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Classification Method for Viability Screening of Naturally Aged Watermelon Seeds Using FT-NIR Spectroscopy

机译:FT-NIR光谱法筛选天然陈年西瓜种子活力的分类方法

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摘要

Viability analysis of stored seeds before sowing has a great importance as plant seeds lose their viability when they exposed to long term storage. In this study, the potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds of three different varieties stored for four years (natural aging) in controlled conditions. Because of the thick seed-coat of triploid watermelon seeds, penetration depth of FT-NIR light source was first confirmed to ensure seed embryo spectra can be collected effectively. The collected spectral data were divided into viable and nonviable groups after the viability being confirmed by conducting a standard germination test. The obtained results showed that the developed partial least discriminant analysis (PLS-DA) model had high classification accuracy where the dataset was made after mixing three different varieties of watermelon seeds. Finally, developed model was evaluated with an external data set (collected at different time) of hundred samples selected randomly from three varieties. The results yield a good classification accuracy for both viable (87.7%) and nonviable seeds (82%), thus the developed model can be considered as a “general model” since it can be applied to three different varieties of seeds and data collected at different time.
机译:播种前储存种子的生存力分析非常重要,因为植物种子长期暴露后会失去生存能力。在这项研究中,研究了傅里叶变换近红外光谱(FT-NIR)的潜力,以区分在受控条件下存储四年(自然老化)的三个不同品种的有生命和无生命的三倍体西瓜种子。由于三倍体西瓜种子的种皮较厚,因此首先确定了FT-NIR光源的穿透深度,以确保可以有效地收集种子胚光谱。通过进行标准的发芽试验确认生存力后,将收集的光谱数据分为生存组和非生存组。获得的结果表明,所建立的偏最小二乘分析(PLS-DA)模型具有很高的分类精度,该模型是在将三种不同品种的西瓜种子混合后建立的。最后,使用外部数据集(在不同时间收集的)评估了开发的模型,该数据集是从三个品种中随机选择的一百个样本。结果对有生命力的种子(87.7%)和无生命的种子(82%)都具有良好的分类精度,因此可以将开发的模型视为“通用模型”,因为该模型可以应用于三种不同的种子品种,并在不同的时间。

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