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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
Published on: November 8, 2019
Real-Time Monitoring of Beer Parameters Using Infrared Spectroscopy-A Process Analytical Technology Approach
Simone Tessarini Estevão1, Felipe Rebello Lourenço2
1Universidade de São Paulo, Instituto de Química, Departamento de Química Fundamental, Ave Prof. Lineu Prestes, 748-Bloco 1, São Paulo, CEP 05508-000 Brazil.
Background:
Quality assessment of a finished beer product is usually performed by determining whether a sample from the batch conforms to the specifications and industry standards. However, if one or more quality parameters do not meet specifications, the batch must be reprocessed or even disposed of entirely.
Objective:
The aim of this work was to propose real-time monitoring of beer parameters using infrared spectroscopy, according to the process analytical technology (PAT) approach.
Method:
Sixteen formulations of beer containing barley malt, rice, maize, and/or oat malt were manufactured and analyzed, using an off-line infrared spectrometer and a beer analyzer, for alcohol content and density. All formulations were also subjected to color analysis, pH measurements, and sensory analysis.
Results:
Partial least square regression models achieved coefficients of determination and prediction of 95.4% and 76.1% for the alcohol content, 90.9% and 69.2% for the density, 93.6% and 85.7% for the pH, and 98.1% and 94.8% for color, respectively. An infrared spectrometric method was used in the real-time monitoring of alcohol content, density, pH, and color of optimized (55% barley malt, 42% oat malt, and 3% maize) and standard (100% barley malt) formulations.
Conclusions:
No statistical differences were observed between the results of the alcoholic content, density, pH, and color predicted by the partial least square regression and those obtained experimentally in the optimized and standard formulations.
Highlights:
The application of the PAT tool is particularly useful for long manufacturing processes and can be applied in the development of food products.
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