Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict...

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Spectroscopy

Background:

• Accurate forage analysis is crucial for animal nutrition and farm management.
• Near-infrared (NIR) spectroscopy offers a rapid, non-destructive method for predicting forage quality.
• Variability in scanning methods and instruments can impact prediction accuracy.

Purpose of the Study:

• To develop and evaluate prediction models for key forage constituents using handheld NIR spectrometers.
• To assess the impact of different scanning methods (sliding vs. non-sliding) on model performance.
• To determine the utility of handheld spectrometers for predicting moisture content in undried silage.

Main Methods:

• Collected near-infrared reflectance spectra from 555 undried corn, grass, and alfalfa silage samples using three NeoSpectra-Scanners.
• Determined laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD), NDF digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), Ash, and moisture content (MO).
• Developed and validated prediction models, comparing performance between instruments and scanning methods.

Main Results:

• The scanning method significantly impacted prediction accuracy; the sliding method improved calibration models for most constituents (p < 0.05).
• Instrument-to-instrument variability generally reduced model performance, except for moisture content.
• High prediction accuracy (R² = 0.97 for alfalfa-grass, R² = 0.93 for corn) was achieved for moisture content using handheld spectrometers on undried samples.

Conclusions:

• Handheld NIR spectrometers are effective tools for predicting moisture content in undried corn and alfalfa-grass silage.
• The sliding scanning method enhances the accuracy of forage constituent prediction models.
• This technology provides a valuable, rapid on-farm method for assessing forage quality.