Molecular characterization of edible vegetable oils via free fatty acid and triacylglycerol fingerprints by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry

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.

Characterization of the Nonpolar and Polar Extractable Components of Glanded Cottonseed for Its Valorization

Cottonseed is the second major product of cotton (Gossypium spp.) crops after fiber. Thus, the characterization and valorization of cottonseed are important parts of cotton utilization research. In this work, the nonpolar and polar fractions of glanded (Gd) cottonseed were sequentially extracted by 100% hexane and 80% ethanol aqueous solutions and subjected to ¹³C and ¹H nuclear magnetic resonance (NMR) spectroscopy and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), respectively. The nonpolar (crude oil) extracts showed the characteristic NMR peak features of edible plant oils with the absence of ω-3 linolenic acid. Quantitative analysis revealed the percentage of polyunsaturated, monounsaturated, and saturated fatty acids as 48.7%, 16.9%, and 34.4%, respectively. Both general unsaturated fatty acid features and some specific olefinic compounds (e.g., oleic, linolenic, and gondonic acids) were found in the nonpolar fraction. In the polar extracts, FT-ICR MS detected 1673 formulas, with approximately 1/3 being potential phenolic compounds. Both the total and phenolic formulas fell mainly in the categories of lipid, peptide-like, carbohydrate, and lignin. A literature search and comparison further identifies some of these formulas as potential bioactive compounds. For example, one compound [2,5-dihydroxy-N'-(2,3,4-trihydroxybenzylidene) benzohydrazide] identified in the polar extracts is likely responsible for the anticancer function observed when used on human breast cancer cell lines. The chemical profile of the polar extracts provides a formulary for the exploration of bioactive component candidates derived from cottonseed for nutritive, health, and medical applications.

Utilizing AgNPt-SALDI to Classify Edible Oils by Multivariate Statistics of Triacylglycerol Profile

Edible oils are valuable sources of nutrients, and their classification is necessary to ensure high quality, which is essential to food safety. This study reports the establishment of a rapid and straightforward SALDI-TOF MS platform used to detect triacylglycerol (TAG) in various edible oils. Silver nanoplates (AgNPts) were used to optimize the SALDI samples for high sensitivity and reproducibility of TAG signals. TAG fingerprints were combined with multivariate statistics to identify the critical features of edible oil discrimination. Eleven various edible oils were discriminated using principal component analysis (PCA). The results suggested the creation of a robust platform that can examine food adulteration and food fraud, potentially ensuring high-quality foods and agricultural products.

Diacylglycerols ions as novel marker indicators for the classification of edible oils using ultrahigh resolution mass spectrometry

Diacylglycerols (DAGs) ions, instead of triacylglycerols (TAGs) ions, were established as marker indicators for an improved classification of edible oils using ultrahigh resolution mass spectrometry (UHRMS). DAGs ions can be used not only to identify triacylglycerols (TAGs) and their embedded fatty acids (FAs), but also to distinguish positional isomers of TAGs. In this work, DAGs ions were determined in edible oils by direct infusion atmospheric pressure chemical ionization-ultrahigh resolution mass spectrometry (APCI-UHRMS), where the ultrahigh resolving power up to 500,000 FWHM (full width at half maximum) can provide accurate molecular compositions and detailed fingerprints MS spectra in a minute. A total of 146 samples belonging to 22 species of plant oils and animal fats, were characterized. Chemometric analyses were performed using principal component analysis, partial least square-discriminant analysis and orthogonal partial least squares-discriminant analysis. DAGs ions were proved to be better than TAGs ions as marker indicators in the chemometric analyses. An overall correct rate of 93.40% was achieved for the classification of tested samples. In addition, blend oils and gutter oils were also characterized by this developed method.

Virgin olive oil metabolomics: A review

Metabolomics involvement in the study of foods is steadily growing. Such a rise is a consequence of the increasing demand in the food sector to address challenges regarding the issues of food safety, quality, and authenticity in a more comprehensive way. Virgin olive oil (VOO) is a key product of the Mediterranean diet, with a globalized consumer interest as it may be associated with various nutritional and health benefits. Despite the strict legislation to protect this high added-value agricultural commodity and offer guarantees to consumers and honest producers, there are still analytical issues needing to be further addressed. Thus, this review aims to present the efforts made using targeted and untargeted metabolomics approaches, namely nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry-based techniques (mainly LC/GC-MS) combined with multivariate statistical analysis. Case-studies focusing on geographical/varietal classification and detection of adulteration are discussed with regards to the identification of possible markers. The advantages and limitations of each of the aforementioned techniques applied to VOO analysis are also highlighted.