Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR.

Abstract

This work was attempted to evaluate the feasibility of a constructed on-line NIR platform coupled with efficient algorithms for rapid and robust quantification of quality parameter in cherry tomato. Specifically, a system was developed based on shortwave NIR spectroscopy for on-line quality inspection of cherry tomatoes. The spectra were recorded in diffuse reflectance mode from 900 to 1700nm, and the conveyor belt speed was fixed to five samples per second. Three novel methods, namely variable combination population analysis (VCPA), uninformative variable elimination (UVE) and competitive adaptive reweighed sampling algorithm (CARS) were coupled with partial least square (PLS) for selecting optimal dataset, and modeling. The obtained results showed that under the optimal tuning parameters (N=100, k=500, ω=14, σ=10%), a total of 512 original variables, only 9 variables (1.75%) were extracted by VCPA. Subsequently, VCPA-PLS yielded outstanding performance in predicting soluble solid content in cherry tomatoes, with a higher correlation coefficient (RP=0.9053), and lower root mean square errors (RMSEP=0.382) in prediction set. This methodology demonstrated the versatile potential of the proposed installation coupled with VCPA methods for on-line detection of total soluble solids in cherry tomatoes.