The substitution of more appreciated animal species by animal species of lower commercial value is a common type of meat product adulteration. DNA metabarcoding, the combination of DNA barcoding with next-generation sequencing (NGS), plays an increasing role in food authentication. In the present study, we investigated the applicability of a DNA metabarcoding method for routine analysis of mammalian and poultry species in food and pet food products. We analyzed a total of 104 samples (25 reference samples, 56 food products and 23 pet food products) by DNA metabarcoding and by using a commercial DNA array and/or by real-time PCR. The qualitative and quantitative results obtained by the DNA metabarcoding method were in line with those obtained by PCR. Results from the independent analysis of a subset of seven reference samples in two laboratories demonstrate the robustness and reproducibility of the DNA metabarcoding method. DNA metabarcoding is particularly suitable for detecting unexpected species ignored by targeted methods such as real-time PCR and can also be an attractive alternative with respect to the expenses as indicated by current data from the cost accounting of the AGES laboratory. Our results for the commercial samples show that in addition to food products, DNA metabarcoding is particularly applicable to pet food products, which frequently contain multiple animal species and are also highly prone to adulteration as indicated by the high portion of analyzed pet food products containing undeclared species.

DNA-metabarcoding is becoming more widely used for routine authentication of meat-based food and feed products. Several methods validating species identification methods through amplicon sequencing have already been published. These use a variety of barcodes and analysis workflows, however, no methodical comparison of available algorithms and parameter optimization are published hitherto for meat-based products’ authenticity. Additionally, many published methods use very small subsets of the available reference sequences, thereby limiting the potential of the analysis and leading to over-optimistic performance estimates. We here predict and compare the ability of published barcodes to distinguish taxa in the BLAST NT database. We then use a dataset of 79 reference samples, spanning 32 taxa, to benchmark and optimize a metabarcoding analysis workflow for 16S rDNA Illumina sequencing. Furthermore, we provide recommendations as to the parameter choices, sequencing depth, and thresholds that should be used to analyze meat metabarcoding sequencing experiments. The analysis workflow is publicly available, and includes ready-to-use tools for validation and benchmarking.