Literature DB >> 19626519

Assessment of quality parameters in grapes during ripening using a miniature fiber-optic near-infrared spectrometer.

Juan Fernández-Novales1, María-Isabel López, María-Teresa Sánchez, José-Antonio García-Mesa, Virginia González-Caballero.   

Abstract

Changes in the chemical properties of wine grapes during ripening were studied using near-infrared (NIR) spectroscopy. A miniature fiber-optic NIR spectrometer system working in transmission mode in the spectral region (700 - 1,060 nm) was evaluated for this purpose. Spectra and analytical data were used to develop partial least square calibration models to quantify changes in the major parameters used to chart ripening in this fruit. NIR spectroscopy provided excellent precision for soluble solid content and for reducing sugars, and good precision for maturity index, while for pH and titratable acidity the miniature NIR spectroscopy instrument proved less accurate. The performance of the instrument in classifying wine grapes by grape type and by irrigation regime was also studied. Percentages of correctly classified samples ranged from 82.7% to 96.2%. The results show that the monitoring of soluble solid content and reducing sugars' changes in wine grape quality parameters during ripening, as well as the classification of grapes, can be performed non-destructively using a miniature fiber-optic NIR spectrometer.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19626519     DOI: 10.1080/09637480903093116

Source DB:  PubMed          Journal:  Int J Food Sci Nutr        ISSN: 0963-7486            Impact factor:   3.833


  4 in total

1.  Novel Application of NIR Spectroscopy for Non-Destructive Determination of 'Maraština' Wine Parameters.

Authors:  Jasenka Gajdoš Kljusurić; Ana Boban; Ana Mucalo; Irena Budić-Leto
Journal:  Foods       Date:  2022-04-18

2.  Optimization of NIR spectral data management for quality control of grape bunches during on-vine ripening.

Authors:  Virginia González-Caballero; Dolores Pérez-Marín; María-Isabel López; María-Teresa Sánchez
Journal:  Sensors (Basel)       Date:  2011-06-07       Impact factor: 3.576

Review 3.  Fruit quality evaluation using spectroscopy technology: a review.

Authors:  Hailong Wang; Jiyu Peng; Chuanqi Xie; Yidan Bao; Yong He
Journal:  Sensors (Basel)       Date:  2015-05-21       Impact factor: 3.576

4.  Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

Authors:  Salvador Gutiérrez; Javier Tardaguila; Juan Fernández-Novales; María P Diago
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.