Quality Control of Vinegar by TN Measurement
Applications | 2017 | ShimadzuInstrumentation
The total nitrogen content in vinegar is directly linked to the concentration of proteins and amino acids originating from the raw materials. This parameter strongly influences vinegar flavor and overall quality. Rapid and accurate TN measurement is therefore critical for quality control in food production and product standardization.
This study demonstrates the application of the Shimadzu TOC-LCPH analyzer equipped with the TNM-L total nitrogen unit for rapid TN determination in five vinegar varieties. The goal is to compare TN levels across rice, brown rice, grain, apple cider, and balsamic vinegars and evaluate method performance for routine quality assessment.
Thermal decomposition-chemiluminescence is used for TN detection, eliminating the need for chemical reagents and lengthy digestion steps. Each vinegar sample is diluted (100× for four types, 500× for balsamic) and measured directly in about 5 minutes per sample.
TN concentrations varied significantly among vinegar types, reflecting differences in raw materials and production methods. Measured values:
The Shimadzu TOC-LCPH combined with the TNM-L unit enables fast, accurate and reagent-free total nitrogen measurement in vinegar. This approach streamlines quality control workflows, supports product consistency, and offers a versatile platform for broader food analysis applications.
TOC
IndustriesFood & Agriculture
ManufacturerShimadzu
Summary
Importance of the Topic
The total nitrogen content in vinegar is directly linked to the concentration of proteins and amino acids originating from the raw materials. This parameter strongly influences vinegar flavor and overall quality. Rapid and accurate TN measurement is therefore critical for quality control in food production and product standardization.
Objectives and Study Overview
This study demonstrates the application of the Shimadzu TOC-LCPH analyzer equipped with the TNM-L total nitrogen unit for rapid TN determination in five vinegar varieties. The goal is to compare TN levels across rice, brown rice, grain, apple cider, and balsamic vinegars and evaluate method performance for routine quality assessment.
Methodology and Instrumentation
Thermal decomposition-chemiluminescence is used for TN detection, eliminating the need for chemical reagents and lengthy digestion steps. Each vinegar sample is diluted (100× for four types, 500× for balsamic) and measured directly in about 5 minutes per sample.
Used Instrumentation
- Shimadzu TOC-LCPH Total Organic Carbon Analyzer with TNM-L Total Nitrogen Unit
- TOC/TN Catalyst
- Single-point calibration using 20 mgN/L potassium nitrate solution
- Autosampler capable of unattended multi-sample analysis
Main Results and Discussion
TN concentrations varied significantly among vinegar types, reflecting differences in raw materials and production methods. Measured values:
- Rice vinegar: 306 mgN/L
- Brown rice vinegar: 1650 mgN/L
- Grain vinegar: 357 mgN/L
- Apple cider vinegar: 24.4 mgN/L
- Balsamic vinegar: 912 mgN/L
Benefits and Practical Applications
- Rapid analysis (~5 minutes) without reagents
- Minimal operator intervention compared to Kjeldahl
- High sample throughput via autosampler
- Broad applicability to various vinegar types and potentially other food products
Future Trends and Potential Applications
- Extension to other food and beverage matrices requiring nitrogen analysis
- Integration with online monitoring systems for continuous quality control
- Development of multi-parameter analyzers combining TN, TOC and other key quality indicators
- Advances in miniaturization and portable instrumentation for on-site testing
Conclusion
The Shimadzu TOC-LCPH combined with the TNM-L unit enables fast, accurate and reagent-free total nitrogen measurement in vinegar. This approach streamlines quality control workflows, supports product consistency, and offers a versatile platform for broader food analysis applications.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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