Development of a computer vision system to measure the color of potato chips

Pedreschi, Franco; León, Jorge; Mery, Domingo; Moyano, Pedro

doi:10.1016/j.foodres.2006.03.009

Cited by 220 publications

(139 citation statements)

References 17 publications

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“…Purlis and Salvadori (2007) studied the relationship between the color of the surface of bread, weight loss of bread, and the oven temperature during baking in order to show that the development of bread surface browning can be predicted. Pedreschi et al (2006) investigated the relationship between the color of potato chips and the temperature of the oil used in deep frying. The study reported a quick color change when the potato chips were fried at a high temperature.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Color Change in Fish during the Grilling Process

Nakamura

Mao

Fukuoka

et al. 2011

FSTR

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Section: Introductionmentioning

confidence: 99%

Analysis of the Color Change in Fish during the Grilling Process

Nakamura

Mao

Fukuoka

et al. 2011

FSTR

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“…The internal walls of the CVS were painted black to avoid light leakage and exterior reflections of the room in a method similar to that realized by Pedreschi et al Pedreschi et al (2006).…”

Section: Computer Vision System For Classifying Cape Gooseberriesmentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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“…Food color usually is the first quality parameter evaluated by consumers and is critical in the acceptance of the products. In addition, color measurement is an objective parameter for the evaluation of quality changes during herb processing, and storage [3].…”

Section: Introductionmentioning

confidence: 99%

Effect of Moisture Sorption Behavior on Color Quality of Cassia Alata Leaves

2017

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Abstract.The Cassia alata leaf is widely used for the medical uses as it is beneficial for treatment of skin diseases such as purities, eczema and allergy. The moisture sorption behavior included desorption and adsorption of C. alata leaves were investigated using standard static gravimetric method. The sorption isotherm was obtained at ambient temperature 24 ˚C and with the equilibrium relative humidity (ERH) range from 11.3 to 84.3 %. The equilibrium moisture content (EMC) of C. alata increased from 8.67 to 28.50 g water/dry matter with increasing ERH during desorption. It is similar to adsorption which the EMC increased from 21.28 to 32.85 g water/dry matter. The color quality of C. alata leaves were lower with increasing the ERH.

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Development of a computer vision system to measure the color of potato chips

Cited by 220 publications

References 17 publications

Analysis of the Color Change in Fish during the Grilling Process

Analysis of the Color Change in Fish during the Grilling Process

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Effect of Moisture Sorption Behavior on Color Quality of Cassia Alata Leaves

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