Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Lipidomic characteristics of three edible cold-pressed oils by LC/Q-TOF for simple quality and authenticity assurance

Distinguishing oil samples from each other is challenging but it is crucial for ensuring food quality, and for detecting and preventing the possible adulteration of these products. Lipidomic profiling is believed to provide sufficient information to get fit-to-purpose confidence of oil identification as well as to deliver oil-specific lipid features which could be used as targets for routine authenticity testing of camelina, flax, and hemp oil in food control laboratories. Conducted di- and triacylglycerol profiling by LC/Q-TOFMS yielded successful differentiation of the oils. A marker panel consisting of 27 lipids (both DAGs and TAGs) useful for quality verification and authenticity assurance of the oils was established. Moreover, sunflower, rapeseed, and soybean oils were analysed as potential adulterants. We identified 6 lipid markers (DAGs 34:6, 35:2, 40:1, 40:2, 42:2, and TAG 63:1) which can be used for revealing the adulteration of camelina, hemp, and flax seed oils with these oils.

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.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis

BACKGROUND

The edible oil storage period is one of the important indicators for evaluating the intrinsic quality of edible oil. The present study aimed to develop a portable electronic nose device for the qualitative identification of the edible oil storage period. First, four metal oxide semiconductor gas sensors, comprising TGS2600, TGS2611, TGS2620 and MQ138, were selected to prepare a sensor array to assemble a portable electronic nose device. Second, the homemade portable electronic nose device was used to obtain the odor change information of edible oil samples during different storage periods, and the sensor features were extracted. Finally, three pattern recognition methods, comprising linear discriminant analysis (LDA), K-nearest neighbors (KNN) and support vector machines (SVM), were compared to establish a qualitative identification model of the edible oil storage period. The input features and related parameters of the model were optimized by a five-fold cross-validation during the process of model establishment.

RESULTS

The research results showed that the recognition performance of the non-linear SVM model was significantly better than that of the linear LDA and KNN models, especially in terms of generalization performance, which had a correct recognition rate of 100% when predicting independent samples in the prediction set.

CONCLUSION

The overall results demonstrate that it is feasible to apply the homemade portable electronic nose device with the help of the appropriate pattern recognition methods to achieve the fast and efficient identification of the edible oil storage period, which provides an effective analysis tool for the quality detection of the edible oil storage. © 2020 Society of Chemical Industry.

Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

Extra Virgin Olive Oil (EVOO), the emblematic food of the Mediterranean diet, is recognized for its nutritional value and beneficial health effects. The main authenticity issues associated with EVOO's quality involve the organoleptic properties (EVOO or defective), mislabeling of production type (organic or conventional), variety and geographical origin, and adulteration. Currently, there is an emerging need to characterize EVOOs and evaluate their genuineness. This can be achieved through the development of analytical methodologies applying advanced "omics" technologies and the investigation of EVOOs chemical fingerprints. The objective of this review is to demonstrate the analytical performance of High Resolution Mass Spectrometry (HRMS) in the field of food authenticity assessment, allowing the determination of a wide range of food constituents with exceptional identification capabilities. HRMS-based workflows used for the investigation of critical olive oil authenticity issues are presented and discussed, combined with advanced data processing, comprehensive data mining and chemometric tools. The use of unsupervised classification tools, such as Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), as well as supervised classification techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Orthogonal Projection to Latent Structure-Discriminant Analysis (OPLS-DA), Counter Propagation Artificial Neural Networks (CP-ANNs), Self-Organizing Maps (SOMs) and Random Forest (RF) is summarized. The combination of HRMS methodologies with chemometrics improves the quality and reliability of the conclusions from experimental data (profile or fingerprints), provides valuable information suggesting potential authenticity markers and is widely applied in food authenticity studies.

Differentiation of avocados according to their botanical variety using liquid chromatographic fingerprinting and multivariate classification tree

BACKGROUND

The oil content, composition and marketing threshold value of an avocado depends on the cultivar hence, identifying the cultivar of the avocado fruit is desirable. However, analytical methods have not been reported with this aim.

RESULTS

A multivariate classification tree method was proposed to discriminate three commercial botanical varieties of avocado: Hass, Fuerte and Bacon, using high-performance liquid chromatography coupled to a charged aerosol detector (HPLC-CAD). Prior to the chromatographic analysis the avocados were lyophilized and then the oil fraction was extracted using a pressurized liquid extraction system. Normal and reverse phase liquid chromatography were applied in order to obtain the chromatographic fingerprint for each sample. Soft independent modelling of class analogies (SIMCA) and partial least-squares discriminant analysis (PLS-DA) were applied. Classification quality metrics were determined to evaluate the performance of the classification. Several strategies to develop the classification models were employed. Finally, the useful application of 'classification trees' methodology, which has been scarcely applied in the field of analytical food control, was evaluated to perform a multiclass classification.

CONCLUSION

Discrimination of the three botanical varieties was achieved. The best classification was obtained when the PLS-DA is applied on the normal-phase chromatographic fingerprints. Classification trees are showed to be useful tools that provide complementary information to single concatenated models showing different results from the same prediction sample set. © 2019 Society of Chemical Industry.

Analysis of Triacylglycerols in Castor Oil Through Liquid Chromatography-Mass Spectrometry Based on Fourier Transform-Ion Cyclotron Resonance-Mass Spectrometry and Gas Chromatography-Mass Spectrometry

Castor oil is a traditional Chinese medicine containing a chemically complex mixture of triacylglycerols (TAGs). Herein, the TAGs in castor oil were analyzed in detail. First, the fatty acid composition of castor oil was examined via methylated derivatisation followed by gas chromatography-mass spectrometry (GC-MS). Then, using the fatty acid composition data, liquid chromatography-mass spectrometry (LC-MS) was applied to analyze the composition and relative content of TAGs. Nine TAGs were identified, wherein triricinolein (RRR) constituted ~81% of the total TAG content based on the peak areas. However, the TAGs in castor oil contain hydroxyl groups, which are incompatible with the equivalent carbon number (ECN) rule. Thus, an extended-ECN rule was expressed as E-ECN = CN (carbon number)-2·DB (double-bond number)-6·OH (hydroxyl group number) to characterize the retention of hydroxyl-containing TAGs in HPLC using a C18 column. Moreover, hydroxyl-containing TAGs may become dehydrated when analyzed using atmospheric pressure chemical ionization (APCI), which was confirmed via APCI Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS). Thus, the TAGs in castor oil were accurately identified through LC-MS together with FT-ICR-MS and GC-MS.

Research advances based on mass spectrometry for profiling of triacylglycerols in oils and fats and their applications

Vegetable oils and animal fats are dietary source of lipids that play critical and multiple roles in biological function. Triacylglycerols (TAGs) are the principal component of oils and fats with significant difference in profile among different oils and fats. TAG profiling is essential for nutritional evaluation, quality control and assurance of safety in oils and fats. However, analysis of TAGs is a challenging task because of the complicated composition of TAGs and their similar physicochemical properties in oils and fats. The rapid development of mass spectrometry (MS) technology in recent years makes it possible to analyze the composition, content and structure of TAGs in the study of the physical, chemical and nutritional properties of oils, fats and related products. This review described the research advancement based on MS for profiling of TAGs in oil, fat and their applications in food. The application of MS, including direct infusion strategies, and its combination with chromatography, gas chromatography-MS (GC-MS) and liquid chromatography-MS (LC-MS), in the analysis of TAGs were reviewed. The advantages and disadvantages of these analytical methods with relevant applications for TAGs analysis in food were also described.

Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality

A wide variety of olive oil samples from different origins and olive types has been chemically analyzed as well as evaluated by trained sensory panelists. Six chemical parameters have been obtained for each sample (free fatty acids, peroxide value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) and linked to their quality using an artificial neural network-based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality. Two different methods were defined to assess the statistical performance of the model (a K-fold cross-validation (K = 6) and three different blind tests), and both of them showed around a 95-96% correct classification rate. These results support that a relationship between the chemical and the sensory analyses exists and that the mathematical tool can potentially be implemented into a device that could be employed for various useful applications.

Exploratory data analysis in the study of 7Be present in atmospheric aerosols

PURPOSE

Exploratory data analysis (EDA) is applied in this research to study the behavior of radioactive aerosols present in the surface atmosphere of Granada, using (7)Be as radiotracer. The reason for this study is to reduce the large number of parameters involved in understanding their behavior, given the complexity of the atmosphere.

METHODS

Aerosol particles were collected weekly in Granada (Spain) over a 5-year period. Low-background gamma spectrometry was used to determine concentrations of (7)Be-aerosol activity. The variables studied were: (7)Be concentration, cosmic ray intensity, temperature, temperature interval, rainfall, relative humidity, and Saharan intrusions. Least significant difference test (LSD), hierarchical cluster analysis (HCA), and principal component analysis (PCA) with varimax rotation have been applied to study the datasets.

RESULTS AND DISCUSSION

The results of our study reveal that aerosol behavior is represented by two principal components which explain 86.23 % of total variance. Components PC1 and PC2 respectively explain 74.61 and 11.62 % of total variance. PC1 explains the cyclical and seasonal pattern of the samples, while PC2 is related to the production of (7)Be. In addition, PCA and HCA show good distribution of the samples by families with two groups, summer and winter, at the extremes and spring-autumn in the middle. This result corroborates that there are no differences between spring and autumn in the climate of Granada.

CONCLUSIONS

EDA has been found to be quite useful in studying the behavior of radioactive aerosols in the surface atmosphere of a city with the climate and geographical characteristics of Granada.