Unknown Oil Samples Research Articles

A kind of multi-dot ensemble regression AI detector for lubricating oil additive content based on lambert-beer law

In this work, we propose a Multi-dot Ensemble Regression AI detector (MER) based on the Lambert-Beer law. We pre-trained a model using the infrared spectral data of target additives collected in advance to detect the target additives in unknown oil samples. The algorithm's feasibility was validated by assessing the content of additives in a series of simulated commercial oil samples that were not part of the training set. We established models for three common lubricating oil additives (anti-friction, anti-wear, and antioxidant agents), demonstrating their effectiveness in oil sample detection. Additionally, by comparing with other algorithms, we established the superiority of MER in small-sample learning scenarios.

Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils.

The peroxide value of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine peroxide values using field portable and process instrumentation; those efforts presented "best-case scenarios" with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique or combination thereof is best for predicting peroxide values. Following peroxide value assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils.

Characterization and authentication of olive, camellia and other vegetable oils by combination of chromatographic and chemometric techniques: role of fatty acids, tocopherols, sterols and squalene

Fatty acids, tocopherols, sterols and squalene were analyzed by chromatographic-based techniques and were selected as variables to build a variety of classification models for the accurate characterization and authentication of olive, camellia oil and six other vegetable oils (soybean, corn, rapeseed, peanut, palm and sunflower). Different unsupervised and supervised chemometrics techniques, such as principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA), have been applied. In addition, the Kennard–Stone algorithm was used to select the training samples for the construction of supervised models. The discriminating power of different components was compared, and the results suggested that fatty acids are the most powerful in distinguishing vegetable oils, followed by tocopherols and sterols, and squalene contributed to the discrimination between olive and camellia oils despite their apparent similarities. This proposed method was straightforward and can be easily implemented to identify unknown oil samples.

EXPRESS: Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils.

The peroxide value (PV) of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine PVs using field portable and process instrumentation; those efforts presented âbest-caseâ scenarios with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique, or combination thereof, is best for predicting PVs. Following PV assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression (PLSR) calibration models to predict the PV of unknown oil samples. A global PV model based on near-infrared (8 mm optical path length â OPL) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm OPL near infrared (5.1), Raman (6.9) and 50 μm OPL mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global PV models based on low-level fusion of the NIR (8 and 24 mm OPL) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes.

Multiwavelength laser induced fluorescence (LIF) LIDAR system for remote detection and identification of oil spills

A multiwavelength laser induced fluorescence (LIF) LIDAR system, which can be employed around the clock for detecting oil spills in a marine environment, has been developed. The system incorporates a pulsed Nd: YAG laser, Nd: YAG laser amplifier and two KD*P crystals for generating 266 nm, 355 nm and 532 nm. Additionally, the LIF LIDAR system incorporates a nitrogen laser pumped dye laser system operating at 428 nm (Stilbene 420 dye), a Newtonian telescope, motorized monochromator, a photomultiplier detection system and a data acquisition system. Fluorescence intensity measurements collected from fresh and weathered oil samples using the LIF LIDAR system are presented. Strong fluorescence spectra were detected from refined and crude oils when excited with 266 nm. Finally, a brief description for the principle components algorithm used for identifying and classifying unknown oil samples is provided.

ATR-FTIR spectroscopy and chemometric techniques for the identification of edible vegetable oils

There is a large variety and trademarks of vegetable oils in Bangladesh. The oils have characteristics very similar to each other and often cannot be classified by the observation of color, odor or taste. This paper proposes a vibrational spectroscopic method like FTIR in association with chemometric techniques to classify vegetable oils like: sunflower, mustard, sesame, soybean, castor, olive and palm oils from different manufacturers. In the FTIR spectra of oil, as information about fatty acid composition is concentrated in the range of 4400-200 cm-1 principal component analysis (PCA) was applied on the standardized full FTIR spectral data of this region for vegetable oils to totally capture the FTIR spectral pattern; seven varieties of vegetable oils could be successfully classified from their PCA scores. PCA of FTIR spectra of different known vegetable oils is used to determine the identity of several unknown vegetable oils. The unknowns are then analyzed, plotted, and identified based on their proximity to the known in principal component space. For the multivariate analysis PCA and soft independent modeling of class analogy (SIMCA) and support vector machines (SVMs) were used. 85% and 14% variability of data was explained by PC1 and PC2 respectively. PC1 has strong positive correlation with soybean, sunflower, palm and olive oil while strong negative correlation with mustard, castor and sesame oil. Soybean oils are positively and sesame oils are negatively correlated with PC2. Unknown oil samples can be identified properly by used supervised methods i.e. SIMCA, SVM by developing model with the help of PCA. The major interest of this method using chemometric analysis of spectral data is in their rapidity, since no chemical treatment of samples is required.Bangladesh J. Sci. Ind. Res. 50(4), 233-240, 2015

Discriminating crude oil grades using laser-induced breakdown spectroscopy

The analysis of crude oil using laser-based analytical techniques such as laser-induced breakdown spectroscopy (LIBS) has become of great interest to various specialists in different fields such as geology, petro-chemistry and environmental science. In this work, a detailed study is presented wherein the implementation of an efficient and simple LIBS technique to identify the elemental constituents of crude oil and to distinguish between different grades of petroleum crude oil is discussed.Laser-induced plasma (LIP) technique has been used in this work for direct measurements of atomic, ionic and molecular species in dry crude oil samples with API gravities ranging between 18 and 36. The technique was implemented using the first harmonic of a pulsed Nd-YAG laser source. Atomic and molecular emission bands were observed, consisting of characteristic spectral lines of atoms and diatomic molecular bands, namely from C, H, Si, Na, Ca, Mg, AL, Fe, Ti, Mo, C2 and CN. The intensities of high-resolution spectral lines for some atoms and molecules of elements such as Ca, Na, Fe, Mo, C2 and CN were evaluated at different wavelengths along the obtained spectra. The molecular bands and the elemental spectral lines were used to assess the possibility of adopting the LIBS technique in differentiating between crude oil samples with different American Petroleum Institute (API) gravity values. The results indicate the presence of a distinct correlation between the API gravity values of the various oil samples and the spectral line intensities of the elements and some molecular radical constituents. In addition, the possibility of identifying the API gravity values of unknown oil samples is also indicated.

Rapid quantification of polar compounds in thermo‐oxidized oils by HPTLC‐densitometry

The level of polar compounds is a good indicator of oil quality and it is the basis of the international regulations limiting the use of degraded frying fats/oils for human consumption. According to ISO 8420:2002 standard, polar compound content is determined by adsorption chromatography extraction followed by gravimetric quantification. This simple and accurate method is time‐consuming and considerable amounts of silica‐gel and solvents are needed. This study aims at developing a fast and reliable alternative method for polar compound quantification in frying oils, using HPTLC‐densitometry. This technique was tested for soybean, sunflower and peanut oils, heated at 180°C, and for frying oil samples. This technique proved to be fast, accurate and repeatable.Practical applications: The developed method is adequate for rapid screening of unknown oil samples, with respect to their level of polar compounds. Using HPTLC‐densitometry to evaluate polar compound content in oils, the number of samples to be analyzed by the ISO standard method may be considerably reduced to those having values close to the maximum allowed. Thus, the proposed methodology allows for a reduction in time and chemicals used, with economic and environmental benefits.HPTLC‐densitometry is an alternative technique to the standard method for polar compound quantification in heated/frying vegetable oils. The method developed is fast, accurate, and repeatable with economic and environmental benefits.

Identification of illegal cooking oils by using <sup>1</sup>H NMR fingerprints combined with multivariate analysis

Illegal cooking oil incident is a serious food safety problem happened in China recently. There are still no efficient methods for illegal cooking oil identification. In this paper, 60 kinds of oils were detected by ¹HNMR, and 12 identification indicators were found. Based on those 12 fingerprintings, 60 kinds of oils could be divided into 8 categories by using cluster analysis. According to the 8 categories, discriminant function was established. When the unknown sample’s ¹H NMR data were put into those functions, the discriminant analysis of the unknown samples could be done to identify the origin of the unknown oil samples. This method was applied twice in blind tests, and the results showed that the correct rate of the two blind tests were 91.9% and 93.8% respectively.

Quantitative determination of saturated and unsaturated fatty acids in edible oils by infrared spectroscopy and chemometrics

The fatty acids of eight edible oil samples with varying composition were saponified, esterified into methyl esters and analysed by gas chromatography. These composition values were used in calculating the composition of 25 syntheic edible oil mixtures containing a wide range of saturation and unsaturation. All these 33 oils were then analysed by infrared spectroscopy using single reflectance technique. The reflectance infrared profiles in 1/R format were calibrated against their saturated, unsaturated, 18:1 and 18:2 fatty acids composition using regions and the whole spectral profiles. Second derivated spectral profiles were also used in the calibration. The results show that the cross-validated calibration model established with second derivated data of the infrared spectral profiles in the 4000–2700 cm − 1 region against the GC evaluated unsaturated fatty acid composition predicted 5 unknown oil samples within an error limit of ± 2.5%. The same was the case with the unsaturated fatty acid composition against second derivated infrared profiles in the region 4000–2700 cm − 1 . Oleic acid and linoleic acid composition in the calibration oil samples were calibrated against the second derivated profiles of the infrared spectra in the region 4000–600 cm − 1 . The prediction values of the cross-validated calibration model for the unknown oil samples give oleic acid and linoleic acid composition in the oils with an error margin ± 3.1% and ± 4.1%, respectively. The calibration and prediction error includes the error in the quantitative determination of the fatty acids by gas chromatography.

Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Very Similar Chemical Composition

This paper describes a case study in which a multi-criterion approach was used to fingerprinting and identifying mystery oil samples. Three unknown oil samples were received from Quebec on March 28, 2001 for chemical analysis. The main purpose of this analysis was to determine the nature and the type of the products, detailed hydrocarbon composition of the samples, and whether these samples came from the same source. The samples were analyzed by gas chromatography with a flame ionization detector (GC-FID) and by gas chromatography coupled with mass spectrometry (GC-MS). Hydrocarbon distribution patterns of unknown oils were recognized. Multiple suites of analytes were quantified and compared. A variety of diagnostic ratios of “source-specific marker” compounds for interpreting chemical data were further determined and analyzed. The chemical fingerprinting results reveal the following: (1) These three oils are most likely a hydraulic-fluid type oil. (2) These three oils are very “pure”, largely composed of saturated hydrocarbons with the total aromatics being only 4–10% of the TPH. (3) The oils are a mixture of two different hydraulic fluids. There is no clear sign indicating they had been weathered. (4) The PAH concentrations are extremely low (<10μg/g oil) in the oil samples, while the biomarker concentration are unusually high (4700–5500μg/g oil). (5) Three major unknown compounds in the oil samples were positively identified. They are antioxidant compounds added to oils. (6) Samples 2996 and 2997 are identical and come from the same source. (7) The sample 2998 has group hydrocarbon compositions (including the GC traces, TPH, and total saturates) very similar to samples 2996 and 2997. But, it is not identical in chemical composition to samples 2996 and 2997, and they do not come from the same source.

Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Very Similar Chemical Composition

This paper describes a case study in which a multi-criterion approach was used to fingerprinting and identifying mystery oil samples. Three unknown oil samples were received from Quebec on March 28, 2001 for chemical analysis. The main purpose of this analysis was to detemine the nature and the type of the products, detailed hydrocarbon composition of the samples, and whether these samples came from the same source. The samples were analyzed by gas chromatography with a flame ionization detector (GC-FID) and by gas chromatography coupled with mass spectrometry (GC-MS). Hydrocarbon distribution patterns of unknown oils were recognized. Multiple suites of analytes were quantified and compared. A variety of diagnostic ratios of "source-specific marker" compounds for interpreting chemical data were further determined and analyzed. The chemical fingerprinting results reveal the following: (1) These three oils are most likely a hydraulic-fluid type oil. (2) These three oils are very "pure", largely composed of saturated hydrocarbons with the total aromatics being only 4-10% of the TPH. (3) The oils are a mixture of two different hydraulic fluids. There is no clear sign indicating they had been weathered. (4) The PAH concentrations are extremely low (<10 µg/g oil) in the oil samples, while the biomarker concentration are unusually high (4700-5500 µ/g oil). (5) Three major unknown compounds in the oil samples were positively identified. They are antioxidant compounds added to oils. (6) Samples 2996 and 2997 are identical and come from the same source. (7) The sample 2998 has group hydrocarbon compositions (including the GC traces, TPH, and total saturates) very similar to samples 2996 and 2997. But, it is not identical in chemical composition to samples 2996 and 2997, and they do not come from the same source.

Discriminant analysis of vegetable oils by near‐infrared reflectance spectroscopy

AbstractDiscriminant analysis of four vegetable oil types (cotton‐seed, peanut, soybean and canola) was performed by near‐infrared reflectance spectroscopy. The objective of this study was to provide an alternate method to differentiate vegetable oil types and to classify unknown oil samples. Second derivative spectra of the vegetable oils were subjected to discriminate analysis with Mahalanobis distances principles. A four‐wavelength (1704, 1802, 1816 and 2110 nm) equation was derived, which produced a sum of inverse squared distance of 0.0548. Although all four groups were successfully separated with a chi square of 18.9, the soybean oil group is more dispersed in space than the other three groups. Iodine values of the soybean oil samples suggest that this group may have a wide range of hydrogenation states. Discriminant analysis can be successfully used to differentiate vegetable oil types and possibly could also be used to differentiate degree of hydrogenation and oxidative states of oils.

Calculation of Ages of Hydrocarbons in Oils--Physical Chemistry Applied to Petroleum Geochemistry I

The compositions of the hydrocarbons in oils change as they mature. Several of these changes may be used to estimate the lengths of time required to attain measured hydrocarbon compositions and hence to estimate the ages of these hydrocarbons. The information needed to calculate the hydrocarbon ages includes detailed chemical analyses of the oils, stratigraphic information for the section with which the oil samples are associated, and temperature information from which geothermal gradients may be calculated. The hydrocarbon ages calculated by this method are usually in good agreement with geologically interpreted ages of the oils. The agreement commonly is better for oils from Cenozoic reservoirs than for oils from older reservoirs, and it is better for oils from clastic eservoirs than for oils for carbonate reservoirs. Applications of the calculation method include the recognition of hydrocarbon assemblages having ages different from those of the reservoirs in which they are found and the recognition of oils that have migrated along faults or across unconformities. Thus, these calculations complement other geochemical methods of relating oils to their probable sources. The changes of hydrocarbon composition occur both in the C5-C7 and C15 + ranges of molecular size. We were able to correlate these changes with time-temperature histories for suites of selected oils believed to be indigenous to the reservoirs in which they are found. For unknown oil samples, the correlations, together with stratigraphic and temperature information for the section in which the oil possibly may have its origin, provide the basis for calculations of the ages of the samples' hydrocarbons, and thus the ages of the beds that may have originated the oils. The ages of single samples commonly can be determined with some confidence because the least-squares correlations are based on groups of 29 and 56 indigenous oils from beds of widely diffe ent ages and temperatures.

Systems chemical analysis of petroleum pollutants.

The application of an established mathematical treatment useful for the characterization and identification of petroleum pollutions is described. Using discriminant analysis of relevant infrared spectrophotometric data, 99% of numerous known and unknown oil samples have been correctly characterized and identified. Unknown samples include weathered crude oils, heavy residual fuel oils, and asphalts, which were all correctly identified by systems chemical analysis and by other chemical analysis such as electron capture detection gas chromatography, flame ionization detection gas chromatography, metal analysis, etc.