Potential of fluorescence spectroscopy to predict fatty acid composition of beef

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

Evaluation of the adulteration of camel milk by non-camel milk using multispectral image, fluorescence and infrared spectroscopy

In the present study, the focus was to evaluate the potential of three spectroscopic techniques (Middle Infrared -MIR-, fluorescence, and multispectral imaging -MSI-) to check the level of adulteration in camel milk with goat, cow, and ewe milks. Camel milk was adulterated with goat, ewe, and cow milks, respectively, at 6 different levels viz. 0.5, 1, 2, 5, 10, and 15%. After preprocessing the data with standard normal variate (SNV), multiplicative scattering correction (MSC), and normalization (area under spectrum = 1), partial least squares regression (PLSR) and partial least squares discriminant analysis (PLSDA) were used to predict the adulteration level and their belonging group, respectively. The PLSR and PLSDA models, validated using external data, highlighted that fluorescence spectroscopy was the most accurate technique giving a Rp² ranging between 0.63 and 0.96 and an accuracy ranging between 67 and 83%. However, no technique has allowed the construction of robust PLSR and PLSDA models for the simultaneous prediction of contamination of camel milk by the three milks.

Determination of Adulteration of Chicken Meat into Minced Beef Mixtures using Front Face Fluorescence Spectroscopy Coupled with Chemometric

The objective of this study was to explore the potential of front face fluorescence spectroscopy (FFFS) as rapid, non-destructive and inclusive technique along with multi-variate analysis for predicting meat adulteration. For this purpose (FFFS) was used to discriminate pure minced beef meat and adulterated minced beef meat containing (1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) of chicken meat as an adulterant in uncooked beef meat samples. Fixed excitation (290 nm, 322 nm, and 340 nm) and fixed emission (410 nm) wavelengths were used for performing analysis. Fluorescence spectra were acquired from pure and adulterated meat samples to differentiate pure and binary mixtures of meat samples. Principle component analysis, partial least square regression and hierarchical cluster analysis were used as chemometric tools to find out the information from spectral data. These chemometric tools predict adulteration in minced beef meat up to 10% chicken meat but are not good in distinguishing adulteration level from 1% to 5%. The results of this research provide baseline for future work for generating spectral libraries using larger datasets for on-line detection of meat authenticity by using fluorescence spectroscopy.

Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat

Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900-1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with R_c of 0.8716, RMSEC of 0.0793 g/100 g, RPD_c of 2.398, and R_p of 0.8121 with RMSEP of 0.1481 g/100 g and RPD_p of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Prediction of chemical parameters and authentication of various cold pressed oils with fluorescence and mid-infrared spectroscopic methods

It was aimed to compare the performances of two spectroscopic methods, fluorescence and mid-infrared spectroscopy, in terms of their adulteration detection and estimation of several chemical properties for various cold pressed seed oils. Spectroscopic profiles, fatty acid, free fatty acid and total phenol contents of pumpkin seed, grape seed, black cumin oil, and sesame seed oils were determined and these oils were mixed with sunflower oil at 1-50% (v/v). Both spectroscopic techniques provided comparable results for determination of adulteration of each oil type and the most successful prediction was obtained for pumpkin seed oil at levels >%1. Combined data set of oils resulted in successful quantification of their free fatty acid value, total phenol and major fatty acids contents with both spectroscopic methods regardless of oil type. Both techniques could be used as reliable, fast and environmentally friendly alternatives in the analyses of different types of seed oils.

Spectrofluorometric Analysis of Autofluorescing Components of Crude Serum from a Rat Liver Model of Ischemia and Reperfusion

Autofluorescence (AF) of crude serum was investigated with reference to the potential of its intrinsic AF biomarkers for the noninvasive diagnosis of liver injury. Spectral parameters of pure compounds representing retinol (vitamin A) and fluorescing free fatty acids were characterized by spectrofluorometry, to assess spectral parameters for the subsequent AF analysis of serum, collected from rats undergoing liver ischemia/reperfusion (I/R). Differences in AF spectral profiles detected between control and I/R were due to the increase in the AF components representing fatty acids in I/R serum samples. No significant changes occurred for retinol levels, consistently with the literature reporting that constant retinol levels are commonly observed in the blood, except for malnutrition or chronic severe liver disease. Conversely, fatty acids, in particular arachidonic and linoleic acid and their derivatives, act as modulating agents in inflammation, representing both a protective and damaging response to stress stimuli. The biometabolic and pathophysiological meaning of serum components and the possibility of their direct detection by AF spectrofluorometry open up interesting perspectives for the development of AF serum analysis, as a direct, cost effective, supportive tool to assess liver injury and related systemic metabolic alterations, for applications in experimental biomedicine and foreseen translation to the clinics.

Autofluorescence excitation-emission matrices as a quantitative tool for the assessment of meat quality

Commercially produced meat is currently graded by a complex and partly subjective multiparameter methodology; a quantitative method of grading, using small samples would be desirable. Here, we investigate the correlation between commercial grades of beef and spectral signatures of native fluorophores in such small samples. Beef samples of different commercial grades were characterized by fluorescence spectroscopy complemented by biochemical and histological assessment. The excitation-emission matrices of the specimens reveal five prominent native autofluorescence signatures in the excitation range from 250 to 350 nm, derived mainly from tryptophan and intramuscular fat. We found that these signatures reflect meat grade and can be used for its determination.

Effect of ELOVL6 on the lipid metabolism of bovine adipocytes

This study investigated the effect of ELOVL6 (elongation of very long chain fatty acids protein 6) and its underlying mechanism on lipid metabolism in bovine adipocytes. The ELOVL6 gene was overexpressed in bovine adipocytes by adenoviruses, and RNA sequencing was performed. Overexpression of ELOVL6 showed reduced proportions of C14:0 (Myristic) and C16:0 (palmitate) fatty acids and increased proportions of C18.0 (stearate) and C20:4n6 (arachidonic) fatty acids in adipocytes. In addition, a total of 2170 differentially expressed genes (DEGs) were found, containing 1802 up-regulated and 368 down-regulated genes. KEGG pathway analysis revealed that the down-regulated genes were linked with the regulation of lipolysis and the Wnt signaling pathway. The up-regulated genes were mainly involved in the FoxO signaling pathway; the PI3K-Akt signaling pathway; and the cAMP signaling pathway. In conclusion, our results suggest that ELOVL6 could affect the fatty acid composition in bovine adipocytes. We identified numerous related DEGs and pathways, which may provide a basis for studying the function and molecular mechanism of the ELOVL6 gene in regulating lipid metabolism.

Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible-Near-Infrared Spectroscopy and Chemometrics

The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R²C) and cross-validation (R²CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R²CV: 0.91 (quartering, 24 h) and R²CV: 0.96 (LTL muscle, 48 h)) and drip loss (R²CV: 0.82 (quartering, 24 h) and R²CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods.

Autofluorescence-based optical biopsy: An effective diagnostic tool in hepatology

Autofluorescence emission of liver tissue depends on the presence of endogenous biomolecules able to fluoresce under suitable light excitation. Overall autofluorescence emission contains much information of diagnostic value because it is the sum of individual autofluorescence contributions from fluorophores involved in metabolism, for example, NAD(P)H, flavins, lipofuscins, retinoids, porphyrins, bilirubin and lipids, or in structural architecture, for example, fibrous proteins, in close relationship with normal, altered or diseased conditions of the liver. Since the 1950s, hepatocytes and liver have been historical models to study NAD(P)H and flavins as in situ, real-time autofluorescence biomarkers of energy metabolism and redox state. Later investigations designed to monitor organ responses to ischaemia/reperfusion were able to predict the risk of dysfunction in surgery and transplantation or support the development of procedures to ameliorate the liver outcome. Subsequently, fluorescent fatty acids, lipofuscin-like lipopigments and collagen were characterized as optical biomarkers of liver steatosis, oxidative stress damage, fibrosis and disease progression. Currently, serum AF is being investigated to improve non-invasive optical diagnosis of liver disease. Validation of endogenous fluorophores and in situ discrimination of cancerous from non-cancerous tissue belong to the few studies on liver in human subjects. These reports along with other optical techniques and the huge work performed on animal models suggest many optically based applications in hepatology. Optical diagnosis is currently offering beneficial outcomes in clinical fields ranging from the respiratory and gastrointestinal tracts, to dermatology and ophthalmology. Accordingly, this review aims to promote an effective bench to bedside transfer in hepatology.

MicroRNA-125a-5p Affects Adipocytes Proliferation, Differentiation and Fatty Acid Composition of Porcine Intramuscular Fat

Intramuscular fat (IMF) content and composition are considered crucial indicators of porcine meat quality. However, the molecular mechanism of porcine IMF development is still mostly unclear. Recently, new evidence suggested that microRNA (miRNAs) play important roles in porcine intramuscular adipogenesis. Previously, microRNA-125a-5p (miR-125a-5p) was identified as an important regulator of adipogenesis. In the present study, we found that the expression of miR-125a-5p is dynamically regulated during porcine intramuscular preadipocytes differentiation and that its expression levels in different porcine muscle tissues were negatively involved with IMF content. To investigate the potential function role of miR-125a-5p in IMF development, porcine intramuscular preadipocytes were collected and transfected with miR-125a-5p mimics, inhibitors, or a negative control (NC), respectively. The results showed that overexpression of miR-125a-5p promoted proliferation and inhibited differentiation of porcine intramuscular preadipocytes while inhibition of miR-125a-5p had the opposite effects. Furthermore, a luciferase reporter assay demonstrated that porcine kruppel like factor 3 (KLF13) is a target gene of miR-125a-5p during porcine intramuscular preadipocytes differentiation. Interestingly, porcine ELOVL fatty acid elongase 6 (ELOVL6), a regulator of fatty acid composition, was also identified as a target gene of miR-125a-5p during porcine intramuscular adipogenesis. Further studies show that miR-125a-5p overexpression reduced total saturated fatty acids (SFA) content and monounsaturated fatty acids (MUFA)/SFA ratios while having no significant impact on polyunsaturated fatty acids (PUFA)/SFA and n-6/n-3 ratios. Taken together, our results identified that miR-125a-5p may be a novel regulator of porcine intramuscular adipogenesis and the fatty acid composition of porcine IMF.

Performance of fluorescence spectroscopy for beef meat authentication: Effect of excitation mode and discriminant algorithms

This study evaluated the performance of classical front face (FFFS) and synchronous (SFS) fluorescence spectroscopy combined with Partial Least Square Discriminant Analysis (PLSDA), Support Vector Machine associated with PLS (PLS-SVM) and Principal Components Analysis (PCA-SVM) to discriminate three beef muscles (Longissimus thoracis, Rectus abdominis and Semitendinosus). For the FFFS, 5 excitation wavelengths were investigated, while 6 offsets were studied for SFS. Globally, the results showed a good discrimination between muscles with Recall and Precision between 47.82 and 94.34% and Error ranging from 6.03 to 32.39%. For the FFFS, the PLS-SVM with the 382nm excitation wavelength gave the best discrimination results (Recall, Precision and Error of 94.34%, 89.53% and 6.03% respectively). For SFS, when performing discrimination of the three muscles, the 120nm offset gave the highest Recall and Precision (from 57.66% to 94.99%) and the lowest Error values (from 6.78 to 8.66%) whatever the algorithm (PLSDA, PLS-SVM and PCA-SVM).

Fluorescing fatty acids in rat fatty liver models

The autofluorescence (AF) of NAD(P)H and flavins has been at the basis of many in-situ studies of liver energy metabolism and functionality. Conversely, few data have been so far reported on fluorescing lipids. In this work we investigated the AF of liver lipid extracts from two fatty liver models, Wistar rats fed with MCD diet for 12 days (Wi-MCD), and obese (fa/fa) Zucker rats. Among the most abundant fatty acids in the lipid extracts, indicated by mass spectrometry, arachidonic acid (AA) exhibited higher quantum yield than the other fluorescing fatty acids (FLFA), and red shifted AF spectrum. This allowed to estimate the AA contribution to the overall emission of lipid extracts by curve fitting analysis. AA prevailed in obese Zucker livers, accounting for the different AF spectral profiles between the two models. AF and mass spectrometry indicated also a different balance between the fluorescing fraction and the overall amount of AA in the two models. The ability of AF to detect directly AA and FLFA was demonstrated, suggesting its supportive role as tool in wide-ranging applications, from the control of animal origin food, to experimental investigations on liver fat accumulation, lipotoxicity and disease progression, with potential translation to the clinics.

Fluorescence Spectroscopy for the Monitoring of Food Processes

Different analytical techniques have been used to examine the complexity of food samples. Among them, fluorescence spectroscopy cannot be ignored in developing rapid and non-invasive analytical methodologies. It is one of the most sensitive spectroscopic approaches employed in identification, classification, authentication, quantification, and optimization of different parameters during food handling, processing, and storage and uses different chemometric tools. Chemometrics helps to retrieve useful information from spectral data utilized in the characterization of food samples. This contribution discusses in detail the potential of fluorescence spectroscopy of different foods, such as dairy, meat, fish, eggs, edible oil, cereals, fruit, vegetables, etc., for qualitative and quantitative analysis with different chemometric approaches.