陈皮年份的高光谱技术鉴别研究

doi:10.3964/j.issn.1000-0593(2017)06-1866-06

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Abstract The shoddy phenomena often occur in the market, and the producing year is an important index to measure the quality of dried tangerine. Thus, this research applied hyperspectral technique in the spectral windows of 380～1 023 and 874～1 734 nm combined with chemometric methods to identify different producing years of dried tangerine. Due to the actual detection of dried tangerine, spectra of front and back of dried tangerine were acquired. Hyperspectral imagesat both sides of a total of 180 samples of four years were collected within 380～1 023 and 874～1 734 nm (720 pictures). Then principal component analysis (PCA) was carried out on the spectral data, which had a qualitativeanalysis about the dried tangerine. Regression coefficient (RC) was chosen to select the sensitive variables. Partial least squares-discrimiant analysis (PLS-DA) were used to compare the performances of full-spectra and sensitive variables. Finally, the linear PLS-DA model and nonlinear ELM model were established based on the sensitive bands. The results demonstrated that most of the predictive effects in 874～1 734 nm were higher than that of 380～1 023 nm. ELM models were outperformed PLS-DA among all the developed models, the highest accuracy was achieved 100% in the model set, and 98.33% in the prediction set. No matter what kind of placement, the prediction accuracy rate was higher than 85%. Hence, hyperspectral technique with chemometrics models can realize nondestructive identification of various producing years of dried tangerine, which provides a theoretical reference and basis for developing instruments of recognition the dried tangerine in further research.

Key words：Hyperspectraltechnique; Dried tangerine; Year; Chemometrics models; Extreme learning machine

Received: 2016-10-08 Accepted: 2017-02-27

ZTFLH:

S567.9

Corresponding Authors: HE Yong E-mail: yhe@zju.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2017)06-1866-06 OR https://www.gpxygpfx.com/EN/Y2017/V37/I06/1866