Application of the radial basis function neural networks to improve the nondestructive Vis/NIR spectrophotometric analysis of potassium in fresh lettuces

Abstract This study was carried out to evaluate the feasibility of using Vis/near-infrared (Vis/NIR) spectroscopy for determining the potassium concentration in fresh lettuce leaves and petioles of single-variety lettuce and mixed lettuce leaves of two varieties. Partial least squares (PLS) and radial basis function (RBF) neural network were systemically studied and compared as regressions tools in developing the prediction models. Competitive adaptive reweighted sampling (CARS) variable selection and spectral preprocessing (first- and second-order derivatives) were applied to optimize the performance of predictions. On the basis of these selected optimum wavelengths, the established PLS prediction models provided the coefficients of determination (R2) of 0.83 and 0.71, residual predictive deviations (RPD) were 1.95 and 1.80, and root mean square errors of prediction (RMSEP) were 39.07 and 38.06 mg/100 g for green leaves and petioles, respectively. By comparison, the RBF approach with first-derivative preprocessing spectra was found to provide the best performance of mixed samples, yielding R2 of 0.86 and 0.88, RMSEP of 31.20 and 27.63 mg/100 g, and RPD of 2.44 and 2.47 for green leaves and petioles, respectively. The overall results of this study revealed the potential for use of Vis/NIR spectroscopy as an objective and non-destructive method to inspect the potassium concentration of fresh lettuces.

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