Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases.
Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases.
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DOI：
10.3389/fpls.2020.577851
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发表时间：
2020
影响因子：
5.6
通讯作者：
Barboza da Silva C
中科院分区：
文献类型：
--
作者：
Galletti PA;Carvalho MEA;Hirai WY;Brancaglioni VA;Arthur V;Barboza da Silva C
基于光的方法正在进一步发展，以满足农业对食品日益增长的需求。光学成像是一种快速、非破坏性和准确的技术，与传统技术相比，可以对产品质量进行一致的测量。在这项研究中，提出了一种新的种子质量预测方法。在所提出的方法中，两个先进的光学成像技术的基础上叶绿素荧光和化学计量学为基础的多光谱成像。化学计量学包括主成分分析（PCA）和二次判别分析（QDA）。在作为作物和科学模型的相关植物中，番茄和胡萝卜被选择用于实验。我们比较了光学成像技术与传统的分析方法用于商业化种子的质量表征。结果表明，基于叶绿素荧光的技术是可行的，以区分品种和鉴定种子的生理潜力较低。探索性分析的多光谱成像数据，使用非监督的方法（双组分PCA）允许胡萝卜品种之间的差异的表征，但不是番茄品种。随机森林（RF）分类器的基础上基尼系数的重要性，应用到多光谱数据，它揭示了最有意义的带宽从19个波长的种子质量表征。为了验证RF模型，我们选择了五个最重要的波长应用于基于QDA的模型中，该模型对具有高活力和低活力种子的批次进行分类达到了很高的准确性，验证集的番茄和胡萝卜的正确分类率分别为86%至95%和88%至97%。进一步分析表明，低质量的种子导致幼苗的光合能力和叶绿素含量发生变化。总之，叶绿素荧光和化学计量学为基础的多光谱成像可以作为可靠的代理的生理潜力，在番茄和胡萝卜种子。从实践的角度来看，这些技术/方法可以潜在地用于筛选食品和农业工业中的低质量种子。
Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.
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影响因子：
8.3
作者：
Che, Wenkai;Sun, Laijun;Liu, Yangyang
通讯作者：
Liu, Yangyang
影响因子：
5.9
作者：
Figueiredo-Gonzalez, Maria;Valentao, Patricia;Andrade, Paula B.
通讯作者：
Andrade, Paula B.
影响因子：
2.6
作者：
Jalink, H.;Frandas, A.;van der Schoor, R.
通讯作者：
van der Schoor, R.
影响因子：
8.7
作者：
Jones MA
通讯作者：
Jones MA
影响因子：
2
作者：
Eller, F. J.;Moser, J. K.;Taylor, S. L.
通讯作者：
Taylor, S. L.