Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6664436 | Journal of Food Engineering | 2018 | 31 Pages |
Abstract
Spatially-resolved spectroscopy (SRS) enables better interrogation of tissue properties at different depths, and it thus has the potential for enhancing quality assessment of horticultural products like tomato, which are heterogeneous in structure and chemical composition. This research was aimed at assessing quality of tomato fruit by using a newly developed SRS system with 30 detection optic fibers covering the wavelength range of 550-1650â¯nm and comparing its performance with two conventional single-point (SP) spectroscopic instruments covering the visible and shortwave near-infrared (Vis/SWNIR) (400-1100â¯nm) and near-infrared (NIR) (900-1300â¯nm) regions, respectively. Spatially-resolved (SR) spectra and SP interactance spectra were acquired for 600 'Sun Bright' tomato fruit. Partial least squares (PLS) models for individual SR spectra and their combinations and for SP Vis/SWNIR and NIR spectra were developed for prediction of soluble solids content (SSC) and pH. Results showed that SSC and pH predictions by SRS varied depending on the light source-detector distance, with the correlation coefficient of prediction (rp) ranging 0.608-0.791 and 0.688-0.800, respectively. Combinations of two or more SR spectra resulted in better, more consistent SSC and pH predictions. SR predictions of pH (rpâ¯=â¯0.819) were better than for SP Vis/SWNIR (rpâ¯=â¯0.743) and NIR (rpâ¯=â¯0.741) predictions, whereas SR predictions of SSC (rpâ¯=â¯0.800) were comparable to SP NIR predictions (rpâ¯=â¯0.810) but better than SP Vis/SWNIR predictions (rpâ¯=â¯0.729). This research showed that owning to its ability of acquiring spatially-resolved spectral information, the SRS technique has advantages over conventional SP spectroscopy for enhancing quality assessment of tomatoes.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Yuping Huang, Renfu Lu, Kunjie Chen,