Near-infrared spectroscopy as a high-throughput phenotyping method for fusiform rust resistance in loblolly pine.

Abstract

L.) in the U.S., causing millions of dollars in damage each year. Using resistant genotypes has proven a successful strategy to limit the disease, but resistance selection still relies on visual inspection for symptoms, which can lead to misclassification due to human error and the presence of 'escaped susceptibles' (i.e., susceptible individuals with no visible symptoms due to either an extended asymptomatic phase of the disease or the lack of adequate disease pressure to become infected). Here, we propose the use of near-infrared (NIR) spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated. We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S. using a portable, handheld NIR spectrometer. We also used a benchtop Fourier-transformed mid-infrared (FT-IR) spectrometer to analyze phloem phenolic extracts of the same samples, as this phenotyping approach has proved successful in other pathosystems. Our results show a moderate association between the phloem spectra and resistance, and models built with NIR spectra were able to classify extremes (i.e., very resistant or very susceptible) with up to 69 ​% testing accuracy. This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.

Plant phenotyping relevance

NIR分光法とケモメトリクスを用いてマツのさび病抵抗性表現型を高スループット評価する方法を提案・検証しており、表現型取得が研究の中心である。

Code and data availability

The article describes NIR/FT-IR spectral data collection and R-based chemometric analysis, but no public phenotype/spectral dataset, author analysis code, or trained model deposit is mentioned. Supplementary data is only referenced via the article DOI, and the caret URL is a generic library reference, not a paper asset

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