Abstract: Preserved duck eggs are made from fresh duck eggs. The protein was denatured in the alkali liquor, and the preserved egg was divided into 3 grades according to the quality of the gel. The first grade eggs are the preserved duck egg that are solidified in a gel state and belong to the high quality eggs. The second grade eggs are the slight alkali-damaged. After peeling, there are cases of sticky shell, rotten head, sallow, etc. But they can still be eaten, which are the inferior eggs. The third grade eggs are the water-sounding egg. They are liquefied into water in eggs. They cannot be eaten, which are bad eggs. Over the years, the classification of preserved duck eggs in the egg industry is entirely dependent on manual work, which is cumbersome and inefficient, and the market urgently needs relevant detection technology.The quality classification of preserved duck egg gel was studied based on machine vision and near-infrared spectroscopy. It was found that the use of machine vision and near-infrared spectroscopy alone could not accurately classify preserved duck eggs, the grading accuracy was 75% and 78.33%, respectively. But the combination of the two technology could achieve the classification of preserved duck eggs. Firstly, the transmitted light images of preserved duck eggs were collected by using industrial camera. The MATLAB was used to extract 18 image color feature values in RGB、HSV and CIELab or Lab color eigenvalues. Then the extracted 18-dimensional features were reduced by principal component analysis (PCA). The 601 samples were randomly divided into 433 training sets and 168 test sets. And the genetic algorithm-support vector machine (GA-SVM) classification model was built for the three principal components of the PCA. The preserved duck egg samples were divided into edible eggs (high quality eggs and inferior eggs) and inedible eggs (bad eggs). The 60 inedible eggs (bad eggs) in the test set were all judged correctly. The 103 of the 108 edible eggs (high quality eggs and inferior eggs) were judged correctly and 5 were misjudged. Test set recognition rate of bad eggs was 100%. Then the near-infrared spectroscopy technique was used to obtain the original spectrum of edible eggs (high quality eggs and inferior eggs). Multiplicative scatter correction (MSC) was performed, and the characteristic wavelength was extracted by using competitive adaptive re-weighted sampling (CARS) which extracted 75 data in the range of 4 420-51 52 cm-1 and 5 538-7 042 cm-1 characteristic bands. The 401 samples were randomly divided into 293 training sets and 108 test sets. Based on the support vector machine (SVM), a hierarchical model was established for the characteristic wavelength variable, and edible high quality eggs and inferior eggs were separated. The test set selected 108 edible eggs, including 57 high quality eggs and 51 inferior eggs. 54 high quality eggs and 49 inferior eggs were judged correctly. The recognition rate of high quality egg test set was 96.49%, and that of inferior egg was 94.12%. The results showed that it was feasible to perform non-destructive classification of preserved duck egg based on machine vision and near-infrared spectroscopy. In practical applications, machine vision technology can be used to separate the inferior eggs, and then the near-infrared spectroscopy technique is used to separate the high-quality eggs from the defective eggs. The result provides a reference for realizing the online non-destructive testing of preserved duck eggs.