FTIR Spectroscopy and PLS-DA Classification and Prediction of Four Commercial Grade Virgin Olive Oils from Morocco

Fourier transform infrared (FTIR) coupled to chemometrics was shown to be a useful method to classify and predict the quality of four commercial grade virgin olive oils (VOO). FTIR and physicochemical data were collected using a set of 70 samples representing extra virgin (EV), virgin (V), ordinary virgin (OV), and lampante (L) commercial grade olive oils collected in Beni Mellal region (central Morocco). Two partial least squares discriminant analysis (PLS-DA) models using physicochemical data and FTIR data were established and compared. The PLS-DA model using only physicochemical data was not accurate enough to distinguish satisfactorily among OV, V, and EV olive oil grades. On the contrary, the PLS-DA model on FTIR data was better in the calibration, able to describe 98 % of the spectral information and predicting 93 % of the VOO grades. In the external validation, this PLS-DA model accurately classified VOO commercial grades with prediction accuracy of 100 %. The proposed procedure is fast, nondestructive, simple, and easy to operate, and it is recommended for the quick monitoring of olive oil’s quality.     

基于傅里叶变换中红外光谱识别正常食用植物油和精炼潲水油模型分析

潲水油回流餐桌等食品安全问题越来越受到社会关注,探寻准确、快速、高效的潲水油鉴别新方法成为食用油安全性检测的新要求。用傅里叶变换中红外光谱技术（Fourier transform mid-infrared spectroscopy,FT-MIR）对精炼潲水油（refining hogwash oils,RHOs）和4 种不同正常食用植物油（菜籽油、大豆油、花生油和玉米油）进行快速检测,结合偏最小二乘判别法（PLS-DA）建立了RHOs和4 种不同正常食用植物油的判别模型。结果表明,在全光谱范围（4 000～450 cm–1）内,经二阶求导（Savitzky-Golay,5 点）后,RHOs和4 种不同正常食用植物油FT-MIR有显著差异。PLS-DA模型对22 个未知样品预测发现,判别模型的整体正确判别率均为100%。此结果表明FT-MIR结合化学计量学方法可以作为RHOs和4 种不同正常食用植物油（菜籽油、大豆油、花生油和玉米油）区分的一种有效技术手段。     

Multivariate analysis of NMR fingerprint of the unsaponifiable fraction of virgin olive oils for authentication purposes

A new approach to the geographical characterisation of virgin olive oils (VOOs) based on the ¹H NMR fingerprint of the unsaponifiable matter is presented. The ¹H NMR spectra of the unsaponifiable fraction of virgin olive oils from Spain, Italy, Greece, Tunisia, Turkey, and Syria were analysed by several pattern recognition techniques (LDA, PLS-DA, SIMCA, and CART). PLS-DA (PLS-1 approach) obtained the best classification results for all classes. Moreover, ¹H NMR spectra of the bulk oil, and its corresponding unsaponifiable fraction, as well as the subfractions of the unsaponifiable fraction (alcohol, sterol, hydrocarbon, and tocopherol fractions) were studied in the search for the markers that multivariate techniques revealed to be related to the geographical origin of olive oils. Additionally, a preliminary study regarding ¹H NMR data of the bulk oil and the corresponding unsaponifiable fraction of VOOs suggested that these spectral data contained complementary information for the geographical characterisation of VOOs.     

基于紫外可见光谱技术和色素组成区分橄榄油与其他食用油

目的 通过紫外可见光谱技术和色素组成,探究区分橄榄油与其他食用油的方法。方法 选择市售橄榄油(特级初榨橄榄油、混合橄榄油)和其他食用油(菜籽油、玉米油、葵花籽油、大豆油、花生油、调和油)作为研究对象,分别在220~800 nm的波长范围内进行全光谱扫描以及在波长为450 nm和670 nm处进行光度测量;通过比较不同食用油的紫外可见吸收光谱并结合主成分分析(principal component analysis, PCA)和偏最小二乘法判别(partial least squares discriminant analysis, PLS-DA)对不同橄榄油和其他食用油进行比较区分。结果 通过观察比较各食用油紫外可见吸收光谱的最大吸收波长所在位置,可以对不同食用油进行初步区分。并且PCA和PLS-DA结果显示,以波长450 nm和670 nm处的吸光度A450和A670以及A450/A670为变量,可用于区分不同橄榄油和其他食用油。结论 基于紫外可见光谱技术对不同食用油中色素的检测结果可以对食用油进行分类,并且A450、A670和A450/A670可以作为区分橄榄油与其他食用油的标记。     

Geographical origin classification of olive oils by PTR-MS

The volatile compositions of 192 olive oil samples from five different European countries were investigated by PTR-MS sample headspace analysis. The mass spectra of all samples showed many masses with high abundances, indicating the complex VOC composition of olive oil. Three different PLS-DA models were fitted to the data to classify samples into ‘country’, ‘region’ and ‘district’ of origin, respectively. Correct classification rates were assessed by cross-validation. The first fitted model produced an 86% success rate in classifying the samples into their country of origin. The second model, which was fitted to the Italian oils only, also demonstrated satisfactory results, with 74% of samples successfully classified into region of origin. The third model, classifying the Italian samples into district of origin, yielded a success rate of only 52%. This lower success rate might be due to either the small class set, or to genuine similarities between olive oil VOC compositions on this tight scale.     

傅里叶变换红外光谱和紫外光谱数据融合对牛肝菌种类的鉴别

采集5?种共272?份牛肝菌样品的傅里叶变换红外光谱和紫外光谱,结合多光谱信息融合策略,建立牛肝菌种类快速鉴别的方法。多元散射校正（multiplicative signal correction,MSC）及二阶导数（second derivative,2D）等预处理方法对原始光谱进行优化,比较优化处理对区分不同种类牛肝菌影响;利用优化处理后的光谱数据及融合数据建立偏最小二乘判别分析（partial least squares discriminant analysis,PLS-DA）模型和支持向量机（support vector machine,SVM）判别模型。结果显示：1）经过2D和MSC预处理后,不同种类牛肝菌的PLS-DA鉴别效果优于未优化模型,表明2D＋MSC预处理优化了光谱信息并提高了分类准确度;2）基于傅里叶变换红外光谱、紫外光谱、低级融合和中级融合数据分别建立PLS-DA模型,预测正确率为86.87%、66.67%、78.89%和95.56%;建立SVM判别模型,预测正确率分别为88.89%、74.44%、91.11%和100.00%,表明中级融合技术对不同种类牛肝菌鉴别效果显著,优于其他技术;3）中级融合技术在PLS-DA模型和SVM判别模型中对样品的预测正确率分别为95.56%和100.00%,表明SVM判别模型对牛肝菌种类区分效果优于PLS-DA模型。采用中级融合技术建立SVM判别模型,快速鉴别牛肝菌种类,为牛肝菌种类鉴别和质量控制提供可靠、稳定的方法。     

Discrimination of olive oil adulterated with vegetable oils using dielectric spectroscopy

The study focused on application of dielectric spectroscopy to identify the adulteration of olive oil. The dielectric properties of binary mixture of oils were investigated in the frequency range of 101 Hz–1 MHz. A partial least squares (PLS) model was developed and used to verify the concentrations of the adulterant. Furthermore, the principal component analysis (PCA) was used to classify olive oil sample as distinct from other adulterants based on their dielectric spectra. The results showed that the dielectric spectra of binary mixture of olive oil spiked with other oils increased with increasing concentration of soy, corn, canola, sesame, and perilla oils from 0% to 100% (w/w) over the measured frequency range. PLS calibration model showed a good prediction capability for the concentrations of the adulterant. For olive oil adulterated with soy oil, the results showed that the RMS was 0.053, sd(RMS), 0.017 and Q² value was 0.967. PCA classification plots for all oil samples showed clear performance in the differentiation for the different concentrations of the adulterant. Each of the oil samples could be easily grouped in different clusters using dielectric spectra. From the results obtained in this research, dielectric spectroscopy could be used to discriminate the olive oil adulterated with the different types of the oils at levels of adulteration below 5%.     

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.     

气相色谱仪结合数据分析软件鉴别橄榄油掺杂

目的基于气相色谱仪和数据分析软件来鉴别橄榄油掺杂。方法取5种橄榄油分别与市售葵花籽油、大豆油、菜籽油、玉米油和花生油以不同的比例混合来模拟掺杂。运用气相色谱-氢火焰检测器检测其脂肪酸甲酯含量,结合数据分析软件(mass profiler professional,MPP)进行数据处理,以偏最小二乘判别分析法建立预测模型。结果 1%(体积比)掺杂样品的鉴别准确率在90%以上。结论通过此方法对各类掺杂橄榄油都能很好地鉴别是否掺杂。     

Verification of fresh grass feeding,pasture grazing and organic farming by FTIR spectroscopy analysis of bovine milk

In the present study, a total of 116 tank milk samples were collected from 30 farms located in The Netherlands and analysed by Fourier-transform infrared (FTIR) spectroscopy. Samples were collected in April, May and June 2011 and in February 2012. The samples differed in the time spent by the cows on pasture, presence/absence of fresh grass in the daily ration and the farming system (organic/biodynamic or conventional). Classification models based on partial least square discriminant analysis (PLS-DA) of FTIR spectra were developed for the prediction of fresh grass feeding, pasture grazing and organic farming. The PLS-DA model discriminated between milk from cows that had fresh grass in the daily ration and milk from cows that had not fresh grass with sensitivity and specificity values of 88% and 83% in external validation and all the samples from cows that had no fresh grass collected in spring were correctly classified. The PLS-DA model developed for the authentication of pasture grazing showed comparable accuracy when the whole sample set is considered but was less accurate on the spring samples (75% of samples from cows indoors in spring correctly classified). Discrimination of organic and conventional milk was also accomplished with acceptable accuracy with % correct classification of 80% and 94% respectively in external validation. The results suggest that milk FTIR spectra contain valuable information on cows' diet that can be used for authentication purposes.     

Rapid classification of virgin and recycled EPS containers by Fourier transform infrared spectroscopy and chemometrics

ABSTRACT

A rapid and sensitive method for classification of virgin and recycled expanded polystyrene (EPS) food containers was developed using Fourier transform infrared spectroscopy (FTIR) and chemometrics. This method includes preparing a transparent film by dissolution, examining by FTIR and developing classification models. The degradation of EPS containers occurring during the recycling process was reflected by the carbonyl region of the infrared spectrum which was used as variables for multivariate data analysis. PCA was used to reduce the data dimension and view the sample similarities. Soft independent modelling of class analogy (SIMCA), partial least squares-discrimination analysis (PLS-DA) and linear discrimination analysis (LDA) were applied to construct three classification models. The best discrimination results were obtained by an LDA model, with all samples correctly classified. PLS-DA and SIMCA could not classify the recycled EPS samples with low levels of adulteration. When applying this method to commercially available EPS containers, about 45% of samples were shown to contain recycled polystyrene resins. It is concluded that the carbonyl region of the infrared spectra coupled with chemometrics could be a powerful tool for the classification of virgin and recycled EPS food containers.     

Combining Partial Least Squares (PLS) Discriminant Analysis and Rapid Visco Analyser (RVA) to Classify Barley Samples According to Year of Harvest and Locality

The aim of this study was to evaluate the usefulness of the Rapid Visco Analyser (RVA) instrument combined with pattern recognition methods as tools to differentiate commercial barley samples from two South Australian localities and three harvests. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise discriminant analysis were applied to classify samples based on the RVA profiles using full cross validation (leave-one-out) as the validation method. The PLS-DA models correctly classify 96.3 and 97.8 % of the barley samples according to harvest and locality, using the profiles generated by the RVA instrument. Analysis and interpretation of the eigenvectors and loadings from the PCA or PLS-DA models developed verified that the RVA profiles contain relevant information related to starch pasting properties that allows sample classification. These results suggest that RVA coupled with PLS-DA holds necessary information for a successful classification of barley samples sourced from different localities and harvests.     

Improving the determination of moisture in edible oils by FTIR spectroscopy using acetonitrile extraction

A Fourier transform infrared (FTIR) method developed for the analysis of moisture in edible oils using dry acetonitrile as the extraction solvent was re-examined with the objective of improving its overall sensitivity and reproducibility. Quantitation was based on the H-O-H bending absorption at ～1630 cm(-1) instead of the bands in the OH stretching region, fewer interferences being an issue in the former as opposed to the latter region. In addition, a spectroscopic dilution correction procedure was developed to compensate for any miscibility of oil samples with acetonitrile, and gap-segment 2nd derivative spectra were employed to minimise the associated possibility of spectral interferences from absorptions of the oils. In comprehensive standard addition experiments using a variety of edible oils, the FTIR method was shown to recover the amounts of water quantitatively added to dry oil with an accuracy of ±20 ppm when the spectra of the acetonitrile extracts of the water-spiked oils were ratioed against the spectra of the acetonitrile extracts of the corresponding dry oils. The accuracy deteriorated substantially when the spectra of the acetonitrile extracts of the water-spiked oils were ratioed against the spectrum of the acetonitrile extraction solvent only. However, the primary variable affecting the apparent difference in the accuracy of the two approaches was determined to be the variability in the residual moisture content of the dried oils used in the standard addition experiments, as confirmed by an FTIR procedure based on H-D exchange with D(2)O. The FTIR method as structured is amenable to automation (>120 samples/h) and provides a very competitive means by which to routinely measure moisture present in a variety of hydrophobic materials that are normally the domain of Karl Fischer titration, such as edible oils, mineral oils, biodiesel and fuels.     

Discrimination of five Tunisian cultivars by Mid InfraRed spectroscopy combined with chemometric analyses of olive Olea europaea leaves

The high biodiversity of olive tree and the economic needs require tools for the correct classification and identification of the different cultivars. Simple and rapid methods are in increasing demand. In the present work, FT-MIR spectroscopy associated to chemometric treatment is proposed as a direct and rapid tool to discriminate cultivars according to their olive leaves, a persistent tissue the whole year. A set of 75 samples of olive leaves representative of five Tunisian cultivars (Chemlali, Sayali, Meski, Zarrazi and Chétoui) cultivated in the same geographical area was analysed. Discrimination between the five Tunisian cultivars was performed by the chemometric approach, principal component analysis (PCA), based on the FT-MIR spectral data provided by olive leaves. Furthermore, a correct classification (100%) of the five Tunisian cultivars was obtained by the Partial Least Square Discriminate Analysis (PLS-DA) method.     

Classification of ‘Chemlali’ accessions according to the geographical area using chemometric methods of phenolic profiles analysed by HPLC-ESI-TOF-MS

The present work describes a classification method of Tunisian ‘Chemlali’ olive oils based on their phenolic composition and geographical area. For this purpose, the data obtained by HPLC-ESI-TOF-MS from 13 samples of extra virgin olive oils, obtained from different production area throughout the country, were used for this study focusing in 23 phenolics compounds detected. The quantitative results showed a significant variability among the analysed oil samples. Factor analysis method using principal component was applied to the data in order to reduce the number of factors which explain the variability of the selected compounds. The data matrix constructed was subjected to a canonical discriminant analysis (CDA) in order to classify the oil samples. These results showed that 100% of cross-validated original group cases were correctly classified, which proves the usefulness of the selected variables.     

NMR and statistical study of olive oils from Lazio: A geographical,ecological and agronomic characterization

NMR and statistical procedures were used to analyse olive oils obtained from trees grown in different areas of Lazio, an Italian region, under different irrigation conditions. In order to obtain information on “real” commercial olive oils and to study the effects of some agronomical and ecological factors on the olive oil composition, we studied commercial multi-varietal olive oils, all produced in well-characterized areas of Lazio. ¹H and ¹³C NMR techniques, coupled to a suitable multivariate statistical procedure, were used to analyse 72 multi-varietal extra virgin and PDO (Protected Denomination of Origin) olive oils harvested in 2003, from the northern area, the centre and the southern area of Lazio. The intensity of selected ¹H and ¹³C NMR variables were submitted to three different statistical methods, namely, analysis of variance (ANOVA), principal component analysis (PCA) and linear discriminant analysis (LDA). ¹H and ¹³C NMR spectroscopy allowed us to obtain a good chemical characterization of the samples, giving information on major and minor compounds with an experimental error exactly the same and always extremely low for all the analyzed components. As a result of the statistical analysis, olive oils from the same geographical areas were well grouped. Since the amounts of some minor volatile components, such as aldehydes, terpenes and squalene, as well as, the content of β-sitosterol, the most important sterol present in olive oils, are sensitive to the pedoclimatic conditions, the intensity of the corresponding NMR signals turned out to be the most discriminating factors in the geographic classification. Moreover, the NMR and statistical protocol allowed us to investigate the roles of irrigation and altitude on the olive oil composition: the contents of oleic and saturated fatty acids turned out to be strongly influenced by the irrigation practice, whereas the content of volatile compounds was sensitive to the altitude of the olive trees. As a result of our study, olive oils were well grouped according to the irrigation practice as well as to the altitude at which olive trees were grown.     

Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil

Fourier transform infrared (FTIR) spectroscopy has been developed for analysis of extra virgin olive oil (EVOO) adulterated with palm oil (PO). Measurements were made on pure EVOO and that adulterated with varying concentrations of PO (1.0–50.0% wt./wt. in EVOO). Two multivariate calibrations, namely partial least square (PLS) and principle component regression (PCR) were optimized for constructing the calibration models, either for normal spectra or its first and second derivatives. The discriminant analysis (DA) was used for classification analysis between EVOO and that adulterated with PO and the other vegetable oils (palm oil, corn oil, canola oil, and sunflower oil). Frequencies at fingerprint region, especially at 1500–1000 cm^?1, were exploited for both quantification and classification. Either PLS or PCR at first derivative spectra revealed the best calibration models for predicting the concentration of adulterated EVOO samples, with coefficient of determination (R²) of 0.999 and root mean standard error of cross validation (RMSECV) of 0.285 and 0.373, respectively. DA was able to classify pure and adulterated samples on the basis of their FTIR spectra with no misclassified group obtained. In addition, DA was also effective enough to classify EVOO samples as the distinct group from the evaluated other vegetable oils.     

PRELIMINARY EVALUATION OF THE APPLICATION OF THE FTIR SPECTROSCOPY TO CONTROL THE GEOGRAPHIC ORIGIN AND QUALITY OF VIRGIN OLIVE OILS

ABSTRACT

A rapid Fourier transform infrared (FTIR) attenuated total reflectance spectroscopic method was applied to determine qualitative parameters such as free fatty acid (FFA) content and the peroxide value (POV) in virgin olive oils. Calibration models were constructed using partial least squares regression on a large number of virgin olive oil samples. The best results (R² = 0.955, root mean square error in cross validation [RMSECV] = 0.15) to evaluate FFA content expressed in oleic acid % (w/w) were obtained considering a calibration range from 0.2 to 9.2% of FFA relative to 190 samples. For POV determination, the result obtained, built on 80 olive oil samples with a calibration range from 11.1 to 49.7 meq O₂/kg of oil, was not satisfactory (R² = 0.855, RMSECV = 3.96). We also investigated the capability of FTIR spectroscopy, in combination with multivariate analysis, to distinguish virgin olive oils based on geographic origin. The spectra of 84 monovarietal virgin olive oil samples from eight Italian regions were collected and elaborated by principal component analysis (PCA), considering the fingerprint region. The results were satisfactory and could successfully discriminate the majority of samples coming from the Emilia Romagna, Sardinian and Sicilian regions. Moreover, the explained variance from this PCA was higher than 96%.

PRACTICAL APPLICATIONS

The verification of the declared origin or the determination of the origin of an unidentified virgin olive oil is a challenging problem. In this work, we have studied the applicability of Fourier transform infrared coupled with multivariate statistical analysis to discriminate the geographic origin of virgin olive oil samples from different Italian regions.

     

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%.