Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning.

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC­HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Self-Responsive Fluorescence Aptasensor for Lactoferrin Determination in Dairy Products.

In this study, a self-responsive fluorescence aptasensor was established for the determination of lactoferrin (Lf) in dairy products. Herein, the aptamer itself functions as both a recognition element that specifically binds to Lf and a fluorescent signal reporter in conjunction with fluorescent moiety. In the presence of Lf, the aptamer preferentially binds to Lf due to its specific and high-affinity recognition by folding into a self-assembled and three-dimensional spatial structure. Meanwhile, its reduced spatial distance in the aptamer-Lf complex induces a FRET phenomenon based on the quenching of 6-FAM by amino acids in the Lf protein, resulting in a turn-off of the fluorescence of the system. As a result, the Lf concentration can be determined straightforwardly corresponding to the change in the self-responsive fluorescence signal. Under the optimized conditions, good linearities (R2 > 0.99) were achieved in an Lf concentration range of 2~10 µg/mL for both standard solutions and the spiked matrix, as well as with the desirable detection limits of 0.68 µg/mL and 0.46 µg/mL, respectively. Moreover, the fluorescence aptasensor exhibited reliable recoveries (89.5-104.3%) in terms of detecting Lf in three commercial samples, which is comparable to the accuracy of the HPCE method. The fluorescence aptasensor offers a user-friendly, cost-efficient, and promising sensor platform for point-of-need detection.

Chemical Composition of Fat Bloom on Chocolate Products Determined by Combining NMR and HPLC-MS.

To reduce unwanted fat bloom in the manufacturing and storage of chocolates, detailed knowledge of the chemical composition and molecular mobility of the oils and fats contained is required. Although the formation of fat bloom on chocolate products has been studied for many decades with regard to its prevention and reduction, questions on the molecular level still remain to be answered. Chocolate products with nut-based fillings are especially prone to undesirable fat bloom. The chemical composition of fat bloom is thought to be dominated by the triacylglycerides of the chocolate matrix, which migrate to the chocolate's surface and recrystallize there. Migration of oils from the fillings into the chocolate as driving force for fat bloom formation is an additional factor in the discussion. In this work, the migration was studied and confirmed by MRI, while the chemical composition of the fat bloom was measured by NMR spectroscopy and HPLC-MS, revealing the most important triacylglycerides in the fat bloom. The combination of HPLC-MS with NMR spectroscopy at 800 MHz allows for detailed chemical structure determination. A rapid routine was developed combining the two modalities, which was then applied to investigate the aging, the impact of chocolate composition, and the influence of hazelnut fillings processing parameters, such as the degree of roasting and grinding of the nuts or the mixing time, on fat bloom formation.

A Cost-Effective and Sensitive Method for the Determination of Lincomycin in Foods of Animal Origin Using High-Performance Liquid Chromatography.

BACKGROUND: Lincomycin (LIN) is extensively used for treating diseases in livestock and promoting growth in food animal farming, and it is frequently found in both the environment and in food products. Currently, most of the methods for detecting lincomycin either lack sensitivity and precision or require the use of costly equipment such as mass spectrometers. RESULT: In this study, we developed a reliable high-performance liquid chromatography-ultraviolet detection (HPLC-UVD) method and used it to detect LIN residue in 11 types of matrices (pig liver and muscle; chicken kidney and liver; cow fat, liver and milk; goat muscle, liver and milk; and eggs) for the first time. The tissue homogenates and liquid samples were extracted via liquid-liquid extraction, and subsequently purified and enriched via sorbent and solid phase extraction (SPE). After nitrogen drying, the products were derivatized with p-toluene sulfonyl isocyanic acid (PTSI) (100 µL) for 30 min at room temperature. Finally, the derivatized products were analyzed by HPLC at 227 nm. Under the optimized conditions, the method displayed impressive performance and demonstrated its reliability and practicability, with a limit of detection (LOD) and quantification (LOQ) of LIN in each matrix of 25-40 µg/kg and 40-60 µg/kg, respectively. The recovery ranged from 71.11% to 98.30%. CONCLUSIONS: The results showed that this method had great selectivity, high sensitivity, satisfactory recovery and cost-effectiveness-fulfilling the criteria in drug residue and actual detection requirements-and proved to have broad applicability in the field of detecting LIN in animal-derived foods.

A rapid and improved method for the determination of ethyl carbamate in foodstuffs of different matrices.

This work deals with the rapid and simple determination of the probable carcinogen ethyl carbamate (EC), which is naturally present in fermented food products. An undemanding, robust, and rapid pre-column derivatization utilizing a 9-xanthydrol reagent has been developed. The resulting derivative was subsequently analysed by reversed-phase high-performance liquid chromatography coupled with fluorescence detection. As a result of the thorough optimisation of the chromatographic conditions, the run was completed in just 5 minutes, considerably speeding up the usual time of EC separation (30-60 min). Thanks to the fast separation, satisfactory yields (around 90%), negligible matrix effects, no interfering peaks, very low detection limit, and simple sample pre-treatment (for the very first time, the derivatization was performed in the presence of light and without any extraction step), the proposed method represents a significant improvement of the EC determination protocol used so far. After method validation, a total of fifty food samples were subjected to analysis without any additional sample pre-treatment despite their diverse matrix. Due to its robustness, simplicity, and low time, cost, and manual demands, this method is suitable for rapid screening of EC in both final food products and during their production.

Quantification of antibiotics in food by octahedral gold-silver nanocages-based SERS sensor coupling multivariate calibration.

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu2O template etching method as SERS substrates, which enhanced the plasmonic effect through sharp edges and hollow nanostructures. Afterwards, five chemometric algorithms, like partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), competitive adaptive reweighted sampling-PLS (CARS-PLS), random frog-PLS (RF-PLS), and convolutional neural network (CNN) were applied for TTC and CAP. RF-PLS performed optimally for TTC and CAP (Rc = 0.9686, Rp = 0.9648, RPD = 3.79 for TTC and Rc = 0.9893, Rp = 0.9878, RPD = 5.88 for CAP). Furthermore, the detection limit of 0.0001 µg/mL for both TTC and CAP was obtained. Finally, satisfactory (p > 0.05) results were obtained with the standard HPLC method. Therefore, SERS combined RF-PLS could be applied for fast, nondestructive sensing of TTC and CAP in milk.

A fluorescent NBD "turn-on" probe for the rapid and on-site analysis of fructose in food.

High fructose intake is an important cause of metabolic disease. Due to the increasing prevalence of metabolic diseases worldwide, the development of an accurate and efficient tool for monitoring fructose in food is urgently needed to control the intake of fructose. Herein, a new fluorescent probe NBD-PQ-B with 7-nitrobenz-2-oxa-1, 3-diazole (NBD) as the fluorophore, piperazine (PQ) as the bridging group and phenylboronic acid (B) as the recognition receptor, was synthesized to detect fructose. The fluorescence of NBD-PQ-B increased linearly at 550 nm at an excitation wavelength of 497 nm with increasing fructose concentration from 0.1 to 20 mM. The limit of detection (LOD) of fructose was 40 µM. The pKa values of NBD-PQ-B and its fructose complexes were 4.1 and 10.0, respectively. In addition, NBD-PQ-B bound to fructose in a few seconds. The present technique was applied to determine the fructose content in beverages, honey, and watermelon with satisfactory results. Finally, the system could not only be applied in an aqueous solution with a spectrophotometer, but also be fabricated as a NBD-PQ-B/polyvinyl oxide (PEO) film by electrospinning for on-site food analysis simply with the assistance of a smartphone.

Rapidly responsive and highly selective NIR fluorescent probe for detecting hydrogen sulfide in food samples and living cells.

Hydrogen sulfide (H2S) is a pungent gas that is one of the key mediators of signal transduction in biological systems, and its presence is related to the freshness of some protein foods. Using phenothiazine derivatives as fluorophores and 2, 4-dinitrobenzene sulfonate (DNBS) fragments as reaction groups, a near-infrared (NIR) probe WX-HS for H2S identification was designed. With the addition of H2S, WX-HS appeared a strong fluorescence signal at 660 nm with short reaction time (90 s) and high sensitivity, and fluorescence state change from non-fluorescent to orange-red. In addition, WX-HS could effectively detect H2S produced during food oxidation. Based on its low cytotoxicity, the WX-HS probe further enabled the detection and imaging of H2S in A549 cells.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis.

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

Portable sensing methods based on carbon dots for food analysis.

Food analysis is significantly important in monitoring food quality and safety for human health. Traditional methods for food detection mainly rely on benchtop instruments and require a certain amount of analysis time, which promotes the development of portable sensors. Portable sensing methods own many advantages over traditional techniques such as flexibility and accessibility in diverse environments, real-time monitoring, cost-effectiveness, and rapid deployment. This review focuses on the portable approaches based on carbon dots (CDs) for food analysis. CDs are zero-dimensional carbon-based material with a size of less than 10 nm. In the manner of sensing, CDs exhibit rich functional groups, low biotoxicity, good biocompatibility, and excellent optical properties. Furthermore, there are many methods for the synthesis of CDs using various precursor materials. The incorporation of CDs into food science and engineering for enhancing food safety control and risk assessment shows promising prospects.

Trends in authentication of edible oils using vibrational spectroscopic techniques.

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring.

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 µA/µM/cm2), a broad linear range (0.002-8 µM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

An electrochemical aptasensor for zearalenone detection based on the Co3O4/MoS2/Au nanocomposites and hybrid chain reaction.

An electrochemical aptasensor was used for the fast and sensitive detection of zearalenone (ZEN) based on the combination of Co3O4/MoS2/Au nanocomposites and the hybrid chain reaction (HCR). The glassy carbon electrode was coated with Co3O4/MoS2/Au nanomaterials to immobilize the ZEN-cDNA that had been bound with ZEN-Apt by the principle of base complementary pairing. In the absence of ZEN, the HCR could not be triggered because the ZEN-cDNA could not be exposed. After ZEN was added to the surface of the electrode, a complex structure was produced on the modified electrode by the combination of ZEN and ZEN-Apt. Therefore, the ZEN-cDNA can raise the HCR to produce the long-strand dsDNA structure. Due to the formation of dsDNA, the methylene blue (MB) could be inserted into the superstructure of branched DNA and the peak currents of the MB redox signal dramatically increased. So the concentration of ZEN could be detected by the change of signal intensity. Under optimized conditions, the developed electrochemical biosensing strategy showed an outstanding linear detection range of 1.0×10-10 mol/L to 1.0×10-6 mol/L, a low detection limit (LOD) of 8.5×10-11 mol/L with desirable selectivity and stability. Therefore, the fabricated platform possessed a great application potential in fields of food safety, medical detection, and drug analysis.

Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products.

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products.

[Study on determination methods and analysis of micronutrients in take-away meals].

OBJECTIVE: To comprehensively analyze the trace nutrient contents in take-away meals, the simultaneous detection method of common vitamins in take-away meals were explored based on the samples' matrix, and the content of trace nutrients in take-away meals was analyzed combined with inductively coupled plasma-mass spectrometry(ICP-MS) detection of common elements. METHODS: Fifty-seven take-away meals were collected randomly and analyzed. Vitamins were determined by high performance liquid chromatography-ultraviolet detector tandem fluorescence detector after pretreatment of samples including enzymatic digestion, hydrolysis and extraction. The separation was performed on a C_(18) column(250 mm×4.6 mm, 5 µm) with ion-pair acid reagents as the mobile phase for water-soluble vitamins and methanol for fat-soluble vitamins. Vitamin B_1, vitamin B_2, nicotinic acid, nicotinamide and vitamin A were detected by ultraviolet detector(UVD), while vitamin B_6 and E by fluorescence detector(FLD). Elemental analysis of calcium, magnesium, sodium, potassium, zinc, selenium and copper in the take-away meals was carried out according to GB 5009.268-2016 by ICP-MS to comprehensively evaluate the contents of micronutrients. RESULTS: Through optimization of chromatography and sample pretreatment conditions, the sensitivity of the established detection method can meet the needs of micronutrient evaluation with the detection limits and quantification limits of vitamins in the range of 0.002-0.098 mg/100 g and 0.007-0.327 mg/100 g, respectively. Good precision was obtained(<10%). The spiked recovery rates were 80.5%-103.8%(n=6). The result showed that the contents of micronutrients in take-away meals were generally low. The detection rates of vitamins ranged from 21.1% to 98.2%. CONCLUSION: The proposed method is simple and sensitive, and the contents of vitamins and elements determined were low in the collected take-away meals.

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin.

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost-profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and "volatilomics," "volatiles," "food authenticity," "adulteration," "origin," "fingerprint," "chemometrics," and variations thereof as keywords, without chronological restriction. One hundred thirty-seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

Recent advances and trends in innovative biosensor-based devices for heavy metal ion detection in food.

Low-cost, reliable, and efficient biosensors are crucial in detecting residual heavy metal ions (HMIs) in food products. At present, based on distance-induced localized surface plasmon resonance of noble metal nanoparticles, enzyme-mimetic reaction of nanozymes, and chelation reaction of metal chelators, the constructed optical sensors have attracted wide attention in HMIs detection. Besides, based on the enrichment and signal amplification strategy of nanomaterials on HMIs and the construction of electrochemical aptamer sensing platforms, the developed electrochemical biosensors have overcome the plague of low sensitivity, poor selectivity, and the inability of multiplexed detection in the optical strategy. Moreover, along with an in-depth discussion of these different types of biosensors, a detailed overview of the design and application of innovative devices based on these sensing principles was provided, including microfluidic systems, hydrogel-based platforms, and test strip technologies. Finally, the challenges that hinder commercial application have also been mentioned. Overall, this review aims to establish a theoretical foundation for developing accurate and reliable sensing technologies and devices for HMIs, thereby promoting the widespread application of biosensors in the detection of HMIs in food.

Comparison of physiologically functional compounds in Sika deer Cervus nippon meats obtained from different regions in Japan.

In Japan, the promotion of effective use of many wild deer as food resource has been conducted. However, they are not necessarily utilized effectively. Thus, we focused physiologically functional compounds to find characteristics of Sika deer meats (commercially available) obtained from different regions such as Hokkaido, Wakayama, Tokushima, and Miyazaki prefectures in Japan, making it a valuable resource for future studies and applications. The amount of carnosine, anserine, and balenine in muscle of deer from Wakayama prefecture was significantly lower than that in muscle of deer from other prefectures. The differences of amount of imidazole dipeptides in different prefectures seems to be caused by feed, rearing environment, and breed. The amount of carnitine in deer meat from Hokkaido was significantly lower than that in muscle of deer from other prefectures, while the amount of acetyl-carnitine in deer meat from Miyazaki prefectures was significantly higher than that from other prefectures. The amounts of glutamine, ornithine, and 3-methylhistidine in muscles of deer from Wakayama prefectures were significantly higher than those in muscle of deer from other prefectures. These results might be caused by differences in feeding habits, habitat, the muscle types, and subspecies of deer obtained from four regions in Japan.

Revolutionizing food science with mass spectrometry imaging: A comprehensive review of applications and challenges.

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.