Monitoring odour of heated extra-virgin olive oils from Arbequina and Manzanilla cultivars using an electronic nose

E-Nose Discrimination of Almond Oils Extracted from Roasted Kernels

Almonds contain around 50% fat with a health-promoting fatty acid profile that can be extracted by pressing to obtain high-quality oils. To improve oil sensory properties, the almonds can be subjected to roasting treatments before oil extraction. However, intense thermal treatments may cause the appearance of undesirable volatile compounds causing unpleasant aromas. Thus, oils from almonds subjected to different roasting treatments (30, 45, 60 and 90 min at 150 °C) were analyzed from sensory and the chemical points of view. In addition, an electronic device (E-nose) was used in order to evaluate its usefulness in discriminating samples according to their aromas. The almonds’ roasting treatments caused changes in the sensory properties, since defects such as a burned, dry smell or wood fragrance appeared when almonds were subjected to roasting treatments (>45 min). These data agree with the analysis of volatile compounds, which showed an increase in the content of aldehyde and aromatic groups in roasted almonds oils while alcohols and terpenes decreased. Partial least squares discriminant analysis and partial least squares obtained from the E-nose were able to classify samples (97.5% success) and quantify the burned defect of the oils (Rp2 of 0.88), showing that the E-nose can be an effective tool for classifying oils.

Application of a solid-phase microextraction-gas chromatography-mass spectrometry/metal oxide sensor system for detection of antibiotic susceptibility in urinary tract infection-causing Escherichia coli - A proof of principle study

PURPOSE

Antibiotic resistance is widespread throughout the world and represents a serious health concern. There is an urgent need for the development of novel tools for rapidly distinguishing antibiotic resistant bacteria from susceptible strains. Previous work has demonstrated that differences in antimicrobial susceptibility can be reflected in differences in the profile of volatile organic compounds (VOCs) produced by dissimilar strains. The aim of this study was to investigate the effect of the presence of cephalosporin antibiotics on the VOC profile of extended spectrum beta-lactamase (ESBL) and non-ESBL producing strains of Escherichia coli.

MATERIAL AND METHODS

In this study, VOCs from strains of Escherichia coli positive and negative for the most commonly encountered ESBL, CTX-M in the presence of cephalosporin antibiotics were assessed using solid-phase microextraction (SPME) coupled with a combined gas chromatography-mass spectrometry/metal oxide sensor (GC-MS/MOS) system.

RESULTS

Our proof-of-concept study allowed for distinguishing CTX-M positive and negative bacteria within 2 ​h after the addition of antibiotics. One MOS signal (RT: 22.6) showed a statistically significant three-way interaction (p ​= ​0.033) in addition to significant two-way interactions for culture and additive (p ​= ​0.046) plus time and additive (p ​= ​0.020). There were also significant effects observed for time (p ​= ​0.009), culture (p ​= ​0.030) and additive (p ​= ​0.028). No effects were observed in the MS data.

CONCLUSIONS

The results of our study showed the potential of VOC analysis using SPME combined with a GC-MS/MOS system for the early detection of CTX-M-producing, antibiotic-resistant E. coli, responsible for urinary tract infections (UTIs).

Mechanism, indexes, methods, challenges, and perspectives of edible oil oxidation analysis

Edible oils are indispensable food components, because they are used for cooking or frying. However, during processing, transport, storage, and consumption, edible oils are susceptible to oxidation, during which various primary and secondary oxidative products are generated. These products may reduce the nutritional value and safety of edible oils and even harm human health. Therefore, analyzing the oxidation of edible oil is essential to ensure the quality and safety of oil. Oxidation is a complex process with various oxidative products, and the content of these products can be evaluated by corresponding indexes. According to the structure and properties of the oxidative products, analytical methods have been employed to quantify these products to analyze the oxidation of oil. Combined with proper chemometric analytical methods, qualitative identification has been performed to discriminate oxidized and nonoxidized oils. Oxidative products are complex and diverse. Thus, proper indexes and analytical methods should be selected depending on specific research objectives. Expanding the mechanism of the correspondence between oxidative products and analytical methods is crucial. The underlying mechanism, conventional indexes, and applications of analytical methods are summarized in this review. The challenges and perspectives for future applications of several methods in determining oxidation are also discussed. This review may serve as a reference in the selection, establishment, and improvement of methods for analyzing the oxidation of edible oil. HighlightsThe mechanism of edible oil oxidation analysis was elaborated.Conventional oxidation indexes and their limited values were discussed.Analytical methods for the determination of oxidative products and qualitative identification of oxidized and non-oxidized oils were reviewed.

Application of a lab-made electronic nose for extra virgin olive oils commercial classification according to the perceived fruitiness intensity

An electronic nose, comprising nine metal oxide sensors, has been built aiming to classify olive oils according to the fruity intensity commercial grade (ripely fruity or light, medium and intense greenly fruity), following the European regulated complementary terminology. The lab-made sensor device was capable to differentiate standard aqueous solutions (acetic acid, cis-3-hexenyl, cis-3-hexen-1-ol, hexanal, 1-hexenol and nonanal) that mimicked positive sensations (e.g., fatty, floral, fruit, grass, green and green leaves attributes) and negative attributes (e.g., sour and vinegary defects), as well as to semi-quantitatively classify them according to the concentration ranges (0.05-2.25 mg/kg). For that, unsupervised (principal component analysis) and supervised (linear discriminant analysis: sensitivity of 92% for leave-one-out cross validation) classification multivariate models were established based on nine or six gas sensors, respectively. It was also showed that the built E-nose allowed differentiating/discriminating (sensitivity of 81% for leave-one-out cross validation) extra virgin olive oils according to the perceived intensity of fruitiness as ripely fruity, light, medium or intense greenly fruity. In conclusion, the gas sensor device could be used as a practical preliminary non-destructive tool for guaranteeing the correctness of olive oil fruitiness intensity labelling.

Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO

The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture.

Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction-gas chromatography-mass spectrometry corresponding to 4-methyl-2-pentanol, (E)-2-hexenal, 1-tridecene, hexyl acetate, (Z)-3-hexenyl acetate, (E)-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

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.