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Non-destructive determination of total polyphenols content and classification of storage periods of Iron Buddha tea using multispectral imaging system

文献类型： 外文期刊

作者： Xiong, Chuanwu 1 ; Liu, Changhong 1 ; Pan, Wenjuan 1 ; Ma, Fei 1 ; Xiong, Can 1 ; Qi, Li 2 ; Chen, Feng 3 ; Lu, Xuzhong 4 ;

作者机构： 1.Hefei Univ Technol, Sch Biotechnol & Food Engn, Hefei 230009, Peoples R China

2.Hefei Meiya Optoelect Technol Inc, R&D Ctr, Hefei 230088, Peoples R China

3.Clemson Univ, Dept Food Nutr & Packaging Sci, Clemson, SC 29634 USA

4.Anhui Acad Agr Sci, Rice Res Inst, Hefei 230031, Peoples R China

5.Hefei Univ Technol, Sch Med Engn, Hefei 230009, Peoples R China

关键词： Multispectral imaging;Near infrared reflectance spectroscopy;Total polyphenols;Non-destructive determination;Chemometrics;Iron Buddha tea

期刊名称：FOOD CHEMISTRY （ 影响因子：7.514； 五年影响因子：7.516 ）

ISSN：

年卷期：

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收录情况： SCI

摘要： Total polyphenols is a primary quality indicator in tea which is consumed worldwide. The feasibility of using near infrared reflectance (NIR) spectroscopy (800-2500 nm) and multispectral imaging (MSI) system (405-970 nm) for prediction of total polyphenols contents (TPC) of Iron Buddha tea was investigated in this study. The results revealed that the predictive model by MSI using partial least squares (PLS) analysis for tea leaves was considered to be the best in non-destructive and rapid determination of TPC. Besides, the ability of MSI to classify tea leaves based on storage period (year of 2004, 2007, 2011, 2012 and 2013) was tested and the classification accuracies of 95.0% and 97.5% were achieved using LS-SVM and BPNN models, respectively. These overall results suggested that MSI together with suitable analysis model is a promising technology for rapid and non-destructive determination of TPC and classification of storage periods in tea leaves. (C) 2014 Elsevier Ltd. All rights reserved.

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