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• W4383065572 endingPage "123075" @default.
• W4383065572 startingPage "123075" @default.
• W4383065572 abstract "Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation." @default.
• W4383065572 created "2023-07-05" @default.
• W4383065572 creator A5019560977 @default.
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• W4383065572 date "2023-12-01" @default.
• W4383065572 modified "2023-09-27" @default.
• W4383065572 title "Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction" @default.
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• W4383065572 doi "https://doi.org/10.1016/j.saa.2023.123075" @default.
• W4383065572 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37423101" @default.
• W4383065572 hasPublicationYear "2023" @default.
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