Two-dimensional correlation spectral analysis of blueberries based on visible/near-infrared spectroscopy was performed for the prediction of the comprehensive storage quality of blueberries. In this study, visible/near-infrared spectra of blueberries were collected for 10 storage periods, and four physicochemical indexes, including hardness, mass loss rate, vitamin C, and soluble solids, were integrated and their combined scores were calculated by linear orthogonal transformation to classify the storage quality levels. The spectral data were subjected to single and combined Savitzky-Golay convolutional smoothing (SG), standard normal transform (SNV), multiple scattering correction (MSC), and iterative adaptive weighted penalized least squares (airPLS) preprocessing, and after comparative analysis, the model established after SG preprocessing had the best prediction accuracy, with 94.59% prediction results. Based on this pretreatment, a two-dimensional correlation spectral analysis of blueberries was performed, and the wavelengths corresponding to the four self-phase peaks were preferentially selected as the characteristic wavelengths using the integrated score of blueberries as the external disturbance, and then the prediction model of integrated storage quality of blueberries was established using support vector machine (SVM). Results showed that the established model obtained superior prediction results, with prediction accuracy of 100%, 93.75%, and 100% for the three classes, respectively. Therefore, the use of two-dimensional correlation spectral analysis technology can achieve accurate prediction of comprehensive storage quality of blueberries and provide new ideas for quality detection of blueberries.
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