Determination of Tomato’s SSC and TS Based on Diffuse Transmittance Spectroscopy

In order to meet the demands for rapid and safe nondestructive testing of fruit and vegetable quality，tomato detection system with a special circular light source was built based on the visible / near infrared diffuse transmission principle. Taking soluble solids content (SSC) and total sugar (TS) as the internal quality index, the prediction of 58 tomato samples was carried out by using this system. First, we collected the spectral data of four points for each tomato. Second, Savitzky-Golay smooth(SG-Smooth), standard normal variable transformation(SNV), multiplication scattering correction(MSC), first derivative (FD) and other methods were used to process the original spectral curve before the partial least squares regression(PLSR) model was established. Finally, we validated the established model. The results show that the correlation coefficient (r) of calibration and prediction of the SSC prediction model -are 0.995 6 and 0.976 0 when using 10 point SG-smooth, and the root mean square error of calibration and prediction are 0.052 4% and 0.082 3%. The partial least square regression (PLSR)model, with respect to the first derivative (FD) spectra, provides better prediction performance for total sugar of tomato, with correlation coefficient (r) of calibration of 0.969 1 and 0.972 9, and prediction, root mean standard error of 0.423 8% and 0.454 9%. In the experimental verification of the prediction model, the relationship of SSC between predicted and true value is 0.985 5, root mean square error is 0.066 3°Brix, the relationship of TS between predicted and true value is 0.944 9 while root mean square error is 0.571 5%. The results show that the content of soluble solids and total sugar in tomato can be realized by using visible / near infrared diffuse reflectance spectroscopy. It provides a real-time, nondestructive and rapid detection method for the evaluation of the internal quality of tomato, and provides a theoretical basis for its online grading.