Zhongguo youzhi (Oct 2023)
基于连续分类策略的动物油红外光谱无损识别Non-destructive recognition of animal oil by infrared spectroscopy based on continuous classification strategy
Abstract
为实现对市面上常见以及实际案件中出现的动物油样本进行快速无损识别，借助光谱分析技术和机器学习算法，基于连续分类策略，对不同动物油样本在种类及品牌/来源地方面进行区分和认定。收集了247份动物油样本(鸡油、牛油、鹅油、猪油、羊油、鸭油)，对其进行红外光谱扫描，采用自动基线校正和峰面积归一化消除样本基线漂移和量纲不一致情况；再分别采用Savitzky-Golay平滑、二项式平滑、邻域平均法、FFT滤波、一阶导数和二阶导数对红外光谱进行预处理，比较了6种预处理方法在降噪方面的差异性，同时构建不同预处理方法下的随机森林、贝叶斯网络以及最小二乘支持向量机3种分类模型，开展各样本“种类—品牌/来源地”的连续分类工作。结果表明，相较于未预处理模型，经过预处理后，模型的识别能力均有提升，其中采用FFT滤波预处理结合随机森林模型可较好区分6种动物油，其对6种动物油样本品牌/来源地的识别准确率由高到低依次为鸡油、牛油、鹅油、猪油、羊油、鸭油；对实际案件中2份检材进行验证性分析，结果与实际情况相符合。红外光谱结合机器学习算法可应用于基于连续分类策略的动物油的快速无损识别。To achieve rapid and non-destructive recognition of animal oil samples commonly found in the market and in actual cases, different animal oil samples were distinguished and identified in terms of category and brand/origin by spectral analysis technology and machine learning algorithms based on continuous classification strategy.A total of 247 animal oil （chicken oil, beef tallow, goose oil, lard, duck fat, mutton fat） were collected and scanned by infrared spectra, and automatic baseline correction and peak area normalization were used to eliminate the baseline drift and dimensional inconsistency. The infrared spectra of animal oil was pretreated by Savitzky-Golay smoothing, binomial smoothing, neighborhood average, FFT filter, first-order derivative and second-order derivative,respectively,and the differences of the six pretreatment methods in noise reduction were compared. Besides, the random forest, Bayesian network and least square support vector machine models were constructed with six pretreatment methods respectively to carry out the continuous classification of "category—brand/origin" of all samples. The results showed that the recognition ability of models was improved after pretreatment. The random forest model after FFT filter pretreatment could better distinguish six types of animal oil, the classification accuracy of recognizing the brands/origin of the six animal oil samples from high to low was chicken oil, beef tallow, goose oil, lard, mutton fat and duck fat. Confirmatory analysis was made on two samples from the actual case, and the results were consistent with the actual situation. Infrared spectroscopy combined with machine learning algorithms can apply to the quick and non-destructive recognition of animal oil based on continuous classification strategy.
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