Feed analysis with near-infrared spectroscopy (NIRS)
Applications | 2025 | MetrohmInstrumentation
Rapid and reliable quality control of animal feed is a critical component of efficient livestock production. Traditional wet-chemistry assays for moisture, protein, fiber, ash, starch and fat are laborious, time-consuming and depend on costly reagents. Near-infrared spectroscopy (NIRS) addresses these challenges by providing multi-parameter analysis in seconds without sample preparation, supporting real-time decision making in feed manufacturing and QC laboratories.
This application note demonstrates the development and validation of NIRS models for determining key nutritional parameters in a broad range of feed types (poultry, swine, cattle, goats and sheep). The study aims to assess prediction accuracy, precision and applicability of a Metrohm NIR Analyzer for routine feed analysis.
Approximately 500 feed samples spanning moisture, starch, fiber, protein, fat and ash content were scanned in reflection mode using a large rotating cup to average out inhomogeneities. Spectra were collected at multiple spots per sample and processed with Metrohm’s software to build and cross-validate calibration models. Reference values were determined by established ISO methods (Kjeldahl, polarimetry, incineration and gravimetric assays).
Generic models covering all feed types yielded strong correlations between predicted and reference values (R2: moisture 0.94, starch 0.93, fat 0.92, protein 0.87; fiber 0.65; ash 0.72). Species-specific calibrations improved performance further:
Figures of merit (SEC, SECV, SEP) demonstrate that NIRS meets or exceeds the precision requirements for routine feed testing, delivering results within minutes rather than hours.
Advances in instrumentation and chemometric algorithms will further enhance sensitivity for minor components (e.g., vitamins, additives) and enable predictive maintenance of feed quality. Integration with cloud-based platforms and IoT sensors promises automated, plant-wide monitoring and data-driven optimization of feed formulations.
NIR spectroscopy on a Metrohm analyzer provides a robust, fast and accurate method for simultaneous quantification of moisture, protein, fiber, ash, starch and fat in diverse animal feeds. Adoption of NIRS streamlines QC workflows, reduces chemical waste and accelerates decision making in feed production.
NIR Spectroscopy
IndustriesFood & Agriculture
ManufacturerMetrohm
Summary
Importance of the Topic
Rapid and reliable quality control of animal feed is a critical component of efficient livestock production. Traditional wet-chemistry assays for moisture, protein, fiber, ash, starch and fat are laborious, time-consuming and depend on costly reagents. Near-infrared spectroscopy (NIRS) addresses these challenges by providing multi-parameter analysis in seconds without sample preparation, supporting real-time decision making in feed manufacturing and QC laboratories.
Aims and Study Overview
This application note demonstrates the development and validation of NIRS models for determining key nutritional parameters in a broad range of feed types (poultry, swine, cattle, goats and sheep). The study aims to assess prediction accuracy, precision and applicability of a Metrohm NIR Analyzer for routine feed analysis.
Methodology and Instrumentation
Approximately 500 feed samples spanning moisture, starch, fiber, protein, fat and ash content were scanned in reflection mode using a large rotating cup to average out inhomogeneities. Spectra were collected at multiple spots per sample and processed with Metrohm’s software to build and cross-validate calibration models. Reference values were determined by established ISO methods (Kjeldahl, polarimetry, incineration and gravimetric assays).
Used Instrumentation
- Metrohm NIR Analyzer configured for reflectance measurements
- Large sample cup with continuous rotation for spectral averaging
- Metrohm software for spectral acquisition, preprocessing and model calibration
Main Results and Discussion
Generic models covering all feed types yielded strong correlations between predicted and reference values (R2: moisture 0.94, starch 0.93, fat 0.92, protein 0.87; fiber 0.65; ash 0.72). Species-specific calibrations improved performance further:
- Poultry feed: R2 range 0.79–0.97, SEP <0.8% for protein and starch, SEP <0.2% for fat
- Swine feed: R2 range 0.82–0.99, SEP <0.3% for moisture and fat, SEP <1% for starch
- Cattle feed: R2 range 0.86–0.99, SEP <0.6% for fiber, SEP <1.3% for starch
Figures of merit (SEC, SECV, SEP) demonstrate that NIRS meets or exceeds the precision requirements for routine feed testing, delivering results within minutes rather than hours.
Benefits and Practical Applications
- Non-destructive, reagent-free analysis
- High throughput: multiple parameters in one scan
- Consistent precision across feed varieties
- Immediate feedback for process control and QC
Future Trends and Opportunities
Advances in instrumentation and chemometric algorithms will further enhance sensitivity for minor components (e.g., vitamins, additives) and enable predictive maintenance of feed quality. Integration with cloud-based platforms and IoT sensors promises automated, plant-wide monitoring and data-driven optimization of feed formulations.
Conclusion
NIR spectroscopy on a Metrohm analyzer provides a robust, fast and accurate method for simultaneous quantification of moisture, protein, fiber, ash, starch and fat in diverse animal feeds. Adoption of NIRS streamlines QC workflows, reduces chemical waste and accelerates decision making in feed production.
References
- ISO 6493:2000 – Animal feeding stuffs: Determination of starch by polarimetry
- ISO 5984:2002 – Animal feeding stuffs: Determination of crude ash
- ISO 6865:2000 – Animal feeding stuffs: Determination of crude fibre by intermediate filtration
- ISO 5983:1997 – Animal feeding stuffs: Nitrogen (Kjeldahl) and crude protein calculation
- ISO 6496:1999 – Animal feeding stuffs: Moisture and other volatile matter
- ISO 6492:1999 – Animal feeding stuffs: Determination of fat content
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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