基于最小二乘支持向量机的橄榄油掺杂拉曼快速鉴别方法

提出了一种基于最小二乘支持向量机(LS-SVM)的橄榄油掺杂拉曼快速鉴别方法。首先,收集若干己知类别的橄榄油样作为训练样本,获取其拉曼谱图,并对其谱图进行预处理和波段选择,进而构建LSSVM分类器;对于未知类别的油样,获取其拉曼谱图,并进行相应的预处理和波段选择,由LSSVM分类器获得鉴别结果。实验以7种已知的特级初榨橄榄油为基础,分别掺入4种其它植物油(大豆油、菜籽油、玉米油、葵花籽油),获得112个掺杂油样。将全部样本随机分成训练集和测试集,对测试集样本的预测实验结果表明,本文方法能有效鉴别橄榄油掺杂,且掺杂量最低检测限为5%。与其它分类方法相比,LSSVM分类法具有最佳的分类性能。该方法快速、简便,为橄榄油掺杂鉴别提供了一种全新的方法。     

Quantitative detection of adulterated olive oil by Raman spectroscopy and chemometrics

Commercially available extra virgin olive oils are often adulterated with some other cheaper edible oils with similar chemical compositions. A set of extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil were characterized by Raman spectra in the region 1000–1800 cm⁻¹. Based on the intensity of the Raman spectra with vibrational bands normalized by the band at 1441 cm⁻¹ (CH₂), external standard method (ESM) was employed for the quantitative analysis, which was compared with the results achieved by support vector machine (SVM) methods. By plotting the adulterant content of extra virgin olive oil versus its corresponding band intensity in the Raman spectrum at 1265 cm⁻¹, the calibration curve was obtained. Coefficient of determination (R²) of each curve was 0.9956, 0.9915 and 0.9905 for extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil, respectively. The mean absolute relative errors were calculated as 7.41, 7.78 and 9.45%, respectively, with ESM, while they were 5.10, 6.96 and 4.55, in the SVM model, respectively. The prediction accuracy shows that the ESM based on Raman spectroscopy is a promising technique for the authentication of extra virgin olive oil. The method also has the advantages of simplicity, time savings and non‐requirement of sample preprocessing; especially, a portable Raman system is suitable for on‐site testing and quality control in field applications. Copyright © 2011 John Wiley & Sons, Ltd.     

Use of Raman spectroscopy for analyzing edible vegetable oils

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil is one of the main ingredients of the Mediterranean diet, which is among the healthiest of eating practices. This article reviews the use of Raman spectroscopy for analyzing edible vegetable oils including olive oil. Although the spectra for edible vegetable oils are similar, they exhibit some differences which, however small, enable their discrimination. Thus, Raman spectra allow one to determine the degree of unsaturation of oils. This property is correlated with the iodine value but much faster and simpler to obtain. The degree of unsaturation can be used to classify and authenticate oils, which is especially useful with high-quality oils. In fact, adulteration with mixtures of more inexpensive oils can be easily detected by Raman spectroscopy. This technique additionally allows some minor components present in unsaponifiable matter to be identified. Fats in general and vegetable oils in particular, are prone to oxidation. Thus, double bonds in them are oxidized to form triglycerides. Vegetable oils are widely used for frying and Raman spectroscopy allows for their oxidative stability against heating at the usual frying temperatures to be assessed.     

基于植物油可见吸收光谱的相关系数鉴别特级初榨橄榄油

目前市场上的橄榄油品牌很多,质量参差不齐,亟需完善橄榄油的等级分类检测和特级初榨橄榄油的鉴别方法。可见吸收光谱光谱法可在不直接接触样品的情况下对样品进行无添加试剂的探测,因此为实现特级初榨橄榄油的鉴别,采用可见吸收光谱法对不同种类植物油进行了光谱测量。实验结果发现特级初榨橄榄油在500~780 nm波段内具有4个明显的吸收峰,而其他种类植物油在此波段内吸光度较弱或无吸收峰,且同种植物油不同品牌之间的光谱特征极其相似。采用相关系数比对不同种类植物油可见吸收光谱,分别计算了四个不同波长范围内植物油的可见吸收光谱的相关系数,实验发现不同波长范围内的植物油可见光谱相关系数差别较大。在520～700 nm范围内,特级初榨橄榄油间的光谱相关系数在0.999 6以上,特级初榨橄榄油与其他种类植物油的光谱相关系数均低于0.267 8,特级初榨橄榄油与其他等级橄榄油的光谱相关系数在0.194 6～0.835 8之间。研究结果表明可见吸收光谱相关系数法是一种快速非接触式鉴别特级初榨橄榄油的可行性方法。建立了一种特级初榨橄榄油快速鉴别方法,即可见吸收光谱相关系数法。该方法在特级初榨橄榄油的实际鉴别中具有一定的应用价值。     

Partial Least Squares Processing of Near‐Infrared Spectra for Discrimination and Quantification of Adulterated Olive Oils

Abstract

A new processing based on partial least squares (PLS) algorithm for the discrimination and determination of adulterants in pure olive oil using near‐infrared (NIR) spectroscopy has been introduced. The 280 adulterations of olive oil with corn oil (n=70), hazelnut oil (n=70), soya oil (n=70), and sunflower oil (n=70) were prepared, and their NIR spectra in the region 12,000–4550 cm^?1 were collected. The 70 spectra of each adulteration of olive oil were divided into two sets, 50 spectra for a calibration set and 20 spectra for a prediction set. The spectra of a total calibration set (n=200) were separated into individual adulterant calibration sets (n_i=50, i=corn, hazelnut, soya, sunflower) by using discriminant PLS (DPLS) analysis, and PLS calibration models for the quantification of adulterants with corn oil, hazelnut oil, soya oil, or sunflower oil were developed separately. A variety of wavelength ranges and data pretreatments were examined for obtaining optimal results for the discrimination and quantification objects. Four PLS models for differentiating the adulterant types were evaluated by classifying the NIR spectra of a total prediction set (n=80) into known adulterant types. Then, these known adulterant spectra were analyzed by the PLS calibration models developed for each type to determine the content of an adulterant in pure olive oil. The results of evaluation revealed that the processing reported in this article works excellently for the discrimination and quantification of the adulterations of olive oil.     

Analysis of Argan Oil Adulteration Using Infrared Spectroscopy

The determination of argan oil adulteration by other vegetable oils is a real analytical challenge. The authentication of argan oil needs fast and simple analytical techniques for quality control and testing. This study focuses on the detection and quantification of argan oil adulteration with different edible oils, using midinfrared spectroscopy with chemometrics. Chemometric treatment of MIR spectra has been assessed for the classification and quantification of argan oil adulteration with sunflower or soybean oils. The potential of MID spectroscopy combined with partial least squares regression (PLS) as a rapid analytical technique for the quantitative determination of adulterants in argan oil has been demonstrated. A PLS model has been established to predict the concentration of soybean and sunflower oil as adulterants in the calibration range between 0% and 30% (w/w) in argan oil with good prediction performances in the external validation.     

Qualitative analysis of desi ghee,edible oils,and spreads using Raman spectroscopy

Keeping in view the importance of dietary fats in modulating disease risk, a study was planned to compare edible oils, spreads, and desi ghee based on fatty acid composition through Raman spectroscopy. The double bonds in unsaturated oils tend to react more with oxygen causing oxidative stress in living cells; therefore, the excessive use of processed vegetable oils may pose risk for human health. In the spectral analysis, Raman peaks at 1063 and 1127 cm⁻¹ represent out‐of‐phase and in‐phase aliphatic C C stretch for saturated fatty acids. The peak at 1300 cm⁻¹, labeled for alkane, decreases with increase in the double bond contents (unsaturation). Further, the Raman peak at 1655 cm⁻¹ showed a monotonic increase as a function of unsaturation. The double bond contents in the Raman spectra from 1650–1657 cm⁻¹ represent unsaturated fatty acids that changes during the synthesis of spreads and banaspati ghee. Desi ghee, extracted from cow and buffalo milk, showed distinctive Raman peaks at 1650 and 1655 cm⁻¹, which originates because of isomers of conjugated linoleic acid. These Raman shifts differentiated desi ghee from other artificially produced banaspati ghee, spreads, and oils. Conjugated linoleic acid has proved to be anti‐carcinogenic, anti‐inflammatory, and anti‐allergic properties; therefore, the limited use of desi ghee may reduce the risk of cardiac diseases. Principal component analysis has been applied on the Raman spectra that clearly differentiated desi ghee, mono‐unsaturated extra virgin olive oil, and extra virgin olive oil spread from other oils, oil mixtures, spreads, and ghee. In addition, principal component analysis has been blindly applied successfully on 13 unknown samples to classify them with reference to the known ghee sample. Copyright © 2016 John Wiley & Sons, Ltd.     

Identification of waste cooking oil and vegetable oil via Raman spectroscopy

This paper made a qualitative identification of ordinary vegetable oil and waste cooking oil based on Raman spectroscopy. Raman spectra of 73 samples of four varieties oil were acquired through the portable Raman spectrometer. Then, a partial least squares discriminant analysis (PLS‐DA) model and a discrimination model based on characteristic wave band ratio were established. A classification variable model of olive oil, peanut oil, corn oil and waste cooking oil that was established through the PLS‐DA model could identify waste cooking oil accurately from vegetable oils. The identification model established based on selection of waveband characteristics and intensity ratio of different Raman spectrum characteristic peaks could distinguish vegetable oils from waste cooking oil accurately. Research results demonstrated that both ratio method and PLS‐DA could identify waste cooking oil samples accurately. The identification model based on characteristic waveband ratio is simpler than PLS‐DA model. It is widely applicable to identification of waste cooking oil. Copyright © 2016 John Wiley & Sons, Ltd.     

基于超连续光谱特级初榨橄榄油的快速检测方法

特级初榨橄榄油作为一种冷榨植物油含有较为丰富的不饱和脂肪酸和多酚类化合物,其营养价值较高。目前,橄榄油的掺假问题是业界最严重的问题之一,中国对橄榄油的消费量与日俱增,国内橄榄油市场较为混乱,掺假造假现象层出不穷,从橄榄油的国外进口到国内二次包装都有可能存在人为干扰和品质造假,如果不加以有效监督和制止,对国民的健康和财产将造成严重损失。如果通过传统的化学分析方法获取所有成分信息势必会增加检测周期,不利于商品的快速流通,对生产厂商和消费者来说都是一种损失。为应对复杂多变的橄榄油掺伪技术及国内具备橄榄油检测资质机构不足的问题,提出一种基于超连续光谱特级初榨橄榄油的快速检测方法,为实现快速鉴别提供了可能性,研究选用特级初榨橄榄油、菜籽油、茶油、芝麻油、稻米油、葵花油、玉米油以及大豆油作为研究对象,分别采集每种植物油的超连续光谱并对初步光谱数据进行光谱预处理,最后计算了不同样本间超连续光谱的皮尔逊相关系数并以此作为特级初榨橄榄油判别的主要依据。实验结果显示不同样本特级初榨橄榄油间的超连续光谱的皮尔逊相关系数在0.901 1以上,而特级初榨橄榄油与其他种类植物油的超连续光谱的皮尔逊相关系数在0.172 2~0.899 0之间。研究表明以皮尔逊相关系数0.901 1作为判别特级初榨橄榄油与其他植物油的检测阈值,可实现快速实时的精准检测识别。该技术与分光光度计的吸收透射光谱相比,最大的优势在于采集周期短和光谱指纹特征丰富,周期短表现为光谱曝光采集时间仅为100 ms,光谱指纹特征丰富表现为除包含吸收光谱外还表现出各种荧光活性物质所特有的荧光光谱。除此之外,可将超连续谱光源应用推广到食品安全检测技术领域。该技术装置简单且易于推广对国内橄榄油的检测和市场规范具有一定的研究意义。     

基于FTIR的芝麻油真伪鉴别和掺伪定量分析模型

把低价油掺入到高价油是食用油脂中的常见掺伪现象,芝麻油由于品质好价格高,市场上时有假冒伪劣产品,因此应用FTIR并结合化学计量学,建立了芝麻油的真伪和掺伪的快速分析方法。首先分析了芝麻油与大豆油、葵花籽油在4 000～650 cm-1范围的FTIR谱图,由于食用植物油都是不同脂肪酸甘油三酯的混合物,其谱图极为相似,很难发现芝麻油与其他油脂的明显差异。但是不同食用油的脂肪酸组成不同,其1 800～650 cm-1红外指纹特征区也有所不同,因此可以选择该区域,对红外光谱数据用化学计量学方法进行分类识别。通过建立主成分分析(PCA)和簇类独立软模式识别(SIMCA)模型,进行了芝麻油的真伪鉴别,该模型聚类效果较为理想,识别正确率达到了100％;采用标准正态化校正(SNV)和偏最小二乘法(PLS),经过PCA分析计算,芝麻油中掺入大豆油、葵花籽油的掺伪检测限均为10%;利用FTIR和PLS,建立了芝麻油掺的定量分析模型,该模型预测值与实际值有着良好的对应关系,预测相对误差为-6.87％～8.07％之间,说明定量模型可行。本方法能够实现芝麻油的快速真伪鉴别和掺伪定量分析,其优点是模型一旦建立,分析简便、快速,可以满足大量样品的日常监测。     

基于拉曼光谱和最小二乘支持向量机的橄榄油掺伪检测方法研究

为实现橄榄油中掺伪油类型的识别和掺伪量预测,对掺入葵花籽油、大豆油、玉米油的橄榄油共117个样品进行拉曼光谱检测,并用基于多重迭代优化的最小二乘支持向量机模型对掺入油的类型进行识别,综合识别率为97%。同时分别采用最小二乘支持向量机、人工神经网络模型、偏最小二乘回归建立橄榄油中葵花籽油、大豆油、玉米油含量的拉曼光谱定标模型,结果显示最小二乘支持向量机具有最优的预测效果,其预测均方根误差(RMSEP)在0.007 4～0.014 2之间。拉曼光谱结合最小二乘支持向量机可为橄榄油掺伪检测提供一种精确、快速、简便、无损的方法。     

近红外光谱和化学计量学方法用于橄榄油品质分析与掺杂量检测

基于橄榄油的近红外光谱数据,用判别分析(Discriminant analysis)方法把20个样品成功地分为特级初榨橄榄油和普通橄榄油两类,正确率为100％。同时测定了纯橄榄油中分别掺入菜籽油、玉米油、花生油、山茶油、葵花籽油、罂粟油的混合油的近红外光谱,掺杂油体积百分数范围为0～100%。选择最佳的光谱波段组合用偏最小二乘(PLS)法分别建立定量分析模型,预测相对误差范围在-5.67%～5.61%之间。研究结果表明,基于化学计量学方法和近红外光谱数据可为橄榄油的品质鉴定和掺杂量检测提供了一种简便、快捷、准确的方法。     

拉曼光谱结合模式识别方法用于大豆原油掺伪的快速判别

大豆原油是我国的战略储备物资,然而目前储油市场上频繁出现大豆原油掺混的现象严重影响了食用油储备安全。基于此,通过大豆原油与部分植物精炼油拉曼谱图的特征差异,并结合主成分分析-支持向量机(PCA-SVM)模式识别建立了大豆原油是否掺伪的快速判别方法。以28个大豆原油、46个精炼油、110个掺伪油的拉曼谱图为模型样本;选择位于780～1 800 cm-1波段的谱图,预处理方法同时采用Y轴强度校正、基线校正和谱图归一化法;在此基础上应用PCA法提取特征变量,即以贡献率最高前7个主成分为变量进行SVM分析。SVM校正模型的建立是以随机选取的20个大豆原油和75个掺伪油样组成校正集,以8个大豆原油和35个掺伪油样组成验证集,分别运用并比较四种核函数算法建立的大豆原油SVM分类模型,并采用网格搜索法(grid-search)优化模型的参数,以四种模型的分类性能作为评判标准。结果表明：应用线性核函数算法构建的SVM分类模型可以很好地完成掺伪大豆原油的判别,校正集识别准确率达到100%,预测结果的误判率为0,判别下限为2.5%。结果表明应用拉曼光谱结合化学计量学能够用于大豆原油掺伪的快速鉴别。拉曼光谱简便、快速、无损、几乎没有试剂消耗,适合现场检测,从而为大豆原油的掺伪分析提供了一种新的备选方法。     

Quantitative analysis of essential oils from rosemary in virgin olive oil using Raman spectroscopy and chemometrics

In this work, virgin olive oil mixed with essential oils from rosemary has been analyzed by means of Raman spectroscopy. First of all, experimental design has been employed in order to define the Raman spectroscopy's parameters, final measuring conditions were: acquisition time of 30 s, five accumulations, and the intensity of the laser power at 75 mW. The Raman spectra were initially measured at full range (150–3000 cm⁻¹), but a narrower window assured faster accumulations and more accurate predictions. The calibration solutions of eucalyptol and camphor in olive oil were prepared following a central composite design and different spectra pre‐processing algorithms were evaluated. To conclude, essential oils obtained by means of Supercritical Fluid Extraction, Ultrasounds, and hydrodistillation were mixed with virgin olive oil and quantified with Raman spectroscopy. Predicted concentrations of the olive oil mixtures were compared with concentrations obtained for the same samples by a Comprehensive Two‐Dimensional Gas Chromatographic (GC × GC) method. Copyright © 2011 John Wiley & Sons, Ltd.     

基于近红外光谱的橄榄油品质鉴别方法研究

目前市面上销售的橄榄油主要分为特级初榨橄榄油和普通初榨橄榄油两类,为了鉴别两种不同品质的橄榄油,提出了一种应用siPLS-IRIV-PCA算法的橄榄油品质鉴别的新方法。基于橄榄油的近红外光谱数据,应用联合区间偏最小二乘法(siPLS)对橄榄油的近红外光谱进行了波长区间优选,使用交叉验证均方根误差(RMSECV)评估模型的性能并选择最优波长区间,通过迭代保留信息变量(IRIV)算法从最优波长区间中选择特征波长,根据选择的特征波长构建主成分分析(PCA)模型。对90组特级初榨橄榄油和90组普通橄榄油样本进行了判别鉴定。PCA将1 427个波长变量作为输入变量,前两个主成分贡献率为51.891 8%和26.473 2%;siPLS-PCA将408个波长变量作为输入变量,前两个主成分贡献率为56.039 1%和36.235 5%;siPLS-IRIV-PCA将6个波长变量作为输入变量,前两个主成分贡献率为66.347 6%和32.304 3%。结果表明,与PCA和siPLS-PCA鉴别方法相比,siPLS-IRIV-PCA具有最佳的鉴别性能。     

激光拉曼光谱检测灵芝孢子油

采用激光拉曼光谱分析比较了灵芝孢子油、橄榄油、葵花籽油及鱼肝油的光谱特征,结果显示激光拉曼光谱法可以用于快速检测灵芝孢子油：灵芝孢子油具有位于1 563 cm-1处峰强较弱线宽较宽的特征拉曼峰,而且位于1 445和1 660 cm-1两处拉曼峰的相对强度比与其他油不同。同时运用激光拉曼光谱法分析了变质的灵芝孢子油和廉价的灵芝孢子油,发现暴露在空气中一段时间后的灵芝孢子油的活性成分基本被氧化了,市场上廉价的灵芝孢子油可能是由变质的灵芝孢子油、葵花籽油、或其他廉价植物油混合掺杂而成的。     

近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析

橄榄油兼有食用和保健的作用,价值与价格远远高于其他食用油,所以橄榄油中以劣充好的现象十分普遍。可采用近红外光谱法测定初榨橄榄油中掺杂芝麻油、大豆油和葵花籽油的光谱数据,运用改进的BP算法——Levenberg-Marquardt方法,建立PCA-BP人工神经网络方法对其进行定性判别。同时采用偏最小二乘法(PLS)建立了初榨橄榄油中芝麻油、大豆油、葵花籽油含量的近红外光谱定标模型,用交互验证法进行验证。结果表明,BP人工神经网络有很好的定性鉴别能力,PLS建立的芝麻油、大豆油、葵花籽油定标模型的相关系数分别为98.77,99.37,99.44,交叉验证的均方根误差分别为1.3,1.1,1.04。该方法无损、快速、简便,为橄榄油掺杂的检测提供了一种新的途径。     

荧光光谱及平行因子分析法在植物油鉴别中的应用

利用FS920荧光光谱仪测量市售的八种植物油(大豆油、玉米油、橄榄油、稻米油、花生油、核桃油、葵花籽油和芝麻油)共22个样品的荧光光谱,并对其数据矩阵(EEMs)进行平行因子分析,结合荧光谱分析的直观物质表征和平行因子法对灰色体系的组分识别优势,实现了植物油的种类区分与鉴别。综合分析植物油在特定范围内(激发波长为250～550 nm,发射波长为260～750 nm)的三维荧光光谱和等高线光谱图,给出了各植物油峰位、峰数和峰强等特征信息,确定了植物油各荧光谱峰相应的荧光物质(不饱和脂肪酸类、维生素E及其衍生物、叶绿素及类胡萝卜素);将平行因子模型应用于植物油光谱数据矩阵的分析,确定了平行因子分析模型的因子数及各因子的物质基础(维生素E及其衍生物、亚油酸和亚麻酸、脂肪酸氧化产物、植物油氧化产物)。建立了植物油的4因子激发-发射光谱轮廓图和样品因子投影得分图。通过对植物油荧光光谱的图谱特征和其数据阵平行因子模型的分析,证实荧光光谱技术和平行因子分析法对植物油进行分析和种类鉴别的有效性。     

Adulteration of sesame oil with corn oil detected by use of two-dimensional infrared correlation spectroscopy and multivariate calibration

An innovative methodology was developed to detect adulteration of sesame oil with corn oil based on two-dimensional mid-infrared correlation spectroscopy with multivariate calibration. Forty pure sesame oils and 40 adulterated sesame oils with corn oil were prepared and the infrared absorption spectra were measured at room temperature, respectively. The synchronous two-dimensional mid-infrared correlation spectra were calculated to develop multivariate calibration models for adulteration of sesame oil with corn oil. The results showed the higher classification accuracy of 96.3% for the prediction set using two-dimensional mid-infrared correlation spectra and N-way partial least square discriminant analysis, versus 88.9% using traditional one-dimensional mid-infrared spectra and partial least squares discriminant analysis. Also, the multivariate calibration models were developed for quantitative analysis of sesame oil adulteration with corn oil. The root mean square error of prediction was 0.98% v/v using two-dimensional mid-infrared correlation spectra and N-PLS, and 1.15% v/v using traditional one-dimensional mid-infrared spectra and PLS. The results of our analyses indicated that the proposed method could provide better predictive results than traditional one-dimensional mid-infrared spectra and multivariate calibration.     

The Use of Raman Spectroscopy and Methods of Quantum Chemistry for Assessing the Relative Concentration of Triglycerides of Oleic and Linoleic Acids in a Mixture of Olive Oil and Sunflower Seed Oil

The Raman spectra of five samples of sunflower seed oil and five samples of cold-pressed olive oil of various brands are recorded in the range of 500–2000 cm^–1. Within the framework of the B3LYP/6-31G(d)/6-31G(d,p)/6-31+G(d,p)/6-311G(d)/6-311G(d, p)/6-311+G(d,p) methods, the structural models of eight fatty acids (oleic, linoleic, palmitic, stearic, α-linolenic, arachidonic, eicosapentaenoic, and docosahexaenoic) are constructed, and also within the framework of the B3LYP/6-31G(d) method, the structural models of triglycerides of the first four of the above acids are obtained. The vibrational wavenumbers and intensities in the IR and Raman spectra are calculated. The Raman spectra of olive oil and sunflower seed oil were simulated by using the supermolecular approach. We investigated the dependence of the relative intensity of the vibrational bands ν_exp = 1660 and 1445 cm^–1 on the concentration of triglycerides in oils of oleic and linoleic acids and the dependence of the intensity of these bands on the degree of saturation of fatty acids. Experimental and empirical dependences are constructed to estimate the relative concentration of triglycerides of oleic and linoleic acids in a mixture of olive oil and sunflower seed oil. The applicability of the density functional theory together with the vibrational spectroscopy for the identification of mixtures of vegetable oils is discussed.