Present and future challenges in food analysis: foodomics

Early Detection of Food Safety and Spoilage Incidents Based on Live Microbiome Profiling and PMA-qPCR Monitoring of Indicators

The early detection of spoilage microorganisms and food pathogens is of paramount importance in food production systems. We propose a novel strategy for the early detection of food production defects, harnessing the product microbiome. We hypothesize that by establishing microbiome datasets of proper and defective batches, indicator bacteria signaling production errors can be identified and targeted for rapid quantification as part of routine practice. Using the production process of pastrami as a model, we characterized its live microbiome profiles throughout the production stages and in the final product, using propidium monoazide treatment followed by 16S rDNA sequencing. Pastrami demonstrated product-specific and consistent microbiome profiles predominated by Serratia and Vibrionimonas, with distinct microbial signatures across the production stages. Based on the established microbiome dataset, we were able to detect shifts in the microbiome profile of a defective batch produced under lactate deficiency. The most substantial changes were observed as increased relative abundances of Vibrio and Lactobacillus, which were subsequently defined as potential lactate-deficiency indicators. PMA-qPCR efficiently detected increased levels of these species, thus proving useful in rapidly pinpointing the production defect. This approach offers the possibility of the in-house detection of defective production events with same-day results, promoting safer food production systems.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Metal(loid)s Profile of Four Traditional Ethiopian Teff Brands: Geographic Origin Discrimination

Among the most renowned Ethiopian food crops, teff (Eragrostis tef (Zucc.)Trotter) is the most nutritious and gluten-free cereal. Because of the increase in demand for teff, it is necessary to establish geographic origin authentication of traditional teff brands based on multi-element fingerprint. For this purpose, a total of 60 teff samples were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Accuracy of the laboratory procedure was verified by the analysis of rice flour standard reference material (NIST SRM 1568b). In this context, four traditional teff brands (Ada'a, Ginchi, Gojam and Tulu Bolo) were analytically characterized using multi-element fingerprint and further treated statistically using linear discriminant analysis (LDA). Due to obvious extrinsic Fe, Al and V contamination, these elements were excluded from the discriminant model. Five elements (Cu, Mo, Se, Sr, and Zn) significantly contributed to discriminate the geographical origin of white teff. On the other hand, Mn, Mo, Se and Sr were used as discriminant variables for brown teff. LDA revealed 90 and 100% correct classifications for white and brown teff, respectively. Overall, multi-element fingerprint coupled with LDA can be considered a suitable tool for geographic origin discrimination of traditional teff brands.

Oilomics: An important branch of foodomics dealing with oil science and technology

Oil is one of three nutritious elements. The application of omics techniques in the field of oil science and technology is attracted increasing attention. Oilomics, which emerged as an important branch of foodomics, has been widely used in various aspects of oil science and technology. However, there are currently no articles systematically reviewing the application of oilomics. This paper aims to provide a critical overview of the advantages and value of oilomics technology compared to traditional techniques in various aspects of oil science and technology, including oil nutrition, oil processing, oil quality, safety, and traceability. Moreover, this article intends to review major issues in oilomics and give a comprehensive, critical overview of the current state of the art, future challenges and trends in oilomics, with a view to promoting the optimal application and development of oilomics technology in oil science and technology.

Critical Evaluation of Two Qualitative Analytical Approaches for Multiclass Determination of Veterinary Drugs in Bovine Muscle Using UHPLC-Q-Orbitrap: The Wind of Change in Brazilian Monitoring

Food safety is recognized as a main requirement for consumers, food industries, and official laboratories. Here, we present the optimization and screening qualitative validation of two multianalyte methods in bovine muscle tissues by ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry with an Orbitrap-type analyzer, operated with a heated ionization source in positive and negative mode. This aims for not only the simultaneous detection of veterinary drugs regulated in Brazil but also the prospection of antimicrobials not yet monitored. Two different sample preparation procedures were applied: method A-generic solid-liquid extraction with 0.1% formic acid (v/v) in an aqueous solution of EDTA 0.1% (w/v)-acetonitrile-methanol (1:1:1, v/v/v), followed by an additional ultrasound-assisted extraction and method B-QuEChERS. In both procedures, selectivity showed satisfactory conformity. From a detection capability (CCβ) equivalent to ½ the maximum residue limit, >34% of the analyte resulted in a false positive rate of <5%, preponderant by the QuEChERS method, which exhibited a higher yield of the sample. The results showed the potential application of both procedures in the routine analysis of foods by official laboratories, enabling the expansion of this methodological portfolio as well as its analytical scopes, thus optimizing the control of residues of veterinary drugs in the country.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

The Study of Yak Colostrum Nutritional Content Based on Foodomics

The utilization of yak milk is still in a primary stage, and the nutrition composition of yak colostrum is not systematically characterized at present. In this study, the lipids, fatty acids, amino acids and their derivatives, metabolites in yak colostrum, and mature milk were detected by the non-targeted lipidomics based on (ultra high performance liquid chromatography tandem quadrupole mass spectrometer) UHPLC-MS, the targeted metabolome based on gas chromatography-mass spectrometer (GC-MS), the targeted metabolome analysis based on UHPLC-MS, and the non-targeted metabolome based on ultra high performance liquid chromatography tandem quadrupole time of flight mass spectrometer (UHPLC-TOF-MS), respectively. Meanwhile, the nutrition composition of yak colostrum was compared with the data of cow mature milk in the literatures. The results showed that the nutritive value of yak colostrum was higher by contrast with yak and cow mature milk from the perspective of the fatty acid composition and the content of Σpolyunsaturated fatty acids (PUFAs), Σn-3PUFAs; the content of essential amino acid (EAA) and the ratio of EAA/total amino acid (TAA) in yak colostrum were higher than the value in yak mature milk; and the content of functional active lipids including phosphatidylcholines (PC), phosphatidylglycerol (PG), phosphatidylserine (PS), lyso-phosphatidylcholine (LPC), lyso-phosphatidylglycerol (LPG), lyso-phosphatidylinositol (LPI), sphingomyelin (SM), ganglioside M3 (GM3), ganglioside T3 (GT3), and hexaglycosylceramide (Hex1Cer) in yak colostrum, was higher than the value of yak mature milk. Moreover, the differences of nutritive value between yak colostrum and mature milk were generated by the fat, amino acids and carbohydrate metabolism that were regulated by the ovarian hormone and referencesrenin-angiotensin-aldosterone system in yaks. These research results can provide a theoretical basis for the commercial product development of yak colostrum.

- Invited Review - Postmortem skeletal muscle metabolism of farm animals approached with metabolomics

Skeletal muscle metabolism regulates homeostatic balance in animals. The metabolic impact persists even after farm animal skeletal muscle is converted to edible meat through postmortem rigor mortis and aging. Muscle metabolites resulting from animal growth and postmortem storage have a significant impact on meat quality, including flavor and color. Metabolomics studies of postmortem muscle aging have identified metabolisms that contain signatures inherent to muscle properties and the altered metabolites by physiological adaptation, with glycolysis as the pivotal metabolism in postmortem aging. Metabolomics has also played a role in mining relevant postmortem metabolisms and pathways, such as the citrate cycle and mitochondrial metabolism. This leads to a deeper understanding of the mechanisms underlying the generation of key compounds that are associated with meat quality. Genetic background, feeding strategy, and muscle type primarily determine skeletal muscle properties in live animals and affect post-mortem muscle metabolism. With comprehensive metabolite detection, metabolomics is also beneficial for exploring biomarker candidates that could be useful to monitor meat production and predict the quality traits. The present review focuses on advances in farm animal muscle metabolomics, especially postmortem muscle metabolism associated with genetic factors and muscle type.

Bioactive compounds as an alternative for the sugarcane industry: Towards an integrative approach

Here, a comprehensive review of sugarcane industrialization and its relationship with bioactive compounds (BCs) detected in various products and by-products generated during its processing is presented. Furthermore, it is discussed how these compounds have revealed important antioxidant, antineoplastic, antidiabetic, and antimicrobial activities. From this bibliographic research highlights the significance of two types of BCs of natural origin (phenolic compounds (PCs) and terpenoids) and a group of compounds synthesized during industrial transformation processes (Maillard reaction products (MRPs)). It was found that most of the studies about the BCs from sugarcane have been conducted by identifying, isolating, and analyzing ones or a few compounds at a specific period, this being a conventional approach. However, given the complexity of the synthesis processes of all these BCs and the biological activities they can manifest in a specific biological context, novel approaches are needed to address these analyses holistically. To overcome this challenge, integrating massive and multiscale methods, such as omics sciences, seems necessary to enrich these studies. This work is intended to contribute to the state of the art that could support future research about the exploration, characterization, or evaluation of different bioactive molecules from sugarcane and its derivatives.

Production performance, egg quality, and uterine gene expression for layers as affected by N-Carbamylglutamate supplementation

Introduction

Supplementation of exogenous additives is a strategy to improve laying performance of layers by regulating uterine function. N-Carbamylglutamate (NCG) as an activator for endogenous arginine synthesis has the potential to regulate the laying performance of layers, but its effects have not been fully understood.

Methods

This study investigated the effects of dietary supplementation of NCG on production performance, egg quality, and uterine gene expression in layers. A total of 360 45-week-old layers with a genetic line of Jinghong No. 1 were used in this study. The experimental period was 14 weeks. All birds were divided into 4 treatments with 6 replicates per treatment and 15 birds per replicate. Dietary treatments were based on a basal diet and supplemented with 0, 0.08, 0.12, or 0.16% NCG to form C, N1, N2, and N3 groups.

Results and discussion

We found that layers in group N1 had higher egg production rate than those in group C. Egg weight was significantly reduced, while eggshell thickness was significantly improved, by treatment. However, the albumen height and Haugh unit were the lowest in group N3. Based on the above results, groups C and N1 were selected for further transcriptomics analysis of uterine tissue by RNA-seq. More than 7.4 Gb clean reads and 19,882 tentative genes were obtained using the Gallus gallus genome as a reference. Transcriptomics analysis in uterus tissue revealed that 95 differentially expressed genes (DEGs) were upregulated and 127 DEGs were downregulated. Functional annotation and pathway enrichment analysis showed that DEGs in uterine tissue were mainly enriched in glutathione metabolism, cholesterol metabolism, and glycerolipid metabolism, etc. Vitamin A metabolism-related gene, RBP1, nutrient transport-related gene, ALB, protein synthesis-related gene, METTL21C, and calcium transport-related gene, RYR2, CACNB2, RAMP3, and STAC, were significantly regulated by 0.08% NCG supplementation. Therefore, we concluded that NCG supplementation at a dose of 0.08% improved production performance and egg quality of layers by regulating uterus function.

Recent Advances in Nanomaterial-Based Sensing for Food Safety Analysis

The increasing public attention on unceasing food safety incidents prompts the requirements of analytical techniques with high sensitivity, reliability, and reproducibility to timely prevent food safety incidents occurring. Food analysis is critically important for the health of both animals and human beings. Due to their unique physical and chemical properties, nanomaterials provide more opportunities for food quality and safety control. To date, nanomaterials have been widely used in the construction of sensors and biosensors to achieve more accurate, fast, and selective food safety detection. Here, various nanomaterial-based sensors for food analysis are outlined, including optical and electrochemical sensors. The discussion mainly involves the basic sensing principles, current strategies, and novel designs. Additionally, given the trend towards portable devices, various smartphone sensor-based point-of-care (POC) devices for home care testing are discussed.

A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional Omics Dataset

With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB® coding basis and three-in-one integrated method, 'Ana', was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research.

Use of laboratory equipment for analysis of external quality of food maize kernel

The purpose of this study was to investigate the influence of the parameters of the grain air-sieve cleaner in laboratory conditions on the external quality of food maize (Zea mays L.)  kernel in terms of the design for the selection of a suitable sieve mesh for cleaning procedures. The object of the research was maize kernel, variety Pionier P0216, year of cultivation 2019. The available laboratory equipment was used in the study. To evaluate the external quality of food maize kernel, indicators were determined, which were investigated before and after cleaning. An Asus notebook computer with software from Microsoft Windows XP and Ofice 2010 was used to evaluate the measurement results. These results were achieved: an average bulk density of 846.77 kg.m-3 was found in the input sample of food maize kernel after harvest, admixtures before cleaning reached an average of 19.1% and impurities of 2.76%, cleanliness of kernels before cleaning averaged 76.9%, the output after cleaning expressed in terms of bulk density reached an average value of 851.15 kg.m-3, admixtures after cleaning reached 0.07% and impurities 4.21%, clean kernels after cleaning reached 94.86% and damage kernels after cleaning decreased slightly by separation of fragments and chipped kernels. In conclusion, it was stated that the laboratory technique for post-harvest treatment of grain is at a high level worldwide. Currently, the issue of post-harvest processing of grain in Slovakia is addressed at an average level. Post-harvest processing and storage of grain in terms of enginery and technological and economic aspects is little researched in the Slovak Republic, so these issues are open to further research.

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.

Liquid chromatographic methods coupled to chemometrics: a short review to present the key workflow for the investigation of wine phenolic composition as it is affected by environmental factors

The guarantee of wine authenticity arises great concern because of its nutritional and economic importance. Phenolic fingerprints have been used as a source of chemical information for various authentication issues, including botanical and geographical origin, as well as vintage age. The local environment affects wine production and especially its phenolic metabolites. Integrated analytical methodologies combined with chemometrics can be applied in wine fingerprinting studies for the determination and establishment of phenolic markers that contain comprehensive and standardized information about the wine profile and how it can be affected by various environmental factors. This review summarizes all the recent trends in the generation of chemometric models that have been developed for treating chromatographic data and have been used for the investigation of critical wine authenticity issues, revealing phenolic markers responsible for the botanical, geographical, and vintage age classification of wines. Overall, the current review suggests that chromatographic methodologies are promising and powerful techniques that can be used for the accurate determination of phenolic compounds in difficult matrices like wine, highlighting the advantages of the applications of supervised chemometric tools over unsupervised for the construction of prediction models that have been successfully used for the classification based on their territorial and botanical origin.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

The Role of Chromatographic and Electromigration Techniques in Foodomics

Foodomics is the discipline aimed at studying the prevention of diseases by food, identifying chemical, biological and biochemical food contaminants, determining changes in genetically modified foods, identifying biomarkers able to confirm the authenticity and quality of foods or studying the safety, quality and traceability of foods, among other issues. It is mainly based on the use of genomic, transcriptomic, proteomic and metabolomic tools, among others, in order to understand the effect of food on animals and humans at the level of genes, messenger ribonucleic acid, proteins and metabolites. Since the first definition of Foodomics, a reasonable number of works have shown the extremely high possibilities of this discipline, which is highly based on the use of advanced analytical hyphenated techniques - especially for proteomics and metabolomics. This book chapter aims at providing a general description of the role of chromatographic and electromigration techniques that are currently being applied to achieve the main objectives of Foodomics, particularly in the proteomic and metabolomic fields, since most published works have been focused on these approaches, and to highlight relevant applications.

Macroalgae as Protein Sources—A Review on Protein Bioactivity, Extraction, Purification and Characterization

The increased demand for protein sources combined with a decrease in the available land and water resources have led to a growing interest in macroalgae as alternative protein sources. This review focuses on strategies for macroalgae protein extraction, enrichment and characterization. To date, the protein extraction methods applied to algae include enzymatic hydrolysis, physical processes and chemical extraction. Novel methods, such as pulsed electric field, microwave-assisted, pressurized liquid and supercritical fluid extractions, and the application of smart solvents are discussed. An overview of the use of membranes and other processes to generate high-value protein concentrates from algae extracts is also presented, as well as some examples of the methods used for their characterization. The potential bioactivities from macroalgae-derived proteins and peptides, including novel glycoproteins and lectins, are briefly reviewed.

Current Trends of Food Analysis, Safety, and Packaging

Food is a basic necessity for life, growth, survival, and maintaining a proper body function. Rising food demand leads both producers and consumers to search for alternative food sources with high nutritional value. However, food products may never be completely safe. The oxidation reaction may alter both the physicochemical and immunological properties of food products. Maillard and caramelization nonenzymatic browning reactions can play a pivotal role in food acceptance through the ways they influence quality factors such as flavor, color, texture, nutritional value, protein functionality, and digestibility. There is a multitude of adulterated foods that portray adverse risks to the human condition. To maintain food safety, the packaging material is used to preserve the quality and freshness of food products. Food safety is jeopardized by plenty of pathogens by the consumption of adulterated food resulting in multiple foodborne illnesses. Though different analytical tools are used in the analysis of food products, yet, adulterated food has repercussions for the community and is a growing issue that adversely impairs human health and well-being. Thus, pathogenic agents' rapid and effective identification is vital for food safety and security to avoid foodborne illness. This review highlights the various analytical techniques used in the analysis of food products, food structure, and quality of food along with chemical reactions in food processing. Moreover, we have also discussed the effect on health due to the consumption of adulterated food and focused on the importance of food safety, including the biodegradable packaging material.

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.

External parameter orthogonalization-support vector machine for processing of attenuated total reflectance-mid-infrared spectra: A solution for saffron authenticity problem

In the present work, a new approach based on external parameter orthogonalization combined with support vector machine (EPO-SVM) is proposed for processing of attenuated total reflectance-Fourier transform mid-infrared (ATR-FT-MIR) spectra with the goal of solving authentication problem in saffron, the most expensive spice in the world. First, one-hundred authentic saffron samples are clustered by principal component analysis (PCA) with EPO as the best preprocessing strategy. Then, EPO-SVM is used for the detection of four commonly used plant-derived adulterants (i.e. safflower, calendula, rubia, and style) in binary mixtures (saffron and each of plant adulterants) and its performance is compared with other common classification methods. The obtained results showed that the EPO-SVM approach has a much better classification accuracy (>95%) than other methods (accuracy<89.2%). Finally, two different sample sets including mixture of saffron and four plant adulterants and commercial saffron samples are used for validation of the developed EPO-SVM model. In this regard, classification figures of merit in terms of sensitivity, specificity and accuracy were respectively 96.6%, 97.1%, and 96.8% which showed good classification performance. It is concluded that the proposed EPO-PCA and EPO-SVM approaches can be considered as reliable tools for authentication and adulteration detection in saffron samples.

Foodborne pathogens in the omics era

Outbreaks and deaths related to Foodborne Diseases (FBD) occur constantly in the world, as a result of the consumption of contaminated foodstuffs with pathogens such as Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, Salmonella spp, Clostridium spp. and Campylobacter spp. The purpose of this review is to discuss the main omic techniques applied in foodborne pathogen and to demonstrate their functionalities through the food chain and to guarantee the food safety. The main techniques presented are genomic, transcriptomic, secretomic, proteomic, and metabolomic, which together, in the field of food and nutrition, are known as "Foodomics." This review had highlighted the potential of omics to integrate variables that contribute to food safety and to enable us to understand their application on foodborne diseases. The appropriate use of these techniques had driven the definition of critical parameters to achieve successful results in the improvement of consumers health, costs and to obtain safe and high-quality products.

Review: Microextraction Technique Based New Trends in Food Analysis

Food chemistry is the study and classification of the quality and origin of foods. The identification of definite biomarkers and the determination of residue contaminants such as toxins, pesticides, metals, human and veterinary drugs, which are a very common source of food-borne diseases. The food analysis is continuously demanding the improvement of more robust, sensitive, highly efficient, and economically beneficial analytical approaches to promise the traceability, safety, and quality of foods in the acquiescence with the consumers and legislation demands. The traditional methods have been used at the starting of the 20th century based on wet chemical methods. Now it existing the powerful analytical techniques used in food analysis and safety. This development has led to substantial enhancements in the analytical accuracy, precision, sensitivity, selectivity, thereby mounting the applied range of food applications. In the present decade, microextraction (micro-scale extraction) pays more attention due to its futures such as low consumption of solvent and sample, throughput analysis easy to operate, greener, robotics, and miniaturization, different adsorbents have been used in the microextraction process with unique nature recognized with wide range applications.

Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection

In this work, the potential of near-infrared (NIR) and mid-infrared (MIR) spectroscopy along with chemometrics was investigated for authentication and adulteration detection of Iranian saffron samples. First, authentication of one-hundred saffron samples was examined by principal component analysis (PCA). The results showed the NIR spectroscopy can better predict the origin of samples than the MIR. Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect four common plant-derived adulterants (i.e., saffron style, calendula, safflower, and rubia). In all cases, PLS-DA classification figures of merit in terms of sensitivity, specificity, error rate and accuracy were satisfactory for both NIR and MIR datasets. The built models were then successfully validated using test set and also commercial samples. Finally, partial least squares regression (PLSR) was used to estimate the amount of adulteration. In this case, only NIR showed a good performance with regression coefficients (R²) in range of 0.95-0.99.

Discrimination between vegetable oil and animal fat by a metabolomics approach using gas chromatography-mass spectrometry combined with chemometrics

Adulteration of olive oil with the other cheap oils and fats plays an important role in economics and has nutritional benefits. In this work, metabolite profiling was performed using gas chromatography-mass spectrometry to identify and quantify animal fat (lard) adulteration in vegetable oil (olive oil). Principal component analysis could correctly identify and clustering olive oil, sunflower oil, sesame oil, lard, and adulterated samples through the changes in their fatty acid methyl esters (FAMEs) profile. A targeted metabolomics method was then optimized and validated through construction of calibration curves of known FAMSs in olive oil and lard. The method was presented high linearity (R2 > 0.96) and good intra and inter day accuracy and precision (79-101 and 86-102% and 2-7 and 3-7, respectively) for determination of FAMEs. Afterwards the absolute concentration and relative percentage of FAMEs were successfully determined in 12 commercial olive oils and 3 lards samples. Methyl myristate, methyl palmitate, methyl oleate, and methyl stearate were selected as discriminant markers to identify and quantify lard adulteration even at a low level of lard (5%w/w), with errors less than 2% in the comparison of the absolute or relative concentrations of FAMEs using several statistical methods. The proposed methodology allowed us to quantify the FAMEs simultaneously and also could predict small amount of lard in the adulterated olive oil samples.

The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars-the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)-that support the development and implementation of vibrational spectroscopy applications.

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.

Detection Strategies of Zearalenone for Food Safety: A Review

Zearalenone (ZEN) is a toxic compound produced by the metabolism of fungi (genus Fusarium) that threaten the food and agricultural industry belonging to the in foods and feeds. ZEN has toxic effects on human and animal health due to its mutagenicity, teratogenicity, carcinogenicity, nephrotoxicity, immunotoxicity, and genotoxicity. To ensure food safety, rapid, precise, and reliable analytical methods can be developed for the detection of toxins such as ZEN. Different selective molecular diagnostic elements are used in conjunction with different detection strategies to achieve this goal. In this review, the use of electrochemical, colorimetric, fluorometric, refractometric as well as other strategies were discussed for ZEN detection. The success of the sensors in analytical performance depends on the development of receptors with increased affinity to the target. This requirement has been met with different immunoassays, aptamer-assays, and molecular imprinting techniques. The immobilization techniques and analysis strategies developed with the combination of nanomaterials provided high precision, reliability, and convenience in ZEN detection, in which electrochemical strategies perform the best.

Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.

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.

Miniaturized liquid chromatography applied to the analysis of residues and contaminants in food: A review

The humankind is pretty dependent on food to control several biological processes into the organism. As the world population increases, the demand for foodstuffs follows the same trend claiming for a high food production situation. For this reason, a substantial amount of chemicals is used in agriculture and livestock husbandries every year, enhancing the likelihood of contaminated foodstuffs being commercialized. This outlook becomes a public health concern; thus, the governmental regulatory agencies impose laws to control the residues and contaminants in food matrices. Currently, one of the most important analytical techniques to perform it is LC. Despite its already recognized effectiveness, it is often time consuming and requires significant volumes of reagents, which are transformed into toxic waste. In this context, miniaturized LC modes emerge as a greener and more effective analytical technique. They have remarkable advantages, including higher sensitivity, lower sample amount, solvent and stationary phase requirements, and more natural coupling to MS. In this review, most of the critical characteristics of them are discussed, focusing on the benchtop instruments and their related analytical columns. Additionally, a discussion regarding the last 10 years of publications reporting miniaturized LC application for the analysis of natural and industrial food samples is categorized. The main chemical classes as applied in the crops are highlighted, including pesticides, veterinary drugs, and mycotoxins.

Recent Trends in the Analysis of Chemical Contaminants in Beverages

Chemical contaminants should not be present in beverages for human consumption, but could eventually be ingested by consumers as they may appear naturally from the environment or be produced by anthropogenic sources. These contaminants could belong to many different chemical sources, including heavy metals, amines, bisphenols, phthalates, pesticides, perfluorinated compounds, inks, ethyl carbamate, and others. It is well known that these hazardous chemicals in beverages can represent a severe threat by the potential risk of generating diseases to humans if no strict quality control is applied during beverages processing. This review compiles the most updated knowledge of the presence of potential contaminants in various types of beverages (both alcoholic and non-alcoholic), as well as in their containers, to prevent undesired migration. Special attention is given to the extraction and pre-concentration techniques applied to these samples, as well as to the analytical techniques necessary for the determination of chemicals with a potential contaminant effect. Finally, an overview of the current legislation is carried out, as well as future trends of research in this field.

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)-mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality.

State of the Art in Alcohol Sensing with 2D Materials

Since the discovery of graphene, the star among new materials, there has been a surge of attention focused on the monatomic and monomolecular sheets which can be obtained by exfoliation of layered compounds. Such materials are known as two-dimensional (2D) materials and offer enormous versatility and potential. The ultimate single atom, or molecule, thickness of the 2D materials sheets provides the highest surface to weight ratio of all the nanomaterials, which opens the door to the design of more sensitive and reliable chemical sensors. The variety of properties and the possibility of tuning the chemical and surface properties of the 2D materials increase their potential as selective sensors, targeting chemical species that were previously difficult to detect. The planar structure and the mechanical flexibility of the sheets allow new sensor designs and put 2D materials at the forefront of all the candidates for wearable applications. When developing sensors for alcohol, the response time is an essential factor for many industrial and forensic applications, particularly when it comes to hand-held devices. Here, we review recent developments in the applications of 2D materials in sensing alcohols along with a study on parameters that affect the sensing capabilities. The review also discusses the strategies used to develop the sensor along with their mechanisms of sensing and provides a critique of the current limitations of 2D materials-based alcohol sensors and an outlook for the future research required to overcome the challenges.

A targeted approach for studying the effect of sugar bee feeding on the metabolic profile of Royal Jelly

Royal Jelly (RJ) constitutes one of the most popular beehive products and for this reason the use of inexpensive sweeteners during its production remains an important quality issue. In the present study we report results of metabolic profiling of RJ samples obtained after the application of artificial bee-feeding using different feeding protocols. The hydrophilic content of RJ samples was assessed by applying (HILIC)UPLC-MS/MS. In total 96 crude RJ samples were analysed with the developed method. Multivariate statistical analysis revealed clear differentiation between the RJ samples obtained from control (non-fed) bees and samples obtained after feeding. In total 27 out of 57 detected molecules were statistically found to be significantly altered in the different comparisons. Among them some amino acids (e.g. tryptophan, lysine), amino acid derivatives (pyroglutamic acid), amines (cadaverine, TMAO, etc.), carbohydrates and vitamins were found as potential markers. The results of the study could be further used for the development of an LC-MS based analytical tool for RJ quality control assessment.