Near Infrared Spectral Analysis Algorithms for Traceability of Fishmeal Origin
LI Qing-bo1, BI Zhi-qi1, SHI Dong-dong2
1. Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
2. Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract：Fish meal is a kind of high-protein feed made up of one or more kinds of fish, which occupies a very important position in the aquaculture industry. In order to maintain market order, a method of tracing the origin of the fish meal should be established to identify and analyze the quality of the fish meal more accurately. In this paper, near-infrared spectroscopy (NIRS) and chemometrics are used to trace the origin of fish meal from different habitats quickly and accurately. The support vector machine with radial basis function (RBF-SVM) as the kernel function is used for pattern recognition, and the gray wolf algorithm is used to select the key parameters of RBF-SVM. By simulating the hunting behavior of wolves, a hierarchical system is set up according to the fitness level. The target parameters gradually approximate the movement of encirclement. After each movement, the adaptability is re-evaluated. The prey is finally captured through the iteration of wolf pack rank, and the optimal penalty factor and the radius of the kernel function are searched. Finally, the optimal parameters are used to establish the support vector machine model to trace the origin of fish meal from different origins. Grey Wolf algorithm can improve the speed and accuracy of selecting key parameters in the support vector machine algorithm, and improve the classification accuracy of support vector machine. In this paper, 144 spectra of fish meal samples from four fishmeal producing areas in ZhejiangWenling, Shandong Rongcheng, Shandong Weihai and Liaoning Dalian were obtained. The spectrum ranges from 3 700 to 12 500 cm-1. The origin of fish meal was traced by the obtained spectra. Seventy percent of the samples from each producing area was randomly selected as the training sample set for modeling and 30 percent as the test sample set. First, the original near infrared spectra are pretreated, and the average spectra of all the collected spectra are calculated by multivariate scattering correction as “ideal spectra”. The other spectra are linearly regressed, and the baseline correction of spectral translation and migration is carried out. The original signal is decomposed by wavelet transform, and the high-frequency signal is thresholded to eliminate the high-frequency noise so as to achieve the smooth denoising effect of the spectral curve. Ten parallel experiments were carried out by support vector machine to reduce error interference, and the classification results were obtained as follows: Zhejiang Wenling, Shandong Rongcheng, Shandong Weihai and Liaoning Dalian were 100%, 98.89%, 96.43% and 97.78%, respectively. Compared with the grid search method, the Improved Grey Wolf algorithm searches for the penalty factor and the radius of the kernel function faster and more accurately, and the classification accuracy is high. It can be seen that the improved grey wolf algorithm’s support vector machine (GWO-SVM) is feasible for tracing the origin of fish meal.
Key words：Near infrared spectroscopy; Preprocessing; Gray wolf algorithm; Support vector machine; Traceability of fish meal origin
李庆波，毕智棋，石冬冬. 鱼粉产地溯源的近红外光谱判别方法研究[J]. 光谱学与光谱分析, 2020, 40(09): 2804-2808.
LI Qing-bo, BI Zhi-qi, SHI Dong-dong. Near Infrared Spectral Analysis Algorithms for Traceability of Fishmeal Origin. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2804-2808.
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