低场核磁共振结合化学计量学方法快速检测掺假核桃油

以掺假核桃油样品为低场核磁共振检测对象,利用主成分分析法(PCA)和偏最小二乘回归法(PLSR)分析处理Carr-Purcell-Meiboom-Gill(CPMG)序列的核磁共振弛豫数据,旨在探求一种能快速检测核桃油品质的新方法。对几种常见掺假形式(掺入大豆油、玉米油、葵花油)的核桃油样品和纯核桃油样品进行检测和评价。实验结果表明:纯核桃油和掺入不同种类食用油的掺假核桃油在主成分得分图上可以得到很好的区分,且掺假样品随掺假比例在图中呈规律性分布;采用PLSR法对CPMG数据和实际掺假率进行回归,可实现对核桃油掺假水平的准确定量测定。方法快速、无损、准确,在食用油制品的品质控制及评价方面具有很大的应用潜力。     

Visible and near-infrared absorption spectroscopy by an integrating sphere and optical fibers for quantifying and discriminating the adulteration of extra virgin olive oil from Tuscany

Because of its high price, extra virgin olive oil is frequently targeted for adulteration with lower quality oils. This paper presents an innovative optical technique capable of quantifying and discriminating the adulteration of extra virgin olive oil caused by lower-grade olive oils. An original set-up for diffuse-light absorption spectroscopy in the wide 400–1,700 nm spectral range was experimented. It made use of an integrating sphere containing the oil sample and of optical fibers for illumination and detection; it provided intrinsically scattering-free absorption spectroscopy measurements. This set-up was used to collect spectroscopic fingerprints of authentic extra virgin olive oils from the Italian Tuscany region, adulterated by different concentrations of olive-pomace oil, refined olive oil, deodorized olive oil, and refined olive-pomace oil. Then, a straightforward multivariate processing of spectroscopic data based on principal component analysis and linear discriminant analysis was applied which was successfully capable of predicting the fraction of adulterant in the mixture, and of discriminating its type. The results achieved by means of optical spectroscopy were compared with the analysis of fatty acids, which was carried out by standard gas chromatography.     

Comparative Study using Raman and Visible Spectroscopy of Cretan Extra Virgin Olive Oil Adulteration with Sunflower Oil

The adulteration of extra virgin olive oil with low-quality and inexpensive seed oil is a serious problem in the industry. In recent years, the characterization of extra virgin olive oil adulteration with various techniques has been successfully implemented. In this work, a comparative study of Raman and visible spectroscopy is presented. These methods are rapid, noninvasive, and no sample pretreatment is required. We used both methods to study Cretan extra virgin olive oil adulterated with sunflower oil. Statistical analysis based on partial least square regression was used to determine the detection limits of the methods. Raman spectroscopy was superior in comparison to visible spectroscopy with adulteration detection limits of 3.5 and 5.5%, respectively, for the same samples. These results indicate that both techniques are suitable for olive oil quality control.     

Direct olive oil authentication: detection of adulteration of olive oil with hazelnut oil by direct coupling of headspace and mass spectrometry, and multivariate regression techniques

Control of adulteration of olive oil, together with authentication and contamination, is one of the main aspects in the quality control of olive oil. Adulteration with hazelnut oil is one of the most difficult to detect due to the similar composition of hazelnut and olive oils; both virgin olive oil and olive oil are subjected to that kind of adulteration. The main objective of this work was to develop an analytical method able to detect adulteration of virgin olive oils and olive oils with hazelnut oil by means of its analysis by a headspace autosampler directly coupled to a mass spectrometer used as detector (ChemSensor). As no chromatographic separation of the individual components of the samples exists, a global signal of the sample is obtained and employed for its characterization by means of chemometric techniques. Four different crude hazelnut oils from Turkey were employed for the development of the method. Multivariate regression techniques (partial least squares and principal components analysis) were applied to generate adequate regression models. Good values were obtained in both techniques for the parameters employed (standard errors of prediction (SEP) and prediction residual error sum of squares (PRESS)) to evaluate its goodness. With the proposed method, minimum adulteration levels of 7 and 15% can be detected in refined and virgin olive oils, respectively. Once validated, the method was applied to the detection of such adulteration in commercial olive oil and virgin olive oil samples.     

Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils

An “electronic nose” has been used for the detection of adulterations of virgin olive oil. The system, comprising 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, feature selection techniques were employed to choose a set of optimally discriminant variables. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and artificial neural networks (ANN) were applied. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils and it was even possible to identify the type of oil used in the adulteration. Promising results were also obtained as regards quantification of the percentages of adulteration.     

Application of lag-k autocorrelation coefficient and the TGA signals approach to detecting and quantifying adulterations of extra virgin olive oil with inferior edible oils

The combination of lag-k autocorrelation coefficients (LCCs) and thermogravimetric analyzer (TGA) equipment is defined here as a tool to detect and quantify adulterations of extra virgin olive oil (EVOO) with refined olive (ROO), refined olive pomace (ROPO), sunflower (SO) or corn (CO) oils, when the adulterating agents concentration are less than 14%. The LCC is calculated from TGA scans of adulterated EVOO samples. Then, the standardized skewness of this coefficient has been applied to classify pure and adulterated samples of EVOO. In addition, this chaotic parameter has also been used to quantify the concentration of adulterant agents, by using successful linear correlation of LCCs and ROO, ROPO, SO or CO in 462 EVOO adulterated samples. In the case of detection, more than 82% of adulterated samples have been correctly classified. In the case of quantification of adulterant concentration, by an external validation process, the LCC/TGA approach estimates the adulterant agents concentration with a mean correlation coefficient (estimated versus real adulterant agent concentration) greater than 0.90 and a mean square error less than 4.9%.     

Chiral separation of amino acids by capillary electrophoresis with octyl-beta-thioglucopyranoside as chiral selector

In the present work, we propose the use of direct coupling of a headspace sampler to a mass spectrometer for the detection of adulterants in olive oil. Samples of olive oils were mixed with different proportions of sunflower oil and olive-pomace oil, respectively, and patterns of the volatile compounds in the original and mixed samples were generated. Application of the linear discriminant analysis technique to the data from the signals was sufficient to differentiate the adulterated from the non-adulterated oils and to discriminate the type of adulteration. The results obtained revealed 100% success in classification and close to 100% in prediction. The main advantages of the proposed methodology are the speed of analysis (since no prior sample preparation steps are required), low cost, and the simplicity of the measuring process.     

The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.

A new procedure has been developed for the classification and quantification of the adulteration of pure olive oil by soya oil, sun flower oil, corn oil, walnut oil and hazelnut oil. The study was based on a chemometric analysis of the near-infrared (NIR) spectra of olive-oil mixtures containing different adulterants. The adulteration of olive oil was carefully carried out gravimetrically in a 4 mm quartz cuvette, starting with pure olive oil in the cuvette first. NIR spectra of the 525 adulterated mixtures were measured in the region of 12,000-4000 cm(-1). The spectra were subjected batch wise to multiplicative signal correction (MSC) before calculating the principal component (PCA) models. The MSC-corrected data were subjected to Savitzky-Golay smoothing and a mean normalization procedure before developing partial least-squares calibration (PLS) models. The results revealed that the models predicted the adulterants, corn oil, sun flower oil, soya oil, walnut oil and hazelnut oil involved in olive oil with error limits +/-0.57, +/-1.32, +/-0.96, +/-0.56 and +/-0.57% weight/weight, respectively. Furthermore, the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty. Quantification of the adulterants was carried out using their respective PLS models within the same error limits as mentioned above.     

Detection and quantification of honey adulteration via direct incorporation of sugar syrups or bee-feeding: preliminary study using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) and chemometrics

Honey adulteration is a complex problem which currently has a significant economic impact and undeniable nutritional and organoleptic consequences. This paper describes the development of an effective anionic chromatographic method (HPAEC-PAD) for honey analysis and adulteration detection. The method relies on the use of chemometric methods to process chromatograms in order to achieve a better discrimination between authentic and adulterated honeys by linear discriminant analysis and to quantify adulteration levels by partial least squares analysis. This approach was investigated using honey samples adulterated from 10 to 40% with various industrial bee-feeding sugar syrups. Good results were obtained in the characterization of authentic and adulterated samples (96.5% of good classification) using linear discriminant analysis followed by a canonical analysis. The application of the partial least squares modeling method provided a corresponding linear regression model allowing the percentage of adulteration of new samples to be estimated directly from sample chromatograms.Additionally, a bee-feeding experiment on a small apiary was conducted in order to evaluate the effect of supplying hives with bee-feeding syrups. This practice is specific to the apicultural area. It has been demonstrated that bee-feeding can modify the sugar composition of the produced honey if it is conducted without safeguards.     

Synchronous fluorescence spectroscopy for quantitative determination of virgin olive oil adulteration with sunflower oil

Adulteration of extra virgin olive oil with sunflower oil is a major issue for the olive oil industry. In this paper, the potential of total synchronous fluorescence (TSyF) spectra to differentiate virgin olive oil from sunflower oil and synchronous fluorescence (SyF) spectra combined with multivariate analysis to assess the adulteration of virgin olive oil are demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 270–720 nm and the wavelength interval (Δλ) in the region from 20 to 120 nm. TSyF contour plots for sunflower, in contrast to virgin olive oil, show a fluorescence region in the excitation wavelength range 325–385 nm. Fifteen different virgin olive oil samples were adulterated with sunflower oil at varying levels (0.5–95%) resulting in one hundred and thirty six mixtures. The partial least-squares regression model was used for quantification of the adulteration using wavelength intervals of 20 and 80 nm. This technique is useful for detection of sunflower oil in virgin olive oil at levels down to 3.4% (w/v) in just two and a half minutes using an 80-nm wavelength interval.     

Development of a methodology based on headspace-gas chromatography-ion mobility spectrometry for the rapid detection and determination of patin fish oil adulterated with palm oil

The present research intends to develop a new method based on headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the detection and determination of patin fish oil adulterated with different percentages of palm oil, because it is a cheaper vegetable oil. Five variables that affect headspace generation (incubation time and temperature, amount of sample, injection volume, and pre-heating time) have been optimized by means of a Box Behnken design in combination with Response Surface Methodology. Then, repeatability and intermediate precision have been studied where coefficients of variation lower than 10% were obtained. This new method has been applied to several samples of patin fish oil adulterated with palm oil at 20 different levels (5–50% palm oil content). The results have confirmed the suitability of the HS-GC-IMS for a rapid, easy, and reliable detection and discrimination of adulterated oil samples since a characteristic fingerprint that allows 100% successful discrimination between adulterated and unadulterated patin oil samples was achieved. Finally, a regression model has been developed to determine each sample’s adulteration level with an error lower than 10% and a coefficient of determination greater than 0.95.     

DSC方法在特级初榨橄榄油掺假鉴别中的应用

采用差示扫描量热法(DSC)对进口特级初榨橄榄油中葵花籽油的掺假鉴别进行了系统研究。由橄榄油入手考察了升降温循环实验条件下油品的重复性及数据可靠性,以此为基础提出采用程序降温的方法研究油品的结晶特性。统计了研究体系内的8种特级初榨橄榄油、6种其他食用油以及5种比例的模拟掺假油的结晶峰温度值,建立了回归方程。结果表明:进口特级初榨橄榄油在-60~-46℃区间内具有尖锐的结晶峰;随着掺入葵花籽油比例的升高,模拟掺假油的结晶温度逐渐向低温区移动,结晶峰形由尖锐逐渐变平坦;由结晶起始温度和结晶峰值温度分别相对于掺假油体积分数建立的回归方程具有很好的相关性,可以快速准确地鉴别特级初榨橄榄油。     

Linear and non linear chemometric models to quantify the adulteration of extra virgin olive oil

Two mathematical methods to quantify adulterations of extra virgin olive oil (EVOO) with refined olive oil (ROO), refined olive-pomace oil (ROPO), sunflower (SO) or corn (CO) oils have been described here. These methods are linear and non linear models based on chaotic parameters (CPs, Lyapunov exponent, autocorrelation coefficients and two fractal dimensions) which were calculated from UV-vis scans (190-900 nm wavelength) of 817 adulterated EVOO samples. By an external validation process, linear and non linear integrated CPs/UV-vis models estimate concentrations of adulterant agents with a mean correlation coefficient (estimated versus real concentration of cheaper oil) greater than 0.80 and 0.97 and a mean square error less than 1% and 0.007%, respectively. In the light of the results shown in this paper, the adulteration of EVOO with ROO, ROPO, SO and CO can be suitably detected by only one chaotic parameter integrated on a radial basis network model.     

傅里叶变换红外光谱法对掺假橄榄油的快速鉴别

利用衰减全反射傅里叶红外光谱法对掺假橄榄油进行了快速鉴别研究。对掺入转基因大豆油、非转基因大豆油、花生油、玉米油、葵花籽油、调和油等的橄榄油采用160℃高温加热8h处理,通过观察样品加热前、后二阶导数光谱在988cm-1处特征吸收峰的吸光度变化,准确鉴别橄榄油是否掺假。该方法操作简便、前处理无需有机试剂,可作为市场筛查掺假橄榄油的快速鉴别方法。     

Extra virgin (EV) and ordinary (ON) olive oils: distinction and detection of adulteration (EV with ON) as determined by direct infusion electrospray ionization mass spectrometry and chemometric approaches

Extra virgin (EV), the finest and most expensive among all the olive oil grades, is often adulterated by the cheapest and lowest quality ordinary (ON) olive oil. A new methodology is described herein that provides a simple, rapid, and accurate way not only to detect such type of adulteration, but also to distinguish between these olive oil grades (EV and ON). This approach is based on the application of direct infusion electrospray ionization mass spectrometry in the positive ion mode, ESI(+)‐MS, followed by the treatment of the MS data via exploratory statistical approaches, PCA (principal component analysis) and HCA (hierarchical clustering analysis). Ten distinct brands of each EV and ON olive oil, acquired at local stores, were analyzed by ESI(+)‐MS and the results from HCA and PCA clearly indicated the formation of two distinct groups related to these two categories. For the adulteration study, one brand of each olive oil grade (EV and ON) was selected. The counterfeit samples (a total of 20) were then prepared by adding assorted proportions, from 1 to 20% w/w, with increments of 1% w/w, of the ON to the EV olive oil. The PCA and HCA methodologies, applied to the ESI(+)‐MS data from the counterfeit (20) and authentic (10) EV samples, were able to readily detect adulteration, even at levels as low as 1% w/w. Copyright © 2010 John Wiley & Sons, Ltd.     

A New Method for Olive Oil Screening Using Multivariate Analysis of Proton NMR Spectra

A new NMR-based method for the discrimination of olive oils of any grade from seed oils and mixtures thereof was developed with the aim of allowing the verification of olive oil authenticity. Ten seed oils and seven monovarietal and blended extra virgin olive oils were utilized to develop a principal component analysis (PCA) based analysis of ¹H NMR spectra to rapidly and accurately determine the authenticity of olive oils. Another twenty-eight olive oils were utilized to test the principal component analysis (PCA) based analysis. Detection of seed oil adulteration levels as low as 5% v/v has been shown using simple one-dimensional proton spectra obtained using a 400 MHz NMR spectrometer equipped with a room temperature inverse probe. The combination of simple sample preparation, rapid sample analysis, novel processing parameters, and easily interpreted results, makes this method an easily accessible tool for olive oil fraud detection by substitution or dilution compared to other methods already published.     

Total luminescence spectroscopy with pattern recognition for classification of edible oils

Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.     

Screening of adulteration in packaging biocomposites by infrared spectroscopy and chemometrics

Fourier transform infrared spectroscopy coupled with chemometrics was employed to detect packaging polylactic acid-based biocomposite samples adulterated with polypropylene (PP) 30–45% and linear low-density polyethylene 2–10%. Principal component analysis, soft independent modeling of class analogy (SIMCA) and partial least square discriminate analysis (PLS-DA) chemometric techniques were utilized to classify samples in different classes. Totally, 362 samples were modeled in three different classes (two adulterated and one non-adulterated). The obtained results revealed that PLS-DA is the most suitable chemometric approach for prediction of probable adulteration in biocomposite samples with reliable specificity and selectivity. It could provide 99% correct class prediction rate between non-adulterated biocomposite samples and adulterated ones, while SIMCA methods provided 73.33% prediction accuracy in classification.     

Rapid Authentication of Olive Oil by Raman Spectroscopy Using Principal Component Analysis

A method of principal component analysis was employed to authenticate genuine olive oil based on Raman spectroscopy, which can reliably distinguish olive oil from other types of oils and can also accurately identify the level of adulteration in a set of olive oil samples contaminated with 5% or more of other types of oils, such as soybean oil, rapeseed oil, sunflower seed oil, and corn oil. The method is very easy, effective, time-saving, and requires minimal sample preparation. Therefore, the method is a promising technique for the rapid authentication application of olive oil.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resource(s): Additional text and table]     

Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics

Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using ¹H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.