Mass Spectrometry for Biomedical and Food Analysis.

Biomedical and food analysis has always been an important topic that closely relates to health [...].

Machine Learning-Assisted Portable Microplasma Optical Emission Spectrometer for Food Safety Monitoring.

To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (µPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and µPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety.

From sound check to encore: A journey through high-resolution mass spectrometry-based food analyses and metabolomics.

This manuscript presents a comprehensive review of high-resolution mass spectrometry in the field of food analysis and metabolomics. We have followed the historical evolution of metabolomics, its associated techniques and technologies, and its increasing role in food science and research. The review provides a critical comparison and synthesis of tentative identification guidelines proposed for over 15 years, offering a condensed resource for researchers in the field. We have also examined a wide range of recent metabolomics studies, showcasing various methodologies and highlighting key findings as a testimony of the versatility of the field and the possibilities it offers. In doing so, we have also carefully provided a compilation of the software tools that may be employed in this type of studies. The manuscript also explores the prospects of high-resolution mass spectrometry and metabolomics in food science. By covering the history, guidelines, applications, and tools of metabolomics, this review attempts to become a comprehensive guide for researchers in a rapidly evolving field.

High performance liquid chromatography coupled with post - Column derivatization methods in food analysis: Chemistries and applications in the last two decades.

High performance liquid chromatography coupled with post-column derivatization is used for increasing the sensitivity and selectivity of the desirable analytes after the chromatographic separation. The transformation of the analytes can be conducted through the addition of a suitable reagent in the eluted stream or the ultraviolet irradiation of the eluted analytes, forming detectable derivatives for ultraviolet or fluorescence detectors. This review focuses on the developed methods using high performance liquid chromatography coupled with post-column derivatization for the determination of substances in food samples during the last two decades. The significance of the determination of each analyte in foods and the existing guidelines in each case are discussed. Preparation of the samples and the analytical methods are commented. For each analyte, official methods and commercially available systems and reagents are mentioned, as well.

An innovative fluorescent probe based on dicyanoisoflurone derivatives for differential detection of Hg2+ and Cu2+ and its applications in bacteria, cell imaging and food analysis.

BACKGROUND: Heavy metal pollution has become one of the world's most important environmental pollution, especially Hg2+ is enriched, it is easy to enter the human body through the food chain, bind to the sulfhydryl group in the protein, cause mercury poisoning. Traditional methods for detecting Hg2+ have obvious drawbacks, such as poor selectivity and long detection time. Fluorescence detection has attracted attention because of its good sensitivity and specificity detection ability. In previously reported probes for detecting Hg2+, Cu2+ often interferes. Therefore, it is of great practical significance to synthesize a fluorescent probe that can distinguish between Hg2+ and Cu2+. RESULTS: We have successfully synthesized the probe DFS, a fluorescent probe that can differentially detect Hg2+ and Cu2+, and the probe DFS has good selectivity and anti-interference ability for Hg2+ and Cu2+. The fluorescence intensity at 530 nm increased rapidly when Hg2+ was detected; during the Cu2+ detection, the fluorescence intensity at 636 nm gradually decreased, fluorescence quenching occurred, and the detection limits of Hg2+ and Cu2+ were 7.29 × 10-9 M and 2.13 × 10-9 M, respectively. Through biological experiments, it was found that probe DFS can complete the fluorescence imaging of Hg2+ and Cu2+ in Staphylococcus aureus and HUVEC cells, which has certain research value in the field of environmental monitoring and microbiology, and the probe DFS has low cytotoxicity, so it also has broad application prospects in the field of biological imaging. In addition, the probe DFS also has good applicability for Hg2+ and Cu2+ detection in actual samples. SIGNIFICANCE AND NOVELTY: This is a fluorescent probe that can distinguish between Hg2+ and Cu2+, the fluorescence emission peak appears at 530 nm when Hg2+ is detected; when detecting Cu2+, fluorescence quenching occurs at 636 nm, the fluorescence emission peak distance between Hg2+ and Cu2+ differs by 106 nm. This reduces mutual interference between Hg2+ and Cu2+ during detection, it provides a new idea for the detection of Hg2+ and Cu2+.

Perovskite Nanocrystals: Superior Luminogens for Food Quality Detection Analysis.

With the global limited food resources receiving grievous damage from frequent climate changes and ascending global food demand resulting from increasing population growth, perovskite nanocrystals with distinctive photoelectric properties have emerged as attractive and prospective luminogens for the exploitation of rapid, easy operation, low cost, highly accurate, excellently sensitive, and good selective biosensors to detect foodborne hazards in food practices. Perovskite nanocrystals have demonstrated supreme advantages in luminescent biosensing for food products due to their high photoluminescence (PL) quantum yield, narrow full width at half-maximum PL, tunable PL in the entire visible spectrum, easy preparation, and various modification strategies compared with conventional semiconductors. Herein, we have carried out a comprehensive discussion concerning perovskite nanocrystals as luminogens in the application of high-performance biosensing of foodborne hazards for food products, including a brief introduction of perovskite nanocrystals, perovskite nanocrystal-based biosensors, and their application in different categories of food products. Finally, the challenges and opportunities faced by perovskite nanocrystals as superior luminogens were proposed to promote their practicality in the future food supply.

First nanoplate digital PCR method to trace allergenic foods: Improved sensitivity for the detection of sesame.

Sesame (Sesamum indicum L.) is an important allergenic food whose presence can be the cause of severe allergic reactions in sensitised individuals. In this work, nanoplate digital PCR (ndPCR) was used to develop two methods to detect trace amounts of sesame in processed foods and compared with previously proposed real-time PCR assays. Two independent ndPCR approaches were successfully advanced, achieving sensitivities of 5 and 0.1 mg/kg of sesame in dough/biscuits, targeting the CO6b-1 and ITS regions, respectively. The sensitivity using both targets was improved by one order of magnitude comparing with real-time PCR and was not affected by food processing. CO6b-1 system was not influenced by food matrix, exhibiting similar performance regardless the use of complex matrix extracts or serial diluted DNA. Herein, ndPCR was proposed for the first time for the detection of allergenic foods with the advantage of providing better performance than real-time PCR regarding sensitivity and robustness.

Performance assessment of a new G12/A1 antibody-based rapid ELISA using commercially available and gluten-spiked food samples.

OBJECTIVE: Food products with <20 mg/kg gluten can be labeled 'gluten-free' according to international regulations. Several antibodies-based ELISAs have been develop to track gluten traces in food products. Among them, R5 and G12 antibody-based ELISAs are the frequently used methods. However, these antibodies have certain limitations. We evaluated the accuracy of G12/A1 antibody-based 'Glutentox ELISA Rapid G12' and compared the results with the current reference method i.e., R5 antibody-based 'Ridascreen R5 ELISA'. METHODS: In the first step, the performance of Glutentox ELISA Rapid G12 kit was inspected by determination of the threshold value i.e., > or <20 mg/kg gluten in different food products. In the second step, quantification accuracy was assessed by quantification of gluten in gluten-free food products spiked with gliadin reference material. RESULTS: In total 47 food products (naturally and labeled gluten-free, and food with traces of gluten) were included. Of them, 29 products were quantified with <20 mg/kg, and 18 with a low level of gluten by both the kits. Six out of 29 gluten-free products were used for the recovery test at different spike levels. Gluten concentration and mean recovery rates of individual kits showed consistency. CONCLUSION: GlutenTox Rapid G12 ELISA could be an appropriate choice for detecting gluten in food products but needs more in-house validation and collaborative tests.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix.

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence.

In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.

Convergent analysis of food products using molecular barcodes, based on LC-HRMS data.

There are various analytical techniques available to address the growing interest in the composition of food products. LC-HRMS(/MS) is the most comprehensive technique, providing detailed information at the molecular level. However, given the vast number of different molecules encountered in food products, it is important to obtain a global overview of the dataset before focusing on similarities and differences. Therefore, a convergent strategy was employed, going from non-targeted to targeted analysis, with insightful data representations, most notably Molecular Barcode. Additionally an intermediate, semi-targeted analysis was defined, aimed at the specific detection of animal tissue in food products, using pG+ and related fragments after all ion fragmentation. The use of Molecular Barcode as a starting point to obtain relevant molecular data was also demonstrated. In conclusion, the convergent approach facilitates the design of suitable targeted methods, either to discriminate between samples or to find a generic target.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives.

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Recent advances in electrochemical cell-based biosensors for food analysis: Strategies for sensor construction.

Owing to their advantages such as great specificity, sensitivity, rapidity, and possibility of noninvasive and real-time monitoring, electrochemical cell-based biosensors (ECBBs) have been a powerful tool for food analysis encompassing the areas of nutrition, flavor, and safety. Notably, the distinctive biological relevance of ECBBs enables them to mimic physiological environments and reflect cellular behaviors, leading to valuable insights into the biological function of target components in food. Compared with previous reviews, this review fills the current gap in the narrative of ECBB construction strategies. The review commences by providing an overview of the materials and configuration of ECBBs, including cell types, cell immobilization strategies, electrode modification materials, and electrochemical sensing types. Subsequently, a detailed discussion is presented on the fabrication strategies of ECBBs in food analysis applications, which are categorized based on distinct signal sources. Lastly, we summarize the merits, drawbacks, and application scope of these diverse strategies, and discuss the current challenges and future perspectives of ECBBs. Consequently, this review provides guidance for the design of ECBBs with specific functions and promotes the application of ECBBs in food analysis.

Determination of copper in rose tea samples using flame atomic absorption spectrometry after emulsification liquid-liquid microextraction.

Rose tea infusion has gained popularity worldwide due to its health benefits. However, it is known that tea plants can be contaminated with heavy metals including copper. Hence, an accurate and applicable analytical method namely emulsification liquid-liquid microextraction based deep eutectic solvent - flame atomic absorption spectrometry (ELLME-DES-FAAS) was proposed to determine copper at trace levels in rose tea samples. Under the optimum experimental conditions, analytical figures of merit for the developed method were examined, and dynamic range, limit of detection (LOD) and limit of quantification (LOQ) were found to be 5.07-246.61 µg/kg (mass-based) with 0.9992 coefficient of determination, 2.50 µg/kg and 8.32 µg/kg, respectively. A matrix matching calibration strategy was employed to boost recovery results, and the acceptable recovery results were recorded between 95.9 % and 118.4 %. According to recovery results, the developed analytical method can be safely employed to determine the concentration of copper in rose tea samples accurately.

Current applications and future trends of artificial senses in fish freshness determination: A review.

Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e-nose), and electronic tongue (e-tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e-tongue determines the freshness of fish in a completely destructive way. There are developed e-noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large-scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses.

Development and validation of sensitive and rapid CRISPR/Cas12-based PCR method to detect hazelnut in unlabeled products.

Hazelnut, one of the most popular tree nuts, is widely found in processed food and even very small amounts can trigger severe allergic reactions in susceptible people. Herein, we developed a sensitive and rapid method based on CRISPR and qPCR capable of detecting low-abundance hazelnut in processed food. The assay, known as CRISPR-based nucleic acid test method (Crinac) can detect 1 % of hazelnut in a mixture and allows the species to be identified in a complex processed sample. The detection process can be completed within 60 min. Contributed to amplification via PCR and CRISPR/Cas12a, enables end-fluorescence measurement for the quantification of hazelnut, thus reducing assay time and eliminating the need for costly real-time fluorescence PCR instruments. The assay based on CRISPR/Cas12 and PCR has potential as a sensitive and reliable analytical tool for the detection of food authenticity.

In-house validation of an LC-MS method for the multiplexed quantitative determination of total allergenic food in chocolate.

Mass spectrometry has been widely accepted as a confirmatory tool for the sensitive detection of undeclared presence of allergenic ingredients. Multiple methods have been developed so far, achieving different levels of sensitivity and robustness, still lacking harmonization of the analytical validation and impairing comparability of results. In this investigation, a quantitative method has been validated in-house for the determination of six allergenic ingredients (cow's milk, hen's egg, peanut, soybean, hazelnut, and almond) in a chocolate-based matrix. The latter has been produced in a food pilot plant to provide a real and well-characterized matrix for proper assessment of method performance characteristics according to official guidelines. In particular, recent considerations issued by the European Committee for Standardization have been followed to guide a rigorous single-laboratory validation and to feature the main method performance, such as selectivity, linearity, and sensitivity. Synthetic surrogates of the peptide markers have been used both in native and labelled forms in matrix-matched calibration curves as external calibrants and internal standards, respectively. A two-order of magnitude range was investigated, focusing on the low concentration range for proper assessment of the detection and quantification limits (LOD and LOQ) by rigorous calibration approach. Conversion factors for all six allergenic ingredients have been determined for the first time to report the final quantitative information as fraction of total allergenic food protein (TAFP) per mass of food (µgTAFP/gfood), since such a reporting unit is exploitable in allergenic risk assessment plans. The method achieved good sensitivity with LOD values ranging between 0.08 and 0.2 µgTAFP/gfood, for all ingredients besides egg and soybean, whose quantitative markers reported a slightly higher limit (1.1 and 1.2 µgTAFP/gfood, respectively). Different samples of chocolate bar incurred at four defined concentration levels close to the currently available threshold doses have been analyzed to test the quantitative performance of the analytical method, with a proper estimate of the measurement uncertainty from different sources of variability. The sensitivity achieved resulted in compliance with the various threshold doses issued or recommended worldwide.

Capillary electromigration methods for food analysis and Foodomics: Advances and applications in the period March 2021 to March 2023.

This work presents a revision of the main applications of capillary electromigration (CE) methods in food analysis and Foodomics. Papers that were published during the period March 2021 to March 2023 are included. The work shows the multiple CE methods that have been developed and applied to analyze different types of molecules in foods and beverages. Namely, CE methods have been applied to analyze amino acids, biogenic amines, heterocyclic amines, peptides, proteins, phenols, polyphenols, pigments, lipids, carbohydrates, vitamins, DNAs, contaminants, toxins, pesticides, additives, residues, small organic and inorganic compounds, and other minor compounds. In addition, new CE procedures to perform chiral separation and for evaluating the effects of food processing as well as the last developments of microchip CE and new applications in Foodomics will be also discussed. The new procedures of CE to investigate food quality and safety, nutritional value, storage, and bioactivity are also included in the present review work.