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• W4310370781 abstract "Bitter pit (BP) is one of the most relevant post-harvest disorders for apple industry worldwide, which is often related to calcium (Ca) deficiency at the calyx end of the fruit. Its occurrence takes place along with an imbalance with other minerals, such as potassium (K). Although the K/Ca ratio is considered a valuable indicator of BP, a high variability in the levels of these elements occurs within the fruit, between fruits of the same plant, and between plants and orchards. Prediction systems based on the content of elements in fruit have a high variability because they are determined in samples composed of various fruits. With X-ray fluorescence (XRF) spectrometry, it is possible to characterize non-destructively the signal intensity for several mineral elements at a given position in individual fruit and thus, the complete signal of the mineral composition can be used to perform a predictive model to determine the incidence of bitter pit. Therefore, it was hypothesized that using a multivariate modeling approach, other elements beyond the K and Ca could be found that could improve the current clutter prediction capability. Two studies were carried out: on the first one an experiment was conducted to determine the K/Ca and the whole spectrum using XRF of a balanced sample of affected and non-affected 'Granny Smith' apples. On the second study apples of three cultivars ('Granny Smith', 'Brookfield' and 'Fuji'), were harvested from two commercial orchards to evaluate the use of XRF to predict BP. With data from the first study a multivariate classification system was trained (balanced database of healthy and BP fruit, consisting in 176 from each group) and then the model was applied on the second study to fruit from two orchards with a history of BP. Results show that when dimensionality reduction was performed on the XRF spectra (1.5 - 8 KeV) of 'Granny Smith' apples, comparing fruit with and without BP, along with K and Ca, four other elements (i.e., Cl, Si, P, and S) were found to be deterministic. However, the PCA revealed that the classification between samples (BP vs. non-BP fruit) was not possible by univariate analysis (individual elements or the K/Ca ratio).Therefore, a multivariate classification approach was applied, and the classification measures (sensitivity, specificity, and balanced precision) of the PLS-DA models for all cultivars evaluated ('Granny Smith', 'Fuji' and 'Brookfield') on the full training samples and with both validation procedures (Venetian and Monte Carlo), ranged from 0.76 to 0.92. The results of this work indicate that using this technology at the individual fruit level is essential to understand the factors that determine this disorder and can improve BP prediction of intact fruit." @default.
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• W4310370781 date "2022-11-30" @default.
• W4310370781 modified "2023-10-05" @default.
• W4310370781 title "Improving bitter pit prediction by the use of X-ray fluorescence (XRF): A new approach by multivariate classification" @default.
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• W4310370781 cites W1965695829 @default.
• W4310370781 cites W1967840722 @default.
• W4310370781 cites W1970458547 @default.
• W4310370781 cites W1976136027 @default.
• W4310370781 cites W1985457629 @default.
• W4310370781 cites W1986822234 @default.
• W4310370781 cites W1993772834 @default.
• W4310370781 cites W2011259025 @default.
• W4310370781 cites W2022509794 @default.
• W4310370781 cites W2025216331 @default.
• W4310370781 cites W2030237984 @default.
• W4310370781 cites W2039717999 @default.
• W4310370781 cites W2044857430 @default.
• W4310370781 cites W2059701125 @default.
• W4310370781 cites W2062892911 @default.
• W4310370781 cites W2063736813 @default.
• W4310370781 cites W2089468765 @default.
• W4310370781 cites W2091140513 @default.
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• W4310370781 cites W2252379778 @default.
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• W4310370781 cites W2314313470 @default.
• W4310370781 cites W2315704119 @default.
• W4310370781 cites W2321622415 @default.
• W4310370781 cites W2321830155 @default.
• W4310370781 cites W2323113834 @default.
• W4310370781 cites W2338509344 @default.
• W4310370781 cites W2403619799 @default.
• W4310370781 cites W2470069085 @default.
• W4310370781 cites W2491147451 @default.
• W4310370781 cites W2493087038 @default.
• W4310370781 cites W2507836980 @default.
• W4310370781 cites W2524900911 @default.
• W4310370781 cites W2525124420 @default.
• W4310370781 cites W2558229612 @default.
• W4310370781 cites W2584097648 @default.
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• W4310370781 cites W2589863237 @default.
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• W4310370781 cites W2593308505 @default.
• W4310370781 cites W2597095173 @default.
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• W4310370781 cites W2767997627 @default.
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• W4310370781 cites W2807326971 @default.
• W4310370781 cites W2807342548 @default.
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• W4310370781 doi "https://doi.org/10.3389/fpls.2022.1033308" @default.
• W4310370781 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36531358" @default.
• W4310370781 hasPublicationYear "2022" @default.
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