Rapid detection and specific identification of offals within minced beef samples utilising ambient mass spectrometry

Simple and rapid identification of beef within 30 min using a new food nucleic acid release agent combined with direct-fast qPCR

Simple and rapid molecular detection technologies for authenticating animal species are urgently needed for food safety and authenticity. This study established a new direct-fast quantitative polymerase chain reaction (qPCR) detection technology for beef to achieve rapid and on-site nucleic acid detection in food. This technology can complete nucleic acid extraction in 4 min using a new type of food nucleic acid-releasing agent, followed by direct amplification of the DNA sample by fast qPCR in 25 min. The results indicated that direct-fast qPCR can specifically identify beef and can also identify 0.00001% of beef components in artificially simulated meat mixtures, with a detection precision variation coefficient of <4%. This method can be used to effectively identify beef in different food samples. As a simple, fast, and accurate molecular detection technology for beef, this method may provide a new tool for the on-site detection of beef components in food.

Discrimination of beef composition and sensory quality by using rapid Evaporative Ionisation Mass Spectrometry (REIMS)

Herein, we investigated the potential of REIMS analysis for classifying muscle composition and meat sensory quality. The study utilized 116 samples from 29 crossbred Angus × Salers, across three muscle types. Prediction models were developed combining REIMS fingerprints and meat quality metrics. Varying efficacy was observed across REIMS discriminations - muscle type (71 %), marbling level (32 %), untrained consumer evaluated tenderness (36 %), flavor liking (99 %) and juiciness (99 %). Notably, REIMS demonstrated the ability to classify 116 beef across four Meat Standards Australia grades with an overall accuracy of 37 %. Specifically, "premium" beef could be differentiated from "unsatisfactory", "good everyday" and "better than everyday" grades with accuracies of 99 %, 84 %, and 62 %, respectively. Limited efficacy was observed however, in classifying trained panel evaluated sensory quality and fatty acid composition. Additionally, key predictive features were tentatively identified from the REIMS fingerprints primarily comprised of molecular ions present in lipids, phospholipids, and amino acids.

Development of a real-time PCR protocol for the detection of chicken DNA in meat products

Food falsification is a pressing issue in today's food industry, with fraudulent substitution of costly ingredients with cheaper alternatives occurring globally. Consequently, developing straightforward and efficient diagnostic systems to detect such fraud is a top priority in scientific research. The aim of the work was to develop a test system and protocol for polymerase chain reaction (PCR) to detect in food products of animal origin the substitution of expensive meat raw materials for by-products of poultry processing. For this, real-time polymerase chain reaction (RT-PCR) was used, which allows determining the qualitative and quantitative substitution in raw and technologically prepared products. Other methods for detecting falsification - enzyme immunoassay (ELISA/ELISA) or express methods in the form of a lateral flow immunoassay are less informative. The extraction of nucleic acids for real-time polymerase chain reaction depends on the source matrix, with higher concentrations obtained from germ cells and parenchymal organs. Extraction from muscle and plant tissues is more challenging, but thorough grinding of these samples improves nucleic acid concentration by 1.5 times using DNA extraction kits. The selection of primers and fluorescent probes through GenBank and PCR Primer Design/DNASTAR software enables efficient amplification and identification of target chicken DNA fragments in various matrices.

Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications

Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.

Absolute quantification of targeted rabbit liver- and meat tissue-specific peptide markers in highly processed food products

A highly sensitive and selective method for the simultaneous absolute quantification of peptides unique to rabbit meat- and liver-specific tissue was developed using liquid chromatography - triple quadrupole mass spectrometry. Two rabbit skeletal muscle-specific peptides (SSVFVADPK and PHSHPALTPEQK), three rabbit liver tissue-specific peptides (FNLEALVTHTLPFEK, AILNYVANK, and TELAEPTSTR) and one peptide specific to both rabbit offal and skeletal muscle tissue (AFFGHYLYEVAR) were monitored. Analyses were performed using peptides labelled with stable isotopes (13C and 15N) as internal standards. Fifteen food samples containing rabbit meat and/or liver were analysed to verify compliance of the rabbit meat and liver composition with product labelling. One sample was adulterated with undeclared rabbit liver. The limit of detection and limit of quantification for the selected peptides of interest were in the range of 0.17 to 0.35 ng/mg and 0.57 to 1.17 ng/mg, respectively. The method may be useful for the determination of rabbit meat and liver tissue in highly processed food samples.

Evaluation of Lipid Oxidation Characteristics in Salmon after Simulation of Cold Chain Interruption Using Rapid Evaporation Ionization Mass Spectrometry

Temperature fluctuations occurring during the cold chain logistics of salmon contribute to lipid oxidation. This study aimed to simulate cold chain interruption through freeze-thaw operations and evaluate the lipidomics data from salmon samples subjected to different numbers of freeze-thaw cycles by using rapid evaporative ionization mass spectrometry (REIMS) combined with an intelligent surgical knife (iKnife). The results indicated significant differences in the relative abundance of characteristic ions among the samples (p < 0.05). A total of 34 ions with variable importance for the projection values ≥1 were identified as potential biomarkers, including m/z 719.4233 ([PCC36:5-NH(CH3)3]-), m/z 337.3134 ([FAC22:1]-), m/z 720.4666 ([PEC35:6-H]-), m/z 309.2780 ([FAC20:1]-), m/z 777.4985 ([PCC40:4-NH(CH3)3]-), m/z 745.4421 ([PCC38:6-NH(CH3)3]-/[PEC38:6-NH3]-), m/z 747.4665 ([PCC38:5-NH(CH3)3]-/[PEC38:5-NH3]-), etc. The degree of lipid oxidation was found to be associated with the number of freeze-thaw cycles, exhibiting the most significant alterations in the relative abundance of lipid ions in the 8T samples. Additionally, sensory evaluation by the CIE-L*a*b* method and volatile analysis by headspace solid-phase microextraction gas chromatography-mass spectrometry demonstrated significant differences (p < 0.05) in color and odor among the salmon samples, with a correlation to the number of freeze-thaw cycles. The primary compounds responsible for alterations in salmon odor were aldehydes with lower odor thresholds. In summary, the iKnife-REIMS method accurately differentiated salmon muscle tissues based on varying levels of lipid oxidation, thus expanding the application of REIMS.

Heat-stable peptide markers specific to rabbit and chicken liver tissue for meat product authentication testing

A three-step analysis was used to detect and identify heat-stable peptide markers specific to liver tissue from rabbit and chicken. It involved peptide discovery by liquid chromatography coupled to high resolution mass spectrometer (LC-HRMS), followed by protein identification using Spectrum Mill software and multiple reaction monitoring (MRM) based confirmation of the discovered peptides using a liquid chromatography coupled to triple quadrupole mass spectrometer (LC-TQ). We identified 50 and 91 heat-stable peptide markers unique to chicken and rabbit liver, respectively. The markers were validated in commercial food samples with declared liver tissue contents ranging from 5% to 30%. The best candidate peptides for distinguishing liver tissue from skeletal muscle were selected and then confirmed using MRM-based approach. Limit of detection of liver was found to be in the range of 0.13 to 2.13% (w/w) for chicken liver-specific peptide markers, and from 0.04 to 0.6% (w/w) for rabbit liver-specific peptide markers.

Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging

Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.

Metabolic fingerprinting using Rapid evaporative ionisation mass spectrometry can discriminate meat quality and composition of lambs from different sexes, breeds and forage systems

Assurance of food quality and allied farming systems is increasingly sought by consumers and food processors. Yet, there are no validated analytical approaches for food-based verification of farming systems. Rapid evaporative ionisation mass spectrometry (REIMS) is an emerging analytical tool that can provide sufficient details to meet this need. M. Longissimus lumborum of 10 groups of lambs (n = 140) from 3 farms, varying by breed, sex, and forage type, were measured using REIMS fingerprinting. Modelling of features detected by REIMS could discriminate for most comparisons of sex (including castration status), breed, and diet. Tentative identification suggested that lipids, hormone-related compounds, amino acids and dipeptides were the main discriminatory features. Several REIMS features were correlated with pH and shear force in Merino lambs. REIMS was able to detect features related to breed, sex and feed in lamb meat, suggesting that these characteristics can be independently measured using rapid metabolic fingerprinting.

Metabolomic profiling to detect different forms of beef fraud using rapid evaporative ionisation mass spectrometry (REIMS)

Organic food fraud is a significant challenge in the food testing sector-high price premiums, ease of access to produce to be relabelled and difficulties in developing testing strategies that can detect such frauds make organic foods particularly attractive and thus highly vulnerable to fraud. Samples of conventional and organic cattle taken across meat plants in Ireland and the United Kingdom, consisting of the neck (supraspinatus), rump (gluteus), and shin (flexor carpi radialis) regions of the carcass were analysed using a high resolution time-of-flight based rapid evaporative ionisation mass spectrometry (REIMS) system. The resulting untargeted lipidomic data (m/z 600-1000) was used to generate PCA-LDA models for production system and for muscle type, for these models, it was found that the production system model could differentiate organic from conventional beef with an accuracy of 84%, whilst the muscle type model could identify the cut of meat with a 98% accuracy; additionally, samples can be assessed against multiple models simultaneously, reducing analysis time and sample numbers. The use of REIMS showed considerable promise in its ability to detect different forms of meat fraud; its accuracy in differentiating organic from conventional beef is superior to stable isotope ratio mass spectrometry, with the added advantages of substantially shorter analysis times and lower sample analysis costs. The ability to rapidly confirm the cut of meat also demonstrates the potential of REIMS to concurrently determine multiple aspects of beef authenticity in a close to real time analysis.

A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration

Primary and secondary food processing had been identified as areas vulnerable to fraud. Besides the food processing area, other stages within the food supply chain are also vulnerable to fraud. This study aims to develop a Bayesian network (BN) model to predict food fraud type and point of adulteration i.e., the occurrence of fraudulent activity. The BN model was developed using GeNie Modeler (BayesFusion, LLC) based on 715 notifications (1979-2018) from Food Adulteration Incidents Registry (FAIR) database. Types of food fraud were linked to six explanatory variables such as food categories, year, adulterants (chemicals, ingredients, non-food, microbiological, physical, and others), reporting country, point of adulteration, and point of detection. The BN model was validated using 80 notifications from 2019 to determine the predictive accuracy of food fraud type and point of adulteration. Mislabelling (20.7%), artificial enhancement (17.2%), and substitution (16.4%) were the most commonly reported types of fraud. Beverages (21.4%), dairy (14.3%), and meat (14.0%) received the highest fraud notifications. Adulterants such as chemicals (21.7%) (e.g., formaldehyde, methanol, bleaching agent) and cheaper, expired or rotten ingredients (13.7%) were often used to adulterate food. Manufacturing (63.9%) was identified as the main point of adulteration followed by the retailer (13.4%) and distribution (9.9%).

A quantitative method for detecting meat contamination based on specific polypeptides

OBJECTIVE

This study was aimed to establish a quantitative detection method for meat contamination based on specific polypeptides.

METHODS

Thermally stable peptides with good responses were screened by high resolution liquid chromatography tandem mass spectrometry. Standard curves of specific polypeptide were established by triple quadrupole mass spectrometry. Finally, the adulteration of commercial samples was detected according to the standard curve.

RESULTS

Fifteen thermally stable peptides with good responses were screened. The selected specific peptides can be detected stably in raw meat and deep processed meat with the detection limit up to 1% and have a good linear relationship with the corresponding muscle composition.

CONCLUSION

This method can be effectively used for quantitative analysis of commercial samples.

Detection of Meat Adulteration Using Spectroscopy-Based Sensors

Minced meat is a vulnerable to adulteration food commodity because species- and/or tissue-specific morphological characteristics cannot be easily identified. Hence, the economically motivated adulteration of minced meat is rather likely to be practiced. The objective of this work was to assess the potential of spectroscopy-based sensors in detecting fraudulent minced meat substitution, specifically of (i) beef with bovine offal and (ii) pork with chicken (and vice versa) both in fresh and frozen-thawed samples. For each case, meat pieces were minced and mixed so that different levels of adulteration with a 25% increment were achieved while two categories of pure meat also were considered. From each level of adulteration, six different samples were prepared. In total, 120 samples were subjected to visible (Vis) and fluorescence (Fluo) spectra and multispectral image (MSI) acquisition. Support Vector Machine classification models were developed and evaluated. The MSI-based models outperformed the ones based on the other sensors with accuracy scores varying from 87% to 100%. The Vis-based models followed in terms of accuracy with attained scores varying from 57% to 97% while the lowest performance was demonstrated by the Fluo-based models. Overall, spectroscopic data hold a considerable potential for the detection and quantification of minced meat adulteration, which, however, appears to be sensor-specific.

Rapid Evaporative Ionization Mass Spectrometry: A Review on Its Application to the Red Meat Industry with an Australian Context

The red meat supply chain is a complex network transferring product from producers to consumers in a safe and secure way. There can be times when fragmentation can arise within the supply chain, which could be exploited. This risk needs reduction so that meat products enter the market with the desired attributes. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a novel ambient mass spectrometry technique originally developed for rapid and accurate classification of biological tissue which is now being considered for use in a range of additional applications. It has subsequently shown promise for a range of food provenance, quality and safety applications with its ability to conduct ex vivo and in situ analysis. These are regarded as critical characteristics for technologies which can enable real-time decision making in meat processing plants and more broadly throughout the sector. This review presents an overview of the REIMS technology, and its application to the areas of provenance, quality and safety to the red meat industry, particularly in an Australian context.

Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork

Hyperspectral imaging (HSI) technique was investigated to explore a feasible protocol for detecting the potential offal (lung) adulteration in ground pork. Tested samples (176 adulterated and 2 controls) were first prepared with adulterant of ground lung in range of 0%-100% (w/w) at 10% intervals. After hyperspectral images were acquired and calibrated in reflectance mode (400-1000 nm), full spectra were extracted from identified regions of interests (ROIs) and then transformed into absorbance and Kubelka-Munck spectral units, respectively. Partial least squares regression (PLSR) models based on full spectra showed that raw reflectance spectra with no preprocessings performed best with coefficient of determination (R_p²) of 0.98, root mean square error (RMSEP) of 4.25%, and ratio performance deviation (RPD) of 7.53 in prediction set. To reduce the high dimensionality of spectra, data was further explored using principal component loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC), respectively. The optimal performance of established simplified PLSR model were acquired using eleven featured wavelengths selected by PC loadings with R_p² of 0.98, RMSEP of 4.47% and RPD of 7.16. Finally, the limit of detection (LOD) was calculated to be a satisfactory 7.58%, and readily discernible visualization procedure using preferred simplified PLSR model yielded satisfactory spatial distribution of adulteration situation. Control samples with known distribution were also visualized to successfully prove the validity. Consequently, this research offers an alternative assay for visually and rapidly detecting offal of lung adulteration in ground pork.

Use of Rapid Evaporative Ionisation Mass Spectrometry fingerprinting to determine the metabolic changes to dry-aged lean beef due to different ageing regimes

Rapid Evaporative Ionisation Mass Spectrometry (REIMS) was used to determine the impact of in-bag ageing regimes (stepwise-ageing at different air velocities and straight-dry-ageing) and trimming on the metabolic profile of dry-aged lean beef. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models based on 1705 tentatively identified m/z features were found for ageing methods (Q² = 0.85), ageing time (0 vs. 21 days, Q² = 0.95) and sampling locations (surface meat vs. trimmings, Q² = 0.94). No significant (P > 0.05) difference in metabolites due to air velocities. Small metabolites such as dipeptides and amino acids were more abundant, especially on the surface of untrimmed lean beef, following 21 days of straight-dry-ageing. Stepwise-ageing produced different metabolic profiles from straight-dry-ageing, suggesting that the two methods may differ in dry-aged meat quality and flavour. This work demonstrates REIMS's potential for real time differentiation of meat on processing parameters.

Metabolic fingerprinting of in-bag dry- and wet-aged lamb with rapid evaporative ionisation mass spectroscopy

The effect of in-bag dry- and wet-ageing on metabolite profiles of lamb legs was determined using Rapid Evaporative Ionisation Mass Spectrometry (REIMS). Using orthogonal projection to latent structures-discriminant analysis (OPLS-DA) with REIMS, 1705 metabolite ions were identified (Q² = 0.86) in four muscles: m. semimembranosus, m. biceps femoris, m. vastus lateralis and m. rectus femoris. A total of 663 metabolites differed between ageing methods (P < 0.05) which mainly resulted from proteolysis and lipid metabolism. Dry-aged lamb had higher pH (P = 0.016) and lower moisture content (P = 0.034) than the wet-aged. Dry-ageing produced more (P < 0.05) smaller sized metabolites including dipeptides and free amino acids and lipid oxidation metabolites compared to wet-aged equivalents. Different muscles had distinct REIMS metabolic profiles. Outcomes of this study demonstrated that REIMS can be used for authentication between in-bag dry- and wet-aged lamb based on their metabolic fingerprints.

Making complex measurements of meat composition fast: Application of rapid evaporative ionisation mass spectrometry to measuring meat quality and fraud

Increasing demands are being placed on meat producers to verify more about their product with regards to safety, quality and authenticity. There are many methods that can detect aspects of these parameters in meat, yet most are too slow to keep up with the demands of modern meat processing plants and supply chains. A new technology, Rapid Evaporative Ionisation Mass Spectrometry (REIMS), has the potential to bridge the gap between advanced laboratory measurements and technology that can screen for quality, safety and authenticity parameters in a single measurement. Analysis with REIMS generates a detailed mass spectral fingerprint representative of a meat sample without the need for sample processing. REIMS has successfully been used to detect species fraud, detect use of hormones in meat animals, monitor meat processing and to detect off flavours such as boar taint. The aim of this review is to summarize these and other applications to highlight the potential of REIMS for meat analysis. Sampling methods and important considerations for data analysis are discussed as well as limitations of the technology and remaining challenges for practical adoption.

Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with R_p² = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that R_p² was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Monitoring recombinant protein expression in bacteria by rapid evaporative ionisation mass spectrometry

RATIONALE

There is increasing interest in methods of direct analysis mass spectrometry that bypass complex sample preparation steps.

METHODS

One of the most interesting new ionisation methods is rapid evaporative ionisation mass spectrometry (REIMS) in which samples are vapourised and the combustion products are subsequently ionised and analysed by mass spectrometry (Synapt G2si). The only sample preparation required is the recovery of a cell pellet from a culture that can be analysed immediately.

RESULTS

We demonstrate that REIMS can be used to monitor the expression of heterologous recombinant proteins in Escherichia coli. Clear segregation was achievable between bacteria harvesting plasmids that were strongly expressed and other cultures in which the plasmid did not result in the expression of large amounts of recombinant product.

CONCLUSIONS

REIMS has considerable potential as a near-instantaneous monitoring tool for protein production in a biotechnology environment.