摘要
植物油种类的准确鉴别对油品的质量控制、欺诈检测、营养健康及粮油贸易等领域具有重要的意义。准确、快速鉴别植物油的种类对保证油品质量和维护市场监督至关重要。本文提出一种主成分分析结合蚁群优化支持向量机(Principal component analysis-Ant colony optimization-Support vector machine,PCA-ACO-SVM)算法结合傅里叶红外光谱(Fourier transform infrared spectroscopy,FTIR)技术快速识别植物油种类。实验采集了6种不同种类的植物油,并利用FTIR测量了样品的吸收、透射红外光谱。通过PCA对红外光谱数据进行降维,实现油品红外光谱特征的提取。运用ACO-SVM分类算法的核心参数进行参数优化,优化后的SVM分类模型核心参数C=1.1024043和G a mma=0.1476193。在该研究中使用PCA-ACO-SVM算法建立了植物油种类的识别模型。利用已知种类的油品进行分类模型训练和参数优化,进一步应用该模型对未知油品进行识别,通过与其他算法的对比,验证PCA-ACO-SVM算法在植物油种类识别中的准确性和高效率。结果表明PCA-ACO-SVM算法结合FTIR技术能够快速、准确地对植物油的种类进行识别。该方法不仅具有较高的分类准确率,而且在数据处理方面表现出较高的运算效率,非常适合大规模植物油种类识别的实际应用。综上所述,本文提出的PCA-ACO-SVM算法结合FTIR技术为植物油种类的快速识别提供了一种可行的解决方案,具有较高的效率和准确率。该方案在食品工业和质量监管等方面具有广阔的应用前景,并对植物油的质量监管具有重要意义。
Accurately identifying vegetable oil species is significant in oil quality control,fraud detection,nutrition and health,and grain and oil trade.Accurate and rapid identification of vegetable oils is essential to ensure oil quality and maintain market supervision.This paper proposes a Principal component analysis-Ant colony optimization-Support vector machine(PCA-ACO-SVM)algorithm combined with Fourier transform infrared spectroscopy(FTIR)technology for rapidly identifying vegetable oil species.Six different kinds of vegetable oils were collected,and the absorption and transmission infrared spectra of the samples were measured by FTIR.PCA reduced the dimension of infrared spectral data,and the infrared spectral characteristics of oil products were extracted.The ACO algorithm optimizes the core parameters of the SVM classification algorithm.The optimized core parameters of the SVM classification model are C=1.1024043 and G a mma=0.1476193.This study uses the PCA-ACO-SVM algorithm to establish the identification model of vegetable oil species.The classification model was trained,and the parameters were optimized using the known types of oil products.The model was further applied to identify unknown oil products.By comparing with other algorithms,the accuracy and efficiency of the PCA-ACO-SVM algorithm in identifying vegetable oil types were verified.The results show that the PCA-ACO-SVM algorithm combined with FTIR technology can quickly and accurately identify the types of vegetable oil.This method has high classification accuracy and high computational efficiency in data processing,which is very suitable for the practical application of large-scale vegetable oil classification.In summary,the PCA-ACO-SVM algorithm proposed in this paper,combined with FTIR technology,provides a feasible solution for rapidly identifying vegetable oil species,which has high efficiency and accuracy.The scheme has broad application prospects in the food industry and quality supervision and is of great significance to the quality supervision of vegetable oil..
作者
贾丹
明米娜
雷蕾
周锐
侯金亮
JIA Dan;MING Mina;LEI Lei;ZHOU Rui;HOU Jinliang(Zhumadian Vocational and technical College,Zhumadian 463000,Henan,China;Henan University of Economics Law,Zhengzhou 450046,Henan,China;College of Information and Management Science,Henan Agricultural University,Zhengzhou 450002,Henan,China;School of Information and Engineering,ZhengZhou University,ZhengZhou,450001,Henan,China)
出处
《光散射学报》
北大核心
2024年第4期454-460,共7页
The Journal of Light Scattering
基金
河南省终身教育专项课题和课程开发立项(202370216)
国家自然科学基金(61806180)。
关键词
主成分分析
蚁群优化支持向量机
傅里叶变换红外光谱
植物油
分类识别
Principal component analysis
Ant colony optimizationsupport vector machine
Fourier transform infrared spectroscopy
Vegetable oil
Classification recognition
作者简介
贾丹(1982.8-),女,本科,讲师,研究方向:图像识别及人工智能算法,E-mail:MichaelJackson2008@126.com。
