Influence of Soil Salinity on Sensory Characteristics and Volatile Aroma Compounds of Nero d’Avola Wine

2010 ◽  
Vol 61 (4) ◽  
pp. 498-505 ◽  
Author(s):  
Antonio Scacco ◽  
Antonella Verzera ◽  
Carmela M. Lanza ◽  
Antonio Sparacio ◽  
Giuseppe Genna ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Aroma Compounds ◽  
Sensory Characteristics ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

scholarly journals Volatile aroma compounds and sensory characteristics of traditional banana wine “Urwagwa” of Rwanda

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
François Lyumugabe ◽  
Innocent Iyamarere ◽  
Michel Kayitare ◽  
Joseph Rutabayiro Museveni ◽  
Emmanuel Bajyana Songa
Keyword(s):  
Aroma Compounds ◽  
Sensory Characteristics ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

Volatile aroma compounds of sugarcane molasses as affected by spray drying at low and high temperature

LWT ◽  
2021 ◽  
pp. 111288
Author(s):  
Katarzyna Samborska ◽  
Radosław Bonikowski ◽  
Danuta Kalemba ◽  
Alicja Barańska ◽  
Aleksandra Jedlińska ◽  
...  
Keyword(s):  
High Temperature ◽  
Spray Drying ◽  
Aroma Compounds ◽  
Sugarcane Molasses ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

Extraction of volatile aroma compounds from toasted oak wood using pulsed electric field

2021 ◽  
Author(s):  
George V. Ntourtoglou ◽  
Foteini Drosou ◽  
Yang Enoch ◽  
Evangelia A. Tsapou ◽  
Eleni Bozinou ◽  
...  
Keyword(s):  
Electric Field ◽  
Pulsed Electric Field ◽  
Aroma Compounds ◽  
Oak Wood ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

Modeling the Partition of Volatile Aroma Compounds from a Cloud Emulsion

2002 ◽  
Vol 50 (7) ◽  
pp. 1985-1990 ◽  
Author(s):  
Michelle E. Carey ◽  
Tom Asquith ◽  
Robert S. T. Linforth ◽  
Andrew J. Taylor
Keyword(s):  
Aroma Compounds ◽  
Volatile Aroma ◽  
Volatile Aroma Compounds
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