Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/6488
Título: 'Rocha' pear firmness predicted by a Vis/NIR segmented model
Autor: Cavaco, A. M.
Pinto, Patricia
Antunes, Maria Dulce
da Silva, J. M.
Guerra, Rui
Data: 2009
Editora: Elsevier
Resumo: We present a segmented partial least squares (PLS) prediction model for firmness of 'Rocha' pear (Pyres communis L) during fruit ripening under shelf-life conditions. Pears were collected from three different orchards. Orchard I provided the pears for model calibration and internal validation (set 1). These were transferred to shelf-life in the dark at 20 +/- 2 degrees C and 70% RH, immediately after harvest. External validation was performed on the pears from the other two orchards (sets 2 and 3), which were stored under different conditions before shelf-life. Fruit was followed in the shelf-life period by visible/near infrared reflectance spectroscopy (Vis/NIRS) in the range 400-950 nm. The correlation between firmness and the reflectance at some wavelength bands was markedly different depending on ripening stage. A segmented partial least squares model was then constructed to predict firmness. This PLS model has two segments: (1) unripe and ripening/ripe pears (high firmness); (2) over-ripe pears (low firmness). The prediction is done in two steps. First, a full range model (full model) is applied. When the full model prediction gives a low firmness value, then the over-ripe model is applied to refine the prediction. The full model is reasonably significant in regression terms, robust, but allows only a coarse quantitative prediction (standard deviation ratio, SDR = 2.48, 1.50 and 2.40 for sets 1, 2 and 3, respectively). Also, RMSEP% = 139%, 91% and 56%, indicating large relative errors at low firmness values. The segmented model improved moderately the correlation, and the values of RMSEC, RMSEP and SDR: it improved significantly the RMSEP% (29%, 55% and 31%), providing an improvement of the relative prediction errors at low firmness values. This method improves the ordinary PLS models. Finally, we tested whether chlorophyll alone was enough for a predictive model for firmness, but the results showed that the absorption of chlorophyll alone does not explain the performance of the PLS models. (C) 2008 Elsevier B.V. All rights reserved.
Peer review: yes
URI: http://hdl.handle.net/10400.1/6488
DOI: https://dx.doi.org/10.1016/j.postharvbio.2008.08.013
ISSN: 0925-5214
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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