Oil Quality Analysis Research Articles

Comparative analysis of edible oil quality in fish nugget frying: Vacuum vs. atmospheric conditions

Consumer awareness about safe and nutritional fried food leads to the development of vacuum frying which overcomes major limitations of traditional frying. In this study effect of vacuum (120◦C, 40kPa) and atmospheric frying (180°C) on the fried fish nuggets quality and frying oil was compared. Oils used for frying (soyabean and mustard oil) were repeatedly used for 25 h without any replenishment. Vacuum frying (VF) oils and fish nuggets show a slower degradation of polyunstaurated fatty acids (PUFA), lesser transfatty acid (TFA) formation and a low C18:2/C16:0 ratio as compared to atmospheric frying (AF). Soyabean oil used for vacuum frying fish nuggets show an increase from 2.14 % to 3.11 %, decrease from 45.25 % to 38.29 %, while as AF fish nuggets show an increase from 2.14 % to 5.48 % and decrease from 45.25 % to 22.19 % in TFA content and PUFA content respectively after 25 h of frying. Oil uptake was also found lower (34.89 %) in VF fish nuggets as compared to AF(37.45 %), while as water loss was higher in AF chips than VF nuggets by 1.42-fold after 10 minutes of frying. Sensory analysis revealed that VF fish nuggets show a higher retention of sensory attributes and overall acceptability but they were less crispy then AF fish nuggets. Overall, vacuum frying results in the production of safe and nutritious fried fish nuggets.

Evaluation of bioactive metabolites, quality analysis and antimicrobial efficacy of essential oil of offered leaves of Aegle marmelos collected from different places of worship

Evaluation of bioactive metabolites, quality analysis and antimicrobial efficacy of essential oil of offered leaves of Aegle marmelos collected from different places of worship

Comparative analysis of groundnut oil quality in the north-central zone of Nigeria: Determination and evaluation of heavy metals, fatty acids, Phospholipids, and iodine values in groundnut oil

The research presents a comparative analysis of the quality of locally produced groundnut oil (Arachis hypogaea) sold in the north-central zone of Nigeria markets (Benue, Nasarawa, Kogi, Kwara, Niger, Plateau States). The aim was to assess and compare the qualities of the oils and to know the safety of human consumption. The groundnut oil produced biodiesel, shampoo lubricants, and soap-making industries. The concentrations of the heavy metals were analyzed with atomic absorption spectrometry (AAS). It showed that the lead, zinc, and copper (Pb, Zn, Cu) were within the FAO/WHO recommended limit, while Cd (0.201-0.331 mg kg-1) was above the limit (0.07 mg kg-1). Also, the gas chromatography (GC-FID) results indicated that twelve fatty acids (linoleic > oleic > palmitic > stearic >lignoceric > arachidic acid > behenic > erucic> arachidonic > margaric > linolenic > palmitoleic) were obtained in the groundnut oils in all markets and fatty acids include caprylic acid, capric acid, lauric acid, and myristic acid were absent in oils. In addition, the magnitude of six Phospholipids (phosphatidylcholine > phosphatidylethanolamine> phosphatidylinositol > phosphatidylserine > phosphatidic acid > lysophosphatidylcholine) were also achieved, respectively. The iodine were below the FAO/WHO and the acid value was higher than the normal range.

The effects of superheated steam roasting on proximate analysis, antioxidant activity, and oil quality of black seed (Nigella sativa).

Nigella sativa, commonly known as the black seed, is a culinary spice therapeutic against many ailments. Common preparation practice of roasting or heating the seeds often deteriorates bioactive compounds, which can be remedied with superheated steam (SHS). With roasting temperatures of 150, 200, and 250°C and roasting times of 10, 15, and 20 min, convection and SHS roasting media were tested, and their effects on proximate analysis, antioxidant assays, and oil quality were evaluated. For proximate content, moisture significantly decreased from 9.08% in unroasted seeds to 4.18%-1.04% in roasted seeds, while fat increased to as high as 44.76% from 32.87% in unroasted seeds. Roasting only slightly increased ash content and had no significant impact on protein and carbohydrate content. SHS roasted black seeds had better DPPH (2,2-Diphenyl-1-picrylhydrazyl) radical scavenging capacity (RSC) than convection roasted seeds. DPPH RSC decreased with elevated roasting time and temperature, conversely related to total phenolic content, which increased with increased roasting time and temperature. Oil of roasted seeds developed an increasingly intense brown color from an initial light, yellow, unroasted oil with better extraction efficiency in SHS roasting. For oil quality analysis, free fatty acid values were significantly lower in both roasted samples. Peroxide value was initially recorded at 84 in convection and 48 (meq O2/kg of oil) in SHS roasted samples. In contrast, p-anisidine values were initially recorded at 28.36 in convection roasted samples compared to 23.73 in SHS roasted samples. Based on all quality analyses, SHS showed better potential in black seed quality preservation.

Quality analysis of oil recovered from used locomotive engine oil using ultrasound-assisted solvent extraction

Herein, we present a process for recovering used locomotive engine oil using ultrasound-assisted extraction and sustainable organic solvents. We evaluated the quality of the oil recovered from used locomotive engine oil through ultrasound-assisted extraction or mechanical stirring. The extraction process involved the direct application of ultrasound (25 °C, 24 kHz, amplitude 20%) or mechanical stirring (225 rpm, 25 °C) to a mixture of used locomotive engine oil and alcoholic solvents (ethanol, propan-2-ol, 2-methlypropan-1-ol, and butan-1-ol) for 0.5, 1, and 5 min. With ultrasound (5 min), the yield of butan-1-ol was the highest (91.4 wt%), followed by that of 2-methylpropan-1-ol (79.4 wt%), propan-2-ol (12.1 wt%), and ethanol (3.6 wt%). With mechanical stirring (5 min), the yield of butan-1-ol was the highest (88.3 wt%), followed by that of 2-methylpropan-1-ol (59.8 wt%), propan-2-ol (9.54 wt%), and ethanol (2.3 wt%). The kinematic viscosities of the recovered oils extracted with 2-methylpropan-1-ol and butan-1-ol were 126.0 and 132.7 mm2 s−1, respectively, which were close to the kinematic viscosity of fresh locomotive engine oil (150.6 mm2 s−1). The recovered oil had decreased Ca, Cr, Fe, Mo, and Na concentrations. The proposed recovery is a promising approach for the support and intensification of used locomotive engine oil treatment.

Jenis dan Konsentrasi Tanah Pengelantang untuk Menurunkan Prekursor Senyawa 3-MCPDE dan GE pada Pemucatan CPO

Refined palm oil contains contaminants such as 3-monochloropropane diol ester (3-MCPDE) and glycidyl ester (GE) which are formed from diacylglycerol (DAG) and chloride (Cl) as precursors during processing. There is a need to control the levels of precursors especially during the bleaching stage using selected bleaching earth (BE). Reducing the number of precursors after the bleaching stage will minimize the formation of 3-MCPDE and GE in bleached palm oil (BPO) for the next refinery stage. The research consisted of three stages: (1) characterization of raw materials consisting of CPO, BE types 1, 2, 3, 4, and 5; (2) study on the effect of BE type; and (3) study on the concentration effect of selected BE for reducing DAG and Cl by chemical analysis of oil quality. The results showed that degummed palm oil (DPO) contained DAG of 6.70%, free fatty acid 5.66%, carotene 424.31 mg/kg, P 20.30 mg/kg, and Fe <0.021 mg/kg. The five types of BE have different physicochemical properties on which the effect will be seen on BPO. The second stage results showed that BE-4 was selected as the most potential BE. Using BE-4 of 2.5% was proven to reduce total Cl as 3-MCPDE precursors in the resulted BPO sample. The Cl level decreased to 3.05 mg/kg with the largest decrease percentage of 55.80% compared to DPO sample. BE-4 concentration of 2.5% has been able to withstand and prevent the increase of DAG as a precursor to GE contaminants during bleaching process.
 
 Keywords: 3-monokloropropanadiol ester, chloride, diacylglycerol, glycidyl ester, palm oil

Quality Analysis of Canola and Mustard Oil Using Fluorescence Spectroscopy.

The potential of Fluorescence spectroscopy has been utilized for the quality analysis of canola and mustard oil along with the effect of heating on their molecular composition has been investigated. Laser diode at 405nm has been employed directly to oil surface to excite both oil type samples and their emission spectra has been recorded by an in-house developed Fluorosensor. The emission spectra of both oil types unveiled that they contain carotenoids, isomers of vitamin E and chlorophylls that exhibit their fluorescence at 525 and 675/720nm, and these can be used as markers for their quality assurance. Fluorescence spectroscopy is a fast, reliable and non-destructive analytical technique for the quality assessment of both oil types. Moreover, the effect of temperature on their molecular composition has been investigated by heating them at 110, 120, 130, 140, 150, 170, 180 and 200°C, each sample for 30min which was done because both oils are used for cooking and frying. On heating, the deterioration of carotenoids and isomers of vitamin E in both oil types occurred with an increase in the oxidised products. However, it was found that up to 150°C, both oil types can be used safely for cooking/frying purpose where they do not lose much of their valuable ingredients and up to 180°C for deep frying, both oils can be used with less deterioration and after that both deteriorated much due to rapid increase of the oxidized products. The portable Fluorosensor, therefore, proved as an excellent device for quality screening of edible oils based on carotenoids and vitamin E.

Applied Complex Diagnostics and Monitoring of Special Power Transformers

As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.

Endangered Curcuma caesia Roxb.: Qualitative and quantitative analysis for identification of industrially important elite genotypes

Black turmeric (Curcuma caesia Roxb.) of Zingiberaceae family is an industrially important critically endangered herb, found to have a captivating influence in the realm of traditional medicine. Now that this species is enlisted under critically endangered category and the repercussion of its unavailability in the terrestrial flora exempts us from scientifically exploring its potential biochemical constituents in terms of pharmaceutical and other industrial uses. So, in the present study, the industrially important qualitative and quantitative traits of black turmeric (n = 50) from different climatic zones were studied to select elite genotypes with respect to high anthocyanin and essential oil content. The genotype of Koraput was found to yield high anthocyanin content (7.14%) and high leaf oil content (1.25%) while genotype of Deoghati yields high rhizome oil content (2.55%). The quality analysis of essential oil by Gas Chromatography-Mass Spectroscopy showed the presence of eucalyptol and camphor as major compounds in leaf and rhizome oils respectively. The selected elite genotypes mostly belong to North Western Plateau, North Eastern Ghat, Eastern Ghat High Land, and South Eastern Ghat agroclimatic zones. Further, 5 elite genotypes from 4 different agroclimatic zones were recommended as industrially important high yielding traits (Anthocyanin > 6%, leaf oil ≥ 1.2%, rhizome oil >1.5%, eucalyptol >20%, and camphor >21%.) and found to be most favourable for the cultivation.

Assessment of Production Rate and Quality Analysis of Essential Oil of Clove Oil Obtained from Hydro-Distillation of Clove Leaves

Clove is one of the leading and potential commodities of the plantation in Madigondo. Currently, the community earns the economic advantage of clove plantation only from the buds. However, other parts of the clove plant have varied potential utilization. For instance, the abundant clove leaves are a valuable and potential source of essential oil. The community considered clove leaves as plantation waste due to a lack of knowledge on further clove leaves processing. The isolation of essential oil from clove leaves can be conducted by applying hydrodistillation. In this research, the hydrodistillation of clove leaves was conducted to characterize the essential oil of clove leaves as well as to investigate the production potency of clove leaves essential oil. The research was also conducted to determine the production rate and capacity of the hydrodistillation of clove leaves. The research shows that the optimum duration of the clove leaves hydrodistillation is 90 minutes. The yield of clove leaves hydrodistillation is 5.4%. The obtained essential oil of clove leaves has to have a density of 1.019 gram/mL and a typical odor of clove oil with a bright and yellow color. Those criteria meet the SNI 06-2387-2006 for oil of clove leaves.

Analisis Minyak Atsiri Daun Cengkeh (Syzygium aromaticum L.) Dari Tempat Tumbuh Dengan Ketinggian Yang Berbeda

ABSTRACT
 A research was carried out to determine the yield, content of constituent compounds, and analysis of the quality of clove leaf essential oil (Syzygium aromaticum L.) at different altitudes. The study used dry clove leaves obtained from Buleleng Regency, Bali Province with an altitude of 300 meters above sea level (masl), 600 masl, and 900 masl. Extraction of essential oils was carried out by steam distillation. The essential oil obtained was analyzed using Gas Chromatography-Mass Spectrometry (GC-MS) and the quality was tested using the quality requirements of SNI 06-2387-2006. Parameters observed were yield, identification of constituent compounds, analysis of essential oil quality including color, odor, specific gravity, and refractive index, solubility in alcohol, optical rotation, total eugenol and caryophyllene. The results showed that clove leaf essential oil in the lowlands (300 masl) had a higher yield of 2.05% and more types of active compounds than in the optimal altitude (600 masl) which was 1.45% and the highlands (900 masl) was 1.85%. The main compounds that make up clove leaf essential oil were eugenol, caryophyllene, and humulene. Other compounds were longifoline, nootkatone, naphthalene, citronellal, and cyclohexene. The clove leaf essential oil from the three heights met the quality requirements of SNI 06-2387-2006.
 Keywords: Essential oil, cloves, eugenol, GC-MS, secondary metabolites

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

This work investigates the state distributions of failure rate, performance curve, maintenance cost and preventive frequency of the transformer population through the Markov Model (MM). The condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age were analyzed from 370 distribution transformers. This work utilized the computed failure rate prediction model of the transformer population based on MM using the nonlinear minimization technique. First, the transition probabilities for each state were adjusted based on pre-determined maintenance repair rates of 10%, 20%, and 30%. Next, the failure rate state distributions and performance curves at various states were analyzed. Finally, the maintenance costs and preventive maintenance frequency were estimated utilizing the proposed maintenance policy models and the failure rate state probabilities. The result reveals that the transition from state 2 to state 1 with a 30% pre-determined maintenance repair rate can provide an average reduction of failure rate up to 11%. Based on the failure rate state probability, an average increment of maintenance cost from RM 18.32 million to RM 251.87 million will be incurred over 30 years. In total, 85% of the transformer population must undergo maintenance every nine months to avoid reaching very poor conditions.

1H NMR and chemometric methods in verification of hemp-seed oil quality

Hemp oil is widely used in the food industry, and also in many other industries. It is characterized by a high content of unsaturated fatty acids with the optimal ratio of ω-6 to ω-3 fatty acids (3:1). Such composition means that this oil is often used in the food, pharmaceutical and cosmetic industries. Due to the popularity of hemp oil and its high content of unsaturated fatty acids, methods for the quick oil quality analysis are sought. Six samples of cold-pressed hemp oil were stored under different conditions (2–8 °C and 30 °C) for one month and then their 1H NMR spectra were recorded. The chemometric methods of PCA and OPLS-DA were used to perform the analysis of the spectra. The combination of those methods made it possible to observe the differences in the oil samples after a month of storage. Based on the 1H NMR spectra, it was possible to differentiate the time and conditions of the oil storage.

Computational studies on physico-chemical properties in the quality analysis of corn and peanut oil

Oils are commonly used in cooking as a frying medium which has been constantly subjected to different levels of heating. In this work, we have considered the most commonly used oils namely peanut oil and corn oil. Quality analyses of corn and peanut oils were made by relating macroscopic properties (ultrasonic velocity, viscosity, and density) to microscopic parameters (intermolecular free length, adiabatic compressibility etc.,) by subjecting them to six cycles of heating (190 ˚C). Variation in the mentioned property indexes, the degree of degradation and reusability for the next heating cycle that could be used in the food industry and processing were monitored. Using Newton-Laplace and Wood’s equation, the adiabatic compressibility, acoustic impedance, and intermolecular free length of the oil were estimated from the experimental data. Ultrasonic velocity was observed linearly as related to viscosity with the dependency factor (R2 = 0.932). With the aid of experiential data, the physical thermodynamic parameters, particularly particle size, packing factor, chemical potential, and L-J potential were computed. A high correlation factor was observed by fitting ultrasonic velocity, viscosity, and density to Parthasarathy and Bakshi, and Rodenbush equations. In the study, ultrasonic velocity, a macroscopic parameter, could be decoded to determine the microscopic variations in oil subjected to different temperatures in an industrial application.

Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.

Failure Rate Estimation for Transformer Population based on Health Index through Markov Model Approach

This work examines the failure rate of the transformer population through the application of the Markov Model (MM) and Health Index (HI). Overall, the condition parameters data extracted from 3,192 oil samples were analysed in this study. The samples were from 370 transformers with the age range between 1 and 25 years. First, both HIs and failure rates of transformers were determined based on the condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age. A two-parameter exponential function model was applied to represent the relationship between the HI and failure rate. Once the failure rate state was obtained, the non-linear optimisation was used to determine the transition probability for each age band. Next, the future failure rate of the transformer population was computed through the MM prediction model. The goodness-of-fit test and Mean Absolute Percentage Error (MAPE) were utilised to determine the performance of the predicted failure rate. The current study reveals that the future state of the transformer population and failure rate could be predicted through MM based on updated transition probabilities. It is observed that the MAPE between predicted and computed failure rates is 7.3%.

Submersible High Sensitivity Microwave Sensor for Edible Oil Detection and Quality Analysis

A high sensitivity complementary multiple split ring resonator (CMSRR) based submersible microwave sensor for edible oil detection and quality analysis is proposed. The main part of the sensor is the CMSRR loaded flared microstrip patch, and the proposed single-port structure is quite attractive for liquid detection. The proposed sensor was fabricated and measured to operate at 8.49 GHz with very high Q factor, which agrees very well with the simulated results obtained by High Frequency Structure Simulator (HFSS). A strong electric field can be observed along the sides of CMSRR at resonance, creating a sensing region sensitive to changes in the nearby dielectric material. By simply immersing the sensor into some familiar oil samples and heated peanut oil, the device’s resonance and peak attenuation change greatly. The measured results have proven the improved sensitivity of the proposed sensor on the liquid samples compared with the methods described in the literature. The complex permittivity of the oil samples can be estimated by establishing an approximate model in HFSS. Two empirical formulas were built and fitted to estimate the complex permittivity of unknown several oil samples and heated peanut oil samples. The estimated dielectric constants agree well with the existing data. The estimated error is within 1.4% and the measured sensitivity is up to 7.25%. The proposed sensor has advantages, being compact, inexpensive, reliable, easy to operate, and highly sensitive, and has potential applications in oil detection and quality analysis.

Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data.

Near-infrared prediction of edible oil frying times based on Bayesian Ridge Regression

In the detection and analysis of edible oil quality, Total Polar Compound (TPC) has been widely used as an evaluation index of frying oil quality. Its content has been shown to have a linear relationship with the number of frying times. The more frying times, the higher the TPC content in the oil, the worse the quality of the oil. In this study, a Bayesian Ridge Regression (BRR) model is established to detect the frying times of soybean oil, peanut oil, and rapeseed oil based on near-infrared spectroscopy (NIRS) detection techniques. The study collects a total of 450 spectral signals from soybean oil, peanut oil and rapeseed oil. Then partial least regression (PLS), Support Vector Regression (SVR) and BRR prediction models are established to predict frying times of the three frying oils and furthermore to reflect the quality of frying oils. The results show that under the full spectra, the SVR and BBR prediction regression models established by the original spectra and the standard normalized variable (SNV) processed spectra can predict the frying times better, while the PLSR model cannot effectively predict the frying times in all cases. After selecting the characteristic wavelength through UFS, the BRR prediction model works best. The coefficient of determination (R2) and the root mean square error of prediction (RMSEP) of the model are 0.9852, 0.5463, respectively. In summary, the method of this study can effectively predict the number of frying times and provide an effective means for the rapid detection of frying oil quality.

Correlation between Chemical Analysis and Sensory Committee Tests with Total Fluorescence Characteristics of Jordanian Olive Oil

Olive oil samples extracted from olive trees grown in different un-irrigated areas of Jordan were classified according to their quality using three different techniques. Two of the techniques; namely, chemical analysis and sensory committee tests, are recognized by the International Olive Oil Council (IOC) and considered as classical techniques. The results were compared with those of the total fluorescence spectroscopy that has been recently adopted for olive oil quality analysis. Nine different olive oil samples were subjected to the analysis by the above techniques. The analysis for each sample was repeated twice to ensure consistency of the results. Chemical analysis was used to determine their acidities, peroxide values, ∆K’s and polyphenol content parameters. The results of chemical analysis indicated that eight of the samples could be classified as EVOO and one as VOO. Sensory tests indicated that four samples could be classified as EVOO and five as VOO. Total fluorescence spectra results revealed that six samples are EVOO and three samples are VOO. The correlation of fluorescence results to chemical and sensory results indicates the ability of fluorescence technique as a powerful tool for classification of olive oil. Reference samples that were already classified by IOC were tested to ensure the accuracy of the fluorescence technique in recognizing EVOO from other types of olive oil. Moreover, fluorescence analysis proved to be fast and consumes minute amounts of samples and chemicals, in contrary to chemical analysis which is known to be time and chemicals-consuming.