The application of near-infrared spectroscopy in beer fermentation for online monitoring of critical process parameters and their integration into a novel feedforward control strategy

Abstract

Traditional methods used in the analysis of fermentation media suffer from a number of limitations. The search for more rapid and efficient methods has led to the development and application of near‐infrared spectroscopy. Near‐infrared spectroscopy has been applied successfully in a variety of industrial processes: agricultural, food, chemical and pharmaceutical, generally in the areas of raw material quality control but also including intermediate and finished product testing. The present research explores its potential for online fermentation monitoring of total cell count (TCC), specific gravity (SG), free amino nitrogen (FAN) and percentage alcohol by volume (% v v−1) in a 300 L pilot‐scale validation batch. Models that were generated from three calibration batches for each of these constituents exhibited overall favourable standard error of cross validation (SECV) and fit of predicted vs actual cross validated results (SECV, R2): SG (0.00072, 0.995), ethanol (0.17% v v−1, 0.990), FAN (16.5 mg L−1, 0.886) and TCC (1.24 × 106 cells mL−1, 0.640). The data that was most relevant to cell metabolism was determined to be sugar consumption rate, ethanol production rate, yield of ethanol and fermentation lag time. These ‘critical performance parameters’ were incorporated into a novel feed‐forward control strategy where yeast pitching rate was modified based on values of the critical performance parameters from the previous batch. Use of this feed‐forward strategy demonstrated how brewers can utilize near‐infrared monitoring for quality assurance through early detection of shifts in fermentation performance. Copyright © 2017 The Institute of Brewing & Distilling

Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

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Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning

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