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傅里叶变换近红外光谱在优化绿茶蒸制工艺条件中的应用。

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions.

机构信息

Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, Japan.

出版信息

J Biosci Bioeng. 2011 Sep;112(3):247-51. doi: 10.1016/j.jbiosc.2011.05.002. Epub 2011 Jun 2.

DOI:10.1016/j.jbiosc.2011.05.002
PMID:21640647
Abstract

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture.

摘要

在这项研究中,我们使用傅里叶变换近红外(FT-NIR)光谱和偏最小二乘(PLS)回归分析构建了预测模型,通过对新鲜绿茶叶片的代谢指纹进行分析,以客观优化绿茶制造过程中的蒸青工艺条件。蒸青是制造高品质绿茶产品的最重要步骤。然而,蒸青机的参数设置目前是由制造商主观确定的。因此,有必要开发一种简单、稳健的系统,用于客观地设置蒸青工艺参数。我们专注于傅里叶变换近红外(FT-NIR)光谱,因为它具有操作简单、测量快速和运行成本低的特点。在通过主成分分析(PCA)去除光谱数据中的噪声后,使用光谱信息作为自变量,有经验的制造商设定的蒸青参数作为因变量进行偏最小二乘(PLS)回归分析。成功构建了具有令人满意精度的预测模型。此外,示范实验的结果表明,在更大的生产规模上可以预测绿茶蒸青工艺参数。该技术将有助于提高绿茶的质量和产量,因为它可以客观地优化复杂的绿茶蒸青工艺,适用于绿茶制造的实际应用。

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