Potential of VIS/NIR spectroscopy to detect and predict bitter pit in ‘Golden Smoothee’ apples

• Main Authors: Estanis Torres, Inmaculada Recasens, Simó Alegre
• Format: Article
• Language: English
• Published: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria 2021-04-01
• Series: Spanish Journal of Agricultural Research
• Subjects: prediction of disorders; calcium disorders; multiclass classification; binary-class classification
• Online Access: https://revistas.inia.es/index.php/sjar/article/view/15656

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.