Application of Wavelet Transform and Successive Projections Algorithm in the Non-Destructive Measurement of Total Acid Content of Pitaya
LUO Xia1, 2, 3, HONG Tian-sheng2, 3, 4*, LUO Kuo2, 3, 4, DAI Fen1, 2, 3, WU Wei- bin2, 3, 4, MEI Hui-lan2, 3, 4, LIN Lin4
1. College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China 2. Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China 3. Division of Citrus Machinery, China Agriculture Research System，Guangzhou 510642, China 4. College of Engineering, South China Agricultural University, Guangzhou 510642, China
Abstract：The objective of present study was to find out an accurate, rapid and nondestructive method to detect total acid content (TA) of pitaya with visible/near-infrared spectrometry, wavelet transform (WT) and successive projections algorithm (SPA), which will provide scientific basis for non- destructive measurement of pitaya. Maya2000 fiber-optic spectrumeter was used to collect spectral data of pitaya on the wavelength in the range of 380~1 099 nm; and then with the methods of WT denosing pretreatment, SPA and partial least squares regression (PLSR) quantitative forecasting model of TA of pitaya was established. The result showed that the precision of WT-SPA-PLSR model, which combine the WT with SPA, was better than that of PLSR model based on the whole wave variables. The relation coefficient of the PLSR model (Rp) that predicted TA based on the original spectrum of all samples as the input variables was 0.851 394 and RMSEP was 0.086 848. The original spectrum variable of the all samples were processed by using wavelet function dbN(N=2, 3, …, 10) for wavelet decomposition and de-noising. The optimal results of noise reduction were decomposed in level 2 using wavelet function db4 (db4-2). The Rp of WT-PLSR model was 0.915 635 and RMSEP was 0.066 752. The prediction of model using wavelet transform de-noising was improved significantly. After the original spectrum processed by db10-3 and SPA, 12 preferred variables were selected from 570 spectrum variables, such as 530, 545, 604, 626, 648, 676, 685, 695, 730, 897, 972, 1 016 nm spectrum variables. The WT-SPA-PLSR model based on these 12 variables as input variables was established. Rp of the WT-SPA-PLSR prediction model was 0.882 83 and RMSEP was 0.077 39. SPA algorithm was suitable for the selection of spectrum variables which could effectively obtain the spectrum variables which were strong correlation with TA and increase the accuracy and stability of the prediction model. The results indicated that the nondestructive detection for TA of pitaya based on the diffuse reflectance visible/near-infrared spectrometry, WT and SPA was feasible.
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