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Direct Moisture Determination of Various Grain/Seed Products Comparing Automated Thermogravimetric Method and the Oven Drying Methods Used by the Southern African Grain Laboratory

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Food & Agriculture
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Summary

Importance of the Topic


The accurate determination of moisture content in grains, seeds, and related products is critical for ensuring product quality, safety, and consistency in downstream analyses. Moisture levels influence microbial stability, shelf life, purity calculations, yield estimates, and cost control in both industrial and research settings. Reliable moisture measurement methods support regulatory compliance and enable efficient quality assurance and process optimization.

Study Objectives and Overview


This study compares an automated thermogravimetric moisture analyser (TGM800) with conventional manual oven drying techniques used by the Southern African Grain Laboratory. The main goals are to evaluate accuracy, precision, analysis time, and practical workflow improvements when using an integrated loss-on-drying instrument versus established AACCI, ICC, and ISO oven methods.

Methodology and Instrumentation


The automated approach employs the TGM800 thermogravimetric analyser, featuring:
  • A computer-controlled oven carousel holding up to 16 aluminium crucibles
  • An integrated four-place balance with 0.0001 g resolution
  • Programmable temperature ramps from ambient to target values (103 C or 130 C)
  • Controlled atmosphere ventilation at 4.0 LPM
  • Mass-constancy endpoint logic for improved throughput

Manual oven methods follow international protocols (AACCI 44-15.02, ICC 110/1, ISO 665-2000), involving:
  • Sample loading (2–5 g) into covered moisture dishes
  • Drying in a ventilated oven at specified temperatures (103 C or 130 C) for 1–4 hours
  • Transfer to a desiccator for cooling and final weighing

Main Results and Discussion


Across sample suites including whole wheat flour, white wheat flour, maize meal, and sunflower seeds, moisture values obtained with the TGM800 agreed with manual methods within 0.1 percent absolute. Key findings:
  • Average moisture differences ranged from −0.09 to +0.11 percentage points
  • TGM800 analysis times were reduced by 36 to 78 percent compared to manual oven routines
  • Elimination of manual cooling and intermediate weighings further streamlined workflow

Results demonstrate that the automated analyser maintains analytical performance while significantly lowering total analysis time, improving laboratory productivity.

Benefits and Practical Applications


The automated thermogravimetric method offers:
  • High throughput via parallel sample processing and mass-constancy endpoints
  • Reduced operator time and potential for human error
  • Consistent temperature and ventilation control
  • Immediate result reporting without desiccator cooling delays

This approach is valuable for quality control laboratories in the grain, milling, and seed industries, where rapid and reliable moisture data support production decisions and compliance testing.

Future Trends and Opportunities


Emerging developments may include:
  • Integration of moisture analysers with laboratory information management systems to automate data tracking
  • Advanced kinetic modelling of moisture loss for optimized method design
  • Expanded application to diverse matrices such as flours, meals, and raw agricultural commodities
  • Hybrid techniques combining infrared or microwave drying with thermogravimetric control for further speed gains

Such innovations will drive even greater efficiency, data quality, and adaptability in moisture determination workflows.

Conclusion


The TGM800 thermogravimetric moisture analyser matches the accuracy of traditional oven drying methods while delivering substantial time savings. Its automation and precise environmental control streamline moisture analysis in grain and seed products, supporting enhanced laboratory productivity and robust quality assurance.

References


No external literature references were provided in the source document.

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

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