Detection of soluble solids content in tomatoes using full transmission Vis-NIR spectroscopy and combinatorial algorithms

Abstract

IntroductionSoluble solids content (SSC) is an important indicator for evaluating tomato flavor, and general physical and chemical methods are time-consuming and destructive.MethodsThis study utilized full transmittance visible and near infrared (Vis-NIR) spectroscopy for multi-posed data acquisition of tomatoes in different orientations. The role of two directions (Z1 and Z2) and four preprocessing techniques, as well as three wavelength selection methods in the exploitation of SSC regression models was investigated.ResultsAfter using the Outlier elimination method, the spectra acquired in the Z2 direction and the raw spectral data processed by preprocessing methods gave the best result by the PLSR model (Rp = 0.877, RMSEP = 0.417 %). Compared to the model built using the full 2048 spectral wavelengths, the prediction accuracy using 20 wavelengths obtained by a combination wavelength selection: backward variable selection - partial least squares and simulated annealing (BVS-PLS and SA) was further improved (Rp = 0.912, RMSEP = 0.354 %).DiscussionThe findings of this research demonstrate the efficacy of full-transmission visible-near infrared (Vis-NIR) spectroscopy in forecasting SSC of tomatoes, and most importantly, the combination of the packing method in wavelength selection with an intelligent optimization algorithm provides a viable idea for accurately and rapidly assessing the SSC of tomatoes.