Foodomics approaches: New insights in phenolic compounds analysis

Fruits, vegetables, and plant-based foods contain several bioactive substances such as phenolic compounds (PCs), that are plant secondary metabolites with attributed health properties. The study of the metabolic pathways of PCs, including those related with their synthesis, transport, accumulation, and degradation are essential to advance in this field of research. In this regard, omics tools such as foodomics are gaining relevance due to their versatility and their tremendous potential to generate significant advances in PC research. In this review, we present a comprehensive overview of the applications of omics technologies in PC analysis, including transcriptomics, micromics, proteomics and metabolomics, highlighting their role in metabolic pathways, current limitations, and emerging insights. Omics techniques as well as data analyses are continuously progressing, emerging new opportunities with onset of artificial intelligence and machine learning. However, significant limitations and challenges still remain. The immense diversity of PC chemical structures and their variability across plant species, varieties, and impact of agronomic factors complicate the analyses and limit the extrapolation of findings. Additionally, high data dimensionality, strong correlations among measured variables, and general lack of standardization in the different omics techniques can impact in the results. Addressing these limitations requires integrating multi-omics approaches and developing standardized protocols to enhance comparability and interpretation in PC research. In summary, foodomics approaches arise as essential for the complete mapping of PC biosynthesis.

Combination of histological and metabolomic assessments to evaluate the potential pharmacological efficacy of saikosaponin D

Saikosaponin D (SsD), a natural triterpenoid saponin compound, exhibits notable potential in suppressing tumor growth and inhibiting metastasis, particularly in breast cancer. However, its underlying mechanism of action for SsD remains unclear. In this study, a combination strategy to reveal the metabolism modulation of SsD on breast cancer was performed by integration of histopathological assessments and untargeted metabolomics analysis. Pathological evaluation of the efficacy of SsD from a visual and intuitive perspective. Accordingly, a non-targeted metabolomics study was used to investigate the pharmacological efficacy using a set of serum samples from mice before and after (0-30 days) modulated with SsD based on ultra-high performance liquid chromatography tandem orbitrap mass spectrometry to discover metabolite biomarkers for finding the key metabolic mechanism in a molecular perspective. As a result, 20 metabolites were selected as potential biomarkers for SsD efficacy evaluation with high sensitivity and specificity. These metabolites changes were involved in sphingolipid metabolism, glycerophospholipid metabolism, phenylalanine and tryptophan metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis pathways, suggesting that SsD exerted anti-breast cancer effects through the regulation of the underlying metabolism. In conclusion, we developed a new analysis strategy that effectively discovers tumor-progressing related metabolite biomarkers in serum for pharmacological efficacy evaluation.

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing

Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL-1) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise t-test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.4~2.0 µg kg-1 and 0.3~2.1 µg kg-1 in lettuce and maize matrices, respectively.

Metabolomics-based non-targeted screening analysis of 34 PPCPs in bovine and piscine muscles

The metabolomics-based analytical strategy has showed superiority on the non-targeted screening of contaminants, especially for unknown and unexpected (U&U) contaminants in the field of food safety, but data analysis is often the bottleneck of the strategy. In this study, a novel metabolomics-based analytical method via searching for marker compounds was developed on the basis of ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) results to accurately, rapidly and comprehensively achieve the non-targeted screening of 34 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked in bovine and piscine muscle matrices. Three concentration groups (20, 50 and 100 ng mL^-1) were intentionally designed to simulate the control and experimental groups for the discovery of marker compounds, for which multivariate and univariate analyses were adopted. In multivariate analysis, each concentration group was fully separated from the other two groups in PCA and OPLS-DA plots, laying a foundation to distinguish marker compounds among groups. The S-plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify marker compounds, which were further verified by pairwise t-test and fold change judgement in univariate analysis. The results indicate that 34 PPCPs spiked in two muscle matrices were all identified as marker compounds, proving the validity and practicability of this novel metabolomics-based non-targeted screening method, which will exhibit great superiority and broad application prospects, especially in the face of massive PPCPs and various animal matrices in the field of food safety control. In addition, the limits of detection (LODs) for 34 PPCPs were calculated to be 0.2-2.6 μg kg^-1 and 0.3-2.1 μg kg^-1 in bovine and piscine muscle matrices, respectively.

Metabolomics as a Tool to Elucidate the Sensory, Nutritional and Safety Quality of Wheat Bread-A Review

Wheat (Triticum aestivum) is one of the most extensively cultivated and used staple crops in human nutrition, while wheat bread is annually consumed in more than nine billion kilograms over the world. Consumers’ purchase decisions on wheat bread are largely influenced by its nutritional and sensorial characteristics. In the last decades, metabolomics is considered an effective tool for elucidating the information on metabolites; however, the deep investigations on metabolites still remain a difficult and longtime action. This review gives emphasis on the achievements in wheat bread metabolomics by highlighting targeted and untargeted analyses used in this field. The metabolomics approaches are discussed in terms of quality, processing and safety of wheat and bread, while the molecular mechanisms involved in the sensorial and nutritional characteristics of wheat bread are pointed out. These aspects are of crucial importance in the context of new consumers’ demands on healthy bakery products rich in bioactive compounds but, equally, with good sensorial acceptance. Moreover, metabolomics is a potential tool for assessing the changes in nutrient composition from breeding to processing, while monitoring and understanding the transformations of metabolites with bioactive properties, as well as the formation of compounds like toxins during wheat storage.

Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)>1, Fold change (FC) > 1.5, and p < 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of >0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

Integrating metabolomics and targeted gene expression to uncover potential biomarkers of fungal/oomycetes-associated disease susceptibility in grapevine

Vitis vinifera, one of the most cultivated fruit crops, is susceptible to several diseases particularly caused by fungus and oomycete pathogens. In contrast, other Vitis species (American, Asian) display different degrees of tolerance/resistance to these pathogens, being widely used in breeding programs to introgress resistance traits in elite V. vinifera cultivars. Secondary metabolites are important players in plant defence responses. Therefore, the characterization of the metabolic profiles associated with disease resistance and susceptibility traits in grapevine is a promising approach to identify trait-related biomarkers. In this work, the leaf metabolic composition of eleven Vitis genotypes was analysed using an untargeted metabolomics approach. A total of 190 putative metabolites were found to discriminate resistant/partial resistant from susceptible genotypes. The biological relevance of discriminative compounds was assessed by pathway analysis. Several compounds were selected as promising biomarkers and the expression of genes coding for enzymes associated with their metabolic pathways was analysed. Reference genes for these grapevine genotypes were established for normalisation of candidate gene expression. The leucoanthocyanidin reductase 2 gene (LAR2) presented a significant increase of expression in susceptible genotypes, in accordance with catechin accumulation in this analysis group. Up to our knowledge this is the first time that metabolic constitutive biomarkers are proposed, opening new insights into plant selection on breeding programs.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

Characterization of Cultivar Differences of Blueberry Wines Using GC-QTOF-MS and Metabolic Profiling Methods

A non-targeted volatile metabolomic approach based on the gas chromatography-quadrupole time of fight-mass spectrometry (GC-QTOF-MS) coupled with two different sample extraction techniques (solid phase extraction and solid phase microextraction) was developed. Combined mass spectra of blueberry wine samples, which originated from two different cultivars, were subjected to orthogonal partial least squares-discriminant analysis (OPLS-DA). Principal component analysis (PCA) reveals an excellent separation and OPLS-DA highlight metabolic features responsible for the separation. Metabolic features responsible for the observed separation were tentatively assigned to phenylethyl alcohol, cinnamyl alcohol, benzenepropanol, 3-hydroxy-benzenethanol, methyl eugenol, methyl isoeugenol, (E)-asarone, (Z)-asarone, and terpenes. Several of the selected markers enabled a distinction in secondary metabolism to be drawn between two blueberry cultivars. It highlights the metabolomic approaches to find out the influence of blueberry cultivar on a volatile composition in a complex blueberry wine matrix. The distinction in secondary metabolism indicated a possible O-methyltransferases activity difference among the two cultivars.

Quantitative trait loci associated with different polar metabolites in perennial ryegrass - providing scope for breeding towards increasing certain polar metabolites

Background

Recent advances in the mapping of biochemical traits have been reported in Lolium perenne. Although the mapped traits, including individual sugars and fatty acids, contribute greatly towards ruminant productivity, organic acids and amino acids have been largely understudied despite their influence on the ruminal microbiome.

Results

In this study, we used a targeted gas-chromatography mass spectrometry (GC-MS) approach to profile the levels of 25 polar metabolites from different classes (sugars, amino acids, phenolic acids, organic acids and other nitrogen-containing compounds) present in a L. perenne F2 population consisting of 325 individuals. A quantitative trait (QTL) mapping approach was applied and successfully identified QTLs regulating seven of those polar metabolites (L-serine, L-leucine, glucose, fructose, myo-inositol, citric acid and 2, 3-hydroxypropanoic acid).Two QTL mapping approaches were carried out using SNP markers on about half of the population only and an imputation approach using SNP and DArT markers on the entire population. The imputation approach confirmed the four QTLs found in the SNP-only analysis and identified a further seven QTLs.

Conclusions

These results highlight the potential of utilising molecular assisted breeding in perennial ryegrass to modulate a range of biochemical quality traits with downstream effects in livestock productivity and ruminal digestion.

Electronic supplementary material

The online version of this article (10.1186/s12863-017-0552-0) contains supplementary material, which is available to authorized users.

NMR-Based Metabolomics of Oral Biofluids

NMR-based metabolomics is an established technique for characterizing the metabolite profile of biological fluids and investigating how metabolite profiles change in response to biological and/or clinical stimuli. Thus, NMR-based metabolomics has the potential to discover biomarkers for diagnosis, prognosis, and/or therapy of clinical conditions, as well as to unravel the physiology underlying clinical conditions. Here, we describe a detailed protocol for NMR-based metabolomics of oral biofluids, including sample collection, sample handling, NMR data acquisition, and processing. In addition, we give a general overview of the statistical analysis of the resulting metabolomic data.

Holistic Analysis Enhances the Description of Metabolic Complexity in Dietary Natural Products

In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic analysis of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concentrations. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its composition, thereby leading to an untargeted analysis. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR–based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented.

Evaluation of unintended effects in the composition of tomatoes expressing a human immunoglobulin A against rotavirus

The production of neutralizing immunoglobulin A (IgA) in edible fruits as a means of oral passive immunization is a promising strategy for the inexpensive treatment of mucosal diseases. This approach is based on the assumption that the edible status remains unaltered in the immunoglobulin-expressing fruit, and therefore extensive purification is not required for mucosal delivery. However, unintended effects associated with IgA expression such as toxic secondary metabolites and protein allergens cannot be dismissed a priori and need to be investigated. This paper describes a collection of independent transgenic tomato lines expressing a neutralizing human IgA against rotavirus, a mucosal pathogen producing severe diarrhea episodes. This collection was used to evaluate possible unintended effects associated with recombinant IgA expression. A comparative analysis of protein and secondary metabolite profiles using wild type lines and other commercial varieties failed to find unsafe features significantly associated with IgA expression. Preliminary, the data indicate that formulations derived from IgA tomatoes are as safe for consumption as equivalent formulations derived from wild type tomatoes.

Validated modeling for German white wine varietal authentication based on headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry fingerprinting

An untargeted analytical approach combined with chemometrics using the volatiles of German white wine was investigated regarding the usefulness for verifying botanical origin. A total of 198 wine samples of Riesling, Müller-Thurgau, Silvaner, Pinot Gris, and Pinot Blanc were examined applying headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry. The resultant three-dimensional raw data were processed by available metabolomics software. After data treatment, a partial least-squares discriminant analysis (PLS-DA) model was validated. External samples were correctly classified for 97% Silvaner, 93% Riesling, 91% Pinot Gris/Blanc, and 80% Müller-Thurgau. This model was related to monoterpenoids, C13-norisoprenoids, and esters. Further, 100% prediction for a two-class model of Riesling versus Pinot Gris/Blanc was confirmed by 74 additional samples measured independently. Hence, the strategy applied was, in particular, reliable and relevant for white wine varietal classification. In addition, the superior classification performance of the Riesling class was revealed.

Changes in the metabolome of lettuce leaves due to exposure to mancozeb pesticide

This paper describes a proton high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) metabolomic study of lettuce (Lactuca sativa L.) leaves to characterise metabolic adaptations during leaf growth and exposure to mancozeb. Metabolite variations were identified through multivariate analysis and checked through spectral integration. Lettuce growth was accompanied by activation of energetic metabolism, preferential glucose use and changes in amino acids, phospholipids, ascorbate, nucleotides and nicotinate/nicotinamide. Phenylalanine and polyphenolic variations suggested higher oxidative stress at later growth stages. Exposure to mancozeb induced changes in amino acids, fumarate and malate, suggesting Krebs cycle up-regulation. In tandem disturbances in sugar, phospholipid, nucleotide and nicotinate/nicotinamide metabolism were noted. Additional changes in phenylalanine, dehydroascorbate, tartrate and formate were consistent with a higher demand for anti-oxidant defence mechanisms. Overall, lettuce exposure to mancozeb was shown to have a significant impact on plant metabolism, with mature leaves tending to be more extensively affected than younger leaves.

Metabolomics in toxicology and preclinical research

Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological status of a biological system, and about the changes caused by chemicals. Metabolomics analysis is used in many fields, ranging from the analysis of the physiological status of genetically modified organisms in safety science to the evaluation of human health conditions. In toxicology, metabolomics is the -omics discipline that is most closely related to classical knowledge of disturbed biochemical pathways. It allows rapid identification of the potential targets of a hazardous compound. It can give information on target organs and often can help to improve our understanding regarding the mode-of-action of a given compound. Such insights aid the discovery of biomarkers that either indicate pathophysiological conditions or help the monitoring of the efficacy of drug therapies. The first toxicological applications of metabolomics were for mechanistic research, but different ways to use the technology in a regulatory context are being explored. Ideally, further progress in that direction will position the metabolomics approach to address the challenges of toxicology of the 21st century. To address these issues, scientists from academia, industry, and regulatory bodies came together in a workshop to discuss the current status of applied metabolomics and its potential in the safety assessment of compounds. We report here on the conclusions of three working groups addressing questions regarding 1) metabolomics for in vitro studies 2) the appropriate use of metabolomics in systems toxicology, and 3) use of metabolomics in a regulatory context.

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

Food Analysis: Present, Future, and Foodomics

This paper presents a revision on the instrumental analytical techniques and methods used in food analysis together with their main applications in food science research. The present paper includes a brief historical perspective on food analysis, together with a deep revision on the current state of the art of modern analytical instruments, methodologies, and applications in food analysis with a special emphasis on the works published on this topic in the last three years (2009–2011). The article also discusses the present and future challenges in food analysis, the application of “omics” in food analysis (including epigenomics, genomics, transcriptomics, proteomics, and metabolomics), and provides an overview on the new discipline of Foodomics.

Present and future challenges in food analysis: foodomics

The state-of-the-art of food analysis at the beginning of the 21st century is presented in this work, together with its major applications, current limitations, and present and foreseen challenges.

The NutriChip project – translating technology into nutritional knowledge

Advances in food transformation have dramatically increased the diversity of products on the market and, consequently, exposed consumers to a complex spectrum of bioactive nutrients whose potential risks and benefits have mostly not been confidently demonstrated. Therefore, tools are needed to efficiently screen products for selected physiological properties before they enter the market. NutriChip is an interdisciplinary modular project funded by the Swiss programme Nano-Tera, which groups scientists from several areas of research with the aim of developing analytical strategies that will enable functional screening of foods. The project focuses on postprandial inflammatory stress, which potentially contributes to the development of chronic inflammatory diseases. The first module of the NutriChip project is composed of three in vitro biochemical steps that mimic the digestion process, intestinal absorption, and subsequent modulation of immune cells by the bioavailable nutrients. The second module is a miniaturised form of the first module (gut-on-a-chip) that integrates a microfluidic-based cell co-culture system and super-resolution imaging technologies to provide a physiologically relevant fluid flow environment and allows sensitive real-time analysis of the products screened in vitro. The third module aims at validating the in vitro screening model by assessing the nutritional properties of selected food products in humans. Because of the immunomodulatory properties of milk as well as its amenability to technological transformation, dairy products have been selected as model foods. The NutriChip project reflects the opening of food and nutrition sciences to state-of-the-art technologies, a key step in the translation of transdisciplinary knowledge into nutritional advice.

Biotechnologies for the management of genetic resources for food and agriculture

In recent years, the land area under agriculture has declined as also has the rate of growth in agricultural productivity while the demand for food continues to escalate. The world population now stands at 7 billion and is expected to reach 9 billion in 2045. A broad range of agricultural genetic diversity needs to be available and utilized in order to feed this growing population. Climate change is an added threat to biodiversity that will significantly impact genetic resources for food and agriculture (GRFA) and food production. There is no simple, all-encompassing solution to the challenges of increasing productivity while conserving genetic diversity. Sustainable management of GRFA requires a multipronged approach, and as outlined in the paper, biotechnologies can provide powerful tools for the management of GRFA. These tools vary in complexity from those that are relatively simple to those that are more sophisticated. Further, advances in biotechnologies are occurring at a rapid pace and provide novel opportunities for more effective and efficient management of GRFA. Biotechnology applications must be integrated with ongoing conventional breeding and development programs in order to succeed. Additionally, the generation, adaptation, and adoption of biotechnologies require a consistent level of financial and human resources and appropriate policies need to be in place. These issues were also recognized by Member States at the FAO international technical conference on Agricultural Biotechnologies for Developing Countries (ABDC-10), which took place in March 2010 in Mexico. At the end of the conference, the Member States reached a number of key conclusions, agreeing, inter alia, that developing countries should significantly increase sustained investments in capacity building and the development and use of biotechnologies to maintain the natural resource base; that effective and enabling national biotechnology policies and science-based regulatory frameworks can facilitate the development and appropriate use of biotechnologies in developing countries; and that FAO and other relevant international organizations and donors should significantly increase their efforts to support the strengthening of national capacities in the development and appropriate use of pro-poor agricultural biotechnologies.

Metabolomics and food processing: from semolina to pasta

The objective of this study was to investigate the metabolite variations during industrial pasta processing (from semolina to dried pasta) for five different commercial products. Up to 76 metabolites were detected. Significant differences were observed between wholemeal and refined pasta samples, with the wholemeal pasta richer in many classes of compounds such as phytosterols, policosanols, unsaturated fatty acids, amino acids, carotenoids, minerals, and so on. Significant differences were also observed between samples of refined pasta apparently similar for the actual parameters used for the assessment of pasta quality. The results indicated that a number of metabolites undergo a transformation during the pasta-making process depending on the processing conditions adopted. The approach used in this work shows the high potential of metabolite profiling for food investigations with regard to process-related transformation, safety, and nutrition.