Authenticating common Australian beef production systems using Raman spectroscopy

Validation of Raman spectroscopic models to verify the origin of Australian beef grown under different production systems

Verification of beef production systems and authentication of origin is becoming increasingly important as consumers base purchase decisions on a greater number of perceived values including the healthiness and environmental impact of products. Previously Raman spectroscopy has been explored as a tool to classify carcases from grass and grain fed cattle. Thus, the aim of the current study was to validate Partial Least Squares Discriminant Analysis (PLS-DA) models created using independent samples from carcases sampled from northern and southern Australian production systems in 2019, 2020 and 2021. Validation of the robustness of discrimination models was undertaken using spectral measures of fat from 585 carcases which were measured in 2022 using a Raman handheld device with a sample excised for fatty acid analysis. PLS-DA models were constructed and then employed to classify samples as either grass or grain fed in a two-class model. Overall, predictions were high with accuracies of up to 95.7% however, variation in the predictive ability was noted with models created for southern cattle yielding an accuracy of 73.2%. While some variation in fatty acids and therefore models can be attributed to differences in genetics, management and diet, the impact of duration of feeding is currently unknown and thus further work is warranted.

Visible spectroscopy on lamb fat and muscle to authenticate the duration of pasture finishing

Pasture-based livestock systems are considered environmentally-sustainable and welfare-friendly farming systems that can meet consumer demand for good-quality produce. However, trust in products labelled as 'grass-fed' depends on the ability to reliably authenticate pasture origin. The two objectives of this study were (i) to test the ability of visible spectroscopy combined with discriminant analysis on lamb perirenal fat (PF), dorsal fat (DF) and longissimus thoracis et lumborum muscle to discriminate different durations of pasture-finishing; and (ii) to determine the timing of appearance of the pasture signature and its stabilization in these tissues. Four groups of 35 lambs were used over two years, i.e. lambs fed concentrate in-stall (L0) and lambs grazing alfalfa for 21d (L21), 42d (L42) and 63d (L63) before slaughter. No one tissue satisfactorily discriminated the four treatment groups, with ≤75% of lambs correctly classified. However, visible spectroscopy discriminated L0 from L21 + L42 + L63 lambs with an accuracy of 92.8%, 92.0%, and 85.3% lambs correctly classified on PF, DF and muscle, respectively, and discriminated L0 + L21 from L42 + L63 lambs with an accuracy of 90.1%, 76.5% and 92.3% on PF, DF and muscle, respectively. The pasture fingerprint or signature on the spectrum appeared in most lambs between 0 and 21d in PF and DF and between 0 and 42d in muscle. Pasture signature gradually stabilized with increasing time on pasture but was not entirely stabilized in any tissue within the range of grazing durations explored. These promising results need to be confirmed on larger datasets with different breeds and grazing conditions.

Traceability of bilberries (Vaccinium myrtillus L.) of the Baltic-Nordic region using surface-enhanced Raman spectroscopy (SERS): DFT simulation-based DNA analysis

Food traceability is a major issue in the industry. We investigated whether bilberries (Vaccinium myrtillus L.) from 4 different locations within the Baltic-Nordic region could be effectively differentiated using surface-enhanced Raman scattering (SERS) based spectral data and chemometric analyses. Furthermore, we aimed to determine if nucleobase (adenine and cytosine) methylation could be responsible for any observed variation. Our experiment was successful in that both principal component (PCA) and discriminant function analyses (DFA) showed differentiation between bilberry DNA from all 4 geographical regions. Density functional theory (DFT) based simulations allowed us to analyze whether DNA's spectral data dissimilarities may be due to nucleobase methylation. Although results were inconclusive on this, our investigation provides valuable data on simulated versus experimental DNA and DNA component spectra. Further research will be directed towards understanding what other epigenetic changes could be responsible for the observed DNA variation as well as determining the optimal parameters for using DFT simulations in upcoming projects.

Authentication of beef cuts by multielement and machine learning approaches

BACKGROUND

Brazil has consolidated a relevant position in the world market, being the largest exporter and second producer of beef. Genetics, feeding system, geographic origin and climate influence the multielement profile of beef. The feasibility of combining classification algorithms with major and trace elements was evaluated as a tool for authentication of beef cuts.

METHODS

Animals of Angus, Nelore and Wagyu crossbreeds, raised in a vertically integrated system, were sampled at the slaughterhouse for chuck steak, rump cap and sirloin steak. Supervised learning algorithms i.e. Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naïve Bayes (NB), Random Forest (RF) and Sequential Minimal Optimization (SMO) were used to build classification models based on the multielement profile of beef determined by neutron activation analysis.

RESULTS

Br, Co, Cs, Fe, K, Na, Rb, Se and Zn were determined in the beef samples. The classification accuracy values obtained for the beef cuts were 96% (MLP), 95% (SMO), 91% (RF), 86% (NB) and 70% (CART).

CONCLUSION

The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions.

Quantitation of carotenoids and fatty acids from Atlantic salmon using a portable Raman device

Raman spectra collected using subsurface measurement on a portable device can be used to quantify carotenoid and lipid levels in Atlantic salmon.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing,  thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.