Detection of Peanut Oil Adulteration Mixed with Rapeseed Oil Using Gas Chromatography and Gas Chromatography–Ion Mobility Spectrometry

Rapid identification of peanut oil adulteration by near infrared spectroscopy and chemometrics

Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near-infrared spectroscopy to non-invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high-quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near-infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R2) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition.

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

Differentiation of Goat Meat Freshness Using Gas Chromatography with Ion Mobility Spectrometry

To investigate the flavor changes in goat meat upon storage, the volatile components observed in goat meat after different storage periods were determined using gas chromatography-ion mobility spectrometry (GC-IMS). A total of 38 volatile organic compounds (VOCs) were determined from the goat meat samples, including alcohols, ketones, aldehydes, esters, hydrocarbons, ethers, and amine compounds. 1-Hexanol, 3-Hydroxy-2-butanone, and Ethyl Acetate were the main volatile substances in fresh goat meat, and they rapidly decreased with increasing storage time and can be used as biomarkers for identifying fresh meat. When combined with the contents of total volatile basic-nitrogen (TVB-N) and the total numbers of bacterial colonies observed in physical and chemical experiments, the characteristic volatile components of fresh, sub-fresh, and spoiled meat were determined by principal component analysis (PCA). This method will help with the detection of fraudulent production dates in goat meat sales.

The Aroma Fingerprints and Discrimination Analysis of Shiitake Mushrooms from Three Different Drying Conditions by GC-IMS, GC-MS and DSA

The aroma fingerprints and discrimination analysis of shiitake mushrooms under different drying conditions were performed by GC-IMS, GC-MS, and descriptive sensory analysis (DSA) with advanced chemometric methods. Three samples (A, B, and C) were treated with varied drying degree and rate. The sample A and C were at the same drying degree and the sample B and C were at the same drying rate. The GC-IMS volatile fingerprints, including the three-dimensional topographic map, topographic map, and gallery plot, showed that 29 compounds showed higher signal intensities in sample B. Moreover, 28 volatile compounds were identified by HS-SPME-GC-MS and only 8 compounds were ever detected by GC-IMS. The sample B not only had more varieties of volatile compounds, but also showed significant higher contents than sample A and C, especially C8 compounds (p < 0.05). Additionally, sample B showed the highest intensity in mushroom-like, chocolate-like, caramel, sweat, seasoning-like, and cooked potato-like odors by DSA. PCA, fingerprint similarity analysis (FSA) and PLSR further demonstrated that the sample B was different from sample A and C. These results revealed that samples with different drying degree were different and drying degree exerted more influence on the volatile flavor quality than the drying rate. This study will provide a foundation and establish a set of comprehensive and objective methods for further flavor analysis.

Non-Target Detection of Diversity of Volatile Chlorine Compounds in Frying Oil and Study on the Influencing Factors of Their Formation

Headspace-gas-chromatography ion-mobility spectrometry (HS-GC-IMS) proved the diversity of volatile chlorinated compounds (VCCs) in frying oil in this work. First, the VCCs were obtained by headspace by heating the frying oil at 80 °C for 30 min. Then, those compounds were separated by GC capillary column in the first dimension and by IMS in the second dimension, respectively. And at last, those compounds were detected in negative ion mode for non-targeting. The study results indicated that VCCs' formation depends on the contents of NaCl and water, heating temperature and time, and the types of oil. The refining process does not affect the detection of VCCs, indicating the durability of such targets as indicators for assessing deep-frying oil. Using HS-GC-IMS, the VCCs were detected to evaluate 16 authentic refined deep-frying oils from the market with an accuracy of 100%.

Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry in Combination with Chemometrics for Detecting the Age of Chinese Liquor (Baijiu)

This paper proposes the combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometrics as a method to detect the age of Chinese liquor (Baijiu). Headspace conditions were optimized through single-factor optimization experiments. The optimal sample preparation involved diluting Baijiu with saturated brine to 15% alcohol by volume. The sample was equilibrated at 70 °C for 30 min, and then analyzed with 200 μL of headspace gas. A total of 39 Baijiu samples from different vintages (1998–2019) were collected directly from pottery jars and analyzed using HS-GC-IMS. Partial least squares regression (PLSR) analysis was used to establish two discriminant models based on the 212 signal peaks and the 93 identified compounds. Although both models were valid, the model based on the 93 identified compounds discriminated the ages of the samples more accurately according to the goodness of fit value (R²) and the root mean square error of prediction (RMSEP), which were 0.9986 and 0.244, respectively. Nineteen compounds with variable importance for prediction (VIP) scores > 1, including 11 esters, 4 alcohols, and 4 aldehydes, played vital roles in the model established by the 93 identified compounds. Overall, we determined that HS-GC-IMS combined with PLSR could serve as a rapid and accurate method for detecting the age of Baijiu.

Edible Oils Adulteration: A Review on Regulatory Compliance and Its Detection Technologies

Various events of edible oils adulteration with inferior ingredients were reported regularly in recent years. This review is aimed to provide an overview of edible oils adulteration practices, regulatory compliance and detection technologies. Many detection technologies for edible oils adulteration were developed in the past such as methods that are based on chromatography or spectroscopy. Electrochemical sensors like electric nose and tongue are also gaining popularity in the detection of adulterated virgin olive oil and virgin coconut oil. It can be concluded that these detection technologies are essential in the combat with food adulterers and can be improved.

Effects of ethanol, activated carbon, and activated kaolin on perilla seed oil: Volatile organic compounds, physicochemical characteristics, and fatty acid composition

Perilla seed oil (PSO) has a special aromatic odor, which is unpleasant to the personal preferences of some consumers. To this end, this article evaluated the differences in volatile organic compounds (VOCs), physicochemical characteristics, and fatty acid composition of PSO treated with ethanol (PSO-EA), activated carbon (PSO-AC), and activated kaolin (PSO-AK). The results showed that in the PSO, PSO-EA, PSO-AC, and PSO-AK samples, the content of linolenic acid, oleic acid, and linoleic acid hardly changed. Among the physicochemical characteristics of the four samples, the color difference between PSO and PSO-EA was greater than the color difference between PSO and PSO-AC, PSO-AK. The three treatment methods had the greatest impact on the PSO peroxide value but had little effect on other indicators. Gas chromatography-ion mobility spectrum results identified 28 known volatiles, of which aldehydes, alkenals, alcohols, ketones, and esters were the main groups. Fingerprint analysis found that PSO had an aromatic odor, which includes 1-hexanol, hexanal, and 2-pentylfuran; the removal effect of ethanol on VOCs in PSO was better than that of activated carbon and activated kaolin. The difference between the four oil samples was found from the strength of the VOCs' signals in a two-dimensional map. From the principal components analysis and the "nearest neighbor" fingerprint analysis, it was found that PSO is generally quite different from PSO-EA, PSO-AC, and PSO-AK, while in the "nearest neighbor" fingerprint analysis, PSO-AC and PSO-AK are similar in general. In short, PSO will have better applications in the food field. PRACTICAL APPLICATION: Treatment of PSO with ethanol, activated carbon, and activated kaolin is conducive to the comprehensive utilization of edible resources. In this work, ethanol, activated carbon, and activated kaolin were used to remove VOCs in PSO, and PSO-EA, PSO-AC, and PSO-AK were obtained. The perilla seed oil after these three treatment methods was tested for VOCs, physicochemical characteristics, and fatty acid composition. They can meet the needs of more consumers without affecting the fatty acid composition in the PSO, and have broad development prospects.

Volatile composition analysis of tree peony (Paeonia section Moutan DC.) seed oil and the effect of oxidation during storage

Peony (Paeonia section Moutan DC.) seed oil is a novel vegetable oil with unique aromatic profile. The objectives of this study were to characterize the volatile compounds with optimized analysis conditions, and to study the effects of oxidation during storage. The Headspace Solid-phase Micro-extraction (HS-SPME) conditions were optimized with factors including extraction fiber, extraction time and temperature, as well as sample amount and desorption time. Then, the aromatic compounds extracted from peony seed oil were determined using the odor activity value (OAV) method coupled with the GC-olfactometry. Results found that the best HS-SPME conditions were DVB/CAR/PDMS fibers with magnetic stirring for 50 min of extraction at 60°C. The optimal sample amount was 4 g with 6 min of desorption. Forty-one volatile components were found, and 19 aromatic components were identified using GC-MS and GC-olfactometry. This is the first study to describe 4,7-dimethylbenzofuran in peony seed oil with a unique aroma of grass, light bitter, and fragrance. The stability of alcohol and terpenoids decreased as accelerated oxidation progresses. This study provides the theoretical basis for the sensory attributes, and shelf-life of peony seeds oil products for industry production and marketing.

Microwave pretreatment of camellia (Camellia oleifera Abel.) seeds: Effect on oil flavor

Microwave is a new pretreatment technology, and microwave processing time of camellia seeds is a factor affecting the flavor of camellia seed oil (CSO). Therefore, this study on the characteristic volatile compounds of CSO from microwaved seeds with different processing time was carried out by electronic nose (E-nose), headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The results of E-nose show that W1W, W2W and W5S were the main sensors to distinguish the flavor profile of CSOs. Through HS-SPME-GC-MS and odor activity value analysis, 80 volatile compounds were detected and 22 key aroma compounds were screened in CSOs. Compared with HS-SPME-GC-MS, 44 volatile compounds were detected by HS-GC-IMS, including 9 identical compounds and 35 different compounds. In general, the volatile compounds of 0, 2 and 3 min CSOs were mainly alcohols and esters, while the 4, 5 and 6 min CSOs were mainly heterocycles and aldehydes.

Comparison of near infrared spectroscopy and Raman spectroscopy for the identification and quantification through MCR-ALS and PLS of peanut oil adulterants

Peanut oil is considered one of the best frying oils, and, consequently there is an increasing worldwide demand. This has led to adulteration practices with unhealthy, synthetic or less expensive oils which raises concerns related with public health safety. Therefore, there is a high need for rapid, versatile, low-cost and reliable analytical methods, such as vibrational spectroscopic techniques, capable of identifying and quantifying the respective adulteration. The objective of this work focused on the application of two different vibrational spectroscopic techniques (NIR and Raman spectroscopy) for the qualitative and quantitative analysis of two adulterants in pure peanut oil, namely corn oil and vegetable oil. For the quantitative analysis two chemometric methods, namely PLS and MCR-ALS, were compared while for the qualitative analysis only MCR-ALS was tested. The analysis of peanut oil adulteration was performed by adding each adulterant individually and also by blending the peanut oil with both adulterants simultaneously. A total of 69 samples were analyzed, which was comprised by two sets of 20 samples each containing just one adulterant and another set of 29 samples containing both adulterants. Several pre-processing techniques were tested. The qualitative analysis performed by MCR-ALS allowed the identification of all the adulterants using both NIR and Raman spectra, with correlation coefficients higher than 0.99. For the quantification, none of the chemometric methods as well as the vibrational spectroscopic techniques tested showed significant better results. Nonetheless, the determination coefficients and the relative percentage errors for the validation samples for most of the developed models were higher than 0.98 and lower than 15%, respectively. Concluding, MCR-ALS was capable of correctly extracting the spectral profiles of all the adulterants in very complex mixtures (as the pure spectra of the adulterants and peanut oil are very similar) and both MCR-ALS and PLS were able to quantify the adulteration with low RE. To the best of our knowledge, it was the first time that MCR-ALS was used for the qualitative analysis of peanut oil adulteration (with all adulterants added simultaneously) and MCR-ALS and PLS were compared for the quantification of peanut oil adulteration using both NIR and Raman spectroscopy.

Response of fatty acids and lipid metabolism enzymes during accumulation, depuration and esterification of diarrhetic shellfish toxins in mussels (Mytilus galloprovincialis)

Bivalve mollusks accumulate diarrhetic shellfish toxins (DSTs) from toxigenic microalgae, thus posing a threat to human health by acting as a vector of toxins to consumers. In bivalves, free forms of DSTs can be esterified with fatty acids at the C-7 site to form acyl esters (DTX3), presumably a detoxification mechanism for bivalves. However, the effects of esterification of DSTs on fatty acid metabolism in mollusks remain poorly understood. In this study, mussels (Mytilus galloprovincialis) were fed the DST-producing dinoflagellate Prorocentrum lima for 10 days followed by an additional 10-days depuration in filtered seawater to track the variation in quantity and composition of DST acyl esters and fatty acids. A variety of esters of okadaic acid (OA) and dinophysistoxin-1 (DTX1) were mainly formed in the digestive gland (DG), although trace amounts of esters also appeared in muscle tissue. A large relative amount of OA (60%-84%) and DTX1 (80%-92%) was esterified to DTX3 in the visceral mass (referred to as digestive gland, DG), and the major ester acyl chains were C16:0, C16:1, C18:0, C18:1, C20:1 and C20:2. The DG and muscle tissues showed pronounced differences in fatty acid content and composition during both feeding and depuration periods. In the DG, fatty acid content gradually decreased in parallel with increasing accumulation and esterification of DSTs. The decline in fatty acids was accelerated during depuration without food. This reduction in the content of important polyunsaturated fatty acids, especially docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), would lead to a reduction in the nutritional value of mussels. Enzymes involved in lipid metabolism, including acetyl-coenzyme A carboxylase (ACC), fatty acid synthase (FAS), lipoprotein lipase (LPL) and hepatic lipase (HL), were actively involved in the metabolism of fatty acids in the DG, whereas their activities were weak in muscle tissue during the feeding period. This study helps to improve the understanding of interactions between the esterification of DSTs and fatty acid dynamics in bivalve mollusks.

Identification of new, very long-chain polyunsaturated fatty acids in fish by gas chromatography coupled to quadrupole/time-of-flight mass spectrometry with atmospheric pressure chemical ionization

The characterization of very long-chain (>C24) polyunsaturated fatty acids (VLC-PUFAs), which are essential in the vision, neural function, and reproduction of vertebrates, is challenging because of the lack of reference standards and their very low concentrations in certain lipid classes. In this research, we have developed a new methodology for VLC-PUFA identification based on gas chromatography coupled to quadrupole/time-of-flight mass spectrometry with an atmospheric pressure chemical ionization source (GC-APCI-QTOF MS). The mass accuracy attainable with the innovative QTOF instrument, together with the soft ionization of the APCI source, provides valuable information on the intact molecule, traditionally lost with electron ionization sources due to the extensive fragmentation suffered. We have identified, for the first time, VLC-PUFAs with chains up to 44 carbons in eyes, brain, and gonads of gilthead sea bream, a commercially important fish in the Mediterranean. The added value of ion mobility-mass spectrometry (IMS), recently developed in combination with GC-QTOF MS, and the contribution of the collisional cross section (CCS) parameter in the characterization of novel VLC-PUFAs (for which reference standards are not available) have been also evaluated. The methodology developed has allowed assessing qualitative differences between farmed and wild fish, and opens new perspectives in a still scarcely known field of research.

Identification of species-specific peptide markers in cold-pressed oils

In recent years, cold-pressed vegetable oils have become very popular on the global market. Therefore, new versatile methods with high sensitivity and specificity are needed to find and combat fraudulent practices. The objective of this study was to identify oilseed species-specific peptide markers, using proteomic techniques, for authentication of 10 cold-pressed oils. In total, over 380 proteins and 1050 peptides were detected in the samples. Among those peptides, 92 were found to be species-specific and unique to coconut, evening primrose, flax, hemp, milk thistle, nigella, pumpkin, rapeseed, sesame, and sunflower oilseed species. Most of the specific peptides were released from major seed storage proteins (11 globulins, 2S albumins), and oleosins. Additionally, the presence of allergenic proteins in the cold-pressed oils, including pumpkin Cuc ma 5, sunflower Hel a 3, and six sesame allergens (Ses i 1, Ses i 2, Ses i 3, Ses i 4, Ses i 6, and Ses i 7) was confirmed in this study. This study provides novel information on specific peptides that will help to monitor and verify the declared composition of cold-pressed oil as well as the presence of food allergens. This study can be useful in the era of widely used unlawful practices.

Gas chromatographic-ion mobility spectrometry combined with a multivariate analysis model exploring the characteristic changes of odor components during the processing of black sesame

Black sesame (Sesamum indicum L.) is a Chinese dietary herb that has been widely used in the medical and healthcare fields in China. According to the theory of Traditional Chinese medicine processing, reasonable processing (steaming and drying many times) can increase the tonic effect and reduce the adverse factors generated during long-term use. At present, the processing degree of black sesame is mainly judged based on subjective experience. However, due to the lack of objective and quantitative control indicators, quality fluctuations easily occur. Therefore, for better application, its processing technology needs scientific monitoring methods. Herein a gas chromatography-ion mobility spectrometry (GC-IMS) technique was applied as a monitoring method to differentiate the processed products of black sesame in different processing stages. The response data of volatile components obtained from the samples were processed by the built-in data processing software in the instrument to identify the different components for further principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). From fingerprint comparison, 70 differential signal peaks were screened, 32 of which were qualitatively identified, mainly monomers and dimers of 20 compounds. On this basis, the PCA model shows that there was a significant difference between the raw product (S1) and the processed products (H1-9); moreover, there was a certain correlation between the differential changes of samples in different processing stages (H1-9) and the processing times. The OPLS-DA model specifically shows the differential components in the processing with potential characteristics peaks of 41, 105, n-nonanal, 2 and ethanol can discriminate whether the BS has undergone the first processed. And the dynamic changes of the three characteristic peaks of 1-hexanol, acetic acid and 107 can determine the specific degree of processing of BS. The research proves that GC-IMS combined with a multivariate analysis model can provide scientific data for identifying the characteristic odor components of black sesame.

Detection of olive oil adulteration with vegetable oils by ultra-performance convergence chromatography-quadrupole time-of-flight mass spectrometry (UPC2-QTOF MS) coupled with multivariate data analysis based on the differences of triacylglycerol compositions

Three different vegetable oils, including soybean, corn, and sunflower oils, were differentiated from olive oil by using ultra-performance convergence chromatography coupled with quadrupole time-of-flight (UPC2-QTOF MS) and multivariate data analysis based on their differences in triacylglycerol compositions. Then, olive oil was adulterated by adding these three vegetable oils in 1%, 0.75%, and 0.5% (v/v), and the adulterated olive oils were differentiated from the pure olive oils using the similar analytical strategies but different data processing approaches. After that, the representative markers in differentiating the adulterations were selected, and a mathematical model was created to detect the olive oil adulteration based on these specific markers. These results indicated that UPC2-QTOF MS coupled with multivariate data analysis is a sensitive and accurate method in detecting olive oil adulteration, even in 0.5% adulteration level (v/v). This method could be applied in olive oil adulteration detection, and potentially beneficial to the oil industry.

Characterization and Biological Activities of Seed Oil Extracted from Berberis dasystachya Maxim. by the Supercritical Carbon Dioxide Extraction Method

Characterization of the structure and pharmacological activity of Berberis dasystachya Maxim., a traditional Tibetan medicinal and edible fruit, has not yet been reported. In this study, central composite design (CCD) combined with response surface methodology (RSM) was applied to optimize the extraction conditions of B. dasystachya oil (BDSO) using the supercritical carbon dioxide (SC-CO₂) extraction method, and the results were compared with those obtained by the petroleum ether extraction (PEE) method. The chemical characteristics of BDSO were analyzed, and its antioxidant activity and in vitro cellular viability were studied by DPPH, ABTS, reducing power assay, and MTT assay. The results showed that the maximum yield of 12.54 ± 0.56 g/100 g was obtained at the optimal extraction conditions, which were: pressure, 25.00 MPa; temperature 59.03 °C; and CO₂ flow rate, 2.25 SL/min. The Gas chromatography (GC) analysis results showed that BDSO extracted by the SC-CO₂ method had higher contents of unsaturated fatty acids (85.62%) and polyunsaturated fatty acids (57.90%) than that extracted by the PEE method. The gas chromatography used in conjunction with ion mobility spectrometry (GC-IMS) results showed that the main volatile compounds in BDSO were aldehydes and esters. BDSO also exhibited antioxidant ability in a dose-dependent manner. Moreover, normal and cancer cells incubated with BDSO had survival rates of more than 85%, which indicates that BDSO is not cytotoxic. Based on these results, the BDSO extracted by the SC-CO₂ method could potentially be used in other applications, e.g., those that involve using berries of B. dasystachya.