The evaluation of wine adulteration is often a cumbersome process not exempt from multiple difficulties.In this work,a valuable methodology to rapidly assess wine adulteration was used.Harnessing the ratio of Compton ...The evaluation of wine adulteration is often a cumbersome process not exempt from multiple difficulties.In this work,a valuable methodology to rapidly assess wine adulteration was used.Harnessing the ratio of Compton and Rayleigh scattering signals obtained in TXRF(total reflection X-ray fluorescence).The Compton/Rayleigh signal ratio as a sensitive way to estimate the average effective atomic number(Z_(eff))of a sample was used.Thus,any addition made into the wine,would cause a change in its Z_(eff) which could be detected by TXRF.Non-adulterated and adulterated wines were selected and its Z_(eff) was estimated.The method was developed using X-ray excitation Molybdenum tube.Deconvolution of independent Compton and Rayleigh signals was performed by non-Gaussian and Gaussian curve resolution methods,and the area ratio was evaluated.A calibration curve for Compton/Rayleigh signal ratio versus Z_(eff) was established and wine adulterated samples were tested in a Z_(eff) range between 4.3 and 7.2.Wine adulteration was detected in all cases.The method is simple,fast,sensitive,precise and non-destructive.It procedure is usefulness as an important tool for wine industry and for the maintenance of origin and quality of wines.展开更多
Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adultera...Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine(RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2%in five-floods crossvalidation that increased 4.818%than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7%which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.展开更多
为了快速识别市场中的劣质食用油,提出了一种结合激光诱导荧光(laser-induced fluorescence,LIF)技术与偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)的高品质食用油掺伪鉴别方法。首先利用实验室搭建的LIF...为了快速识别市场中的劣质食用油,提出了一种结合激光诱导荧光(laser-induced fluorescence,LIF)技术与偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)的高品质食用油掺伪鉴别方法。首先利用实验室搭建的LIF系统采集了橄榄油、芝麻油和花生油及其掺伪样本的荧光光谱数据;然后基于PLS-DA方法分别为橄榄油、芝麻油和花生油构建了掺伪鉴别模型;最后通过预测集对模型性能进行了评估。结果表明,PLS-DA模型能够准确捕捉掺伪样本与真实样本荧光光谱之间的差异性特征,在实验所得数据验证下,达到了100%的分类准确率。该方法可实现对掺伪食用油的高精度鉴别,为食品安全监管提供了科学的鉴别手段。展开更多
基金the support of Vicerrectoria de Investigacion,Universidad de Concepcion VRID N◦2022000461INV,Agencia Nacional de Investigacion y Desarrollo by means of FONDEQUIP EQM-160100(JN)and FONDECYT 11181153(JA).
文摘The evaluation of wine adulteration is often a cumbersome process not exempt from multiple difficulties.In this work,a valuable methodology to rapidly assess wine adulteration was used.Harnessing the ratio of Compton and Rayleigh scattering signals obtained in TXRF(total reflection X-ray fluorescence).The Compton/Rayleigh signal ratio as a sensitive way to estimate the average effective atomic number(Z_(eff))of a sample was used.Thus,any addition made into the wine,would cause a change in its Z_(eff) which could be detected by TXRF.Non-adulterated and adulterated wines were selected and its Z_(eff) was estimated.The method was developed using X-ray excitation Molybdenum tube.Deconvolution of independent Compton and Rayleigh signals was performed by non-Gaussian and Gaussian curve resolution methods,and the area ratio was evaluated.A calibration curve for Compton/Rayleigh signal ratio versus Z_(eff) was established and wine adulterated samples were tested in a Z_(eff) range between 4.3 and 7.2.Wine adulteration was detected in all cases.The method is simple,fast,sensitive,precise and non-destructive.It procedure is usefulness as an important tool for wine industry and for the maintenance of origin and quality of wines.
基金Supported by the Natural Science Foundation of Heilongjiang Province(LH2020C003)。
文摘Taoren and Xingren are commonly used herbs in East Asian medicine with different medication functions but huge economic differences,and there are cases of adulterated sales in market transactions.An effective adulteration recognition based on hyperspectral technology and machine learning was designed as a non-destructive testing method in this paper.A hyperspectral dataset comprising 500 Taoren and 500 Xingren samples was established;six feature selection methods were considered in the modeling of radial basis function-support vector machine(RBF-SVM),whose interaction between the two optimization methods was further researched.Two mixed metaheuristics modeling methods,Mixed-PSO and Mixed-SA,were designed,which fused both band selection and hyperparameter optimization from two-stage into one with detailed process analysis.The metrics of this mixed model were improved by comparing with traditional two-stage method.The accuracy of Mixed-PSO was 89.2%in five-floods crossvalidation that increased 4.818%than vanilla RBF-SVM;the accuracy of Mixed-SA was 88.7%which could reach the same as the traditional two-stage method,but it only relied on 48 crux bands in full 100 bands in RBF-SVM model fitting.
文摘为了快速识别市场中的劣质食用油,提出了一种结合激光诱导荧光(laser-induced fluorescence,LIF)技术与偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)的高品质食用油掺伪鉴别方法。首先利用实验室搭建的LIF系统采集了橄榄油、芝麻油和花生油及其掺伪样本的荧光光谱数据;然后基于PLS-DA方法分别为橄榄油、芝麻油和花生油构建了掺伪鉴别模型;最后通过预测集对模型性能进行了评估。结果表明,PLS-DA模型能够准确捕捉掺伪样本与真实样本荧光光谱之间的差异性特征,在实验所得数据验证下,达到了100%的分类准确率。该方法可实现对掺伪食用油的高精度鉴别,为食品安全监管提供了科学的鉴别手段。
