Determination of moisture, fat, and nitrogen in human feces by NIR spectroscopy
Applications | | MetrohmInstrumentation
Human fecal composition—especially moisture, fat, and nitrogen content—provides critical insights into digestive health and malabsorption syndromes such as pancreatic insufficiency and hepatic disorders. Traditional wet-chemistry methods are laborious and time-consuming, underscoring the need for rapid, non-destructive alternatives. Near-Infrared Spectroscopy (NIRS) offers a high-throughput, reagent-free approach suited to clinical and research laboratories.
This study develops and validates quantitative NIRS models to predict moisture (humidity), fat (STOT), and nitrogen (CRTOT) in human feces. A total of 522 samples were collected, with 465 spectra retained after outlier screening. The dataset was partitioned into calibration (75%), cross-validation (25%), and an independent external set of 15 samples to guard against overfitting.
All spectra were acquired on a NIRS DS2500 Analyzer across 1200–2300 nm. Preprocessing consisted of a second derivative (0 nm gap, 10 nm segment) combined with 10-point smoothing to compensate for baseline shifts and heterogeneity in fecal matrices. Partial Least Squares (PLS) regression was applied with four factors for fat and moisture, and five factors for nitrogen. Model performance was evaluated by coefficient of determination (R²), standard error of calibration (SEC), and standard error of prediction (SEP).
• Nitrogen (CRTOT): Calibration R² = 0.92, SEC = 0.07; validation R² = 0.95, SEP = 0.11.
• Fat (STOT): Calibration R² = 0.96, SEC = 0.37; validation R² = 0.91, SEP = 0.42.
• Moisture: Calibration R² = 0.90, SEC = 1.69; validation R² = 0.97, SEP = 1.81.
Samples spanned 23 concentration classes: moisture from ~64.7 % to 92.0 %, fat from 0.24 % to 22.15 %, and nitrogen from 0.19 % to 1.59 %. These robust correlations demonstrate NIRS accuracy comparable to conventional assays.
Rapid, simultaneous determination of key fecal biomarkers accelerates diagnostic workflows, reduces chemical waste, and lowers per-sample cost. The method is amenable to routine QA/QC in clinical labs and large cohort studies.
Emerging trends include miniaturized, portable NIR devices for point-of-care testing, advanced chemometric algorithms for enhanced noise reduction, and integration with machine learning for predictive diagnostics. Expansion to other biological matrices (urine, blood) and multiplexed analyte panels is also anticipated.
The NIRS DS2500 platform effectively quantifies moisture, fat, and nitrogen in human feces in a single, non-destructive measurement. High model precision and accuracy support its adoption in clinical and research environments.
NIR Spectroscopy
IndustriesClinical Research
ManufacturerMetrohm
Summary
Significance of the Topic
Human fecal composition—especially moisture, fat, and nitrogen content—provides critical insights into digestive health and malabsorption syndromes such as pancreatic insufficiency and hepatic disorders. Traditional wet-chemistry methods are laborious and time-consuming, underscoring the need for rapid, non-destructive alternatives. Near-Infrared Spectroscopy (NIRS) offers a high-throughput, reagent-free approach suited to clinical and research laboratories.
Objectives and Study Overview
This study develops and validates quantitative NIRS models to predict moisture (humidity), fat (STOT), and nitrogen (CRTOT) in human feces. A total of 522 samples were collected, with 465 spectra retained after outlier screening. The dataset was partitioned into calibration (75%), cross-validation (25%), and an independent external set of 15 samples to guard against overfitting.
Methodology
All spectra were acquired on a NIRS DS2500 Analyzer across 1200–2300 nm. Preprocessing consisted of a second derivative (0 nm gap, 10 nm segment) combined with 10-point smoothing to compensate for baseline shifts and heterogeneity in fecal matrices. Partial Least Squares (PLS) regression was applied with four factors for fat and moisture, and five factors for nitrogen. Model performance was evaluated by coefficient of determination (R²), standard error of calibration (SEC), and standard error of prediction (SEP).
Instrumentation Used
- NIRS DS2500 Analyzer
- Customer-modified ring cup
- Mini ISI ring cup (FOSS)
- Vision 4.01 software
Main Results and Discussion
• Nitrogen (CRTOT): Calibration R² = 0.92, SEC = 0.07; validation R² = 0.95, SEP = 0.11.
• Fat (STOT): Calibration R² = 0.96, SEC = 0.37; validation R² = 0.91, SEP = 0.42.
• Moisture: Calibration R² = 0.90, SEC = 1.69; validation R² = 0.97, SEP = 1.81.
Samples spanned 23 concentration classes: moisture from ~64.7 % to 92.0 %, fat from 0.24 % to 22.15 %, and nitrogen from 0.19 % to 1.59 %. These robust correlations demonstrate NIRS accuracy comparable to conventional assays.
Benefits and Practical Applications
Rapid, simultaneous determination of key fecal biomarkers accelerates diagnostic workflows, reduces chemical waste, and lowers per-sample cost. The method is amenable to routine QA/QC in clinical labs and large cohort studies.
Future Trends and Applications
Emerging trends include miniaturized, portable NIR devices for point-of-care testing, advanced chemometric algorithms for enhanced noise reduction, and integration with machine learning for predictive diagnostics. Expansion to other biological matrices (urine, blood) and multiplexed analyte panels is also anticipated.
Conclusion
The NIRS DS2500 platform effectively quantifies moisture, fat, and nitrogen in human feces in a single, non-destructive measurement. High model precision and accuracy support its adoption in clinical and research environments.
References
- van de Kamer J.H., te Bokkel Huinink H., Weyers H.A. Rapid method for the determination of fat in feces. J. Biol. Chem. 1949, 177, 347–355.
- Fecal-NIRS-Review (JNIRS).
- Comparison of NIR reflectance analysis of fecal fat, nitrogen, and water with conventional methods, and fecal energy content. Clinical Biochemistry.
- Quantification of fecal carbohydrates by NIR reflectance analysis. Clinical Chemistry.
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