A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support
Vector Machines
YANG Cheng-en1, WU Hai-wei1*, YANG Yu2, SU Ling2, YUAN Yue-ming1, LIU Hao1, ZHANG Ai-wu3, SONG Zi-yang3
1. College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, China
2. Engineering Research Center of Edible and Medicinal Fungi Ministry of Education, Jilin Agricultural University, Changchun 130118, China
3. College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
Abstract：Sika deer antler caps are of great medicinal and economic value. Because of its hard texture, the finished product is generally presented as powder. It is difficult for consumers to determine the authenticity of sika deer antler cap powder from its appearance, which leads to endless series of counterfeit and adulterated products. Therefore, this paper proposes a FTIR technology and machine learning method to identify counterfeited and adulterated sika deer antler cap powder. This method can identify counterfeited sika deer antler cap powder by horse stag deer antler cap powder, sika deer bone powder, and adulterated sika deer antler cap powder by beef bone powder. This research’s sika deer antler caps, stag deer antler caps and sika deer bones are from five regions of the three provinces of Heilongjiang, Jilin and Liaoning. The samples are divided into 360 portions, including 120 portions of sika deer antler caps, 120 portions of red deer antlers caps and 120 portions of sika deer bones. The beef bone powder is purchased in Changchun Nanguan District Farmers’ Market. Adulterate the beef bone powder into 120 portions of sika deer antlers powder with 5%, 10%, 20%, 30%, 40%, and 50% for every 20 portions. Sample spectral data were collected by mid-infrared spectroscopy, preprocessed by multiple scattering correction (MSC), and sampled by the K-S method. After the training and test sets were divided by 2∶1, Normalization and principal component analysis (PCA) dimension reduction was conducted on spectral data. According to the principle of cumulative contribution rate of the number of principal components≥85% and principal component characteristic value≥1, the first 7 principal components were selected to form the spectral data after dimensionality reduction. The recognition models of support vector machine (SVM), random forest (RF) and Extreme learning machine (ELM) were established by using full-spectrum (FS) data and PCA dimensional-reduction spectral data as model inputs. The results showed a difference between the authentic and counterfeit and adulterated products in the waveband of 1 300~1 800 and 2 800~3 600 cm-1. The difference between the pure sika deer antler cap powder and sika deer antler cap powder of the adulteration rate ≥10% was the most obvious. FS-SVM, PCA-SVM and FS-RF models all have excellent recognition effects in identifying fake and adulterated sika deer antler hat powder. The recognition rate of the training and test set is 100%, and the recognition rate of other models is less than 98%. From the perspective of simplified models, the modeling time of FS-SVM and FS-RF is 4 859.36 and 1 818.96 s respectively, while the modeling time of PCA-SVM is only 19.91 s. Therefore, PCA-SVM has the best overall effect among the six recognition models. The research shows that the method based on mid-infrared spectroscopy combined with support vector machine modeling can be used as a fast, accurate and non-destructive identification method for counterfeiting and adulteration of sika deer antler cap powder.
Key words：Sika deer antler cap; Mid-infrared spectrum; Principal component analysis; Support vector machine; Random forest; Extreme learning machine
杨承恩，武海巍，杨 宇，苏 玲，袁月明，刘 浩，张爱武，宋子洋. 中红外光谱和支持向量机的梅花鹿角帽粉假冒与掺假识别模型[J]. 光谱学与光谱分析, 2022, 42(08): 2359-2365.
YANG Cheng-en, WU Hai-wei, YANG Yu, SU Ling, YUAN Yue-ming, LIU Hao, ZHANG Ai-wu, SONG Zi-yang. A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support
Vector Machines. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2359-2365.
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