Potential of VIS/NIR spectroscopy to detect and predict bitter pit in ‘Golden Smoothee’ apples
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 ultivariate 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.
632 - Malalties i protecció de les plantes
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
Is part of
Spanish Journal of Agricultural Research
Torres, Estanis, Inmaculada Recasens, and Simó Alegre. 2021. "Potential Of VIS/NIR Spectroscopy To Detect And Predict Bitter Pit In ‘Golden Smoothee’ Apples". Spanish Journal Of Agricultural Research 19 (1): e1001. doi:10.5424/sjar/2021191-15656.
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