Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC-MS fingerprints

Rapid classification of Camellia seed varieties and non-destructive high-throughput quantitative analysis of fatty acids based on non-targeted fingerprint spectroscopy combined with chemometrics

Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared [MIR] spectroscopy) and analytical models (Discriminant Analysis [DA], Partial Least Squares [PLS], and Artificial Neural Networks [ANN]), seeking to classify Camellia seed varieties and estimate oil and principal FAs composition. The PCA analysis effectively discriminated among various Camellia seed varieties, likely due to variations in their oil and principal FAs compositions. Significantly, the NIR-based DA model significantly outperformed MIR, achieving 100 % accuracy in distinguishing Camellia seed varieties. In terms of predicting the oil and principal FAs compositions in Camellia seeds, NIR-based predictions models outperformed those derived from MIR, with PLS models surpassing ANN models. This study validated the potential of NIR technology combined with chemometrics for rapid, high-throughput, non-destructive identification of Camellia seeds.

Detection of camellia oil adulteration based on near-infrared spectroscopy and smartphone combined with deep learning and multimodal fusion

Camellia oil (CO) is known for its nutritional value and health benefits, but its high price makes it susceptible to adulteration. This study developed a binary adulteration system for CO in response to the adulteration of rapeseed oil (RO) into CO that been observed in the market. A total of 243 oil samples adulterated with various concentrations of RO were prepared. The spectral information of the adulterated oil samples was obtained using near-infrared (NIR) spectroscopy. Additionally, visual data obtained from smartphone-captured images and videos were analysed. Deep-learning models trained on video data reached the highest accuracy of 96.30 %. To improve detection accuracy, a multimodal approach was adopted by combing spectral and visual data. Generally, this study presented a novel method for detecting the authenticity of CO in real time, providing technical support to address increasingly serious food safety concerns and laying the foundation for future rapid online detection using smartphones.

Authentication of Edible Oil by Real-Time One Class Classification Modeling

Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it is challenging to collect representative samples from all over the world. Moreover, it is also very hard to evaluate the representativeness of collected samples. In this study, we blazed a new trail to propose an authentication method to identify adulterated edible oils without building a prediction model beforehand. An authentication method developed by real-time one-class classification modeling, and model population analysis was designed to identify adulterated oils in the market without building a classification model beforehand. The underlying philosophy of the method is that the sum of the absolute centered residual (ACR) of the good model built by only authentic samples is higher than that of the bad model built by authentic and adulterated samples. In detail, a large number of OCC models were built by selecting partial samples out of inspected samples using Monte Carlo sampling. Then, adulterated samples involved in the test of these good models were identified. Taking the inspected samples of avocado oils as an example, as a result, 6 out of 40 avocado oils were identified as adulterated and then validated by chemical markers. The successful identification of avocado oils adulterated with soybean oil, corn oil, or rapeseed oil validated the effectiveness of our method. The proposed method provides a novel idea for oils as well as other high-value food adulteration detection.

Comparison of extraction and refinement techniques for volatile compound analysis in camellia oil

The processing techniques of camellia oil, containing freshly squeezed (FSCO), refined (DFCO), cold-pressed (OFCO), and hot-pressed (RFCO), significantly influence flavor compounds and organoleptic properties. In this study, the preference for FSCO and RFCO was revealed by sensory evaluation due to the "fruity" and "roasted" flavors, respectively. Flavor differences among oils were accurately distinguished by the E-nose. A total of 77 and 116 odorants were identified by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and gas chromatography-mass spectrometry (GC-MS), respectively. The gas chromatography-olfactometry (GC-O) and detection frequency analysis identified 34 active-aroma compounds with DF ≥ 2. Aroma recombination and omission experiments further confirmed that (E)-2-nonenal and nonanal, derived from the degradation of unsaturated fatty acids, significantly contributed to "green" and "fruity" aromas. Notably, these concentrations positively correlated with sensory preference. This study provides valuable insights into the flavor identification of camellia oil and the improvement of processing techniques.

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

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

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

Currently, the identification and quantification of complex adulteration of high-value vegetable oils are still challenging. In this study, the extreme vertex design method was adopted to design representative multivariate adulterated camellia oil samples. Thereafter, 11 characteristic lipid species were identified by considering the statistically significant difference and categorical contribution. A discriminant method and linear regression model were established based on 11 key lipids. The accuracy rate of the model was 100 %, which could correctly discriminate the camellia oil with an adulteration ratio as low as 2.5 %. The root mean square error (RMSE) of the regression equation was close to 0, and the coefficient of determination (R2P) was 0.9054. This method has been successfully applied to commercially available samples for validation purposes, detecting 27.3 % of camellia oil adulteration. Overall, the results indicate that the discriminating method and content prediction model can provide a potential strategy for authentication of multivariate vegetable oils.

Quantitative Analysis of Peanut Skin Adulterants by Fourier Transform Near-Infrared Spectroscopy Combined with Chemometrics

Peanut skin is a potential medicinal material. The adulteration of peanut skin samples with starchy substances severely affects their medicinal value. This study aimed to quantitatively analyze the adulterants present in peanut skin using Fourier transform near-infrared (FT-NIR) spectroscopy. Two adulterants, sweet potato starch and corn starch, were included in this study. First, spectral information of the adulterated samples was collected for characterization. Then, the applicability of different preprocessing methods and techniques to the obtained spectral data was compared. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to extract effective variables from the preprocessed spectral data, and Partial Least Squares Regression (PLSR), a Support Vector Machine (SVM), and a Black Kite Algorithm-Support Vector Machine (BKA-SVM) were employed to predict the adulterant content in the samples, as well as the overall adulteration level. The results showed that the BKA-SVM model performed excellently in predicting the content of sweet potato starch, corn starch, and overall adulterants, with determination coefficients (RP2) of 0.9833, 0.9893, and 0.9987, respectively. The experimental results indicate that FT-NIR spectroscopy combined with advanced machine learning techniques can effectively and accurately detect adulterants in peanut skin, providing a reliable technological support for food safety detection.

Unraveling the Metabolomics Mysteries in Camellia Oil: From Cognition to Application

Camellia oil is a high-value edible seed oil, recommended by the Food and Agriculture Organization (FAO). It is essential to develop accurate and rapid analytical methods to authenticate camellia oil due to its susceptibility to adulteration. Recently, hyphenated chromatography-mass spectrometry, especially high-resolution mass spectrometry using chemometrics, has become a promising platform for the identification of camellia oil. Based on the compositional analysis, the fatty acid, sterol, phenol, and tocopherol profiles (or fingerprints) were utilized as predictor variables for assessing authenticity. The review systematically summarizes the workflow of chromatography-mass spectrometry technologies and comprehensively investigates recent metabolomic applications combined with chemometrics for camellia oil authentication. Metabolomics has significantly improved our understanding of camellia oil composition at the molecular level, contributing to its identification and full characterization. Hence, its integration with standard analytical methods is essential to enhance the tools available for public and private laboratories to assess camellia oil authenticity. Integrating metabolomics with artificial intelligence is expected to accelerate drug discovery by identifying new metabolic pathways and biomarkers, promising to revolutionize medicine.

Quantitative Analysis of Blended Oils Based on Intensity Ratios of Marker Ions in MALDI-MS Spectra

Determination of quantitative compositions of blended oils is an essential but challenging step for the quality control and safety assurance of blended oils. We herein report a method for the quantitative analysis of blended oils based on the intensity ratio of triacylglycerol marker ions, which could be obtained from the highly reproducible spectra acquired by using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to directly analyze blended oils in their oily states. We demonstrated that this method could provide good quantitative results to binary, ternary, and quaternary blended oils, with simultaneous quantitation of multiple compositions, and was applicable for quantitative analysis of commercial blended oil products. Moreover, the intensity ratio-based method could be used to rapidly measure the proportions of oil compositions in blended oils, only based on the spectra of the blended oils and related pure oils, making the method as a high-throughput approach to meet the sharply growing analytical demands of blended oils.

Amelioration of walnut, peony seed and camellia seed oils against D-galactose-induced cognitive impairment in mice by regulating gut microbiota

Diet adjustment will affect the health of gut microbiota, which in turn influences the development and function of the organism's brain through the gut-brain axis. Walnut oil (WO), peony seed oil (PSO) and camellia seed oil (CSO), as typical representatives of woody plant oils, have been shown to have the potential to improve cognitive impairment in mice, but the function mechanisms are not clear. In this study, we comparatively investigated the neuroprotective effects of these three oils on D-galactose (D-gal)-induced cognitive impairment in mice, and found that the ameliorative effect of WO was more prominent. During the behavioral experiments, supplementation with all three oils would improve spatial learning and memory functions in D-gal mice, with a significant reduction in the error times (p < 0.001) and a significant increase in step-down latency (p < 0.001); walnut oil supplementation also significantly increased the number of hidden platform traversals, the target quadrant spent times and percentage of distance (p < 0.05). The results of biomarker analysis showed that WO, in addition to significantly inhibiting D-gal-induced oxidative stress and neuroinflammation as did PSO, significantly increased the ACh content in the mouse brain (p < 0.05) and modulated neurotransmitter levels. The results of further microbiota diversity sequencing experiments also confirmed that dietary supplementation with all three oils affected the diversity and composition of the gut microbiota in mice. Among them, WO significantly restored the balance of the mouse gut microbiota by increasing the abundance of beneficial bacteria (Bacteroidetes, Actinobacteria, Firmicutes) and decreasing the abundance of harmful bacteria (Clostridium, Shigella, Serratia), which was consistent with the results of behavioral experiments and biomarker analyses. Based on the analysis of the fatty acid composition of the three oils and changes in the gut microbiota, it is hypothesized that there is a correlation between the fatty acid composition of the dietary supplement oils and neuroprotective effects. The superiority of WO over PSO and CSO in improving cognitive impairment is mainly attributed to its balanced composition of omega-6 and omega-3 fatty acids.

Traditional uses, phytochemistry, pharmacology, toxicity and clinical application of traditional Chinese medicine Cynoglossum amabile: a review

Cynoglossum amabile, a member of the Boraginaceae family, is a well-known traditional Chinese medicine and ethnomedicine known as Daotihu. Despite several studies confirming the presence of bioactive pyrrolizidine alkaloids such as amabiline, ambelline, echinatine, europine, and others in C. amabile, there has been no comprehensive review of its traditional uses, phytochemistry, and pharmacology thus far. This review was conducted by thoroughly examining the literature and analyzing network databases. It covers various aspects of C. amabile, including botanical characteristics, geographical distribution, traditional applications, phytochemistry, pharmacological activities, toxicology, and clinical applications. The results have shown that C. amabile has been traditionally used for medicinal, edible, and ornamental purposes in China for many centuries. The whole plant, root, and leaf of C. amabile are used by different ethnic groups, such as Lisu, Bai, Naxi, Yi, Jinuo, and Han, to treat malaria, hepatitis, dysentery, leucorrhea, tuberculosis cough, fracture, joint dislocation, trauma bleeding, and skin carbuncle abscess. A total of 47 chemical components, including alkaloids (pyrrolizidine alkaloids, PAs), sterols, organic acids, and saccharides, were isolated from C. amabile. Pharmacological studies show that the chemical extracts of C. amabile possess various biological activities, such as anti-inflammatory, anti-tumor, anti-microbial, cardiovascular effects, ganglionic action, and acetylcholinesterase inhibition. However, it is important to note that C. amabile exhibits hepatotoxicity, with its toxicity being linked to its primary PAs components. Although preliminary studies suggest potential applications in the treatment of prostate diseases and alopecia, further research is needed to validate these clinical uses. Our review highlights the traditional uses, phytochemistry, biological activity, toxicity, and clinical applications of C. amabile. It emphasizes the essential guiding role of the indigenous medicinal knowledge system in developing new drugs. Previous studies have shown that the phytochemical and pharmacological characteristics of C. amabile are significantly related to its traditional medicinal practices. Cynoglossum amabile has excellent market potential and can be further analyzed in terms of phytochemistry, pharmacology, and toxicology, which are critical for its clinical drug safety, quality evaluation, and resource development.

Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.

Differential analysis of volatiles in five types of mosquito-repellent products by chemometrics combined with headspace GC-Orbitrap HRMS nontargeted detection

This paper reports a method for the differential analysis of volatile chemical components in five novel types of mosquito-repellent products based on chemometrics combined with headspace gas chromatography-Orbitrap high-resolution mass spectrometry (HS-GC-Orbitrap HRMS) nontargeted screening. A total of 358 unknown substances were detected in 30 samples under specific headspace conditions. Through principal component analysis and orthogonal partial least-squares discriminant analysis, 36 significantly different substances with variable importance in the projection values greater than 1 were further screened, and these substances were accurately identified by GC-Orbitrap HRMS. Most substances were found for the first time in mosquito-repellent products. The clustered heat map, Venn diagram and peak area histogram showed that the mosquito-repellent products had similar volatile composition, and the volatile species and content of different types of mosquito-repellent products significantly varied. Substances, such as eucalyptol, d-limonene, α-pinene, β-pinene, dl-menthol and methyl salicylate, may be the main sources of odour in mosquito-repellent products. This work explored the characteristic volatile components in mosquito-repellent products and comparatively analysed the chemical composition of different types of products. It can be generalised to consumer products as a case study and has positive implications for promoting product quality and safety and improving production processes.

Enzymatic Synthesis of Structured Lipids Enriched with Medium- and Long-Chain Triacylglycerols via Pickering Emulsion-Assisted Interfacial Catalysis: A Preliminary Exploration

Medium- and long-chain triacylglycerol (MLCT), as a novel functional lipid, is valuable due to its special nutritional properties. Its low content in natural resources and inefficient synthesis during preparation have limited its practical applications. In this study, we developed an effective Pickering emulsion interfacial catalysis system (PE system) for the enzymatic synthesis of MLCT by trans-esterification. Lipase NS 40086 served simultaneously as a catalyst and a solid emulsifier to stabilize the Pickering emulsion. Benefitting from the sufficient oil-water interface, the obtained PE system exhibited outstanding catalytic efficiency, achieving 77.5% of MLCT content within 30 min, 26% higher than that of a water-free system. The Km value (0.259 mM) and activation energy (14.45 kJ mol-1) were 6.8-fold and 1.6-fold lower than those of the water-free system, respectively. The kinetic parameters as well as the molecular dynamics simulation and the tunnel analysis implied that the oil-water interface enhanced the binding between substrate and lipase and thus boosted catalytic efficiency. The conformational changes in the lipase were further explored by FT-IR. This method could give a novel strategy for enhancing lipase activity and the design of efficient catalytic systems to produce added-value lipids. This work will open a new methodology for the enzymatic synthesis of structured lipids.

Identification of Adulteration of Flaxseed Oil From QINGHAI Area Using GC-MS Profiling of Phytosterol Composition and Chemometrics

Flaxseed oil is an important source of vegetable oil with a polyunsaturated fatty acid. It is significant to establish a method to quickly identify adulterated flaxseed oil. In the present study, the qualitative and quantitative analysis of phytosterol of flaxseed oil from different varieties and different production areas in the Qinghai area was first performed by gas chromatography-mass spectrometry (GC-MS) and the phytosterol standard profile of flaxseed oil was established. Then, a combination of similarity evaluation and cluster analysis was used to distinguish pure flaxseed oil from flaxseed oil adulterated with concentrations of 10-50% rapeseed oil, peanut oil, sunflower oil, and sesame oil, and discriminant analysis was used to identify the types of adulterated flaxseed oil. The results showed that similarity evaluation combined with cluster analysis can distinguish pure and adulterated flaxseed oil when the concentration of the adulterant was greater than 10%. Discriminant analysis models accurately identified the types of adulterating oil in flaxseed oil when the concentration of rapeseed, peanut, or sunflower oil was greater than 20%, and that of sesame oil was greater than 30%. This study shows that the determination of the phytosterol composition and chemometrics is a valuable tool to evaluate the purity of flaxseed oil.

Lipid Metabolism and Improvement in Oilseed Crops: Recent Advances in Multi-Omics Studies

Oilseed crops are rich in plant lipids that not only provide essential fatty acids for the human diet but also play important roles as major sources of biofuels and indispensable raw materials for the chemical industry. The regulation of lipid metabolism genes is a major factor affecting oil production. In this review, we systematically summarize the metabolic pathways related to lipid production and storage in plants and highlight key research advances in characterizing the genes and regulatory factors influencing lipid anabolic metabolism. In addition, we integrate the latest results from multi-omics studies on lipid metabolism to provide a reference to better understand the molecular mechanisms underlying oil anabolism in oilseed crops.

Detection of walnut oil adulterated with high-linoleic acid vegetable oils using triacylglycerol pseudotargeted method based on SFC-QTOF-MS

Authentication of walnut oil (WO) is challenging due to the adulteration of high-linoleic acid vegetable oils (HLOs) with similar fatty acid composition. To allow the discrimination of WO adulteration, a rapid, sensitive and stable scanning method based on supercritical fluid chromatography quadrupole time-of-flight mass spectrometry (SFC-QTOF-MS) was established to profile 59 potential triacylglycerol (TAGs) in HLOs samples within 10 min. Limit of quantitation of the proposed method is 0.002 µg mL^-1 and the relative standard deviations range from 0.7% to 12.0%. TAGs profiles of WO samples from various varieties, geography origins, ripeness, and processing methods were used to construct orthogonal partial least squares-discriminant analysis (OPLS-DA) and OPLS models that were highly accurate in both qualitative and quantitative prediction at adulteration levels as low as 5% (w/w). This study advances the TAGs analysis to characterize vegetable oils and holds promise as an efficient method for oil authentication.

Comprehensive adulteration detection of sesame oil based on characteristic markers

Sesame oil has a unique flavor and is very popular in Asian countries, and this leads to frequent adulteration. In this study, comprehensive adulteration detection of sesame oil based on characteristic markers was developed. Initially, sixteen fatty acids, eight phytosterols, and four tocopherols were utilized to construct an adulteration detection model, which screened seven potentially adulterated samples. Subsequently, confirmatory conclusions were drawn based on the characteristic markers. Adulteration with rapeseed oil in 4 samples was confirmed using the characteristic marker of brassicasterol. The adulteration of soybean oil in 1 sample was confirmed using the isoflavone. The adulteration of 2 samples with cottonseed oil was demonstrated by sterculic acid and malvalic acid. The results showed that sesame oil adulteration could be detected by screening positive samples using chemometrics and verifying with characteristic markers. The comprehensive adulteration detection method could provide a system approach for market supervision of edible oils.

Effects of Endogenous Anti-Oxidative Components from Different Vegetable Oils on Their Oxidative Stability

The effects of endogenous anti-oxidative components of ten common edible vegetable oils (palm olein, corn oil, rapeseed oil, soybean oil, perilla seed oil, high oleic sunflower oil, peanut oil, camellia oil, linseed oil, and sesame oil) on oxidation were explored in this research. The oxidation processes and patterns of the oils were investigated with the Schaal oven test using fatty acids and the oxidative stability index, acid value, peroxide value, p-anisidine value, total oxidation value, and content of major endogenous anti-oxidative components as indicators. The major endogenous anti-oxidative components in vegetable oils were tocopherols, sterols, polyphenols, and squalene, among which α-tocopherol, β-sitosterol, and polyphenols showed good anti-oxidative activity. However, squalene and polyphenols were relatively low and showed limited anti-oxidative effects. Moreover, the oxidative stability index of edible vegetable oils oxidized at high temperature (120 °C) was positively correlated with the content of saturated fatty acids (r = 0.659) and negatively correlated with the content of polyunsaturated fatty acids (r = -0.634) and calculated oxidizability (r = -0.696). When oxidized at a low temperature (62 °C), oxidative stability was influenced by a combination of fatty acid composition as well as endogenous anti-oxidative components. An improved TOPSIS based on Mahalanobis distance was used to evaluate the oxidative stability of different types of vegetable oils. Moreover, the oxidative stability of corn oil was better than the other vegetable oils, while perilla seed oil was very weak.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

Accurate quantification of TAGs to identify adulteration of edible oils by ultra-high performance liquid chromatography-quadrupole-time of flight-tandem mass spectrometry

Edible oils play important roles in biological functions, and triacylglycerols (TAGs) in edible oils are complex mixtures. This makes accurate TAGs quantitation quite difficult that bring economically motivated food adulteration. Herein, we demonstrated a strategy for accurate quantification of TAGs in edible oils, which could be applied in identification of olive oil adulteration. The results showed that the proposed strategy could significantly improve the accuracy of TAG content determination, reduce the relative error of fatty acids (FAs) content determination, and present a wider accurate quantitative range than that of gas chromatography-flame ionization detection. Most important, this strategy coupled with principal component analysis could be used to identify adulteration of high-priced olive oil with cheaper soybean oils, rapeseed oils or camellia oils at a lower concentration of 2%. These findings indicated that the proposed strategy could be regarded as a potential method for edible oils quality and authenticity analysis.

Comprehensive Evaluation of Quality Characteristics of Four Oil-Tea Camellia Species with Red Flowers and Large Fruit

Red-flowered oil-tea camellia (ROC) is an important woody oil species growing in the south, and its oil has high nutritional value. There are four main species of ROC in China, namely, Camellia chekiangoleosa (CCH), Camellia polyodonta (CPO), Camellia semiserrata (CSE) and Camellia reticulata (CRE). Reports on the comprehensive comparative analysis of ROC are limited. This study investigated the fruit characteristics and nutritional components of four ROC fruits, and the results showed that ROC had high oil content with levels of 39.13%-58.84%, especially the CCH fruit, which reached 53.6-58.84%. The contents of lipid concomitants of ROC oil were also substantial, including β-amyrin (0.87 mg/g-1.41 mg/g), squalene (0.43 mg/g-0.69 mg/g), β-sitosterin (0.47 mg/g-0.63 mg/g) and α-tocopherol (177.52 μg/g-352.27 μg/g). Moreover, the transverse diameter(TD)/longitudinal diameter (LD) of fruits showed a significant positive correlation with the oil content, and ROC fruits with thinner peels seemed to have better oil quality, which is similar to the result of the oil quality evaluation obtained by the gray correlation coefficient evaluation method. Four ROC oils were evaluated using the gray correlation coefficient method based on 11 indicators related to the nutritional value of ROC. CCH oil had the highest score of 0.8365, and YS-2 (a clone of CCH) was further evaluated as the best CCH oil. Finally, the results of heatmap analysis showed that triglycerides could be used as a characteristic substance to distinguish CCH oil from the other three ROC oils. The PLSDA (Partial least squares regression analysis) model and VIP (Variable important in projection) values further showed that P/S/O, P/O/O, P/L/L, P/L/Ln, S/S/O, S/O/O and P/S/S (these all represent abbreviations for fatty acids) could be used as characteristic differential triglycerides among the four ROC oils. This study provides a convenient way for planters to assess the nutritional quality of seed oil depending on fruit morphology and a potential way to distinguish between various ROC oils.

Purification of Camellia Oil by Inorganic Ceramic Membrane

Camellia oil is an edible health oil with high medicinal value. While phospholipids, peroxides, and free fatty acids are present in unrefined camellia virgin oil (CVO), which has a negative impact on the quality characteristics and storage stability. This paper is to investigate the testing effects of transmembrane pressure and temperature on the membrane flux and degumming (the removal of colloidal substances from crude oil and which is mainly phospholipids) to determine the optimum process parameters for the purification of CVO. On this basis, the effects of purification treatments applied by using a membrane system with membranes of different pore sizes (200, 140, 20, 15, and 10 nm) on CVO were tested. The results indicate that the purification treatments of ceramic membrane on CVO reduced the contents of phospholipids (87.0% reduction), peroxides (29.2% reduction), and free fatty acids (16.2% reduction) at a transmembrane pressure of 0.4 MPa and temperature of 60 °C. At the same time, these treatments did not significantly alter the fatty acid composition. Thus, ceramic membranes have the potential for the purification of camellia oil, which could be an effective way to achieve the purification of camellia oil.

Typical Characterization of Commercial Camellia Oil Products Using Different Processing Techniques: Triacylglycerol Profile, Bioactive Compounds, Oxidative Stability, Antioxidant Activity and Volatile Compounds

The processing technique is one of the key factors affecting the quality of camellia oil. In this study, camellia oils were obtained using four different processing techniques (cold-pressed, roast-pressed, fresh-pressed, and refined), and their triacylglycerols (TAGs) profile, bioactive compound (tocopherols, sterols, squalene, and polyphenols) level, oxidative stability, and volatile compounds were analyzed and compared. To further identify characteristic components in four camellia oil products, the TAG profile was analyzed using UPLC-QTOF-MSE. Five characteristic markers were identified, including OOO (m/z 902.8151), POL (m/z 874.7850), SOO (m/z 904.8296), PPL (m/z 848.7693), PPS (m/z 852.7987). Regarding the bioactive compound level and antioxidant capacity, the fresh-pressed technique provided higher α-tocopherols (143.15 mg/kg), β-sitosterol (93.20 mg/kg), squalene (102.08 mg/kg), and polyphenols (35.38 mg/kg) and showed stronger overall oxidation stability and antioxidant capacity. Moreover, a total of 65 volatile compounds were detected and identified in four camellia oil products, namely esters (23), aldehydes (19), acids (8), hydrocarbons (3), ketones (3), and others (9), among which pressed oil was dominated by aldehydes, acid, and esters, while refined oil had few aroma components. This study provided a comprehensive comparative perspective for revealing the significant influence of the processing technique on the camellia oil quality and its significance for producing camellia oil of high quality and with high nutritional value.

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

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

Integration of lipidomics and metabolomics for the authentication of camellia oil by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry coupled with chemometrics

The integration of lipidomics and metabolomics approaches, based on UPLC-QTOF-MS technology coupled with chemometrics, was established to authenticate camellia oil adulterated with rapeseed oil, peanut oil, and soybean oil. Lipidomics revealed that the glyceride profile provides a prospective authentication of camellia oil, but no characteristic markers were available. Sixteen characteristic markers were identified by metabolomics. For camellia oil, all six markers were sapogenins of oleanane-type triterpene saponins. Lariciresinol, sinapic acid, doxercalciferol, and an unknown compound were identified as markers for rapeseed oil. Characteristic markers in peanut oil were formononetin, sativanone, and medicarpin. In the case of soybean oil, the characteristic markers were dimethoxyflavone, daidzein, and genistein. The established OPLS-DA and OPLS prediction models were highly accurate in the qualitative and quantitative analyses of camellia oil adulterated with 5% other oils. These results indicate that the integration of lipidomics and metabolomics approaches has great potential for the authentication of edible oils.

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

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

Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics

Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, alkaloid, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.

Unsaturated fatty-acid based HPLC fingerprints in combination with quantitative analysis of multi-components by single-marker for the classification of Rana chensinensis ovum

Comprehensive quality evaluation strategy was established for Rana chensinensis ovum based on analytical chemistry and chemometrics.

Fast 1H-NMR Species Differentiation Method for Camellia Seed Oils Applied to Spanish Ornamentals Plants. Comparison with Traditional Gas Chromatography

Camellia genus (Theaceae) is comprised of world famous ornamental flowering plants. C. japonica L. and C. sasanqua Thunb are the most cultivated species due to their good adaptation. The commercial interest in this plant linked to its seed oil increased in the last few years due to its health attributes, which significantly depend on different aspects such as species and environmental conditions. Therefore, it is essential to develop fast and reliable methods to distinguish between different varieties and ensure the quality of Camellia seed oils. The present work explores the study of Camellia seed oils by species and location. Two standardized gas chromatography methods were applied and compared with that of data obtained from proton nuclear magnetic resonance spectroscopy (¹H-NMR) for fatty acids profiling. The principal component analysis indicated that the proposed ¹H-NMR methodology can be quickly and reliably applied to separate specific Camellia species, which could be extended to other species in future works.

Composition Profiling and Authenticity Assessment of Camellia Oil Using High Field and Low Field 1H NMR

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.