Assessing the varietal origin of extra-virgin olive oil using liquid chromatography fingerprints of phenolic compound, data fusion and chemometrics

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Analysis of Quality Differences in Radix Dipsaci before and after Processing with Salt Based on Quantitative Control of HPLC Multi-Indicator Components Combined with Chemometrics

Radix Dipsaci (RD) is the dry root of the Dipsacus asper Wall. ex DC., which is commonly used for tonifying the kidney and strengthening bone. The purpose of this study was to analyze the difference between raw and salt-processed RD from the chemical composition comprehensively. The fingerprints of raw and salt-processed RD were established by HPLC-DAD to determine the contents of loganin (LN), asperosaponin VI (AVI), caffeic acid (CaA), dipsanoside A (DA), dipsanoside B (DB), chlorogenic acid (CA), loganic acid (LA), isochlorogenic acid A (IA), isochlorogenic acid B (IB), and isochlorogenic acid C (IC). The results showed that after processing with salt, the components with increased contents were LA, CaA, DA, and AVI, and the components with decreased contents were CA, LN, IB, IA, IC, and DB. Then, the chemometric methods such as principal component analysis (PCA) and fisher discriminant analysis (FDA) were used to evaluate the quality of raw and salt-processed RD. In the classification of raw and salt-processed RD, the order of importance of each chemical component was LA > DB > IA > IC > IB > LN > CA > DA > AVI > CaA. These integrated methods successfully assessed the quality of raw and salt-processed RD, which will provide guidance for the development of RD as a clinical medication.

Identification of Adulteration of Flaxseed Oil From QINGHAI Area Using GC-MS Profiling of Phytosterol Composition and Chemometrics

Flaxseed oil is an important source of vegetable oil with a polyunsaturated fatty acid. It is significant to establish a method to quickly identify adulterated flaxseed oil. In the present study, the qualitative and quantitative analysis of phytosterol of flaxseed oil from different varieties and different production areas in the Qinghai area was first performed by gas chromatography-mass spectrometry (GC-MS) and the phytosterol standard profile of flaxseed oil was established. Then, a combination of similarity evaluation and cluster analysis was used to distinguish pure flaxseed oil from flaxseed oil adulterated with concentrations of 10-50% rapeseed oil, peanut oil, sunflower oil, and sesame oil, and discriminant analysis was used to identify the types of adulterated flaxseed oil. The results showed that similarity evaluation combined with cluster analysis can distinguish pure and adulterated flaxseed oil when the concentration of the adulterant was greater than 10%. Discriminant analysis models accurately identified the types of adulterating oil in flaxseed oil when the concentration of rapeseed, peanut, or sunflower oil was greater than 20%, and that of sesame oil was greater than 30%. This study shows that the determination of the phytosterol composition and chemometrics is a valuable tool to evaluate the purity of flaxseed oil.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

A Reaction-Based Optical Fingerprinting Strategy for the Recognition of Fat-Soluble Samples: Discrimination of Motor Oils

Optical "fingerprints" are widely used for chemometrics-assisted recognition of samples of different types. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions analyzed over time. Indicator reactions are usually carried out in aqueous solutions; in this study, we developed reactions that proceed in an organic solvent, thereby making it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40, and 5W30 motor oils from four manufacturers, with six samples in total. The procedure involved mixing a dye, sample, and reagents (HNO3, HCl, or tert-butyl hydroperoxide) in an ethanolic solution in a 96-well plate and measuring absorbance or near-infrared fluorescence intensity every several minutes for 20-55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and the k-nearest neighbors algorithm (kNN). Discrimination accuracy was evaluated by a validation procedure. A reaction of oxidation of a dye by nitric acid allowed us to recognize all six samples with 100% accuracy for LDA. Merging of data from the four reactions that did not provide complete discrimination ensured an accuracy of 93% for kNN. The newly developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetics-based discrimination strategy.

Estimation of doses absorbed by potato tubers under electron beam or X-ray irradiation using an optical fingerprinting strategy

High-energy electron beam and X-ray processing of foods can be used for extending their storage life and for combating pests and pathogens. Several instrumental techniques are used to estimate irradiation doses in foods, but these methods are complex and laborious, require expensive equipment, and do not always allow to determine low doses. This study was aimed at developing simple methods for detecting irradiation in potato tubers and for dose estimation. We used a "fingerprinting" strategy that does not involve quantitation of any compound; instead, the rate of indicator reactions involving carbocyanine dyes is measured. The dye content was monitored by its near-infrared fluorescence intensity and visible-light absorption. Potatoes not subjected to treatment and those irradiated with different doses (10, 100, 1000, 5000, or 10,000 Gray) could be distinguished by linear discriminant analysis. Thus, the order of magnitude of the absorbed dose can be estimated with 89% ± 3% accuracy for a mixture of tubers of two potato varieties irradiated with an electron beam or with 95% ± 8% accuracy for one variety irradiated with an X-ray source.

Applications of UV–Visible, Fluorescence and Mid-Infrared Spectroscopic Methods Combined with Chemometrics for the Authentication of Apple Vinegar

Spectroscopic techniques as untargeted methods have great potential in food authentication studies, and the evaluation of spectroscopic data with chemometric methods can provide accurate predictions of adulteration even for hard-to-identify cases such as the mixing of vinegar with adulterants having a very similar chemical nature. In this study, we aimed to compare the performances of three spectroscopic methods (fluorescence, UV–visible, mid-infrared) in the detection of acetic-acid/apple-vinegar and spirit-vinegar/apple-vinegar mixtures (1–50%). Data obtained with the three spectroscopic techniques were used in the generation of classification models with partial least square discriminant analysis (PLS-DA) and orthogonal partial least square discriminant analysis (OPLS-DA) to differentiate authentic and mixed samples. An improved classification approach was used in choosing the best models through a number of calibration and validation sets. Only the mid-infrared data provided robust and accurate classification models with a high classification rate (up to 96%), sensitivity (1) and specificity (up to 0.96) for the differentiation of the adulterated samples from authentic apple vinegars. Therefore, it was concluded that mid-infrared spectroscopy is a useful tool for the rapid authentication of apple vinegars and it is essential to test classification models with different datasets to obtain a robust model.

Study on the Correlation Between the Appearance Traits and Intrinsic Chemical Quality of Bitter Almonds Based on Fingerprint-Chemometrics

Bitter almond is a well-known and commonly used traditional Chinese medicine (TCM) for relieving coughs and asthma. However, the bioactive chemical composition of bitter almonds, especially their amygdalin content, which determines their quality for TCM use, is variable and this can cause problems with formulating and prescribing TCMs based on bitter almonds. Therefore, a simple method was developed to evaluate the compositional quality of bitter almonds from their appearance traits, based on a combination of chromatographic fingerprinting and chemometrics. Bitter almonds were analyzed by high-performance liquid chromatography (HPLC). Hierarchical cluster analysis (HCA) and principal components analysis (PCA) were applied to classify bitter almonds, which split the samples into two independent clusters. Three chemical markers (amygdalin, prunasin, and one unidentified component) were found by partial least squares-discriminant analysis (PLS-DA). What's more, a new PLS-DA model was reconstructed to confirm the obtained chemical markers from PLS-DA. Additionally, the appearance trait indices and amygdalin content of bitter almond were determined and the classification was confirmed by one-way analysis of variance. This method can easily determine the quality of bitter almonds from their appearance alone, high quality correlated closely with kernels that were larger, oblong in shape and heavier.

Analytical Methods for the Characterization of Vegetable Oils

The determination of the authenticity of extra virgin olive oils (EVOOs) has become more interesting in recent years. Italy is the first country in Europe in terms of number of Protected Designation of Origin (PDO) oils, which connects consumers to a feeling of tradition and thus to higher quality standards. This work focused on the consideration of the inorganic content as a possible marker of EVOOs. Ten vegetable oils (VOs), eight Italian EVOOs and seven not Italian EVOOs were analyzed. After pretreatment by acid mineralization, Al, Ba, Ca, Cu, Fe, K, Li, Mg, Mn, Na, P, Sb, Se and Zn were determined by ICP-OES. The electrochemical properties of a selected group of EVOOs and other vegetal oils of different botanical origin were investigated by voltammetry. Carbon paste electrodes (CPEs) were prepared. The features observed in the voltammograms reflect the reactions of electroactive compounds, which are present in the oils. A chemometric treatment of the results was performed to assess the possibility to distinguish (i) the region of provenience considering the inorganic profile; and (ii) the plant species from which each oil was obtained on the basis of the current profile registered during voltammetric analysis. Inorganic composition seems to be a useful marker for the assessment of the geographical origin of an EVOO. The EVOO-CPEs voltammetry seems to have a good ability to distinguish the plant species of origin. This method could be useful to monitor the conservation status of the oils, as the redox profile is linked to the oxidative degradation state.

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

Detection and Quantitation of Adulterated Paprika Samples Using Second-Order HPLC-FLD Fingerprints and Chemometrics

Paprika is a widely consumed spice in the world and its authentication has gained interest considering the increase in adulteration cases in recent years. In this study, second-order fingerprints acquired by liquid chromatography with fluorescence detection (HPLC-FLD) were first used to detect and quantify adulteration levels of Chinese paprika samples. Six different adulteration cases, involving paprika production region, cultivar, or both, were investigated by pairs. Two strategies were employed to reduce the data matrices: (1) chromatographic fingerprints collected at specific wavelengths and (2) fusion of the mean data profiles in both spectral and time dimensions. Afterward, the fingerprint data with different data orders were analyzed using partial least squares (PLS) and n-way partial least squares (N-PLS) regression models, respectively. For most adulteration cases, N-PLS based on second-order fingerprints provided the overall best quantitation results with cross-validation and prediction errors lower than 2.27% and 20.28%, respectively, for external validation sets with 15-85% adulteration levels. To conclude, second-order HPLC-FLD fingerprints coupled with chemometrics can be a promising screening technique to assess paprika quality and authenticity in the control and prevention of food frauds.

Hydroxytyrosol in Foods: Analysis, Food Sources, EU Dietary Intake, and Potential Uses

Hydroxytyrosol (HT) is a phenolic compound with proven biological properties present in a limited number of foods such as table olives, virgin olive oil (VOO) and wines. The present work aims to evaluate the dietary intake of HT in the European (EU) population by compiling scattered literature data on its concentration in foods. The consumption of the involved foods was estimated based on the EFSA Comprehensive European Food Consumption Database. The updated average contents of HT are as follows: 629.1, 5.2 and 2.1 µg/g for olives, olive oil and wine, respectively. The HT estimated intake in the European Union (EU) adult population falls within 0.13–6.82 mg/day/person, with table olives and wine being the main contributors. The estimated mean dietary intake of HT in EU countries is 1.97 ± 2.62 mg/day. Greece showed the highest HT intake (6.82 mg/day), while Austria presented the lowest (0.13 mg/day). Moreover, HT is an authorized novel food ingredient in the EU that can be added to different foods. Since the estimated HT intake is substantially low, the use of HT as a food ingredient seems feasible. This opens new possibilities for revalorizing waste products from olive oil and olive production which are rich HT sources.

The potential of wild olive leaves (Olea europaea L. subsp. oleaster) addition as a functional additive in olive oil production: the effects on bioactive and nutraceutical compounds using LC-ESI-QTOF/MS

This study aims to investigate the influence of traditional maceration upon the enrichment of olive oil with oleaster leaves. The phenolic and tocopherolic compositions of control olive oil and enriched olive oils were determined. The influence of these oil preparation procedures on oil quality indicators was also investigated through spectrophotometric indices and fatty acid profiles. The total contents of bioactive compounds and pigments improved in oils obtained by maceration of fresh wild olive leaves, and were in statistically significant correlation with leaves proportions additions. The obtained results revealed that 15 phenolic compounds belonging to different phenolic types were characterized and quantified by an effective HPLC-DAD-ESI-MS/MS method. In all expected olive oils, the oleuropein aglycon (3,4-DHPEA-EA), and ligstroside aglycon (p-HPEAEA) derivatives were the most abundant compounds. Similarly, to phenolic compounds, tocopherols strongly increased with leaves addition during maceration process. The data obtained from this study suggested that the addition of olive leaf to oils allowed more functional olive oils with higher antioxidant contents. Thus, Extra Virgin Olive Oil (EVOO) extracted with 10% of olive leaves presented the highest amount of phenolic and tocopherol compounds.

Evaluation of Olive Oil Quality with Electrochemical Sensors and Biosensors: A Review

Electrochemical sensors, sensor arrays and biosensors, alongside chemometric instruments, have progressed remarkably of late, being used on a wide scale in the qualitative and quantitative evaluation of olive oil. Olive oil is a natural product of significant importance, since it is a rich source of bioactive compounds with nutritional and therapeutic properties, and its quality is important both for consumers and for distributors. This review aims at analysing the progress reported in the literature regarding the use of devices based on electrochemical (bio)sensors to evaluate the bioactive compounds in olive oil. The main advantages and limitations of these approaches on construction technique, analysed compounds, calculus models, as well as results obtained, are discussed in view of estimation of future progress related to achieving a portable, practical and rapid miniature device for analysing the quality of virgin olive oil (VOO) at different stages in the manufacturing process.

Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations

The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.

Preliminary study on the potential application of Fourier-transform mid-infrared for the evaluation of overall quality and authenticity of Moroccan virgin olive oil

BACKGROUND

Olive oil provides a wide range of health-promoting compounds. The quality of olive oil is an even more complex concept as it is affected by several factors, such as variety, season, stage of maturation, extraction processing, and so on. The main objective of this study was to determine the potential of chemical and mid-infrared spectroscopy techniques to determine the quality and authenticity of virgin olive oil (VOO). For this, we studied 41 VOOs originating from five regions of Morocco (Fez/Meknes, Eastern, Northern, Beni-Mellal/Khenifra, and Marrakech/Safi) and produced using different agricultural and technological conditions during two successive crop seasons (2015-2016 and 2016-2017).

RESULTS

By applying principal component analysis and factorial discriminant analysis with leave-one-out validation to the mid-infrared spectroscopy, clear discrimination between VOO samples according to their geographic origin and variety was observed, with correct classification rates of 91.87% and 91.87% being observed respectively. The application of partial least-squares regression to mid-infrared and chemical data sets allowed excellent prediction of free acidity, peroxide value, k₂₇₀ , and chlorophyll level with R² of 0.99, 0.97, 0.98, and 0.93 respectively, and good prediction of k₂₃₂ (R² = 0.84).

CONCLUSION

The results demonstrate that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool for evaluating the overall quality and authenticity of VOO. © 2020 Society of Chemical Industry.

Multiple-fingerprint analysis of Poria cocos polysaccharide by HPLC combined with chemometrics methods

In this study, the multiple fingerprints, which were integrated with HPGFC-ELSD (high performance gel filtration chromatography - evaporative light scattering detector) fingerprint, PMP-HPLC-DAD (1-phenyl-3-methyl-5-pyrazolone-high performance liquid chromatography - diode array detector) fingerprint of complete acid hydrolysates and HILIC-HPLC-ELSD (hydrophilic interaction - high performance liquid chromatography - evaporative light scattering detector) fingerprint of enzyme hydrolysates, were established to evaluate the quality of polysaccharides from Poria cocos (PCPs). The similarity evaluation showed that 16 batches of PCPs from different origins had high similarity in structural characteristics based on the multiple fingerprints. The chromatographic data of multiple fingerprints of PCPs were fused, processed and analyzed by chemometric methods including HCA (hierarchical cluster analysis), PCA (principal component analysis) and PLS-DA (partial least squares discriminant analysis). The 16 batches of PCPs were divided into 3 categories in PCA, indicating a certain relationship between the structural characteristics and the origins. PLS-DA analysis indicated that Man, Glc, Gal, Fuc, the components with m/z of 2.22 × 10⁴∼1.53 × 10⁴ Da and 3.46 × 10³∼2.69 × 10³ Da, oligosaccharides with DPs of 6 and 7, respectively, could be regarded as potential chemical markers for the classification of PCPs from different origins. According to the multiple fingerprints and chemometric analysis, the two commercial samples were proved to be adulterants.

Assessment of paprika geographical origin fraud by high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprinting

Paprika production under the protected designation of origin (PDO) standardized procedures leads to more quality products. However, it is also related to higher retail prices, making them susceptible to adulteration with low-quality paprika or its agricultural origin's mislabeling. Therefore, in this study, high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints, strongly related to phenolic acid and polyphenolic compounds, were proposed as chemical markers to assess the classification of paprika from five European regions (three Spanish PDO, Hungary, and the Czech Republic), through a classification decision tree constructed by partial least squares regression-discriminant analysis (PLS-DA) models. After external validation, an excellent classification accuracy of 97.9% was achieved. Moreover, the chromatographic fingerprints were also proposed to detect and quantitate two different paprika geographical origin blend scenarios by partial least squares (PLS) regression. Low external validation and prediction errors -with values below 1.6 and 10.7%, respectively- were obtained.

A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra

Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

Determination of the concentration of total phenolic compounds in aged cachaça using two-dimensional fluorescence and mid-infrared spectroscopy

In this work, we investigate alternative quantification methods based on optical spectroscopy and variable selection approach, using as a case study the aging process of cachaça. The cachaça was aged in an Amburana barrel and the samples were analyzed with a traditional analytical method for total phenolic quantification (Folin-Ciocalteu), with 2-D fluorescence and infrared spectroscopy. We applied a methodology based on Ant Colony Optimization to optimize variable selection and model fitting to predict total phenolic content. Our results demonstrated that fluorescence spectroscopy was more sensitive than IR in the quantification of total phenolic compounds for both global and local models, presenting good results (R² superior to 0.979), significantly reducing the number of original variable (1995) for only 4 pairs of Ex/Em. Variable selection combined with spectroscopy reveals potential because this technique eliminates the need for sample preparation and allows the construction of customized sensors for application in the food industry.

Intelligent evaluation of total polar compounds (TPC) content of frying oil based on fluorescence spectroscopy and low-field NMR

The purpose of this study was to construct a fusion model using probe-based and non-probe-based fluorescence spectroscopy and low-field nuclear magnetic resonance spectroscopy (Low-field NMR) for rapid quality evaluation of frying oil. Iron tetraphenylporphyrin (FeTPP) was selected as the probe to detect polar compounds in frying oil samples. Non-probe-based fluorescence spectroscopy and low-field NMR were employed to determine the fluorescence changes of antioxidants, triglycerides and fatty acids in frying oil samples. Compared to the models constructed using non-fusion data, the fusion-data models achieved a better regression prediction performance and correlation coefficients with values of 0.9837 and 0.9823 for the training and test sets, respectively. This study suggested that the multiple data fusion method was capable to construct better regression models to rapidly evaluate the quality of frying oil and other food with high oil contents.

Evaluation and Origin Discrimination of Two Monocultivar Extra Virgin Olive Oils, Cultivated in the Coastline Part of North-Western Greece

Extra virgin olive oil (EVOO) quality and authenticity are important and challenging factors nowadays for the assurance of consumers’ protection, prevention of unfair competition, and disruption of the national economy by a false declaration of origin. Hence, the recognition of EVOO authenticity is of great interest in terms of commercial and quality aspects. The objective of this study was to evaluate and discriminate monovarietal extra virgin olive oils of the two dominant olive cultivars, Lianolia Kerkyras and Koroneiki, produced in the coastline part of Western Greece, based on their chemical characteristics, followed by statistical and chemometric analysis in order to profile for the first time the typical characteristics of Lianolia Kerkyras as well as to identify possible markers for authenticity purpose. A total of 104 olive oil samples were collected. Both cultivars had an overall high quality profile as far as their basic qualitative parameters (free fatty acid, peroxide value, and UV spectrometric indices) are concerned. A higher concentration in the mono-unsaturated oleic acid characterize olive oils of cv. Koroneiki compared to cv. Lianolia Kerkyras, while a clearly higher concentration in the poly-unsaturated linoleic acid was observed in olive oils of cv. Lianolia Kerkyras. In addition, olive oil samples of cv. Koroneiki showed a clear lower total sterols concentration with a percentage of 40.9% not surpassing the required EU Regulatory limit of 1000 mg/kg, an observation which strengthens previous published results of our research group and depicts an overall “intrinsic characteristic” of cv. Koroneiki. As far as the profile of the individual sterols is concerned, Lianolia Kerkyras samples exhibited higher mean value for the total sterol content as well as for β-sitosterol, the major phytosterol in olive oils, compared to the relative values of Koroneiki. Significant differences in the sterolic and fatty acid composition of the examined olive oil samples were shown by means of statistical analysis demonstrating a strong botanical effect and depicting that those compositional markers can be suggested as possible authenticity tools.

Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

Varietal and Geographical Discrimination of Greek Monovarietal Extra Virgin Olive Oils Based on Squalene, Tocopherol, and Fatty Acid Composition

Extra virgin olive oil (EVOO) is an important component of the Mediterranean diet and a highly priced product. Despite the strict legislation to protect it from fraudulent practices, there is an increasing demand to characterize EVOOs and evaluate their authenticity. For this purpose, 68 monovarietal EVOOs, originating from three regions of Greece (Peloponnese, Crete, and Lesvos) and two local cultivars (Koroneiki and Kolovi), were obtained during the harvesting period of 2018-2019. Fatty acids, squalene, and tocopherols were determined chromatographically according to official methods in order to study the effect of cultivar and geographical origin. Squalene and γ-tocopherol differed significantly amongst the cultivars tested. Koroneiki samples exhibited higher squalene content than Kolovi samples, whereas the opposite was observed for γ-tocopherol. The tocopherol level was highly geographical dependent, with EVOOs from Peloponnese displaying the highest concentration of α-tocopherol, whereas the content of γ-tocopherol was significantly higher in samples from Lesvos. Unsupervised and supervised multivariate analysis resulted in a satisfactory grouping of EVOOs according to cultivar. γ-Tocopherol, squalene, and the majority of fatty acids were the most discriminant variables, with γ-tocopherol, linoleic, linolenic, and gadoleic acid being present at higher levels in samples from the Kolovi cultivar. Koroneiki samples were characterized with higher levels of squalene, palmitic, palmitoleic, and arachidic acid.

Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

Extra Virgin Olive Oil (EVOO), the emblematic food of the Mediterranean diet, is recognized for its nutritional value and beneficial health effects. The main authenticity issues associated with EVOO's quality involve the organoleptic properties (EVOO or defective), mislabeling of production type (organic or conventional), variety and geographical origin, and adulteration. Currently, there is an emerging need to characterize EVOOs and evaluate their genuineness. This can be achieved through the development of analytical methodologies applying advanced "omics" technologies and the investigation of EVOOs chemical fingerprints. The objective of this review is to demonstrate the analytical performance of High Resolution Mass Spectrometry (HRMS) in the field of food authenticity assessment, allowing the determination of a wide range of food constituents with exceptional identification capabilities. HRMS-based workflows used for the investigation of critical olive oil authenticity issues are presented and discussed, combined with advanced data processing, comprehensive data mining and chemometric tools. The use of unsupervised classification tools, such as Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), as well as supervised classification techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Orthogonal Projection to Latent Structure-Discriminant Analysis (OPLS-DA), Counter Propagation Artificial Neural Networks (CP-ANNs), Self-Organizing Maps (SOMs) and Random Forest (RF) is summarized. The combination of HRMS methodologies with chemometrics improves the quality and reliability of the conclusions from experimental data (profile or fingerprints), provides valuable information suggesting potential authenticity markers and is widely applied in food authenticity studies.

Rapidly monitoring the quality of flavoring essence based on microwave-induced plasma ionization mass spectrometry and multivariate statistical analysis

Microwave-induced plasma ionization mass spectrometry (MIPI-MS) combined with multivariate statistical analysis was first applied to rapidly monitor the quality of tobacco flavoring essence. A small isolation and reaction chamber was set up between the ion source and the injection port of mass spectrometer to effectively eliminate the interference of external environment in the process of analyzing samples. The improved experimental apparatus (MIPI-MS) could achieve online and high-throughput analysis, with minimal sample preparation steps. Further, two types of tobacco flavoring essences with the similar appearance and physicochemical parameters were employed to verify the usability of the promising method in the field of quality monitoring. Firstly, the mass spectral fingerprint of each essence was established by the improved MIPI-MS method within 2 min. Then, two multivariate statistical processes were carried out to analyze mass spectral data. The similarity results indicated that the thresholds of tobacco flavoring essences from different batches were 1.512 and 2.638, respectively. The first three principal components of the established PLS-DA described 93.6% of the total variability, and provided a visualized comparison for the two types of flavoring materials. Finally, the adulterated samples were successfully distinguished by employing the two multivariate statistical processes.