Seeds are notoriously high in polyphenols and polysaccharides, which reduce RNA quality and yield, and interfere with downstream applications. We present simple modifications to a rapid RNA extraction protocol for use with seeds. The inclusion of polyethylene glycol in place of polyvinylpyrrolidone reduced polyphenol and polysaccharide contamination. In addition, replacing NaCl with KCl improved the RNA yield from Arabidopsis seeds still bound by mucilage. On extraction of Arabidopsis seed recovered from field soils clean RNA pellets with no accompanying gelatinous matrix (polysaccharide) were seen, with A260/230 ratios greater than 1.8 confirming the lack of polysaccharide carry-over. When Brassica oleracea and Sinapis arvensis seeds were extracted, 260/230 ratios greater than 1.8 were seen. RNA yields in excess of 10 µg per 100 mg seed suitable for RT-QPCR were obtained.

Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950–1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.