A feature dimensionality reduction strategy coupled with an electronic nose to identify the quality of egg

Abstract In this study, a feature dimensionality reduction strategy is proposed to reduce the feature dimensionality of the electronic nose (e‐nose) sensor, combined with support vector machine (SVM) to distinguish the gas information of eggs produced by chickens with different breeding methods. First, to characterize the overall properties of the original detection signal, five different time domain features are extracted from each sensor. Second, max‐relevance and min‐redundancy (MRMR) is introduced to obtain a preliminary optimal feature set. Finally, kernel principal component analysis (KPCA) is introduced to further eliminate the correlation between features and obtain the optimal feature set. The result shows that the optimal feature set is obtained by MRMR–KPCA, and good classification accuracy is obtained based on SVM. In conclusion, the feature dimensionality reduction strategy effectively reduces the feature dimensionality of the e‐nose sensor, eliminates the correlation between features, realizes the nondestructive detection for the quality of egg, and provides an effective technical method for the market quality supervision of egg. Practical applications In different breeding conditions, the nutritional value of eggs produced by chickens is different. To get more benefit, some inferior eggs are brought into the market instead of those with a higher nutritional value. Therefore, it is very important to use the nondestructive detection technology to quickly identify the quality of egg. In this work, e‐nose is used to obtain the gas information of eggs produced by chickens with different breeding methods. A feature dimensionality reduction strategy is proposed to process the e‐nose data, which realizes the effective identification of gas information of eggs. Moreover, it provides an effective detection method for the quality monitoring of the egg market.