𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Application of principal component analysis to ripening indices for wine grapes

✍ Scribed by M.L. González-San José; L.J.R. Barren; B. Junquera; L.M. Robredo

Publisher
Elsevier Science
Year
1991
Tongue
English
Weight
817 KB
Volume
4
Category
Article
ISSN
0889-1575

No coin nor oath required. For personal study only.

✦ Synopsis

The influence of lipids, photosynthetic pigments, and phenols in grapes in determining optimum harvesting time and their relationship with traditional grape ripening indices were studied. The relationships between ripening indices and the different types-physiological and technologicalof ripening were also examined. Three factors representing physiological ripening, technological ripening, and energy stores were selected for principal component analysis. The results obtained indicate differences in the meaning of traditional ripening indices, with the choice of index used depending upon the type of ripening desired. Analysis of photosynthetic pigments and phenols during ripening can serve to complement the information furnished by the ripening indices. Phenolic compounds were particularly important as markers of ripening and were closely associated with technological ripening. 0 1991 Academic PW. IX.

📜 SIMILAR VOLUMES

Application of Time–Frequency Principal
Application of Time–Frequency Principal Component Analysis to Speaker Verification
✍ Ivan Magrin-Chagnolleau; Geoffrey Durou 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 133 KB

This article presents the RIMO/ELISA speaker verification system which has been used in the 1999 NIST speaker recognition evaluation. This system is based on a new technique for analyzing speech signals called time-frequency principal component (TFPC) analysis. This technique consists in extracting

APPLICATION OF PROJECTION-PURSUIT PRINCI
APPLICATION OF PROJECTION-PURSUIT PRINCIPAL COMPONENT ANALYSIS METHOD TO CLIMATE STUDIES
✍ CHAN, JOHNNY C. L.; SHI, JIU-EN 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 194 KB 👁 2 views

Projection-pursuit (PP) principal component analysis (PCA) is a new statistical method that can deal with high-dimensional problems. In this paper, we show how this method can be applied to analyse regional monthly sea surface temperature and rainfall. Comparisons are made with results derived from