基于IABC-SVR算法的拉曼光谱定量分析山羊血清蛋白含量

doi:10.3964/j.issn.1000-0593(2021)02-0540-06

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Abstract A method based on Raman spectroscopy and improved artificial bee colony algorithm to optimize the support vector machine regression (IABC-SVR) algorithm for rapid quantitative detection of goat serum protein content was proposed. The traditional artificial bee colony algorithm gradually slows down the convergence rate when the data area is large in size, which causes problems such as low efficiency, decreased accuracy, and high optimal local solution probability. The proposed algorithm solves these problems so that the algorithm avoids falling into the local optimal solution in the early stage of evolution, and can maintain the global search ability of the solution in the middle and late stages of evolution. Conventional methods for measuring total serum protein usually use Kjeldahl method, biuret method, etc., which has the disadvantages of slow aging and contaminated samples. This paper uses Raman spectroscopy for detection, which has the advantages of fast and non-destructive. Using goat serum as the analysis object, configure 35 groups of samples to be measured according to a certain volume ratio. Raman spectra were collected using a Raman spectrometer with a spectral collection range of 300~1 300 cm^-1. Baseline correction was used to remove the fluorescent background, and Savitzky-Golay spectra were used to smooth this method smoothes the original spectrum, normalizes the spectral data, and assigns the characteristic peaks of the Raman spectrum. The experimental results show that Raman spectroscopy can characterize the information of the main chemical groups in the serum, and due to the difference in functional group concentration, the spectral characteristic peak intensity changes significantly with the concentration, so the total serum protein can be determined based on the characteristic peak information. In the experiment, based on the purchased goat serum protein content, the protein content of each group of serum samples was obtained by configuring the volume ratio of the samples. The volume of a single liquid sample was 3 mL. Eight groups of experimental samples were randomly selected as the model test set. The remaining 27 groups are used as model training sets. The processed spectral characteristic peak intensity and the corresponding serum protein content were used as the input and output values of the model to establish a quantitative model of IABC-SVR, ABC-SVR, and BP algorithms to predict the total serum protein in the test set. Finally, the mean square error (MSE), correlation coefficient (r) was compared with the modeling time, and the results showed that the goat serum protein quantitative correction model established by IABC-SVR had the best effect. The correlation coefficient of the model was 0.990 27, and the mean square error was 0.244 3, the modeling time is 1.9 s, the variance of the predicted values are less than 0.001 g·mL^-1, and the prediction accuracy is 99.8%. The experimental results show that the laser Raman spectroscopy technology combined with the IABC-SVR algorithm has high accuracy and stability for the rapid quantitative detection of goat serum protein content.

Key words：Laser Raman spectroscopy; Serum protein; Improved artificial bee colony optimization; Support vector machine regression; Quantitative analysis

Received: 2019-12-29 Accepted: 2020-04-27

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)02-0540-06 OR http://www.gpxygpfx.com/EN/Y2021/V41/I02/540