Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system

• Published in: Postharvest biology and technology 2014-04, Vol.90, p.1-6
• Main Authors: Jie, Dengfei, Xie, Lijuan, Rao, Xiuqin, Ying, Yibin
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Food industries; Fruit and vegetable industries; Fundamental and applied biological sciences. Psychology; General aspects; Methods of analysis, processing and quality control, regulation, standards; Monte Carlo-uninformative variable elimination; Near infrared spectroscopy; On-line determination; Stepwise multiple linear regression; Watermelon
• ISSN: 0925-5214; 1873-2356
• DOI: 10.1016/j.postharvbio.2013.11.009

•NIR diffuse transmittance technique is suitable for on-line detection of SSC in watermelon.•The on-line detection system designed by us showed good performance.•BOC-MC-UVE-GA algorithm was considered as a better variable selection method.•Thirteen variables were picked out as the input of multiple linear regression modeling. Sugar content is one of the most important factors determining the eating quality of watermelon fruit. In order to detect the fruit soluble solids content (SSC) on-line, this work develops a nondestructive on-line detection prototype system using visible and near-infrared (Vis/NIR) technology. For the acquisition of the diffuse transmittance spectrum of watermelon, the conveyor was set at a speed of 0.3m/s and ten 150W tungsten halogen lamps were used as the light source. The crucial model for SSC value prediction was optimized by chemometrics. Partial least squares regression (PLSR), stepwise multiple linear regressions (SMLR), Monte-Carlo uninformative variable elimination (MC-UVE) and genetic algorithms (GA) were applied to the spectra in the range of 687–920nm. The data pre-processing methods were optimized to transmittance spectra with baseline offset correction (BOC), and the BOC-MC-UVE-SMLR calibration model was the best with a correlation coefficient (rpre) of 0.70, root mean square error of prediction (RMSEP) of 0.33°Brix for the prediction set. In on-line testing of 30 samples, the rpre was 0.66 and RMSEP was 0.39°Brix. The results showed that a nondestructive on-line SSC value determination prototype based on Vis/NIR technology was feasible.