Nondestructive detection of tomato quality based on multiregion combination model

To quickly and accurately identify the quality of tomatoes, a method was proposed to predict the total soluble solid content (SSC), total titratable acidity (TA), and vitamin C (VC) content of tomatoes based on a multiregion combined model of the visible–near-infrared spectrum. The results show that the competitive adaptive re-weighted sampling algorithm combined with the partial least squares regression (CARS-PLSR) model has the best prediction effect on SSC, TA, and VC content in “stem + equator”, “stem + bottom” and “stem + bottom” combinations. The prediction accuracy is 97.2%, 96.7%, and 97.7%, respectively, and the relative percent deviation (RPD) value is 5.870, 5.401, and 5.942, respectively. Practical Application This indicates that the CARS-PLSR model based on the multiregion combination of visible–near-infrared spectroscopy is reliable for predicting tomatoes' SSC, TA, and VC content. The results provide a theoretical basis for developing a portable fruit quality detector.