Comparison of NIRS approach for prediction of internal quality traits in three fruit species
| Autor: | Sylvie Bureau, Adaucto Bellarmino Pereira-Netto, Catherine M.G.C. Renard, Gabrieli Alves de Oliveira, Fernanda de Castilhos |
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| Přispěvatelé: | Alvès De Oliveira, Gabrieli, Sécurité et Qualité des Produits d'Origine Végétale (SQPOV), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Graduate Program in Food Engineering, Universidade Federal do Parana (UFPR), CAPESBrazil: UMR-A408 |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences
modèle de prédiction passiflora edulis Apricot Near infrared abricot chimiométrie Titratable acid acidité titrable Passion fruit Tomato qualité du fruit solide soluble Analytical Chemistry Chemometrics tomate longueur d'onde Soluble solids Soluble solids content Partial least squares regression Botany spectroscopie proche infrarouge de réflectivité NIRS erreur quadratique moyenne de prédiction proche infrarouge Spectroscopy Near-Infrared Chemistry Passiflora prunus armeniaca morphologie du fruit General Medicine Total acidity Internal quality Agricultural sciences acidité totale Horticulture méthode des moindres carrés partiels solanum lycopersicum Fruit Thick skin Prunus Sciences agricoles Food Science |
| Zdroj: | Food Chemistry (143), 223-230 . (2014) Food Chemistry Food Chemistry, Elsevier, 2014, 143, pp.223-230. ⟨10.1016/j.foodchem.2013.07.122⟩ |
| ISSN: | 0308-8146 |
| DOI: | 10.1016/j.foodchem.2013.07.122⟩ |
| Popis: | International audience; NIR Spectroscopy ability was investigated to assess the fruit structure effect (passion fruit, tomato and apricot) on prediction performance of soluble solids content (SSC) and titratable acidity (TA). Relationships between spectral wavelengths and SSC and TA were evaluated through the application of chemometric techniques based on partial least squares (PLS). Good prediction performance was obtained for apricot with correlation coefficients of 0.93 and 0.95 for SSC and TA and root mean square errors of prediction (RMSEP%) of 3.3% and 14.2%, respectively. For the passion fruit and tomato, the prediction models were not satisfactorily accurate due to the high RMSEP. Results showed that NIR technology can be used to evaluate apricot internal quality, however, it was not appropriate to evaluate internal quality in fruits with thick skin, (passion fruit), and/or heterogeneous internal structure (tomato). |
| Databáze: | OpenAIRE |
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