AUTHOR=Moggia Claudia , Bravo Manuel A. , Baettig Ricardo , Valdés Marcelo , Romero-Bravo Sebastián , Zúñiga Mauricio , Cornejo Jorge , Gosetti Fabio , Ballabio Davide , Cabeza Ricardo A. , Beaudry Randolph , Lobos Gustavo A. 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.1033308

DOI=10.3389/fpls.2022.1033308

ISSN=1664-462X

ABSTRACT=Bitter pit (BP) is one of the most relevant post-harvest disorders for apple industry worldwide. The disorder is often related to a calcium (Ca) deficiency at the calyx end of the fruit; incidence can be higher in orchards planted on acid or slightly acidic soils where the exchangeable Ca is low. The occurrence of BP takes place along with an imbalance with other minerals, such as potassium (K). Although the K/Ca ratio is still 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. The prediction systems currently available have different levels of effectiveness, mainly because they come from combined samples of fruit. On the other hand, when XRF technology is used, although the concentration is not measured directly, there is an association with the signal intensities of each element. Thus, it is possible to characterize several mineral elements at a given position in the fruit. Therefore, it was hypothesized that using a multivariate modeling approach, other elements beyond the K/Ca ratio could be found that could improve the current clutter prediction capability. 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). When a multivariate classification approach was applied, 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.