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ABSTRACT
1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied.
2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky–Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output.
3. The SWR–CS–SVM model performed better than the other models, including SWR–GS–SVM, SWR–GA–SVM, SWR–PSO–SVM and others based on full spectral data. The training and test classification accuracy of the SWR–CS–SVM model were respectively 99.3% and 96%.
4. SWR–CS–SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.
Acknowledgement
The first author would like to express gratitude to all those who have helped during the writing of this paper and particularly Professor Sun and my family and friends who have consistently assisted and supported me.
Conflict of interest
No potential conflict of interest was reported by the authors.
Disclosure statement
No potential conflict of interest was reported by the authors.