  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
($23\pm1$\deg 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.
