Evaluation of the Deliciousness of Artifcial Meat

Significance of the Topic

The rapid global population increase and growing meat consumption place pressure on food security, environmental sustainability, and public health. Artificial meat – including plant-based formulations and cultured cell-derived products – offers a path to reduce greenhouse gas emissions, land use, and resource depletion while providing meat-like taste and texture.

Study Objectives and Overview

The study aims to establish quantitative analytical methods for assessing the deliciousness of artificial meat by evaluating:

• Volatile flavor profiles in plant-based meat (PBM) versus organic beef.
• Quality discrimination of meat samples via machine learning on GC-MS data.
• Amino acid composition differences between soy-based meat and chicken.
• Broader primary metabolite profiling in PBM products and ground beef using LC-MS.
• Texture characteristics of meatballs from PBM and chicken by mechanical testing and DSC.
• Cultured-meat bioprocess monitoring through medium analysis and cell aggregate mechanics.

Methodology and Instrumentation

Various analytical platforms were employed: solid-phase microextraction–GC-MS for volatiles, support vector machines for flavor-based classification, post-column OPA derivatization LC for amino acids, LC-MS/MS for primary metabolomics, texture analyzers and differential scanning calorimetry (DSC) for mechanical properties, and high-throughput LC-MS profiling plus micro-compression testing for cell culture monitoring.

Použitá instrumentace

• GCMS-QP2020 NX with AOC-6000 Plus autosampler and SPME Arrow for volatile aroma analysis.
• Nexera-e High-Performance Liquid Chromatograph with post-column OPA derivatization for amino acid profiling.
• LCMS-8060NX triple quadrupole mass spectrometer and primary metabolites method package for targeted metabolomics.
• Shimadzu Texture Analyzer for fracture and compression tests on meat samples.
• Differential Scanning Calorimeter for protein denaturation studies in cooked meats.
• Micro Compression Testing Machine MCT-510 for deformation strength of cell aggregates.

Main Results and Discussion

• Flavor Analysis: SPME-GC/MS revealed overlapping fatty acid and Maillard reaction products in PBM and organic beef; PBM showed additional compounds from diverse plant precursors. A support vector machine classifier achieved 95.8 % precision in distinguishing fresh versus heat-deteriorated meats.
• Taste Analysis: Post-column LC demonstrated distinct free amino acid profiles in soy meat versus chicken, with glutamic acid, glycine, and alanine levels correlating to umami and sweetness. LC-MS metabolite profiling and PCA clearly separated ground beef from PBM products based on patterns of amino acids, organic acids, and nucleosides.
• Texture Analysis: Meatball shear and compression tests showed plant-based samples to be harder but less elastic than chicken. DSC coupled with texture assays on fried chicken indicated that protein denaturation progresses over two hours of heat retention, coinciding with increased hardness.
• Cell Culture Monitoring: Simultaneous LC-MS/MS quantitation of 95 medium constituents tracked nutrient uptake (glucose, glutamine depletion) and by-product accumulation (lactate increase) during a five-day hybridoma culture. Micro-compression tests on HEK293 and two iPS cell aggregates measured deformation strengths of 1.9 MPa, 1.26 MPa, and 1.77 MPa, respectively, reflecting differences in aggregate mechanics.

Benefits and Practical Applications

• Objective flavor and taste metrics accelerate R&D and quality control of artificial meat formulations.
• Machine learning on GC-MS data supports rapid, automated freshness and quality screening.
• Targeted metabolite profiling guides formulation adjustments to enhance umami and overall taste.
• Mechanical and thermal analysis informs processing conditions to achieve desired texture and juiciness.
• Comprehensive cell culture monitoring enables process optimization for scaled-up cultured meat production.

Future Trends and Potential Applications

• Integration of multi-omics data with sensory evaluation for predictive taste and texture modeling.
• Artificial intelligence and advanced chemometrics to design personalized meat analogs.
• Real-time, in-line bioprocess sensors for continuous cultured meat quality control.
• Novel biopolymer scaffolds and 3D bioprinting for improved tissue-like texture in cultured products.

Conclusion

This comprehensive analytical framework demonstrates that advanced chromatography, mass spectrometry, mechanical testing, and data analytics can robustly characterize the flavor, taste, and texture of artificial meat products. Such tools are critical for optimizing formulations, ensuring quality, and accelerating the adoption of sustainable protein alternatives.