RationaleAvenanthramides (AVNs) are constituents unique to oats and have many outstanding health benefits. AVNs are antioxidants and possess anti‐inflammatory, antifungal and antibacterial activity. The number of known AVNs increased recently because of the latest developments in high‐resolution tandem mass spectrometry (HRMS/MS) techniques.MethodsOat seed extract from 10 oat cultivars was analysed using ultra‐high‐performance liquid chromatography (UHPLC) and Q Exactive hybrid quadrupole‐Orbitrap mass spectrometry (HRMS/MS) with positive heated electrospray ionization.ResultsThirty‐five AVNs were identified and characterized in seed extracts, and the structures of 10 novel AVNs were tentatively elucidated, among which were AVNs bearing a cinamoyl or sinapoyl moiety. These AVNs are reported in oats for the first time. The method was validated using AVN standards (AVNs 2c, 2f and 2p), with limits of detection and quantitation at low picomole levels. Recovery of AVN standards varied from 83% to 106%, and relative standard deviations ranged from 2% to 9%. The total AVNs in the selected oat varieties ranged from 36.0 to 302.5 μg/g (dry weight), with AVN 2c, AVN 2f and AVN 2p representing approximately 65%–70% of that total.ConclusionsOur comprehensive method for detecting the full avenanthramide spectrum can contribute to better understanding the chemical and biological properties of individual AVNs for utilization in developing new oat cultivars and novel functional foods.

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An analysis of the population structure and genetic diversity for any organism often depends on one or more molecular marker techniques. Nonetheless, these techniques are not absolutely reliable because of various sources of errors arising during the genotyping process. Thus, a complex analysis of genotyping error was carried out with the AFLP method in 169 samples of the oil seed plant Plukenetia volubilis L. from small isolated subpopulations in the Peruvian Amazon. Samples were collected in nine localities from the region of San Martin. Analysis was done in eight datasets with a genotyping error from 0 to 5%. Using eleven primer combinations, 102 to 275 markers were obtained according to the dataset. It was found that it is only possible to obtain the most reliable and robust results through a multiple-level filtering process. Genotyping error and software set up influence both the estimation of population structure and genetic diversity, where in our case population number (K) varied between 2–9 depending on the dataset and statistical method used. Surprisingly, discrepancies in K number were caused more by statistical approaches than by genotyping errors themselves. However, for estimation of genetic diversity, the degree of genotyping error was critical because descriptive parameters (He, FST, PLP 5%) varied substantially (by at least 25%). Due to low gene flow, P. volubilis mostly consists of small isolated subpopulations (ΦPT = 0.252–0.323) with some degree of admixture given by socio-economic connectivity among the sites; a direct link between the genetic and geographic distances was not confirmed. The study illustrates the successful application of AFLP to infer genetic structure in non-model plants.