Nondestructive evaluation of soluble solids content in tomato with different stage by using Vis/NIR technology and multivariate algorithms

In this study Vis/NIR spectroscopy was applied to evaluate soluble solids content (SSC) of tomato. A total of 168 tomato samples with five different maturity stages, were measured by two developed systems with the wavelength ranges of 500-930 nm and 900-1400 nm, respectively. The raw spectral data were pre-processed by first derivative and standard normal variate (SNV), respectively, and then the effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and random frog (RF). Partial least squares (PLS) and least square-support vector machines (LS-SVM) were employed to build the prediction models to evaluate SSC in tomatoes. The prediction results revealed that the best performance was obtained using the PLS model with the optimal wavelengths selected by CARS in the range of 900-1400 nm (Rp = 0.820 and RMSEP = 0.207 degrees Brix). Meanwhile, this best model yielded desirable results with Rp and RMSEP of 0.830 and 0.316 degrees Brix, respectively, in 60 samples of the independent set. The method proposed from this study can provide an effective and quick way to predict SSC in tomato. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The study applied Vis/NIR spectroscopy to evaluate the soluble solids content (SSC) of tomatoes and developed a method for predicting SSC effectively. By measuring tomato samples at different maturity stages and using spectral data from different wavelength ranges, the study established the best prediction model through preprocessing and model building steps.