A single model to monitor multistep craft beer manufacturing using near infrared spectroscopy and chemometrics

•NIR and MSPC to monitor and control the beer production.•Simple multivariate control charts established for all the steps of the process.•Variability within-batches is smaller than the variability within-steps.•The complete procedure monitor and control with a single PCA model. This manuscript pres...

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Veröffentlicht in:Food and bioproducts processing 2021-03, Vol.126, p.95-103
Hauptverfasser: França, Leandro, Grassi, Silvia, Pimentel, Maria Fernanda, Amigo, José Manuel
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container_title Food and bioproducts processing
container_volume 126
creator França, Leandro
Grassi, Silvia
Pimentel, Maria Fernanda
Amigo, José Manuel
description •NIR and MSPC to monitor and control the beer production.•Simple multivariate control charts established for all the steps of the process.•Variability within-batches is smaller than the variability within-steps.•The complete procedure monitor and control with a single PCA model. This manuscript presents a comprehensive approach to monitoring the whole process of craft beer production (mashing, circulation, boiling, fermentation and carbonatation), using a simple, rapid and green methodology like Near Infrared spectroscopy combined with MSPC (Multivariate Statistics Process Control). A Principal Component Analysis model is calculated with near infrared spectra (range between 800–2500 nm) collected in all the steps of the process (i.e., using a batch-to-batch approach), and a multivariate control chart is generated in order to monitor the beer development. Each batch was composed of a variable number of samples (average of 55 samples per batch) depending on the sampling time of every step. Four batches working under normal operating conditions are used to construct the model. Three external batches are used to validate the proposal (two of them with induced disturbances and another one working under normal operating conditions). The results were compared to those obtained by monitoring the solid soluble content (SSC) by using Partial Least Squares regression to ascertain the richness of the information given by NIR. The results illustrate the versatility and simplicity of the proposal and its reliability towards a global monitor and control of the beer-making procedure.
doi_str_mv 10.1016/j.fbp.2020.12.011
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The results were compared to those obtained by monitoring the solid soluble content (SSC) by using Partial Least Squares regression to ascertain the richness of the information given by NIR. 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subjects Analytical methods
Beer
Beer fermentation
Carbonatation
Chemometrics
Control charts
Fermentation
Full process
Infrared analysis
Infrared spectra
Infrared spectroscopy
Least squares method
Mashing
Monitoring
Multivariate analysis
Multivariate Statistics Process Control
Near infrared radiation
NIR
Principal components analysis
Process control
Process controls
Process monitoring
Spectrum analysis
Statistical analysis
Statistical methods
title A single model to monitor multistep craft beer manufacturing using near infrared spectroscopy and chemometrics
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