Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA)

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.

[1]  Roberto Kawakami Harrop Galvão,et al.  A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm , 2008 .

[2]  Alexandre C. B. Delbem,et al.  Mutation-based compact genetic algorithm for spectroscopy variable selection in determining protein concentration in wheat grain , 2014 .

[3]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[4]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[5]  Dong Wang,et al.  Successive projections algorithm combined with uninformative variable elimination for spectral variable selection , 2008 .

[6]  Zou Xiaobo,et al.  Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.

[7]  Edward Hodgson,et al.  Measurement of key compositional parameters in two species of energy grass by Fourier transform infrared spectroscopy. , 2009, Bioresource technology.

[8]  S. Engelsen,et al.  Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy , 2000 .

[9]  Yibin Ying,et al.  Variable selection in visible and near-infrared spectra: Application to on-line determination of sugar content in pears , 2012 .

[10]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[11]  W. Cai,et al.  A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra , 2008 .

[12]  Qian Du,et al.  Segmented Principal Component Analysis for Parallel Compression of Hyperspectral Imagery , 2009, IEEE Geoscience and Remote Sensing Letters.

[13]  T. Ma,et al.  Micro-Kjeldahl Determination of Nitrogen.A New Indicator and An Improved Rapid Method , 1942 .

[14]  T. Næs,et al.  The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy , 1988 .

[15]  K. Tremper,et al.  Near-Infrared Spectroscopy : Theory and Applications , 2005 .

[16]  Wenxiu Gao,et al.  Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods , 2013 .

[17]  Xianghong Fan,et al.  Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model , 2016, PloS one.

[18]  Beata Walczak,et al.  Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets , 2000 .

[19]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[20]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[21]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[22]  Kássio M. G. Lima,et al.  Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection , 2011 .

[23]  Roberto Kawakami Harrop Galvão,et al.  A method for calibration and validation subset partitioning. , 2005, Talanta.

[24]  John A. Richards,et al.  Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[25]  Roberto Kawakami Harrop Galvão,et al.  The successive projections algorithm for interval selection in PLS , 2013 .

[26]  Yafit Cohen,et al.  Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance , 2013 .

[27]  B. Mistele,et al.  Estimating the nitrogen nutrition index using spectral canopy reflectance measurements , 2008 .

[28]  M A Arnold,et al.  Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. , 1996, Analytical chemistry.