Classification and adulteration detection of vegetable oils based on fatty acid profiles

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.

O-Alkyl derivatives of ferulic and syringic acid as lipophilic antioxidants: effect of the length of the alkyl chain on the improvement of the thermo-oxidative stability of sunflower oil

Lipid oxidation is the major cause of the deterioration of fat-containing foods, especially those containing polyunsaturated fatty acids (PUFAs). Antioxidant additives of synthetic origin are added to matrices rich in PUFAs, such as sunflower oil (SO). However, there is controversy regarding their safety, and their low solubility in both water and fat has led to the search for new covalent modifications through lipophilicity. This work presents the synthesis of O-alkyl acid derivatives from ferulic and syringic acids and the study of their antioxidant capacity and effect on the thermoxidative degradation of SO. Antioxidant activities were evaluated by employing ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays in a concentration range of 10-100 µg mL-1. The IC50 values for DPPH scavenging activity ranged from 15.61-90.43 µg mL-1. The results of the FRAP assay for both O-alkyl ferulic (3a-f) and syringic (5a-f) series revealed a "cut-off" effect on antioxidant activity in carbon five (C5). Thermoxidation study of additives 3b-c and 5b-c showed a decrease in the slope of extinction coefficients K 232 and K 270 in comparison with SOcontrol. Furthermore, 3c presented higher antioxidant activity than 3b and 1, with a power to decrease the thiobarbituric acid reactive species (TBARS) 6 times higher than SOcontrol at 220 °C. Additives 5b-c exerted a protective effect on the thermoxidation of SO. The results suggest that increasing lipophilic and thermal properties of antioxidants through O-alkyl acid derivatization is an effective strategy for accessing lipophilic antioxidant additives with potential use in food matrices.

Chemical Composition and Geographic Variation of Cold Pressed Balanites aegyptiaca Kernel Oil

With the increasing impacts of climate change, establishing more sustainable and robust plants such as desert dates (Balanites aegyptiaca) seems to be necessary. Known for its resilience in arid conditions, this tree has the potential to become a more important food source, particularly for its potential to yield edible oil. This study characterized Balanites kernel oil (BKO) as a promising oil source in arid regions, studying the influence of geographical origin and environmental factors. Moroccan and Sudanese BKO samples were analyzed and compared with Mauritanian BKO. In the fatty acid profile, unsaturated fatty acids constituted over 70% of the BKO profile, with a predominance of linoleic acid (Li), oleic acid (Ol), palmitic acid (Pa), and stearic acid (St). Consequently, the predominant triacylglycerols were PaLiLi, PaLiOl, LiLiOl, OlLiOl, and StLiOl. α-Tocopherol dominated the tocochromanol composition (324 to 607 mg/kg), followed by γ-tocopherol (120 to 226 mg/kg), constituting 90% of the total tocochromanols. The total phytosterol content in BKO ranged from 871 to 2218 mg/kg oil, with β-sitosterol dominating (58% to 74%). Principal Component Analysis revealed that the geographical origin significantly influences BKO composition, emphasizing environmental factors, particularly water deficit and/or temperatures. Notably, Moroccan BKO collected from an area characterized by high aridity and relatively low winter temperatures, showcased a unique profile in fatty acid, phytosterols, and tocochromanols. The valorization of BKO presents an opportunity for local agricultural development in arid regions and a role model for plant development and agricultural practices in other parts of the world.

Volatile aroma compounds of passion fruit seed Oils: HS-GC-IMS analysis and interpretation

The physicochemical properties, fatty acid composition and volatile aroma compounds of cold-pressed passion fruit seed oils were analyzed. The oils were rich in linoleic acid, oleic acid and volatile compounds. A total of 108 volatile compounds including 17 aldehydes, 23 alcohols, 21 esters, 19 ketones, 6 acids, 9 alkenes, 5 pyrazines and 8 others were identified using HS-GC-IMS. The significant differences of volatile compounds in the purple and yellow passion fruit seed oils were observed via the GalleryPlot graph and distinguished by principal component analysis. The results showed that acids, alcohols, esters and ketones were major aromatic compounds in purple passion fruit seed oils, which contribute to flavors such as flowery, fruity, creamy, yogurt. Whereas the contents of aldehydes, pyrazines, alkenes were higher in yellow passion fruit seed oils, which contributes to fatty and nutty odors. The findings filled in our understanding of volatilization characteristics in passion fruit seed oils.

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation

Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.

Gas chromatography ion mobility spectroscopy: A rapid and effective tool for monitoring oil oxidation

Oil autoxidation is an early process of food deterioration, monitoring oil oxidation is therefore of great significance to ensure food quality and safety. In this study, a detection method of the primary and secondary oxidative products was developed by gas chromatography ion mobility spectrometry (GC-IMS).The secondary oxidative products was analyzed by GC-IMS. Then, the relationships between peroxide values and the contents of secondary oxidative products were investigated by constructing a prediction model of peroxide value of rapeseed oil with the help of secondary oxidative products and chemometrics. The coefficient of determination Q2 of the model validation set is 0.96, and the RMSECV is 0.1570 g/100 g. These validation results indicated that secondary oxidative products could also reflect the content of the primary oxidative products. Moreover, 10 characteristic markers related to oxidative rancidity were identified for monitoring edible oil rancidity and oxidative stability.

Identification of the interference spectra of edible oil samples based on neighborhood rough set attribute reduction

Due to numerous edible oil safety problems in China, an automatic oil quality detection technique is urgently needed. In this study, rough set theory and Fourier transform spectrum are combined for proposing a digital identification method for edible oil. First, the Fourier transform spectra of three different types of edible oil samples, including colza oil, waste oil, and peanut oil, are measured. After the input spectra are differentially and smoothly processed, the characteristic wavelength bands are selected with neighborhood rough set attribution reduction (NRSAR). Moreover, the classification models are established based on random forest (RF) and extreme learning machine (ELM) algorithms. Finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. The results show that by using the third-order difference pre-processing method, 193 wavelength bands in the visible range can be reduced to 10 characteristic wavelengths, with a compression ratio of over 88.61%. Using the established NRS-RF and NRS-ELM models, the total identification accuracies are 91.67% and 93.33%, respectively. In particular, the identification accuracy of peanut oil using the NRS-ELM model reaches up to 100%, whereas the identification accuracies obtained using the principal component analysis (PCA)-based models that are commonly used in information processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively. As compared with feature extraction methods, the proposed NRSAR shows directive advantages in terms of precision, sensitivity, specificity, and the distribution of judgment. In addition, the execution time is also reduced by approximately 1/3. Conclusively, the NRSAR method and NRS-ELM the model in the spectral identification of edible oil show favorable performance. They are expected to bring forth insightful oil identification techniques.

Differentiation of lard from other animal fats based on n-Alkane profiles using chemometric analysis

Adulteration of lard with other fats and oils in food production affects many areas including economics, religion, and health. Previous studies discriminated lard based on major components of fats, i.e. triglycerides and fatty acids. This study aimed to differentiate lard and other animal fats (beef, chicken and mutton fat) based on n-alkane profiles established by gas chromatography-mass spectrometry (GC-MS). Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to initiate clustering of lard and other animal fats. Good result was obtained using Random Forest (RF) and Partial Least Squares-Discriminant Analysis (PLS-DA). Statistical models propose tetracosane (C₂₄) as a potential n-alkane marker and it was found that C₂₄ was the major alkane with composition of 15.72% (GC-MS) of total alkanes identified. Based on this finding, more interesting study may potentially be explored for the interest of various fats and oils consumers in vast applications especially using chemometrics analysis.

The phytochemical components of walnuts and their application for geographical origin based on chemical markers

Place of origin has an important influence on walnut quality and commercial value, which results in the requirement of rapid geographical traceability method. Thus, a method for geographical origin identification of walnuts on the basis of nutritional quality of walnut from China was conducted. The concentrations of 43 phytochemical components were analyzed in walnut samples from five different walnut-producing regions of China. Based on 14 chemical markers selected by the Random Forest method from these phytochemical components, a new discriminant model for geographical origin was built, with the corresponding correct classification rate of 99.3%. In addition, the quantitative quality differences of walnuts from five regions were analyzed, with the values of 0.17-1.43. Moreover, the top three chemical markers for the geographical origin discriminant analysis were Mo, V and stearic acid, with the contribution rates of 26.8%, 18.9% and 10.9%. This study provides a potentially viable method for application in the food authentication.

Machine learning directed discrimination of virgin and recycled poly(ethylene terephthalate) based on non-targeted analysis of volatile organic compounds

The use of non-decontaminated recycled poly(ethylene terephthalate) (PET) in food packages arouses consumer safety concerns, and thus is a major obstacle hindering PET bottle-to-bottle recycling in many developing regions. Herein, machine learning (ML) algorithms were employed for the discrimination of 127 batches of virgin PET and recycled PET (rPET) samples based on 1247 volatile organic compounds (VOCs) tentatively identified by headspace solid-phase microextraction comprehensive two-dimensional gas chromatography quadrupole-time-of-flight mass spectrometry. 100% prediction accuracy was achieved for PET discrimination using random forest (RF) and support vector machine (SVM) algorithms. The features of VOCs bearing high variable contributions to the RF prediction performance characterized by mean decrease Gini and variable importance were summarized as high occurrence rate, dominant appearance and distinct instrument response. Further, RF and SVM were employed for PET discrimination using the simplified input datasets composed of 62 VOCs with the highest contributions to the RF prediction performance derived by the AUCRF algorithm, by which over 99% prediction accuracy was achieved. Our results demonstrated ML algorithms were reliable and powerful to address PET adulteration and were beneficial to boost food-contact applications of rPET bottles.

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.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Variety classification of coated maize seeds based on Raman hyperspectral imaging

As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm^-1 were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield.

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 R ² 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 essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry

Sesame oil is a traditional and delicious edible oil in China and Southeast Asia with a high price. However, sesame oil essence was often illegally added to cheaper edible oils to counterfeit sesame oil. In this study, a rapid and accurate headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) method was proposed to detect the counterfeit sesame oil where the other cheap oils were adulterated with essence. Combined with chemometric methods including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), authentic and counterfeit sesame oils adulterated with sesame essence (0.5%, w/w) were easily separated into two groups. More importantly, 2-methylbutanoic acid, 2-furfurylthiol, methylpyrazine, methional, and 2,5-dimethylpyrazine were found to be markers of sesame essence, which were used to directly identify the sesame essence. The determination of volatile compounds based on HS-GC-IMS was proven to be an effective method for adulteration detection of essence in sesame oil.

Phytochemical Profile of Eight Categories of Functional Edible Oils: A Metabolomic Approach Based on Chromatography Coupled with Mass Spectrometry

Functional vegetable oils are highly considered not only for their nutritional value, but also for their health benefits. The profile of phytochemicals responsible for their quality is useful also for the identification of possible mislabeling or adulteration. The comparative composition of eight categories (sunflower, pumpkin, hempseed, linseed, soybean, walnut, sea buckthorn and olive) of commercial vs. authentic oils was determined. Fatty acids, volatiles, carotenoids, tocopherols, and phenolic components were analyzed by gas- and liquid chromatography-based techniques coupled with diode array, mass spectrometry, or fluorescence detection. Classification models, commonly used in metabolomics, e.g., principal component analysis, partial least squares discriminant analysis, hierarchical clusters and heatmaps have been applied to discriminate each category and individual samples. Carotenoids, tocopherols, and phenolics contributed mostly, qualitatively, and quantitatively to the discrimination between the eight categories of oils, as well as between the authentic and the commercial ones. This metabolomic approach can be easily implemented and the heatmaps can be considered as “identity” cards of each oil category and the quality of commercial oils, comparative to the authentic ones of the same botanical and geographical origin.

Application of machine learning to the monitoring and prediction of food safety: A review

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

In-depth free fatty acids annotation of edible oil by mCPBA epoxidation and tandem mass spectrometry

The analysis of free fatty acids (FFAs) in edible oils, especially their fine structure, can provide information for nutritional value evaluation and authentication. Here, a strategy based on epoxidation reaction by mCPBA combined with tandem MS was developed to identify and relatively quantify FFAs, including CC location isomers, which can rapidly distinguish different edible oils. Notably, low-abundant FFAs can be detected directly in the presence of high-abundant triacylglycerol (TAG) without complicated pretreatment. We identified a series of CC location isomers via mCPBA-nanoESI-MS/MS, among them, FA 24:1 (Δ13) and FA 24:1 (Δ17) were first identified in edible oils, and the predominant UFAs was FA 18:1 (Δ9), which occupies 98.35% of FA 18:1 in peanut oil while 89.68% in rapeseed oil. The results demonstrated that the proposed method could provide further in-depth CC positional information of oils, promoting the development of structural determination of fatty acids in food chemistry.

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints

Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.

Dietary Antioxidant Anthocyanins Mitigate Type II Diabetes through Improving the Disorder of Glycometabolism and Insulin Resistance

Insulin resistance (IR) is one of the pathological reasons for type II diabetes mellitus (T2DM). Therefore, it is important to prevent the body from developing T2DM by improving IR and maintaining glucose homeostasis. Anthocyanins (ACNs) are water-soluble pigments and are widely distributed in natural products. This article summarizes research on the bioavailability and metabolism of ACNs. Moreover, we further elaborate on how ACNs reduce IR and hyperglycemia during the development of T2DM based on studies over the past 20 years. Many studies have demonstrated that ACNs are small molecules that target the pancreatic, liver, muscle, and adipose tissues, preventing IR and hyperglycemia. However, the molecular mechanisms are still unclear. Therefore, we envision whether the molecular mechanism of reducing T2DM by ACNs could be more deeply investigated.

Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers

Since consuming gutter oil does great harm to people's health, the Food Safety Administration has always been seeking for a more effective and timely supervision. As laboratory tests consume much time, and existing field tests have excessive limitations, a more comprehensive method is in great need. This is the first time a study proposes machine learning algorithms for real-time gutter oil detection under multiple feature dimensions. Moreover, it is deployed on FPGA to be low-power and portable for actual use. Firstly, a variety of oil samples are generated by simulating the real detection environment. Next, based on previous studies, sensors are used to collect significant features that help distinguish gutter oil. Then, the acquired features are filtered and compared using a variety of classifiers. The best classification result is obtained by k-NN with an accuracy of 97.18%, and the algorithm is deployed to FPGA with no significant loss of accuracy. Power consumption is further reduced with the approximate multiplier we designed. Finally, the experimental results show that compared with all other platforms, the whole FPGA-based classification process consumes 4.77 µs and the power consumption is 65.62 mW. The dataset, source code and the 3D modeling file are all open-sourced.

Detection and quantification of palmolein and palm kernel oil added as adulterant in coconut oil based on triacylglycerol profile

Economically motivated adulteration of expensive coconut oil with low cost oil, like palm kernel oil and palmolein is difficult to detect and quantify by available methods primarily due to their overlapping physicochemical properties with coconut oil. In the present work, a HPLC method has been developed to detect and quantify the degree of adulteration of coconut oil with palmolein and palm kernel oil based on triglyceride structure. The normalized area percentage of trilaurin (C36) among the three major TAG molecular species dilaurin-monocaprin/myristin-caprylin-laurin (C34), trilaurin (C36) and dilaurin-monomyristin (C38) of coconut oil was chosen as detection index for quantifying degree of adulteration of coconut oil with palm kernel oil, while the area ratio of dipalmitoyl-monoolein: trilaurin was chosen as detection index for quantifying adulteration of coconut oil with palmolein. The RP-HPLC based method developed in the present work is effective with a 2-4% minimum detection limit of adulterant oils and 78-98% detection accuracy depending on the degree of adulteration and types of oil.

Rapid quantification of fatty acids in plant oils and biological samples by LC-MS

Analysis of fatty acids (FA) in food and biological samples such as blood is indispensable in modern life sciences. We developed a rapid, sensitive and comprehensive method for the quantification of 41 saturated and unsaturated fatty acids by means of LC-MS. Optimized chromatographic separation of isobaric analytes was carried out on a C8 reversed phase analytical column (100 × 2.1 mm, 2.6 μm core–shell particle) with a total run time of 15 min with back pressure lower than 300 bar. On an old triple quadrupole instrument (3200, AB Sciex), pseudo selected reaction monitoring mode was used for quantification of the poorly fragmenting FA, yielding limits of detection of 5–100 nM. Sample preparation was carried out by removal of phospholipids and triglycerides by solid-phase extraction (non-esterified fatty acids in oils) or saponification in iso-propanol (fatty acyls). This is not only a rapid strategy for quantification of fatty acyls, but allows the direct combination with the LC-MS-based analysis of fatty acid oxidation products (eicosanoids and other oxylipins) from the same sample. The concentrations of fatty acyls determined by means of LC-MS were consistent with those from GC-FID analysis demonstrating the accuracy of the developed method. Moreover, the method shows high precisions with a low intra-day (≤ 10% for almost all fatty acids in plasma and ≤ 15% in oils) and inter-day as well as inter-operator variability (< 20%). The method was successfully applied on human plasma and edible oils. The possibility to quantify non-esterified fatty acids in samples containing an excess of triacylglycerols and phospholipids is a major strength of the described approach allowing to gain new insights in the composition of biological samples.

Sterols as a biomarker in tracing niger and sesame seeds oils adulterated with palm oil

Food adulteration is a serious social problem all over the world. The oil obtained from the niger and sesame is known for its quality and has a high market value in Ethiopia. The cost of the oil obtained from these oilseed crops is high unlike other plant oils, thus prone to be mixed with other cheap oils to increase profits. The study aimed to quantify the sterol profile of niger seed and sesame oils thereby trace adulteration of these oils with palm oil. Gas Chromatography coupled to Mass Spectrometry was used to analyze the sterol fractions of oils. A blend of palm oil, at a level of 10%, with niger seed and sesame oil was prepared. In all the studied oils; sitosterol (467.2–2778.96 mg/kg), campesterol (295.9–869.85 mg/kg) and stigmasterol (125.6–920 mg/kg) were the dominant sterols identified. Lupeol, Lanosterol, and Olean-12-en-3-one were only identified in a significant proportion for niger seed oil. Moreover, cholestroltrimethyl silyl ether (19.9 mg/kg) and 24-Nor-22,23- methylenecholest-5-en-3β-ol trimethylsilyl (TMS) ethers (139.14 mg/kg) were only identified in palm oil and used to trace adulteration. An attempt made to trace these compounds by mixing palm oil at a level of 10% with niger seed and sesame oils was successfully detected its presence. Hence, as the physicochemical properties of oils can be arranged to cover adulteration, marker identification provides a reliable identity of the specific oil.

Characterization of iron walnut in different regions of China based on phytochemical composition

Little is known about the phytochemical composition of iron walnuts. Differences in the geographical origin of iron walnuts associated with economic benefits should also be examined. In this study, the phytochemical composition (fatty acids, Vitamin E, total polyphenols and flavonoids, amino acids, and minerals) of iron walnuts in China was investigated. The results showed that there were significant differences (p < 0.05) in the phytochemical composition of iron walnut oils and flours from different regions. Positive (r > 0.5, p < 0.05) and negative (r < - 0.5, p < 0.05) correlations were found between amino acids/minerals and amino acids/oleic acid, with the highest correlation coefficient (r = 0.742, p < 0.05) between Cu and tyrosine. In addition, based on the 12 phytochemical fingerprints selected by random forest, a geographical-origin identification model for iron walnuts was established, with a corresponding correct classification rate of 96.6%. The top three phytochemical fingerprints for the geographical-origin identification of iron walnut were microelements, macroelements, and antioxidant composition, with contribution rates of 61.7%, 18.1%, and 9.9%, respectively.

Degradation of the polar lipid and fatty acid molecular species in extra virgin olive oil during storage based on shotgun lipidomics

Among the bioactive compounds present in extra-virgin olive oil, polar lipids and free fatty acids are minor compounds with well-known nutritional values and have been studied for traceability and adulteration investigations as well. In the present paper, the simultaneous characterization of polar lipids and free fatty acids in a pool of fifteen EVOO samples was achieved by means of reversed phase C18 analysis coupled to negative polarity high-resolution mass spectrometry. A total of 24 polar lipids, comprising 19 phospholipids and 5 sulfolipids, and 27 free fatty acids were tentatively identified, including several odd-chain and very long-chain fatty acids at trace levels. Moreover, a one-month study of lipid degradation on simulated storage conditions was carried out thanks to the set-up of a dedicated approach for degradation product analysis which was implemented of Compound Discoverer software. By virtue of the customized data processing workflow, more than forty compounds were tentatively identified, including compounds deriving from hydrolysis and oxidation reactions. Finally, by analysis of peak area trends, phosphoester hydrolyses of polar heads of phospholipids emerged as the fastest reactions, followed by glycerol ester hydrolyses and oxidative processes.

Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures

Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid profile of a specific oil type can be mimicked by a mixture of 2 or more oil types. This has led to fraudulent oil adulteration and intentional mislabeling of edible oils threatening food safety and endangering public health. Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures. Trained on a large number of simulated oil mixtures, independent test dataset validation demonstrates that the model has a 50^th percentile absolute error between 1.4–1.8% and a 90^th percentile error of 4–5.4% for any 3-way mixtures of the ten oil types. The deep learning model can also be further refined with on-line training. Because oil-producing plants have diverse geographical origins and hence slightly varying fatty acid profiles, an online-training method provides also a way to capture useful knowledge presently unavailable. Our method allows the ability to control product quality, determining the fair price of purchased oils and in-turn allowing health-conscious consumers the future of accurate labeling.

Fraudulent adulteration of edible oils is based on the fact that their characteristic fatty acid profile can be mimicked with mixtures of other oil types. Here, the authors use a deep learning method to uncover fatty acid patterns discriminative for ten different plant oil types and to discern composition of mixtures.

Sustainable Palm Oil-The Role of Screening and Advanced Analytical Techniques for Geographical Traceability and Authenticity Verification

Palm oil production from oil palm (Elaeis guineensis Jacq.) is vital for the economy of Malaysia. As of late, sustainable production of palm oil has been a key focus due to demand by consumer groups, and important progress has been made in establishing standards that promote good agricultural practices that minimize impact on the environment. In line with the industrial goal to build a traceable supply chain, several measures have been implemented to ensure that traceability can be monitored. Although the palm oil supply chain can be highly complex, and achieving full traceability is not an easy task, the industry has to be proactive in developing improved systems that support the existing methods, which rely on recorded information in the supply chain. The Malaysian Palm Oil Board (MPOB) as the custodian of the palm oil industry in Malaysia has taken the initiative to assess and develop technologies that can ensure authenticity and traceability of palm oil in the major supply chains from the point of harvesting all the way to key downstream applications. This review describes the underlying framework related to palm oil geographical traceability using various state-of-the-art analytical techniques, which are also being explored to address adulteration in the global palm oil supply chain.

Identification of camellia oil using FT-IR spectroscopy and chemometrics based on both isolated unsaponifiables and vegetable oils

Camellia oil is one of editable high-quality oils recommended by Food and Agriculture Organization. Thus the method to authenticate camellia oil is significant research. Saponification is one of the simple and inexpensive processes have been used to identify the adulteration in edible oil. At present, the saponification takes a long time, higher temperature and the isolation of unsaponifiables from saponifiables is tedious. In this research, the enriched saponification process has been developed using ultrasonication technique instead of a conventional reflux method. The process has been significantly reduced to 15 min at 55 °C from the regular saponification which need about 2 h by ISO 18609:2000. The special solid phase extraction (SPE) cartridge has been designed and prepared to separate the unsaponifiables, which separates the residual alkaline substance as well as absorbs water in the organic phase in a single cycle. PLS-DA is used to establish model I based on isolated unsaponifiables and model II based on of vegetable oils for identification of camellia oil. The combined FT-IR and chemometrics based on the isolated unsaponifiables was first used to authenticate vegetable oil. Model I had more sensitivity to discriminate adulterated camellia oils by adulterants whose fatty acid compositions similar to camellia oil such as hazelnut oil, soybean oil, corn oil and cheap mixed oil. On the contrary, model II had more sensitivity to discriminate adulterated camellia oils by adulterant whose fatty acid compositions were different from camellia oil such as palm oil. The results concluded that the FT-IR spectroscopy combined with chemometrics based on both isolated unsaponifiables and vegetable oils could be fast and effective to authenticate camellia oil.

Identification and Validation of Metabolic Markers for Adulteration Detection of Edible Oils Using Metabolic Networks

Food adulteration is a challenge faced by consumers and researchers. Due to DNA fragmentation during oil processing, it is necessary to discover metabolic markers alternative to DNA for adulteration detection of edible oils. However, the contents of metabolic markers vary in response to various factors, such as plant species, varieties, geographical origin, climate, and cultivation measures. Thus, it is difficult to identify a universal marker for all adulterants that may be present in some authentic samples. Currently, the specificity and selectivity of metabolic biomarkers are difficult to validate. Therefore, this study developed a screening strategy based on plant metabolic networks by developing a targeted analytical method for 56 metabolites in a metabolic network, using liquid/liquid extraction-liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified a chain of 11 metabolites that were related to isoflavonoid biosynthesis, which were detected in soybean oils but not rapeseed oils. Through multiple-marker mutual validation, these metabolites can be used as species-specific universal markers to differentiate soybean oil from rapeseed oil. Moreover, this method provides a model for screening characteristic markers of other edible vegetable oils and foods.

Optical screening for presence of banned Sudan III and Sudan IV dyes in edible palm oils

Due to the proven carcinogenicity of Sudan III and IV dyes, they are considered global public health issues. They are banned in all forms as food colourants. We propose the monitoring of simple and easy-to-measure optical properties of palm oils, such as the refractive indices and spectrophotometric properties, as efficient indicators to detect adulteration. Coupling these results with principal component analysis, excess refractive index, and integration of transmittance introduces a novel detection tool for the authentication of edible palm oil. This opens a new opportunity for accurate handheld devices to detect adulteration and provide control in the field. This work assessed in total of 49 samples, some collected from different parts of Ghana and others, in-house adulterated samples. The Ghana Food and Drugs Authority, who performed a complex and expensive chemical analysis of the samples, confirmed our results with good agreement.

Direct Discrimination of Edible Oil Type, Oxidation, and Adulteration by Liquid Interfacial Surface-Enhanced Raman Spectroscopy

The quality and safety of edible oils is a momentous but formidable challenge, especially regarding identification of oil type, oxidation, and adulteration. Most conventional analytical methods have bottlenecks in sensitivity, specificity, accessibility, or reliability. Surface-enhanced Raman spectroscopy (SERS) is promising as an unlabeled and ultrasensitive technique but limited by modification of inducers or surfactants on metal surfaces for oil analysis. Here, we develop a quantitative SERS analyzer on two-liquid interfacial plasmonic arrays for direct quality classification of edible oils by a portable Raman device. The interfacial plasmonic array is self-assembled through mixing the gold nanoparticle (GNP) sols and oil sample dissolved in chloroform without any surfactants or pretreatments. Different kinds of edible oils dissolved in chloroform directly participate in self-assembly of plasmonic arrays that finally localizes onto a three-dimensional (3D) oil/water interface. The 3D plasmonic array is self-healing, shape adaptive, and can be transferred to any glass containers as a substrate-free SERS analyzer for direct Raman measurements. It produces sensitive responses of SERS on different kinds of edible oils. By virtue of principal component analysis (PCA), this analyzer is able to quickly distinguish six edible oils, oxidized oils, and adulterated oils. Moreover, the solvent chloroform generates unique and stable SERS bands that can utilized as an inherent internal standard (IIS) to calibrate SERS fluctuation and greatly improve quantitation accuracy. Compared to conventional lab methods, this analyzer avoids complex and time-consuming preprocessing and provides significant advantages in cost, speed, and utility. Our study illuminates a facile way to determine edible oil quality and promises great potential in food quality and safety analysis.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops

Oilseed crops are one of the most important sources of vegetable oils for food and industry. Nutritional and technical properties of vegetable oil are primarily determined by its fatty acid (FA) composition. The content and composition of FAs in plants are commonly determined using gas chromatography-mass spectrometry (GS-MS) or gas chromatography-flame ionization detection (GC-FID) techniques. In the present work, we applied ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) technique to FA profiling of sunflower and rapeseed seeds and compared this method with the GC-FID technique. GC-FID detected 11 FAs in sunflower and 13 FAs in rapeseed, while UPLC-MS appeared to be more sensitive, detecting about 2.5 times higher numbers of FAs in both plants. In addition to even-chain FAs, UPLC-MS was able to detect odd-chain FAs. The longest FA detected using GC-FID was an FA with 24 carbon atoms, whereas UPLC-MS could reveal the presence of longer FAs with the tails of up to 28 carbon atoms. Based on our results, we may conclude that UPLC-MS has great potential to be used for the assessment of FA profiles of oil crops.

Stimulated Brillouin scattering in combination with visible absorption spectroscopy for authentication of vegetable oils and detection of olive oil adulteration

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil (EVOO) possesses a higher price than that of other vegetable oils. Adulteration of pure EVOO with other types of vegetable oils has attracted increasing attentions. In this work, a stimulated Brillouin scattering (SBS) combined with visible absorption spectroscopy method is proposed for authentication of vegetable oils and detection of olive oil adulteration. The results provided here have demonstrated that the different vegetable oils and adulteration oils exhibit significant differences in normalized absorbance values of two relevant wavelengths (455 and 670 nm) and frequency shifts of SBS. The normalized absorbance values of all spectra at the two relevant wavelengths of 670 nm and 455 nm linearly decrease with the increase of the adulteration concentration. The Brillouin frequency shifts exponentially increase with the increase of the adulteration concentration. Due to non-destructive and requiring no sample pretreatment procedure, this method can be effectively employed for authentication and detection of oils adulteration.

Ratiometric analysis using Raman spectroscopy as a powerful predictor of structural properties of fatty acids

Raman spectroscopy has been used extensively for the analysis of biological samples in vitro, ex vivo and in vivo. While important progress has been made towards using this analytical technique in clinical applications, there is a limit to how much chemically specific information can be extracted from a spectrum of a biological sample, which consists of multiple overlapping peaks from a large number of species in any particular sample. In an attempt to elucidate more specific information regarding individual biochemical species, as opposed to very broad assignments by species class, we propose a bottom-up approach beginning with a detailed analysis of pure biochemical components. Here, we demonstrate a simple ratiometric approach applied to fatty acids, a subsection of the lipid class, to allow the key structural features, in particular degree of saturation and chain length, to be predicted. This is proposed as a starting point for allowing more chemically and species-specific information to be elucidated from the highly multiplexed spectrum of multiple overlapping signals found in a real biological sample. The power of simple ratiometric analysis is also demonstrated by comparing the prediction of degree of unsaturation in food oil samples using ratiometric and multivariate analysis techniques which could be used for food oil authentication.