学术文献库

Nowadays, consumers are more concerned about freshness and quality of food. Poultry egg storage time is a freshness and quality indicator in industrial and consumer applications, even though egg marking is not always required outside the European Union. Other authors have already published works using expensive laboratory equipment in order to determine the storage time and freshness in eggs. Oppositely, this paper presents a novel method based on low-cost devices for rapid and non-destructive prediction of egg storage time at room temperature ($23\pm1$°C). H&N brown flock with 49-week-old hens were used as source for the sampled eggs. Those samples were daily scanned with a low-cost smartphone-connected near infrared reflectance (NIR) spectrometer for a period of 22 days starting to run from the egg laid. The resulting dataset of 660 samples was randomly splitted according to a 10-fold cross validation in order to be used in a contrast and optimization process of two machine learning algorithms. During the optimization, several models were tested to develop a robust calibration model. The best model use a Savitzky Golay preprocessing technique, and an Artificial Neural Network with ten neurons in one hidden layer. Regressing the storage time of the eggs, tests achieved a coefficient of determination (R-squared) of $0.8319\pm0.0377$ and a root mean squared error (RMSE) of 1.97. Although further work is needed, this technique has shown industrial potential and consumer utility to determine the egg's freshness by using a low-cost spectrometer connected to a smartphone.