Effect of Methyl Bromide on the Internal Browning, Firmness and Elasticity of Flesh in Un-bagged Apple 'Fuji' Fruit.

敬明, 長内; 佳恵, 元村; 直樹, 桜井

doi:10.3136/nskkk.50.254

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Enzymatic browning has been quantified using browning indicators through a biochemical index (Lunadei et al 2011). For example, using polyphenol oxidase activity (Hosoda et al 2005;Osanai et al 2003) or physical indicators such as surface colour have been used (Kang et al 2004;Lambrecht 1995).…”

Section: Browning Indexmentioning

confidence: 99%

Colour Measurement and Analysis in Fresh and Processed Foods: A Review

Pathare

Opara

Al‐Said

2012

Food Bioprocess Technol

1,529

899

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Colour is an important quality attribute in the food and bioprocess industries, and it influences consumer's choice and preferences. Food colour is governed by the chemical, biochemical, microbial and physical changes which occur during growth, maturation, postharvest handling and processing. Colour measurement of food products has been used as an indirect measure of other quality attributes such as flavour and contents of pigments because it is simpler, faster and correlates well with other physicochemical properties. This review discusses the techniques and procedures for the measurement and analysis of colour in food and other biomaterial materials. It focuses on the instrumental (objective) and visual (subjective) measurements for quantifying colour attributes and highlights the range of primary and derived objective colour indices used to characterise the maturity and quality of a wide range of food products and beverages. Different approaches applied to model food colour are described, including reaction mechanisms, response surface methodology and others based on probabilistic and non-isothermal kinetics. Colour is one of the most widely measured product quality attributes in postharvest handling and in the food processing research and industry. Apart from differences in instrumentation, colour measurements are often reported based on different colour indices even for the same product, making it difficult to compare results in the literature. There is a need for standardisation to improve the traceability and transferability of measurements. The correlation between colour and other sensory quality attributes is well established, but future prospects exist in the application of objective non-destructive colour measurement in predictive modelling of the nutritional quality of fresh and processed food products.

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Section: Browning Indexmentioning

confidence: 99%

Colour Measurement and Analysis in Fresh and Processed Foods: A Review

Pathare

Opara

Al‐Said

2012

Food Bioprocess Technol

1,529

899

View full text Add to dashboard Cite

show abstract

“…Traditionally, enzymatic browning has been quantified using browning indicators through a biochemical index, for example using polyphenol oxidase activity (Hosoda et al, 2005;Murata, 2001;Osanai et al, 2003;Sannomaru et al, 1998;Sharon and Kahn, 1979), or physical indicators, such as color surface (Elshimi, 1993;Kang et al, 2004;Lambrecht, 1995;Lozano de Gonzalez et al, 1993;Lozano et al, 1994;Lu et al, 2007;Luo and Barbosa, 1997;Shengmin et al, 2007). In the case of physical indicators based on color, Lab space or CIELab has been the most extensively used color model; especially the L value, which has been used as a browning indicator in fruits (Luo and Barbosa, 1994, 1997Luo and Patterson, 1994;Pristijono et al, 2006;Sapers and Ziolkowski, 1987;Severini et al, 2003;Soliva-Fortuny et al, 2002;Valentines et al, 2005).…”

Section: Introductionmentioning

confidence: 98%

Quantification of enzymatic browning in apple slices applying the fractal texture Fourier image

Quevedo

Jaramillo

Díaz

et al. 2009

Journal of Food Engineering

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“…For example, using polyphenol oxidase activity (Osanai et al, 2003;Hosoda et al, 2005), or physical indicators such as surface color have been used (Lambrecht, 1995;Kang et al, 2004). In the case of physical indicators based on color, CIÉ L*a*b* color space has been the most extensively used color model due to the uniform distribution of colors and because it is very cióse to the human perception of color (Yam and Papadakis, 2004).…”

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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Effect of Methyl Bromide on the Internal Browning, Firmness and Elasticity of Flesh in Un-bagged Apple 'Fuji' Fruit.

Cited by 10 publications

References 9 publications

Colour Measurement and Analysis in Fresh and Processed Foods: A Review

Colour Measurement and Analysis in Fresh and Processed Foods: A Review

Quantification of enzymatic browning in apple slices applying the fractal texture Fourier image

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

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