Online detection of tomato internal and external quality attributes by an optical sensing system

Abstract

A new method for detection both external and internal quality attributes of tomato was proposed in this paper. The comprehensive quality detection could be completed by the image and spectra analyses based on the optical sensing system. The images processing method contained three steps: (1) morphological filtering; (2) binarization and (3) circle fitting. The first step was applied to reduce the random noises in the raw images. The second step was aimed to obtain the binary images that contained the contour information of the tomato edge. And the circle fitting algorithm was used to obtain the final diameter information of the tomato samples. The values of R and the RMSE for size prediction results of the tomato samples were 0.9813 and 1.269 mm, respectively. For the spectra analysis, the light scatter effects, including addition coefficient and multiplication coefficient in the raw spectra, were the main reasons for the calibration failure of the multivariate linear model such as PLS, MLR and PCR. Thus, the NSR method was used to eliminate the light scatter effects in this paper. Compared with the other method, NSR method was advantages in higher prediction performance and the simpler calculation. The RMSEP values of the final PLS model were 0.2936 % and 2.0129 a.u. for the SSC and a*, respectively. Thus, the optical sensing system combined with effective information processing method was able to detect the tomato external and internal quality attributes, which could be more suitable to apply in the food processing enterprise in the practice.