The use of infrared spectroscopy in combination with chemometrics for quality control and authentication of edible fats and oils: A review

Edible fats and oils provide a significant contribution in our diet and daily life, as cooking or frying oil, or as components used in food, pharmaceutical, and cosmetics products. Fats and oils are characterized by specific values, including acid value, saponification value, iodine value, and peroxide value, as well as the oxidation products which occur during storage due to oxidative and hydrolytic deterioration. Currently, due to the high price of edible fats and oils, some unethical producers adulterate high-value edible oils like olive oil with low-priced oils like palm and corn oils; therefore the authentication analysis of edible fats and oils must be assured by introducing reliable and fast methods like infrared spectroscopy. Fourier transform infrared (FTIR) spectroscopy is an ideal technique for monitoring the quality control of fats and oils due to its property as a “fingerprint spectra technique,” which allows analysts to differentiate among fats and oils. FTIR spectra signals of fats and oils are very complex. Fortunately, a statistical technique called chemometrics can be used to handle the complex FTIR spectral data. Chemometrics in combination with FTIR spectroscopy has been widely used in many aspects of monitoring quality control of edible fats and oils including their authenticity.     

基于拉曼光谱的食用植物油快速鉴别

提出了一种基于拉曼光谱的食用植物油快速鉴别方法。基于已知类别的食用植物油样本进行建模,首先对原始拉曼谱图进行基线校正和标准归一化等预处理,并选取食用油不饱和度特征的两处拉曼峰值作为特征向量,计算训练样本特征空间上各个植物油类别的中心坐标;然后,将食用植物油测试样本的拉曼谱图经过相同预处理和特征提取,获取测试样本的特征向量,计算其与各类别中心坐标的欧式距离,根据类中心最小距离法,取欧式距离最小的那一类作为预测样本的类别。针对7类43个食用植物油样本的实验结果表明,采用食用油不饱和度两点描述法进行特征提取,各类别样本聚集效果比PCA好,类间距更大。上述鉴别方法可以准确地实现食用植物油品种的快速分类。     

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。     

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.     

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.     

Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration

We have investigated the potential of Raman spectroscopy with excitation in the visible spectral range (VIS Raman) as a tool for the classification of different vegetable oils and the quantification of adulteration of virgin olive oil as an example. For the classification, principal component analysis (PCA) was applied, where 96% of the spectral variation was characterized by the first two components. A significant similarity between sunflower oil and extra‐virgin olive oil was found using this approach. Therefore, sunflower oil is a potential candidate for adulteration in most commercially available olive oils. Beside the classification of the different vegetable oils, we have successfully applied Raman spectroscopy in combination with partial least‐squares (PLS) regression analysis for very fast monitoring of adulteration of extra‐virgin olive oil with sunflower oil. Different mixtures of extra‐virgin olive oil with three different sunflower oil types were prepared between 5 and 100% (v/v) in 5% increments of sunflower oil. While in the present context the adulteration usually refers to the addition of reasonable amounts of the adulterant (given the similarity with the basic product), we show that the technique proposed can also be used for trace analysis of the adulterant. Without using techniques like surface‐enhanced Raman scattering (SERS), a quantitative detection limit down to 500 ppm (0.05%) could be achieved, a limit irrelevant for adulteration in commercial terms but significant for trace analysis. The qualitative detection limit even was at considerably lower concentration values. Based on PCA, a clear discrimination between pure extra‐virgin olive oil and olive oil adulterated with sunflower oil was achieved. The adulterant content was successfully determined using PLS regression with a high correlation coefficient and small root mean‐square error for both prediction and validation. Copyright © 2009 John Wiley & Sons, Ltd.     

基于荧光光谱及矩阵分析的植物油鉴别技术

利用FS920荧光光谱仪测量42个油样(包括36个纯植物油样,3个调和油样和3个混合油样)的荧光光谱,并对其数据矩阵(EEMs)进行归一化处理,确定了植物油特征激发波长及矩阵分析模型。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)的等高线光谱图和特征发射谱线图,将植物油划分为三类;将矩阵分析模型应用于纯植物油鉴别,分类正确率100%;为验证矩阵分析的定量判别能力,对三种混合油样进行分析,得到接近实际配比的分析结果;对市售三种调和油样本进行分析,得出调和油以大豆和菜籽油为基底的结论。通过对植物油荧光光谱的图谱特征和其矩阵模型的分析,证实荧光光谱技术和矩阵分析法对植物油进行分析和种类鉴别的有效性。     

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。     

食用植物油中反式脂肪酸含量的激光拉曼光谱检测

油脂中的反式脂肪酸(TFA)有害人们的身体健康,有必要对其含量进行监测。共收集各类食用植物油样本79个,涉及9个品种和27个品牌,分配到校正集和预测集的样本数分别为53个和26个。采用QE65000拉曼光谱仪采集79个样本的拉曼光谱,利用自适应迭代惩罚最小二乘法去除样本拉曼光谱的荧光背景;在此基础上,采用多种归一化方法对样本拉曼光谱进行处理,并对拉曼光谱的建模波数范围进行初选;再利用竞争性自适应重加权采样(CARS)方法筛选与食用植物油TFA含量相关的光谱变量,并应用偏最小二乘(PLS)回归将食用植物油TFA的特征变量光谱强度与气相色谱测定的TFA真实含量进行关联,建立食用植物油中TFA含量的定量预测模型。研究结果表明,多种归一化方法中,有4种归一化方法均能提高PLS定量预测模型的性能,其中Area normalization方法的效果最优;经建模波数范围初选,波数范围由686~2 301 cm-1缩减为737~1 787 cm-1,确定较优的建模波数范围为737~1 787 cm-1;经CARS方法筛选,共有31个光谱变量被选择,其选择的光谱变量主要分布在1 265,1 303,1 442及1 658 cm-1拉曼振动峰附近,且974 cm-1拉曼振动峰两侧均有光谱变量被选择;此外,CARS方法的PLS建模结果优于常用的无信息变量消除及连续投影算法。由此可知,激光拉曼光谱技术结合化学计量学方法检测食用植物油中的TFA含量是可行的。归一化方法、建模波数范围初选及竞争性自适应重加权采样(CARS)方法能有效提高TFA定量预测模型的预测精度和稳定性,优化后的TFA定量预测模型的校正集及预测集的相关系数和均方根误差分别为0.949,0.953和0.188%,0.191%。与未优化的预测模型相比,预测均方根误差由0.361%下降为0.191%,下降幅度为47.1%;建模所用的变量数由683个下降为31个,仅占原变量数的4.54%。     

部分动植物油的拉曼光谱研究

利用拉曼光谱技术分别研究了植物油和动物脂肪油的光谱特性。通过自适应迭代惩罚最小二乘法的基线校正方法, 对所测得的拉曼光谱进行了数据处理,获得了它们的微观信息。进而通过选取其中部分油的拉曼光谱,进行了植物油的光谱、动物脂肪油的光谱以及动物脂肪油和植物油之间的光谱比较和分析,从而发现了多种植物油光谱(包括多个频移范围内的频移大小以及强度大小)的差异,也发现了动物脂肪油与植物油的拉曼光谱间的差异。不但提供了辨识不同食用油的光谱依据,也证明了拉曼光谱是一种识别不同油品的有效方法。     

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。     

三指标值法快速筛查不合格植物油

通过测定南方地区常用的花生油、玉米油、菜籽油、大豆油、葵花油、茶籽油、橄榄油等植物油以及地沟油和过期植物油的A3005(代表不饱和度)、A₉₈₅(代表共轭脂肪酸含量)、A₉₆₀+A₉₈₅(代表反式脂肪酸含量)三个指标值,得到了合格植物油三个指标值设定范围。在此基础上,建立快速筛查不合格植物油(过期、添加低价油、添加地沟油)的方法,有效地提高了植物油的监控效率。利用该法筛查出的若干疑不合格油,通过脂肪酸构成法和11, 12, 13, 17脂肪酸含量判定法等,均证实它们是掺杂油或过期油,几种检测方法的结合应用,可进一步推断植物油不合格的原因。     

反射光谱结合光谱基二维卷积回归网络快速检测食用油中饱和脂肪酸

人们日常膳食中常见的食用油含有丰富的饱和脂肪酸,饱和脂肪酸能为人体提供能量和必须营养物质,但过量摄入会导致多种心血管疾病.结合反射率光谱和深度学习方法发展一种食用油中饱和脂肪酸含量的分析方法.首先,测量了菜籽油、大豆油、葵花籽油、玉米油、橄榄油、芝麻油及花生油等7种食用植物油350～2500 nm范围的反射光谱,并通过...     

基于衰减全反射式太赫兹时域光谱技术的食用油光谱特性研究

食用油是人类营养和能量的重要来源,为人体提供必需的脂肪酸,研究食用油在太赫兹波段光学特性,对食用油成分分析及品质评价具有重要价值。衰减全反射式太赫兹时域光谱技术是一种新型的太赫兹时域光谱技术,通过样品与倏逝波的相互作用,获取样品的太赫兹光谱。与透射式或反射式太赫兹时域光谱技术相比,该技术能有效地避免测量食用油等液体样品时样品池对光学参数的影响,并能获得样品的精确光学参数。分别利用透射式太赫兹时域光谱技术和衰减全反射式太赫兹时域光谱技术测量了大豆油的吸收光谱。结果表明,与透射式太赫兹时域光谱技术相比,衰减全反射式太赫兹时域光谱技术能更有效地提取大豆油的吸收系数、吸收峰分布等光学特性。进一步利用衰减全反射式太赫兹时域光谱技术研究了大豆油、核桃油、葡萄籽油在太赫兹波段的光学特性,获得了三种食用油在1~1.8 THz范围内的折射率谱和吸收光谱。利用密度泛函理论计算了食用油中四种主要成分(软脂酸、硬脂酸、油酸和亚油酸)在太赫兹波段的振动、转动模式,理论计算结果同实验测量结果吻合较好。研究表明,在太赫兹波段食用油的吸收峰与所含脂肪酸分子种类与含量有关,其主要来源为脂肪酸分子的低频振动和转动。研究成果对食用油成分定性定量分析及品质检测等具有指导意义。     

Characterization of Edible Oils Using Total Luminescence Spectroscopy

Total luminescence spectroscopy was used to characterise and differentiate edible oils and additionally, to control one of the major problems in the oil quality--the effect of thermal and photo-oxidation. We studied several vegetable oils available on the Polish market, including soybean, rapeseed, corn, sunflower, linseed and olive oils. Total luminescence spectroscopy measurements were performed using two different sample geometries: front-face for pure oil samples and right-angle for transparent samples, diluted in n-hexane. All the samples studied as n-hexane solutions exhibit an intense peak, which appears at 320 nm in emission and 290 nm in excitation, attributed to tocopherols. Some of the oils exhibit a second long-wavelength peak, appearing at 670 nm in emission and 405 nm in excitation, belonging to pigments of the chlorophyll group. Additional bands were present in the intermediate range of excitation and emission wavelengths; however, the compounds responsible for this emission were not identified. The front-face spectra for pure oils included chlorophyll peaks for most samples, and some additional peaks in the intermediate range, while the tocopherol peaks were comparatively less intense. The results presented demonstrate the capability of the total luminescence techniques to characterise and differentiate vegetable oil products, and additionally, to characterize the effect of thermal and photo-oxidation on such products. In the photo-oxidation experiments, special attention was paid to possible involvement of singlet oxygen. Experiments were done to monitor the highly specific O2(1delta(g)) --> O2(3sigma(g)-) singlet oxygen emission at 1270 nm. Thus, total luminescence spectroscopy presents an interesting alternative to time-consuming and expensive techniques such as gas or liquid chromatography, mass spectrometry and other methods requiring wet chemistry steps.     

地沟油特异性指标研究

精炼地沟油相比于食用植物油,经历了更长时间的高于200 ℃的高温历程,第三类地沟油或称煎炸老油也经历了长时间高温历程。本文应用ATR红外光谱法,研究了六种常用植物油中共轭脂肪酸甘油酯含量、反式脂肪酸甘油酯含量、不饱和度等三个指标值随加热温度及加热时间的变化情况,以期找出地沟油的特异性指标。研究结果表明: 从160 ℃开始,各植物油的共轭脂肪酸甘油酯含量及反式脂肪酸甘油酯含量随着加热温度升高及加热时间增长而增加,不饱和度则降低,当加热温度为200 ℃或以上、加热时间达到4 h或更长时,六种植物油的三个指标值有大幅变化。共轭脂肪酸甘油酯含量的变化幅度还与植物油中亚油酸含量有关,亚油酸含量越大其变化幅度越大;反式脂肪酸甘油酯含量变化幅度则还与植物油中油酸含量有关,油酸含量高者变化幅度大。此外,上述三个指标值与存储时间有关,随着六种植物油存放时间增长,共轭脂肪酸含量变大,不饱和度变小,而反式脂肪酸含量变化规律与高温长时间加热不同,其含量不是变大而是变小,保质期内三个指标值的变化幅度都小于经过高温长时间加热的变化幅度。因此,从三个指标值及其变化规律可以了解植物油是否经历了长时间高温历程,它们可以作为地沟油及精炼地沟油的特异性指标。     

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。     

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.     

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。