Near-infrared spectroscopy allows detection and species identification of Epichloë endophytes in Lolium perenne.

BACKGROUND: Perennial ryegrass (Lolium perenne) is systemically infected by seed-transmitted fungal endophytes (Epichloë sp.). The presence of Epichloë endophytes alters the nutritive quality of its hosts by modifying several plant traits. The aim of this research was to develop a fast method based on near-infrared reflectance spectroscopy (NIRS) to discriminate between perennial ryegrass plants infected (E+) or not infected (E-) with two endophyte species, Epichloë festucae var. lolii, and Epichloë typhina, using a heterogonous set of perennial ryegrass samples collected from wild grasslands and cultivars. Epichloë festucae var. lolii cultures show two morphotypes, M1 and M3, and Epichloë typhina cultures have a different M2 morphotype. RESULTS: Near-infrared reflectance spectra from E+ and E- ryegrass plants were recorded. Applying the best NIRS model for the detection of Epichloë, 93.3% of E+ plants were classified correctly. The NIRS morphotype classification was correct for 92.9% of M1 morphotype and 100% of M2 morphotypes. The NIRS classification of M3 morphotypes was not as accurate, but it was in accordance with the fungal species classification, identifying some M3 as M1 morphotypes. CONCLUSION: Near-infrared reflectance spectroscopy can detect the presence of Epichloë fungal endophytes directly in samples of perennial ryegrass, and it is adequate for discriminating among fungal species. © 2018 Society of Chemical Industry.

Qualitative and quantitative analysis of endophyte alkaloids in perennial ryegrass using near-infrared spectroscopy.

BACKGROUND: Near-infrared reflectance spectroscopy (NIRS) has been widely used in forage quality control because it is faster, cleaner and less expensive than conventional chemical procedures. In Lolium perenne (perennial ryegrass), one of the most important forage grasses, the infection by asymptomatic Epichloë fungal endophytes alters the plant nutritional quality due to the production of alkaloids. In this research, we developed a rapid method based on NIRS to detect and quantify endophyte alkaloids (peramine, lolitrem B and ergovaline) using a heterogeneous set of L. perenne plants obtained from wild grasslands and cultivars. RESULTS: NIR spectra from dried grass samples were recorded and classified according to the absence or presence of alkaloids, based on reference methods. The best discriminant equations for detection of alkaloids classified correctly 94.4%, 87.5% and 92.9% of plants containing peramine, lolitrem B and ergovaline, respectively. The quantitative NIR equations obtained by modified partial least squares (MPLS) algorithm had coefficients of correlation of 0.93, 0.41, and 0.76 for peramine, lolitrem B and ergovaline respectively. CONCLUSION: NIRS is a suitable tool for qualitative analysis of endophyte alkaloids in grasses and for the accurate quantification of peramine and ergovaline. © 2017 Society of Chemical Industry.