Determination of acceptability and shelf life of ready-to-use lettuce by digital image analysis

Zhou, Ting; Harrison, A. D.; McKellar, R.C.; Young, J. Christopher; Odumeru, Joseph A.; Piyasena, P.; Lu, Xuewen; Mercer, Donald G.; Karr, S.

doi:10.1016/j.foodres.2004.05.005

Cited by 66 publications

(47 citation statements)

References 14 publications

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“…The color model most often used is the RGB model in which an imaging sensor captures the light intensity of the red (R), green (G), and blue (B) components, respectively. RGB, HSV (hue, saturation, and value), and CIELab are the most popular space color models used in food computer vision (Dana and Ivo 2008;Du and Sun 2005;Kang et al 2008;Leemans et al 1998;Leon et al 2006;Magdic and Dobricevic 2007;Mendoza et al 2006;Misimi et al 2007;O'Sullivan et al 2003;Sun 2004;Zhang et al 2003;Zhou et al 2004), among others. The RGB color model is used for computer color representation.…”

Section: Methodsmentioning

confidence: 99%

Color of Salmon Fillets By Computer Vision and Sensory Panel

Quevedo

Aguilera

Pedreschi

2008

Food Bioprocess Technol

148

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A computer vision method was developed and used to assign color score in salmon fillet according to SalmonFan™ card. The methodology was based on the transformation of RGB to L*a*b* color space. In the algorithm, RGB values assigned directly to each pixel by the camera in the salmon fillet image, were transformed to L*a*b* values, and then matched with other L*a*b* values that represent a SalmonFan score (between 20 and 34). Colors were measured by a computer vision system (CVS) and a sensorial panel (eight panelists) under the same illumination conditions in ten independent sets of experiments. Errors from transformation of RGB to L*a*b* values by the CVS were 2.7%, 1%, and 1.7%, respectively, with a general error range of 1.83%. The coefficient of correlation between the SalmonFan score assigned by computer vision and the sensory panel was 0.95. Statistical analysis using t test was performed and showed that there were no differences in the measurements of the SalmonFan score between both methods (t c =1.65 ≤ t=1.96 at α= 0.05%). The methodology presented in this paper is very versatile and can potentially be used by computer-based vision systems in order to qualify salmon fillets based on color according to the SalmonFan card.

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Section: Methodsmentioning

confidence: 99%

Color of Salmon Fillets By Computer Vision and Sensory Panel

Quevedo

Aguilera

Pedreschi

2008

Food Bioprocess Technol

148

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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%

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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“…This demand has changed the status of foodborne diseases worldwide and had an important economic and social impact . In recent years, minimally processed vegetables (MPV) gained significant acceptance by consumers, which could be attributed to an increased consumer desire for fresh vegetables with reduced preparation times (Odumeru et al, 2002;Zhou et al, 2004;Millan-Sango, McElhatton & Valdramidis, 2015). Prior to their sale, MPV are submitted to simple operations such as washing, peeling, slicing, shredding, sanitization, rinsing, drying and packaging to extend their shelf life and preserve their nutritive value while retaining the characteristics of fresh food (FDA, 2008).…”

Section: Introductionmentioning

confidence: 99%