AUTHOR=Bresolin Tiago , Dórea João R. R. 

TITLE=Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00923

DOI=10.3389/fgene.2020.00923

ISSN=1664-8021

ABSTRACT=High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive and accurate animal-level information. Assembling such information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectroscopy, which is based on the analysis of the interaction between electromagnetic radiation and matter. The electromagnetic radiation spans an enormous range of wavelengths and frequencies known as electromagnetic spectrum. The spectrum is divided in different regions, with near- and mid-infrared regions being the main spectra regions used in livestock applications. The advantage of using infrared spectroscopy include speed, nondestructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectroscopy techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them have not implemented modern data mining techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on the predictive quality.