Near infrared spectroscopy determination of chemical and sensory properties in tomato
| Autor: | Silvia Sans, Jordi Cruz, Dong Sun, Manel Alcalà, Roser Romero del Castillo, Joan Casals |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Agroalimentària i Biotecnologia |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Fruit quality
Materials science 010401 analytical chemistry Near-infrared spectroscopy Espectroscòpia d'infraroigs Sensory system 04 agricultural and veterinary sciences Sensory analysis 01 natural sciences Tomato 0104 chemical sciences Characterization (materials science) Chemometrics Glucose Soluble solids Tomatoes--Quality Phenomics 0405 other agricultural sciences Biological system Sugars Enginyeria agroalimentària::Agricultura::Producció vegetal [Àrees temàtiques de la UPC] Spectroscopy 040502 food science |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| DOI: | 10.1177/09670335211018759 |
| Popis: | Fast and massive characterization of quality attributes in tomatoes is a necessary step toward its improvement; for sensory attributes this process is time-consuming and very expensive, which causes its absence in routine phenotpying. We aimed to assess the feasibility of near infrared (NIR) spectroscopy as a fast and economical tool to predict both the chemical and sensory properties of tomatoes. We built partial least squares models from spectra recorded from tomato puree and juice in 53 genetically diverse varieties grown in two environments. Samples were divided in calibration (210 samples for chemical traits, 45 samples for sensory traits) and validation sets (60 and 10, respectively) using the Kennard Stone algorithm. Models from puree spectra gave validation r2 values higher than 0.97 for fructose, glucose, soluble solids content, and dry matter (relative standard error of prediction, RSEP% ranged 3.5–5.8), while r2 values for sensory properties were lower (ranging 0.702–0.917 for taste-related traits (RSEP%: 9.1–20.0), and 0.009–0.849 for texture related traits (RSEP%: 3.6–72.1)). For sensory traits such as explosiveness, juiciness, sweetness, acidity, taste intensity, aroma intensity, and mealiness, NIR spectroscopy is potentially useful for scanning large collections of samples to identify likely candidates to select for tomato quality. This work was supported by the Departament d’Agricultura Ramaderia, Pesca i Alimentacio de la Generalitat de Catalunya (grant number AAM/259/2013); and the Ministerio de Economıa y Competitividad del Gobierno de Espa~na (grant number CTQ 2016-79696-P, AEI/FEDER, EU). |
| Databáze: | OpenAIRE |
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