PT - JOURNAL ARTICLE AU - Mark A. Nisbet AU - Tim E. Martinson AU - Anna Katharine Mansfield TI - Preharvest Prediction of Yeast Assimilable Nitrogen in Finger Lakes Riesling using Linear and Multivariate Modeling AID - 10.5344/ajev.2013.13030 DP - 2013 Jul 08 TA - American Journal of Enology and Viticulture PG - ajev.2013.13030 4099 - http://www.ajevonline.org/content/early/2013/07/02/ajev.2013.13030.short 4100 - http://www.ajevonline.org/content/early/2013/07/02/ajev.2013.13030.full AB - A three-year study was conducted to determine if regression models could be developed to predict yeast assimilable nitrogen (YAN) prior to harvest, using Riesling in the New York Finger Lakes region as a model. Berry samples were taken from 62 commercial Riesling vineyards around the Finger Lakes at three time points: veraison, two weeks prior to harvest, and harvest. Samples were measured for berry weight, Brix, pH, titratable acidity, ammonia(AMM), primary amino nitrogen (PAN), and yeast assimilable nitrogen(YAN) . The average YAN concentration at harvest was 91.8 mg/L, and there were no significant differences in harvest YAN concentration among years. Linear regression models created using preharvest YAN concentrations (p <0.05) had a cross-validated R2 (Q2) of 70%. Models using only preharvest AMM had less predictive power (Q2 = 63%), but may allow winemakers more analytical flexibility than those requiring complete YAN measurements. Models created using multiple linear regression (MLR) were also developed, and provided better predictive power, with a Q2 of 74%. Finally, a multivariate approach using partial least squares regression (PLSR) was used to create models with the highest predictive power, with a Q2 of 74%. The additional analysis required to obtain values for additional prediction variables may limit the practicality of MLR and PLS approaches. Because many winemakers are not able to perform the analyses required to calculate YAN during the busy time of harvest, the development of these regression models as predictive tools may allow winemakers to use preharvest analysis to calculate accurate supplemental nitrogen additions, allowing targeted supplementation and lowering the risk of excessive prophylactic additions.