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Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques

Paper ID Volume ID Publish Year Pages File Format Full-Text
23596 43454 2012 6 PDF Available
Title
Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques
Abstract

Three multivariate statistical techniques (Multiway Principal Component Analysis, Multiway Partial Least Squares, and Stepwise Linear Discriminant Analysis) and one artificial intelligence method (Artificial Neural Networks) were evaluated to detect and predict early abnormal behaviors of wine fermentations. The techniques were tested with data of thirty-two variables at different stages of fermentation from industrial wine fermentations of Cabernet Sauvignon. All the techniques studied considered a pre-treatment to obtain a homogeneous space and reduce the overfitting. The results were encouraging; it was possible to classify at 72 h 100% of the fermentation correctly with three variables using Multiway Partial Least Squares and Artificial Neural Networks. Additional and complementary results were obtained with Stepwise Linear Discriminant Analysis, which found that ethanol, sugars and density measurements are able to discriminate abnormal behavior.

Keywords
Multivariate statistics; Prediction; Fault detection; Wine fermentations; Artificial neural networks
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Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques
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Publisher
Database: Elsevier - ScienceDirect
Journal: Journal of Biotechnology - Volume 159, Issue 4, 30 June 2012, Pages 336–341
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us