Non-destructive identification of native egg by near-infrared spectroscopy and data driven-based class-modeling

Eggs are very important parts of human diets worldwide. It is very common to pass feed eggs off as native ones of high commercial values in Chinese markets. One urgent and challenging work is to develop a non-destructive method for verifying the authenticity of native eggs. The present work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy with data driven-based class-modeling (DDCM) and model-independent variable selection, i.e., joint mutual information (JMI). A total of 122 eggs of three types were collected. Principal component analysis (PCA) was utilized for exploratory analysis. The JMI algorithm selected only 20 informative variables out of 1557 original variables for class-modeling. DDCM constructed a class-model for each kind of eggs by optimizing parameters such as degrees of freedom (DoF) and the number of principal components (NPC). All class-models and the decision rules were validated on the corresponding test sets. In short, these models achieved an acceptable performance and are also more consistent with actual needs than classification models. The results show that NIR spectroscopy combined with class-modeling is a potential tool for detecting the authenticity of a specific kind of native eggs.