木薯块根灰分和水分近红外光谱预测模型的构建与优化
Construction and optimization of prediction models for cassava ash and water content by using near-infrared spectroscopy
张逸杰 1王思琦 2陆小静 3宋记明 4王睿 2曹敏 2张瑞 2王红刚 2吴金山1
作者信息
- 1. 海南大学林学院,海口 570228
- 2. 海南大学热带作物学院,海口 570228
- 3. 中国热带农业科学院热带作物品种资源研究所,海口 571101
- 4. 云南省农业科学院热带和亚热带经济作物研究所,云南保山 678000
- 折叠
摘要
为快速检测木薯的灰分和水分含量,以同地块同一时期木薯种质资源为材料进行建模,采用GB 5009.3-2016和GB/T5009.4-2016法对木薯灰分和水分含量进行测定,同时使用近红外光谱分析仪对137份样品进行光谱采集,利用TQ Analyst 9.0分析软件,采用偏最小二乘法(PLS)构建木薯灰分和水分近红外定标模型.实验结果显示,木薯灰分、水分模型相关系数(R)分别为0.94、0.93,校正均方根误差(RMSEC)分别为0.22、0.48,预测均方根误差(RMSEP)分别为0.21、1.46,交叉验证均方差(RMSECV)分别为0.40、1.54;选用未参与建模的20份木薯种质资源对该模型进行外部验证,预测值与真实值进行差异性分析(P＞0.05),P值分别为0.464、0.459说明差异无显著性,表明该模型可适用于木薯灰分和水分检测.
Abstract
Ash content and moisture content are important indexes for cassava quality evaluation.It is of great significance to establish a method for rapid detection of ash and moisture contents in cassava by using near infrared spectroscopy combined with multivariate mathematical analysis.Tubers of 137 accessions of cassava were harvested at the cassava growing site and their ash and water contents were determined by using the methods described in the standards GB5009.3-2016 and GB/T5009.4-2016 and at the same time the near-infrared spectroscopy.The NIR spectral data collected were analyzed by using the software TQ Analyst 9.0.The NIR calibration models for ash and water contents of cassava were constructed by using the partial least squares(PLS)method.The results showed that correlation coefficients(R)of the ash and water content models were 0.94 and 0.93,respectively.Root mean square error of correction(RMSEC)was 0.22 and 0.48,respectively.Root mean square error of prediction(RMSEP)was 0.21 and 1.46,respectively.Root mean square error of cross-validation(RMSECV)was 0.40 and 1.54,respectively.Twenty accessions of cassava germplasm that were not involved in the modelling were selected for external validation of the model.The results showed that there is no significant difference between the predicted value and the actual value,with the p-values(P＞0.05)being 0.464 and 0.459,respectively,indicating that the model could be applied to the detection of cassava ash and water contents.The near-infrared quantitative detection models of ash and moisture established can be used for rapid detection.
关键词
木薯/灰分/水分/近红外光谱/预测模型/快速检测Key words
cassava/ash/water/near-infrared spectroscopy/prediction model/rapid detection method引用本文复制引用
基金项目
国家现代木薯产业技术体系建设项目(CARS-11-HNCYH)
出版年
2024
NETL
NSTL
万方数据