Study on Varieties Discrimination of Nectarine by Hyperspectral Technology Combined with CARS-ELM Algorithm
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Abstract:
The varieties discrimination feasibility of nectarines by hyperspectra technology combined with competitive adaptive reweighted sampling (CARS) and extreme learning machine(ELM) were discussed. Hyperspectra imaging technology was used to select the hyperspectra images data of three different types of nectarines in the range of 420 to 1000nmin this study.The raw spectra were processed by Savitzky-Golay, Multiplicative Scatter Correction, Baseline, and Standard Normalized Varite. The most appropriate pretreatment method was confirmed by the PLSR model. In order to extract characterized wavelengths, there are three different approaches have been used: principal component analysis (PCA), successive projections algorithm (SPA), and CARS. Furthermore, partial least square(PLS)and extreme learning machine (ELM) on the basis of three different characterized wavelengths were used to build the model for identifying the species of nectarines. The results of tests implicated that the performance of CARS-PLS and CARS-ELM classification model was optimized using optimal characterized wavelengths of CARS algorithm. The PLS and ELM model correlation coefficient (Rp), the root mean squared error (RMSEP) of prediction set were 0.942, 0.205 and 0.931, 0.119, respectively. It indicated that CARS algorithm was one of the effective methods for exacting the characterized wavelengths,and ELM model prediction ability was equivalent with conventional PLS model. It approves that the hyperspectral imaging technology combined with CARS-ELM is feasible to discriminate the nectarine varietiesn.
