Marques da Silva Jorge, Figueiredo Andreia, Cunha Jorge, Eiras-Dias José Eduardo, Silva Sara, Vanneschi Leonardo, Mariano Pedro
Biosystems and Integrative Sciences Institute (BioISI), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
National Station of Viticulture and Enology, 2565-191 Dois Portos, Portugal.
Plants (Basel). 2020 Feb 1;9(2):174. doi: 10.3390/plants9020174.
When a dark-adapted leaf is illuminated with saturating light, a fast polyphasic rise of fluorescence emission (Kautsky effect) is observed. The shape of the curve is dependent on the molecular organization of the photochemical apparatus, which in turn is a function of the interaction between genotype and environment. In this paper, we evaluate the potential of rapid fluorescence transients, aided by machine learning techniques, to classify plant genotypes. We present results of the application of several machine learning algorithms (k-nearest neighbors, decision trees, artificial neural networks, genetic programming) to rapid induction curves recorded in different species and cultivars of vine grown in the same environmental conditions. The phylogenetic relations between the selected species and cultivars were established with molecular markers. Both neural networks (71.8%) and genetic programming (75.3%) presented much higher global classification success rates than k-nearest neighbors (58.5%) or decision trees (51.6%), genetic programming performing slightly better than neural networks. However, compared with a random classifier (success rate = 14%), even the less successful algorithms were good at the task of classifying. The use of rapid fluorescence transients, handled by genetic programming, for rapid preliminary classification of genotypes is foreseen as feasible.
当用饱和光照射暗适应的叶片时，会观察到荧光发射的快速多相上升（考茨基效应）。曲线的形状取决于光化学装置的分子组织，而分子组织又是基因型与环境相互作用的函数。在本文中，我们借助机器学习技术评估了快速荧光瞬变对植物基因型进行分类的潜力。我们展示了几种机器学习算法（k近邻、决策树、人工神经网络、遗传编程）应用于在相同环境条件下生长的不同葡萄品种和种的快速诱导曲线的结果。利用分子标记确定了所选品种和种之间的系统发育关系。神经网络（71.8%）和遗传编程（75.3%）的整体分类成功率均远高于k近邻（58.5%）或决策树（51.6%），遗传编程的表现略优于神经网络。然而，与随机分类器（成功率 = 14%）相比，即使是不太成功的算法在分类任务中也表现良好。利用遗传编程处理的快速荧光瞬变对基因型进行快速初步分类被认为是可行的。
