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Spain has a legal limit of 3% for undeclared vegetable proteins in meat patties.  The aim of this open-access study was to evaluate the feasibility of point-based near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) to verify compliance.

The model was trained on patties prepared in-house.  They were all prepared from the same cut of beef, so the robustness of the model has not been verified.  A total of 240 patties were fabricated, of which 60 contained pea (PP), 60 contained soybean (SP), and 60 chickpea protein (CP) at levels from 1 up to 6 % (w/w). 60 pure beef patties were included.

The authors report that they could clearly discriminate the type of protein added, using either partial least squares-discriminant analysis (PLS-DA) or linear discriminant analysis (LDA), with >90 % of the samples in the test set correctly classified. Based on protein inclusion, LDA discriminated 100 % of the PP, SP and CP samples with both NIR and HSI. PLS-DA classified 100 % of the PP and CP burgers using the NIR instrument. To manage double classification tasks, a hierarchical model classifier (HMC) was proposed for both NIR and HSI spectra, achieving classification rates of at least 83% by combining LDA and PLS-DA models at the nodes.

The authors conclude that NIR spectroscopy is suitable for detecting low levels (1 %) of vegetable protein flours added to beef burgers.

There is a premium market, particularly in the US, for beef labelled as “grass-fed”.  From 2007 to 2016, the United States Department of Agriculture (USDA) carried voluntary marketing claim standards to help regulate grass-fed beef (GFB). These standards were discontinued, but producers can still seek approval from the USDA to market GFB.  This can only come from meat derived from cattle fed 100% forage, but the USDA also allows for partial claims (e.g., 50% grass-fed). Participating producers can define their own claim and need to comply with written protocols and sign an affidavit, but no audits are conducted.

In this study, (open access) three reference populations were used; 100% grass-fed, grain-fed, and grape-supplemented.  Red Angus steers (n = 54) were randomly allocated to one of the three feed regimes. Beef samples were collected in September 2019 and October 2020 in a USDA-regulated slaughter facility. All animals were slaughtered on the same day at 16–18 months old for GRAIN and GRAPE and 24–26 months old for GRASS. Ribeye samples were collected from the left side of the carcass between the 11th and 13th rib.

A multi-omics approach (gene expression quantification, metabolomics, and fatty acid [FA] profiling) was used to classify the three groups.  FAs were measured by gas chromatography-mass spectrometry (GC–MS), secondary metabolites were identified using ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS), and gene expression analysis was performed using quantitative reverse transcription polymerase chain reaction (RT–qPCR).

The authors report that all target genes were upregulated in beef from GRASS compared to the other two groups. Multivariate analyses showed that long-chain n-3 polyunsaturated FAs, the n-6:n-3 ratio, vitamin E, organic acids, amino acid derivatives, and the nephronectin isoform X1 (NPNT-1) gene were the most important compounds for group separation. These compounds showed higher concentrations in beef from GRASS.

The success of beef separation by dietary treatment was highlighted by the 90.4% prediction accuracy of the random forest model, with beef from GRASS being 100% accurately predicted and beef from GRAPE being 94.4% accurately predicted. Beef from GRAIN was 76.5% accurately predicted.

The authors conclude that coupling gene expression analysis to metabolomics and FA profiling allowed for the separation of beef samples from varying dietary backgrounds with a high degree of confidence.

Thank you to FAN member Lucas Krusinski for flagging this article

DNA analysis will not help if a processed meat product has been adulterated with offal from the same species.  The traditional test approach is microscopy but this is challenging for highly processed products and requires expert interpretation.  Analysis of protein profiles can also be used, but proteins may not be stable to food processing.

In this study (purchase required) the authors propose using a molecular diagnostics method, testing for the messenger RNA (mRNA) that drives the protein production, rather than for the proteins themselves.  They scanned through a bovine gene expression database for mRNAs expressed at elevated levels in 10 unwanted offal tissues but not in muscle or adipose tissue. Out of 27,095 candidate transcripts, 3 were eventually selected as markers. Primers and probe sets for RT-PCR analysis of each transcript were designed. Two of the transcripts were shown to be detected by the developed RT-PCR method. The method was validated by specificity, sensitivity, repeatability, and reproducibility parameters

Photo by Laura Ohlman on Unsplash