Food authentication from shotgun sequencing reads with an application on high protein powders

Abstract

Here we propose that using shotgun sequencing to examine food leads to accurate authentication of ingredients and detection of contaminants. To demonstrate this, we developed a bioinformatic pipeline, FASER (Food Authentication from SEquencing Reads), designed to resolve the relative composition of mixtures of eukaryotic species using RNA or DNA sequencing. Our comprehensive database includes >6000 plants and animals that may be present in food. FASER accurately identified eukaryotic species with 0.4% median absolute difference between observed and expected proportions on sequence data from various sources including sausage meat, plants, and fish. FASER was applied to 31 high protein powder raw factory ingredient total RNA samples. The samples mostly contained the expected source ingredient, chicken, while three samples unexpectedly contained pork and beef. Our results demonstrate that DNA/RNA sequencing of food ingredients, combined with a robust analysis, can be used to find contaminants and authenticate food ingredients in a single assay.

Keywords: Food microbiology; Metagenomics.

Fig. 1

Pipeline applied to food sample sequencing data to determine matrix species and their relative proportions. In the taxonomic assignment step with exemplary diagram, reads are placed on the lowest common ancestor (LCA) of the nodes that they hit, in case of multiple hits per read. In the relative quantification step the read counts at internal nodes are re-assigned to the species at the leaf nodes

Fig. 2

Illustration of the minimum size of a subsample to obtain a desired limit of detection. The required number of reads is shown as a function of frequency of species S (in the full sample). In this example with a total number of N = 300 million reads, we desire with high probability P (here P ≥ 0.9999) to have limit of detection at least L = 100 sampled reads coming from species S when S is present. For example, when frequency of S is 0.1% (x = 0.001), a subsample of 141,499 reads from the total 300 million reads is required (marked with a square). When frequency is S is 2% (x = 0.02), fewer than 10,000 reads are required (marked with a circle)

Fig. 3

FASER pipeline accuracy on two simulated food mixtures. a Simulated food matrix 1. b Simulated food matrix 2. Insets are shown separately to accommodate different scales. Details regarding the input genomes are given in Supplementary Table 6

Fig. 4

Raw high protein powder (poultry meal) samples’ FASER results showing unexpected non-chicken components. a Percentage of expected content (chicken). b Percentage of unexpected content showing species with relative proportion >0.1% of total matrix composition. Content from Bos taurus (beef) and Sus scrofa (pork) is detected for samples MFMB-04, MFMB-20, and MFMB-38