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Varietal discrimination of hop pellets by near and mid infrared spectroscopy

机译:近红外光谱法的啤酒花粒度的变异鉴别

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AbstractHop is one of the most important ingredients of beer production and several varieties are commercialized. Therefore, it is important to find an eco-real-time-friendly-low-cost technique to distinguish and discriminate hop varieties. This paper describes the development of a method based on vibrational spectroscopy techniques, namely near- and mid-infrared spectroscopy, for the discrimination of 33 commercial hop varieties. A total of 165 samples (five for each hop variety) were analysed by both techniques. Principal component analysis, hierarchical cluster analysis and partial least squares discrimination analysis were the chemometric tools used to discriminate positively the hop varieties. After optimizing the spectral regions and pre-processing methods a total of 94.2% and 96.6% correct hop varieties discrimination were obtained for near- and mid-infrared spectroscopy, respectively. The results obtained demonstrate the suitability of these vibrational spectroscopy techniques to discriminate different hop varieties and consequently their potential to be used as an authenticity tool. Compared with the reference procedures normally used for hops variety discrimination these techniques are quicker, cost-effective, non-destructive and eco-friendly.Graphical abstractDisplay Omitted展开▼
机译:<![cdata [ 抽象 跳跃是啤酒生产最重要的成分之一,几个品种商业化。因此,找到一种生态实时友好的低成本技术,以区分和鉴别跳跃品种。本文介绍了基于振动光谱技术,即近红外光谱的方法的开发,用于33种商业跳体品种的鉴别。两种技术分析了总共165个样品(每个HOP品种的五种)。主要成分分析,分层聚类分析和偏最小二乘歧视分析是用于歧视跳乐品种的化学计量工具。在优化光谱区和预处理方法之后,共有94.2%和96.6%的正确跳体变量分别用于近红外光谱法。所获得的结果证明了这些振动光谱技术的适用性,以区分不同的啤酒花变量,从而用作真实性工具的潜力。与通常用于啤酒花种类的参考程序相比,这些技术可以更快,经济效益,无损和环保。 图形抽象 < CE:简单段落>显示省略

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