基于拉曼光谱和CNN算法的特级初榨 橄榄油的掺假量化

旨在为特级初榨橄榄油掺假快速定量分析提供参考,以掺假菜籽油的特级初榨橄榄油为例,采用激光拉曼光谱实验系统获取油样的拉曼光谱数据,运用基于Inception V2结构的卷积神经网络（CNN）算法提取拉曼光谱特征并完成光谱特征与掺假量的非线性关系映射。结果表明：特级初榨橄榄油与菜籽油的拉曼光谱存在较大的差异,其中类胡萝卜素、碳碳双键、甲基和亚甲基产生的拉曼特征峰是引起差异的主要因素;所建立的CNN模型效果较好,训练集、验证集、测试集的决定系数均大于099,均方根误差均小于0.026;在低剂量掺假中,模型的预测结果仍具有一定的参考价值。综上,拉曼光谱结合基于Inception V2结构的CNN算法所建立的模型可以满足特级初榨橄榄油掺假量的快速检测。     

反向传播神经网络算法结合拉曼荧光光谱法定量检测特级初榨橄榄油掺假

目的 建立基于反向传播神经网络算法结合拉曼荧光光谱技术定量检测低等级橄榄油掺假特级初榨橄榄油的分析方法。方法 制备11种不同掺伪浓度的特级初榨橄榄油混合油样各10份，在相同时间、空间及目标的前提下，使用同台光谱探测系统，采集样品的拉曼光谱和荧光光谱。经过卷积神经网络去除拉曼光谱的基线，实现拉曼光谱和荧光光谱的数据预处理。根据分子光谱与电子光谱的特征差异，人为干预并设定拉曼光谱的权重，建立低等级橄榄油掺假特级初榨橄榄油的反向传播神经网络回归模型。结果 综合评估了反向传播神经网络回归模型的评价参数，特级初榨橄榄油掺假的反向传播神经网络模型的测试集决定系数为0.9716，均方根误差为0.0569，模型预测效果较好。结论 本研究提出的反向传播神经网络算法结合拉曼光谱与荧光的探测方法，满足快速检测低等级橄榄油掺假特级初榨橄榄油的定量分析需求，为评价或跟踪特级初榨橄榄油的品质提供了一种无损伤、高效率、低成本的新检测思路。     

便携式激光拉曼光谱法快速鉴别橄榄油掺假

目的采用便携式激光拉曼光谱仪,建立激光拉曼光谱对橄榄油进行快速鉴别的方法。方法对橄榄油样品进行光谱扫描及基线校正后,以1440 cm-1作为参考波数,对拉曼光谱数据进行归一化处理。结果对80余份橄榄油样品进行统计分析,发现75%的样品在1265 cm-1的拉曼光谱强度值低于540。特级初榨橄榄油中掺加果渣油,会使1265 cm-1和1650 cm-1的特征峰增强,1525 cm-1处的精细结构变小直至消失。结论拉曼光谱具有便捷、快速、无损分析的特点,可作为橄榄油真伪鉴别在线初步筛查的工具。     

基于光谱法的特级初榨橄榄油快速鉴伪技术

本文研究了特级初榨橄榄油中掺入不同比例橄榄果榨油(精炼橄榄油)、菜籽油、玉米油和大豆油的光谱特征,采用荧光光谱和紫外光谱,对掺假样品及纯油样品进行了快速检测。结果表明,特级初榨橄榄油的光谱特征与其他植物油之间差异较大,且掺假体积与吸光度之间存在良好的线性关系(R²>0.89),实现了特级初榨橄榄油的定性鉴别与定量检测,建立了特级初榨橄榄油质量控制体系及其掺假检测分析技术,最低检出限为1%,线性范围为5%～100%(v/v)。系统聚类分析将所有特级初榨橄榄油准确地分为一个亚类,也佐证了此方法的稳定性与可靠性。这种简单快捷的检测技术,有助于特级初榨橄榄油实时、在线橄榄油检测分析技术的研发,为我国橄榄油品质鉴定及产业发展提供有利的技术保障。     

液质联用分析常见植物油甘油三酯

采用高效液相色谱-串联飞行时间质谱法分析了常见植物油如大豆油、芝麻油、花生油、特级初榨橄榄油、葵花籽油、玉米油、油茶籽油、棉籽油和菜籽油的甘油三酯。结果显示每种植物油甘油三酯的种类和含量均不相同。该方法测定甘油三酯有效可行,可为甘油三酯结构信息研究及油脂掺伪鉴别提供基础支持。     

紫外光谱结合化学计量学检测初榨橄榄油掺伪研究

以紫外光谱为技术手段,结合偏最小二乘法和BP人工神经网络2种化学计量学方法建立了初榨橄榄油/混合橄榄油二元掺伪体系的定量预测模型.试验结果表明,2种统计模型定量预测性能良好,偏最小二乘模型的训练集交叉验证均方根误差RMSEcv和预测集均方根误差RMSEP均达到0.011,预测值与真实值相关性达到0.996 2;BP人工神经网络迭代次数为61步,训练集拟合残差为9.684×10-5,网络预测值和真实值相关系数为0.998 3,对于5％以上掺伪比例的油样BP神经网络能够精确地预测.     

橄榄油中脂肪酸烷基酯含量测定

采用商品化固相萃取柱净化,气相色谱-质谱联用法检测,建立了一种前处理简单快捷的橄榄油中脂肪酸烷基酯含量测定方法。该方法测定橄榄油中8种脂肪酸烷基酯的检出限均为1.0 mg/kg,加标回收率为99.05%~111.13%,相对标准偏差为0.50%~6.70%。方法重复性好,准确度高。通过测定橄榄油中脂肪酸烷基酯的含量,可以帮助区分橄榄油品质,为特级初榨橄榄油掺伪的鉴别提供技术支持。     

油脂同步荧光光谱检测及橄榄油掺假鉴别

采用同步荧光光谱仪,在激发波长250~720 nm,波长间隔Δλ=15 nm时,采集20种食用植物油和掺杂的特级初榨橄榄油的荧光光谱图,分析比较了各种植物油脂的同步荧光光谱图。结果表明,同步荧光光谱法能够将特级初榨橄榄油与其他17种植物油明显地区分开来。在橄榄油掺杂鉴别中,其中14种植物油掺兑量在1%的情况下,同步荧光光谱图与特级初榨橄榄油有着明显的差异。同步荧光光谱法对橄榄油掺假鉴别,无需复杂的样品前处理,本方法简便、快速、灵敏,适合快速筛查。     

化学计量法结合气相色谱鉴别米糠油掺伪菜籽油

为研究米糠油掺伪菜籽油的快速定性定量检测方法,采用毛细管气相色谱法测定掺伪不同比例菜籽油的米糠油脂肪酸含量,将C14∶0、C16∶0、C16∶1、C17∶0、C18∶0、C18∶1、C18∶2、C18∶3、C20∶0、C20∶1、C22∶1含量作为变量,通过聚类分析和判别分析对掺伪油样进行定性分析,采用一元线性法与指纹图谱相似度法对掺伪油样进行定量分析。结果表明:米糠油掺伪菜籽油2%以上,聚类分析和判别分析均能正确进行辨别;特征脂肪酸一元线性模型为YC22∶1=0.158 2X+0.350 7(R2=0.991 0),检出限为2%;利用向量夹角余弦法计算纯米糠油与掺伪米糠油的相似度,建立的掺伪量与相似度的线性模型为Y=-23.62X3-8.380 6X2-6.138 3X+100.12(R2=0.999 4)。     

快速鉴别掺伪橄榄油的拉曼光谱-聚类分析方法

以不同产地、不同品牌的多批次橄榄油、大豆油、玉米油、菜籽油、葵花籽油、棕榈油、棉籽油及精炼地沟油为样品,探索建立快速鉴别掺伪橄榄油的拉曼光谱-聚类分析方法。在780、532 nm激光光源普通光栅、532 nm激光光源扩展光栅条件下,研究了橄榄油、低价食用植物油与精炼地沟油的拉曼光谱形态;并采用聚类分析法鉴别掺伪橄榄油。结果表明:在532 nm激光光源下,橄榄油与低价食用植物油及精炼地沟油扩展及其一阶导数拉曼光谱的信息量极为丰富,而且各类样品间的光谱形态差异显著。基于全波段光谱信息和形态建立的聚类分析模型既可准确鉴定橄榄油,还可准确鉴定各种类型的掺伪橄榄油。对30份不同橄榄油、105份不同低价食用植物油和38份不同精炼地沟油的判别正确率均为100%,对180份5%及以上的掺假橄榄油的判别正确率达94%以上,对75份5%及以上的掺杂橄榄油的判别正确率为100%,对72份5%及以上的掺杂植物油的判别正确率达88%以上。样品测量时无需制备样品及消耗化学试剂,测量和分析1份样品仅耗时5min左右,可实现对掺伪橄榄油的快速、无损和准确鉴别。     

Authentication of Olive Oil Adulterated with Vegetable Oils Using Fourier Transform Infrared Spectroscopy

Determination of the authenticity of extra virgin olive oils has become more important in recent years following some infamous adulteration and contamination scandals. The study focused on application of Fourier transform infrared spectroscopy to identify the adulteration of olive oils. Single-bounce attenuated total reflectance measurements were made on pure olive oil and olive oil samples adulterated with varying concentrations of sunflower oil (20-100 mL vegetable oil/L of olive oil). Discriminant analysis using 12 principal components was able to classify the samples as pure and adulterated olive oils based on their spectra. A partial least squares model was developed and used to verify the concentrations of the adulterant. Furthermore, the discriminant analysis method was used to classify olive oil samples as distinct from other vegetable oils based on their infrared spectra.     

Differential scanning calorimeter application to the detectionof refined hazelnut oil in extra virgin olive oil

The potential application of differential scanning calorimetry (DSC) to verify adulteration of extra virgin olive oil with refined hazelnut oil was evaluated. Extra virgin olive oil and hazelnut oil were characterised by significantly different cooling and heating DSC thermal profiles. Addition of hazelnut oil significantly enhanced crystallisation enthalpy (at hazelnut oil ?20%) and shifted the transition towards lower temperatures (at hazelnut oil ?5%). Lineshape of heating thermograms of extra virgin olive oil was significantly altered by hazelnut oil addition: a characteristic exothermic event originated at −27 °C in extra virgin olive oil and progressively disappeared with increasing hazelnut oil content, while the major endothermic peak at −3.5 °C broadened (at hazelnut oil ?40%) and the minor endothermic peak at 8 °C shifted toward lower temperatures (at hazelnut oil ?5%). The preliminary results presented in this study suggest that DSC analysis may be a useful tool for detecting adulteration of extra virgin olive oil with refined hazelnut oil.     

三维荧光光谱法快速检测橄榄油中掺假廉价油

目的 建立三维荧光光谱结合机器学习快速检测橄榄油中掺假廉价油的方法。方法 采集橄榄油及掺入大豆油、玉米油、棕榈油三种不同浓度梯度油的荧光光谱数据,利用标准差标准化（standardscaler）、标准正态变换（standard normal variate,SNV）、归一化（normalize）三种光谱预处理方法,基于K近邻(K-nearest neighbor,KNN)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)、偏最小二乘法(partial least squares,PLS)和卷积神经网络(convolutional neural network,CNN) 5种机器学习方法,构建5种橄榄油定量掺假模型。结果 在定性模型中,基于PLS算法构建的模型效果最好,对3种掺假橄榄油的准确率为79%~97%,其中,在鉴定掺假大豆油的橄榄油中正确率高达97%。在构建的掺假油定量模型中,Standardscaler预处理结合RF算法,构建的定量模型最优,Rc2、Rp2、RMSEC、RMSEP最高,分别为1.00、0.99、0.01、0.02。结论 构建橄榄油掺假3种油的定性定量模型,并建立一种快速、实时、低成本的橄榄油掺假检测方法,能够准确判断是否掺入廉价油,并量化掺假程度,提供更全面的橄榄油质量评估。     

鲜榨山茶油与特级初榨橄榄油营养价值的比较

以鲜榨山茶油和特级初榨橄榄油为研究对象,对其理化指标、脂肪酸组成和营养成分进行测定并比较。结果表明:鲜榨山茶油在理化指标上与特级初榨橄榄油相当,脂肪酸组成与特级初榨橄榄油相似,但油酸含量高于特级初榨橄榄油,棕榈酸和硬脂酸含量低于特级初榨橄榄油;在维生素E、角鲨烯、植物甾醇等天然生物活性物质方面媲美特级初榨橄榄油。     

Quantification of soybean oil adulteration in extra virgin olive oil using portable raman spectroscopy

Extra virgin olive oil is produced through either a cold press procedure or a centrifugation with no thermal and chemical treatments and it is considered as the best quality oil under the category of olive oils. The superior properties of olive oil due to its rich in phenolic and antioxidant content and its contribution to prevent several health problems has increased the demand for olive oil over the years. Consequently, it is nowadays sold at remarkably higher price than regular vegetable oils in the market. Unfortunately, extra virgin olive oil (EVOO) has been adulterated with other cheap oils due to potential high commercial profit. Even though, there are methods available to detect the adulteration in EVOO (such as chromatographic methods and PCR), alternative simpler and faster methods are being studied. In this study, performance of portable Raman spectroscopy to quantify soybean oil (SO) adulteration [up to 25?% (w/w)] in EVOO has been evaluated. Partial Least Square Regression (PLSR) calibration models were developed and both internally (using cross-validation, leave-one-out approach) and externally (using an independent sample set) validated. The model gave standard error of prediction (SEP) of 1.34?% (w/w) SO in EVOO and correlation coefficient of prediction (rPred) of 0.99. Additionally, the residual predictive deviation (RPD) value calculated for the model was found to be 5.71, indicating that the model was considered as “good” and could be used for routine analysis and quality control applications.     

基于近红外光谱的橄榄油中玉米油掺杂量检测

利用近红外光谱和模式识别技术建立了橄榄油中掺杂玉米油的快速鉴别方法。对191个橄榄油样本及混合油样本（玉米油和橄榄油）进行近红外光谱扫描,并对近红外光谱进行一阶导数和平滑处理,利用主成分分析法（PCA）对数据进行降维,通过前三个主成分的载荷图确定了相关性较大的特征波段（7520⁶927cm-1、6270⁵440cm-1和4970⁴400cm-1）,分别在3个波段内利用偏最小二乘回归（PLS）方法建立了玉米油含量的预测模型。结果表明在6270⁵440cm-1波段内,因子数为7,建立的模型精密度和稳定性最好,在玉米油质量分数为0.5%¹00%的范围内,校正集样本和检验集样本的R2均能达到0.9999,标准偏差在0.126⁰.139之间。因此,利用近红外光谱可以实现橄榄油品质的快速无损分析,以合频波段（6270⁵440cm-1）为建模区域可以得到更好的预测效果。      

Self-organizing maps based on chaotic parameters to detect adulterations of extra virgin olive oil with inferior edible oils

A nonlinear algorithm based on chaotic parameters (CPs) has been employed to determine the nature of different output signals obtained from UV–vis spectrophotometer (UV) measurements. These signals come from UV scans of adulterated samples of extra virgin olive oil (EVOO) with refined olive oil or refined olive pomace oil, or from pure samples of EVOO with white random or sinusoidal white random noises. The data collected from this equipment was used to calculate CP values. Then, a self-organizing map was used to detect different types of signals. Using this method, the signals can be identified and classified into five groups depending on their type, the percentage of noise added, and the concentration of adulterant agents, with a misclassification rate of less than 1.3%.