Detection of Counterfeit Perfumes by Using GC-MS Technique and Electronic Nose System Combined with Chemometric Tools.

The Scientific Committee on Cosmetic and Non-Food Products has identified 26 compounds that may cause contact allergy in consumers when present in concentrations above certain legal thresholds in a product. Twenty-four of these compounds are volatiles and can be analyzed by gas chromatography-mass spectrometry (GC-MS) or electronic nose (e-nose) technologies. This manuscript first describes the use of the GC-MS approach to identify the main volatile compounds present in the original perfumes and their counterfeit samples. The second part of this work focusses on the ability of an e-nose system to discriminate between the original fragrances and their counterfeits. The analyses were carried out using the headspace of the aqueous solutions. GC-MS analysis revealed the identification of 10 allergens in the perfume samples, some of which were only found in the imitated fragrances. The e-nose system achieved a fair discrimination between most of the fragrances analyzed, with the counterfeit fragrances being clearly separated from the original perfumes. It is shown that associating the e-nose system to the appropriate classifier successfully solved the classification task. With Principal Component Analysis (PCA), the three first principal components represented 98.09% of the information in the database.

Comparative analysis of volatile organic compounds of breath and urine for distinguishing patients with liver cirrhosis from healthy controls by using electronic nose and voltammetric electronic tongue.

Advanced stage detection of liver cirrhosis (LCi) would lead to high mortality rates in patients. Therefore, accurate and non-invasive tools for its early detection are highly needed using human emanations that may reflect this disease. Human breath, along with urine and blood, has long been one of the three main biological media for assessing human health and environmental exposure. The primary objective of this study was to explore the potential of using volatile organic compounds (VOCs) assay of exhaled breath and urine samples for the diagnosis of patients with LCi and healthy controls (HC). For this purpose, we used a hybrid electronic nose (E-nose) combining two sensor families, consisting of an array of five commercial chemical gas sensors and six interdigitated chemical gas sensors based on pristine or metal-doped WO3 nanowires for sensing volatile gases in exhaled breath. A voltammetric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. 54 patients were recruited for this study, comprising 22 patients with LCi, and 32 HC. The two-sensing systems coupled with pattern recognition methods, namely Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), were trained to classify data clusters associated with the health status of the two groups. The diagnostic performances of the E-nose and VE-tongue systems were studied by using the receiver operating characteristic (ROC) method. The use of the E-nose or the VE-tongue separately, trained with these appropriate classifiers, showed a slight overlap indicating no clear discrimination between LCi patients and HC. To improve the performance of both electronic sensing devices, an emerging strategy, namely a multi-sensor data fusion technique, was proposed as a second aim to overcome this shortcoming. The data fusion approach of the two systems, at a medium level of abstraction, has demonstrated the ability to assess human health and disease status using non-invasive screening tools based on exhaled breath and urinary VOC analysis. This suggests that exhaled breath as well as urinary VOCs are specific to a disease state and could potentially be used as diagnostic methods.

An electrochemical molecularly imprinted sensor based on chitosan capped with gold nanoparticles and its application for highly sensitive butylated hydroxyanisole analysis in foodstuff products.

One of the most widely used synthetic antioxidants in food, butylated hydroxyanisole (BHA) has raised serious concerns due to its potential toxic effects on human health. Hence, elaboration of simple, effective and sensitive methods for BHA detection is pressing. In this regards, the present research work highlights a facile, simple, and fast synthesis approach for the development of an electrochemical sensor for the analysis of BHA in foodstuffs. In this study, the chitosan (CS) capped with gold nanoparticles (AuNPs) were self-assembled on a screen-printed carbon electrode (SPCE) and complete the elaboration of the molecularly imprinted polymer (MIP) sensor in the presence of BHA as templates. The electrochemical behaviour of the MIP sensor was investigated by using electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV), and cyclic voltammetry (CV). Similarly, the morphology of the electrodes surface of the different elaboration steps was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and atomic force microscopy (AFM). In addition, the obtained results demonstrate satisfactory sensitivity and selectivity to BHA compared to interfering species, including ascorbic acid and citric acid. Under optimal experimental conditions, the MIP sensor exhibits responses proportional to concentrations over a range of 0.01-20 µg mL-1, with a low detection limit (LOD) of 0.001 µg mL-1 (signal-to-noise ratio S/N = 3). Besides, the reproducibility, stability, and repeatability of the MIP sensor were proven. Taking into account all these outcomes, the MIP sensor well demonstrates its ability towards the determination of BHA in food samples with a relative standard deviation (RSD ≤ 8%). Spectrophotometry was utilized as a validation method. Partial least squares (PLS) prediction models were constructed from the MIP sensor and spectrophotometer data with a regression coefficient (R = 0.99). According to the achieved outcomes, the MIP sensor could be a viable tool for food control.

Tramadol sensing in non-invasive biological fluids using a voltammetric electronic tongue and an electrochemical sensor based on biomimetic recognition.

Tramadol (TRA) is a weak opioid analgesic, prescribed to relieve mild to moderately severe pain. However, side effects of TRA overdoses, including vomiting, depression, tachycardia, convulsions, morbidity and mortality are often reported. In this study, an electrochemical sensor based on molecularly imprinted conductive polymer was firstly developed for the quantitative and non-invasive detection of TRA. Secondly, a voltammetric electronic tongue (VE-Tongue) combined with chemometric methods was used for the qualitative analysis. The MIP sensor was constructed by self-assembling a poly-aniline layer coated with silver nanoparticles (PANI-AgNPs) on a screen-printed gold electrode (Au-SPE). Then, 2-amino-thiophenol was polymerised in the presence of TRA. The electronic device exhibits, under optimal conditions, responses proportional to TRA concentrations (0.01-100 µg/mL) with detection and quantification limits of 9.42 µg/mL and 28.55 µg/mL, respectively. Moreover, its selectivity was proven by insignificant interferences of substances (paracetamol and citric acid). Spiked saliva and urine samples were used for the sensor practical application with a significant recovery above 90% and standard deviations below 4.5%. Besides, urine samples' analyses using VE-Tongue and pattern recognition methods show good discrimination, classification, and prediction results with scores above 95%. Correspondingly, both electro-analytical devices could be viable for monitoring drugs in biological matrices.

Wastewater monitoring by means of e-nose, VE-tongue, TD-GC-MS, and SPME-GC-MS.

The presence of wastewater and air pollution has become an important risk factor for citizens, not only in terms of problems related to health risks, but also because of its negative impact on the country's image. For this reason, malodorous emission monitoring and control techniques are in high demand in urban areas and industries. The aim of this work is first to build an electronic nose (e-nose) and a Voltammetric Electronic tongue (VE-tongue) in order to study their ability to discriminate between polluted and clean environmental samples. Secondly, Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS), and Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) are utilized to explain this discrimination by identifying specific compounds from these samples. Indeed, the e-nose, consisted of metal oxide semiconductor gas sensors, is used for the assessment of the studied odorous air and headspace samples from water and wastewater sites. Moreover, the VE-tongue, based on metal electrodes, is utilized to determine the patterns of the sensor array responses, which serve as fingerprints profiles of the analyzed liquid samples. Chemometric tools, such as Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vector Machines (SVMs) are operated for the processing of data from the e-nose and the VE-tongue. By using the both systems, the analyses of headspace and liquid samples from the seven sites allow better discrimination. To explain the cause of the obtained discrimination, TD-GC-MS and SPME-GC-MS analyses are well performed to identify compounds related sites. According to these outcomes, the proposed e-nose and VE-tongue are proved to be rapid and valuable tools for analysis of environmental polluted matrices.

Characterization and Analysis of Okoume and Aiele Essential Oils from Gabon by GC-MS, Electronic Nose, and Their Antibacterial Activity Assessment.

Essential oil resins of Aucoumea klaineana (Okoume) and Canarium schweinfurthii (Aiele) species, of the Burseraceae family, were studied to investigate their bioactive constituents and their antibacterial activities. Aiele resin had a higher yield (6.86%) of essential oil than Okoume (3.62%). Twenty-one compounds for Okoume and eighteen for Aiele essential oil were identified using a gas chromatography-mass spectrometry (Gp-C-MS) technique. The main compounds identified in Okoume essential oil were benzenemethanol, α, α,4-trimethyl (28.85%), (+)-3-carene (3,7,7-trimethyl bicyclo[4.1.0]hept-3-ene) (17.93%), D-Limonene ((4R)-1-methyl-4-prop-1-en-2-ylcyclohexene) (19.36%). With regard to the Aiele essential oil, we identified (1R,4S)-1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.64%), and 1-methyl-4-propan-2-ylcyclohex-2-en-1-ol (26.83%). Two strains of bacteria, Escherichia coli and Staphylococcus aureus, were used in antibacterial tests. S. aureus was found to be more sensitive to Okoume and Aiele essential oils, with a high inhibition zone ranging from 20 to 16 mm. In comparison, the inhibition zone ranged from 6 to 12 mm for E. coli. An electronic nose (e-nose) combined with pattern analysis methods such as principal component analysis (PCA), discriminant function analysis (DFA), and hierarchical cluster analysis (HCA) were used to discriminate the essential oil samples. In summary, the e-nose and GC-MS allowed the identification of bioactive compounds in the essential oil samples, which have a strong antimicrobial activity, with satisfactory results.

Synthesis and characterization of a highly sensitive and selective electrochemical sensor based on molecularly imprinted polymer with gold nanoparticles modified screen-printed electrode for glycerol determination in wastewater.

Glycerol is widely used as humectant in cosmetics to improve skin's smoothness and moisture. However, its level must be controlled in cosmetics at the risk of causing irritation or allergy. Therefore, determining glycerol concentration in environmental waters with more advanced, inexpensive and accurate sensing systems is of great importance. In this work, a fast, simple, portable and cheap molecular imprinted polymer (MIP) approach is used to develop an electrochemical sensor for glycerol determination. The MIP based screen-printed gold electrode (Au-SPE) is prepared by electro-polymerizing Acrylamide/Bisacrylamide (AAM/NNMBA) and gold nanoparticles (AuNPs) in the presence of glycerol as a template. Techniques, such as cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) are used for electrochemical measurements. Energy-dispersive X-ray spectroscopy (EDS) is utilized to characterize the chemical composition analysis. In contrast to its high response towards glycerol, the electrochemical sensor exhibits negligible responses when exposed to interfering species, such as glycolic acid, glycerol monostearate, tartaric acid, sodium citrate, ammonium sulfate, decyl-glucoside, caprylyl glucoside and glutamic acid. Under optimal experimental conditions, a detection limit (LOD) as low as 0.001 µg/mL (signal-to-noise ratio S/N = 3) is calculated over a linear concentration range (20.00-227.81 µg/mL). Interestingly, the sensor was successfully applied to wastewater samples relating to glycerol determination with a relative standard deviation (RSD) less than 4%. Besides, the reproducibility, the working and storage stabilities of the sensor were proven. According to these outcomes, the electrochemical MIP sensor could be viable enough to detect the presence and levels of pollutants in real water samples.

Assessment of Electronic Sensing Techniques for the Rapid Identification of Alveolar Echinococcosis through Exhaled Breath Analysis.

Here we present a proof-of-concept study showing the potential of a chemical gas sensors system to identify the patients with alveolar echinococcosis disease through exhaled breath analysis. The sensors system employed comprised an array of three commercial gas sensors and a custom gas sensor based on WO3 nanowires doped with gold nanoparticles, optimized for the measurement of common breath volatile organic compounds. The measurement setup was designed for the concomitant measurement of both sensors DC resistance and AC fluctuations during breath samples exposure. Discriminant Function Analysis classification models were built with features extracted from sensors responses, and the discrimination of alveolar echinococcosis was estimated through bootstrap validation. The commercial sensor that detects gases such as alkane derivatives and ethanol, associated with lipid peroxidation and intestinal gut flora, provided the best classification (63.4% success rate, 66.3% sensitivity and 54.6% specificity) when sensors' responses were individually analyzed, while the model built with the AC features extracted from the responses of the cross-reactive sensors array yielded 90.2% classification success rate, 93.6% sensitivity and 79.4% specificity. This result paves the way for the development of a noninvasive, easy to use, fast and inexpensive diagnostic test for alveolar echinococcosis diagnosis at an early stage, when curative treatment can be applied to the patients.

An electrochemical sensor based on chitosan capped with gold nanoparticles combined with a voltammetric electronic tongue for quantitative aspirin detection in human physiological fluids and tablets.

Inflammatory diseases increase has recently sparked the research interest for drugs diagnostic tools development. At therapeutic doses, acetylsalicylic acid (ASA or aspirin) is widely used for these diseases' treatment. ASA overdoses can however give rise to adverse side effects including ulcers, gastric damage. Hence, development of simple, portable and sensitive methods for ASA detection is desirable. This paper reports aspirin analysis in urine, saliva and pharmaceutical tablet using an electrochemical sensor and a voltammetric electronic tongue (VE-Tongue). The electrochemical sensor was fabricated by self-assembling chitosan capped with gold nanoparticles (Cs + AuNPs) on a screen-printed carbon electrode (SPCE). It exhibits a logarithmic-linear relationship between its response and the ASA concentration in the range between 1 pg/mL and 1 µg/mL. A low detection limit (0.03 pg/mL), good selectivity against phenol and benzoic acid interference, and successful practical application were demonstrated. Qualitative analysis was performed using the VE-Tongue based unmodified metal electrodes combined with two chemometric approaches to classify urine samples spiked with different aspirin concentrations. Partial least squares (PLS) method provided prediction models obtained from the data of both devices with a regression correlation coefficient R2 = 0.99. Correspondingly, the SPCE/(Cs + AuNPs) electrochemical sensor and VE-Tongue could be viable tools for biological analysis of drugs.

Development of a molecularly imprinted polymer electrochemical sensor and its application for sensitive detection and determination of malathion in olive fruits and oils.

Malathion (MAL) is an organophosphorus (OP) insecticide. It is a cholinesterase inhibitor, which can pose serious health and environmental problems. In this study, a sensitive and selective molecular imprinted polymer (MIP) based on screen-printed gold electrodes (Au-SPE) for MAL detection in olive oils and fruits, was devised. The MIP sensor was prepared using acrylamide as the functional monomer and MAL as the template. Subsequently, the morphology of the electrode surface was studied by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The electrochemical characterization of the developed MIP sensor was performed by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) techniques. The operational repeatability and stability of the sensor were studied. It was found to have a dynamic concentration range of (0.1 pg mL-1-1000 pg mL-1) and a low limit of detection (LOD) of 0.06 pg mL-1. Furthermore, the sensor was employed to determine MAL content in olive oil with a recovery rate of 87.9% and a relative standard deviation of 8%. It was successfully applied for MAL determination in real samples and promise to open new opportunities for the detection of OP pesticides residues in various food products, as well as in environmental applications.

A novel electrochemical sensor based on ion imprinted polymer and gold nanomaterials for nitrite ion analysis in exhaled breath condensate.

Human Exhaled Breath Condensate (EBC) contains markers of several inflammatory diseases. Its analysis is of interest to a number of researchers. Nitrite ions (NO2-), which are widely used in our daily lives, are nevertheless among these indicators. In this study, a simple, fast, portable, non-invasive and cheap electrochemical sensor is developed for the analysis of the nitrite profile in EBC. In this regard, sodium nitrite (NaNO2) was first immobilized on self-assembled 2-aminothiophenol (2-ATP) on a screen-printed gold electrode (Au-SPE). Then, a polymer matrix composed of polyvinyl alcohol (PVA) crosslinked with glutaraldehyde (GA) was combined with gold nanoparticles (Au-NPs) to cover the modified Au-SPE and complete the fabrication of the Ion Imprinted Polymer (IIP) sensor. The electrochemical behaviour of the sensor was monitored using Cyclic Voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS) and Differential Pulse Voltammetry (DPV) methods, while the morphology and chemical composition of its layers were observed by infrared Fourier transform (FTIR), Atomic Force Microscopy (AFM) and Scanning Electron Microscopy coupled with energy dispersion X-Ray spectroscopy (SEM-EDS) techniques. In addition, after a successful control test using a Non-Imprinted Ion Polymer (NIIP) sensor, the obtained results demonstrated satisfactory sensitivity and selectivity to nitrite compared to co-existing interfering substances in EBC, such as nitrate, acetate and ammonium nitrate. Under improved experimental conditions, the nitrite IIP sensor exhibits responses proportional to nitrite concentrations (R2 = 0.96) over a concentration range of 0.5-50 µg mL-1 with a detection limit (LOD) of 4 µmol L-1 (signal-to-noise ratio S/N = 3). The proposed approach was well applied for the nitrite determination in EBC samples with a relative standard deviation (RSD = 4%) and could open clinical applications in respiratory medicine.

A nonenzymatic electrochemical glucose sensor based on molecularly imprinted polymer and its application in measuring saliva glucose.

Diabetes mellitus (DM) is often diagnosed by invasive or enzymatic methods being progressively somewhat undesirable. If salivary glucose (SG) level correlates with blood glucose (BG), it could be useful for early DM detection. In this work, a molecular imprinted polymer (MIP) approach was used to develop an electrochemical non-enzymatic sensor to determine SG in micro-molar levels. The MIP based Screen printed gold electrode (Au-SPE) was prepared by electropolymerizing Acrylamide/Bis-Acrylamide (AAM/NNMBA) in the presence of glucose (G) as a template. The Cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques were employed for the electrochemical measurements with Ferri/Ferrocyanide as redox probe in phosphate buffer saline. Morphological characterizations of the elaborated sensors were performed by using atomic force microscopy (AFM) and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDS). Important parameters influencing the MIP sensor performances such as extraction, incubation time, potential range and number of CV cycles, were studied and optimized. Under optimum conditions, the sensor could effectively detect glucose avoiding interferences of structural similar substances like lactose and sucrose. In a working range from 0.5 to 50 µg/mL, it exhibits a detection and quantification limits of 0.59 µg/mL and 1.9 µg/mL, respectively. Additionally, the real saliva glucose determination was compared to those of finger prick blood with satisfactory results (R2 = 0.99) by using partial least squares (PLS) statistical technique. Correspondingly, this work has demonstrated a cheap, simple and effective sensing platform for non-enzymatic glucose detection thus making it a promising tool for future evolution of accurate and reliable non-invasive DM diagnosis.

Electrochemical sensor based on molecularly imprinted polymer for sensitive triclosan detection in wastewater and mineral water.

Triclosan (TCS) is a topical antiseptic widely used in different cosmetic products. It is also a common additive in many antimicrobial household consumables. Over a certain concentration, it becomes risky for human and environmental health. This work describes the development of an electrochemical sensor based on molecularly imprinted polymer (MIP), assembled on screen-printed gold electrode (Au-SPE), dedicated to the TCS detection in environmental water sources. To achieve this goal, an acrylamide/bisacrylamide solution was polymerized after linking TCS with the carboxylic polyvinyl chloride (PVC-COOH) layer onto the Au-SPE. The sensor device fabrication and its retention capabilities were characterized through cyclic voltammetry (CV), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), atomic force microscopy (AFM) and Fourier transform infrared (FTIR) spectroscopy. As control experiment, negligible responses were obtained during the non-imprinted polymer (NIP) test. The sensor could effectively detect TCS avoiding interferences of structural similar substances like 2,4,6-trichlorophenol and catechol. Under optimal conditions, the sensor responses were found logarithmic in the concentration range from 0.1 to 1000 pg mL-1. Indeed, compared with reported works, this sensor exhibits lower detection limit (LOD) and quantification limit (LOQ) of 0.23 and 0.78 pg mL-1, respectively. The developed sensor was effectively applied to wastewater samples for TCS detection and displayed satisfactory performances. Moreover, the different wastewater samples, regarding their TCS contents, were correctly classified by using principal component analysis (PCA) technique. Correspondingly, this work has demonstrated a cheap, simple and effective sensing platform for TCS detection thus making it a promising tool for future evolution of accurate and reliable environmental analysis.

Development and application of a novel electrochemical immunosensor for tetracycline screening in honey using a fully integrated electrochemical Bio-MEMS.

Tetracycline (TC) is a veterinary drug, wildly prescribed for prophylactic and therapeutic purposes. Consequently, its remaining residues in food products have to be regularized. We report in this paper about the development of a novel immunosensor based on an integrated bio micro-electromechanical system (Bio-MEMS) containing eight gold microelectrodes (µWEs), an integrated silver and platinum reference and counter electrodes, respectively. TC immobilization on the µWEs surface was conducted using three methods. The first through functionalization with 4-aminophenylacetic acid (CMA), the second by functionalization with CMA followed by preconcentration of a new structure of magnetic nanoparticles (MNPs) coated with poly (pyrrole-co-pyrrole-2-carboxylic acid) (Py/Py-COOH/MNPs) cross-linked with Ab-TC, and the last one directly through the functionalization with Py/Py-COOH/MNPs. The analyte was quantified by competitive detection with TC immobilized on the µWEs surface toward specific polyclonal antibody (Ab-TC), using a mixture of a fixed concentration of Ab-TC and decreasing levels of TC one from 0.1 pg mL-1 to 1000 pg mL-1. Microcontact printing, followed by fluorescence microscopy characterization were performed during the functionalization of the immunosensor surface to certify that the corresponding immune detection process is taking place. This immunosensor was found to be highly sensitive with a limit of detection of 1.2 pg mL-1 and specific in the presence of interferents. The standard addition method was exploited to detect TC in honey samples. The present immunosensor platform is up-and-coming for TC detection which can dramatically decrease the time of analysis providing a new pathway for advanced immunoassays development in industrial food control.

Emerging approach for analytical characterization and geographical classification of Moroccan and French honeys by means of a voltammetric electronic tongue.

Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented.

Distinguishment, identification and aroma compound quantification of Portuguese olive oils based on physicochemical attributes, HS-GC/MS analysis and voltammetric electronic tongue.

BACKGROUND: In this paper, various extra-virgin and virgin olive oils samples from different Portuguese markets were studied. For this purpose, a voltammetric electronic tongue (VE-tongue), consisting of two kinds of working electrode within the array, together with physicochemical analysis and headspace gas chromatography coupled with mass spectrometry (HS-GC-MS), were applied. In addition, preliminary considerations of relationships between physicochemical parameters and multisensory system were reported. RESULTS: The physicochemical parameters exhibit significant differences among the analyzed olive oil samples that define its qualities. Regarding the aroma profile, 14 volatile compounds were characterized using HS-GC-MS; among these, hex-2-enal, hexanal, acetic acid, hex-3-ene-1-ol acetate and hex-3-en-1-ol were semi-quantitatively detected as the main aroma compounds in the analyzed samples. Moreover, pattern recognition methods demonstrate the discrimination power of the proposed VE-tongue system. The results reveal the VE-tongue's ability to classify olive oil samples and to identify unknown samples based of built models. In addition, the correlation between VE-tongue and physicochemical analysis exhibits a remarkable prediction model aimed at anticipating carotenoid content. CONCLUSION: The preliminary results of this investigation indicate that physicochemical and HS-GC-MS analysis, together with multisensory system coupled with chemometric techniques, presented a satisfactory performance regarding olive oil sample discrimination and identification. © 2017 Society of Chemical Industry.

DPPG Liposomes Adsorbed on Polymer Cushions: Effect of Roughness on Amount, Surface Composition and Topography.

The adsorption of intact liposomes onto solid supports is a fundamental issue when preparing systems with encapsulated biological molecules. In this work, the adsorption kinetic of 1,2-dipalmitoyl-sn-glycero-3-[phospho-rac-(1-glycerol)] (sodium salt) liposomes onto cushions prepared from commom polyelectrolytes by the layer-by-layer technique was investigated with the main objective of finding the surface conditions leading to the adsorption of intact liposomes. For this purpose, different cushion surface roughnesses were obtained by changing the number of cushion bilayers. The adsorbed amount per unit area was measured through quartz crystal microbalance, surface morphology was characterized by atomic force microscopy, and the surface composition was assessed by X-ray photoelectron spectroscopy. The results show that (1) the amount of adsorbed lipids depends on the number of cushion bilayers, (2) the cushions are uniformly covered by the adsorbed lipids, and (3) the surface morphology of polymer cushions tunes liposome rupture and its adsorption kinetics. The fraction of ruptured liposomes, calculated from the measured amount of adsorbed lipids, is a function of surface roughness together with other surface morphology parameters, namely the dominating in-plane spatial feature size, the fractal dimension, and other textural features as well as amplitude and hybrid parameters.

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue.

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage day's discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor.

Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat.

The aim of the present study was to develop an electronic nose for the quality control of red meat. Electronic nose and bacteriological measurements are performed to analyse samples of beef and sheep meat stored at 4°C for up to 15 days. Principal component analysis (PCA) and support vector machine (SVM) based classification techniques are used to investigate the performance of the electronic nose system in the spoilage classification of red meats. The bacteriological method was selected as the reference method to consistently train the electronic nose system. The SVM models built classified meat samples based on the total microbial population into "unspoiled" (microbial counts < 6 log10 cfu/g) and "spoiled" (microbial counts ≥ 6 log10 cfu/g). The preliminary results obtained by the bacteria total viable counts (TVC) show that the shelf-life of beef and sheep meats stored at 4 °C are 7 and 5 days, respectively. The electronic nose system coupled to SVM could discriminate between unspoiled/ spoiled beef or sheep meats with a success rate of 98.81 or 96.43 %, respectively. To investigate whether the results of the electronic nose correlated well with the results of the bacteriological analysis, partial least squares (PLS) calibration models were built and validated. Good correlation coefficients between the electronic nose signals and bacteriological data were obtained, a clear indication that the electronic nose system can become a simple and rapid technique for the quality control of red meats.