Pizza sauce spread classification using colour vision and support vector machines

Du, Cheng-Jin; Sun, Da‐Wen

doi:10.1016/j.jfoodeng.2004.03.011

Cited by 129 publications

(33 citation statements)

References 21 publications

(17 reference statements)

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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

145

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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

145

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“…There have been many studies conducted with digital image processing technology on food quality control and evaluation (Teena et al 2013;Jackman et al 2009;Du and Sun 2005b). In terms of rice quality evaluation (Camelo-Méndez et al 2012;Shimizu et al 2008), the studies have been mainly conducted on evaluation of rice appearance quality (Camelo-Méndez et al 2012), the ratio of broken rice (Van Dalen 2004), grain shape (Bjork et al 2009), rice varieties (Liu et al 2005) and chalkiness ratio and chalkiness degree.…”

Section: Introductionmentioning

confidence: 99%

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Wang

Liu

et al. 2014

Food Anal. Methods

Self Cite

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This paper investigated the use of hyperspectral imaging (HSI) to discriminate the variety and quality of rice. Hyperspectral images (400-1,000 nm) of paddy rice samples were acquired to extract both spectral and image information. Dimension reduction was carried out on the region of interest (ROI) of the images by principal component analysis (PCA). The first principal components (PCs) explained over 98 % of variances of all spectral bands. Chalkiness degree and shape feature ('MajorAxisLength', 'MinorAxisLength', lengthwidth ratio, 'Perimeter' and 'Eccentricity') were further extracted and used for subsequent rice variety discrimination by PCA and back propagation neural network (BPNN). An integration of spectral and image data was used for BPNN classification. The BPNN model based on spectral data (seven optimal wavelengths) achieved better results than PCA based on spectral data (seven optimal wavelengths) in variety discrimination with classification accuracy of 89.18 and 89.91 % for PCA and BPNN model, respectively. The BPNN model based on data fusion achieved the best results (94.45 %), which was superior to the results based on spectral data (89.91 %) or image data (88.09 %) alone. Finally, the resulting classification maps were able to visualize different rice varieties. The results demonstrated that discrimination of rice variety and quality with HSI technology was feasible and could be utilized for quality control purposes and/or for innovative sorting of rice.

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“…Since colour is a critical quality parameter in food purchases, colour measurement has gained much attention from food scientists and industry (Quevedo et al 2008;Du and Sun 2005). To investigate colour quality in a systematic way, it is necessary to objectively measure colour as well as pigment concentration (Ngo et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Stability and Degradation Kinetics of Bioactive Compounds and Colour in Strawberry Jam during Storage

Patras

Brunton

Tiwari

et al. 2009

Food Bioprocess Technol

162

109

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The effect of storage time and temperature on degradation of bioactive compounds such as ascorbic acid, anthocyanins, total phenols, colour and total antioxidant capacity of strawberry jam were investigated. The results indicated that lightness (L) value decreased significantly (p< 0.05) over 28 days of storage at 4 and 15°C, with lower values measured at higher temperatures. Anthocyanins, ascorbic acid and colour degradation followed first-order kinetics where the rate constant increased with an increase in the temperature. The reaction rate constant (k) increased from 0.95×10 −2 day −1 to 1.71×10 −2 day −1 at 4 and 15°C for anthocyanins. Similarly, k increased from 2.08×10 −2 day −1 to 4.54×10 −2 day −1 at 4 and 15°C for ascorbic acid. In general, total antioxidant activity for strawberry jam samples stored for 28 days at 4 and 15°C exhibited lower values as compared to control (day 0). The results showed greater stability of nutritional parameters at 4°C compared to 15°C.

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Pizza sauce spread classification using colour vision and support vector machines

Cited by 129 publications

References 21 publications

Color of Salmon Fillets By Computer Vision and Sensory Panel

Color of Salmon Fillets By Computer Vision and Sensory Panel

Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice

Stability and Degradation Kinetics of Bioactive Compounds and Colour in Strawberry Jam during Storage

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