建立近红外特征波长模型快速测定羊草常规营养成分的研究

doi:10.11733/j.issn.1007-0435.2019.04.010

草地学报 ›› 2019, Vol. 27 ›› Issue (4): 867-873.DOI: 10.11733/j.issn.1007-0435.2019.04.010

建立近红外特征波长模型快速测定羊草常规营养成分的研究

陈积山, 朱瑞芬, 张强, 杜优颖, 孔晓蕾

黑龙江省农业科学院草业研究所, 黑龙江哈尔滨, 150086

收稿日期:2018-09-25 修回日期:2019-03-19 出版日期:2019-08-15 发布日期:2019-09-26
作者简介:陈积山(1979-),男,副研究员,主要从事牧草栽培及草地管理;E-mail:cyszps@163.com
基金资助:
黑龙江省自然科学基金（C2018058）资助

Research on the Rapid Determination of Conventional Nutritional Components of Leymus Chinensis by Establishing Near Infrared Characteristic Wavelength Model

CHEN Ji-shan, ZHU Rui-fen, ZHANG Qiang, DU You-ying, KONG Xiao-lei

Institute of Pratacultural Science, Heilongjiang Academy of Agricultural Science, Harbin, Heilongjiang Province 150086, China

Received:2018-09-25 Revised:2019-03-19 Online:2019-08-15 Published:2019-09-26

摘要/Abstract

摘要： 本研究采用近红外光谱法快速测定羊草（Leymus chinensis）中的常规营养成分，利用无信息变量消除法（unknown variable elimination，UVE）、随机蛙算法（random frog algorithm，RF）结合偏最小二乘法（partial least squares，PLS）建立了羊草品质测定模型，有效降低了冗余无信息变量，提高了模型的测量精度和稳定性。研究发现利用UVE-PLS筛选建立的羊草品质测定模型优于全光谱PLS和RF-PLS筛选建立的模型；UVE-PLS模型显著降低了交叉验证均方根误差和预测均方根误差，提高了校正集决定系数、交叉验证决定系数及预测集决定系数。研究表明UVE-PLS模型在测定羊草中的水分、粗蛋白、酸性洗涤纤维和中性洗涤纤维是可行的，校正集决定系数和预测集决定系数95%~98%。

关键词: 羊草, 常规营养成分, 近红外特征波长, 无信息变量消除法, 随机蛙跳

Abstract: Near infrared spectroscopy (NIRS) was used to determine the content of quality components (water,crude protein,acid detergent fiber,neutral detergent fiber) of Leymus chinensis. In order to effectively reduce the redundant unknown variables and to increase the prediction accuracy and stability of the model,this study used the method of unknown variable elimination (UVE),random frog algorithm (RF) and partial least squares (PLS) to establish the quality prediction model of Leymus chinensis. The results showed that UVE-PLS screening model was superior to both full-spectrum PLS and RF-PLS screening model. UVE-PLS model when using for predicting water content,crude protein content,acid detergent fiber content and neutral detergent fiber content of Leymus chinensis significantly reduced cross-validation root mean square error (RMSECV) and prediction root mean square error (RMSEP),and increased the determination coefficient of calibration set determination coefficient (R_C²),cross-validation determination coefficient (R_CV²) and prediction set. (R_P²),indicating that UVE-PLS near infrared spectroscopy model is feasible to predict the contents of quality components (moisture,crude protein,acid detergent fiber,neutral detergent fiber) of Leymus chinensis,with a high prediction accuracy,ranging from 95% to 98%.

Key words: Leymus chinensis, Quality components, Near infrared spectroscopy, Unknown variable elimination, Random frog

中图分类号:

Q943

陈积山, 朱瑞芬, 张强, 杜优颖, 孔晓蕾. 建立近红外特征波长模型快速测定羊草常规营养成分的研究[J]. 草地学报, 2019, 27(4): 867-873.

CHEN Ji-shan, ZHU Rui-fen, ZHANG Qiang, DU You-ying, KONG Xiao-lei. Research on the Rapid Determination of Conventional Nutritional Components of Leymus Chinensis by Establishing Near Infrared Characteristic Wavelength Model[J]. Acta Agrestia Sinica, 2019, 27(4): 867-873.

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参考文献

[1] 陆婉珍,袁洪福,徐广通,等. 现代近红外光谱分析技术[M]. 第一版.北京:中国石化出版社,2000,25-28
[2] 李民赞,韩东海,王秀. 光谱分析技术及其应用[M]. 北京:科学出版社,2006,125-127
[3] 陆婉珍,袁洪福,徐广通,等. 现代近红外光谱分析技术[M]. 第二版.北京:中国石化出版社,2007,102-108
[4] 陆婉珍,袁洪福,褚小立. 近红外光谱仪器[M]. 北京:化学工业出版社,2010,75-78
[5] 陆婉珍,褚小立. 近红外光谱(NIR)和过程分析技术(PAT)[J]. 现代科学仪器,2007(4):13-17
[6] 梁逸曾,俞汝勤.化学计量学[M]. 北京:化学工业出版社,2003,35-37
[7] 屈亚堃,宫元娟. 近红外光谱技术在苹果品质无损检测中的应用[J]. 农业科技与装备,2014(6):32-33
[8] 介邓飞,谢丽娟,饶秀勤,等. 近红外光谱变量筛选提高西瓜糖度预测模型精度[J]. 农业工程学报,2013,29(12):264-270
[9] 褚小立,袁洪福,陆婉珍. 近红外分析中光谱预处理及波长选择方法进展与应用[J]. 化学进展,2004,16(4):528-542
[10] 吴迪,吴洪喜,蔡景波,等. 基于无信息变量消除法和连续投影算法的可见-近红外光谱技术白虾种分类方法研究[J]. 红外与毫米波学报,2009,28(6):423-427
[11] 陈立旦,赵艳茹. 可见-近红外光谱联合随机蛙跳算法检测生物柴油含水量[J]. 农业工程学报,2014,30(8):168-173
[12] Yan H,LU D L,Chen B,et al. Development of A Hand-hled Near Infrared System Based on An Android OS and MivroNIR,and Its Application in Measuring Soluble Solids Content in Fuji Apples[J]. Spectroscopy and Spectral Analysis,2014,25(4):16
[13] Zou X B,Zhao J W,PoceyMJW,et al. Variables Selection Methods in Near-infrared Spectroscopy[J].AnalyticaChimica Acta,2010,667(1/2):14-32
[14] Li H D,Xu Q S,Liang Y Z. Random Frog:An Efficient Reversible Jump Markov Chain Monte Carlo-like Approach for Variable Selection With Applications to Gene Selection and Disease Classification[J].Analytica Chimica Acta,2012(740):20-26
[15] Kasemsumran S,Du Y P,Li B Y,et al. Moving Window Cross Validation:A New Cross Validation Method for The Selection of A Rational Number of Components in A Partial Least Squares Calibration Model[J]. The Analyst,2006,131(4):529-537
[16] KoshoubuJ,IwataT,Minami S. Elimination of The Uninformative Calibration Sample Subset in The Modified UVE(Uninformative Variable Elimination)-PLS (Partial Least Squares) Method[J]. Analytical Sciences,2001,17(2):319-22
[17] Jiang H,Liu G H,Mei C L,et al. Measurement of Process Variables in Solid-State Fermentation of Wheat Straw Using FT-NIR Spectroscopy and Synergy Interval PLS Algorithm[J].SpectrochimicaActa,Part A (Molecular and Biomolecular Spectroscopy),2012(97):227-283
[18] Li J B,Guo Z M,Huang W Q,et al. Near-Infrared Spectra Conbining With CARS and SPA Algorithms to Screen The Variables and Samples for Quantitatively Determining The Soluble Solids Content in Strawberry[J]. Spectroscopy and Spectral Analysis,2015,35(2):372-378
[19] Cao N N,Wang J H,Li P F,et al. Study on Variable Selection of NIR Spectral Information Based on GA and SCMWPLS[J]. Spectroscopy and Spectral Analysis,2010,30(4):915-919
[20] Sun T,Xu W L,Hu T. Determination of Soluble Solids Content in Nanfeng Mandarin by Vis/NIR Spectroscopy and UVE-ICA-LS-SVM[J].Spectroscopy and Spectral Analysis,2013,33(12):3235-3239
[21] Li X L,Sun C J,Luo L B. Determination of Tea Polyphenols Content by Infrared Spectroscopy Coupled With iPLS and Random Frog Techniques[J]. Computers and Electronics in Agriculture,2015,112(Sp. Iss. SI):28-35

建立近红外特征波长模型快速测定羊草常规营养成分的研究

Research on the Rapid Determination of Conventional Nutritional Components of Leymus Chinensis by Establishing Near Infrared Characteristic Wavelength Model

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