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便携式多品种花生种子活力无损检测装置研究

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开发了一种基于近红外光谱技术的便携式多品种花生种子活力无损检测装置.该装置以近红外光谱仪为核心,具有成本低廉、检测速度快等优势,可实现对多品种、多状态花生种子的高效非破坏性活力评估.研究发现,种子老化过程中,脂肪和水分等营养成分明显消耗,与种子活力呈显著关联性.为提高检测准确性,采用竞争自适应重加权抽样算法精确识别了水分和脂肪的特征波长,主要分布在1 000~1 150 nm、1 250~1 350 nm和1 400~1 500 nm.基于这些特征波段,建立水分和脂肪质量分数的定量预测模型.对于含水率,采用SNV预处理方法的模型在预测集上达到0.948 6的相关系数和0.292 7%的均方根误差.对于脂肪质量分数,使用SG-MSC预处理后获得了0.852 1的预测集相关系数和2.569 9%的均方根误差.在上述基础上,引入稀疏偏最小二乘判别分析建立了花生种子活力判别模型.结果表明,改进后的模型在所有状态种子的分类准确率均有显著提高.鲁花8号、粒粒红、落日红和小白沙分类准确率分别达到91.20%、90.80%、90.00%和90.00%.相比不考虑特征波长的建模分类准确率(小白沙,74.40%),改进后的分类方法提高15.60个百分点.特别地,当脂肪质量分数低于45%且含水率低于4%时,判定为非活性种子.本研究开发的无损检测装置为花生种子活力的快速、准确评估提供了创新方法,具有在种子质量控制、育种选择以及农业生产中广泛应用的潜力.
Design of Portable Non-destructive Device for Viability Assessment of Multiple Peanut Seed Varieties
A portable nondestructive testing device was developed based on near infrared spectroscopy technology,which was used to evaluate the viability of various peanut seeds.With a near-infrared spectrometer as its core component,the device offered advantages such as low cost and rapid detection,enabling efficient non-destructive viability assessment of peanut seeds across multiple varieties and states.It was found that during seed aging,nutritional components such as fat and moisture were significantly consumed,showing a strong correlation with seed viability.In order to improve detection accuracy,competitive adaptive re-weighted sampling(CARS)algorithm was used to accurately identify characteristic wavelengths of water and fat,which were mainly distributed in the ranges of 1 000~1 150 nm,1 250~1 350 nm and 1 400~1 500 nm.On this basis,quantitative prediction models of moisture and fat content were established.For moisture content,the SNV pretreatment model achieved a high correlation coefficient of 0.948 6 and a low RMS of only 0.292 7%on the prediction set.For fat content,SG-MSC pretreatment still produced the correlation coefficient of prediction set of 0.852 1 and a root mean square error of 2.569 9.On this basis,the sparse partial least squares discriminant analysis(SPLS-DA)model was introduced to establish a peanut seed viability discriminant model.Results showed that the improved model significantly improved the classification accuracy for seeds under various conditions.The classification accuracies for Luhua No.8,Lili Hong,Luori Hong,and Xiaobaisha varieties reached 91.20%,90.80%,90.00%and 90.00%,respectively,an average increase of 15.60 percentage points compared with models not considering characteristic wavelengths.Specifically,seeds were determined to be non-viable when fat content was less than 45%and moisture content was below 4%.This method was particularly helpful in distinguishing mildly aged seeds and low-viability seeds that are difficult to accurately identify through traditional spectral classification methods.A Matlab-based peanut seed detection software was developed to achieve"one-click operation"for rapid seed viability detection,providing users with a convenient testing experience.The non-destructive testing device developed provided a method for quickly and accurately evaluating peanut seed viability,and had a wide application potential in seed quality control,breeding selection and agricultural production.

seed viabilitynon-destructive detectioncharacteristic wavelengthssparse partial least squares discriminant analysis

尹田振、彭彦昆、李永玉、胡黎明、王炳伟、马振浩

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中国农业大学工学院,北京 100083

国家农产品加工技术装备研发分中心,北京 100083

种子活力 无损检测 特征波长 稀疏偏最小二乘判别分析

2024

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCD北大核心
影响因子:1.904
ISSN:1000-1298
年,卷(期):2024.55(z2)