The application of transcriptomic data in the authentication of beef derived from contrasting production systems.

BACKGROUND: Differences between cattle production systems can influence the nutritional and sensory characteristics of beef, in particular its fatty acid (FA) composition. As beef products derived from pasture-based systems can demand a higher premium from consumers, there is a need to understand the biological characteristics of pasture produced meat and subsequently to develop methods of authentication for these products. Here, we describe an approach to authentication that focuses on differences in the transcriptomic profile of muscle from animals finished in different systems of production of practical relevance to the Irish beef industry. The objectives of this study were to identify a panel of differentially expressed (DE) genes/networks in the muscle of cattle raised outdoors on pasture compared to animals raised indoors on a concentrate based diet and to subsequently identify an optimum panel which can classify the meat based on a production system. RESULTS: A comparison of the muscle transcriptome of outdoor/pasture-fed and Indoor/concentrate-fed cattle resulted in the identification of 26 DE genes. Functional analysis of these genes identified two significant networks (1: Energy Production, Lipid Metabolism, Small Molecule Biochemistry; and 2: Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry), both of which are involved in FA metabolism. The expression of selected up-regulated genes in the outdoor/pasture-fed animals correlated positively with the total n-3 FA content of the muscle. The pathway and network analysis of the DE genes indicate that peroxisome proliferator-activated receptor (PPAR) and FYN/AMPK could be implicit in the regulation of these alterations to the lipid profile. In terms of authentication, the expression profile of three DE genes (ALAD, EIF4EBP1 and NPNT) could almost completely separate the samples based on production system (95 % authentication for animals on pasture-based and 100 % for animals on concentrate- based diet) in this context. CONCLUSIONS: The majority of DE genes between muscle of the outdoor/pasture-fed and concentrate-fed cattle were related to lipid metabolism and in particular ß-oxidation. In this experiment the combined expression profiles of ALAD, EIF4EBP1 and NPNT were optimal in classifying the muscle transcriptome based on production system. Given the overall lack of comparable studies and variable concordance with those that do exist, the use of transcriptomic data in authenticating production systems requires more exploration across a range of contexts and breeds.

BAYESIAN WAVELET-BASED CURVE CLASSIFICATION VIA DISCRIMINANT ANALYSIS WITH MARKOV RANDOM TREE PRIORS.

Discriminant analysis is an effective tool for the classification of experimental units into groups. When the number of variables is much larger than the number of observations it is necessary to include a dimension reduction procedure into the inferential process. Here we present a typical example from chemometrics that deals with the classification of different types of food into species via near infrared spectroscopy. We take a nonparametric approach by modeling the functional predictors via wavelet transforms and then apply discriminant analysis in the wavelet domain. We consider a Bayesian conjugate normal discriminant model, either linear or quadratic, that avoids independence assumptions among the wavelet coefficients. We introduce latent binary indicators for the selection of the discriminatory wavelet coefficients and propose prior formulations that use Markov random tree (MRT) priors to map scale-location connections among wavelets coefficients. We conduct posterior inference via MCMC methods, we show performances on our case study on food authenticity and compare results to several other procedures..

Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum.

The aim of this study was to calibrate chemometric models to predict beef M. longissimus thoracis et lumborum (LTL) sensory and textural values using visible-near infrared (VISNIR) spectroscopy. Spectra were collected on the cut surface of LTL steaks both on-line and off-line. Cooked LTL steaks were analysed by a trained beef sensory panel as well as undergoing WBSF analysis. The best coefficients of determination of cross validation (R2CV) in the current study were for textural traits (WBSF = 0.22; stringiness = 0.22; crumbly texture = 0.41: all 3 models calibrated using 48 h post-mortem spectra), and some sensory flavour traits (fatty mouthfeel = 0.23; fatty after-effect = 0.28: both calibrated using 49 h post-mortem spectra). The results of this experiment indicate that VISNIR spectroscopy has potential to predict a range of sensory traits (particularly textural traits) with an acceptable level of accuracy at specific post-mortem times.

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.

Direct classification of related species of fungal endophytes (Epichloë spp.) using visible and near-infrared spectroscopy and multivariate analysis.

The aim of this work was to investigate the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for the classification of three morphologically similar species of fungal endophytes of grasses. Vis-NIR spectra (400-2498 nm) from 34 isolates of Epichloë sylvatica, 32 of Epichloë typhina and 38 of Epichloë festucae were recorded directly from fresh mycelium growing in potato dextrose agar plates. Multivariate procedures applied to the spectral data were discriminant modified partial least squares regression, soft independent modelling of class analogy and discriminant partial least squares regressions (PLS1, PLS2). Several types of data pretreatments were tested to develop the classification models. The best predictive models were achieved with PLS2 analysis; with this method, 90% of E. typhina and 100% of E. festucae and E. sylvatica external validation samples were successfully classified. These results show the potential of Vis-NIR spectroscopy combined with multivariate analysis as a rapid method for classifying morphologically similar species of filamentous fungi.

Multivariate analysis of attenuated total reflection-Fourier transform infrared spectroscopic data to confirm the origin of honeys.

Fourier transform infrared (FT-IR) spectroscopy and chemometrics were used to verify the origin of honey samples (n=150) from Europe and South America. Authentic honey samples were collected from five sources, namely unfiltered samples from Mexico in 2004, commercially filtered samples from Ireland and Argentina in 2004, commercially filtered samples from the Czech Republic in 2005 and 2006, and commercially filtered samples from Hungary in 2006. Samples were diluted with distilled water to a standard solids content (70 degrees Brix) and their spectra (2500-12 500 nm) recorded at room temperature using an FT-IR spectrometer equipped with a germanium attenuated total reflection (ATR) accessory. First- and second-derivative and standard normal variate (SNV) data pretreatments were applied to the recorded spectra, which were analyzed using partial least squares (PLS) regression analysis, factorial discriminant analysis (FDA), and soft independent modeling of class analogy (SIMCA). In general, when an attenuated wavelength range (6800-11 500 nm) rather than the whole spectrum (2500-12 500 nm) was studied, higher correct classification rates were achieved. An overall correct classification of 93.3% was obtained for honeys by PLS discriminant analysis, while FDA techniques correctly classified 94.7% of honey samples. Correct classifications of up to 100% were achieved using SIMCA, but models describing some classes had very high false positive rates.

Studies on proofing of yeasted bread dough using near- and mid-infrared spectroscopy.

Dough proofing is the resting period after mixing during which fermentation commences. Optimum dough proofing is important for production of high quality bread. Near- and mid-infrared spectroscopies have been used with some success to investigate macromolecular changes during dough mixing. In this work, both techniques were applied to a preliminary study of flour doughs during proofing. Spectra were collected contemporaneously by NIR (750-1100 nm) and MIR (4000-600 cm(-1)) instruments using a fiberoptic surface interactance probe and horizontal ATR cell, respectively. Studies were performed on flours of differing baking quality; these included strong baker's flour, retail flour, and gluten-free flour. Following principal component analysis, changes in the recorded spectral signals could be followed over time. It is apparent from the results that both vibrational spectroscopic techniques can identify changes in flour doughs during proofing and that it is possible to suggest which macromolecular species are involved.

Detection and identification of bacteria in an isolated system with near-infrared spectroscopy and multivariate analysis.

Near-infrared (NIR) transflectance spectra of Listeria innocua FH, Lactococcus lactis, Pseudomonas fluorescens, Pseudomonas mendocina, and Pseudomonas putida suspensions were collected and investigated for their potential use in the identification and classification of bacteria. Unmodified spectral data were transformed (first and second derivative) using the Savitzsky-Golay algorithm. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA), and soft independent modeling of class analogy (SIMCA) were used in the analysis. Using either full cross-validation or separate calibration and prediction data sets, PLS2 regression classified the five bacterial suspensions with 100% accuracy at species level. At Pseudomonas genus level, PLS2 regression classified the three Pseudomonas species with 100% accuracy. In the case of SIMCA, prediction of an unknown sample set produced correct classification rates of 100% except for L. innocua FH (77%). At genus level, SIMCA produced correct classification rates of 96.7, 100, and 100% for P. fluorescens, P. mendocina, and P. putida, respectively. This successful investigation suggests that NIR spectroscopy can become a useful, rapid, and noninvasive tool for bacterial identification.

Prediction of beef eating quality from colour, marbling and wavelet texture features.

Beef longissimus dorsi colour, marbling fat and surface texture are long established properties that are used in some countries by expert graders to classify beef carcasses, with subjective and inconsistent decision. As a computer vision system can deliver objective and consistent decisions rapidly and is capable of handling a greater variety of image features, attempts have been made to develop computerised predictions of eating quality based on these and other properties but have failed to adequately model the variation in eating quality. Therefore, in this study, examination of the ribeye at high magnification and consideration of a broad range of colour and marbling fat features was used to attempt to provide better information on beef eating quality. Wavelets were used to describe the image texture of the beef surface at high magnification rather than classical methods such as run lengths, difference histograms and co-occurrence matrices. Sensory panel and Instron analyses were performed on duplicate steaks to measure the quality of the beef. Using the classical statistical method of partial least squares regression (PLSR) it was possible to model a very high proportion of the variation in eating quality (r(2)=0.88 for sensory overall acceptability and r(2)=0.85 for 7-day WBS). Addition of non-linear texture terms to the models gave some improvements.

Performances of full cross-validation partial least squares regression models developed using Raman spectral data for the prediction of bull beef sensory attributes.

The data presented in this article are related to the research article entitled "Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef" [1]. Partial least squares regression (PLSR) models were developed on Raman spectral data pre-treated using Savitzky Golay (S.G.) derivation (with 2nd or 5th order polynomial baseline correction) and results of sensory analysis on bull beef samples (n = 72). Models developed using selected Raman shift ranges (i.e. 250-3380 cm-1, 900-1800 cm-1 and 1300-2800 cm-1) were explored. The best model performance for each sensory attributes prediction was obtained using models developed on Raman spectral data of 1300-2800 cm-1.

Assessment of infant formula quality and composition using Vis-NIR, MIR and Raman process analytical technologies.

In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytical technologies were investigated for assessment of infant formula quality and compositional parameters namely preheat temperature, storage temperature, storage time, fluorescence of advanced Maillard products and soluble tryptophan (FAST) index, soluble protein, fat and surface free fat (SFF) content. PLS-DA models developed using spectral data with appropriate data pre-treatment and significant variables selected using Martens' uncertainty test had good accuracy for the discrimination of preheat temperature (92.3-100%) and storage temperature (91.7-100%). The best PLS regression models developed yielded values for the ratio of prediction error to deviation (RPD) of 3.6-6.1, 2.1-2.7, 1.7-2.9, 1.6-2.6 and 2.5-3.0 for storage time, FAST index, soluble protein, fat and SFF content prediction respectively. Vis-NIR, MIR and Raman were demonstrated to be potential PAT tools for process control and quality assurance applications in infant formula and dairy ingredient manufacture.

Application of Raman spectroscopy and chemometric techniques to assess sensory characteristics of young dairy bull beef.

This work aims to develop a rapid analytical technique to predict beef sensory attributes using Raman spectroscopy (RS) and to investigate correlations between sensory attributes using chemometric analysis. Beef samples (n = 72) were obtained from young dairy bulls (Holstein-Friesian and Jersey×Holstein-Friesian) slaughtered at 15 and 19 months old. Trained sensory panel evaluation and Raman spectral data acquisition were both carried out on the same longissimus thoracis muscles after ageing for 21 days. The best prediction results were obtained using a Raman frequency range of 1300-2800 cm-1. Prediction performance of partial least squares regression (PLSR) models developed using all samples were moderate to high for all sensory attributes (R2CV values of 0.50-0.84 and RMSECV values of 1.31-9.07) and were particularly high for desirable flavour attributes (R2CVs of 0.80-0.84, RMSECVs of 4.21-4.65). For PLSR models developed on subsets of beef samples i.e. beef of an identical age or breed type, significant improvements on prediction performances were achieved for overall sensory attributes (R2CVs of 0.63-0.89 and RMSECVs of 0.38-6.88 for each breed type; R2CVs of 0.52-0.89 and RMSECVs of 0.96-6.36 for each age group). Chemometric analysis revealed strong correlations between sensory attributes. Raman spectroscopy combined with chemometric analysis was demonstrated to have high potential as a rapid and non-destructive technique to predict the sensory quality traits of young dairy bull beef.

Geographical classification of honey samples by near-infrared spectroscopy: a feasibility study.

The potential of near-infrared (NIR) spectroscopy to determine the geographical origin of honey samples was evaluated. In total, 167 unfiltered honey samples (88 Irish, 54 Mexican, and 25 Spanish) and 125 filtered honey samples (25 Irish, 25 Argentinean, 50 Czech, and 25 Hungarian) were collected. Spectra were recorded in transflectance mode. Following preliminary examination by principal component analysis (PCA), modeling methods applied to the spectral data set were partial least-squares (PLS) regression and soft independent modeling of class analogy (SIMCA); various pretreatments were investigated. For unfiltered honey, best SIMCA models gave correct classification rates of 95.5, 94.4, and 96% for the Irish, Mexican, and Spanish samples, respectively; PLS2 discriminant analysis produced a 100% correct classification for each of these honey classes. In the case of filtered honey, best SIMCA models produced correct classification rates of 91.7, 100, 100, and 96% for the Argentinean, Czech, Hungarian, and Irish samples, respectively, using the standard normal variate (SNV) data pretreatment. PLS2 discriminant analysis produced 96, 100, 100, and 100% correct classifications for the Argentinean, Czech, Hungarian, and Irish honey samples, respectively, using a second-derivative data pretreatment. Overall, while both SIMCA and PLS gave encouraging results, better correct classification rates were found using PLS regression.

Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics.

Raman spectroscopy and chemometrics were investigated for the prediction of eating quality related physico-chemical traits of Holstein-Friesian bull beef. Raman spectra were collected on the 3rd, 7th and 14th days post-mortem. A frequency range of 1300-2800cm-1 was used for partial least squares (PLS) modelling. PLS regression (PLSR) models for the prediction of WBSF and cook loss achieved an R2CV of 0.75 with RMSECV of 6.82 N and an R2CV of 0.77 with RMSECV of 0.97%w/w respectively. For the prediction of intramuscular fat, moisture and crude protein content, R2CV values were 0.85, 0.91 and 0.70 with RMSECV of 0.52%w/w, 0.39%w/w and 0.38%w/w respectively. An R2CV of 0.79 was achieved for the prediction of both total collagen and hydroxyproline content, while for collagen solubility the R2CV was 0.88. All samples (100%) from 15- and 19-month old bulls were correctly classified using PLS discriminant analysis (PLS-DA), while 86.7% of samples from different muscles (longissimus thoracis, semitendinosus and gluteus medius) were correctly classified. In general, PLSR models using Raman spectra on the 3rd day post-mortem had better prediction performance than those on the 7th and 14th days. Raman spectroscopy and chemometrics have potential to assess several beef physical and chemical quality traits.

Effects of the Adulteration Technique on the Near-Infrared Detection of Melamine in Milk Powder.

The United States Pharmacopeial Convention has led an international collaborative project to develop a toolbox of screening methods and reference standards for the detection of milk powder adulteration. During the development of adulterated milk powder reference standards, blending methods used to combine melamine and milk had unanticipated strong effects on the near-infrared (NIR) spectrum of melamine. The prominent absorbance band at 1468 nm of melamine was retained when it was dry-blended with skim milk powder but disappeared in wet-blended mixtures, where spray-dried milk powder samples were prepared from solution. Analyses using polarized light microscopy, Raman spectroscopy, dielectric relaxation spectroscopy, X-ray diffraction, and mass spectrometry indicated that wet blending promoted reversible and early Maillard reactions with lactose that are responsible for differences in melamine NIR spectra between wet- and dry-blended samples. Targeted detection estimates based solely on dry-blended reference standards are likely to overestimate NIR detection capabilities in wet-blended samples as a result of previously overlooked matrix effects arising from changes in melamine hydrogen-bonding status, covalent complexation with lactose, and the lower but more homogeneous melamine local concentration distribution produced in wet-blended samples. Techniques used to incorporate potential adulterants can determine the suitability of milk reference standards for use with rapid detection methods.

Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey adulterated with various sugar syrups.

A collection of authentic artisanal Irish honeys (n = 580) and certain of these honeys adulterated by fully inverted beet syrup (n = 280), high-fructose corn syrup (n = 160), partial invert cane syrup (n = 120), dextrose syrup (n = 160), and beet sucrose (n = 120) was assembled. All samples were adjusted to 70 degrees Bx and scanned in the midinfrared region (800-4000 cm(-1)) by attenuated total reflectance sample accessory. By use of soft independent modeling of class analogy (SIMCA) and partial least-squares (PLS) classification, authentic honey and honey adulterated by beet sucrose, dextrose syrups, and partial invert corn syrup could be identified with correct classification rates of 96.2%, 97.5%, 95.8%, and 91.7%, respectively. This combination of spectroscopic technique and chemometric methods was not able to unambiguously detect adulteration by high-fructose corn syrup or fully inverted beet syrup.

Detection of apple juice adulteration using near-infrared transflectance spectroscopy.

Near-infrared transflectance spectroscopy was used to detect adulteration of apple juice samples. A total of 150 apple samples from 19 different varieties were collected in two consecutive years from orchards throughout the main cultivation areas in Ireland. Adulterant samples at 10, 20, 30, and 40% w/w were prepared using two types of adulterants: a high fructose corn syrup (HFCS) with 45% fructose and 55% glucose, and a sugars solution (SUGARS) made with 60% fructose, 25% glucose, and 15% sucrose (the average content of these sugars in apple juice). The results show that NIR analysis can be used to predict adulteration of apple juices by added sugars with a detection limit of 9.5% for samples adulterated with HFCS, 18.5% for samples adulterated with SUGARS, and 17% for the combined (HFCS + SUGARS) adulterants. Discriminant partial least squares (PLS) regression can detect authentic apple juice with an accuracy of 86-100% and adulterant apple juice with an accuracy of 91-100% depending on the adulterant type and level of adulteration considered. This method could provide a rapid screening technique for the detection of this type of apple juice adulteration, although further work is required to demonstrate model robustness.

Detection of sugar adulterants in apple juice using fourier transform infrared spectroscopy and chemometrics.

Fourier transform infrared spectroscopy and attenuated total reflection sampling have been used to detect adulteration of single strength apple juice samples. The sample set comprised 224 authentic apple juices and 480 adulterated samples. Adulterants used included partially inverted cane syrup (PICS), beet sucrose (BS), high fructose corn syrup (HFCS), and a synthetic solution of fructose, glucose, and sucrose (FGS). Adulteration was carried out on individual apple juice samples at levels of 10, 20, 30, and 40% w/w. Spectral data were compressed by principal component analysis and analyzed using k-nearest neighbors and partial least squares regression techniques. Prediction results for the best classification models achieved an overall (authentic plus adulterated) correct classification rate of 96.5, 93.9, 92.2, and 82.4% for PICS, BS, HFCS, and FGS adulterants, respectively. This method shows promise as a rapid screening technique for the detection of a broad range of potential adulterants in apple juice.

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.