What question did this study set out to answer?

To evaluate the feasibility and performance of handheld near-infrared reflectance spectrometers for predicting nutritive constituent values and moisture content in undried, unground ensiled forage.

February 4, 2023SensorsOpen Access

Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict the Nutritive Value of Undried Ensiled Forage

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Authors

XFXiaoyu FengNorth Dakota State University JCJerry H. CherneyCornell University DCD. J. R. CherneyCornell University

Discussion

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Implication

Analytical validation reveals accurate moisture and nutrient prediction in undried ensiled forage using handheld spectrometers, indicating viable on-farm feed analysis.

Key Points

To evaluate the feasibility and performance of handheld near-infrared reflectance spectrometers for predicting nutritive constituent values and moisture content in undried, unground ensiled forage.
Analyzed N=555 undried ensiled corn, grass, and alfalfa samples using three handheld NeoSpectra-Scanner spectrometers.
Developed calibration models for neutral detergent fiber, acid detergent fiber, crude protein, lignin, digestibility, ash, and moisture against wet chemistry reference values.
Evaluated scanning protocols (including the sliding method) and instrument-to-instrument variability across calibration and independent validation sets.
Employing a sliding scanning method significantly improved calibration model performance across constituents compared to stationary scanning (p < 0.05).
Instrument-to-instrument variability significantly impacted moisture prediction (p = 0.02), yielding a root mean square error of prediction of 2.39 with an independent instrument versus 1.44 using identical instruments.
Handheld devices predicted moisture content with high accuracy in both undried alfalfa-grass (R² = 0.97, validation error 1.67) and corn silage (R² = 0.93, validation error 1.62).

Cite This Study

Feng et al. (2023) studied this question.

synapsesocial.com/papers/6aa9ec45f6e8aa835a48155a https://doi.org/10.3390/s23041750

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February 4, 2023SensorsOpen Access

Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict the Nutritive Value of Undried Ensiled Forage

View Full Paper

Ask AI

Bookmark

Authors

XFXiaoyu FengNorth Dakota State University JCJerry H. CherneyCornell University DCD. J. R. CherneyCornell University

Discussion

Member takes

Implication

Analytical validation reveals accurate moisture and nutrient prediction in undried ensiled forage using handheld spectrometers, indicating viable on-farm feed analysis.

Key Points

To evaluate the feasibility and performance of handheld near-infrared reflectance spectrometers for predicting nutritive constituent values and moisture content in undried, unground ensiled forage.
Analyzed N=555 undried ensiled corn, grass, and alfalfa samples using three handheld NeoSpectra-Scanner spectrometers.
Developed calibration models for neutral detergent fiber, acid detergent fiber, crude protein, lignin, digestibility, ash, and moisture against wet chemistry reference values.
Evaluated scanning protocols (including the sliding method) and instrument-to-instrument variability across calibration and independent validation sets.
Employing a sliding scanning method significantly improved calibration model performance across constituents compared to stationary scanning (p < 0.05).
Instrument-to-instrument variability significantly impacted moisture prediction (p = 0.02), yielding a root mean square error of prediction of 2.39 with an independent instrument versus 1.44 using identical instruments.
Handheld devices predicted moisture content with high accuracy in both undried alfalfa-grass (R² = 0.97, validation error 1.67) and corn silage (R² = 0.93, validation error 1.62).

Cite This Study

Feng et al. (2023) studied this question.

synapsesocial.com/papers/6aa9ec45f6e8aa835a48155a https://doi.org/10.3390/s23041750

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