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• W4211109331 endingPage "104517" @default.
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• W4211109331 abstract "Fruit firmness is a complex trait that develops throughout fruit development, including post-harvest, and is influenced by both ripening and dehydration. There is a wide interest in predicting the firmness with non-destructive sensing techniques such as spectral analyses. However, often used reference techniques, such as acoustic firmness (AF), limited compression (LC) and Magness-Tyler (MT), respond differently to dehydration and ripening. This study aims to detangle how the firmness of ‘Conference’ pears relates to dehydration and ripening and to model ripening-related firmness using non-destructive sensing. Hereto, a pear fruit matrix was created with varying firmness and dehydration levels. To model fruit firmness (LC and MT) with Vis-NIR spectroscopy and explore whether AF information could complement Vis-NIR spectroscopy, a sequential multi-block analysis was performed. Single block Vis-NIR spectral data were made multi-block by partitioning the variance in spectral data into acoustic-dependent and -independent parts. A variation partitioning based approach was also presented to select the best pre-processing operation for Vis-NIR spectral data modelling. Multi-block regression to predict firmness and classification modelling of pear fruit in different firmness classes was also practised. The obtained results led to enhanced insights into the different fruit firmness measures and the capability of Vis-NIR and acoustic for non-destructive fruit firmness prediction. The results can benefit the scientific community working in the domain of fruit optical spectroscopy and chemometric modelling." @default.
• W4211109331 created "2022-02-13" @default.
• W4211109331 creator A5005783876 @default.
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• W4211109331 date "2022-03-01" @default.
• W4211109331 modified "2023-10-16" @default.
• W4211109331 title "Improved understanding and prediction of pear fruit firmness with variation partitioning and sequential multi-block modelling" @default.
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• W4211109331 doi "https://doi.org/10.1016/j.chemolab.2022.104517" @default.
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