Detection of soybean oil adulteration in cow ghee ( clarified milk fat ): An ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics

2022 ◽  
Author(s):  
Mrinmoy Roy ◽  
Manoj Doddappa ◽  
Binod K Yadav ◽  
Rangarajan Jaganmohan ◽  
Vadakkepulppara R N Sinija ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Soybean Oil ◽  
Electronic Nose ◽  
Milk Fat

Fatty acid profile of milk from Nordestina donkey breed raised on Caatinga pasture

2021 ◽  
pp. 1-5
Author(s):  
Tayanna Bernardo Oliveira Nunes Messias ◽  
Susana Paula Alves ◽  
Rui José Branquinho Bessa ◽  
Marta Suely Madruga ◽  
Maria Teresa Bertoldo Pacheco ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Gas Chromatography ◽  
Milk Fat ◽  
Fat Content ◽  
Donkey Milk ◽  
Fat Percentage ◽  
Mid Infrared ◽  
Milk Samples ◽  
The Third ◽  
Commercial Viability

Abstract In this research communication we describe the composition of fatty acids (FA) present in the milk of the Nordestina donkey breed, and how they differ during lactation. Milk samples were taken from 24 multiparous lactating Nordestina donkeys that grazed the Caatinga, comprising 5 animals at each of around 30, 60 and 90 d in milk (DIM) and a further 9 animals ranging from 120 to 180 DIM. The milk fat content was analysed by mid infrared spectroscopy and the FA profile by gas chromatography. The milk fat percentage ranged from 0.45 to 0.61%. The main FA found in milk were 16:0 and 18:1c9. These did not differ among DIM classes and comprised 23% and 25% of total FA. Notably, the α-Linolenic acid (18:3 n-3) was the third most abundant FA and differed (P < 0.05) with DIM, being lowest in the 30 and 60 DIM samples (around 10.7% of total FA) and highest in the 60 and 90 DIM classes (around 14.6% of total FA). The low-fat content and the FA profile of the donkey milk gives it potential as a functional ingredient, which could help to preserve the commercial viability of the Nordestina donkey breed.

scholarly journals Chemosensory Device Assisted-Estimation of the Quality of Edible Oils with Repetitive Frying

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 972
Author(s):  
Jookyeong Lee ◽  
Changguk Boo ◽  
Seong-jun Hong ◽  
Eui-Cheol Shin
Keyword(s):  
Soybean Oil ◽  
Electronic Nose ◽  
Canola Oil ◽  
Fast Food ◽  
Food Industry ◽  
Multivariate Analyses ◽  
Ethyl Butyrate ◽  
Chemical Parameters ◽  
Positive Correlations

This study investigated chemosensory degradations of soybean and canola oils with repeated frying in order to estimate the quality of the oils. Methods: Chemical parameters including oxygen induction time, acid value, p-anisidine value, malondialdehyde, and total polar compounds were measured. Electronic nose and electronic tongue analyses were performed to assess sensory properties. Multivariate analyses were employed to investigate relationships among tastes and volatile compounds using principal component analysis (PCA) and Pearson’s correlation analysis. Results: All chemical parameters increased with repeated frying in both oils. Electronic nose analysis found ethyl butyrate, 2-heptenal, and 2,4-pentanedione as major volatiles for soybean oil and ethyl butyrate and linalool for canola oil. As the numbers of frying increased, all volatiles showed an increased concentration in various extents. In multivariate analyses, ethyl butyrate revealed strong positive correlations with sourness, umami, and sweetness, and umami showed strong positive correlations with sourness and saltiness (p < 0.05). PCA confirmed that in PC1 with 49% variance, sourness, saltiness, and umami were at similar rates while acetyl pyrazine, 2,4-pentadieone, and 1-octanol were found at similar rates. Canola oil was chemically more stable and less susceptible to deterioration in all chemical parameters compared to soybean oil, resulting in a relatively better quality oil when repeatedly fried. Conclusion: The results suggested that minimum repeated frying (5 times) degrades chemosensory characteristics of both oils, thereby compromising their quality. The findings of this study will be utilized as a foundation for quality control of fried foods in food industry, fried food development, and fast-food industry.

Rapid Measuring Flavor Quality Changes of Frying Rapeseed Oils using a Flash Gas Chromatography Electronic Nose

2019 ◽  
Vol 121 (5) ◽  
pp. 1800260 ◽  
Author(s):  
Lirong Xu ◽  
Xu Li ◽  
Jianhua Huang ◽  
Pan Gao ◽  
Qingzhe Jin ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Electronic Nose ◽  
Quality Changes ◽  
Flavor Quality

scholarly journals Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 286
Author(s):  
Thijs T. Wingelaar ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Pieter-Jan A.M. van Ooij ◽  
Rigo Hoencamp ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Oxygen Toxicity ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Exhaled Breath ◽  
Chromatography Mass Spectrometry

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

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