The potential of different techniques for volatile compounds analysis coupled with PCA for the detection of the adulteration of olive oil with hazelnut oil

Visible feature engineering to detect fraud in black and red peppers

Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics' values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics' values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Screening Method for the Visual Discrimination of Olive Oil from Other Vegetable Oils by a Multispecies DNA Sensor

Olive oil is a prominent agricultural product which, in addition to its nutritional value and unique organoleptic characteristics, offers a variety of health benefits protecting against cardiovascular disease, cancer, and neurodegenerative diseases. The assessment of olive oil authenticity is an extremely important and challenging process aimed at protecting consumers and producers. The most frequent adulteration involves blending with less expensive and readily available vegetable/seed oils. The methods for adulteration detection, whether based on changes in metabolite profiles or based on DNA markers, require advanced and expensive instrumentation combined with powerful chemometric and statistical tools. To this end, we present a simple, multiplex, and inexpensive screening method based on the development of a multispecies DNA sensor for sample interrogation with the naked eye. It is the first report of a DNA sensor for olive oil adulteration detection with other plant oils. The sensor meets the 2-fold challenge of adulteration detection, i.e., determining whether the olive oil sample is adulterated and identifying the added vegetable oil. We have identified unique, nucleotide variations, which enable the discrimination of seven plant species (olive, corn, sesame, soy, sunflower, almond, and hazelnut). Following a single PCR step, a 20 min multiplex plant-discrimination reaction is performed, and the products are applied directly to the sensing device. The plant species are visualized as red spots using functionalized gold nanoparticles as reporters. The spot position reveals the identity of the plant species. As low as <5-10% of adulterant was detected with particularly good reproducibility and specificity.

Detection of Hazelnut and Almond Adulteration in Olive Oil: An Approach by qPCR

Virgin olive oil (VOO), characterized by its unique aroma, flavor, and health benefits, is subject to adulteration with the addition of oils obtained from other edible species. The consumption of adulterated olive oil with nut species, such as hazelnut or almond, leads to health and safety issues for consumers, due to their high allergenic potential. To detect almond and hazelnut in olive oil, several amplification systems have been analyzed by qPCR assay with a SYBR Green post-PCR melting curve analysis. The systems selected were Cora1F2/R2 and Madl, targeting the genes coding the allergenic protein Cor a 1 (hazelnut) and Pru av 1 (almond), respectively. These primers revealed adequate specificity for each of the targeted species. In addition, the result obtained demonstrated that this methodology can be used to detect olive oil adulteration with up to 5% of hazelnut or almond oil by a single qPCR assay, and with a level as low as 2.5% by a nested-qPCR assay. Thus, the present research has shown that the SYBR-based qPCR assay can be a rapid, precise, and accurate method to detect adulteration in olive oil.

Direct Coupling of SPME to Mass Spectrometry

Solid-phase microextraction devices are normally analyzed by gas or liquid chromatography. Their use has become increasingly widespread since their introduction in 1990, and nowadays most analytical laboratories use or have used SPME as an efficient and green method to perform analyte extraction and sample clean-up in one step. The SPME technique is intrinsically flexible, and allows for a high degree of optimization with regard to the extracting phase, as well as the way sample is analyzed. Since its introduction, researchers have been trying different ways to transfer analytes extracted from the solid phase to a mass spectrometer, with the aim to increase throughput and reduce solvent, gas usage and costs associated with conventional chromatographic techniques. Furthermore, but not less important, for pure fun of developing new, more efficient and sensitive analytical strategies! This chapter aims at providing a comprehensive overview of the most relevant non-chromatographic mass spectrometric approaches developed for SPME. Technical aspects of each SPME-MS approach will be discussed, highlighting their advantages, disadvantages and future potential developments. Particular emphasis will be given on the most recent direct coupling approaches using novel ionization approaches, and a concise overview of the existing applications will also be provided.

An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil

Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples.

The menace of saffron adulteration: Low-cost rapid identification of fake look-alike saffron using Foldscope and machine learning technology

Saffron authenticity is important for the saffron industry, consumers, food industry, and regulatory agencies. Herein we describe a combo of two novel methods to distinguish genuine saffron from fake in a user-friendly manner and without sophisticated instruments. A smartphone coupled with Foldscope was used to visualize characteristic features and distinguish "genuine" saffron from "fake." Furthermore, destaining and staining agents were used to study the staining patterns. Toluidine blue staining pattern was distinct and easier to use as it stained the papillae and the margins deep purple, while its stain is lighter yellowish green toward the central axis. Further to automate the process, we tested and compared different machine learning-based classification approaches for performing the automated saffron classification into genuine or fake. We demonstrated that the deep learning-based models are efficient in learning the morphological features and classifying samples as either fake or genuine, making it much easier for end-users. This approach performed much better than conventional machine learning approaches (random forest and SVM), and the model achieved an accuracy of 99.5% and a precision of 99.3% on the test dataset. The process has increased the robustness and reliability of authenticating saffron samples. This is the first study that describes a customer-centric frugal science-based approach to creating an automated app to detect adulteration. Furthermore, a survey was conducted to assess saffron adulteration and quality. It revealed that only 40% of samples belonged to ISO Category I, while the average adulteration percentage in the remaining samples was 36.25%. After discarding the adulterants from crude samples, their quality parameters improved significantly, elevating these from ISO category III to Category II. Conversely, it also means that Categories II and III saffron are more prone to and favored for adulteration by fraudsters.

Electronic nose for detection of food adulteration: a review

The food products may attract unscrupulous vendors to dilute it with inexpensive alternative food sources to achieve more profit. The risk of high value food adulteration with cheaper substitutes has reached an alarming stage in recent years. Commonly available detection methods for food adulteration are costly, time consuming and requires high degree of technical expertise. However, a rapid and suitable detection method for possible adulterant is being evolved to tackle the aforesaid issues. In recent years, electronic nose (e-nose) system is being evolved for falsification detection of food products with reliable and rapid way. E-nose has the ability to artificially perceive aroma and distinguish them. The use of chemometric analysis together with gas sensor arrays have shown to be a significant procedure for quality monitoring in food. E-nose techniques with numerous provisions are reliable and favourable for food industry in food fraud detection. In the present review, the contributions of gas sensor based e-nose system are discussed extensively with a view to ascertain the adulteration of food products.

Edible Oils Adulteration: A Review on Regulatory Compliance and Its Detection Technologies

Various events of edible oils adulteration with inferior ingredients were reported regularly in recent years. This review is aimed to provide an overview of edible oils adulteration practices, regulatory compliance and detection technologies. Many detection technologies for edible oils adulteration were developed in the past such as methods that are based on chromatography or spectroscopy. Electrochemical sensors like electric nose and tongue are also gaining popularity in the detection of adulterated virgin olive oil and virgin coconut oil. It can be concluded that these detection technologies are essential in the combat with food adulterers and can be improved.

Advanced techniques in edible oil authentication: A systematic review and critical analysis

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

In-House Validation of an SPE-GC-FID Method for the Detection of Free and Esterified Hydroxylated Minor Compounds in Virgin Olive Oils

Minor compounds in vegetable oils are distributed between free and esterified forms, and the ratio of these two fractions could represent an important parameter for assessment of oil authenticity. A simple method based on offline SPE-GC-FID for the analysis of free and esterified hydroxylated minor compounds in olive and sunflower oils has been developed and in-house validated. A satisfactory repeatability relative standard deviation (<7.5%) was obtained in all cases. The method, which requires simple instrumentation, allows for reliable quantification in a single chromatographic run with the advantages of minimizing sample manipulation, use of toxic solvents and reagents, and time consumption. The analytical procedure was applied to pure oil samples, including 15 authentic extra virgin olive oils collected from different European countries (Spain, Italy, Greece, and Portugal). Finally, the proposed SPE-GC-FID methodology could detect changes in the ratio between the free and esterified forms in pure extra virgin olive oil when mixed with refined sunflower oil at different percentages of 2, 5, 10, 15, and 20% (w/w) to simulate adulteration.

Evolution of Flavors in Extra Virgin Olive Oil Shelf-Life

Extra virgin olive oil (EVOO) is one of the most distinctive ingredients of the Mediterranean diet. There are many properties related to this golden ingredient, from supreme organoleptic characteristics to benefits for human health. EVOO contains in its composition molecules capable of exerting bioactivities such as cardio protection, antioxidant, anti-inflammatory, antidiabetic, and anticancer activity, among others, mainly caused by unsaturated fatty acids and certain minor compounds such as tocopherols or phenolic compounds. EVOO is considered the highest quality vegetable oil, which also implies a high sensory quality. The organoleptic properties related to the flavor of this valued product are also due to the presence of a series of compounds in its composition, mainly some carbonyl compounds found in the volatile fraction, although some minor compounds such as phenolic compounds also contribute. However, these properties are greatly affected by the incidence of certain factors, both intrinsic, such as the olive variety, and extrinsic, such as the growing conditions, so that each EVOO has a particular flavor. Furthermore, these flavors are susceptible to change under the influence of other factors throughout the oil's shelf-life, such as oxidation or temperature. This work offers a description of some of the most remarkable compounds responsible for EVOO's unique flavor and aroma, the factors affecting them, the mechanism that lead to the degradation of EVOO, and how flavors can be altered during the shelf-life of the oil, as well as several strategies suggested for the preservation of this flavor, on which the quality of the product also depends.

Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil

Waste cooking oil (WCO) is a valuable feedstock for the synthesis of biodiesel but the product exhibits poor oxidative stability. Techniques available for assessing this parameter are generally expensive and time-consuming, hence the purpose of this study was to develop and validate a rapid and reliable predictive system based on signals from the sensors of a commercial hand-held e-nose instrument. Biodiesels were synthesized from soybean oil and six samples of WCO, and their physicochemical characteristics and oxidative stabilities determined before and after storage in different types of containers for 30 or 60 days at room temperature or 43 °C. Linear regression models were constructed based on principal component analysis of the signals generated by all 32 e-nose sensors and stochastic modeling of signal profiles from individual sensors. The regression model with principal components as predictors was unable to explain the oxidative stability of biodiesels, while the regression model with stochastic parameters (combining signals from 11 sensors) as predictors showed an excellent goodness of fit (R² = 0.91) with a 45-sample training set and a good quality of prediction (R² = 0.84) with a 18-sample validation set. The proposed e-nose system was shown to be accurate and efficient and could be used to advantage by producers/distributors of biodiesel in the assessment fuel quality.

Oxidative Stability at Different Storage Conditions and Adulteration Detection of Prickly Pear Seeds Oil

Lipid oxidation and adulteration have a negative impact on functionality and notoriety of foods especially vegetable oils and cause economic losses. The present study investigates the control of two commercial quality aspects of prickly pear seeds oil (PPSO): oxidative stability during storage and detection of adulteration. Peroxide index, specific extinction coefficients K232 and K270, free acidity, and fatty acids composition were evaluated during different periods of incubation (6, 12, and 18 months) at various temperatures (4°C, 25°C, 40°C, and uncontrolled room temperature ranging between 4°C and 40°C) with different packaging (protected and unprotected from sunlight, with and without nitrogen gas bubbling). Based on the physicochemical and biochemical parameters evolution, this study has shown that PPSO stored at 4°C for 18 months preserves the initial quality. However, at 40°C, an intense lipid oxidative process occurred after 6 months of storage. The changes have also affected fatty acids composition, especially rates of linoleic and oleic acids. The shelf-life of oils stored at 25°C and at uncontrolled room temperature can be limited to 6 months. Regarding the impact of light and nitrogen bubbling, sunlight has affected seriously the oxidative stability of oils after 12 months of storage and the bubbling with nitrogen has improved their stability when they have been stored in clear glass bottles. The levels of adulteration detection using fatty acids as markers are relatively high. The detection of oil adulteration can be depicted by fatty acids composition up to 15% of olive and almond oils and up to 20% of rapeseed oil. The iodine value could also be an indicator of the sunflower oil presence in PPSO. Therefore, other minor compounds including sterols and tocopherols should be investigated to depict PPSO adulteration with cheaper oils and to determine lower levels of detection in order to ensure the authenticity of PPSO.

Detection of adulteration in food based on nondestructive analysis techniques: a review

In recent years, people pay more and more attention to food quality and safety, which are significantly relating to human health. Food adulteration is a world-wide concerned issue relating to food quality and safety, and it is difficult to be detected. Modern detection techniques (high performance liquid chromatography, gas chromatography-mass spectrometer, etc.) can accurately identify the types and concentrations of adulterants in different food types. However, the characteristics as expensive, low efficient and complex sample preparation and operation limit the use of these techniques. The rapid, nondestructive and accurate detection techniques of food adulteration is of great and urgent demand. This paper introduced the principles, advantages and disadvantages of the nondestructive analysis techniques and reviewed the applications of these techniques in food adulteration screen in recent years. Differences among these techniques, differences on data interpretation and future prospects were also discussed.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

Elucidation of hulling-induced changes in the aroma and aroma-active compounds of cv. Uzun pistachio (Pistacia vera)

BACKGROUND

The purpose of this study was to define the effects of the hulling process on the aroma and aroma-active composition of cv. Uzun pistachio. Four different hulling processes, namely wet, wet-dry, dry-dry and brine, were applied. In addition, solvent-assisted flavor evaporation (SAFE), simultaneous distillation/extraction (SDE) and purge and trap (P&T) extraction methods were also tested to obtain the best representative aroma extract.

RESULTS

The results revealed that the dry-dry hulling method was the most prominent according to the aroma and aroma-active compounds, and this was especially the case with terpene compounds. The method increased the content of terpenes, pyrazines and total aroma compounds. By application of aroma extract dilution analysis (AEDA), a total of 18 compounds were detected, of which 11 were identified for the first time in the fruit of pistachio. 2,3-Dimethylpyrazine, trimethylpyrazine and 2-ethyl-3,5-dimethylpyrazine were the compounds that had the highest flavour dilution factors.

CONCLUSION

In general, the results of the present research indicated that the hulling method highly affected the overall aroma structure of pistachios. The dry-dry hulling method is therefore suggested to manufacturers to obtain better pistachios with rich and high-quality aroma. © 2019 Society of Chemical Industry.

Electrochemical Sensor-Based Devices for Assessing Bioactive Compounds in Olive Oils: A Brief Review

Electrochemical bioinspired sensor devices combined with chemometric tools have experienced great advances in the last years, being extensively used for food qualitative and quantitative evaluation, namely for olive oil analysis. Olive oil plays a key role in the Mediterranean diet, possessing unique and recognized nutritional and health properties as well as highly appreciated organoleptic characteristics. These positive attributes are mainly due to olive oil richness in bioactive compounds such as phenolic compounds. In addition, these compounds enhance their overall sensory quality, being mainly responsible for the usual olive oil pungency and bitterness. This review aims to compile and discuss the main research advances reported in the literature regarding the use of electrochemical sensor based-devices for assessing bioactive compounds in olive oil. The main advantages and limitations of these fast, accurate, bioinspired voltammetric, potentiometric and/or amperometric sensor green-approaches will be addressed, aiming to establish the future challenges for becoming a practical quality analytical tool for industrial and commercial applications.

Identification of volatile markers for the detection of adulterants in red ginseng (Panax ginseng) juice using headspace stir-bar sorptive extraction coupled with gas chromatography and mass spectrometry

Red ginseng (Panax ginseng) products are frequently adulterated by manufacturers with cheaper medicinal plant products including deodeok (Codonopsis lanceolata) and doraji (Platycodon grandiflorum) to increase profits. To identify possible volatile markers for the adulteration of red ginseng juices with deodeok or doraji, a headspace stir-bar sorptive extraction method was developed. Gas chromatography with mass spectrometry and untargeted metabolomics analysis revealed that 1-hexanol, cis-3-hexen-1-ol, and trans-2-hexen-1-ol are abundantly present in deodeok and doraji but not red ginseng. The peak area ratios in gas chromatograms of these compounds in red ginseng juices mixed with deodeok or doraji indicate that these volatile chemicals can be used as markers to detect the adulteration of red ginseng juice.

Assessment of a combined gas chromatography mass spectrometer sensor system for detecting biologically relevant volatile compounds

There have been a number of studies in which metal oxide sensors (MOS) have replaced conventional analytical detectors in gas chromatography systems. However, despite the use of these instruments in a range of applications including breath research the sensor responses (i.e. resistance changes w.r.t. concentration of VCs) remain largely unreported. This paper addresses that issue by comparing the response of a metal oxide sensor directly with a mass spectrometer (MS), whereby both detectors are interfaced to the same GC column using an s-swafer. It was demonstrated that the sensitivity of an in-house fabricated ZnO/SnO₂ thick film MOS was superior to a modern MS for the detection of a wide range of volatile compounds (VCs) of different functionalities and masses. Better techniques for detection and quantification of these VCs is valuable, as many of these compounds are commonly reported throughout the scientific literature. This is also the first published report of a combined GC-MS sensor system. These two different detector technologies when combined, should enhance discriminatory abilities to aid disease diagnoses using volatiles from e.g. breath, and bodily fluids. Twenty-nine chemical standards have been tested using solid phase micro-extraction; 25 of these compounds are found on human breath. In all but two instances the sensor exhibited the same or superior limit of detection compared to the MS. Twelve stool samples from healthy participants were analysed; the sensor detected, on average 1.6 peaks more per sample than the MS. Similarly, analysing the headspace of E. coli broth cultures the sensor detected 6.9 more peaks per sample versus the MS. This greater sensitivity is primarily a function of the superior limits of detection of the metal oxide sensor. This shows that systems based on the combination of chromatography systems with solid state sensors shows promise for a range of applications.

Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment

The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand for novel methods that could be used for rapid food quality assessment. A technique based on the use of an array of chemical sensors for holistic analysis of the sample’s headspace is called electronic olfaction. In this article, a prototype of a portable, modular electronic nose intended for food analysis is described. Using the SVM method, it was possible to classify samples of poultry meat based on shelf-life with 100% accuracy, and also samples of rapeseed oil based on the degree of thermal degradation with 100% accuracy. The prototype was also used to detect adulterations of extra virgin olive oil with rapeseed oil with 82% overall accuracy. Due to the modular design, the prototype offers the advantages of solutions targeted for analysis of specific food products, at the same time retaining the flexibility of application. Furthermore, its portability allows the device to be used at different stages of the production and distribution process.

Chemical and sensory differences between high price and low price extra virgin olive oils

The aim of the study was to identify new potential chemical markers of extra virgin olive oil (EVOO) quality by using a multicomponent analysis approach. Sixty-six EVOOs were purchased from the Italian market and classified according to their price as low price EVOOs (LEVOOs) and high price EVOOs (HEVOOs) costing 3.60-5.90euro/L and 7.49-29.80euro/L respectively. Sensory and chemical parameters strictly related to olive oil quality have been investigated, like volatile substances, polar phenolic substances, antioxidant activity, fatty acid composition, and α-tocopherol. Significant differences in terms of chemical composition and sensory features have been highlighted between the two EVOOs classes investigated, proving a generally lower level of quality of LEVOOs, clearly showed also by means of principal component analysis. Among the most interesting outcomes, R ratio (free tyrosol and hydroxytyrosol over total free and bound forms), measuring the extent of secoiridoids hydrolysis, resulted to be significantly higher in LEVOOs than in HEVOOs. Other key differences were found in the volatile substances composition, in the stearic acid percentage and in p-coumaric acid content.

Sensor-array-based evaluation and grading of beef taste quality

A sensor array composed of ion electrodes including 2 glass electrodes, 3 liquid-membrane electrodes and 7 insoluble salt electrodes was built. Before detection, the working electrodes were activated as required in activate fluids, and the stability states of sensors were analyzed in deionized water. Beef samples were evaluated after all working electrodes stabilized. The response signals from the samples were recorded by an electrochemical workstation and used as the evaluation results. A beef taste sensory evaluation criterion was built and used into sensory evaluation of beef samples. The samples were scored with quality grades according to this criterion, and the results were compared with the results of the sensor array in evaluation of beef broth samples. The evaluation results were processed by principal component analysis and used to build a beef taste quality evaluation model based on artificial neural networks. Tests show this model has an accuracy of 90% in classification of beef taste quality grades.

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

The adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naïve Bayesian, K-Nearest Neighbors (k-NN), Linear Discriminate Analysis (LDA), Decision Tree, Artificial Neural Networks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.

Trace Element Content of Monovarietal and Commercial Portuguese Olive Oils

Olive (Olea europaea L.) represents one of the most important and ancient crops in the Mediterranean countries, being widely known due to the optimal organoleptic characteristics of its oil, besides the important health benefits assigned to this product in the last few years. Since olive oil composition, respecting specific trace elements, has a direct impact on the quality and safety of this product, eleven monovarietal and twelve commercial olive oils were analysed spectrometrically concerning their metal composition. Regarding quality parameters, the monovarietal olive oils display larger quantities of Mg, Fe, Mn, while the commercial samples are characterized by greater amounts of Na, Ca, Cu and Zn. In which respects to safety parameters, the commercial samples present higher values for Al and Pb, and lower quantities of Cd, respecting the monovarietal ones. The assessment of the values observed for the trace elements, resorting to multivariate statistical methods, allowed to clearly distinguish the type of olive oil production-monovarietal or commercial blend. For this purpose, besides Cluster analysis, the data have been subjected to Principal Component Analysis-undertaken for either all the evaluated parameters simultaneously, or the quality/safety parameters separately, both data sets allowing the discrimination of the samples.

Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics

At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.