Analysis of Volatile Compounds in Sea Bass (Lateolabrax japonicus) Resulting from Different Slaughter Methods Using Electronic-Nose (E-Nose) and Gas Chromatography-Ion Mobility Spectrometry.

Sea bass (Lateolabrax japonicus) is known for its unique flavor and high nutritional value. In this study, the influence of slaughter methods on the volatile compounds (VOCs) in sea bass was investigated using electronic nose (E-nose) technology and gas chromatography-ion mobility spectrometry (GC-IMS). VOCs in raw and cooked sea bass resulting from different slaughter methods were effectively distinguished using both techniques. Aldehydes, ketones, and alcohols were associated with the basic flavor of sea bass, whereas esters, organic acids, and furans enriched the aroma. In raw sea bass, the fishy odor was the strongest in the HSD group (head shot control death), followed by that in the IFD (ice faint to death) and BDS (bloodletting to death) groups. The VOC content increased and stabilized after steaming, enhancing pleasant odors such as fatty and fruity aromas. In cooked sea bass, the content of diacetyl and ethanol was the highest in the EAD group (eugenol anesthesia to death), which may be a residue of eugenol, imparting a distinct irritating chemical odor. Furthermore, abundant (E)-2-octenal, 2-heptanone, benzaldehyde, and esters in the BDS group imparted a strong, pleasant aroma. The findings indicate that heart puncture and bloodletting is the preferred slaughter method to maintain sea bass quality, providing new insights into the volatile changes in sea bass induced by different slaughter methods.

The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients.

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver-operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66-0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43-0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49-0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50-0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Quality evaluation of table grapes during storage by using 1H NMR, LC-HRMS, MS-eNose and multivariate statistical analysis.

Three non-targeted methods, i.e. 1H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes, mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified.

A modified mid-level data fusion approach on electronic nose and FT-NIR data for evaluating the effect of different storage conditions on rice germ shelf life.

Evaluating the possibility of extending shelf life of rice germ (a by-product of rice milling process) by reducing water activity in combination with storage atmosphere packaging, without any heat treatment, is the aim of the present study. Samples at different water activities (0.55, 0.45 and 0.36) were packed in air, argon or under vacuum, and stored at 27 °C for 150 days. To the aim, a non-targeted approach was applied by means of an FT-NIR spectrometer in reflectance with a rotating sample holder and a portable electronic nose, equipped with 10 non-specific sensors. For understanding the impact of the factors under study on the rice germ shelf life, a modified mid-level data fusion approach was applied to enhance the information most correlated with time. Moreover, Principal Component Analysis was applied on fused data to follow samples evolution during storage and identify different clusters according to the storage conditions. The rice germ case study allowed to better understand the information captured by the non-specific sensors: a 2D correlation map was developed combining the e-nose data with the NIR spectral information, highlighting relationships among NIR absorption bands and classes of chemical compounds inducing e-nose responses. A data fusion approach highlighted the importance of water activity on rice germ storage, while no interesting differences were ascribable to storage atmosphere packaging systems. In terms of correlation, the sensors could be divided in two groups, negatively inter-correlated: sensors ascribable to aromatic compounds (WC) and correlated with the NIR band around 4800-4900 cm-1 (N-H bending of primary amides, typical for peptides coming from protein hydrolysis); broad-range response sensors (WS), linked with the NIR band at 5128 cm-1 (second overtone of CO stretching of esters).

A method for early detection and identification of fungal contamination of building materials using e-nose.

The aim of the study was to develop a method for early detection and identification of fungal contamination of building materials using an electronic nose. Therefore, the laboratory experiments based on the analysis of the air in the vicinity of fungal isolates potentially found in the building materials were performed. The results revealed that the employed gas sensors array consisting of MOS-type sensors enables the detection of the differences among the examined samples of fungi and distinguishing between the non-contaminated and contaminated samples, shortly after fungal contamination occurs. Electronic nose readouts were analysed using Principal Component Analysis and the results were verified with standard chromatographic analysis by means of SPME-GC/MS method, which proved that gas sensors array can be applied for early detection of fungal contamination.

A simple voltammetric electronic tongue for the analysis of coffee adulterations.

This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.

Evolution of Volatile Compounds and Spoilage Bacteria in Smoked Bacon during Refrigeration Using an E-Nose and GC-MS Combined with Partial Least Squares Regression.

The changes in the concentration of volatile organic compounds (VOCs) and biogenic amines (BAs) in smoked bacon during 45-day refrigerated storage is investigated using solid-phase micro-extraction coupled with gas chromatography-mass spectrometry and high-performance liquid chromatography. In total, 56 VOCs and 6 BAs were identified and quantified. The possible pathways leading to their formation are analyzed and considered as the potential signs of microbial activity, especially by specific spoilage microorganisms (SSOs). Leuconostoc and Lactobacillus, which levels increased markedly with the extension of storage time, were recognized as SSOs. An electronic nose (e-nose) was employed to determine the changes in concentration of the odor components per sample present within half an hour. Partial least squares regression was then carried out to analyze the correlation between SSO growth, metabolite concentration, BA accumulation, and e-nose response. The results show that ten VOCs (ethanol, 2-furanmethanol, 1-hexanol, 1-propanol, phenol, 2-methoxyphenol, acetic acid, 3-ethyl-2-cyclopenten-1-one, furfural, and ethyl hexanoate) and three BAs (putrescine, cadaverine, and tyramine) can be associated with the growth of SSOs. Thus, they can be adopted as potential indicators to evaluate and monitor the quality of the bacon and develop appropriate detection methods. E-noses can used to recognize odors and diagnose quality of bacon.

Clinical and Inflammatory Phenotyping: Can Electronic Nose and NMR-based Metabolomics Work at the Bedside?

Electronic nose (eNose) and nuclear magnetic resonance (NMR)-based metabolomics seem to be able to identify metabolic and inflammatory profiles in patients with chronic obstructive diseases. The hypothesis arises from three recent studies using two different methods in patients with asthma and chronic obstructive pulmonary diseases (COPD), opening promising diagnostic perspectives. The possibility that the use of eNose and NMR-based metabolomics might provide clinical/inflammatory characteristics is intriguing. This might classify specific phenotypes of chronic airway disease regardless of the diagnosis asthma or COPD, therefore suggesting therapeutical targets for a personalized respiratory medicine through more efficient "tailored" strategies.

Rapid prediction of deoxynivalenol contamination in wheat bran by MOS-based electronic nose and characterization of the relevant pattern of volatile compounds.

BACKGROUND: Deoxynivalenol (DON) is a mycotoxin, mainly produced by Fusarium sp., most frequently occurring in cereals and cereal-based products. Wheat bran refers to the outer layers of the kernel, which has a high risk of damage due to chemical hazards, including mycotoxins. Rapid methods for DON detection in wheat bran are required. RESULTS: A rapid screening method using an electronic nose (e-nose), based on metal oxide semiconductor sensors, has been developed to distinguish wheat bran samples with different levels of DON contamination. A total of 470 naturally contaminated wheat bran samples were analyzed by e-nose analysis. Wheat bran samples were divided in two contamination classes: class A ([DON] ≤ 400 µg kg-1 , 225 samples) and class B ([DON] > 400 µg kg-1 , 245 samples). Discriminant function analysis (DFA) classified wheat bran samples with good mean recognizability in terms of both calibration (92%) and validation (89%). A pattern of 17 volatile compounds of wheat bran samples that were associated (positively or negatively) with DON content was also characterized by HS-SPME/GC-MS. CONCLUSIONS: These results indicate that the e-nose method could be a useful tool for high-throughput screening of DON-contaminated wheat bran samples for their classification as acceptable / rejectable at contamination levels close to the EU maximum limit for DON, reducing the number of samples to be analyzed with a confirmatory method. © 2018 Society of Chemical Industry.

Prediction of egg freshness during storage using electronic nose.

The aim of the present study was to investigate the potential of a fast gas chromatography (GC) e-nose for freshness discrimination and for prediction of storage time as well as sensory and internal quality changes during storage of hen eggs. All samples were obtained from the same egg production farm and stored at 20 °C for 20 d. Egg sampling was conducted every 0, 3, 6, 9, 12, 16, and 20 d. During each sampling time, 4 egg cartons (each containing 10 eggs) were randomly selected: one carton for Haugh units, one carton for sensory evaluation and 2 cartons for the e-nose experiment. The e-nose study included 2 independent test sets; calibration (35 samples) and validation (28 samples). Every sampling time, 5 replicates were prepared from one egg carton for calibration samples and 4 replicates were prepared from the remaining egg carton for validation samples. Sensors (peaks) were selected prior to multivariate chemometric analysis; qualitative sensors for principal component analysis (PCA) and discriminant factor analysis (DFA) and quantitative sensors for partial least square (PLS) modeling. PCA and DFA confirmed the difference in volatile profiles of egg samples from 7 different storage times accounting for a total variance of 95.7% and 93.71%, respectively. Models for predicting storage time, Haugh units, odor score, and overall acceptability score from e-nose data were developed using calibration samples by PLS regression. The results showed that these quality indices were well predicted from the e- nose signals, with correlation coefficients of R2 = 0.9441, R2 = 0.9511, R2 = 0.9725, and R2 = 0.9530 and with training errors of 0.887, 1.24, 0.626, and 0.629, respectively. As a result of ANOVA, most of the PLS model results were not significantly (P > 0.05) different from the corresponding reference values. These results proved that the fast GC electronic nose has the potential to assess egg freshness and feasibility to predict multiple egg freshness indices during its circulation in the supply chain.

Detection of Lung Cancer and EGFR Mutation by Electronic Nose System.

INTRODUCTION: Early detection of lung cancer (LC) has been well established as a significant key point in patient survival and prognosis. New highly sensitive nanoarray sensors for exhaled volatile organic compounds that have been developed and coupled with powerful statistical programs may be used when diseases such as LC are suspected. Detection of genetic aberration mutation by nanoarray sensors is the next target. METHODS: Breath samples were taken from patients who were evaluated for suspicious pulmonary lesions. Patients were classified as those with benign nodules, as patients with LC with or without the EGFR mutation, and according to their smoking status. Breath prints were recognized by nanomaterial-based sensor array, and pattern recognition methods were used. RESULTS: A total of 119 patients participated in this study, 30 patients with benign nodules and 89 patients with LC (16 with early disease and 73 with advanced disease). Patients with LC who harbored the EGFR mutation (n = 19) could be discriminated from those with wild-type EGFR (n = 34) with an accuracy of 83%, sensitivity of 79%, and specificity of 85%. Discrimination of early LC from benign nodules had 87% accuracy and positive and negative predictive values of 87.7 and 87.5% respectively. Moderate discrimination (accuracy of 76%) was found between LC of heavy smokers and that of never-smokers or distant past light smokers. CONCLUSIONS: Breath analysis could discriminate patients with LC who harbor the EGFR mutation from those with wild-type EGFR and those with benign pulmonary nodules from those patients with early LC. A positive breath print for the EGFR mutation may be used in treatment decisions if tissue sampling does not provide adequate material for definitive mutation analysis.

Measurements of the effects of wine maceration with oak chips using an electronic tongue.

The use of oak products as a cheaper alternative to expensive wood barrels was recently permitted in Europe, which led to a continuous increase in the use of oak chips and staves in winemaking. The feasibility of the potentiometric electronic tongue as a tool for monitoring the effects of wine maceration with oak chips was evaluated. Four types of commercially available oak chips subjected to different thermal treatments and washing procedures and their mixture were studied. Ethanolic extracts of the chips were analysed using electrospray mass spectrometry and 28 phenolic and furanic compounds were identified. The electronic tongue comprising 22 potentiometric chemical sensors could distinguish artificial wine solutions and Cabernet Sauvignon wine macerated with different types of oak chips, quantify total and non-flavonoid phenolic content, as well as the concentrations of added oak chips. Using measurements at two pH levels, 3.2 and 6.5, improved the accuracy of quantification.

Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics.

Electronic nose and tongue sensors and chemometric multivariate analysis were applied to characterize and classify 7 Chinese robusta coffee cultivars with different roasting degrees. Analytical data were obtained from 126 samples of roasted coffee beans distributed in the Hainan Province of China. Physicochemical qualities, such as the pH, titratable acidity (TA), total soluble solids (TSS), total solids (TS), and TSS/TA ratio, were determined by wet chemistry methods. Data fusion strategies were investigated to improve the performance of models relative to the performance of a single technique. Clear classification of all the studied coffee samples was achieved by principal component analysis, K-nearest neighbour analysis, partial least squares discriminant analysis, and a back-propagation artificial neural network. Quantitative models were established between the sensor responses and the reference physicochemical qualities, using partial least squares regression (PLSR). The PLSR model with a fusion data set was considered the best model for determining the quality parameters.

The prediction of food additives in the fruit juice based on electronic nose with chemometrics.

Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R2s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring.

Evaluation of the synergism among volatile compounds in Oolong tea infusion by odour threshold with sensory analysis and E-nose.

Twenty-four kinds of representative aroma compounds in Oolong tea were selected to evaluate the interactions by the variation in the threshold values of these compounds before, and after, they were mixed. Result demonstrated that the ratios of the referenced threshold values of heptanal, ß-damascenone, and methional to their determined thresholds were larger, namely, 5, 3.85, and 2.5, respectively. On the other hand, the mixed compounds with similar structure and aroma mainly presented a synergistic effect and additive action. Moreover, a masking effect was found among compounds with different structures. (E)-2-hexenal was added to tea infusion at a concentration below its threshold level to investigate whether, or not, the sub-threshold compounds affected the overall odour sensation of Oolong tea by sensory analysis and electronic nose (E-nose). The result indicated that the aroma of the tea infusion with added (E)-2-hexenal had changed before, and after, they were mixed.

Application of an electronic nose in the diagnosis of head and neck cancer.

OBJECTIVES/HYPOTHESIS: Electronic nose (E-nose) technology has various applications such as the monitoring of air quality and the detection of explosive and chemical agents. We studied the diagnostic accuracy of volatile organic compounds (VOC) pattern analysis in exhaled breath by means of an E-nose in patients with head and neck squamous cell carcinoma (HNSCC). STUDY DESIGN: Cohort study. Exhaled breath samples from patients with HNSCC were analyzed by using an E-Nose. METHODS: Thirty-six patients diagnosed with HNSCC exhaled into a 5-litre Tedlar bag. The control group consisted of 23 patients visiting the outpatient clinic for other (benign) conditions. Air samples were analyzed using an E-nose. RESULTS: Logistic regression showed a significant difference (P < 0.05) in VOC resistance patterns between patients diagnosed with HNSCC and the control group, with a sensitivity of 90% and a corresponding specificity of 80%. CONCLUSIONS: E-nose application holds a promising potential for application in the diagnosis of HNSCC due to its rapid, simple, and noninvasive nature. LEVEL OF EVIDENCE: 3b.

Power-efficient oscillator-based readout circuit for multichannel resonant volatile sensors.

This work presents a multichannel electronic nose system that enables a range of novel applications owing to high sensitivity, low form factor and low power consumption. Each channel is based on a combination of doubly-clamped piezoelectric MEMS resonators and CMOS oscillator-based readout designed in TSMC 0.25 µm technology. Using "application specific" polymer coatings, the individual resonators can be tuned to detect mixtures of volatile organic compounds (VOCs). This system achieves ppm-level theoretical limit of detection for ethanol which paves the way towards a broad range of applications such as personalized health and environment air quality.