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Nondestructive detection for egg freshness grade based on hyperspectral imaging technology
Author(s) -
Yao Kunshan,
Sun Jun,
Zhou Xin,
Nirere Adria,
Tian Yan,
Wu Xiaohong
Publication year - 2020
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.13422
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , artificial intelligence , support vector machine , mahalanobis distance , imaging spectrometer , computer science , feature selection , mathematics , spectrometer , physics , quantum mechanics
Abstract In order to identify the freshness grade of eggs nondestructively and rapidly, hyperspectral imaging technology was used in this article. The hyperspectral data of 200 samples of three freshness grades was acquired by using hyperspectral image acquisition system (400.68–1,001.612 nm), and then the freshness grade of egg samples was measured by stoichiometry. First, Mahalanobis distance algorithm was used to remove abnormal sample data. Second, savitzky–golay and wavelet threshold denoising combined with standard normalized variable were used to pretreat the spectral data, respectively. Third, iteratively retains informative variables (IRIV), variable iterative space shrinkage approach, and competitive adaptive reweighted sampling were used for feature wavelength selection. Since the classification accuracy of support vector machine (SVM) model was affected by the selection of parameters, genetic algorithm (GA) was introduced to search the optimal parameters in SVM and compared with grid search algorithm. Finally, the result indicated that the classification accuracy of training set and test set of the optimal classification model (IRIV‐GA‐SVM) reached 99.29% and 97.87%, respectively. Thus, it is feasible to use hyperspectral image technology to detect egg freshness grade. Practical Applications The freshness grade is one of the most important indexes to measure the quality of eggs. Traditional methods of detecting egg freshness grade are time‐consuming and destructive, which cannot meet the test needs of modern agriculture. Hyperspectral imaging, an emerging technology, can provide both spectral and spatial information simultaneously, and has the advantages of nondestructive, fast and nonpollution. The result indicated that hyperspectral imaging technology for the detection of freshness grade of eggs is feasible.