Reversing Naive Bayes to find extreme points of data sets

Question

I'd like to know if this is a sensible idea and if there exist any already formed methods to do this (I'm new to the data science area).

Essentially, I have used Naive Bayes to accurately classify three types of food, based on their nutritious value (fat, salt, sugar, protein, and carbohydrates as my features).

Now that I can accurately classify these foods, Is there a method which uses the Naive Bayes to reverse this approach, and find the extreme values these features can be to be still classified as a type of food?

E.g: The max fat food1 can be, to still be considered food1.

I realize that these values will change, as other nutrient variables are changed, but I wondered if an optimized set of equations could be obtained in 5 dimensions?