Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Engineering and Technology, Southwest University, Chongqing 400715, China.
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Food Chem. 2024 Oct 15;455:139889. doi: 10.1016/j.foodchem.2024.139889. Epub 2024 May 29.
The development of nondestructive technology for the detection of seed viability is challenging. In this study, to establish a green and effective method for the viability assessment of single maize seeds, a two-stage seed viability detection method was proposed. The catalase (CAT) activity and malondialdehyde (MDA) content were selected as the most key biochemical components affecting maize seed viability, and regression prediction models were developed based on their hyperspectral information and a data fusion strategy. Qualitative discrimination models for seed viability evaluation were constructed based on the predicted response values of the selected key biochemical components. The results showed that the double components thresholds strategy achieved the highest discrimination accuracy (92.9%), providing a crucial approach for the rapid and environmentally friendly detection of seed viability.
检测种子活力的无损技术的发展具有挑战性。在这项研究中，为了建立一种绿色有效的单粒玉米种子活力评估方法，提出了一种两阶段的种子活力检测方法。选择过氧化氢酶（CAT）活性和丙二醛（MDA）含量作为影响玉米种子活力的最关键生化成分，并基于其高光谱信息和数据融合策略，建立了回归预测模型。基于所选关键生化成分的预测响应值，构建了种子活力评价的定性判别模型。结果表明，双组分阈值策略实现了最高的判别准确率（92.9%），为快速、环保的种子活力检测提供了重要方法。
