Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data

This study focuses on the detection and quantification of extra-virgin olive oil adulteration with different edible oils using mid-infrared (IR) spectroscopy with chemometrics. Mid-IR spectra were manipulated with wavelet compression previous to principal component analysis (PCA). Detection limit of adulteration was determined as 5% for corn–sunflower binary mixture, cottonseed and rapeseed oils. For quantification of adulteration, mid-IR spectral data were manipulated with orthogonal signal correction (OSC) and wavelet compression before partial least square (PLS) analysis. The results revealed that models predict the adulterants, corn–sunflower binary mixture, cottonseed and rapeseed oils, in olive oil with error limits of 1.04, 1.4 and 1.32, respectively. Furthermore, the data were analysed with a general PCA model and PLS discriminant analysis (PLS-DA) to observe the efficiency of the model to detect adulteration regardless of the type of adulterant oil. In this case, detection limit for adulteration is determined as 10%.     

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.     

Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef

Chemometric MID-FTIR methods were developed to detect and quantify the adulteration of mince meat with horse meat, fat beef trimmings, and textured soy protein. Also, a SIMCA (Soft Independent Modeling Class Analogy) method was developed to discriminate between adulterated and unadulterated samples. Pure mince meat and adulterants (horse meat, fat beef trimmings and textured soy protein) were characterized based upon their protein, fat, water and ash content. In order to build the calibration models for each adulterant, mixtures of mince meat and adulterant were prepared in the range 2–90% (w/w). Chemometric analyses were obtained for each adulterant using multivariate analysis. A Partial Least Square (PLS) algorithm was tested to model each system (mince meat + adulterant) and the chemical composition of the mixture. The results showed that the infrared spectra of the samples were sensitive to their chemical composition. Good correlations between absorbance in the MID-FTIR and the percentage of adulteration were obtained in the region 1800–900 cm^− 1. Values of R² greater than 0.99, standard errors of calibration (SEC) in the range to 0.0001–1.278 and standard errors of prediction (SEP estimated) between 0.001 and 1.391 for the adulterant and chemical parameters were obtained. The SIMCA model showed 100% classification of adulterated meat samples from unadulterated ones. Chemometric MID-FTIR models represent an attractive option for meat quality screening without sample pretreatments which can identify the adulterant and quantify the percentage of adulteration and the chemical composition of the sample.     

基于傅里叶变换红外光谱的大豆原油掺伪定量 分析模型研究

目的应用傅里叶变换红外光谱(FTIR)结合最小偏二乘法(PLS)建立大豆原油-棕榈油二元掺伪体系的定量分析模型。方法以42个大豆原油、21个精炼油、88个掺伪油的FIIR谱图为模型样本,预处理方法选用标准正态变量(SNV),在此基础上应用主成分分析(PCA)提取特征变量,随机选取60个掺伪油样组成校正集,28个掺伪油样组成验证集,以PLS方法建立大豆原油的掺伪定量模型。结果 PCA可将大豆原油及精炼油分成独立的2类。经PCA分析,大豆原油中掺入棕榈油的掺伪检测限为5%。PLS校正模型的判定系数R2为0.9926,校正误差均方根RMSEC为1.8121。预测模型的R2为0.9823,交叉验证误差均方根RMSECV为2.8189。同时得到的预测结果的偏差在1.3909%~3.1019%之间,差异不显著,说明此模型可行。结论 FTIR-PLS模型能够实现大豆原油的掺伪定量分析,分析速度快,能够满足大豆原油入库要求,是一种可行的大豆原油掺伪分析方法。     

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R² > 0.9961, standard errors of calibration (SEC) in the range of 0.3963–0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.     

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。     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.     

气相离子迁移谱对山茶油掺假的检测

以掺假山茶油样为气相离子迁移谱（gas chromatography-ion mobility spectrometry,GC-IMS）检测对象,利用多维主成分分析（multi-way principal component analysis,MPCA）法和偏最小二乘（partial least squares,PLS）回归分析处理二维谱图数据,探索并建立一种山茶油纯度检测方法。对配制的不同比例3 种食用植物油的掺假油样进行GC-IMS检测,采用MPCA压缩并提取矩阵中的得分矩阵进行主成分分析,将提取的得分矩阵进行PLS分析,建立掺假量的定量预测模型。结果表明,MPCA处理后的主成分图可以明显区分山茶油样和掺入不同种类食用油的掺假山茶油样,且不同掺入比例组有其明显的归属区域;采用PLS对MPCA的得分矩阵进行回归分析,可实现对山茶油掺假比例的准确定量测定。该方法具有快速、准确、无损的特点,可应用推广到其他联用仪器的数据分析处理中,在食用油品质控制与评价方法中具有很大的应用前景。     

电子鼻鉴别核桃油中掺入大豆油、菜籽油及玉米油研究

为了快速简便地鉴别核桃油掺伪,利用电子鼻技术鉴别核桃油中掺入大豆油、菜籽油及玉米油,并采用主成分分析(PCA)和线性判别式分析(LDA)对结果进行分析,研究表明:采用PCA方法可以鉴别核桃油掺入大于20%大豆油、7%菜籽油和7%玉米油;采用LDA方法可以鉴别核桃油中掺入大于1%大豆油、1%菜籽油和7%玉米油,LDA方法比PCA方法能更加有效地鉴别核桃油中掺入大豆油、菜籽油和玉米油的现象。电子鼻技术可以作为鉴别核桃油掺假的一种快速简便的检测技术。     

电子鼻对芝麻油掺假的检测

使用电子鼻系统PEN3 对芝麻油中掺入大豆油、玉米油、葵花籽油进行检测分析,分别对芝麻油中不同量的掺假进行辨别,用主成分分析(PCA)和线性判别式分析(LDA)两种方法分析。结果表明：电子鼻能够较好的识别芝麻油掺假不同比例的大豆油、玉米油和葵花籽油,而且LDA 方法比PCA 方法的效果好。PCA 方法对掺入大豆油、玉米油超过50% 和葵花籽油超过70% 的芝麻油能明显区分,而LDA 方法对芝麻油中掺入不同量的大豆油、玉米油和葵花籽油均能明显区分。     

Identification and Detection of Adulterated Camellia Oleifera Abel. Oils by Near Infrared Transmittance Spectroscopy

Under the serious circumstances of Camellia oleifera adulteration, the accurate examination for quality trait of C. oleifera oil is extremely urgent. The use of near infrared transmittance spectroscopy as a rapid and cost-efficient classification technique for the authentication of Camellia oil was investigated. At the same time, the feasibility of near infrared transmittance spectroscopy for the rapid determination of soybean oil and maize oil adulterated in binary and ternary system Camellia oils was explored. The results showed that identifications was made based on the slight difference in raw near infrared transmittance spectra in Camellia oils, soybean oils, maize oils, and those adulterated with soybean and maize oil with discriminant equations techniques. Furthermore, the performance of near infrared transmittance spectroscopy models for binary and ternary system adulterated Camellia oils was satisfactory. Moreover, the near infrared transmittance spectroscopy calibration model of soybean oil (0–50%) in binary system adulterated Camellia oils was the best, and correlation coefficients of the cross-validation (R_cv) was 0.99999. For the near infrared transmittance spectroscopy calibration model of maize oil in binary system (0–50%) and ternary system (0–40%) adulterated Camellia oils, the R_cv were 0.99996 and 0.99961, respectively. In addition, the coefficients of external validation for three models were obtained (0.9998, 0.9999, and 0.9967, respectively). In all, near infrared transmittance spectroscopy could be conducted to identify Camellia oils and detect soybean oil and maize oil adulterated in binary and ternay system Camellia oils from the methodology.     

GC和LF-NMR结合化学计量学方法检测掺假油茶籽油

为了对油茶籽油品质控制及评价提供支撑,以纯油茶籽油和掺假油茶籽油(分别掺入菜籽油、花生油、棕榈油和高油酸花生油)为试验材料,采用气相色谱法(GC)分析其脂肪酸组成,采用低场核磁共振技术(LF-NMR)测定其横向弛豫特性数据,结合主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)和偏最小二乘分析(PLS)等化学计量学方法建立油茶籽油掺假的定性和定量分析模型。结果表明：5种植物油的脂肪酸组成和LF-NMR横向弛豫特性数据存在显著区别;油茶籽油和其他4种植物油在PCA得分图上可清晰区分;PLS-DA模型可有效区分油茶籽油和掺假油茶籽油,判别正确率均可达100%;建立的油茶籽油中掺入菜籽油、花生油、棕榈油、高油酸花生油的PLS定量预测模型,真实值与预测值的相关系数(R²)分别为0.994 1、0.998 6、0.997 6、0.978 1。综上,GC和LF-NMR结合PCA、PLS-DA以及PLS等化学计量学方法可用于油茶籽油掺假类别判定及掺假量分析。     

Quantification of adulterant agents in extra virgin olive oil by models based on its thermophysical properties

Two methods to quantify the adulteration of extra virgin olive oil (EVOO) based on the physical characteristic of adulterated samples have been here described. Firstly, the adulterant agent concentration is determined using the density and/or refractive indices (RIs) of adulterated samples of EVOO with sunflower (SO) or corn (CO) oils by suitable linear correlations between density and/or RI. Finally, models based on the combination of differential scanning calorimetry (DSC) equipment and a chaotic parameter (lag-k autocorrelation coefficients, LCCs) is defined here to quantify adulterations of EVOO with refined olive (ROO), refined olive pomace (ROPO), SO or CO oils. This quantification was carried out using successful linear correlation of LCCs and ROO, ROPO, SO or CO concentrations in 462 adulterated samples of EVOO. The LCCs are calculated from DSC scans of adulterated EVOO samples. In both models studied, the adulterant agent concentrations are less than 14% w/w. The former is adequate to calculate the concentration of the adulterant agents (CO and SO) with a correlation coefficient (R²) higher than 0.927 and mean square error (MSE) lower than 8.9%. By the external validation process, the LCC/DSC approach estimates the adulterant agent concentrations with a R² (estimated vs. real adulterant agent concentration) greater than 0.921 and a MSE less than 4.9%.     

常见植物油鉴别及掺伪的气相色谱新检测法

用气相色谱法分析测定了常见植物油脂酸组成与含量,对其有关实验条件进行了优选,获得常见植物油脂脂肪酸组成与含量正常值。测定模拟掺伪常见植物油脂脂肪酸组成与含量,获得掺伪常见植物油脂脂肪酸组成与含量的变化规律。建立了常见植物油油品的鉴别及其掺伪的气相色谱检测法,可快速鉴别常见植物油的种类,对常见植物油是否掺掺伪可作出快速判别,同时对掺伪植物油可作定性、定量分析。用本法对市场销售食用植物油进行抽检,共抽检了262件油样,检出掺伪芝麻油83件、掺伪菜油47件,掺伪花生油菜23件,掺伪橄榄油11件,掺伪量10%～95%不等。表明这种方法行之有效的。     

Evaluation of the bellier test in the detection of olive oil adulteration with vegetable oils

Virgin olive oil was mixed with eight vegetable oils (sunflower, soya bean, palm, linseed, cottonseed, corn, sesame, and olive residue) at various levels. The Bellier test was applied to find the minimum detectable adulteration level and the ‘sensitivity score’ for each oil. The test was inapplicable to sunflower and linseed oils regardless of the level in olive oil. It was successful in detecting olive residue, soya bean, palm, cottonseed, corn, and sesame oils at minimal levels of 730, 150, 130, 90, 60 and 10 g kg^?1, respectively. The rancidity level of the adulterant oils did not affect the performance of the test in the case of sunflower, linseed and sesame oils. The sensitivity of the test decreased considerably with increasing peroxide value of the adulterant oil: soya bean, palm, cottonseed, corn and olive residue. However, the change in sensitivity level commenced at so high a peroxide value that it has no significance for practical purposes; at such levels of peroxidation the adulterated olive would be unmarketable and rejected by inspectors due to its poor sensory quality.     

Detection of the adulteration of olive oils by solid phase microextraction and multidimensional gas chromatography

The presence or absence of filbertone in 21 admixtures of olive oil with virgin and refined hazelnut oils obtained using various processing techniques from different varieties and geographical origins was evaluated by solid phase microextraction and multidimensional gas chromatography (SPME–MDGC). The obtained results showed that the sensitivity achievable with the proposed procedure was enough to detect filbertone and, hence, to establish the adulteration of olive oil of different varieties with virgin hazelnut oils in percentages of up to 7%. The very low concentrations in which filbertone occurs in some refined hazelnut oils made difficult its detection in specific admixtures. In any case, the minimum adulteration level to be detected depends on the oil varieties present in the adulterated samples. In the present study, the presence of R- and S-enantiomers of filbertone could be occasionally detected in olive oils adulterated with 10–20% of refined hazelnut oil.     

The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil

Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000–650 cm⁻¹ on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R²) of 0.999.     

MONITORING THE ADULTERATION OF VIRGIN COCONUT OIL BY SELECTED VEGETABLE OILS USING DIFFERENTIAL SCANNING CALORIMETRY

The crystallization and melting enthalpy of virgin coconut oil adulterated with palm kernel oil (PKO) and soybean oil (SBO) were studied by using differential scanning calorimetry. Virgin coconut oil was spiked separately with PKO and SBO from 2% to 40% (w/w) of adulterant oils. Fatty acids of all oils were determined to complement the differential scanning calorimetry data. The heating curve of SBO-adulterated samples showed the adulteration peak appearing at the lower temperature region at 10% adulteration level. Regression analyses using stepwise multiple linear regression were used to predict the percentage adulterant with R ² of 0.9490. PKO-adulterated oils did not show any adulteration peak but demonstrated a gradual decrease in the peak height of the major exothermic peak.

PRACTICAL APPLICATIONS

An alternative method for detection of adulteration based on differential scanning calorimetry in virgin coconut oil is presented. Application of differential scanning calorimetry is rapid, does not require sample preparation and does not involve use of solvents or toxic chemicals.     

基于PCA-SVM结合共聚焦拉曼光谱的特级 初榨橄榄油掺伪压榨菜籽油定量分析

为了促进国内橄榄油市场的健康发展，对掺伪同样存在天然类胡萝卜素的低温压榨菜籽油的特级初榨橄榄油进行了定量鉴别研究。采用共聚焦拉曼光谱技术对不同掺伪浓度油样进行测试，基于密度泛函理论对油样的拉曼光谱峰的归属进行了理论分析，并对拉曼光谱数据进行主成分分析（PCA），然后利用支持向量机（SVM）构建PCA-SVM模型。另外，对PCA-SVM模型的检出限进行了研究。结果表明：特级初榨橄榄油与低温压榨菜籽油的拉曼光谱存在一定差异，最明显的光谱差异主要集中在谱峰1 008、1 161、1 528 cm-1和谱段2 800～3 000 cm-1内，与密度泛函理论对不同油样拉曼光谱峰的分析一致；不考虑类胡萝卜素特征信号建立的PCA-SVM模型决定系数大于0.989，均方根误差小于2.990%，检出限为2%（低温压榨菜籽油体积分数）;在特级初榨橄榄油掺伪定量分析中，考虑类胡萝卜素的特征信号有助于提高模型预测精度，但仅限于掺伪低价植物油中无类胡萝卜素存在的情况;PCA-SVM模型在不考虑类胡萝卜素特征信号的情况下依然具有良好的定量预测效果。综上，所建立的PCA-SVM模型可以用于掺伪2%以上低温压榨菜籽油的特级初榨橄榄油的定量鉴别。     

High-power gradient diffusion NMR spectroscopy for the rapid assessment of extra-virgin olive oil adulteration

A high gradient diffusion NMR spectroscopy was applied to measure diffusion coefficients (D) of a number of extra-virgin olive, seed, and nut oils in order to ascertain the suitability of this rapid and direct method for discrimination of adulterated olive oils. Minimum adulteration levels that could be detected by changes in D were 10% for sunflower (SuO) and soybean oil (SoO), and 30% for hazelnut (HO) and peanut oil (PO). Qualitative and quantitative prediction of adulteration was achieved by discriminant analysis (DA). The highest prediction accuracy (98–100%) was observed only when two DA models were concomitantly used for sample classification. The first DA model provided recognition of high adulterated EVOO with more than 20% of SuO or SoO, and 30% with PO, whilst the second model could differentiate EVOO adulterated with 10% of SuO or SoO, and more than 30% of HO. The validation test performed with an independent set of randomly adulterated EVOO samples gave 100% classification success. The high accuracy levels together with minimal requirements of sample preparation, and short analyses time, prove the high-power gradient diffusion NMR spectroscopy as an ideal method for rapid screening of adulteration in valuable olive oils.