Application of MPCA and MPLS on industrial batch bioprocesses

Urtubia, Alejandra; Emparan, M.; Almonacid, Sergio; Pinto, Marlene; Valdenegro, M.

doi:10.1016/j.jbiotec.2010.09.284

Cited by 4 publications

(2 citation statements)

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“…Specifically in wine, statistical approaches have been applied for discriminating between different wine varieties [9], and only few studies for monitoring wine fermentations [10] and detection of abnormal behaviors [11][12][13]. However, PLS and PCA are linear in nature whereas many processes-such as winemaking-exhibit non-linear relations between the process parameters and the quality parameters [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of problematic wine fermentations using artificial neural networks

Román

Hernández

Urtubia

2011

Bioprocess Biosyst Eng

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Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.

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Section: Introductionmentioning

confidence: 99%

Prediction of problematic wine fermentations using artificial neural networks

Román

Hernández

Urtubia

2011

Bioprocess Biosyst Eng

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“…Some statistical studies have been developed for fault detection and diagnosis in batch and fed-batch processes. Methods of multivariate statistics, such as multiway principal component analysis (MPCA), multiway partial least squares (MPLS), parallel factor analysis (PARAFAC) and Mean Hypothesis Testing, have been tested [6][7][8][9][10][11] [12][13][14]. However, and since PCA does not use class information, LDA usually outperforms PCA for pattern classification [15].…”

Section: Introductionmentioning

confidence: 99%

Predictive power of LDA to discriminate abnormal wine fermentations

Urtubia

Roger

2011

Journal of Chemometrics

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Wine fermentation is a critical step of winemaking. Unfavorable conditions can seriously affect the quality of the final product; however, it is difficult to anticipate these abnormal behaviors. In this study, the predictive power of stepwise linear discriminant analysis (SLDA) was evaluated to discriminate the behavior of wine fermentation. Information on different chemical concentrations from 18 industrial wine fermentations of Cabernet Sauvignon was used in this study. The statistical procedure consisted of curve fitting with exponential curve, and Stepwise LDA applied to the parameters of the curve. This methodology was applied to different times between the beginning and the end of fermentation (72, 95, 100, 150, 200 and 400 h). The results revealed that between seven and eight, of the 28 variables studied, minimized the Standard Error of Cross-Validation (SECV) for the different times. In almost all times studied, correlation coefficient of alcoholic degree, initial concentration of glucose, initial density and correlation coefficient of tartaric acid were the variables more discriminant, and they indicated some differences between a normal and an abnormal fermentation, which need to be corroborated with more information. In this work, before 95 h, it was not possible to minimize the prediction error and find the most discriminant variables.

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