Alignment-free microbiome-based classification of fresh produce safety and quality

Abstract

Small samples sizes and loss of up to 50-70% of sequencing data during the data denoising step of preprocessing can limit the statistical power of fresh produce microbiome analyses and prevent detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored an alignment-free analysis strategy using k-mer hashes to identify DNA signatures predictive of produce safety and produce quality, and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step. Random forests (RF)-based classifiers for fresh produce safety and quality using 7-mer hash datasets had significantly higher classification accuracy than those using the ASV datasets. We also demonstrated that the proposed combination of integrating multiple datasets and leveraging an alignment-free 7-mer hash strategy leads to better classification performance for fresh produce safety and quality. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing datasets for the application of machine learning in the area of microbial safety and quality of food.