An Adaptive Partial Least Square Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging

Abstract

Partial least square (PLS) regression is a well-known chemometric method used for predictive
 modelling, especially in the presence of many variables. Although PLS was not initially developed
 as a technique for classification tasks, scientists have reportedly used this approach successfully
 for discrimination purposes. Whereas some non-supervised learning approaches including but not
 limited to PCA, and k-means clustering do well in identifying/understanding grouping and
 clustering patterns in multidimensional data, they are limited when the end target is discrimination,
 making PLS a preferable alternative. A total of fertilized 672 chicken egg hyperspectral imaging
 data, consisting of 336 white eggs and 336 brown eggs were used in this study. Hyperspectral
 images in the NIR region of 900-1700 nm wavelength range were captured prior to incubation on
 day 0 and on days 1-4 after incubation. Eggs were candled on incubation day 5 and broken out on
 day 10 to confirm fertility. While a total number of 312 and 314 eggs were found to be fertile in
 the brown and white egg batches respectively, total numbers of non-fertile eggs in the same set of
 batches were 23 and 21 respectively. Spectral information was extracted from a segmented region
 of interest (ROI) of each hyperspectral image and spectral transmission characteristics were
 obtained by averaging the spectral information. A moving-thresholding technique was
 implemented for discrimination based on PLS regression results on the calibration set. With true
 positive rates (TPR) of up to 100% obtained at selected threshold values of between 0.50-0.85 and
 on different days of incubation, the results indicated that the proposed PLS technique can
 accurately discriminate between fertile and non-fertile eggs. The adaptive PLS approach was
 thereby presented as suitable for handling hyperspectral imaging-based chicken egg fertility data