Thermal processing and gastrointestinal digestion influence the aroma and nutritional properties of meat products, but the effects of mushroom-derived ingredients remain unclear. This study evaluated the effects of Pleurotus ostreatus cap and stipe extracts on volatile profiles and free amino acid bioaccessibility in beef burgers after in vitro gastrointestinal digestion. Four formulations were investigated: control, 0.01% sodium erythorbate, and 0.5% cap or stipe extract. Volatile compounds and free amino acids were analyzed using HS-SPME/GC–MS and HPLC-FLD, respectively. Treatment significantly influenced volatile profiles (pseudo-F = 9.30, p = 0.002) and free amino acid profiles in cooked burgers (p = 0.012) and intestinal digestates (p = 0.038). Cap extract nearly doubled the umami amino acid fraction (ASP + GLU) in cooked burgers (18.1 versus 9.7 mg/100 g). Mushroom-treated burgers exhibited higher post-digestion concentrations of several essential and umami-related amino acids. Digestion stage explained most variation in the combined amino acid profiles (R2 = 0.79), whereas the stage × treatment interaction was not significant (p = 0.059), indicating no detectable global treatment-dependent transition. Nevertheless, amino acid-specific transition factor (FTF) and preservation index (PI) analyses revealed selective treatment differences, particularly for ASP and HIS. Mushroom extracts also reduced several oxidation-related volatiles, including hexanal, 1-octanol, and 2-pentyl furan. Overall, P. ostreatus extracts, particularly cap extract, enhanced amino acid bioaccessibility and modulated volatile profiles, supporting their potential as sustainable meat ingredients.
Quality attributes of fresh meat represent crucial determinants shaping consumer perception and satisfaction, directly influencing purchase decisions and economic value. Over the last two decades, proteomics has enabled a deeper investigation of multiple beef quality traits, allowing to decipher the unknowns and elucidate biochemical mechanisms underlying post-mortem muscle processes and meat tenderization. Furthermore, increasing attention has been directed toward the identification of biomarkers capable of understanding biochemical changes in post-mortem muscle and their use for predicting the potential meat quality. Based on an integromics study, this work reviews on one hand the proteomic evidence on beef marbling, color, and tenderness to investigate the biological mechanisms underpinning beef quality, and on the other hand elucidates both common and trait-specific molecular pathways and compile recurrent protein biomarkers for multi-trait beef quality. A database has been built from 43 eligible proteomic-based studies with a focus on bovine longissimus muscle. From this dataset, a total of 79 putative biomarkers were identified, among which 11 were consistently identified across the three quality traits, representing a putative set of biomarkers for multi-trait beef quality. The key protein biomarkers generated in this integromics and the related biochemical pathways revealed thanks to innovative and adapted bioinformatics analyses have been thoroughly discussed. The most prominent include catalytic, metabolism & ATP metabolic processes, particularly those related to glycolysis, as well as muscle contraction, structure & associated proteins. These are followed by pathways involving chaperones and heat shock proteins, binding, transport & calcium homeostasis, oxidative stress, and proteolysis. However, differences in their relative contributions were observed across the different beef quality traits.
pH is an indicator of meat quality, affecting colour, tenderness, and shelf life. This study is aimed to carry a comprehensive assessment of non-invasive prediction model for pH using Near-Infrared Spectroscopy (NIRS) across red meat species. A calibration model based on 2539 samples was built using a deep convolutional neural network (DCNN), integrating data from 17 muscles, 3 species (beef, lamb, venison), and 4 NIRS devices. The model achieved a strong calibration performance (R2 = 0.95, SEP = 0.07) and a competent validation performance (R2 = 0.82, SEP = 0.14). External testing confirmed the model's ability to predict pH across different animals and muscles, although reduced accuracy was observed with the INDIGO handheld device. The lack of ability to capture information related to muscle structure reduced the prediction performance of the sensor and generated bias. Results confirm the feasibility of a robust NIRS-based tool for meat pH assessment, provided device used to collect NIR spectra is suitable for red meat. This approach opens new opportunities for real-time quality control in meat processing, with a potential to improve consistency and reduce losses due to pH-related defects.
This study proposed a framework for predicting eating quality clusters (EQCs) of pork shoulder butt using visible/near-infrared hyperspectral imaging (HSI). HSI data from 189 samples were acquired, and their EQCs were defined by applying k-means clustering (k = 2 and 3) to an integrated feature matrix composed of results from sensory and electronic tongue analyses. Three datasets, including muscle, fat, and whole-meat regions, were generated using a partial least squares-discriminant analysis-based segmentation procedure. The muscle region outperformed other datasets in both class classification tasks. Optimal feature selection and data augmentation methods were then explored. In the binary classification, the combination of uninformative variable elimination, combined transformation, and a logistic regression model achieved the best performance. In the three-class classification, the combination of a support vector machine with all variables and scale and noise addition showed the best performance. Consequently, HSI would serve as a rapid and non-destructive tool for predicting pork EQCs.
