“… it is not feasible to test for all 900 chemicals,” says the product inquiry reply.
I don’t think that’s true, though. Besides, once you decide to find a way, you’ll probably find a way.
Modern machine learning can make such large scale testing possible.
In fact, that is a thing — Large Scale Machine Learning.
This article refers to “chemical intuition” in analyzing and predicting:
A Universal Machine Learning Algorithm for Large-Scale Screening of Materials
This emergent combination is chemical engineering alongside computer science and data analysis, drawing on of course large databases.
Let’s put that Big Data to good use, right?
A set of algorithms would be coded for the specific Prop 65 testing purposes.
One notable company working on “anti-corruption” machine learning is giant Microsoft. Not that it would have be them spearheading, but tests of the necessary magnitude are on the horizon.
“Technology resources such as cloud computing, data visualization, artificial intelligence (AI), and machine learning provide powerful tools for governments and corporations to aggregate and analyze their enormous and complex datasets in the cloud, ferreting out corruption…”
Advancements in machine learning and AI are rippling across many fields, and new programs are emerging universities that are cross disciplinary. Of course, it’s important to put beneficial aspects to real world use. And I think that product quality and safety chemical testing is a great application for this.