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Application of NIRS for hay evaluation at different degrees of sample preparation |
Eun Chan Jeong1 
, Farhad Ahmadi2
, Yan Fen Li1
, Li Li Wang1
, Young Sang Yu1
, Jong Geun Kim1,2
, Kun Jun Han1,3,*
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1Graduate School of International Agricultural Technology, Seoul National University, Pyeongchang 25354, Korea
2Research Institute of Eco-friendly Livestock Science, Institute of GreenBio Science Technology, Seoul National University, Pyeongchang 25354, Korea
3School of Plant, Environmental, and Soil Sciences, LSU AgCenter, Baton Rouge LA 70803, USA |
Correspondence:
Kun Jun Han,Email: forage@snu.ac.kr
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Received: 6 November 2023 • Revised: 27 November 2023 • Accepted: 8 January 2024 |
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| Abstract |
Objective
A study compared the performance of the NIRS (Near-Infrared Spectroscopy) calibration models developed with different degrees of hay sample preparations.
Methods
Spectral data of 1-mm ground or whole hay samples were regressed against wet chemistry results of moisture, NDF (neutral detergent fiber), ADF (acid detergent fiber), CP (crude protein), and IVDMD (in vitro dry matter digestibility). A total of 227 imported alfalfa (Medicago sativa L.) and another 360 timothy (Phleum pratense L.) hay samples were used to develop the calibration models. The models developed with ground hay samples were more robust and accurate than whole hay based on cross-validation's R2 (coefficient of determination), standard error, and RPD (ratio percentage deviation).
Results
The R2 of cross-validation ranged from 0.61 (moisture of alfalfa) to 0.95 (CP prediction of timothy). Although R2 of calibration models was mainly greater than 0.90, the R2 of cross-validations remained marginal.
Conclusion
Estimation of nutrient concentrations in imported hay can be achieved by calibrated NIRS. The NIRS calibration models must be improved by including more imported hay samples from different years and origins. Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparation inputs compromising analysis accuracy by NIRS.
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| Keywords:
Hay Quality; NIR Spectroscopy; Predictive Equation |
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