Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research.

The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).

Analysis and Annotation of Phospholipids by Mass Spectrometry-Based Metabolomics.

Phospholipids are essential components of membrane lipid bilayers and serve as precursors of multiple signaling molecules, so alterations in their homeostasis are associated with the pathogenesis of numerous diseases. In this context, the application of mass spectrometry-based metabolomics has demonstrated great potential to comprehensively characterize the human phospholipidome. In this chapter, we describe an untargeted method for the determination of phospholipids and other related metabolites in a variety of biological matrices, including plasma/serum, erythrocytes, and tissues, based on the combination of high-throughput direct mass spectrometry fingerprinting and subsequent profiling by ultra-high-performance reversed-phase liquid chromatography coupled to mass spectrometry. Furthermore, we also review the characteristic fragmentation patterns of phospholipids with the aim of providing simple guidelines for their straightforward annotation.

Comprehensive and High-Throughput Method for Quantitative Fingerprinting of Phenolic Compounds.

Accurate, robust, and wide-coverage analytical tools are needed in polyphenol research to deal with the high physicochemical complexity of the secondary plant metabolome. In this chapter, a novel method based on reversed-phase ultrahigh-performance liquid chromatography coupled with a diode array detector and mass spectrometry is presented, which enables high-throughput, comprehensive, and quantitative fingerprinting of a broad spectrum of phenolic compounds and related metabolites in different food products. The simplicity, low-cost, and excellent analytical performance of this method would facilitate its implementation in food science for quality control and authenticity purposes.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability.

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

High-Throughput Method for Wide-Coverage and Quantitative Phenolic Fingerprinting in Plant-Origin Foods and Urine Samples.

The use of mass spectrometry is currently widespread in polyphenol research because of its sensitivity and selectivity, but its usual high cost, reduced robustness, and nonavailability in many analytical laboratories considerably hinder its routine implementation. Herein, we describe the optimization and validation of a high-throughput, wide-coverage, and robust metabolomics method based on reversed-phase ultra-high-performance liquid chromatography with diode array detection for the identification and quantification of 69 phenolic compounds and related metabolites covering a broad chemical space of the characteristic secondary metabolome of plant foods. The method was satisfactorily validated following the Food and Drug Administration guidelines in terms of linearity (4-5 orders of magnitude), limits of quantification (0.007-3.6 mg L-1), matrix effect (60.5-124.4%), accuracy (63.4-126.7%), intraday precision (0.1-9.6%), interday precision (0.6-13.7%), specificity, and carryover. Then, it was successfully applied to characterize the phenolic fingerprints of diverse food products (i.e., olive oil, red wine, strawberry) and biological samples (i.e., urine), enabling not only the detection of many of the target compounds but also the semi-quantification of other phenolic metabolites tentatively identified based on their characteristic absorption spectra. Therefore, this method represents one step further toward time-efficient and low-cost polyphenol fingerprinting, with suitable applicability in the food industry to ensure food quality, safety, authenticity, and traceability.

Impact of dietary mercury intake during pregnancy on the health of neonates and children: a systematic review.

CONTEXT: Growing evidence suggests that prenatal exposure to methyl mercury through the maternal diet could have great influence on the neurological and physical development of neonates and young children. OBJECTIVE: The aim of this review was to evaluate the clinical repercussions of maternal exposure to methyl mercury during pregnancy on the health of fetuses, neonates, and children up to 8 years of age. DATA SOURCES: The Web of Science, PubMed, CINAHL, Scopus, and Dialnet Plus databases were searched for articles published in English or Spanish from 1990 to 2020. STUDY SELECTION: Original articles published in English or Spanish from 1990 to 2020 were eligible for inclusion. All study designs were eligible. Animal studies were excluded. Two authors independently screened studies for inclusion. From the 971 records initially identified, 19 studies were included in the systematic review. DATA EXTRACTION: PRISMA guidelines were followed. Outcomes extracted included maternal dietary exposure to methyl mercury during pregnancy, possible health repercussions in offspring up to 8 years of age, and quantitative measurement of mercury in a biological sample. All studies met the requirements established for assessing both study quality and risk of bias. RESULTS: Prenatal exposure to mercury was consistently associated with lower birth weight, but only one study reported a negative association with length at birth. Higher mercury levels were also related to lower scores in various neuropsychological and developmental tests. CONCLUSION: The literature shows clear evidence of the adverse effects of maternal methyl mercury exposure on anthropometric variables and cognitive or physical development in children. It is noteworthy, however, that mercury toxicity may sometimes be mitigated by other essential nutrients in the maternal diet, such as polyunsaturated fatty acids.

Potential of Ultraviolet-Visible Spectroscopy for the Differentiation of Spanish Vinegars According to the Geographical Origin and the Prediction of Their Functional Properties.

High-quality wine vinegars with unique organoleptic characteristics are produced in southern Spain under three Protected Designations of Origin (PDO), namely "Jerez", "Condado de Huelva" and "Montilla-Moriles". To guarantee their authenticity and avoid frauds, robust and low-cost analytical methodologies are needed for the quality control and traceability of vinegars. In this study, we propose the use of ultraviolet-visible spectroscopy in combination with multivariate statistical tools to discriminate Spanish wine vinegars according to their geographical origin, as well as to predict their physicochemical and functional properties. Linear discriminant analysis provided a clear clustering of vinegar samples according to the PDO with excellent classification performance (98.6%). Furthermore, partial least squares regression analysis demonstrated that spectral data can serve as accurate predictors of the total phenolic content and antioxidant activity of vinegars. Accordingly, UV-Vis spectroscopy stands out as a suitable analytical tool for simple and rapid authentication and traceability of vinegars.

Mechanistic Insights into Alzheimer's Disease Unveiled through the Investigation of Disturbances in Central Metabolites and Metabolic Pathways.

Hydrophilic metabolites are closely involved in multiple primary metabolic pathways and, consequently, play an essential role in the onset and progression of multifactorial human disorders, such as Alzheimer's disease. This review article provides a comprehensive revision of the literature published on the use of mass spectrometry-based metabolomics platforms for approaching the central metabolome in Alzheimer's disease research, including direct mass spectrometry, gas chromatography-mass spectrometry, hydrophilic interaction liquid chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Overall, mounting evidence points to profound disturbances that affect a multitude of central metabolic pathways, such as the energy-related metabolism, the urea cycle, the homeostasis of amino acids, fatty acids and nucleotides, neurotransmission, and others.

Evolution of Physicochemical Parameters during the Thermal-Based Production of Água-mel, a Traditional Portuguese Honey-Related Food Product.

The purpose of this work was to investigate the physicochemical changes occurring during the thermal-based production of água-mel, a traditional Portuguese honey-related food product. The refractive index, color parameters (hue angle, H°; chroma, C*), and the content of total reducing sugars, glucose, fructose, total brown pigments, and 5-hydroxymethylfurfural were monitored along the entire production process, and their evolution was kinetically modelled. Thermal processing caused a gradual decrease in sugars, which was accompanied by the formation of brown pigments and 5-hydroxymethylfurfural, increased concentration of soluble solids as evaluated through refractive index measurements, as well as the appearance of darker colors. In particular, a zero-order kinetic model could explain the changes in H° and reducing sugars, while the evolution of refractive index, brown pigments, 5-hydroxymethylfurfural, C*, fructose, and glucose were best fitted using a first-order kinetics model.

Volatile Profiling of Strawberry Fruits Cultivated in a Soilless System to Investigate Cultivar-Dependent Chemical Descriptors.

Volatile compounds are essential for food organoleptic characteristics and of great utility for the food industry as potential markers for authenticity purposes (e.g., variety, geographical origin, adulteration). The aim of this study was to determine the characteristic volatile compounds of strawberry samples grown in a soilless system by using headspace solid phase micro-extraction coupled with gas chromatography and to investigate the influence of cultivar (Festival, Candonga, Camarosa) on this volatile profile. We observed that Festival and, to a lesser extent, Candonga varieties were characterized by the richest aroma-related profiles, including higher levels of esters, furanones and terpenes. In particular, methyl butyrate, hexyl hexanoate, linalool, geraniol and furaneol were the most abundant aromatic compounds detected in the three varieties of strawberries. Complementarily, the application of pattern recognition chemometric approaches, including principal component analysis and linear discriminant analysis, demonstrated that concentrations of specific volatiles can be employed as chemical descriptors to discriminate between strawberry cultivars. Accordingly, geraniol and hexyl hexanoate were found to be the most significant volatiles for the discrimination of strawberry varieties.

Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics.

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research.

Fatty Acid Profiling for the Authentication of Iberian Hams According to the Feeding Regime.

The quality and sensory characteristics of Iberian ham are closely related to the pig feeding regime. These are mainly due to the inclusion or not of acorns into the diet, which significantly increases the content of monounsaturated fatty acids in this food product. In this work, the fatty acid profile from subcutaneous fat samples was evaluated and modeled with various chemometric approaches as a potential tool for authentication of Iberian ham from three categories according to the rearing system: "Jamón de Bellota", "Jamón de Cebo de Campo", and "Jamón de Cebo". The application of artificial neural networks provided satisfactory classification and prediction rates, with oleic acid being the most important variable driving this differentiation.

Multi-Chemical Profiling of Strawberry as a Traceability Tool to Investigate the Effect of Cultivar and Cultivation Conditions.

The chemical composition of foods is tightly regulated by multiple genotypic and agronomic factors, which can thus serve as potential descriptors for traceability and authentication purposes. In the present work, we performed a multi-chemical characterization of strawberry fruits from five varieties (Aromas, Camarosa, Diamante, Medina, and Ventana) grown in two cultivation systems (open/closed soilless systems) during two consecutive campaigns with different climatic conditions (rainfall and temperature). For this purpose, we analyzed multiple components closely related to the sensory and health characteristics of strawberry, including sugars, organic acids, phenolic compounds, and essential and non-essential mineral elements, and various complementary statistical approaches were applied for selecting chemical descriptors of cultivar and agronomic conditions. Anthocyanins, phenolic acids, sucrose, and malic acid were found to be the most discriminant variables among cultivars, while climatic conditions and the cultivation system were behind changes in polyphenol contents. These results thus demonstrate the utility of combining multi-chemical profiling approaches with advanced chemometric tools in food traceability research.

Simple and Efficient Green Extraction of Steviol Glycosides from Stevia rebaudiana Leaves.

The food industry has currently shown great interest in alternative sweeteners to sugars with the aim of producing healthier products. In light of this, steviol glycosides are natural low-caloric sweeteners present in Stevia rebaudiana, which have additionally been described as bioactive components with potential therapeutic properties. In this work, a green method for the extraction of steviol glycosides from stevia leaves was optimized by applying a factorial screening design of five variables (time, temperature, agitation, grinding, and sample-solvent ratio) and the subsequent response surface design of Box-Behnken. The optimized extraction method allows for the recovery of stevia sweeteners in a simple and efficient manner by using tap water as the extractant, without the application of an auxiliary energy source to reduce costs, thus representing an interesting strategy for their industrial-scale production.

Assessment of Virgin Olive Oil Adulteration by a Rapid Luminescent Method.

The adulteration of virgin olive oil with hazelnut oil is a common fraud in the food industry, which makes mandatory the development of accurate methods to guarantee the authenticity and traceability of virgin olive oil. In this work, we demonstrate the potential of a rapid luminescent method to characterize edible oils and to detect adulterations among them. A regression model based on five luminescent frequencies related to minor oil components was designed and validated, providing excellent performance for the detection of virgin olive oil adulteration.

High-Throughput Metabolomics Based on Direct Mass Spectrometry Analysis in Biomedical Research.

Metabolomics based on direct mass spectrometry analysis shows a great potential in biomedical research because of its high-throughput screening capability and wide metabolome coverage. This chapter contains detailed protocols to perform comprehensive metabolomic fingerprinting of multiple biological samples (serum, plasma, urine, brain, liver, spleen, thymus) by using complementary analytical platforms. The most important issues to be considered are discussed, including sample treatment, metabolomic analysis, raw data preprocessing, and data analysis.

Optimization of Growth and Carotenoid Production by Haloferax mediterranei Using Response Surface Methodology.

Haloferax mediterranei produces C50 carotenoids that have strong antioxidant properties. The response surface methodology (RSM) tool helps to accurately analyze the most suitable conditions to maximize C50 carotenoids production by haloarchaea. The effects of temperature (15⁻50 °C), pH (4-10), and salinity (5⁻28% NaCl (w/v)) on the growth and carotenoid content of H. mediterranei were analyzed using the RSM approach. Growth was determined by measuring the turbidity at 600 nm. To determine the carotenoid content, harvested cells were lysed by freeze/thawing, then re-suspended in acetone and the total carotenoid content determined by measuring the absorbance at 494 nm. The analysis of carotenoids was performed by an HPLC system coupled with mass spectrometry. The results indicated the theoretical optimal conditions of 36.51 or 36.81 °C, pH of 8.20 or 8.96, and 15.01% or 12.03% (w/v) salinity for the growth of haloarchaea (OD600 = 12.5 ± 0.64) and production of total carotenoids (3.34 ± 0.29 mg/L), respectively. These conditions were validated experimentally for growth (OD600 = 13.72 ± 0.98) and carotenoid production (3.74 ± 0.20 mg/L). The carotenoid profile showed four isomers of bacterioruberin (89.13%). Our findings suggest that the RSM approach is highly useful for determining optimal conditions for large-scale production of bacterioruberin by haloarchaea.

High-Throughput Direct Mass Spectrometry-Based Metabolomics to Characterize Metabolite Fingerprints Associated with Alzheimer's Disease Pathogenesis.

Direct mass spectrometry-based metabolomics has been widely employed in recent years to characterize the metabolic alterations underlying Alzheimer's disease development and progression. This high-throughput approach presents great potential for fast and simultaneous fingerprinting of a vast number of metabolites, which can be applied to multiple biological matrices including serum/plasma, urine, cerebrospinal fluid and tissues. In this review article, we present the main advantages and drawbacks of metabolomics based on direct mass spectrometry compared with conventional analytical techniques, and provide a comprehensive revision of the literature on the use of these tools in the investigation of Alzheimer's disease.

Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition.

This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research.

Mass Spectrometry-Based Metabolomic Multiplatform for Alzheimer's Disease Research.

The integration of complementary analytical platforms has emerged as a suitable strategy to perform a comprehensive metabolomic characterization of complex biological systems. In this work, we describe the most important issues to be considered for the application of a mass spectrometry multiplatform in Alzheimer's disease research, which combines direct analysis with electrospray and atmospheric pressure photoionization sources, as well as orthogonal hyphenated approaches based on reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus). It is noteworthy that the metabolomic pipeline here detailed has demonstrated a great potential for the investigation of metabolic perturbations underlying Alzheimer's disease pathogenesis.