Potential of VIS/NIR spectroscopy to detect and predict bitter pit in ‘Golden Smoothee’ apples
| Autor: | Estanis Torres, Inmaculada Recasens, Simó Alegre |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Spanish Journal of Agricultural Research, Vol 19, Iss 1, Pp e1001-e1001 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2171-9292 1191-1565 |
| DOI: | 10.5424/sjar/2021191-15656 |
| Popis: | Aim of study: A portable VIS/NIR spectrometer and chemometric techniques were combined to identify bitter pit (BP) in Golden apples. Area of study: Worldwide Material and methods: Three different classification algorithms – linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support-vector machine (SVM) –were used in two experiments. In experiment #1, VIS/NIR measurements were carried out at postharvest on apples previously classified according to 3 classes (class 1: non-BP; class 2: slight symptoms; class 3: severe symptoms). In experiment #2, VIS/NIR measurements were carried out on healthy apples collected before harvest to determinate the capacity of the classification algorithms for detecting BP prior to the appearance of symptoms. Main results: In the experiement #1, VIS/NIR spectroscopy showed great potential in pitted apples detection with visibly symptoms (accuracies of 75–81%). The linear classifier LDA performed better than the multivariate non-linear QDA and SVM classifiers in discriminating between healthy and bitter pitted apples. In the experiment #2, the accuracy to predict bitter pit prior to the appearance of visible symptoms decreased to 44–57%. Research highlights: The identification of apples with bitter pit through VIS/NIR spectroscopy may be due to chlorophyll degradation and/or changes in intercellular water in fruit tissue. |
| Databáze: | Directory of Open Access Journals |
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