Analysis of Data Mining Method for Near Infrared Spectral Data of Dairy Products Based Orthogonality

Abstract:

There are advantages such as real time, quick, multi-constituents simultaneous measurement, green and no pollution, and so on, of using near infrared spectral method to detect concentration information of dairy products constituents. From time domain and frequency domain, near infrared spectral data mining methods based on orthogonality were separately researched to realize noise filtering and useful information extracting. For milk spectrum, methods in the time domain including Orthogonal Signal Correction and Direct Orthogonalization, Wavelet Transform de-noising method in the frequency domain, were separately explored and used in spectral preprocessing, PLS method was used to build calibration model, and processing effect of different data mining methods was compared with and analyzed. It is showed that in the time domain, using the orthogonality methods to make data mining for milk spectrum, the complex interfering signal and noise information uncorrelated with the measured constituents can be can reduced effectively, and the correlated information with the measured constituents is reserved in maximal limitation, prediction capability of calibration model is improved, furthermore, the Orthogonal Signal Correction method is better than the Direct Orthogonalization method, moreover, in the frequency domain, wavelet de-noising is better than the orthogonality methods within the time domain.

• Periodical: Advanced Materials Research (Volume 981)
• Pages: 641-646
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.981.641
• Online since: July 2014
• Authors: Li Jie Wang*, Zhen Zhou, Yong Qin, Jin Ying Yin, Jian Ying Guo, Wen Ning Dong, Dan Wang
• Keywords: Data Mining (DM), Frequency Domain, Orthogonality, Spectrum Analysis, Time Domain

References

[1] H.Y. Wu, Y. Liu, Y.M. Zuo: Academic Periodical of Farm Products Processing, No. 3 (2010), p.76 In Chinese.

[2] W.Z. Lu, H.F. Yuan, X.L. Chu: Near Infrared Spectrum apparatus (Chemical Industry Press, China 2010). In Chinese.

[3] R. Liu, L.N. Lv, W.L. Chen, K.X. Xu: Spectroscopy and Spectral Analysis, Vol. 24 (2004), p.1042 In Chinese.

[4] S. Wold, H. Antti, F. Lindgren, J. Ohman: Chemometrics and Intelligent Laboratory Systems, Vol. 44 (1998), p.175.

DOI: 10.1016/s0169-7439(98)00109-9

[5] Claus A. Andersson: Chemometrics and Intelligent Laboratory Systems, No. 47 (1999), p.51.

[6] X.J. Chen: ANALYTICA CHIMICA ACTA, Vol. 634 (2009), p.166.