Poultry meat freshness evaluation using electronic nose technology and ultra-fast gas chromatography.
Ontology highlight
ABSTRACT:
SUBMITTER: Wojnowski W
PROVIDER: S-EPMC5541116 | biostudies-other | 2017
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Oil blending is often used to enhance the properties of vegetable oils. The admixture of a more thermally stable oil makes the resulting blend more suitable for use in frying. A new method of quality assessment of vegetable oils used in frying is presented in this paper. In this method, ultra-fast gas chromatography coupled with flame ionization detector and chemometrics is employed. Principal component analysis was used for data processing. The results obtained with this method were compared with the results of the Rancimat test and sensory evaluation. It is demonstrated that the addition of olive oil improves the stability of rapeseed oil, and also changes its flavour and aroma profile. In addition, it was found that ultra-fast GC coupled with chemometrics is an effective tool for the assessment of the quality of edible oils. The proposed method does not require sample preparation, and the total time of analysis is less than 2 min.
| S-EPMC5541118 | biostudies-other
Project description:Background:Volatile organic compounds (VOC) are end products of human metabolism (normal and disease-associated) that can be mainly excreted in breath, urine, and feces. Therefore, VOC can be very useful as markers of diseases and helpful for clinicians since its sampling is noninvasive, inexpensive, and painless. Electronic noses, or eNoses, provide an easy and inexpensive way to analyze gas samples. Thus, this device may be used for diagnosis, monitoring or phenotyping diseases according to specific breathprints (breath profile). Objective:In this review, we summarize data showing the ability of eNose to be used as a noninvasive tool to improve diagnosis in clinical settings. Methods:A PRISMA-oriented search was performed in PubMed and Cochrane Library. Only studies performed in humans and published since 2000 were included. Results:A total of 48 original articles, 21 reviews, and 7 other documents were eligible and fully analyzed. The quality assessment of the selected studies was conducted according to the Standards for Reporting of Diagnostic Accuracy. Airway obstructive diseases were the most studied and Cyranose 320 was the most used eNose. Conclusions:Several case-control studies were performed to test this technology in diverse fields. More than a half of the selected studies showed good accuracy. However, there are some limitations regarding sampling methodology, analysis, reproducibility, and external validation that need to be standardized. Additionally, it is urgent to test this technology in intend-to-treat populations. Thus, it is possible to think in the contribution of VOC analysis by eNoses in a clinical setting.
| S-EPMC6924976 | biostudies-literature
Project description:Malignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell's metabolism.The goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose).Breath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose.Using GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively.This study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening.
| S-EPMC5710949 | biostudies-literature
Project description:Aroma is a crucial factor determining the market value and consumer satisfaction of fresh oriental melon. However, few studies focus on the volatile flavor of fresh oriental melon, and the effect of forchlorfenuron application on the aroma profile is unclear. This study characterized the volatile profile of fresh oriental melon fruit after forchlorfenuron application by E-nose and HS-GC-IMS. The holistic variation of volatile compounds exhibited evident distinction based on linear discriminant analysis (LDA) with E-nose. Forty-eight volatile compounds were identified from fresh oriental melon via GC-IMS, mainly esters, alcohols, aldehydes, and ketones, along with smaller quantities of sulfides and terpenes. Compared to pollination melon fruits, 13 critical different volatile flavor compounds were screened out in forchlorfenuron application groups by the PLS-DA model, imparting sweet fruity flavor. The results of the current study provide a valuable basis for evaluating the flavor quality of oriental melon after forchlorfenuron treatment.
| S-EPMC10048207 | biostudies-literature
Project description:Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.
| S-EPMC6891334 | biostudies-literature
Project description:There is an urgent need to have reliable technologies to diagnose post-coronavirus disease syndrome (PCS), as the number of people affected by COVID-19 and related complications is increasing worldwide. Considering the amount of risks associated with the two chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), there is an immediate requirement for a screening method for PCS, which also produce symptoms similar to these conditions, especially since very often, many COVID-19 cases remain undetected because a good share of such patients is asymptomatic. Breath analysis techniques are getting attention since they are highly non-invasive methods for disease diagnosis, can be implemented easily for point-of-care applications even in primary health care centres. Electronic (E-) nose technology is coming up with better reliability, ease of operation, and affordability to all, and it can generate signatures of volatile organic compounds (VOCs) in exhaled breath as markers of diseases. The present report is an outcome of a pilot study using an E-nose device on breath samples of cohorts of PCS, asthma, and normal (control) subjects. Match/no-match and k-NN analysis tests have been carried out to confirm the diagnosis of PCS. The prediction model has given 100% sensitivity and specificity. Receiver operating characteristics (ROC) has been plotted for the prediction model, and the area under the curve (AUC) is obtained as 1. The E-nose technique is found to be working well for PCS diagnosis. Our study suggests that the breath analysis using E-nose can be used as a point-of-care diagnosis of PCS.Trial registrationBreath samples were collected from the Kasturba Hospital, Manipal. Ethical clearance was obtained from the Institutional Ethics Committee, Kasturba Medical College, Manipal (IEC 60/2021, 13/01/2021) and Indian Council of Medical Research (ICMR) (CTRI/2021/02/031357, 06/02/2021) Government of India; trials were prospectively registered.
| S-EPMC8930465 | biostudies-literature
Project description:Modern solid-state gas sensors approaching ppb-level limit of detection open new perspectives for process control, environmental monitoring and exhaled breath analysis. Organic field-effect transistors (OFETs) are especially promising for gas sensing due to their outstanding sensitivities, low cost and small power consumption. However, they suffer of poor selectivity, requiring development of cross-selective arrays to distinguish analytes, and environmental instability, especially in humid air. Here we present the first fully integrated OFET-based electronic nose with the whole sensor array located on a single substrate. It features down to 30 ppb limit of detection provided by monolayer thick active layers and operates in air with up to 95% relative humidity. By means of principal component analysis, it is able to discriminate toxic air pollutants and monitor meat product freshness. The approach presented paves the way for developing affordable air sensing networks for the Internet of Things.
| S-EPMC8140082 | biostudies-literature
Project description:RationaleA proof of concept showing GC-MS/MS analysis time for pesticides can be dramatically reduced while maintaining a similar separation efficiency by combining a low-pressure gas chromatography (LPGC) column with the enhanced selected reaction monitoring (SRM) switching speed of the short collision cell of a JEOL JMS-TQ4000GC.MethodsTriple-quadrupole tandem mass spectrometry (standard EI + at 70 eV) was used to measure pesticides eluting from a low-pressure gas chromatograph capillary column. Three transitions for each of 244 pesticide compounds were measured within an 11-min analysis time, and the data were checked to confirm the method's reproducibility and ability to distinguish all three transitions for each pesticide.ResultsAll three transitions for all 244 pesticides were detected in the standard mixture at 1X concentration within the 11-min analysis time. Relative standard deviation (RSD) of peak areas was less than 15% for 242 pesticides, and I/Q RSDs were less than 10% for 242 compounds. Retention time RSD over 15 replicates was less than 0.1%.ConclusionsResults show that analysis time can be markedly decreased using an LPGC column, and that the ability of the short collision cell to distinguish a large number of coeluting peaks makes the two technologies a natural pairing. The effective measurement of pesticides within a short time could benefit any scientists doing pesticide analysis work.
| S-EPMC9286808 | biostudies-literature
Project description:Chinese jasmine tea is a type of flower-scented tea, which is produced by mixing green tea with the Jasminumsambac flower repeatedly. Both the total amount and composition of volatiles absorbed from the Jasminum sambac flower are mostly responsible for its sensory quality grade. This study aims to compare volatile organic compound (VOC) differences in authoritative jasmine tea grade samples. Automatic thermal desorption-gas-chromatography-mass spectrometry (ATD-GC-MS) and electronic nose (E-nose), followed by multivariate data analysis is conducted. Consequently, specific VOCs with a positive or negative correlation to the grades are screened out. Partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) show a satisfactory discriminant effect on rank. It is intriguing to find that the E-nose is good at distinguishing the grade difference caused by VOC concentrations but is deficient in identifying essential aromas that attribute to the unique characteristics of excellent grade jasmine tea.
| S-EPMC7024305 | biostudies-literature
Project description:The aim of this study is to determine the performance of a lab-made electronic nose (e-nose) composed of an array of metal oxide semiconductor (MOS) gas sensors in the detection and differentiation of Listeria monocytogenes (L. monocytogenes) and Bacillus cereus (B. cereus) incubated in trypticsoy broth (TSB) media. Conventionally, the detection of L. monocytogenes and B. cereus is often performed by enzyme link immunosorbent assay (ELISA) and polymerase chain reaction (PCR). These techniques require trained operators and expert, expensive reagents and specific containment. In this study, three types of samples, namely, TSB media, L. monocytogenes (serotype 4b American Type Culture Collection (ATCC) 13792), and B. cereus (ATCC) 10876, were used for this experiment. Prior to measurement using the e-nose, each bacterium was inoculated in TSB at 1 × 103-104 CFU/mL, followed by incubation for 48 h. To evaluate the performance of the e-nose, the measured data were then analyzed with chemometric models, namely linear and quadratic discriminant analysis (LDA and QDA), and support vector machine (SVM). As a result, the e-nose coupled with SVM showeda high accuracy of 98% in discriminating between TSB media and L. monocytogenes, and between TSB media and B. cereus. It could be concluded that the lab-made e-nose is able to detect rapidly the presence of bacteria L. monocytogenes and B. cereus on TSB media. For the future, it could be used to identify the presence of L. monocytogenes or B. cereus contamination in the routine and fast assessment of food products in animal quarantine.
| S-EPMC7158669 | biostudies-literature