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

被引：12
作者：
Franca, Leandro [1 ]
Grassi, Silvia [2 ]
Pimentel, Maria Fernanda [3 ]
Manuel Amigo, Jose [1 ,4 ,5 ]
机构：
[1] Univ Fed Pernambuco, Dept Fundamental Chem, Recife, PE, Brazil
[2] Univ Milan, Dept Food Environm & Nutr Sci DeFENS, Via Celoria 2, I-20133 Milan, Italy
[3] Univ Fed Pernambuco, Dept Chem Engn, LITPEG, Av Arquitetura Cidade Univ, BR-50740540 Recife, PE, Brazil
[4] Basque Fdn Sci, Ikerbasque, Bilbao 48011, Spain
[5] Univ Basque Country, Dept Analyt Chem, UPV EHU, POB 644, Bilbao 48080, Basque Country, Spain
来源：
FOOD AND BIOPRODUCTS PROCESSING | 2021年 / 126卷
基金：
巴西圣保罗研究基金会;
关键词：
Beer fermentation; Chemometrics; Full process; NIR; Multivariate Statistics Process; Control; Process monitoring; STATISTICAL PROCESS-CONTROL; MULTIVARIATE SPC; QUALITY-CONTROL; FERMENTATION; NIR; CHARTS;
D O I：
10.1016/j.fbp.2020.12.011
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
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. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：95 / 103
页数：9
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