Near-infrared Spectroscopy in the Brewing Industry

被引:21
|
作者
Sileoni, Valeria [1 ]
Marconi, Ombretta [2 ]
Perretti, Giuseppe [1 ]
机构
[1] Univ Perugia, Italian Brewing Res Ctr, I-06126 Perugia, Italy
[2] Univ Perugia, Dept Agr Food & Environm Sci, I-06126 Perugia, Italy
关键词
NIR; barley; beer; malt; malting; LEAST-SQUARES REGRESSION; REFLECTANCE SPECTROSCOPY; BARLEY POLYSACCHARIDES; NIR SPECTROSCOPY; RAPID DETECTION; MALT EXTRACT; BETA-GLUCAN; QUALITY; TRANSMISSION; PREDICTION;
D O I
10.1080/10408398.2012.726659
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
This article offers an exhaustive description of the use of Near-Infrared (NIR) Spectroscopy in the brewing industry. This technique is widely used for quality control testing of raw materials, intermediates, and finished products, as well as process monitoring during malting and brewing. In particular, most of the reviewed works focus on the assessment of barley properties, aimed at quickly selecting the best barley varieties in order to produce a high-quality malt leading to high-quality beer. Various works concerning the use of NIR in the evaluation of raw materials, such as barley, malt, hop, and yeast, are also summarized here. The implementation of NIR sensors for the control of malting and brewing processes is also highlighted, as well as the use of NIR for quality assessment of the final product.
引用
收藏
页码:1771 / 1791
页数:21
相关论文
共 50 条
  • [21] Lautering Performance Prediction from Malt by Combining Whole Near-Infrared Spectral Information with Lautering Process Evaluation as Reference Values
    Holtz, C.
    Krause, D.
    Hussein, M.
    Gastl, M.
    Becker, T.
    JOURNAL OF THE AMERICAN SOCIETY OF BREWING CHEMISTS, 2014, 72 (03) : 214 - 219
  • [22] A Review of Machine Learning for Near-Infrared Spectroscopy
    Zhang, Wenwen
    Kasun, Liyanaarachchi Chamara
    Wang, Qi Jie
    Zheng, Yuanjin
    Lin, Zhiping
    SENSORS, 2022, 22 (24)
  • [23] Near-infrared spectroscopy for structural bone assessment
    Sharma, V. J.
    Adegoke, J. A.
    Afara, I. O.
    Stok, K.
    Poon, E.
    Gordon, C. L.
    Wood, B. R.
    Raman, J.
    BONE & JOINT OPEN, 2023, 4 (04): : 250 - 261
  • [24] Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
    Jiang, Hongzhe
    Zhuang, Hong
    Sohn, Miryeong
    Wang, Wei
    APPLIED SCIENCES-BASEL, 2017, 7 (01):
  • [25] Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications
    Yu, Huiwen
    Guo, Lili
    Kharbach, Mourad
    Han, Wenjie
    FOODS, 2021, 10 (04)
  • [26] A METHOD FOR DETERMINING ORGANOPHOSPHORUS PESTICIDE CONCENTRATION BASED ON NEAR-INFRARED SPECTROSCOPY
    Chen, J.
    Peng, Y.
    Li, Y.
    Wang, W.
    Wu, J.
    TRANSACTIONS OF THE ASABE, 2011, 54 (03) : 1025 - 1030
  • [27] Comparison of different modes of visible and near-infrared spectroscopy for detecting internal insect infestation in jujubes
    Wang, J.
    Nakano, K.
    Ohashi, S.
    Takizawa, K.
    He, J. G.
    JOURNAL OF FOOD ENGINEERING, 2010, 101 (01) : 78 - 84
  • [28] Rapid determination of cabbage quality using visible and near-infrared spectroscopy
    Kramchote, Somsak
    Nakano, Kazuhiro
    Kanlayanarat, Sirichai
    Ohashi, Shintaroh
    Takizawa, Kenichi
    Bai, Geng
    LWT-FOOD SCIENCE AND TECHNOLOGY, 2014, 59 (02) : 695 - 700
  • [29] Prediction of acrylamide content in potato chips using near-infrared spectroscopy
    Xie, Chuanqi
    Wang, Changyan
    Zhao, Mengyao
    Zhao, Liming
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2023, 301
  • [30] Unscrambling the Provenance of Eggs by Combining Chemometrics and Near-Infrared Reflectance Spectroscopy
    Hoffman, Louwrens Christiaan
    Ni, Dongdong
    Dayananda, Buddhi
    Ghafar, N. Abdul
    Cozzolino, Daniel
    SENSORS, 2022, 22 (13)