首页 | 简介 | 作者 | 编者 | 读者 | Ei(光盘版)收录本刊数据 | 网络预印版 | 点击排行前100篇 | English
王雪,马铁民,杨涛,宋平,谢秋菊,陈争光.基于近红外光谱的灌浆期玉米籽粒水分小样本定量分析[J].农业工程学报,2018,34(13):203-210.DOI:10.11975/j.issn.1002-6819.2018.13.024
基于近红外光谱的灌浆期玉米籽粒水分小样本定量分析
投稿时间:2018-02-25  修订日期:2018-05-19
中文关键词:  近红外光谱  水分  模型  定量分析  小样本集  灌浆期玉米籽粒  Bootstrap重抽样本  样本优化选择
基金项目:国家自然科学基金青年基金(31701318);黑龙江八一农垦大学校内课题培育资助项目(XZR2016-09)。
作者单位
王雪 1. 沈阳农业大学信息与电气工程学院沈阳 110866
2. 黑龙江八一农垦大学电气与信息学院大庆 163319
 
马铁民 2. 黑龙江八一农垦大学电气与信息学院大庆 163319
3. 东北大学计算机科学与工程学院沈阳 110819
 
杨涛 1. 沈阳农业大学信息与电气工程学院沈阳 110866
 
宋平 1. 沈阳农业大学信息与电气工程学院沈阳 110866
 
谢秋菊 2. 黑龙江八一农垦大学电气与信息学院大庆 163319
 
陈争光 2. 黑龙江八一农垦大学电气与信息学院大庆 163319
 
摘要点击次数: 139
全文下载次数: 40
中文摘要:玉米灌浆期含水率测定是考种育种的重要指标。为了节约样本且快速准确测定灌浆期玉米水分,该文应用近红外光谱技术,提出了基于小样本条件下的自举算法(Bootstrap)与基于x-y距离结合的样本划分方法(SPXY, sample set partitioning based on joint x-y distances)相结合的样本优化方法的偏最小二乘(PLS,partial least square)水分定量分析模型Bootstrap-SPXY-PLS模型。试验结果表明,当Bootstrap重抽样本次数等于500,样本数量大于等于10时,模型的性能稳定,并且随着样本数量增加,重抽样本次数相对减少;样本数量为10和50时,全谱Bootstrap-SPXY-PLS模型的预测均方根误差(RMSEP, root-mean-square error of prediction)均值分别为0.38%和0.40%,预测相关系数(correlation coefficients of prediction)分别为0.975 1和0.968 5,决定系数R2分别为0.999 9和0.993 6;基于竞争性自适应重加权采样算法(CARS,competitive adaptive reweighed sampling)波长变量筛选后的CARS-Bootstrap-SPXY-PLS模型的预测均方根误差RMSEP均值分别为0.36%和0.35%,预测相关系数分别为0.973 6和0.975 0,模型决定系数R2分别为0.924 5和0.918 0。因此,全谱Bootstrap-SPXY-PLS模型和CARS-Bootstrap-SPXY-PLS模型均具有稳定的预测能力,为玉米育种时灌浆期种子水分测定提供了一种稳定、高效的方法。
Wang Xue,Ma Tiemin,Yang Tao,Song Ping,Xie Qiuju,Chen Zhengguang.Moisture quantitative analysis with small sample set of maize grain in filling stage based on near infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(13):203-210.DOI:10.11975/j.issn.1002-6819.2018.13.024
Moisture quantitative analysis with small sample set of maize grain in filling stage based on near infrared spectroscopy
Author NameAffiliation
Wang Xue 1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
2. College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China
 
Ma Tiemin 2. College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China
3. School of Computer Science And Engineering, Northeastern University, Shenyang 110819, China
 
Yang Tao 1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
 
Song Ping 1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
 
Xie Qiuju 2. College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China
 
Chen Zhengguang 2. College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China
 
Key words:near infrared spectroscopy  water  models  quantitative analysis  small sample set  maize grain in filling stage  bootstrap resample  sample optimized selection
Abstract:Near infrared spectroscopy (NIRS) and its analytical techniques are increasingly used for the rapid quantitative and qualitative analysis in the field of agriculture, food, industry, and so on. Generally, the sample size in most research is between 100 and 200. In maize breeding, the sampling quantity and its cost for maize grain moisture measurement in filling stage are limited due to some objective limitations of the planting area of new varieties, the maize plants number per square meter, the effective experimental spikes number and other conditions. However, the filling period is a critical stage for maize grain variety changes and breeding test. In the traditional measurement method for moisture drying, 150-250 grains are taken for the moisture measurement, which are a large number of samples. Therefore, it is one of the urgent problems to provide a high efficient moisture measurement method using small sample size in maize breeding process. In NIRS research field, the size of sample set is a key factor for the performance and prediction ability of the algorithm. In general, the smaller the size of sample set, the lower the efficiency of model, so it is very important to find a critical value for the small sample set in practical applications. In recent years, data analysis methods for small sample set based on Bootstrap were proposed, and most of them were considered reliable for the small sample set data validation. In order to reduce sample size and measure the moisture content of maize grainin filling period quickly and accurately, a quantitative analysis model of moisture was presented based on sample set optimized selection and partial least squares (PLS) algorithm using NIRS. The method of sample set optimized selection was on the basis of Bootstrap resampling strategy and sample set partitioning based on joint x-y distances (SPXY). The models were evaluated by correlation coefficient of prediction and root-mean-square error of prediction (RMSEP) in different resampling times and the sizes of sample set. Firstly, the full spectrum and wavelength selection spectrum were resampled for 100-800 times at the sample size of 5, 10, 20 and 50, respectively, using Bootstrap algorithm. Secondly, the resampled set was selected for the calculation of SPXY samples to optimize selection to form modeling sample set. Thirdly, the modeling sample set was divided into multiple subsets and PLS sub-models were constructed using these subsets respectively, and multiple predictive values were obtained by using the PLS sub-models regression analysis. Finally, the predictive values of maize grain moisture in filling period were obtained by the weighted mean of multiple predictive values. It is shown that a model with stable performance is gotten when the number of Bootstrap resampling is 500 and resampling size is greater than 10, and the number of resampled samples decreases with the increasing of sample size. When the sample size is 10 and 50, the RMSEP mean values of full spectrum Bootstrap-SPXY-PLS model are 0.38% and 0.40% respectively, the correlation coefficients of prediction are 0.975 1 and 0.968 5 respectively, and the determination coefficients (R2) of the calibration are 0.999 9 and 0.993 6 respectively; the RMSEP mean values of CARS-Bootstrap-PLS are 0.36% and 0.35% respectively, the correlation coefficients of prediction are 0.973 6 and 0.975 0 respectively, and the R2 values were 0.924 5 and 0.918 0 respectively. Therefore, the 2 models of full-spectrum Bootstrap-SPXY-PLS and the CARS-Bootstrap-PLS both have good prediction ability and can provide a new stable and efficient method for maize grain moisture determination in filling stage in breeding process. It is helpful for maize breeding research, and also provides a new idea for quantitative analysis of NIR spectra in small sample set.
查看全文   下载PDF阅读器

京ICP备06025802号-3

主办单位:中国农业工程学会 单位地址:北京朝阳区麦子店街41号

服务热线:010-59197076、59197077 传真: 邮编:100125 Email:tcsae@tcsae.org
本系统由北京勤云科技发展有限公司设计

京公网安备 11010502031390号