Oil Adulteration Research Articles

Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.).

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification.

Competitive Electrochemical Immunosensor for Detection of Capsaicinoids in Edible Vegetable Oil Based on Computer Aided Molecular Modeling

The adulteration of edible vegetable oil is a serious threat to the health of consumers. Previous studies have shown that capsaicinoids could be used as a marker of adulteration to identify vegetable oils. The key sites of antibody and capsaicinoids were analyzed by computer aided molecular modeling (CAMM), which provided a basis for the selection of modified material and the implanting of antigen and antibody on the electrode in the subsequent construction of the electrochemical immunosensor. Simultaneously, it was determined that the different forces between capsaicinoids and antibody were the important reason for the differences in sensitivity of capsaicinoids. On this basis, combined with the good conductivity and excellent redox characteristic of Fe3O4NPs@CeO2, a competitive immunosensor was constructed. Under the optimal conditions, the proposed immunosensor provided a linear range of 10–3 to 104 ng ml−1 with a detection limit of 0.33 pg ml−1. This study may broaden the interaction between capsaicinoids and antibody, encourage the design of novel performance-enhanced antibody, and provide a new method for the detection of capsaicinoids.

Control of olive oil adulterated by soybean oil in a population where low olive oil consumption is an issue. A case study in Brazil

The objectives of this work were to evaluate the ability of spectroscopic techniques to distinguish unadulterated and adulterated olive oil (OL) and to investigate consumers perception of adulterated OL samples by sensory tests in a population with low OL consumption. The adulterated OL samples were prepared with the addition of soybean oil (SO) in varying concentrations of 5 to 50% (v v-1). Samples were characterized using Fourier transform infrared (FTIR) spectroscopy and ultraviolet-visible (UV–visible) absorption spectroscopy. The capability of spectroscopy techniques to distinguish unadulterated and adulterated OL was evaluated by using a chemometric tool, principal component analysis (PCA). A questionnaire about the frequency of OL consumption was used. Multiple comparison and hedonic tests were used to discriminate and evaluate the acceptance of the adulterated OL samples. FTIR and UV–visible spectroscopy coupled with PCA proved to be good instruments to be used to distinguish OL samples intentionally adulterated with the addition of 5, 10, 15, 20, 30, 40, and 50% SO, and have great potential for classification of adulteration involving OL. The results confirmed that this group of Brazilian consumers has low OL consumption. Consequently, there was low discrimination of adulterated OL and they had the same acceptance as unadulterated and adulterated OL.

Spectroscopic and Thermal Characterization of Extra Virgin Olive Oil Adulterated with Edible Oils.

The substitution of extra virgin olive oil with other edible oils is the primary method for fraud in the olive-oil industry. Developing inexpensive analytical methods for confirming the quality and authenticity of olive oils is a major strategy towards combatting food fraud. Current methods used to detect such adulterations require complicated time- and resource-intensive preparation steps. In this work, a comparative study incorporating Raman and infrared spectroscopies, photoluminescence, and thermal-conductivity measurements of different sets of adulterated olive oils is presented. The potential of each characterization technique to detect traces of adulteration in extra virgin olive oils is evaluated. Concentrations of adulterant on the order of 5% can be detected in the Raman, infrared, and photoluminescence spectra. Small changes in thermal conductivity were also found for varying amounts of adulterants. While each of these techniques may individually be unable to identify impurity adulterants, the combination of these techniques together provides a holistic approach to validate the purity and authenticity of olive oils.

Rapid Detection of Avocado Oil Adulteration Using Low-Field Nuclear Magnetic Resonance.

Avocado oil (AO) has been found to be adulterated by low-price oil in the market, calling for an efficient method to detect the authenticity of AO. In this work, a rapid and nondestructive method was developed to detect adulterated AO based on low-field nuclear magnetic resonance (LF-NMR, 43 MHz) detection and chemometrics analysis. PCA analysis revealed that the relaxation components area (S23) and relative contribution (P22 and P23) were crucial LF-NMR parameters to distinguish AO from AO adulterated by soybean oil (SO), corn oil (CO) or rapeseed oil (RO). A Soft Independent Modelling of Class Analogy (SIMCA) model was established to identify the types of adulterated oils with a high calibration (0.98) and validation accuracy (0.93). Compared with partial least squares regression (PLSR) models, the support vector regression (SVR) model showed better prediction performance to calculate the adulteration levels when AO was adulterated by SO, CO and RO, with high square correlation coefficient of calibration (R2C > 0.98) and low root mean square error of calibration (RMSEC < 0.04) as well as root mean square error of prediction (RMSEP < 0.09) values. Compared with SO- and CO-adulterated AO, RO-adulterated AO was more difficult to detect due to the greatest similarity in fatty acids’ composition being between AO and RO, which is characterized by the high level of monounsaturated fatty acids and viscosity. This study could provide an effective method for detecting the authenticity of AO.

Casting red light for bad oil by dual turning-on mechanisms of fluorescence and its application in the portable platform

Cooking oil undergoes complicated reactions and generates harmful chemicals during the cooking process. Unfortunately, part of the bad cooking oil was illegally recycled and sold back to consumers. The conventional detection methods usually require heavy equipment and professional skills, which are not easily accessible to the public. Herein, we describe a robust turn-on fluorescent molecular probe, BOS (Bad Oil Sensor), for the straightforward and highly sensitive detection of bad cooking oil. BOS detects bad oil through the inevitable intrinsic change of viscosity and pH during cooking. Therefore, BOS enables the monitoring of cooking extent for any bad oil regardless of the origins, including adulterated oil. With the dual turn-on mechanisms and high sensitivity of BOS, a portable platform named Bad Oil Sensing System (BOSS) was constructed for the quick and simple in-field detection of bad oil. We believe that BOS and its portable platform will become a widely applicable tool for oil quality monitoring for the public and in the field of food chemistry.

Food fraud vulnerability assessment in the edible vegetable oil supply chain: A perspective of Chinese enterprises

The adulteration of edible vegetable oil is simple, but the detection is difficult, so it is a common target for fraudsters. To gain insight into the fraud vulnerability of the edible vegetable oil supply chain and then take more targeted measures to prevent and mitigate food fraud, we assessed the perceived fraud vulnerability of Chinese enterprises using the SSAFE food fraud vulnerability assessment (FFVA) tool and further applied multiple correspondence analysis (MCA) and nonparametric tests (Mann–Whitney U test & Kruskal–Wallis test) to compare the differences in fraud vulnerability between tier groups. Food fraud vulnerability in the FFVA questionnaire consists of three key elements: opportunities, motivations, and control measures. The results show that (1) Fraud factors related to opportunities in the Chinese edible vegetable oil supply chain have an overall medium-high vulnerability, while those related to motivations and control measures have medium and low vulnerability, respectively. (2) Producers have significantly higher perceived fraud vulnerability in terms of opportunities and motivations than retailers. (3) In the comparison between companies and workshops of producers, the perceived fraud vulnerability was significantly higher for workshops in terms of motivations and control measures and higher for companies in terms of opportunities, indicating that workshops are more vulnerable to committing fraud driven by economics and business culture and that they lack adequate measures to control fraud, in addition to the perception by companies that they are more likely to be victims of fraud. (4) The perceived vulnerability to fraud varies by the scale of enterprises.

Extra virgin olive oil: A comprehensive review of efforts to ensure its authenticity, traceability, and safety.

The growing demand for extra virgin olive oil (EVOO), appreciated for its unique organoleptic properties and health benefits, has led to various fraudulent practices to maximize profits, including dilution with lower value edible oils. The adulterated oils would be of poor nutritional quality, more readily oxidized, and may contain unhealthy substances formed during processing. Nevertheless, the range of available techniques to detect fraud in EVOO production has been growing. Reliable markers of EVOO adulteration include fatty acids and minor components such as sterols, tocopherols, triterpene alcohols, phenolic compounds, phospholipids, volatile compounds, and pigments. Additionally, increasing consumer interest in high-quality EVOO has led to the development of robust scientific methods for its traceability. This review focuses on (i) the usefulness of certain compounds as markers of EVOO adulteration; (ii) the potential health risks of consuming adulterated EVOO; and (iii) reliable methods for the geographical traceability of olive oil. In conclusion, fraudulent production practices need to be detected to preserve the beneficial health effects of EVOO and to avoid the potential risks associated with ingesting substandard oil. In this work, as EVOO certification and regulatory framework limitations have already been extensively reviewed, we focus our attention on biomarkers that guarantee both the authenticity and traceability of oil, and consequently its health properties. When it is unavailable to obtain a high-resolution mass spectrometry full fingerprint, stigmastadienes and the sterolic profile are proposed as reliable markers.

Metabolic fingerprinting strategy: Investigation of markers for the detection of extra virgin olive oil adulteration with soft-deodorized olive oils

As extra virgin olive oil (EVOO) is a high value commodity, it might be subject of various fraudulent practices. This study is focused on a challenging authentication issue, addition of lower grade, soft-deodorized olive oil to EVOO. In the first step, sample sets of authentic EVOOs, soft-deodorized oils and their admixtures were extracted by aqueous methanol; obtained polar fractions were then analysed by ultra-high performance liquid chromatography coupled to hybrid quadrupole time-of-flight high-resolution tandem mass spectrometry (UHPLC-QTOF-HRMS/MS). Subsequent chemometric evaluation of metabolic fingerprints enabled suggestion of several ions that might be characteristic for deodorized oils; most of tentatively identified compounds were oxidized fatty acid derivatives. In the second phase, the ‘marker' ions were employed for target analysis by ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS) what enabled achieving lower the detection limits. Two compounds were selected as the best markers for detection of soft-deodorized olive oil addition, tentatively identified as methyl ester of hydroxy octadecenoic acid and ester derivative of oleic acid. • Addition of soft-deodorized olive oil into extra virgin olive oil was studied. • Untargeted approach revealed new markers emerging in soft-deodorization process. • Markers were successfully tested by target approach to be used in routine practice. • Best markers were tentatively identified as fatty acid esters.

Multivariate qualitative methodology for semi-quantitative information. A case study: Adulteration of olive oil with sunflower oil

This paper proposes a strategy to assess the performance of a multivariate screening method for semi-quantitative purposes. The adulteration of olive oil with sunflower oil was considered as a case study using fluorescence spectroscopy and two-class Partial Least Squares Discriminant Analysis (PLS-DA). Building the proper screening methodology based on two-class multivariate classification model involve setting the cut-off value for the adulterated class (class 2). So, four classification models were established for four levels of adulterant (cut-off). Model validation involved calculating the main quality parameters (sensitivity, specificity and efficiency) and three additional semi-quantitative parameters (limit of detection, detection capability and unreliability region).The probability of successfully recognizing non-adulterated samples as such was set by the main performance parameters of the two-class model. However, the probability of successfully recognizing adulterated samples as such was more accurately extracted from the performance characteristic curves (PCC) curves instead of just from the sensitivity of the adulterated class.The main performance parameters of the PLS-DA models increased as the cut-off level increased although after a particular value the increase was less pronounced. As an example, when the cut-off was changed from 5% to 20%, sensitivity changed from 70 to 93%, specificity changed from 87 to 97%, and efficiency changed from 78 to 95%. The same can be stated for the semi-quantitative parameter's decision limit and detection capability, which moved from 0 to 1.6 and from 17.7 to 21.6 (% of adulterant), respectively.

Discriminant analysis of vegetable oils by thermogravimetric-gas chromatography/mass spectrometry combined with data fusion and chemometrics without sample pretreatment

This paper describes the feasibility of thermogravimetric-gas chromatography/mass spectrometry (TGA-GC/MS) combined with data fusion and chemometrics for discrimination of various vegetable oils (soybean, rapeseed, peanut, sunflower, olive and camellia) and determination of adulteration of olive oil with soybean oil. Separate and fused data of TGA and GC/MS analysis of various vegetable oils were subjected to chemometric analyses by principal component analysis (PCA) and linear discriminant analysis (LDA). The results demonstrate that data fusion can effectively enhance the discrimination power of PCA-LDA model, with a prediction rate of 96.2% being obtained at the optimum weight ratio of GC/MS: TGA (0.9: 0.1). The developed method was applied to the determination of adulteration of olive oil with soybean oil, allowing for distinguishment between olive oil samples and adulterated oil samples with adulteration levels ranging from 25% to 75%, and a prediction rate of 100% was achieved. The newly developed method based on TGA-GC/MS technique in combination with data fusion provides a new idea for the authentication of vegetable oils.

FOURIER TRANSFORM INFRARED SPECTROSCOPY FOR ANALYSIS OF DAIRY GHEE ADULTERATED WITH MINERAL OIL

Concept of food integrity to health is a hot topic with research ongoing globally to reduce adulteration in food. In an effort to follow up this cause, present research focused on detection of adulterated lower quality fats with genuine milk fats (ghee) is a key concern. Ghee is a pure clarified butter fat, widely used milk product in Indian sub-continent. Because of its satisfactory flavor and appetizing aroma, excessive dietary value, unique nature and its health benefits intend for its adulteration has usually been an extreme concern. The available methods to detect adulteration of mineral oil are time consuming and laborious. In order to get rid of these in discrepancies, the present paper deals with a promising, fast and effective method to detect the adulteration in ghee circulating in the market. Attenuated total reflectance – Fourier transform infrared spectroscopy (ATR- FTIR) method for identifying mineral oil even at 0.1% was established. The results reveal that six peaks are common, especially 721.38cm-1 (C-Cl) and 1137.17cm-1 (C-H stretching) peaks area percentage decreases and intensity increases with the concentration of adulteration. From these results, FTIR techniques play a potential role and serves as a robust method for detection of mineral oil adulteration in dairy ghee.

Whispering-Gallery-Mode Microwave Sensing Platform for Oil Quality Control Applications

A growing demand has been established over the recent years for quick and inexpensive oil adulteration detection testing to convoy the automated and computerized processes in the industry. This research study presents a practical application of a simple low-resource microwave sensor of small size and high sensitivity to rapidly identify oil types, and monitor its quality and authenticity without having to open any bottles off-shelf. The sensor utilizes the nonreciprocal whispering-gallery-modes (WGMs) traveling on a ferrite ring resonator (FRR) when coupled to a microstrip line (MTL). The magnetic anisotropy of the ferrite is exploited to acquire four sensitive WGM resonances of nonreciprocal nature in the 22–32-GHz spectrum. A fabricated prototype is practically tested for identifying oil samples of different ingredients and brands, when loaded onto the FRR at consistent volume inside glass bottles of identical geometry. The measured scattering responses have shown a high detection sensitivity for the small contrast between the edible oils as demonstrated by the explicit frequency shifts in magnitude and phase of both S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> and S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sub> . The article also discusses a generalized concept for the complementing system layers in an Internet of Things (IoT) architecture for potential implementation in the industry.

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data.

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904–922 nm and 1675–1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

Highly efficient authentication of edible oils by FTIR spectroscopy coupled with chemometrics

A novel improved method for the authentication of edible oil samples based on Fourier-transform infrared (FTIR) spectroscopy coupled with chemometrics has been developed. A discrimination analysis model has been developed. On this basis, 100% correct classification of 135 samples from eleven species has been achieved. Recognition rates with respect to external validation for 91 pure oil samples and 231 blend samples were 100% and 92.6%, respectively. A general quantitative model for detecting edible oil adulteration (taking Camellia oil as an example) has also been built. An optimal backward interval partial least-squares model, based on the spectral regions ν = 3100–2900, 1800–1700, 1500–1400, and 1200–1100 cm−1, has been determined, giving good performances. A specific sub-model using a single adulterant oil has also been constructed, which showed higher prediction accuracy. Based on the developed qualitative and quantitative FTIR methods, adulterant oils in Camellia blends could be rapidly detected, effectively differentiated, and accurately quantified.

Rapid Iodine Value Estimation Using a Handheld Raman Spectrometer for On-Site, Reagent-Free Authentication of Edible Oils.

Edible oil adulteration is a common and serious issue faced by human societies across the world. Iodine value (IV), the total unsaturation measure, is an authentication tool used by food safety officers and industries for edible oils. Current wet titrimetric methods (e.g., Wijs method) employed for IV estimation use dangerous chemicals and elaborate procedures for analysis. Alternate approaches for oil analysis require sophisticated and costly equipment such as gas chromatography (GC), liquid chromatography, high-performance liquid chromatography, mass spectrometry (MS), UV-Visible, and nuclear magnetic resonance spectroscopies. Mass screening of the samples from the market and industrial environment requires a greener, fast, and more robust technique and is an unmet need. Herein, we present a handheld Raman spectrometer-based methodology for fast IV estimation. We conducted a detailed Raman spectroscopic investigation of coconut oil, sunflower oil, and intentionally adulterated mixtures with a handheld device having a 785 nm excitation source. The obtained data were analyzed in conjunction with the GC–MS results and the conventional wet Wijs titrimetric estimated IVs. Based on these studies, a specific equation for IV estimation is derived from the intensity of identified Raman spectral bands. Further, an algorithm is designed to automate the signal processing and IV estimation, and a stand-alone graphical user interface is created in user-friendly LabVIEW software. The data acquisition and analysis require < 2 minutes, and the estimated statistical parameters such as the R2 value (0.9), root-mean-square error of calibration (1.3), and root-mean-square error of prediction (0.9) indicate that the demonstrated method has a high precision level. Also, the limit of detection and the limit of quantification for IV estimation through the current approach is ∼1 and ∼3 gI2/100 g oil, respectively. The IVs of different oils, including hydrogenated vegetable oils, were evaluated, and the results show an excellent correlation between the estimated and reported ones.

Adulteration detection of Qinghai-Tibet Plateau flaxseed oil using HPLC-ELSD profiling of triacylglycerols and chemometrics

The purpose of this study was to develop a hierarchical approach by combining similarity evaluation (SE), discriminant analysis (DA) and principal component analysis (PCA) to detect the adulteration of Qinghai-Tibet Plateau flaxseed oil using triacylglycerol (TAG) profiles with HPLC-ELSD. SE could effectively differentiate pure flaxseed oil from non-flaxseed oil when the adulteration concentration of six vegetable oils was greater than 10%. In TAG discriminant analysis, there was 100% accuracy in detecting adulteration by rapeseed, corn, peanut, soybean, and sesame oils in flaxseed oil when the adulteration concentration was greater than 10%, while the accuracy of detecting sunflower oil in flaxseed oil was 98.75%. PCA detected the adulteration of flaxseed oil by rapeseed, corn, peanut, sunflower seed, soybean, and sesame oils with an accuracy of 100% when TAG was used as an indicator. The comparative results showed that TAG was more effective in identifying the types and specific concentrations of adulteration of the six vegetable oils in flaxseed oil than equivalent carbon numbers (ECN), indicating that the TAG approach is a valuable tool to detect and quantify the adulteration of Qinghai-Tibet Plateau flaxseed oil.

Evaluation of Portable Vibrational Spectroscopy Sensors as a Tool to Detect Black Cumin Oil Adulteration

Black cumin oil adulteration has become a concern because it has numerous health benefits and a high price. Therefore, a simple, non-destructive, and rapid method to identify adulterations in black seed oil is necessary to protect the quality of the oils. This study aimed to perform a non-invasive method to authenticate black cumin oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to identify pure black cumin oil and partial least squares regression (PLSR) to predict the adulterant levels. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). SIMCA and PLSR models provided a very high performance in detecting adulterated samples in all portable units. These portable units showed great potential for rapid and non-destructive monitoring to identify adulterated black cumin oils.

Camellia oil adulteration detection using fatty acid ratios and tocopherol compositions with chemometrics

The fatty acid ratios and tocopherol compositions, coupled to chemometrics were used to verify camellia oil (CAO) adulteration with corn oil (COO), rapeseed oil (RAO), rice bran oil (RBO), sesame oil (SEO), and soybean oil (SOO). Using hierarchical clustering analysis (HCA), all CAO samples were classified into one category accounting for their high oleic acid/α-linolenic acid, oleic acid/palmitic acid, oleic acid/linoleic acid ratios, and α-tocopherol%. Based on partial least squares-discriminant analysis (PLS-DA), well clustering could be observed among CAO, CAO + COO, CAO + SOO, CAO + RBO, CAO + SEO, as well as CAO + RAO samples, with higher than 97.67% of the total discrimination accuracy, at an adulteration ratio above 40%. Using soft-independent modelling of class analogy (SIMCA), the adulterated CAO samples (5%–100%) possessed good correct classification rates above 90.00%. Both two complementary classification methods were confirmed to be feasible and accurate for camellia oil authentication.

Evaluation of fresh palm oil adulteration with recycled cooking oil using GC-MS and ATR-FTIR spectroscopy: A review

Palm oil (PO) is an edible vegetable oil that is extracted from the mesocarp of oil palm fruit (Elaeis guineensis), which is known to contain an almost equal proportion of saturated fatty acids (SFAs) and unsaturated fatty acids (USFAs). PO is used globally, because of its wide application as a frying medium. Extracted from the mesocarp of the oil palm fruit, PO needs to be processed to make it of edible quality. However, to meet growing global demand, it is often adulterated with recycled cooking oil (RCO), which is of inedible quality. As the methods of fresh palm olein (FPO) adulteration are sophisticated, it created an urgent need for commensurate analytical techniques with which to detect FPO adulteration. As such, chromatography and spectroscopy are commonly used to detect adulterations in edible oil. Therefore, this study evaluated the efficacy of utilising gas chromatography-mass spectrometry (GC-MS) and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to detect the adulteration of FPO with inedible RCO. Although previous studies attest to the efficacy of utilising GC-MS and ATR-FTIR spectroscopy in adulteration detection, both these techniques only provided specific qualitative and quantitative insights into the compounds present in oil samples. As such, further extensive studies on the application of a variety of adulteration detection methods are needed to provide regulatory authorities with information on the reliability of these modern adulteration detection methods.