Abstract: Aiming at the deficiency of the traditional artificial sensory evaluation method, this paper established two rapid evaluation models of blueberry freshness by combining visible/near-infrared spectroscopy technology with a support vector machine (SVM) and random forest (RF). Turquoise blueberries stored at 10 ℃ for different days were detected by visible/near-infrared spectrometer. Six physical and chemical indexes including storage days, appearance and quality loss rate, hardness, soluble solid and vitamin C, were comprehensively considered to calculate the comprehensive score of freshness. The spectral data were preprocessed by S-G convolution smoothing, and then the spectral characteristic information was extracted by principal component analysis. To make the selection of optimal principal components more reasonable, a particle swarm optimization algorithm was used to optimize the parameters of SVM, and the number of principal components was tested within the range of [1,20]. Combined with the optimal classification accuracy value under the five-fold cross test, the optimal number of principal components was determined to be 5. The previous 5 principal component scores were input variables, and the freshness category was output quantity. Two kinds of rapid freshness evaluation models were established based on SVM and RF. The results showed that the recognition accuracy of the training set and a test set of the SVM model was 97.78% and 88% respectively, while for the RF model they were 100% and 84% respectively. These results showed that visible/near-infrared spectroscopy combined with principal component analysis and SVM could be used for the rapid evaluation of blueberry freshness.
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2022, Vol. 48 
