Computer Vision Classification of Potato Chips by Color

Abstract

In this research the automatic classification of commercial potato chips by computer vision was studied. The general objective was to design a tool that would be able to classify objectively potato chips according to their color in different categories. For this purpose, sensory measurements of color in 100 potato chips were correlated with the corresponding objective measurements obtained by computer vision system. Potato chips with and without ruffles of different brands were used for training and validation experiments. Sensory evaluations were done with a special chart that classifies potato chips in seven color categories. Simultaneously, the color of the same potato chips classified by the sensory panel, was determined objectively by a computer vision system in L*, a*, b* units. A linear regression model was good enough to predict potato chip sensory color values from the corresponding instrumental measurements by computer vision. The linear model after following the process of crossed validation crossed presented an error of ∼4% for smooth chips (without ruffles) and ∼7% for chips with ruffles. PRACTICAL APPLICATIONS The automatic classification methodology presented for potato chips is general and has a wide range of potential uses. It could be applied not only to other potato cultivars and frying conditions but also to other less heterogeneous raw materials and unit operations different than potato and frying, respectively. The computer vision system used in this research could as well be very useful in the food industry as a large amount of information can now be obtained from measurements at the pixel level that allows a better characterization of foods and thus improves quality control.

Artificial Neural Network-Based Image Analysis for Evaluation of Quality Attributes of Agricultural Produce

“…Pedreschi et al . () also observed that sensory assessors of potato chips were highly correlated with the color determined objective in L *, a * and b * units by a CV system; similar studies were also conducted by Mendoza and Aguilera in for classification of ripening stages of banana by image analysis.…”

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

“…Scanlon et al (1994), Marique et al (2003), and Romani et al (2009), applied such techniques with r values of 0.94, 0.90, and 0.98, respectively. Surface shape of chips was proven to reduce such correlation as shown by Pedreschi et al (2010a), in which the r values were 0.97 and 0.82 for smooth and undulated chips respectively. Mendoza et al (2007), found that texture-based features (energy, entropy, contrast, and homogeneity) yielded better classification rates (90%) than using color-based features.…”

Assessment of peach internal flesh browning through colorimetric measures

“…The judges were trained during two 2.5-hour sessions, aimed to improve their visual acuity and to become more familiar with specific IFB symptoms. In the first session, a ranking test to evaluate the assessors' visual acuity was performed using a series of diluted tartrazine (0.010%) solutions: the color standards were 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 90%, and 100% (Pedreschi et al, 2011). The panel validation was performed via the Page test (Page, 1963).…”

“…Additionally, L* has been used to measure the flesh browning of freshly cut apple and peach purees using a Hunter D-25 colorimeter (Lee et al, 1990). The CIELAB space provides parameters that are highly correlated with human perception of food color (Pedreschi et al, 2011;León et al, 2006). The CIELAB space has been used as an indicator of the enzymatic flesh browning of fresh peach subjected to minimal processing, which showed high correlations with sensory evaluations (Gonzalez-Buesa et al, 2011).…”