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

High-throughput identification of meat ingredients in adulterated foods based on centrifugal integrated purification-CRISPR array

Rapid, accurate, and sensitive analytical methods for the detection of food fraud are now an urgent requirement in the global food industry to ensure food quality. In response to this demand, a centrifugal integrated purification-CRISPR array for meat adulteration (CIPAM) was established. In detail, CIPAM system combines microneedles for DNA extraction and RAA-CRISPR/Cas12a integrated into a centrifugal microfluidic chip for the detection of meat adulteration. The RAA-CRISPR/Cas12a reaction reagents were pre-embedded into the different reaction chambers on the microfluidic chip to achieve the streamline of operations, markedly simplifying the detection process. The whole reaction was completed within 30 min with a detection limit of 0.1 % (w/w) in pig, chicken, duck, and lamb products. Referring to the results of the standard method, CIPAM system achieved 100 % accuracy. The automatic multiplex detection process implemented in the developed CIPAM system met the needs of food regulatory authorities.

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

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.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Rapid identification of mosquito species and age by mass spectrometric analysis

BACKGROUND

A rapid, accurate method to identify and to age-grade mosquito populations would be a major advance in predicting the risk of pathogen transmission and evaluating the public health impact of vector control interventions. Whilst other spectrometric or transcriptomic methods show promise, current approaches rely on challenging morphological techniques or simple binary classifications that cannot identify the subset of the population old enough to be infectious. In this study, the ability of rapid evaporative ionisation mass spectrometry (REIMS) to identify the species and age of mosquitoes reared in the laboratory and derived from the wild was investigated.

RESULTS

The accuracy of REIMS in identifying morphologically identical species of the Anopheles gambiae complex exceeded 97% using principal component/linear discriminant analysis (PC-LDA) and 84% based on random forest analysis. Age separation into 3 different age categories (1 day, 5-6 days, 14-15 days) was achieved with 99% (PC-LDA) and 91% (random forest) accuracy. When tested on wild mosquitoes from the UK, REIMS data could determine the species and age of the specimens with accuracies of 91 and 90% respectively.

CONCLUSIONS

The accuracy of REIMS to resolve the species and age of Anopheles mosquitoes is comparable to that achieved by infrared spectroscopy approaches. The processing time and ease of use represent significant advantages over current, dissection-based methods. Importantly, the accuracy was maintained when using wild mosquitoes reared under differing environmental conditions, and when mosquitoes were stored frozen or desiccated. This high throughput approach thus has potential to conduct rapid, real-time monitoring of vector populations, providing entomological evidence of the impact of alternative interventions.

Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats

Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the m/z range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.

Real-time traceability of sorghum origin by soldering iron-based rapid evaporative ionization mass spectrometry and chemometrics

Sorghum is an important grain with a high economic value for liquor production. Tracing the geographical origin of sorghum is vital to guarantee the liquor flavor. Soldering iron-based rapid evaporative ionization mass spectrometry (REIMS) combined with chemometrics was developed for the real-time discrimination of the sorghum's geographical origin. The working conditions of soldering iron-based ionization were optimized, and then the obtained MS profiling data were processed using chemometrics analysis methods, including principal component analysis-linear discriminant analysis and orthogonal projection to latent structures discriminant analysis (OPLS-DA). A recognition model was established, and discriminations of sorghum samples from 10 provinces in China were achieved with a correct rate higher than 90%. On the basis of OPLS-DA, the specific ions of m/z 279.2327, 281.2479, and 283.2639 had relatively strong discrimination power for the geographical origins of sorghum. The developed method was successfully applied in the discrimination of sorghum origins. The results indicated that the soldering iron-based REIMS technique combined with chemometrics is a useful tool for direct, fast, and real-time ionization of poor conductivity samples and acquisition of metabolic profiling data.

Evaluating the ability of rapid evaporative ionization mass spectrometry to differentiate beef palatability based on consumer preference

Rapid Evaporative Ionization Mass Spectrometry (REIMS) is a type of ambient ionization mass spectrometry, which enables real-time evaluation of several complex traits from a single measurement. The objective of this study was to evaluate the capability of REIMS analysis of raw samples coupled with chemometrics to accurately identify and predict cooked beef palatability. REIMS analysis and consumer sensory evaluation were conducted for beef arm center roasts (n = 20), top loin steaks (n = 20), top sirloin steaks (n = 20), and 20% lipid ground beef (n = 20). These data were used to train predictive models for six classification sets representing different sensory traits. The maximum prediction accuracies achieved (from high to low): beefy flavor acceptance (86.25%), juiciness acceptance (83.75%), overall acceptance (81.25%), overall flavor acceptance (81.25%), grilled flavor acceptance (78.75%), and tenderness acceptance (75%). The current study demonstrates that REIMS analysis of raw meat has the potential to predict and classify cooked beef palatability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-022-05562-6.

Visual Detection of Chicken Adulteration Based on a Lateral Flow Strip-PCR Strategy

The aim of this study was to develop an accurate, easy-to-use, and cost-effective method for the detection of chicken adulteration based on polymerase chain reaction (PCR) and lateral flow strip (LFS). We compared six DNA extraction methods, namely the cetyltrimethylammonium bromide (CTAB) method, salt method, urea method, SDS method, guanidine isothiocyanate method, and commercial kit method. The chicken cytb gene was used as a target to design specific primers. The specificity and sensitivity of the PCR-LFS system were tested using a self-assembled lateral flow measurement sensor. The results showed that the DNA concentration obtained by salt methods is up to 533 ± 84 ng µL⁻¹, is a suitable replacement for commercial kits. The PCR-LFS method exhibits high specificity at an annealing temperature of 62 °C and does not cross-react with other animal sources. This strategy is also highly sensitive, being able to detect 0.1% of chicken in artificial adulterated meat. The results of the test strips can be observed with the naked eye within 5 min, and this result is consistent with the electrophoresis result, demonstrating its high accuracy. Moreover, the detection system has already been successfully used to detect chicken in commercial samples. Hence, this PCR-LFS strategy provides a potential tool to verify the authenticity of chicken.

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.

Review: Improving the nutritional, sensory and market value of meat products from sheep and cattle

This paper focuses on improving the sensory, health attributes and meat yield of beef and lamb meats. Value for meat is defined as the weight of meat × price/kg received with price linked to eating quality. To maximise value across the supply chain, accurate carcass grading systems for eating quality and yield are paramount. Grading data can then be used to target consumers' needs at given price points and then to tailor appropriate production and genetic directions. Both the grading methodologies and key phenotypes are complex and still under intensive research with international collaboration to maximise opportunities. In addition, there is value in promoting the health aspects of red meats served as whole trimmed meats. Typically, the total fat content is relatively low (less than 5%) and for forage systems, they deliver a very significant content of long-chain n-3 fatty acids. Further research is needed to clarify the healthiness or otherwise of ground beef served as burgers given the fat content is typically 20% or more. It is important to continue to improve the feedback to producers regarding the quantity and quality of the products they produce to target new value opportunities in a transparent and quantitative manner.

Differentiation between Fresh and Frozen-Thawed Meat using Rapid Evaporative Ionization Mass Spectrometry: The Case of Beef Muscle

An intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the lipidomic profiling of fresh and frozen-thawed beef muscle. The data were obtained by REIMS and then processed using multivariate statistical analysis methods including principal component analysis-linear discriminant analysis (PCA-LDA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The discrimination of fresh and frozen-thawed meat has been achieved, and the real-time identification accuracy was 92-100%. Changes in the composition and content of fatty acids and phospholipids were statistically analyzed by OPLS-DA, and the ions of m/z 279.2317, m/z 681.4830, and m/z 697.4882 were selected as differential compounds/metabolites. The developed method was also successfully applied in the discrimination of fresh and frozen-thawed meat samples. These results showed that REIMS as a high-throughput, rapid, and real-time mass spectrometry detection technology can be used for the identification of fresh and frozen-thawed meat samples.

Objective measurement technologies for transforming the Australian & New Zealand livestock industries

This paper introduces the special edition of Meat Science focused upon the development, calibration and validation of technologies that measure traits influencing meat eating quality, or carcass fat and lean composition. These papers reflect the combined research efforts of groups in Australia, through the Advanced Livestock Measurement Technologies project, and New Zealand through AgResearch. We describe the various technologies being developed, how these devices are being trained upon common gold-standard measurements, and how their outputs are being simultaneously integrated into existing industry systems. We outline how this enhances the industry uptake and adoption of these technologies, and how this is further accelerated by education programs and strategic industry investment into their commercialisation.

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.