A study was conducted to assess the influence of soil properties and underlying geology on the characteristics of Falanghina wine grape in a viticultural area of southern Italy. Soil, along with climate, is one of the main physical-environmental factors affecting the composition of wine grape. Soil properties reflect to a more o less extent the characteristics of underlying geology, depending on the nature of geologi- cal substrate and the age of soil. Therefore, if relationships can be found between soil properties and grape characteristics, an indirect influence of geological substrate on grape composition should be expected. The structure of geology-soil-grape data is then characterized by a chain of dependence relationships between the three sets of variables. As such, it can evaluated using a single theoretical approach based on the metric generalization of PLS regression.

Assessment of the chain dependence relationships between wine grape quality, soil, and geological substrate using a metric generalization of PLS regression

AMENTA P;
2008-01-01

Abstract

A study was conducted to assess the influence of soil properties and underlying geology on the characteristics of Falanghina wine grape in a viticultural area of southern Italy. Soil, along with climate, is one of the main physical-environmental factors affecting the composition of wine grape. Soil properties reflect to a more o less extent the characteristics of underlying geology, depending on the nature of geologi- cal substrate and the age of soil. Therefore, if relationships can be found between soil properties and grape characteristics, an indirect influence of geological substrate on grape composition should be expected. The structure of geology-soil-grape data is then characterized by a chain of dependence relationships between the three sets of variables. As such, it can evaluated using a single theoretical approach based on the metric generalization of PLS regression.
2008
978-88-8305-060-2
wine grape quality; soil properties; geology; Generalized PLS Regression.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/8779
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