The rapid detection of the grade and storage life of eggs is important for consumers and producers. In this study, an electrochemical system based on voltammetry was used for the rapid detection of egg yolk and egg white mixtures for classification and prediction of storage days. Measuring the Haugh units (H.U.) index of Lindian eggs is aimed at identifying the grades of these eggs and the corresponding storage days. Discriminant function analysis was used to identify accurately the grades of eggs on the basic of storage life. The Gaussian kernel function and a suitable penalty factor were selected to establish a support vector machine for predicting the storage days of eggs. By avoiding deviation in the result due to the correlation of data, which was acquired by square-wave voltammetry technology, the partial least squares and support vector machine were combined, and the predictive accuracy of egg storage days was improved by up to 97.14%. A tenfold cross-validation has been used to evaluate the classification performance of the model; the average accuracy of prediction of storage life was 88.57%.