Oil Adulteration Research Articles

Using three-dimensional fluorescence spectroscopy and machine learning for rapid detection of adulteration in camellia oil

Camellia oil had been widely utilized in the realms of cooking, healthcare, and beauty. Nevertheless, merchants frequently adulterated pure camellia oil with low-priced oils to cut costs. This study was aimed at identifying the authenticity of camellia oil. Through the employment of three-dimensional fluorescence spectroscopy combined with the parallel factor analysis (PARAFAC) method, the characteristics of different vegetable oils were analyzed to establish a foundation for classification modeling. In the identification of pure vegetable oil types, methods such as partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) were adopted. The classification accuracy reached 100 %, demonstrating the effectiveness of feature extraction by PARAFAC. For the identification of camellia oil and its adulterants, traditional machine learning methods and convolutional neural network (CNN) models were introduced. The results indicated that traditional methods had limitations in the classification of single and binary adulterated oils. However, the optimized CaoCNN model achieved an accuracy of 97.78 % in identifying adulterated oil types, showcasing the potential of deep learning in adulterated oil detection. Further, feature visualization analysis verified the ability of CaoCNN to effectively capture and distinguish the characteristics of adulterated oils, providing an effective approach for the identification of camellia oil and its adulterated oils.

Identification and quantitation of multiplex camellia oil adulteration based on 11 characteristic lipids using UPLC-Q-Orbitrap-MS

Identification and quantitation of multiplex camellia oil adulteration based on 11 characteristic lipids using UPLC-Q-Orbitrap-MS

Development of a time-resolved laser-induced fluorescence fingerprinting method for detecting low-level adulteration in extra virgin olive oil

Adulteration identification in extra virgin olive oil (EVOO) is a significant concern in the olive oil industry. This study aimed to detect low-level adulteration of EVOO with other edible oils by using a novel time-resolved laser-induced fluorescence (TRLIF) fingerprinting method. Five EVOO brands were first analyzed to assess its potential for classification based on their differing fluorophore content. The developed method effectively reduces the effects associated with the optical path length and excitation light intensity. Subsequently, three sets of binary mixtures were tested: EVOO adulterated with refined olive oil, soybean oil, and sunflower oil. Quantitative analysis was performed using parallel factor analysis, which achieved a cross-validation coefficient of determination (R2) exceeding 0.90, with a prediction error < 1.40 %. These findings demonstrate that this method has the potential to determine the purity and quality of EVOO.

Detection of tea seed oil adulteration based on near-infrared and Raman spectra information fusion

The adulteration of tea seed oil severely harms consumer interests, such as affecting the taste experience and economic losses, creating an urgent need for a rapid, efficient, and non-destructive detection method. In this study, near-infrared and Raman spectroscopy were used to detect the adulteration of tea seed oil with four common edible vegetable oils. After preprocessing the raw spectral data, three variable selection methods namely uninformative variable elimination, competitive adaptive reweighted sampling, and successive projections algorithm were used individually and consecutively to select key spectral variables. And the NIR and Raman characteristic variables were then fused to develop a discrimination model. The results indicate that normalization is the superior preprocessing method and consecutive variable selection methods are generally superior to single ones. The best NIR and Raman models, based on consecutive variable selection methods, achieve accuracy and F1 score of the prediction set of 95.652%, 97.479% and 92.754%, 95.868%, respectively. Spectral information fusion can fully utilize the spectral information, the accuracy and F1 score of the optimal binary adulteration discrimination model based on information fusion for calibration and prediction sets are all 100%. Meanwhile, the accuracy and F1 score of the multiple adulteration discrimination model are 98.225% and 98.932%, respectively. Therefore, the fusion of NIR and Raman spectral information can enable highly accurate discrimination of adulterated oil. The proposed method in this study will aid in developing portable detection devices for tea seed oil adulteration.

A machine learning-based approach for predicting the level of palm oil adulteration in coconut oil

Coconut oil, prized for its health benefits, faces quality threats from adulteration, particularly with cheaper palm oil. This not only degrades the quality but also poses health risks. Traditional detection methods are often labor-intensive, destructive, and time-consuming. This study addresses the issue by applying multispectral imaging technology combined with machine learning to detect palm oil adulteration in coconut oil. We selected four machine learning algorithms—Support Vector Machines (SVM), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), and Bagging—due to their robustness in handling complex datasets. These models achieved classification accuracies of up to 100 %, far surpassing traditional chemical tests, which are slower and dependent on tester expertise. To further enhance detection accuracy, we employed both hard- and soft-voting mechanisms, integrating the strengths of individual models to improve overall reliability. This research marks a significant advancement in detecting coconut oil adulteration, offering a faster, more efficient solution to ensure product quality and consumer health.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

A microwave permittivity sensor on a Mach–Zehnder Interferometer of: Spoof surface plasmon polariton waveguides

A microwave Mach–Zehnder Interferometer (MZI) sensor for the permittivity characterization of liquids is presented in this paper. Two spoof surface plasmon polariton (SSPP) waveguide transmission lines (TLs) composed of compact spiral units is utilized in a differential architecture. Loading a dielectric material on the sensing arm induces a phase difference between the two arms, leading to periodic interference dips on the transmission curve. The spectral positions and magnitudes of these dips change with different dielectric materials. Due to the nonlinear dispersion characteristics of the SSPP TL, the proposed SSPP-based MZI sensor achieves improved sensitivity compared with microstrip-based MZI sensor. To validate the sensor’s performance, six different oil samples and a set of adulterated oils were measured, incorporating a polydimethylsiloxane (PDMS) channel on the sensing arm. The variations in the spectral position and magnitude of the interference dip centered at 3.2 GHz are employed to determine the dielectric constants and loss tangents of the tested oils. The sensitivity in measuring the dielectric constant is 3.61 %. The maximum errors for measuring the dielectric constant and the loss tangent are 4.3 % and 6.7 %, respectively. Compared to other sensors for liquid oil detection, the proposed sensor provides a relatively high sensitivity while maintaining a low liquid volume, making it a promising candidate for permittivity measurements of liquid analytes and solid materials with similar dielectric constants.

Independently Operational Dual-Band Metamaterial-Based Smart EM Sensing System for Detection and Quantification of Adulteration in Cooking Oils

Independently Operational Dual-Band Metamaterial-Based Smart EM Sensing System for Detection and Quantification of Adulteration in Cooking Oils

Lavender essential oil market integrity: A comprehensive study of commercial Lavandula angustifolia essential oils adulteration and assessment of industrial standards

Adulteration of essential oils (EOs) is a common and unethical practice that affects both consumers and honest suppliers and harms market integrity. Despite the awareness of this problem, scarce scientific studies are described in the scientific literature. Therefore, 55 samples of lavender EOs were purchased and extensively analyzed to the trace levels, including chromatographic profile, quantitative analysis, and chiral profile. Of the 51 samples labeled as Lavandula angustifolia, 51 % were genuine, 6 % were cheaper lavandin EO, 14 % were fortified with synthetic additives and over 29 % of the samples were adulterated with both lavandin EO and synthetics. Usually, adulterated EOs were cheaper than genuine products, still some expensive products were also falsified. The most common synthetic adulterants were synthetic linalool and linalyl acetate, 3,5,5-trimethylhexyl acetate, α-terpinyl acetate, and dipropylene glycol. Only four of the studied samples were diluted with a medium not fully visible in the recorded GC profile. Industry standards were assessed to eliminate adulterated EOs and ISO 3515 was found to be impractical and too strict, but Ph. Eur. demonstrated its efficiency in eliminating adulterated samples without rejecting many genuine ones.PCA enabled the differentiation of lavender EOs containing synthetics from those of purely natural origin, and in the latter group, to distinguish L. angustifolia from lavandin. Some samples had an unusual composition suggesting that they were artificial blends of synthetics and some terpene fractions of non-lavender origin.This is the first such extensive study of adulteration of lavender EOs, regarding both the number of commercial samples as well as implications on their biological activity. The adulteration via dilution influenced the antimicrobial activity of the essential oils. In conclusion, adulteration is a significant problem in the market, and a large share of products on the market is somehow counterfeited, not only the cheapest products.

Application of Multivariate Data Analysis Methods for Rapid Detection and Quantification of Adulterants in Lavender Essential Oil Using Infrared Spectroscopy

Application of Multivariate Data Analysis Methods for Rapid Detection and Quantification of Adulterants in Lavender Essential Oil Using Infrared Spectroscopy

Quick and reagent-free monitoring of edible oil saponification values using a handheld Raman device

Saponification value, the average molecular weight of fatty acids, is a crucial parameter for detecting adulteration of edible oils. Conventionally, it is determined in a laboratory setup through a time-consuming, laborious titration process using chemical reagents. Herein, the application of Raman spectroscopy for quick SV estimation of oils is demonstrated. It was hypothesized that the SV can be predicted from Raman spectra since the spectral patterns reflect the composition of fatty acid triglycerides. Two model oil systems were studied: coconut-gingelly oil and coconut-sunflower oil. Univariate models built from Raman spectra were successful only for the specific oil system; hence, PLS-Regression was executed across the two systems. The PLSR model on the validation set returned the average error, percentage error, and root mean square error of prediction as 2.1, 0.99 %, and 2.4, respectively. This method offers several advantages of portability, little reagent use, minimal sample preparation, and reduced analysis time.

Securing food authenticity by translating triacylglycerol profiles of edible oils into a versatile identification method for pumpkin seed oil adulteration

Edible plant oils provide a crucial source of lipids for human nutrition. Owing to the complex processing of some high-quality variants, including Styrian pumpkin seed oil, edible plant oils have become susceptible to food fraud by adulteration with cheaper vegetable oils, compromising both authenticity and quality. To address this issue, a workflow was developed utilizing QTOF-MS/MS to search for triacylglycerol markers indicative of adulteration and subsequently adapted them for routine analysis using triple quadrupole MS/MS. By developing a transparent classification system utilizing a multi-feature triacylglycerol panel, reliable detection of adulteration down to 3 % (w/w) is possible. Calculating ratios of selected markers and establishing intervals derived from pure oils further enables easy scalability to adjust marker ratios and ensure robustness against permanent or seasonal changes. Our work aims to make advances towards a rapid and accurate detection of oil adulteration in food industry, crucial for maintaining customer trust and safety.

Rapid method for identifying diacylglycerol edible oils using Raman spectroscopy combined with the “oil microscopy” method

This study aims to identify diacylglycerol (DAG) edible oils using Raman spectroscopy to protect consumers. Raman spectral data from 28 samples were collected, including DAG (1,3-diolein), triacylglycerol (triolein), DAG edible oils, adulterated oils, and ordinary edible oils. These samples were analyzed using a combined linear discriminant analysis–k nearest neighbors algorithm, characteristic band ratios, peak area ratios, and oil microscopy. The results of Raman spectroscopy analysis demonstrated DAG edible oils, adulterated oil, and ordinary cooking oil at 1746 cm−1 with Iordinary edible oil >Iadulterated oil >IDAG edible oil. Further analyses revealed differences in esterification between DAG edible oils and triacylglycerol. Moreover, the results demonstrated that the combination of Raman spectroscopy and oil microscopy can quickly and accurately detect DAG edible oil adulteration, which is expected to be an effective tool for food safety regulation to protect consumers’ rights.

Raman spectroscopy and multivariate analysis for the waste and edible vegetable oil classification

Twelve samples of waste cooking oil (WCO) were prepared by four different deep-frying procedures. The edible and the waste oil samples were characterised by Raman spectroscopy, revealing few and almost negligible differences between them. Therefore, the possibility of classifying the different groups of samples by extracting valuable data from the Raman spectra through statistical multivariate analysis was explored. Even if the number of samples was not enough to draw definitive conclusions, unsupervised principal component analysis (PCA) and supervised partial least square discriminant analysis (PLS-DA) conducted on the raw Raman signals, allowed to distinguish within edible and waste vegetable oil, and to select the most relevant combination of variables associated with each family. Using sparse partial least square discriminant analysis (S-PLS-DA), we determined a chemical fingerprint characteristic of each sample by creating a Variable In Projection (VIP) plot. The methodology herein presented could find relevant application in the detection of waste adulteration in vegetable oils sold for industrial purposes other than food.

Effects of different processing methods on the lipid composition of seabuckthorn fruit oil based on lipidomics.

Employing lipidomics, this study investigated the lipid composition of seabuckthorn fruit oil processed via supercritical CO2 extraction and centrifugal separation. Qualitative analysis showed that a total of 2861 lipid molecules were identified in seabuckthorn fruit oil. Quantitative analysis showed that the content of lipids in seabuckthorn fruit oil extracted by supercritical CO2 extraction (927,539.84µg/mL) was significantly higher than that in centrifugal-separated seabuckthorn fruit oil (735,717.63µg/mL), with 17 distinct lipid classes and 215 lipid molecules differentiated through multivariate statistical analysis. Lipid molecules, such as diacylglycerol (DG), ceramides (Cer), monohexosyl ceramide, phosphatidylglycerol, phosphatidylinositol, phosphatidylethanolamine, and monogalactosyl DG, were predominantly found in the oil extracted using supercritical CO2. In contrast, monogalactosyl monoacylglycerol, diglycosyl ceramide, and Cer phosphate were significantly present in the oil extracted by centrifugal separation. These findings contribute new insights into how processing methods affect the quality and composition of seabuckthorn fruit oil and provide a basis for detecting oil adulteration.

Qualitative and quantitative detection of camellia oil adulteration using electronic nose based on wavelet decomposition humidity correction

This study addresses the challenge of environmental humidity impacting the accuracy of metal oxide semiconductor (MOS)-based electronic nose systems in detecting adulterated camellia oil. A self-developed electronic nose platform with eight MOS sensors was used to detect adulteration in camellia oil mixed with varying ratios of rapeseed, soybean, and corn oils at different humidity levels (20%, 40%, and 60%). Wavelet decomposition using fourth-order Symlet wavelet was applied to correct response signals, mitigating humidity's effect on detection accuracy. Linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) were employed to build qualitative identification models using pre- and post-correction datasets, while partial least squares regression (PLSR) and backpropagation neural network (BPNN) were used for quantitative prediction. Results showed that both qualitative and quantitative models significantly improved performance after correcting signal drift with wavelet decomposition. The qualitative model's 10-fold cross-validation prediction accuracy increased to 98.67% from 88.67%, while the quantitative model's average R2 reached 0.99 with RMSE reduced to 3.64 mL/100 mL, compared to pre-correction R2 of 0.86 and RMSE of 9.63 mL/100 mL. Notably, the RF and BPNN models outperformed others in qualitative and quantitative detection, respectively. These findings highlight the potential of electronic nose technology with humidity correction for authenticating camellia oil.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

Detection and quantification of groundnut oil adulteration with machine learning using a comparative approach with NIRS and UV–VIS

Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet–visible (UV–Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV–Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV–Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV’s up to 0.8799 and RMSECV’s lower than 3 ml/100 ml for NIR spectra and R2CV’s up to 0.81 and RMSECV’s lower than 4 ml/100 ml for UV–Vis spectra. NIR spectra produced better models as compared to UV–Vis spectra.

Detecting adulteration in mustard oil using low-frequency dielectric spectroscopy

Detecting adulteration in mustard oil using low-frequency dielectric spectroscopy

Authentication of Argentinean extra-virgin olive oils using three-way fluorescence and two-way near-infrared data fused with multi-block DD-SIMCA

A trending problem of Extra Virgin Olive Oil (EVOO) adulteration is investigated using two analytical platforms, involving: (1) Near Infrared (NIR) spectroscopy, resulting in a two-way data set, and (2) Fluorescence Excitation-Emission Matrix (EEFM) spectroscopy, producing three-way data. The related instruments were employed to study genuine and adulterated samples. Each data set was first separately analyzed using the Data Driven-Soft Independent Modeling of Class Analogies (DD-SIMCA) method, based on Principal Component Analysis (for the two-way NIR data) and PARallel FACtor analysis (for the three-way EEFM data). The data sets were then processed together using the multi-block fusion method, based on the concept of Cumulative Analytical Signal (CAS). A comparison of the data processing methods in terms of sensitivity, specificity and selectivity showed the following order of excellence: NIR < EEFM < NIR + EEFM. This finding confirms the effectiveness of multi-block data fusion, which cumulatively improves the model performance.