Vrešak Martina, Olesen Merete Halkjaer, Gislum René, Bavec Franc, Ravn Jørgensen Johannes
Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Pivola, Hoce, Slovenia.
Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark.
PLoS One. 2016 Mar 24;11(3):e0152011. doi: 10.1371/journal.pone.0152011. eCollection 2016.
Application of rapid and time-efficient health diagnostic and identification technology in the seed industry chain could accelerate required analysis, characteristic description and also ultimately availability of new desired varieties. The aim of the study was to evaluate the potential of multispectral imaging and single kernel near-infrared spectroscopy (SKNIR) for determination of seed health and variety separation of winter wheat (Triticum aestivum L.) and winter triticale (Triticosecale Wittm. & Camus). The analysis, carried out in autumn 2013 at AU-Flakkebjerg, Denmark, included nine winter triticale varieties and 27 wheat varieties provided by the Faculty of Agriculture and Life Sciences Maribor, Slovenia. Fusarium sp. and black point disease-infected parts of the seed surface could successfully be distinguished from uninfected parts with use of a multispectral imaging device (405-970 nm wavelengths). SKNIR was applied in this research to differentiate all 36 involved varieties based on spectral differences due to variation in the chemical composition. The study produced an interesting result of successful distinguishing between the infected and uninfected parts of the seed surface. Furthermore, the study was able to distinguish between varieties. Together these components could be used in further studies for the development of a sorting model by combining data from multispectral imaging and SKNIR for identifying disease(s) and varieties.
将快速且高效省时的健康诊断与鉴定技术应用于种业产业链，可加快所需分析、特征描述，并最终加快新优品种的可得性。本研究的目的是评估多光谱成像和单籽粒近红外光谱（SKNIR）在测定冬小麦（Triticum aestivum L.）和冬小黑麦（Triticosecale Wittm. & Camus）种子健康状况及品种鉴别方面的潜力。2013年秋季在丹麦奥胡斯 - 弗拉克伯格进行的分析，包括斯洛文尼亚马里博尔农业与生命科学学院提供的9个冬小黑麦品种和27个小麦品种。利用多光谱成像设备（波长405 - 970 nm），种子表面感染镰刀菌属和黑点病的部分能够成功地与未感染部分区分开来。本研究应用SKNIR基于化学成分差异导致的光谱差异来区分所有36个参与品种。该研究得出了一个有趣的结果，即成功区分了种子表面感染和未感染的部分。此外，该研究还能够区分不同品种。综合这些因素，可在进一步研究中结合多光谱成像和SKNIR的数据，用于开发识别病害和品种的分选模型。
