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Chemometric Models for the Quantitative Descriptive Sensory Properties of Green Tea (Camellia sinensis L.) Using Fourier Transform Near Infrared (FT-NIR) Spectroscopy

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Abstract

Multivariable models based on chemometric analyses of the tea infusion sensory data and FT-NIR spectra of 70 “Biluochun” green tea (Camellia sinensis L.) samples were generated aiming to predict the scores of sensory attributes of green tea. Modified BP_AdaBoost algorithm was used to develop the models. The synergy interval partial least square (siPLS) algorithm was applied to select the wavenumbers for the prediction model of sensory properties in order to take only significant spectral intervals into account. Some parameters were optimized by cross-validation in model calibrations. Experimental results showed that the optimal BP_AdaBoost model was achieved with four principal components (PCs), when 184 variables in the combination of four spectral intervals [3 17 19 21] were selected by siPLS. The predicted precision of the best model obtained were as follows: the root mean square error of cross-validation (RMSECV) was 5.0305 and the correlation coefficient (R c) was 0.8554 in the calibration set; the root mean square error of prediction (RMSEP) was 6.0807, the correlation coefficient (R p) was 0.7717, and the ratio performance deviation (RPD) was 1.59 in the prediction set. Finally, the BP_AdaBoost model revealed its superior performance when compared with back propagation neural network (BPNN) model. The overall results demonstrate that FT-NIR spectroscopy technique can be successfully used in the evaluation of sensory quality of green tea, and BP_AdaBoost algorithm shows its superiority in model calibration.

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Acknowledgments

The authors gratefully acknowledge the financial support provided by the Natural Science Foundation of Jiangsu Province (Youth) (Grant no. BK20140538), the China Postdoctoral Science Foundation (Grant no. 2014 M550273), and the Advanced Talents Science Foundation of Jiangsu University (Grant No. 13JDG094). We also wish to thank many of our colleagues for many stimulating discussions in this field.

Compliance with Ethics Requirements

All the authors that have been involved with the work agree to submit this paper to Food Analytical Methods, and all authors claim that none of the material in the paper has been published or is under consideration for publication elsewhere.

Conflict of Interest

Hui Jiang declares that he has no conflict of interest. Quansheng Chen declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, People’s Republic of China

    Hui Jiang

  2. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, People’s Republic of China

    Quansheng Chen

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  1. Hui Jiang
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  2. Quansheng Chen
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Correspondence to Hui Jiang or Quansheng Chen.

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Jiang, H., Chen, Q. Chemometric Models for the Quantitative Descriptive Sensory Properties of Green Tea (Camellia sinensis L.) Using Fourier Transform Near Infrared (FT-NIR) Spectroscopy. Food Anal. Methods 8, 954–962 (2015). https://doi.org/10.1007/s12161-014-9978-4

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  • DOI: https://doi.org/10.1007/s12161-014-9978-4

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