Preharvest Prediction of Yeast Assimilable Nitrogen in Finger Lakes Riesling Using Linear and Multivariate Modeling

Abstract

A three-year study was conducted to determine if regression models could be developed to predict yeast assimilable nitrogen (YAN) before 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 veraison, two weeks before harvest, and harvest. Samples were measured for berry weight, Brix, pH, titratable acidity, ammonia, primary amino nitrogen, 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 ammonia 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 provided better predictive power (Q² = 73.6%). Finally, a multivariate approach using partial least squares regression was used to create models with the highest predictive power (Q² = 74.2%). The additional analysis required to obtain values for additional prediction variables may limit the practicality of multiple linear regression and partial least squares 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.