Metabolomic selection–based machine learning improves fruit taste prediction

Abstract

At least with regard to horticultural crops, we are currently experiencing a step change in crop breeding targets. Historically, breeders focused on high-yielding, resilient varieties; however, this has led to considerable dissatisfaction with modern varieties of fruits and vegetables (1). The recent increasing willingness of consumers to pay a premium for quality is, however, driving a renaissance of breeding for quality traits. That said, flavor is a highly complex composite trait made up of the interactions between the chemical composition of the crop as well as the taste, olfaction, and psychology of the consumer (2, 3). In recent years, flavor has been assessed by costly consumer sensory panels or by breeders themselves in the field. Both approaches have disadvantages. Field evaluation, while allowing the evaluation of many varieties in a day, is highly subjective and error prone. Although population-based sensory panels are well established and accurate, they are difficult to scale to large breeding programs. These limitations in flavor phenotyping are elegantly addressed via the employment of metabolomic selection–based machine learning in the report by Colantonio et al. (4). Machine learning has been gaining increasing traction as a means to analyze various high-throughput phenotyping applications, which enable researchers to identify meaningful patterns in relevant plant data. For example, it has proven utility in two-dimensional light imaging as a proxy for plant biomass, reflectance ratios as proxies for yield, hyperspectral reflectance as proxies for leaf chlorophyll and nitrogen content, and canopy temperatures as proxies for the drought response (reviewed in … [↵][1]1To whom correspondence may be addressed. Email: fernie{at}mpimp-golm.mpg.de. [1]: #xref-corresp-1-1

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