Non-destructive quality assessment of hens' eggs using hyperspectral images

Freshness of hens' eggs is important for consumers and the food processing industry and the Haugh unit (HU) is a commonly used index for freshness. Measurement of HU is destructive and also assumes that the sample that is tested accurately reflects the batch of eggs being processed. This research tests the use reflectance near infrared hyperspectral imaging in the wavelength range of 900-1700 nm for nondestructive prediction of eggs freshness and compared these measurements to HU. To achieve this fresh eggs were stored at 25 °C and were measured after storage for 0, 4, 7, 10, 14, 18 and 21 days by hyperspectral imaging technique and compared to HU for each egg. Hyperspectral imaging technique combines between conventional imaging and NIR spectroscopy to achieve spatial and spectral information from eggs. The acquired near infrared hyperspectral imaging data from samples in the calibration set were analyzed in order to develop a calibration model for HU using partial least squares regression (PLSR) and then crossvalidated. The standard normal variate transformation (SNV) spectral pretreatment gave the optimum conditions for establishing the calibration model with a coefficient of determination (R2) of 0.91 and root mean square error of calibration (RMSEC) of 4.58. Distribution maps of HU were generated from the acquired calibration model by interpretation of predicted HU to different colors using image processing algorithms. Displayed colors of acquired image of eggs were different correspond to the freshness of the eggs based on HU. The results show that the near infrared hyperspectral imaging technique can be possible to use for presenting the images of egg related to HU in order to nondestructively evaluate hens' eggs freshness.