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|Title: ||UV spectrophotometry method for the monitoring of galacto-oligosaccharides production|
|Authors: ||Dias, L.G.|
Veloso, Ana C.A.
Correia, Daniela M.
Rodrigues, Lígia R.
Peres, António M.
|Keywords: ||Fermentation processes|
Partial least squares regression
Artificial neural networks
|Issue Date: ||2009|
|Citation: ||Food Chemistry. ISSN 0308-8146. 113:1 (2009) p. 246-252. http://www.sciencedirect.com/science/journal/03088146|
|Abstract: ||Monitoring the industrial production of galacto-oligosaccharides (GOS) requires a fast and accurate
methodology able to quantify, in real time, the substrate level and the product yield. In this work, a simple,
fast and inexpensive UV spectrophotometric method, together with partial least squares regression
(PLS) and artificial neural networks (ANN), was applied to simultaneously estimate the products (GOS)
and the substrate (lactose) concentrations in fermentation samples. The selected multiple models were
trained and their prediction abilities evaluated by cross-validation and external validation being the
results obtained compared with HPLC measurements. ANN models, generated from absorbance spectra
data of the fermentation samples, gave, in general, the best performance being able to accurately and precisely
predict lactose and total GOS levels, with standard error of prediction lower than 13 g kg-1 and
coefficient of determination for the external validation set of 0.93–0.94, showing residual predictive deviations
higher than five, whereas lower precision was obtained with the multiple model generated with
PLS. The results obtained show that UV spectrophotometry allowed an accurate and non-destructive
determination of sugars in fermentation samples and could be used as a fast alternative method for monitoring
|Appears in Collections:||ARN - Artigos em Revistas Indexados ao ISI|
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