Discriminant Model of Maturity of Red Globe Grapes Based on Visible/Near-infrared Spectroscopy
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Abstract:
Maturity is an important criterion for evaluating fruit, as it directly affects the quality and economic value. A discriminant model based on visible/near-infrared spectroscopy was established to determine the maturity of red globe grapes to simplify the process of assessing the maturity, uneven nutritional value, and low competitiveness of red globe grapes. Spectral information on the samples was collected from four stages of the red globe grapes growth period (immature, semi-mature, mature, and super-mature). The spectral band of 550~1 000 nm was selected for modeling. Pre-processed spectra were extracted by competitive adaptive reweighted sampling, uninformative variable elimination, and successive projection algorithm (SPA) to establish the discriminant models of support vector machine, extreme learning machine (ELM), and partial least squares discriminant analysis (PLS-DA), respectively. The best discriminant classification model for the maturity of red globe grapes based on visible/near-infrared spectroscopy was established. The results showed that the ELM model for discrimination and classification of maturity, established by applying the SPA for feature wavelength extraction after spectral pre-processing using the Savitzky–Golay (SG) algorithm showed the best results, followed by the support vector machine model and then the PLS-DA model. Therefore, the best discriminant classification model for red globe grape maturity was SG-SPA-ELM. The accuracy of this model was 97.50% and 96.67% for the training sets and test sets. Therefore, visible/near-infrared spectroscopy can be applied to determine the maturity of red globe grape using a non-destructive method.
