Nondestructive detection for egg freshness grade based on hyperspectral imaging technology

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.

Spectroscopic approaches for non-destructive shell egg quality and freshness evaluation: Opportunities and challenges

“…4) as a rapid online system for the egg classification based on freshness. Yao et al (2020) proposed a solution implementing VIS-NIR-HSI for egg classification based on HU. They measured 188 eggs at three freshness grades, acquiring images in the 400-1000 nm spectral range and using a Region of Interest (ROI) of 32 × 32 pixel from the centre of the samples.…”

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

“…SVM was a machine learning algorithm based on the principle of structural risk minimization, and which performed well in nonlinear classification problems with small‐scale samples (Sun et al, 2019). Therefore, SVM was attempted to classify five kinds of rice seeds in this article, and kernel function of SVM was configured to radial basis function (RBF) because of its excellent stability in many SVM classification studies (Yao et al, 2020).…”

Nondestructive detection for Panax notoginseng powder grades based on hyperspectral imaging technology combined with CARS‐PCA and MPA‐LSSVM

“…HSI is a multi‐information fusion technology integrating image and spectrum, which has been gradually used in the quality detection of agricultural products. Yao et al (2020) used HSI technology to detect freshness grades of eggs, with an accuracy rate of 97.89%. Sun, Zhang, Mao, Cong, and Wu (2017) utilized HSI technology combined with the softmax model to identify moldy tea, with an accuracy rate of 98.5%.…”