Determination of technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars during ripening by near infrared hyperspectral image: A preliminary approach

被引:113
作者
Nogales-Bueno, Julio [1 ]
Miguel Hernandez-Hierro, Jose [1 ]
Jose Rodriguez-Pulido, Francisco [1 ]
Jose Heredia, Francisco [1 ]
机构
[1] Univ Seville, Fac Farm, Dept Nutr & Food Sci, Food Colour & Qual Lab, E-41012 Seville, Spain
关键词
Technological maturity; Phenolic maturity; Grapes; Near infrared hyperspectral imaging; Chemometrics; QUALITY;
D O I
10.1016/j.foodchem.2013.12.030
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
Hyperspectral images of intact grapes during ripening were recorded using a near infrared hyperspectral imaging system (900-1700 nm). Spectral data have been correlated with grape skin total phenolic concentration, sugar concentration, titratable acidity and pH by modified partial least squares regression (MPLS) using a number of spectral pre-treatments and different sets of calibration. The obtained results (RSQ and SEP, respectively) for the global model of red and white grape samples were: 0.89 and 1.23 mg g-1 of grape skin for total phenolic concentration, 0.99 and 1.37 'Brix for sugar concentration, 0.98 and 3.88 g L-1 for titratable acidity and for pH 0.94 and 0.12. Moreover, separate calibration models for red and white grape samples were also developed. The obtained results present a good potential for a fast and reasonably inexpensive screening of these parameters in intact grapes and therefore, for a fast control of technological and phenolic maturity. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:586 / 591
页数:6
相关论文
共 22 条
[1]  
[Anonymous], CURRENT CONTENTS AGR
[2]   Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes [J].
Baiano, Antonietta ;
Terracone, Carmela ;
Peri, Giorgio ;
Romaniello, Roberto .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2012, 87 :142-151
[3]   Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging [J].
Barbin, Douglas F. ;
ElMasry, Gamal ;
Sun, Da-Wen ;
Allen, Paul .
FOOD CHEMISTRY, 2013, 138 (2-3) :1162-1171
[4]  
Brereton R.G., 2003, DATA ANAL LAB CHEM P
[5]  
Cozzolino D., 2006, J NEAR INFRARED SPEC, V14
[6]   Near Infrared Spectroscopy in Natural Products Analysis [J].
Cozzolino, Daniel .
PLANTA MEDICA, 2009, 75 (07) :746-756
[7]  
Dhanoa M. S., 1995, APPL SPECTROSCOPY, V49
[8]   Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review [J].
Elmasry, Gamal ;
Kamruzzaman, Mohammed ;
Sun, Da-Wen ;
Allen, Paul .
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION, 2012, 52 (11) :999-1023
[9]   Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks [J].
Fernandes, Armando Manuel ;
Oliveira, Paula ;
Moura, Joao Paulo ;
Oliveira, Ana Alexandra ;
Falco, Virgilio ;
Correia, Maria Jose ;
Melo-Pinto, Pedro .
JOURNAL OF FOOD ENGINEERING, 2011, 105 (02) :216-226
[10]   Influence of climatic conditions on the phenolic composition of Vitis vinifera L. cv. Graciano [J].
Ferrer-Gallego, Raul ;
Miguel Hernandez-Hierro, Jose ;
Rivas-Gonzalo, Julian C. ;
Teresa Escribano-Bailon, M. .
ANALYTICA CHIMICA ACTA, 2012, 732 :73-77