Embrapa Instrumentation, São Carlos 13560-970, SP, Brazil.
Agrorobótica, São Carlos 13571-512, SP, Brazil.
Molecules. 2021 May 21;26(11):3092. doi: 10.3390/molecules26113092.
The correct recognition of sweet orange ( L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very close genetic characters of three varieties of fresh citrus leaves at the nursery stage of growth by measuring the differences in elemental concentration by laser-induced breakdown spectroscopy (LIBS). The accessions were discriminated by both principal component analysis (PCA) and a classifier based on the combination of classification via regression (CVR) and partial least square regression (PLSR) models, which used the elemental concentrations measured by LIBS as input data. A correct classification of 95.1% and 80.96% was achieved, respectively, for set 1 and set 2. These results showed that LIBS is a valuable technique to discriminate among citrus accessions, which can be applied in the productive sector as an excellent cost-benefit tool in citrus breeding programs.
在苗圃生长阶段，正确识别甜橙（L. Osbeck）品种的品种是生产部门面临的一个挑战，因为它们在表型特征上没有任何差异。此外，由于其遗传痕迹狭窄，没有任何 DNA 标记能够区分品种内的橙子品种。由于树冠和砧木的不同组合会影响元素从土壤中的吸收，每个品种在叶片中都具有典型的元素浓度。因此，这项工作的主要目的是通过激光诱导击穿光谱（LIBS）测量元素浓度的差异，分析生长在苗圃阶段的三个新鲜柑橘品种的二十个不同遗传特征的品种。通过主成分分析（PCA）和基于分类回归（CVR）和偏最小二乘回归（PLSR）模型组合的分类器对品种进行了区分，这些模型将 LIBS 测量的元素浓度作为输入数据。对于第 1 组和第 2 组，分别实现了 95.1%和 80.96%的正确分类。这些结果表明，LIBS 是一种区分柑橘品种的有价值技术，可作为柑橘育种计划中的优秀成本效益工具应用于生产部门。
