Non-destructive Quality Analysis of Wheat Protein Based on SPA-RBF Neural Network

Chinese Agricultural Science Bulletin ›› 2013, Vol. 29 ›› Issue (9) : 208-212. DOI: 10.11924/j.issn.1000-6850.2012-1947
23

Non-destructive Quality Analysis of Wheat Protein Based on SPA-RBF Neural Network

Author information +
History +

Abstract

The traditional detection method of the protein content of wheat was tedious and time-consuming. NIRS (Near Infrared Reflectance Spectroscopy) and SPA-RBF artificial neural network were used to non-destructively measure the protein content of wheat in this paper. A representative set of correction samples was selected by SPXY algorithm, and then the spectral data was pretreated with first derivative and SNV methods to enhance spectral features, on the basis of which, making use of SPA to extract sensitive wave points which are used to establish SPA-RBF neural network model of wheat grain protein. Root-Mean-Square Error of Prediction (RMSEP) and prediction correlation coefficient (R) were 0.26576 and 0.975 respectively, which could basically complete the division that was used in grain reserves and food processing profession and breeding preliminary generation. The study showed that: NIRS combing with SPA-RBF neural network could achieve the detection of the protein content of wheat, which could satisfy the need of non-destructive and real-time detection of wheat to meet the development of modern agriculture.

Key words

wheat; protein; near infrared spectroscopy; SPA; RBF neural network

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
Non-destructive Quality Analysis of Wheat Protein Based on SPA-RBF Neural Network. Chinese Agricultural Science Bulletin. 2013, 29(9): 208-212 https://doi.org/10.11924/j.issn.1000-6850.2012-1947

References

[1] 王乐凯.2003年中国小麦分布及品质状况[J].粮食加工,2004(04): 21-29.
[2] 梁晓艳,吉海彦.近红外光谱技术在农作物品质分析方面的应用[J].中国农学通报,2006,22(01):366-371.
[3] 严衍禄,赵龙莲,韩东海,等.近红外光谱分析基础与应用[M].北京:中国轻工业出版社,2005:32-33.
[4] 闻新,等.MATLAB神经网络应用设计[M].科学出版社,2000.
[5] Büchmann Nils Bo, Henrik Josefsson, Ian A Cowe. Performance of European artificial neural network(ANN) calibrations for moisture and protein in cereals using the dnish near infrared transmission (NIT) network[J].Cereal Chemistry,2001,78(05):572-577.
[6] 逯家辉,张益波,张卓勇,等.小波变换近红外光谱结合径向基神经网络快速分析异福片 [J]. 光谱学与光谱分析,2008,28(06): 1264-1267.
[7] 郑怀礼,张鹏,陈雨,等.近红外反射光谱法测定 PAD阳离子度的研究[J].光谱学与光谱分析,2012,32(12):334-337.
[8] 黄光群,韩鲁佳.基于非线性径向基核函数支持向量机的堆肥产品近红外光谱分析研究[J].光学学报,2009,29(02):3556-3560.
[9] 姚鑫淼,张瑞英,李霞辉,等.近红外透射光谱法(NITS)分析大豆品质的研究[J].大豆科学,2006(04):417-420+424.
[10] 赵强,张工力,陈星旦.多元散射校正对近红外光谱分析定标模型的影响[J].光学精密工程,2005(01):39-47.
[11] 刘洁,李小昱,李培武,等.基于近红外光谱的板栗水分检测方法[J].农业工程学报,2010,26(02):338-341.
[12] 展晓日,朱向荣,史新元,等.SPXY样本划分法及蒙特卡罗交叉验证结合近红外光谱用于橘叶中橙皮苷的含量测定[J].光谱学与光谱分析,2009(06):964-968.
[13] 高洪智,卢启鹏,丁海泉,等.基于连续投影算法的土壤总氮近红外特征波长的选取[J].光谱学与光谱分析,2009(11):2951-2954.
[14] 陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用[J].分析测试学报,2007(01):66-69.
[15] 沈永良,孙来军,刘明亮,等.基于径向基函数网络的高压断路器机械状态分类[C].中国仪器仪表与测控技术大会,2009(7):592-596.
Share on Mendeley

Collection(s)

Triticum aestivum L.

26

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉