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作 者:张凤娟[1] 黄敏 Zhang Fengjuan;Huang Min(School of Internet of things technology,Wuxi Professional College of Science and Technology,Wuri214028,Jiangsu,China;Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),Jiangnan University,Wuri 214122,Jiangsu,China)
机构地区:[1]无锡科技职业学院物联网技术学院,江苏无锡214028 [2]江南大学轻工业过程先进控制教育部重点实验室,江苏无锡214122
出 处:《应用激光》2023年第10期164-169,共6页Applied Laser
基 金:国家自然科学基金项目(61775086)。
摘 要:采用便携式激光拉曼光谱仪对208个特级初榨橄榄油(EVOO)掺伪亚麻籽油(FSO)样品进行拉曼光谱扫描,结合主成分分析(PCA)和偏最小二乘回归(PLSR)分析方法实现了EVOO掺伪FSO的定量检测研究。开发了偏最小二乘回归校准模型,并在内部(使用交叉验证、留一法)和外部(使用独立样本集)进行验证。采用预测值与真实值之间的相关系数(R^(2))、均方根误差(RMSE)及相对分析误差(RPD)这3个参数对系统的预测性能进行评价。研究试验表明,该系统的预测均方根误差为2.31%,预测集相关系数为0.995;交叉验证均方根误差为2.24%,交叉验证集相关系数为0.995,同时得到该系统的相对分析误差为14.19。结果显示该系统在EVOO掺伪FSO定量检测方面具有优秀的预测性能,使用该系统可以对EVOO掺伪进行简易、快速、无损定量检测。A portable laser Raman spectrometer was used to scan 208 adulterated flaxseed oil(FSO)samples of extra virgin olive oil(EVOO).Combined with principal component analysis(PCA)and partial least squares regression(PLSR),the quantitative detection of EVOO adulterated flaxseed oil was realized.Partial least squares regression(PLSR)calibration models were developed and validated internally(using cross validation,leave-one-out method)and externally(using independent sample sets).The correlation coefficient(R^(2)),root mean square error(RMSE)and relative analysis error(RPD)were used to evaluate the prediction performance of the system.The experimental results show that the root mean square error of the prediction system is 2.31%and the correlation coefficient of the prediction set is 0.995.The root mean square error of cross validation is 2.24%,the correlation coefficient of cross validation set is 0.995,and the relative analysis error of the system is 14.19.The results show that the system has excellent predictive performance in quantitative detection of EVOO adulterated flaxseed oil.The system can be used for simple,rapid,and nondestructive quantitative detection of EVOO adulteration.
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