança alimentar para o consumidor.Palavras-chave: Echinometra lucunter, agregação de valor, nama uni, intenção de compra.

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Color of Salmon Fillets By Computer Vision and Sensory Panel

Cited by 147 publications

References 30 publications

Characterisation of liquid food colour from digital images

Characterisation of liquid food colour from digital images

A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

<b>Rock-boring urchin roe from Brazilian subtropical coastal zone for human consumption

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