Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging.
Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging.
复制标题
DOI：
10.1016/j.focha.2022.100141
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Sreenivasulu, Nese
中科院分区：
文献类型：
--
作者：
Buenafe, Reuben James;Tiozon, Rhowell Jr;Boyd, Lesley A;Sartagoda, Kristel June;Sreenivasulu, Nese
揭示具有多种营养特性的色素大米的遗传多样性。通过多光谱指纹数据高效预测生物活性物质。用于预测多种营养性状的随机森林和人工神经网络模型。将黑米的营养价值与持续的人类健康联系起来。世界上超过一半的人口依赖大米作为能源，但作为一种以碳水化合物为基础的作物，大米提供的营养益处有限。为了实现亚洲的营养安全目标，我们必须了解具有治疗特性的多种营养特性的遗传变异，并将这些知识应用于未来的水稻育种。利用偏最小二乘(偏最小二乘)回归模型的高预测能力，高通量、视频测定仪LAB光谱成像数据已有效地估计总花色苷含量，特别是结合的花色苷含量。利用酚类化合物和矿物质的多管齐下的营养特性，以及视频分析仪的LAB特征，开发了模型，将一系列黑米品种划分为三种不同的营养品质理想类型。这些黑米多样性面板的派生模型是利用视频分析仪的LAB数据(L、A、B参数)、选定的酚类(总酚、总花青苷和结合的黄酮类)和矿物(钼和磷)创建的。随机森林模型和人工神经网络模型对黑米多种营养特征的描述准确率分别为85.35%和99.9%。这些预测算法将帮助水稻育种者基于简单、高通量的视频分析仪、实验室读数和少量营养分析，战略性地培育出具有营养价值的基因类型。
Unraveling the genetic diversity of pigmented rice for multi-nutritional properties. Efficient prediction of bioactives through multi-spectral fingerprinting data. Random forest and artificial neural network models to predict multi-nutritional traits. Linking black rice nutritional value for sustained human health. Over half the world population relies on rice for energy, but being a carbohydrate-based crop, it offers limited nutritional benefits. To achieve nutritional security targets in Asia, we must understand the genetic variation in multi-nutritional properties with therapeutic properties and deploy this knowledge to future rice breeding. High throughput, VideometerLAB spectral imaging data has been effective in estimating total anthocyanin content, particularly bound anthocyanin content, using the high prediction power of partial least square (PLS) regression models. Multi-pronged nutritional properties of phenolic compounds and minerals, together with videometerLAB features, were utilized to develop models to classify a collection of black rice varieties into three distinct nutritional quality ideotypes. These derived models for black rice diversity panels were created utilizing videometerLAB data (L, A, B parameters), selected phenolic types (total phenolics, total anthocyanins, and bound flavonoids), and minerals (Molybdenum and Phosphorous). Random forest and artificial neural network models depicted the multi-nutritional features of black rice with 85.35 and 99.9% accuracy, respectively. These prediction algorithms would help rice breeders strategically breed nutritionally valuable genotypes based on simple, high-through-put videometerLAB readings and a small number of nutritional assays.