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Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves

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Abstract

Honeysuckle (Lonicerae japonicae Flos, LJF) tea is a favorite cool tea in China and Southeast Asia. However, some unscrupulous traders usually use Lonicera Flos (LF) as LJF to sell for earning high profit. In order to identify true and false honeysuckle tea leaves rapidly and precisely, hyperspectral imaging technology was applied to develop a nondestructive identification model for LJF and LF. Firstly, the original spectral data were analyzed by three pretreatment methods including Savitzky–Golay (SG) convolution smoothing, multiple scatter correct and standard normal variate transformation (SNV). Then, a full-band analysis model was established by using the partial least squares-discriminant analysis method. And after the selection of characteristic wavelengths by regression coefficients algorithm, the identification analysis models based on the back-propagation neural network and extreme learning machine (ELM) discriminant were established. The results showed that the BP neural network and ELM discriminant analysis model based on SNV denoising at 9 characteristic wavelengths could achieve the best identification results. The recognition rates of both modeling sets and forecasting sets could reach 100%. Therefore, the application of hyperspectral imaging technology can identify LJF and LF effectively and nondestructively, and has potential in the identification of true and false honeysuckle tea leaves.

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Abbreviations

LJF:

Lonicerae japonicae Flos

LF:

Lonicera Flos

HPLC:

High performance liquid chromatography

SG:

Savitzky–Golay convolution smoothing

MSC:

Multiple scatter correct

SNV:

Standard normal variate transformation

PLS-DA:

Partial least squares-discriminant analysis

RC:

Regression coefficients

BPNN:

Back-propagation neural network

ELM:

Extreme learning machine

ROI:

Region of interest

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Acknowledgements

The authors express their sincere appreciation to the National Natural Science Foundation of China (Project U1404334), the Science and Technology Project of Henan Province (Project Nos. 172102310617 and 172102210256), the College Young Teachers Development Program of Henan province (Project 2015GGJS-048) and the Natural Science Foundation of Henan Province (Project 162300410100) for support this study financially.

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Authors and Affiliations

  1. College of Food and Bioengineering, Henan University of Science and Technology, 263 Kaiyuan Road, Luolong District, Luoyang, 471003, China

    Jie Feng, Yunhong Liu, Xiaowei Shi & Qingqing Wang

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  1. Jie Feng
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  2. Yunhong Liu
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  3. Xiaowei Shi
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  4. Qingqing Wang
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Correspondence to Yunhong Liu.

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Feng, J., Liu, Y., Shi, X. et al. Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. Food Measure 12, 2184–2192 (2018). https://doi.org/10.1007/s11694-018-9834-0

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  • DOI: https://doi.org/10.1007/s11694-018-9834-0

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