Classification of adipose tissue species using Raman spectroscopy

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.

Preliminary Investigation on the Relationship between Raman Spectra of Beef and Metmyoglobin and Metmyoglobin Reductase Activity

A hand-held Raman spectroscopic device was used as a rapid nondestructive testing device to predict the metmyoglobin (MetMb) and metmyoglobin reductase activity (MRA) values on the surface layer of fresh beef. Longissimus dorsi muscles were from 10 young bulls (Holstein-Friesian) from two different cattle farms (group A = 5 and B = 5). The Raman spectra of 100 samples were correlated with the MetMb and MRA values using partial least squares regression (PLSR). Two groups could be discriminated, and the separate correlation models were better than the joint correlation model for the fresh beef. The coefficients of determination are R² = 0.81 (group A) and R² = 0.87 (group B) for MetMb and R² = 0.80 (group A) and R² = 0.85 (group B) for MRA. The results show the usefulness of Raman spectra in predicting the inner traits such as MetMb and MRA during meat storage. In conclusion, it is feasible to determine the MetMb and MRA values by Raman spectroscopy. Color is an important indicator of beef freshness and can vary depending on the age, sex, and breed of the cow. They play a very important role in human nutrition. The color of meat is an important indicator of meat freshness, and many researchers are already investigating the causes of color changes. The research was conducted in this environment.

Raman studies of the adipose tissue: Current state-of-art and future perspectives in diagnostics

The last decades revealed that the adipose tissue shows an unexplored therapeutic potential. In particular, targeting the perivascular adipose tissue (PVAT), that surrounds blood vessels, can prevent cardiovascular pathologies and browning of the adipose tissue can become an effective strategy against obesity. Therefore, new analytical tools are necessary to analyze this tissue. This review reports on the recent developments of various Raman-based techniques for the identification and quantification of the adipose tissue compared to conventional analytical methods. In particular, the emphasis is on analysis of PVAT, investigation of pathological changes of the adipose tissue in model systems and possibilities for its characterization in the clinical context. Overall, the review critically discusses the potential and limitations of Raman techniques in adipose tissue-targeted diagnostics and possible future anti-obesity therapies.

Multimodal Label-Free Monitoring of Adipogenic Stem Cell Differentiation Using Endogenous Optical Biomarkers

Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping-based machine learning analysis is reported to distinguish differentiating human adipose-derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single-cell resolution. The label-free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton-based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix-assisted laser desorption/ionization-mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution.

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.

Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef

Australian grass and grain-fed beef products attract premium prices at sale and several beef processors market beef underwritten by production system claims. This preliminary investigation assessed the feasibility of using Raman spectroscopy to detect differences in the chemical composition of subcutaneous fat from cattle raised in extensive and intensive production systems. Raman spectra, fatty acid composition, β-carotene composition and objective colour measurements were measured on 150 grass and 150 grain-fed cattle. Spectral differences at peaks including 1069 cm^-1, 1127 cm^-1, 1301 cm^-1 and 1445 cm^-1 suggest that Raman spectra is able to detect differences in saturated fatty acids, which were significantly higher in carcases from grain-fed cattle. Differences in spectra at 1658 cm^-1 were observed, however further research is required to investigate the cause of this spectral feature. Overall, this study indicated that Raman spectroscopy is a potential tool for the authentication of beef carcases from grass and grain-fed production systems.

Advances in Sheep and Goat Meat Products Research

The main goal of this chapter was to review the state of the art in the recent advances in sheep and goat meat products research. Research and innovation have been playing an important role in sheep and goat meat production and meat processing as well as food safety. Special emphasis will be placed on the imaging and spectroscopic methods for predicting body composition, carcass and meat quality. The physicochemical and sensory quality as well as food safety will be referenced to the new sheep and goat meat products. Finally, the future trends in sheep and goat meat products research will be pointed out.

High pressure Raman study of type-I collagen

The high pressure response of type-I collagen from bovine Achilles tendon is investigated with micro-Raman spectroscopy. Fluorinert™ and methanol-ethanol mixtures were used as pressure transmitting media (PTM) in a diamond anvil cell. The Raman spectrum of collagen is dominated by three bands centred at approximately 1450, 1660 and 2930 cm^-1 , attributed to C-H deformation, C=O stretching of the peptide bond (amide-I band) and C-H stretching modes respectively. Upon pressure increase, using Fluorinert™ as PTM, a shift towards higher frequencies of the C-H stretching and deformation peaks is observed. Contrary, the amide-I band peaks are shifted to lower frequencies with moderate pressure slopes. On the other hand, when using the alcohol mixture as PTM, the amide-I band exhibits more pronounced C=O bond softening, deduced from the shift to lower frequencies, suggesting a strengthening of the hydrogen bonds between glycine and proline residues of different collagen chains due to the presence of the polar alcohol molecules. Furthermore, some of the peaks exhibit abrupt changes in their pressure slopes at approximately 2 GPa, implying a variation in the compressibility of the collagen fibres. This could be attributed to a pitch change from 10/3 to 7/2, sliding of the tropocollagen molecules, twisting variation at the molecular level and/or elimination of the D-gaps induced by kink compression. All spectral changes are reversible upon pressure release, which indicates that denaturation has not taken place. Finally, a minor lipid phase contamination was detected in some sample spots. Its pressure response is also monitored.

Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods

Conventional methods for determining fat content and fatty acids (FAs) composition are generally based on the solvent extraction and gas chromatography techniques, respectively, which are time consuming, laborious, destructive to samples and require use of hazard solvents. These disadvantages make them impossible for large-scale detection or being applied to the production line of meat factories. In this context, the great necessity of developing rapid and nondestructive techniques for fat and FAs analyses has been highlighted. Measurement techniques based on near-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance and hyperspectral imaging have provided interesting and promising results for fat and FAs prediction in varieties of foods. Thus, the goal of this article is to give an overview of the current research progress in application of the four important techniques for fat and FAs analyses of muscle foods, which consist of pork, beef, lamb, chicken meat, fish and fish oil. The measurement techniques are described in terms of their working principles, features, and application advantages. Research advances for these techniques for specific food are summarized in detail and the factors influencing their modeling results are discussed. Perspectives on the current situation, future trends and challenges associated with the measurement techniques are also discussed.

Rapid detection of bovine adipose tissue using lateral flow strip assay

Currently no rapid immunoassays are developed to identify the species content of fat tissue in mixtures. We report a simple protocol enabling the effective detection of bovine fat in highly processed materials using a lateral flow (LF) immunoassay which targets a ruminant-specific muscle protein. A portion (50 gm) of muscle-free fat samples was rendered to separate the molten fat from the proteinaceous residue, then soluble proteins were extracted from the solid residue with 0.5 mol/L NaCl for the LF analysis. The assay could detect 2% bovine fat-in-pork fat, 1% bovine fat-in-porcine meat-and-bone meal, and 0.5% bovine fat-in-soy meal mixtures. Rendered bovine fat could be detected up to 213°C. These results demonstrate that low levels of bovine fat tissue can be detected in processed materials using an immunoassay based on the presence of the muscle protein which serves as a species marker in the fat tissue.

Prediction of total fatty acid parameters and individual fatty acids in pork backfat using Raman spectroscopy and chemometrics: Understanding the cage of covariance between highly correlated fat parameters

This study investigates how Partial Least Squares regression models for predicting individual fatty acids (FAs) and total FA parameters depend on Raman spectral variation associated with the iodine value in pork backfat. The backfat was sampled from pigs, which were fed with different dietary fat sources and levels. Good correlations between the Raman spectra and the total FA composition parameters and most individual FAs were obtained (R(CV)(2)=0.78-0.90). However, the predictions of the individual FAs are indirect and to a high degree depend on co-variance with the total FA parameters. A new procedure was demonstrated for identifying and characterizing such indirect or non-targeted calibrations. This information is very useful when Raman spectroscopy or other vibrational spectroscopic techniques are used to predict non-targeted quality parameters such as individual FAs as they may lead to inaccurate predictions of future sample if the underlying covariance structure is changed e.g. by new dietary regimes or genotypes.

Prediction of intramuscular fat content and major fatty acid groups of lamb M. longissimus lumborum using Raman spectroscopy

A hand held Raman spectroscopic device was used to predict intramuscular fat (IMF) levels and the major fatty acid (FA) groups of fresh intact ovine M. longissimus lumborum (LL). IMF levels were determined using the Soxhlet method, while FA analysis was conducted using a rapid (KOH in water, methanol and sulphuric acid in water) extraction procedure. IMF levels and FA values were regressed against Raman spectra using partial least squares regression and against each other using linear regression. The results indicate that there is potential to predict PUFA (R(2)=0.93) and MUFA (R(2)=0.54) as well as SFA values that had been adjusted for IMF content (R(2)=0.54). However, this potential was significantly reduced when correlations between predicted and observed values were determined by cross validation (R(2)cv=0.21-0.00). Overall, the prediction of major FA groups using Raman spectra was more precise (relative reductions in error of 0.3-40.8%) compared to the null models.

Analysis of the effects of dietary fat on body and skin lipids of hamsters by Raman spectroscopy

Raman spectroscopy has previously been applied for studying lipid metabolism. In this study, a ball lens-installed hollow optical fiber Raman probe (BHRP) was used for the noninvasive measurement of skin lipids in hamsters. Our analysis suggested that multi-unsaturated lipids, once converted into a structure containing conjugated double bonds, were oxidized to form peroxides. These results were applied for analyzing lipid metabolism in adipose and skin tissues in hamsters fed tricaprin, saturated medium-chain triglyceride and trilinolein, unsaturated long-chain triglyceride fat diets. Unsaturated lipids formed conjugated structures in skin tissue but not in adipose tissue. Principal component analysis (PCA) revealed that the dietary fat intake correlated strongly with lipid composition in body and skin tissues. Hence, the present results successfully demonstrate that Raman spectroscopy with a BHRP can be a powerful tool for analyzing lipid metabolism.

Dispersive Raman spectroscopy and multivariate data analysis to detect offal adulteration of thawed beefburgers

Beef offal (i.e., kidney, liver, heart, lung) adulteration of beefburgers was studied using dispersive Raman spectroscopy and multivariate data analysis to explore the potential of these analytical tools for detection of adulterations in comminuted meat products with complex formulations. Adulterated (n = 46) and authentic (n = 36) beefburger samples were produced based on formulations derived using market knowledge and an experimental design. Raman spectral data in the fingerprint range (900-1800 cm(-1)) were examined using both a classification (partial least-squares discriminant analysis, PLS-DA) and class-modeling (soft independent modeling of class analogy, SIMCA) approach to identify offal-adulterated and authentic beefburgers. PLS-DA models correctly classified 89-100% of authentic and 90-100% of adulterated samples. SIMCA models were developed using either PCA or PLS scores as input data. For authentic beefburgers, they exhibited sensitivity, specificity, and efficiency values of 0.94-1, 0.64-1, and 0.80-0.97, respectively. PLS regression quantitative models were also developed in an attempt to quantify total offal and added fat in these samples. The performance of PLS regression quantitative models for prediction of added fat may be acceptable for screening purposes, with the most accurate model producing a coefficient of determination in prediction of 0.85 and a root-mean-square error of prediction equal to 3.8% w/w.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.

A Raman spectroscopy bio-sensor for tissue discrimination in surgical robotics

We report the development of a fiber-based Raman sensor to be used in tumour margin identification during endoluminal robotic surgery. Although this is a generic platform, the sensor we describe was adapted for the ARAKNES (Array of Robots Augmenting the KiNematics of Endoluminal Surgery) robotic platform. On such a platform, the Raman sensor is intended to identify ambiguous tissue margins during robot-assisted surgeries. To maintain sterility of the probe during surgical intervention, a disposable sleeve was specially designed. A straightforward user-compatible interface was implemented where a supervised multivariate classification algorithm was used to classify different tissue types based on specific Raman fingerprints so that it could be used without prior knowledge of spectroscopic data analysis. The protocol avoids inter-patient variability in data and the sensor system is not restricted for use in the classification of a particular tissue type. Representative tissue classification assessments were performed using this system on excised tissue.

Fluorescence suppression using wavelength modulated Raman spectroscopy in fiber-probe-based tissue analysis

In the field of biomedical optics, Raman spectroscopy is a powerful tool for probing the chemical composition of biological samples. In particular, fiber Raman probes play a crucial role for in vivo and ex vivo tissue analysis. However, the high-fluorescence background typically contributed by the auto fluorescence from both a tissue sample and the fiber-probe interferes strongly with the relatively weak Raman signal. Here we demonstrate the implementation of wavelength-modulated Raman spectroscopy (WMRS) to suppress the fluorescence background while analyzing tissues using fiber Raman probes. We have observed a significant signal-to-noise ratio enhancement in the Raman bands of bone tissue, which have a relatively high fluorescence background. Implementation of WMRS in fiber-probe-based bone tissue study yielded usable Raman spectra in a relatively short acquisition time (∼30  s), notably without any special sample preparation stage. Finally, we have validated its capability to suppress fluorescence on other tissue samples such as adipose tissue derived from four different species.

Fast and minimally invasive determination of the unsaturation index of white fat depots by micro-Raman spectroscopy

In the last 20 years increasing interest has been devoted to the investigation of white adipose tissue (WAT) because hypo- or hyperfunction of WAT is involved in the pathogenesis of obesity and other pathologies. The investigation and discrimination of different characteristics in adipose tissues by means of spectroscopic techniques appears as a topic of current interest, also in view of possible medical-technological applications. The aim of this work was to establish micro-Raman spectroscopy as a tool for the characterization of mammals fat tissue. After preliminary tests aimed at defining a suitable sample preparation protocol, Raman spectra of WAT specimens excised from mice of different ages were recorded in the energy range 750-3,350 cm⁻¹. Quantitative values of the unsaturation index were obtained through the calibration with HR-NMR spectra of lipid extracts. Raman spectroscopy detected a sharp increase in the unsaturation index between 22 and 30 days of age in close correspondence with the weaning of mice (21 days). The present results show that Raman spectroscopy is an inexpensive, fast and robust technique to analyze the unsaturation index of mammals fat tissues that could be routinely used in bioptic samples.

Sclera as a surrogate marker for determining AGE-modifications in Bruch's membrane using a Raman spectroscopy-based index of aging

PURPOSE

Raman spectroscopy is an effective probe of advanced glycation end products (AGEs) in Bruch's membrane. However, because it is the outermost layer of the retina, this extracellular matrix is difficult to analyze in vivo with current technology. The sclera shares many compositional characteristics with Bruch's membrane, but it is much easier to access for in vivo Raman analysis. This study investigated whether sclera could act as a surrogate tissue for Raman-based investigation of pathogenic AGEs in Bruch's membrane.

METHODS

Human sclera and Bruch's membrane were dissected from postmortem eyes (n = 67) across a wide age range (33-92 years) and were probed by Raman spectroscopy. The biochemical composition, AGEs, and their age-related trends were determined from data reduction of the Raman spectra and compared for the two tissues.

RESULTS

Raman microscopy demonstrated that Bruch's membrane and sclera are composed of a similar range of biomolecules but with distinct relative quantities, such as in the heme/collagen and the elastin/collagen ratios. Both tissues accumulated AGEs, and these correlated with chronological age (R(2) = 0.824 and R(2) = 0.717 for sclera and Bruch's membrane, respectively). The sclera accumulated AGE adducts at a lower rate than Bruch's membrane, and the models of overall age-related changes exhibited a lower rate (one-fourth that of Bruch's membrane) but a significant increase with age (P < 0.05).

CONCLUSIONS

The results suggest that the sclera is a viable surrogate marker for estimating AGE accumulation in Bruch's membrane and for reliably predicting chronological age. These findings also suggest that sclera could be a useful target tissue for future patient-based, Raman spectroscopy studies.

A prototype hand-held Raman sensor for the in situ characterization of meat quality

As a tool for the in situ characterization of meat quality, a hand-held Raman sensor head using an excitation wavelength of 671 nm was developed. A microsystem-based external cavity diode laser module was integrated into the sensor head and attached to a Raman probe, which is equipped with lens optics for excitation and signal collection as well as a Raman filter stage for Rayleigh rejection. The Raman signal was guided by an optical fiber to the detection unit, which was in the initial phase a laboratory spectrometer with a charge-coupled device (CCD) detector. The laser and the sensor head were characterized in terms of stability and performance for in situ Raman investigations. Raman spectra of meat were obtained with 35 mW within 5 seconds or less, ensuring short measuring times for the hand-held device. In a series of measurements with raw and packaged pork meat, the Raman sensor head was shown to detect microbial spoilage on the meat surface, even through the packaging foil.

Long-term stability of a Raman instrument determining iodine value in pork adipose tissue

The stability of a calibration model from a non-destructive Raman instrument during a period of three years was studied. A calibration model created on a dataset measuring pork adipose tissue in 2005 determining iodine value (IV), was transferred to a dataset measuring pork adipose tissue three years later in 2008. During these three years the fibre optic cable had been changed and the output of the laser was reduced to 60% compared with the power in 2005. The samples were also taken from different parts of the carcass. Aligning the peak positions and pre-processing with multiplicative scatter correction together with a selection of wavelengths/wavenumbers gave, for IV, a correlation coefficient of 0.95 for measured versus predicted IV of the 2008 samples. The accuracy expressed as root mean square error of prediction was 2.04 g iodine added to 100g of melted fat with 6 partial least squares factors for the 2008 samples. This study shows that it is possible, with minor modifications, to transfer the model from spectra measured three years later on the same instrument. It is concluded that a quantitative use of Raman instruments are robust over time.