Nondestructive detection of egg freshness based on a decision-level fusion method using hyperspectral imaging technology

Egg freshness is essential for evaluating the internal quality of eggs. Here, we propose a method based on feature fusion to improve the accuracy of freshness classification. First, hyperspectral reflectance images of 264 egg samples of three freshness grades were acquired by a hyperspectral image a...

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Bibliographic Details
Published in:	Journal of food measurement & characterization Vol. 18; no. 6; pp. 4334 - 4345
Main Authors:	Liu, Yeqiong, Jin, Shangzhong, Alimu, Abuduaini, Jiang, Li, Jin, Huaizhou
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-06-2024 Springer Nature B.V
Subjects:	Accuracy Chemistry Chemistry and Materials Science Chemistry/Food Science Classification Engineering Ensemble learning Food Science Freshness Hyperspectral imaging Image acquisition Image processing Nondestructive testing Original Paper Principal components analysis Reflectance Texture Decision-level fusion Spectral features Egg freshness Texture features Stacking ensemble learning Reflectance hyperspectral data
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Summary:	Egg freshness is essential for evaluating the internal quality of eggs. Here, we propose a method based on feature fusion to improve the accuracy of freshness classification. First, hyperspectral reflectance images of 264 egg samples of three freshness grades were acquired by a hyperspectral image acquisition system. Spectral features were extracted from the region of interest of the reflectance hyperspectral images in the range of 440.51-950.24 nm. Second, after pre-processing and characteristic bands selection, the characteristic images were extracted from the images corresponding to the characteristic bands using principal component analysis; then the texture features of each egg sample were obtained from the characteristic images by gray-level co-occurrence matrices. Third, a variety of freshness discrimination models based on spectral and texture features were established, and the models based on the single feature had a maximum accuracy of 89.39%. Finally, well-performing models based on a single feature were merged into a robust model by the stacking ensemble learning method to realize decision-level fusion of the two features, and the highest accuracy of the prediction set was increased to 92.42%. Thus, the feature fusion method based on decision level is feasible for egg freshness classification.
ISSN:	2193-4126 2193-4134
DOI:	10.1007/s11694-024-02497-8