Proximate biochemical composition and caloric content calculated from elemental CHN analysis: a stoichiometric...

Area of Science:

• Biogeochemistry
• Biomass analysis
• Elemental analysis

Background:

• Biochemical composition and caloric value are crucial for understanding organismal and ecosystem energetics.
• Traditional methods for determining these properties can be labor-intensive and may lack precision.
• Stoichiometric relationships between elemental composition (CHN) and biochemical fractions offer a potential for more efficient analysis.

Purpose of the Study:

• To establish and validate a stoichiometric method for calculating proximate biochemical composition and bomb caloric value from elemental CHN analysis.
• To refine the nitrogen-to-protein conversion factor for biomass.
• To investigate linear relationships between elemental fractions and biochemical components.

Main Methods:

• Elemental CHN (carbon, hydrogen, nitrogen) analysis of total biomass and ash.
• Calculation of proximate biochemical composition (carbohydrate, lipid, protein) and bomb caloric value using stoichiometric principles.
• Comparison of results with direct biochemical analysis for various biological samples.

Main Results:

• The classical nitrogen-to-protein conversion factor of 6.25 was revised to 5.8±0.13.
• Linear relationships were identified between non-protein carbon and carbohydrate/lipid content, and between bomb caloric value and organic carbon fractions.
• The CHN method showed agreement within 10% of ash-free dry biomass compared to direct biochemical analysis, with corrections for detrital material.
• Residual water estimation using hydrogen measurements was demonstrated.

Conclusions:

• Stoichiometric CHN analysis offers a highly automatic and efficient method for determining biomass biochemical composition and caloric value.
• This method extends ecophysiological concepts for constructing balanced carbon, nitrogen, biochemical, and energy budgets.
• The revised nitrogen-to-protein conversion factor improves accuracy in protein estimation from CHN data.