There are obviously limitations such as an inability to quantify supplements or very processed foods with little or no DNA, but overall it seems to work quite well and will combine well with marker gene and metabolomics approaches since they are very complementary.
https://www.pnas.org/doi/full/10.1073/pnas.2304441120
https://www.nature.com/articles/s41587-022-01368-1

Many thanks to @gibbological and the ISB for the patience with this long-running project.

Food genomic material was detected only in about half of infant stool samples but increased at the onset of solid food consumption and was ubiquitous in adult stool samples.

We also ran a proof-of-concept identifying foods and nutrients that were associated with the onset of metabolic disease in an adult cohort.

Building a comprehensive genomic database for as many foods in FOODB as possible we could connect individual genomes to nutrient content. For now we can match 77% of all foods in FOODB with taxonomic information and the next version of the database will push this to 90%.

Using a decoy-aware mapping approach with additional consistency filtering we could show good sensitivity and specificity in simulated sequencing samples with a false positive rate around 1-10 reads per million.