Advanced process analytical tools for identification of adulterants in edible oils - A review

Authentication of coriander oil and adulterant identification using electronic nose and spectroscopic techniques

Coriander oil possesses excellent antimicrobial, antioxidant, and anti-inflammatory properties, making it a valuable product for health care and food preservation. Due to its high value and desirable properties, this type of oil is often targeted for adulteration, either by blending it with less expensive oils or by completely substituting it. This study aims to authenticate coriander oil and distinguish the adulterants using non-invasive, green, fast and reliable methods. Performances of portable Raman and near infrared (NIR) spectrometers, as well as an electronic nose, were compared to distinguish between authentic and adulterated samples, using data driven soft independent class analogy (DD-SIMCA) and one class partial least squares (OC-PLS) models. DD-SIMCA achieved accuracies ranging from 87 % with NIR to 97 % with Raman spectroscopy, while OC-PLS obtained 69 % with NIR and up to 95 % with Raman. Moreover, a multiclass model was developed using only the adulterated samples to classify them into three types of adulterants: canola and soybean oils and palm olein. Partial Least Squares Discriminant (PLS-DA) successfully classified the type of adulterant, achieving 97 % accuracy with Raman spectroscopy. Raman demonstrated the highest performance; however, the custom-built, low-cost electronic nose also achieved high accuracy, with 94 % using DD-SIMCA and 96 % using PLS-DA, highlighting its potential as a rapid and effective screening tool. Therefore, the synergy of multivariate techniques and spectroscopic instruments was demonstrated by addressing both the verification of the authenticity of coriander oil and the detection of the type of adulterants.

Effect of Formulated Edible Oils From Groundnut and African Walnut Oils on Some Hematological, Inflammation, and Oxidative Stress Markers in High-Fat Diet-Induced Obese Wistar Rats

The objective of this study was to evaluate the effects of groundnut oil, African walnut oil, and their blends on some biochemical parameters in obese Wistar rats. Obesity was induced with a high-fat diet for 60 days and managed with oils and orlistat for 28 days. The rats were sacrificed on the 29th day, and the serum and blood were collected. The serum was used to evaluate oxidative stress and cytokine markers, while the blood was used for hematology studies. Results showed that oil quality indices were within standard ranges as recommended by the norm. Hematological assessments showed no significant differences in most parameters across groups, except for platelet counts, which were lower in the group taking 100% of groundnut oil. Catalase activity and glutathione peroxidase (GSH) levels were evaluated in various organs. The normal group exhibited the highest catalase activity in the brain and liver compared with the rats that received the high-fat diet. Notably, GSH activity was higher in the brains of rats receiving the 60:40 blend. Thiobarbituric acid (TBA) values indicated lower oxidative stress in the normal and 100% walnut oil groups. Nitric oxide concentrations were significantly higher in the normal and walnut oil groups compared with the negative control, suggesting a protective effect against oxidative stress. Cytokine analysis revealed elevated inflammatory markers in the negative control group, highlighting the potential anti-inflammatory properties of the oils. These findings suggest that groundnut oil, African walnut oil, and their blends might have anti-inflammatory activities, might preserve hematological markers, and protect against oxidative stress.

NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil

This study investigates the potential of 1H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with 1H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging 1H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples.

Critical overview of mass spectrometry-based lipidomics approach for evaluating lipid oxidation in foods

Mass spectrometry-based lipidomics, developed through rapid advancements in instruments and techniques, provides comprehensive analyses of individual lipidomes in diverse biological systems. This contribution summarizes the limitations of classical methods for measuring lipid oxidation in foods and presents current novel technologies for evaluating lipid oxidation. Notably, this study introduces the mass spectrometry-based lipidomics approach and its utility in assessing lipid oxidation through various analytical modes, supported by numerous examples. This overview offers significant insights into the use of mass spectrometry-based lipidomics for measuring lipid oxidation in foods, proposing lipidomics analysis as a promising method to address the limitations of classical approaches.

Nondestructive and robust detection of different kinds of vegetable oils adulterated with used frying oil by multi-optical properties

Developing a rapid and non-invasive detection tool for frying oil adulteration in pure vegetable oils holds the considerable significance. In this study, a self-developed optical system which combined double integrating spheres (DIS) and laser induced fluorescence (LIF) techniques was applied to obtain the optical properties of adulterated oils, which involved four categories: peanut, corn, rapeseed, and soybean oils, with each category from two distinct brands. The captured multi-optical properties included absorption coefficient (μa), reduced scattering coefficient (μ's) and fluorescence intensity (F). The relationships between optical properties and the primary chemical attributes in oils were explored. And a "two-step" modelling method was proposed for estimating the adulteration ratios of frying oil, which involved initial clustering for eight groups of vegetable oils by principal component analysis (PCA). Subsequently, six quantitative prediction models were developed using multiple linear regressions (MLR) based on clustering outcomes. Furthermore, least squares support vector regression (LSSVR) models were calibrated based on F at 475 nm and fluorescence correction parameters (μa at 375 and 475 nm), to predict the peroxide values (PV) and acid values (AV) of oils. The results demonstrated evident distinctions in the F and μa spectra among the four types of vegetable oils. Complex matrix interference and fluctuating absorption capacities precluded the detection of a consistent pattern between chemical composition and spectral data. The proposed "two-step" modelling method showed good predictive performance for adulteration ratio, with determination coefficient for prediction set (r2p) of 0.851, root mean square errors for prediction set (RMSEP) of 5.204 %, and relative percent difference (RPD) of 2.540. LSSVR models exhibited excellent predictive performance for PV (r2p = 0.925, RMSEP = 0.200, RPD = 3.490) and AV (r2p = 0.996, RMSEP = 0.011, RPD = 16.740). The overall results of this study indicated the feasibility of utilising multi-optical properties for the rapid, non-invasive and accurate detection of adulteration of frying oils.

Dithiothreitol-functionalized perovskite-based visual sensing array capable of distinguishing food oils

At present, the combination of fingerprint recognition methods and environmentally friendly and economical analytical instruments is becoming increasingly important in the food industry. Herein, a dithiothreitol (DTT)-functionalized CsPbBr3-based colorimetric sensor array is developed for qualitatively differentiating multiple food oils. In this sensor array composition, two types of iodides (octadecylammonium iodide (ODAI) and ZnI2) are used as recognition elements, and CsPbBr3 is used as a signal probe for the sensor array. Different food oils oxidize iodides differently, resulting in different amounts of remaining iodides. Halogen ion exchange occurs between the remaining iodides and CsPbBr3, leading to different colors observed under ultraviolet light, enabling a unique fingerprint for each food oil. A total of five food oils exhibit their unique colorimetric array's response patterns and were successfully differentiated by linear discriminant analysis (LDA), realizing 100% classification accuracy.

Analytical approaches for the determination of adulterated animal fats and vegetable oils in food and non-food samples

Edible oils and fats are crucial components of everyday cooking and the production of food products, but their purity has been a major issue for a long time. High-quality edible oils are contaminated with low- and cheap-quality edible oils to increase profits. The adulteration of edible oils and fats also produces many health risks. Detection of main and minor components can identify adulterations using various techniques, such as GC, HPLC, TLC, FTIR, NIR, NMR, direct mass spectrometry, PCR, E-Nose, and DSC. Each detection technique has its advantages and disadvantages. For example, chromatography offers high precision but requires extensive sample preparation, while spectroscopy is rapid and non-destructive but may lack resolution. Direct mass spectrometry is faster and simpler than chromatography-based MS, eliminating complex preparation steps. DNA-based oil authentication is effective but hindered by laborious extraction processes. E-Nose only distinguishes odours, and DSC directly studies lipid thermal properties without derivatization or solvents. Mass spectrometry-based techniques, particularly GC-MS is found to be highly effective for detecting adulteration of oils and fats in food and non-food samples. This review summarizes the benefits and drawbacks of these analytical approaches and their use in conjunction with chemometric tools to detect the adulteration of animal fats and vegetable oils. This combination provides a powerful technique with enormous chemotaxonomic potential that includes the detection of adulterations, quality assurance, assessment of geographical origin, assessment of the process, and classification of the product in complex matrices from food and non-food samples.

Non-Targeted Nuclear Magnetic Resonance Analysis for Food Authenticity: A Comparative Study on Tomato Samples

Non-targeted NMR is widely accepted as a powerful and robust analytical tool for food control. Nevertheless, standardized procedures based on validated methods are still needed when a non-targeted approach is adopted. Interlaboratory comparisons carried out in recent years have demonstrated the statistical equivalence of spectra generated by different instruments when the sample was prepared by the same operator. The present study focused on assessing the reproducibility of NMR spectra of the same matrix when different operators performed individually both the sample preparation and the measurements using their spectrometer. For this purpose, two independent laboratories prepared 63 tomato samples according to a previously optimized procedure and recorded the corresponding 1D 1H NMR spectra. A classification model was built using the spectroscopic fingerprint data delivered by the two laboratories to assess the geographical origin of the tomato samples. The performance of the optimized statistical model was satisfactory, with a 97.62% correct sample classification rate. The results of this work support the suitability of NMR techniques in food control routines even when samples are prepared by different operators by using their equipment in independent laboratories.

Advanced analytical techniques for authenticity identification and quality evaluation in Essential oils: A review

Essential oils (EO), secondary metabolites of plants are fragrant oily liquids with antibacterial, antiviral, anti-inflammatory, anti-allergic, and antioxidant effects. They are widely applied in food, medicine, cosmetics, and other fields. However, the quality of EOs remain uncertain owing to their high volatility and susceptibility to oxidation, influenced by factors such as the harvesting season, extraction, and separation techniques. Additionally, the huge economic value of EOs has led to a market marked by widespread and varied adulteration, making the assessment of their quality challenging. Therefore, developing simple, quick, and effective identification techniques for EOs is essential. This review comprehensively summarizes the techniques for assessing EO quality and identifying adulteration. It covers sensory evaluation, physical and chemical property evaluation, and chemical composition analysis, which are widely used and of great significance for the quality evaluation and adulteration detection of EOs.

Quantitative analysis of spectral data based on stochastic configuration networks

In quantitative analysis of spectral data, traditional linear models have fewer parameters and faster computation speed. However, when encountering nonlinear problems, their predictive accuracy tends to be lower. Nonlinear models provide higher computational accuracy in such situations but may suffer from drawbacks such as slow convergence speed and susceptibility to get stuck in local optima. Taking into account the advantages of these two algorithms, this paper introduces the single-hidden layer feedforward neural network named stochastic configuration networks (SCNs) into chemometrics analysis. Firstly, the model termination parameters, that is, the error tolerance and the allowed maximum number of hidden nodes are analyzed. Secondly, times of random configuration are discussed and analyzed, and then the appropriate number is determined by considering the efficiency and stability comprehensively. Finally, predictions made by the SCN are tested on two public datasets. The performance of the SCN is then compared with that of other techniques, including principal component regression (PCR), partial least squares (PLS), back propagation neural network (BPNN), and extreme learning machine (ELM). Experimental results show that the SCN has good stability, high prediction accuracy and efficiency, making it suitable for quantitative analysis of spectral data.

Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.

Reverse design of haptens based on antigen spatial conformation to prepare anti-capsaicinoids&gingerols antibodies for monitoring of gutter cooking oil

Rapid simultaneous detection of multi-component adulteration markers can improve the accuracy of identification of gutter cooking oil in edible oil, which is made possible by broad-spectrum antibody (bs-mAb). This study used capsaicinoids (CPCs) and gingerol derivatives (GDs) as adulteration markers, and two broad-spectrum haptens (bs-haptens) were designed and synthesized based on a reverse design strategy of molecular docking. Electrostatic potential (ESP) and monoclonal antibodies (mAbs) preparation verified the strategy's feasibility. To further investigate the recognition mechanism, five other reported antigens and mAbs were also used. Finally, the optimal combination (Hapten 5-OVA/1-F12) and key functional groups (f-groups) were determined. The half maximal inhibitory concentration (IC50) for CPCs-GDs was between 88.13 and 499.16 ng/mL. Meanwhile, a preliminary lateral flow immunoassay (LFIA) study made practical monitoring possible. The study provided a theoretical basis for the virtual screening of bs-haptens and simultaneous immunoassay of multiple exogenous markers to monitor gutter oil rapidly and accurately.

Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry

Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.

Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review

Low-field nuclear magnetic resonance (NMR) has three general modalities: spectroscopy, imaging, and relaxometry. In the last twelve years, the modality of spectroscopy, also known as benchtop NMR, compact NMR, or just low-field NMR, has undergone instrumental development due to new permanent magnetic materials and design. As a result, benchtop NMR has emerged as a powerful analytical tool for use in process analytical control (PAC). Nevertheless, the successful application of NMR devices as an analytical tool in several areas is intrinsically linked to its coupling with different chemometric methods. This review focuses on the evolution of benchtop NMR and chemometrics in chemical analysis, including applications in fuels, foods, pharmaceuticals, biochemicals, drugs, metabolomics, and polymers. The review also presents different low-resolution NMR methods for spectrum acquisition and chemometric techniques for calibration, classification, discrimination, data fusion, calibration transfer, multi-block and multi-way.

Multivariate Curve Resolution Methodology Applied to the ATR-FTIR Data for Adulteration Assessment of Virgin Coconut Oil

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky-Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79-2.66 and 6.48-8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively.

Process analytical technology as in-process control tool in semi-continuous manufacturing of PLGA/PEG-PLGA microspheres

Nowadays, among 3rd generation drug delivery systems, biodegradable polymeric based long-acting injectable depot has achieved tremendous success in clinical application. So far, there have been two dozen of commercial products of Poly (lactic-co-glycolic acid) microspheres available in the market. Recently, continuous manufacturing concept has been successfully applied on oral solid formulation from buzzword to reality. However, the polymeric injectable microspheres are still stayed at batch manufacturing phase due to the lack of understanding of knowledge matrix. In this study, micro-mixer as a plug-and-play emulsification modules, Raman spectroscopy and focused beam reflectance measurement as real-time monitoring modules are integrated into a novel semi-continuous manufacturing streamline to provides more efficient upscaling flexibility in microspheres production. In this end to end semi-continuous manufacturing process, amphiphilic block polymer monomethoxy-poly (ethylene glycol) modified PLGA (mPEG-PLGA) was used for encapsulating Gallic acid. Additionally, with guarantee of good robustness, the correlation relationship between critical process parameters, critical material attributes and critical quality attributes were investigated. The time-space evolution process and mechanism for formation of PEG-PLGA microsphere with particular morphology were elaborated. Altogether, this study firstly established semi-continuous manufacturing streamline for PLGA/PEG-PLGA microspheres, which would not only lower the cost of production, narrow process variability and smaller equipment/environmental footprint but also applied in-process control (IPC) and QbD principle on complicated production process of microspheres. Therefore, this study build confidence in the industrial development of PLGA/PEG-PLGA microspheres and establish best practice standards, which might be a quantum leap for developing PLGA microspheres in the future.

Optimization of Ultrasonic Extraction of Nutraceutical and Pharmaceutical Compounds from Bee Pollen with Deep Eutectic Solvents Using Response Surface Methodology

In recent years, there has been increasing interest in green extraction methods and green solvents due to their many advantages. In this study, the effects of an ultrasonic extraction method and deep eutectic solvents (DESs) on the extraction of different bioactive substances from bee pollen were investigated. In this regard, the effects of process variables such as the molar ratio of the DES (1, 1.5, and 2), sonication time (15, 30, and 45 min), and ultrasonic power (90, 135 and 180 W) on total individual amino acids, total individual organic acids, and total individual phenolic compounds were investigated by response surface methodology (RSM). The optimal conditions were found to be a molar ratio of 2, sonication time of 45 min, and ultrasonic power of 180 W (R² = 0.84). Extracts obtained via the maceration method using ethanol as a solvent were evaluated as the control group. Compared with the control group, the total individual amino acid and total individual organic acid values were higher using DESs. In addition, compounds such as myricetin, kaempferol, and quercetin were extracted at higher concentrations using DESs compared to controls. The results obtained in antimicrobial activity tests showed that the DES groups had broad-spectrum antibacterial effects against all bacterial samples, without exception. However, in yeast-like fungus samples, this inhibition effect was negligibly low. This study is the first to evaluate the impact of DESs on the extraction of bioactive substances from bee pollen. The obtained results show that this innovative and green extraction technique/solvent (ultrasonic extraction/DES) can be used successfully to obtain important bioactive compounds from bee pollen.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

Determination of Maximum Oil Yield, Quality Indicators and Absorbance Spectra of Hulled Sunflower Seeds Oil Extraction under Axial Loading

The present study aims to estimate the maximum oil yield of hulled sunflower seed samples in a uniaxial process under a load of 40 kN and speed of 4 mm/min. The oil samples were assessed for their quality parameters and spectra curves within the wavelength range of 325–600 nm. The results show that heating temperatures in the range of 40 °C to 80 °C increased the oil output; however, a maximum oil yield of 48.869 ± 6.023% with a minimum energy of 533.709 ± 65.644 J at the fifth repeated pressing was obtained from the unheated sample compared to the heated samples. The peroxide values ranged from 6.898 ± 0.144 to 7.290 ± 0.507 meq O₂/kg, acid values from 1.043 ± 0.166 to 1.998 ± 0.276 mg KOH/g oil and free fatty acid values from 0.521 ± 0.083 to 0.999 ± 0.138 mg KOH/g oil, which were within the recommended quality threshold. There were significant spectral differences among the oil samples. A single absorbance peak was observed at 350 nm for all oil samples, indicating low levels of pigment molecules in the oil. The study revealed the need for repeated pressings to recover the considerable residual oil remaining in the seedcake after the first pressing.

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

Influence of Wheatgrass Juice on Techno-Functional Properties and Bioactive Characteristics of Pasta

Pasta is an excellent source for fortification of ingredients and wheatgrass juice (WGJ) as a natural source of vital nutrients and antioxidants; the study was taken to develop WGJ-rich functional pasta. Wheatgrass juice (WGJ) was added at the rate of 33, 66, and 100% by replacing water during the mixing process of pasta. The samples were assessed by cooking quality, proximate composition, antioxidant properties, color, texture attributes, and sensory evaluation. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed. Incorporation of WGJ significantly ( $p<0.05$ ) decreased the optimum cooking time of pasta, whereas water absorption capacity was increased. Cooking loss in pasta increased from 2.89 to 3.21% with increasing levels of WGJ from 33 to 100%. The addition of wheatgrass juice to pasta improved the nutritional and antioxidant profile significantly ( $p<0.05$ ), as evidenced by increases in protein, phenolics, flavonoids, chlorophyll, and antioxidant activities (FRAP, DPPH, and ABTS). The incorporation of wheatgrass juice reduced the L value, whereas $a^{\ast }$ of the pasta enhanced gradually. With the addition of WGJ, the stiffness and hardness of the pasta changed dramatically. FTIR spectra validated the existence of bioactive compounds and chlorophyll pigments in pasta. Sensory data revealed that pasta containing 100% of WGJ was acceptable with the highest overall acceptability score of 7.72.

Recent Advances in Analytical Methods for the Detection of Olive Oil Oxidation Status during Storage along with Chemometrics, Authenticity and Fraud Studies

Olive oil is considered to be a food of utmost importance, especially in the Mediterranean countries. The quality of olive oil must remain stable regarding authenticity and storage. This review paper emphasizes the detection of olive oil oxidation status or rancidity, the analytical techniques that are usually used, as well as the application and significance of chemometrics in the research of olive oil. The first part presents the effect of the oxidation of olive oil during storage. Then, lipid stability measurements are described in parallel with instrumentation and different analytical techniques that are used for this particular purpose. The next part presents some research publications that combine chemometrics and the study of lipid changes due to storage published in 2005-2021. Parameters such as exposure to light, air and various temperatures as well as different packaging materials were investigated to test olive oil stability during storage. The benefits of each chemometric method are provided as well as the overall significance of combining analytical techniques and chemometrics. Furthermore, the last part reflects on fraud in olive oil, and the most popular analytical techniques in the authenticity field are stated to highlight the importance of the authenticity of olive oil.

A Review of Advanced Methods for the Quantitative Analysis of Single Component Oil in Edible Oil Blends

Edible oil blends are composed of two or more edible oils in varying proportions, which can ensure nutritional balance compared to oils comprising a single component oil. In view of their economical and nutritional benefits, quantitative analysis of the component oils in edible oil blends is necessary to ensure the rights and interests of consumers and maintain fairness in the edible oil market. Chemometrics combined with modern analytical instruments has become a main analytical technology for the quantitative analysis of edible oil blends. This review summarizes the different oil blend design methods, instrumental techniques and chemometric methods for conducting single component oil quantification in edible oil blends. The aim is to classify and compare the existing analytical techniques to highlight suitable and promising determination methods in this field.

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.

Effects of adulterated palm cooking oil on the quality of fried chicken nuggets

Introduction. There is a rising concern over food safety caused by an increasing trend towards adulterating fresh cooking oil with used cooking oil in Malaysia. Recent decades have seen more cases of high-quality edible cooking oil adulteration with reused oil, driven by high market demand and profit margins. In this study, we aimed to analyze the properties of vegetable oils and their effect on the quality of fried chicken nuggets. Study objects and methods. We determined free fatty acid contents and characterized the properties of fresh palm olein, used cooking oil, and adulterated oil. We also compared the sensory quality attributes of chicken nuggets fried in fresh and adulterated oils. Results and discussion. The content of free fatty acids consistently increased with rising adulteration levels. The FTIR spectral analyses revealed significant differences between fresh, used, and adulterated oils at 3006, 2922, 2853, 2680, 1744, 1654, 987, 968, and 722 cm–1. The oil samples with high adulterant concentrations demonstrated a linear increasing trend in K232 and K 270 values, where higher absorbance values indicated severe deterioration in the oil quality. The sensory evaluation showed no significant effect (P > 0.05) of adulteration with used cooking oil on the quality of fried chicken nuggets. Conclusion. Our findings filled in a gap in the previous studies which only focused on the effects of adulteration on the oil properties. The study also provides valuable information to regulatory authorities on the reliability of quality parameters and modern instruments in edible oil adulteration detection.

HPLC Fingerprints for the Characterization of Walnuts and the Detection of Fraudulent Incidents

A high-pressure liquid chromatographic method coupled to diode array detector (HPLC-DAD) was developed for the determination of phenolic compounds that could be used as markers in authentication studies of walnuts belonging to the Chandler variety, originating from Bulgaria, Greece, and France. An ultrasound-assisted extraction (UAE) protocol applied in the extraction of phenolic compounds was optimized. The method was validated and the relative standard deviations (RSD%) of the within-day, and between-day assays was lower than 6.3 and 11.1, respectively, showing adequate precision, and good accuracy ranging from 86.4 (sinapic acid) to 98.4% (caffeic acid) for within-day assay, and from 90.1 (gallocatechin gallate) to 100.6% (gallic acid) for between-day assay. Eighteen phenolic compounds were determined belonging to the classes of phenolic acids and flavonoids. The quantification results were further processed with chemometrics, and a robust partial least square-discriminant analysis (PLS-DA) model was developed for the classification of the samples according to their geographical origin, proposing markers that could be used for the control of walnuts authenticity and the detection of fraudulent incidents.