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Nitrogen/Protein in Meat Products (TruMac N)

Applications | 2022 | LECOInstrumentation
Elemental Analysis
Industries
Food & Agriculture
Manufacturer
LECO

Summary

Significance of the Topic


The accurate measurement of protein content in meat products is vital for nutritional labeling, quality control, and the economic valuation of food materials. Reliable nitrogen determination underpins consumer safety and regulatory compliance, while enabling manufacturers to ensure product consistency and cost-effectiveness.

Objectives and Overview of the Study


This application note assesses the performance of the LECO TruMac® N combustion nitrogen/protein determinator for meat product analysis. Key goals include:
  • Evaluating macro sample handling (>1 g) for heterogeneous meat matrices
  • Comparing helium versus argon carrier gas models and aliquot loop sizes
  • Determining precision, accuracy, and throughput under typical operating conditions

Methodology


Samples were prepared to uniform consistency following AOAC 992.15 guidelines. Approximately 1 g of each meat product was weighed into ceramic boats and combusted in a horizontal furnace at 1100°C in a pure oxygen environment. Moisture was removed via a thermoelectric cooler. Combustion gas volumes of 3 cc and 10 cc were sampled and analyzed using a thermal conductivity cell. Standard operating parameters included a two-cycle purge, zero dehydration time, and a conversion factor of 1.00 for nitrogen measurement. Calibration utilized single-point EDTA standards, with optional verification using nicotinic acid or phenylalanine.

Used Instrumentation


The study employed the LECO TruMac® N macro combustion nitrogen analyzer configured in two modes:
  • Helium carrier gas: available with 3 cc or 10 cc aliquot loops for enhanced sensitivity and extended reagent life
  • Argon carrier gas: 10 cc loop option to mitigate helium supply constraints

Complementary components included a ceramic horizontal furnace, thermoelectric cooler, autoloader, and thermal conductivity detector.

Key Results and Discussion


Across five meat types—pre-cooked bacon, roast beef, chicken, smoked turkey, and ham—the TruMac N demonstrated consistent nitrogen recovery and protein calculation (using a 6.25 conversion factor). Typical findings:
  • Helium 3 cc loop: average %N ranging from 2.80 to 5.58, RSD ≤0.05, corresponding protein 17.5–34.9% with RSD ≤0.3
  • Helium 10 cc loop: average %N ranging from 2.82 to 5.60, RSD ≤0.03, protein 17.6–35.0% with RSD ≤0.2
  • Argon 10 cc loop: average %N ranging from 2.83 to 5.60, RSD ≤0.05, protein 17.7–35.0% with RSD ≤0.3

Sensitivity was highest with helium, particularly at lower nitrogen levels. Argon models yielded comparable practical performance with a modest increase in analysis time. Extended burn profiles and reduced sample masses were effective mitigations for high-fat or soot-forming samples.

Benefits and Practical Applications


The TruMac N offers:
  • Macro sample capability (>1 g) for representative analysis of heterogeneous meat products
  • Rapid cycle times (35–55 s minimum analysis) for high throughput
  • Low cost-per-analysis via reduced reagent consumption and loop volume optimization
  • Flexible carrier gas options to accommodate supply constraints and sensitivity needs

Applications include food quality assurance, nutritional labeling, contract testing, and research in meat chemistry.

Future Trends and Applications


Emerging directions include automation of sample preparation, integration with laboratory information management systems, and development of alternative carrier gases or detector technologies to enhance sensitivity. Refinement of protein conversion factors for diverse meat and plant-based matrices will support more accurate nutritional profiling.

Conclusion


The LECO TruMac® N delivers reliable, precise, and efficient nitrogen/protein analysis for a range of meat products. Its macro sample capacity and adaptable configuration make it a valuable tool for food laboratories seeking robust performance and cost-effective operation.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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