In this paper, several linear multiple regression models of aroma descriptors were built from potential active odorants in Cabernet Sauvignon red wines in Changli County. The modified frequency (MF%) of ten aroma description terms in sample wines were evaluated by 30 panelists trained using the aroma standards of " Le Nez du Vin" Aroma compounds of sample wines were detected by Solid Phase Microextraction-Gas Chromatography-Mass (SPME-GC-MS), and 65 aroma compounds were identified and quantified. Those aroma compounds with odor active values (OAV) > 0.5 were chosen to build regression models for the eight characteristic aroma terms. Finally, five models were developed for five typical sensory terms: Blackcurrant, Bilberry, Green pepper, Vanilla and Smoked. These models were related to 13 aroma compounds. These compounds included 3-ethoxy-1-propanol, phenethyl acetate, 4-terpinenol, 2-hexen-1-ol, di-tert-butyl-phenol, β-terpinenol, hexanoic acid, octanoic acid, ethyl myristate, ethyl 3-hydroxy butyrate, isobutyl alcohol and 4-methyl-5-butyl-2(3H)-furan. ANOVA statistical analysis indicated that all five models regressed at 95% significant levels. t detections of the models showed regression coefficients of 99% or 95% significant levels. Correlation coefficients between the measured and predicted Y ranged from 0.714 to 0.999. © 2010 Elsevier Ltd.
Tao, Y., & Zhang, L. (2010). Intensity prediction of typical aroma characters of cabernet sauvignon wine in Changli County (China). LWT - Food Science and Technology, 43(10), 1550–1556. https://doi.org/10.1016/j.lwt.2010.06.003