Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network

• Published in: Journal of food measurement & characterization Vol. 12; no. 3; pp. 1449 - 1459
• Main Authors: Nematinia, Elham, Abdanan Mehdizadeh, Saman
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2018; Springer Nature B.V
• Subjects: Albumen; Algorithms; Artificial neural networks; Bayesian analysis; Chemical properties; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Computer vision; Correlation coefficient; Correlation coefficients; egg weight; Eggs; Engineering; Food Science; Freshness; image analysis; Neural networks; Original Paper; Parameters; pH effects; prediction; Protein sources; storage time; Vision systems; Yolk; Computer vision; Freshness; Artificial neural network; Egg; Haugh unit
• ISSN: 2193-4126; 2193-4134
• DOI: 10.1007/s11694-018-9760-1

Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25 ± 2 °C and 20 ± 3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables ( p  < 0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively.