Particle size regression correction for NIR spectrum based on the relationship between absorbance and particle size

doi:10.1007/s12200-013-0320-3

Front Optoelec

2013, Vol. 6

Issue (2) : 216-223 https://doi.org/10.1007/s12200-013-0320-3

RESEARCH ARTICLE

Particle size regression correction for NIR spectrum based on the relationship between absorbance and particle size

Jinrui MI^1,2, Luda ZHANG², Longlian ZHAO¹, Junhui LI¹(

)

1. College of Information and Electrical Engineering, China Agriculture University, Beijing 100083, China; 2. College of Science, China Agriculture University, Beijing 100083, China

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Abstract

Based on the effect of sample size on the near-infrared (NIR) spectrum, the absorbance (log(R)) in any wavelength is divided into two parts, and one of them is defined as non-particle-size-related spectrometry (nPRS) because it is not influenced by particle size. To study the relationship between the absorbance and particle size, the experiment material including nine samples with different particle size was used. According to the regression analysis, the relationship was studied as the reciprocal regression model, y = a + bx + c/x. Meanwhile, the model divides absorbance into two parts, one of them forms nPRS. According to the nPRS, a new correction method, particle size regression correction (PRC) was introduced. In discriminate analysis, the spectra from three different samples (rice, glutinous rice and sago), pretreated by PRC, could be directly and accurately distinguished by principal component analysis (PCA), while by the traditional correction method, such as multiplicative signal correction (MSC) and standard normal variate (SNV), could not do that.

Keywords near-infrared diffuse reflectance spectrometry (NIRDRS) regression analysis non-particle-size-related spectrum (nPRS) particle-size regress correction (PRC)

Corresponding Author(s): LI Junhui,Email:caunir@cau.edu.cn

Issue Date: 05 June 2013

Cite this article:

Jinrui MI,Luda ZHANG,Longlian ZHAO, et al. Particle size regression correction for NIR spectrum based on the relationship between absorbance and particle size[J]. Front Optoelec, 2013, 6(2): 216-223.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-013-0320-3
https://academic.hep.com.cn/foe/EN/Y2013/V6/I2/216

Tab.1 Relationship between sieve mesh and mesh size

Fig.1 log() distribution at 7726 (a) and 4127 (b) cm

Fig.2 and RMSE of four regression models by using quadric polynomial model (a); logistic model (b); reciprocal model (c) and exponential model (d)

Tab.2 and RMSE from different regression model

Fig.3 Results of nPRS, mean k/s spectrum and PRS. nPRS: non-particle-size-related spectrum; PRS: particle-size-related spectrum

Tab.3 Vectors and

Fig.4 NIR spectra (up) and first-derivative spectra (down) from original spectra (a); MSC (b); SNV (c) and PRC (d)

Fig.5 Spatial distribution maps of the first principal component from first-derivative original spectra (a); MSC (b); SNV (c) and PRC (d). PC: principal component

Fig.6 Spatial distribution of the first two principal components from PRC_EACH without first-derivative. PC: principal component

1	Burns D A, Ciurczak E W. Handbook of Near-Infrared Analysis. 3rd eds. Boca Raton: CSC Press LLC, 2006, 23–26
2	Martens H, Jensen S A, Geladi P. Multivariate linearity transformation for near-infrared reflectance spectrometry. In: Proceedings of the Nordic symposium on applied statistics . 1983, 205–234
3	Tomas I, Bruce K. Piese-wise multiplicative scatter correction applied to near-infrared diffuse transmittance data from meat products. Applied Spectroscopy , 1993, 47(6): 702–709 doi: 10.1366/0003702934066839
4	Geladi P, MacDougall D, Martens H. Linearization and scatter-correction for nir-infrared reflectance spectra of meat. Applied Spectroscopy , 1985, 39(3): 491–500 doi: 10.1366/0003702854248656
5	Tomas I, Naes T. Effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy , 1988, 42(7): 1273–1284 doi: 10.1366/0003702884429869
6	Lu Q Y, Chen Y M, Mikami T, Kawano M, Li Z G. Adaptability of four-samples sensory tests and prediction of visual and near-infrared reflectance spectroscopy for Chinese indica rice. Journal of Food Engineering , 2007, 79(4): 1445–1451 doi: 10.1016/j.jfoodeng.2006.04.046
7	Xu K X, Qiu Q J, Jiang J Y, Yang X Y. Non-invasive glucose sensing with near-infrared spectroscopy enhanced by optical measurement conditions reproduction technique. Optics and Lasers in Engineering , 2005, 43(10): 1096–1106 doi: 10.1016/j.optlaseng.2004.06.018
8	Martens H, Nielsen J P, Engelsen S B. Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry , 2003, 75(3): 394–404 doi: 10.1021/ac020194w pmid:12585463
9	Bruun S W, S?ndergaard I, Jacobsen S. Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 1. Gluten Power. Journal of Agricultural and Food Chemistry , 2007, 55(18):7234–7243 doi: 10.1021/jf063680j pmid:17676753
10	Bruun S W, S?ndergaard I, Jacobsen S. Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 2. Hydrated Gluten. Journal of Agricultural and Food Chemistry , 2007, 55(18): 7244–7251 doi: 10.1021/jf063724o
11	Lui L, Ye X P, Arnold M. Saxton, Womac A I. Pretreatment of near infrared spectral data in fast biomass analysis. Journal of Near Infrared Spectroscopy , 2010, 18(5): 317–331
12	Prahl S A, Keijzer M, Jacques S L, Welch A J. A Monte Carlo model of light propagation in tissue. In: SPIE Proceeding of Dosimetry of Laser Radiation in Medicine and Biology . 1989, 102–111
13	Prince S, Malarvizhi S. Monte Carlo simulation of NIR diffuse reflectance in the normal and diseased human breast tissues. BioFactors , 2007, 30(4): 255–263 doi: 10.1002/biof.5520300407 pmid:18607075
14	Hou R F, Huang L, Wang Z Y, Xu Z L. Preliminary study of the light migration in farm product tissue. Transactions of the Chinese Society of Agricultural Engineering , 2005, 21(9): 12–15 (in Chinese)
15	Xu Z L, Wang Z Y, Huang L, Liu Z C, Hou R F, Wang C. Double-integrating-sphere system for measuring optical properties of farm products and its application. Transactions of the Chinese Society of Agricultural Engineering , 2006, 22(11): 244–249 (in Chinese)
16	Wang Z Y, Hou R F, Huang L, Xu Z L, Wang C, Qiao X J. Light transport in multi-layered farm products by using Monte Carlo simulation and experimental investigation. Transactions of the Chinese Society of Agricultural Engineering , 2007, 23(5): 1–7 (in Chinese)

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