In order to distinguish different varieties of Lycium barbarum effectively, a fast nondestructive detection method based on hyperspectral imaging technology was proposed. Six varieties of L. barbarum were selected as the research objects to obtain hyperspectral images. With the whole L. barbarum taken as the object, the region of interest was obtained by threshold segmentation, and the average spectra value of the image points of a single L. barbarum was extracted as the spectral data of the sample. Initially, standard normalized variate was used to preprocess the original spectral data. Furthermore, compared with other methods, competitive adaptive reweighted sampling (CARS) was chosen to extract the characteristic wavelengths. Additionally, the model of support vector machine (SVM) was set. The results showed that the SVM model based on CARS had the best classification effect. The training set accuracy was 100%, and the prediction set accuracy was 85%. Finally, in order to improve the classification accuracy, the whale optimization algorithm (WOA) was introduced. The accuracy of training set and prediction set obtained by WOA‐SVM model were 89.44 and 88.33% respectively. Therefore, it was feasible to use hyperspectral imaging technology combined with CARS‐WOA‐SVM model to identify different varieties of L. barbarum.
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.
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