首页|Nondestructive detection of tomato quality based on multiregion combination model
Nondestructive detection of tomato quality based on multiregion combination model
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NETL
NSTL
Wiley
To quickly and accurately identify the quality of tomatoes, a method was proposedto predict the total soluble solid content (SSC), total titratable acidity (TA), and vitaminC (VC) content of tomatoes based on a multiregion combined model of thevisible–near-infrared spectrum. The results show that the competitive adaptive reweightedsampling 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 predictionaccuracy is 97.2%, 96.7%, and 97.7%, respectively, and the relative percentdeviation (RPD) value is 5.870, 5.401, and 5.942, respectively.
NETL
NSTL
Wiley
