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      Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks

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          Abstract

          Table grape browning is a complex physiological disorder that occurs during cold storage. There is a need to investigate novel and innovative ways to manage the problem that hampers the progressive and sustainable growth of table grape industries. Given the complex nature of the browning phenomenon, techniques such as near-infrared (NIR) spectroscopy can be utilized for the non-destructive classification of different browning phenotypes. In this study, NIR coupled with partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANN) were used to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact ‘Regal Seedless’ table grape bunches that were cold-stored over different periods. Friction browning appears as circular spots close to the pedicel area that are formed when table grape berries move against each other, and chocolate browning appears as discoloration, which originates mostly from the stylar-end of the berry, although the whole berry may appear brown in severe instances. The evaluation of the models constructed using PLS-DA was done using the classification error rate (CER), specificity, and sensitivity and for the models constructed using ANN, the kappa score was used. The CER for chocolate browning (25%) was better than that of friction browning (46%) for weeks 3 and 4 for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 for friction browning were not as good as that of chocolate browning. With ANN, the kappa score was tested to classify table grape bunches as clear or having chocolate browning or friction browning and showed that chocolate browning could be classified with a strong agreement during weeks 3 and 4 and weeks 5 and 6 and that friction browning could be classified with a moderate agreement during weeks 3 and 4. These results open up new possibilities for the development of quality checks of packed table grape bunches before export. This has a significant impact on the table grape industry for it will now be possible to evaluate bunches non-destructively during packaging to determine the possibility of these browning types being present when reaching the export market.

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          Most cited references44

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          Interrater reliability: the kappa statistic

          The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.
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            Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit.

            J. Cohen (1968)
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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
                29 October 2021
                2021
                : 12
                : 768046
                Affiliations
                [1] 1Department of Viticulture and Oenology, Faculty of AgriSciences, Stellenbosch University , Stellenbosch, South Africa
                [2] 2ARC Infruitec-Nietvoorbij , Stellenbosch, South Africa
                [3] 3South African Grape and Wine Research Institute, University of Stellenbosch , Stellenbosch, South Africa
                [4] 4Council for Scientific and Industrial Research, Modelling and Digital Science , Stellenbosch, South Africa
                [5] 5SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University , Stellenbosch, South Africa
                [6] 6UNESCO International Centre for Biotechnology , Nsukka, Nigeria
                Author notes

                Edited by: Julio Nogales-Bueno, Seville University, Spain

                Reviewed by: Sergio Ruffo Roberto, State University of Londrina, Brazil; Francisco J. Rodríguez-Pulido, Universidad de Sevilla, Spain

                *Correspondence: Umezuruike Linus Opara, opara@ 123456sun.ac.za

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

                Article
                10.3389/fpls.2021.768046
                8589818
                d0a6e60e-d6ed-4f64-bfa5-4f4ac6eb75ba
                Copyright © 2021 Daniels, Poblete-Echeverría, Nieuwoudt, Botha and Opara.

                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 2021
                : 01 October 2021
                Page count
                Figures: 7, Tables: 3, Equations: 0, References: 44, Pages: 10, Words: 5872
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                browning,intact table grape bunches,contactless scanning,near-infrared spectroscopy,partial least squares discriminant analysis,artificial neural networks

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