Intensity prediction of typical aroma characters of cabernet sauvignon wine in Changli County (China)

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.