欧亚国际

科学家刚刚利用Videometer多光谱成像系统发表了题Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality的文章，这是利用该产品发布的种子科学研究中非常重要的种子老化研究的文章，Videometer多光谱成像技术是种子光谱成像研究领域的前沿技术，广泛应用于种子表型组学研究等。

自体荧光光谱成像技术是一种快速、无损、可靠评估大豆种子品质的创新方法

在农业领域，基于快速和非破坏性方法的光学成像技术的进步有助于提升由于人口增长的所需粮食产量。本研究采用自荧光光谱成像和机器学习算法，建立不同的模型，用于人工老化后生理质量不同的大豆种子的分类。来自365/400nm激发-发射组合的自体荧光信号（与胚中的总酚表现出完美的相关性）能够有效地分离处理。此外，还可以证明自发荧光光谱数据与一些质量指标之间存在着很强的相关性，如早期发芽和种子对胁迫条件的耐受性。基于人工神经网络、支持向量机或线性判别分析开发的机器学习模型对不同质量水平的种子分类显示出高性能（0.99精度）。综上所述，我们的研究表明，大豆种子的生理潜能随着自荧光化合物浓度和结构的变化而降低。此外，改变种子的自荧光特性会影响幼苗的光合作用。从实用角度来看，基于自体荧光的成像可用于检查大豆种子组织光学特性的改变，并可一致性区分高活力和低活力种子。

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

Clíssia Barboza da Silva, Nielsen Moreira Oliveira, Marcia Eugenia Amaral de Carvalho, André Dantas de Medeiros, Marina de Lima Nogueira & André Rodrigues dos Reis 

Scientific Reports volume 11, Article number: 17834 (2021) 

Abstract

In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.