Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (5): 1465-1473.doi: 10.13229/j.cnki.jdxbgxb.20230274

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Robust discrimination method for tea varieties based on spectral transformation and high⁃order Sparsity⁃aided Hodrick⁃Prescott decomposition

Xiu-zhi ZHAO1,2(),Jing-ming NING3,De-hong XIE4()   

  1. 1.College of Artificial Intelligence，Zhejiang Industry & Trade Vocational College，Wenzhou 325002，China
    2.School of Computer and Artificial Intelligence，Wenzhou University，Wenzhou 325002，China
    3.State Key Laboratory of Tea Plant Biology and Utilization，Anhui Agricultural University，Hefei 230036，China
    4.College of Information Science and Technology，Nanjing Forestry University，Nanjing 210037，China
  • Received:2023-03-27 Online:2023-05-01 Published:2023-05-25
  • Contact: De-hong XIE E-mail:cassyzxz@126.com;dehong.xie@gmail.com

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Abstract:

In order to improve the accuracy of qualitative analysis of visible near infrared spectroscopy， noise reduction pretreatment is needed. Aiming at the problem that it is easy to produce additional small spectral peaks which deteriorate the accuracy of qualitative analysis during noise reduction， a noise reduction method based on spectral transformation and high-order sparse Hodrick-Prescott decomposition is proposed. In the optimization model of this method， it is assumed that the visible near infrared spectroscopy is composed of low-pass basic waveform spectrum， band-pass characteristic waveform spectrum and noise. In this method， the L2 norm of the residual between the noisy spectroscopy， the basic waveform spectroscopy and the band-pass characteristic waveform spectroscopy is taken as the residual term to ensure that the estimated value is close to the real value. According to the sparsity of the characteristic waveform spectrum， taking the L1 norm of the second-order difference of it as the regularization term， it is constrained to estimate that the characteristic waveform spectrum has a certain sparsity to characterize absorption peaks of the important chemical components in tea. In this method， the low-pass and band-pass zero phase filter matrices are obtained by using the spectral transformation of the filter to help decompose the basic waveform spectroscopy and the characteristic waveform spectroscopy， and the optimal regularization parameters in the optimization equation are obtained by using the L-curve method. The experiment takes the visible near infrared spectroscopy of six kinds of tea as the basic experimental data. In the experiment， our method compares with the wavelet decomposition method， improved Hodrick-Prescott method and savitzky-Golay method by the signal-to-noise ratio，root mean square difference， and the accuracy of the classification model of qualitative analysis of tea varieties as the measures. The experimental results show that our method has the highest signal-to-noise ratio in synthetic spectra containing Gaussian noise and synthetic spectra containing Gaussian pulse mixed noise. For synthetic spectral data sets and real spectral data sets， the test accuracy of the classification model is higher than that of the above three methods， and much higher than the results obtained using their noisy spectral data sets. Therefore， this method has been proved to have advantages in noise reduction of visible near infrared spectroscopy， and can be applied to the pretreatment of the qualitative detection of tea varieties based on visible near infrared spectroscopy.

Key words: visible near infrared spectroscopy, tea, variety, noise reduction, sparsity

CLC Number: 

  • O657.3

Fig.1

Image plane corresponding to six different tea samples （from left to right： oolong， black， green， yellow， Pu'er and white）"

Fig.2

VNIR spectroscopy with simulated Gaussian noises and its denoised VNIR spectroscopy"

Fig.3

Denoised VNIR spectroscopy with simulated mixed noises and its denoised VNIR spectroscopy"

Table 1

Statistical value of signal-to-noise ratio and root mean square error between before and after noise reduction of VNIR spectral data"

指标VNIR数据集DWTMHPS-G本文方法
AveStdAveStdAveStdAveStd
SNR （dB）高斯噪声24.424.4933.203.1132.883.4236.252.04
混合噪声24.315.4130.404.4831.674.7235.123.02
RMSE高斯噪声0.441 40.120 50.168 60.057 00.175 90.063 80.066 10.020 2
混合噪声0.423 00.294 20.192 20.111 640.188 50.121 60.070 70.031 9

Table 2

Test accuracy of tea varieties identification model before and after noise reduction of VNIR spectral data and trained by SVM"

VNIR数据集方 法
含噪DWTMHPS-G本文方法
高斯噪声（合成）61.4268.6678.4875.1287.73
混合噪声（合成）57.5664.3277.5676.3887.95
含噪（真实）51.2365.5675.9677.1683.54

Table 3

Test accuracy of tea varieties identification model before and after noise reduction of VNIR spectral data and trained by KNN"

VNIR数据集方 法
含噪DWTMHPS-G本方法
高斯噪声（合成）54.2261.5977.1274.1588.09
混合噪声（合成）51.7557.4876.5475.9288.40
含噪（真实）48.7959.1674.8776.2485.43
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