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      Improving bitter pit prediction by the use of X-ray fluorescence (XRF): A new approach by multivariate classification

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          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.

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              Partial least squares for discrimination

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                30 November 2022
                2022
                : 13
                : 1033308
                Affiliations
                [1] 1 Plant Breeding and Phenomics Center, Faculty of Agricultural Sciences, Universidad de Talca , Talca, Chile
                [2] 2 Laboratorio de Química Analítica y Ambiental, Instituto de Química, Pontificia Universidad Católica de Valparaíso , Valparaíso, Chile
                [3] 3 Facultad de Ciencias Forestales y de la Conservación de la Naturaleza, Universidad de Chile , Santiago, Chile
                [4] 4 Department of Agricultural Sciences, Universidad Católica del Maule , Curicó, Chile
                [5] 5 Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca , Milano, Italy
                [6] 6 Plant Nutrition Laboratory, Department of Crop Sciences, Faculty of Agricultural Sciences, University of Talca , Talca, Chile
                [7] 7 Department of Horticulture, Michigan State University , East Lansing, MI, United States
                Author notes

                Edited by: Patricio Sandaña, Universidad Austral de Chile, Chile

                Reviewed by: Sanaz Jarolmasjed, Donald Danforth Plant Science Center, United States; Yosef Al Shoffe, Cornell University, United States

                *Correspondence: Gustavo A. Lobos, globosp@ 123456utalca.cl ; Claudia Moggia, cmoggia@ 123456utalca.cl ; Manuel A. Bravo, manuel.bravo@ 123456pucv.cl

                This article was submitted to Plant Nutrition, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.1033308
                9748620
                3ef04c77-3c8a-4d6f-aedd-ec5001d17772
                Copyright © 2022 Moggia, Bravo, Baettig, Valdés, Romero-Bravo, Zúñiga, Cornejo, Gosetti, Ballabio, Cabeza, Beaudry and Lobos

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 August 2022
                : 08 November 2022
                Page count
                Figures: 5, Tables: 2, Equations: 1, References: 82, Pages: 12, Words: 5419
                Funding
                Funded by: Fondo de Fomento al Desarrollo Científico y Tecnológico , doi 10.13039/501100008736;
                Award ID: ID18I10214
                Funded by: Michigan State University , doi 10.13039/100007709;
                Funded by: National Institute of Food and Agriculture , doi 10.13039/100005825;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                malus × domestica,calyx,k/ca,refrigerated storage,modelling
                Plant science & Botany
                malus × domestica, calyx, k/ca, refrigerated storage, modelling

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