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| Feature Wavelength Selection and Efficiency Analysis for Paddy Moisture Content Prediction by Near Infrared Spectroscopy |
| HUANG Hua1, WU Xi-yu2, ZHU Shi-ping1* |
1. School of Engineering and Technology, Southwest University, Chongqing 400716, China
2. College of Food Science and Technology, Southwest University, Chongqing 400716, China |
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Abstract In order to obtain the best feature wavelength region for predicting paddy moisture content (PMC) by near infrared spectroscopy (NIR), this research selected 364 paddy samples of “Gangyou 916” which moisture content varied from 32.66% to 2.24%, the pretreatments methods such as mean centering (Mean), standard normalized variate (SNV), Savitzky-Golay derivative (SG1) and multiplicative scatter correction (MSC) were performed, and adopted the feature wavelengths selection methods include interval method (IM), synergy interval method (SIM), moving window method (MWM) and backward interval method (BIM), then used partial least squares (PLS) and principal component regression (PCR) quantitative analysis algorithms for the PMC NIR modeling. The calculation formulas of complexity for wavelengths selection methods with IM, SIM, MWM and BIM are firstly provided in this paper. And this paper also compares and analyzes the program operating efficiency of these methods. The results show that the prediction ability of PLS is better than PCR, but the modeling efficiency of PLS is lower than PCR. The BIM is the optimum prediction model for PMC among the four wavelengths selection methods, which root mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set are 0.995 6 and 0.78%, respectively. The second is MWM, which RMSEP and RP are 0.994 3 and 0.89%, respectively. However, the program running efficiency of these two methods is relatively low. The average running time of BIM is 4.87 h, and average running time of MWM is 29.82 h. This work provided a reference comparison for a fast algorithm of near infrared spectroscopy prediction model in parallel computing and distributed computing.
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Received: 2016-11-28
Accepted: 2017-04-25
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Corresponding Authors:
ZHU Shi-ping
E-mail: zspswu@126.com
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