This is a bit challenging, but perhaps can be done.
I'll assume you are using CBC with the "standard None", where None is one of the concepts in every CBC task.
Next, I'll assume you are using HB estimation such that you obtain a vector of utilities for each respondent.
I'll assume you want to remain with frequentist t-tests rather than delve into Bayesian statistical testing "on the draws." You've said you want to to t-tests using SPSS.
Given all that, please recognize that the None utility is typically understated in marketing contexts. Meaning: people tend to use the None alternative a lot less than they would in the real world. People's intent to buy is thus overstated. Still, you might want to treat None as a relative measure of purchase interest, to compare different groups.
Also, please note that you should use the zero-centered Diffs rescaled utilities in T-tests, as these are normalized to better put each person on the same part-worth utility scale. The raw utilities can show pretty large differences in scaling between people due to respondents answering with different amounts of response error. Lower response error leads to larger magnitude utilities across the boards, and vice-versa.