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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

机译:近红外光谱技术在啤酒发酵中用于关键过程参数的在线监控及其与新型前馈控制策略的集成

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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, R-2): 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 x 10(6) 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 (c) 2017 The Institute of Brewing & Distilling
机译:用于发酵培养基分析的传统方法存在许多局限性。对更快速有效的方法的探索导致了近红外光谱学的发展和应用。近红外光谱已成功应用于各种工业过程:农业,食品,化学和制药,通常用于原材料质量控制领域,还包括中间产品和成品测试。本研究探索了其在线发酵监测300 L中试规模的总细胞数(TCC),比重(SG),游离氨基氮(FAN)和酒精体积百分比(%vv(-1))的潜力验证批处理。对于这些成分中的每一个,从三个校准批次中生成的模型均显示出总体有利的交叉验证标准误差(SECV),以及预测与实际交叉验证结果的拟合度(SECV,R-2):SG(0.00072,0.995),乙醇( 0.17%vv(-1),0.990),FAN(16.5 mg L-1,0.886)和TCC(1.24 x 10(6)细胞mL(-1),0.640)。确定与细胞代谢最相关的数据为糖消耗速率,乙醇产生速率,乙醇产率和发酵滞后时间。这些“关键性能参数”被合并到一种新颖的前馈控制策略中,在该策略中,基于前一批关键性能参数的值修改了酵母的沥青添加速率。通过使用这种前馈策略,酿酒商可以通过早期检测发酵性能变化来利用近红外监测来保证质量。版权所有(c)2017酿酒与蒸馏研究所

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