Wali Khan, Khan Haris Ahmad, Sica Pietro, Van Henten Eldert J, Meers Erik, Brunn Sander
Agricultural Biosystems Engineering Group, Wageningen University & Research, Wageningen, 6708 PB, Netherlands.
Data Science, Crop Protection Development, Syngenta, Basel, Switzerland.
Heliyon. 2024 Mar 31;10(7):e28487. doi: 10.1016/j.heliyon.2024.e28487. eCollection 2024 Apr 15.
In this study, we assess the feasibility of using Fourier Transform Infrared Photoacoustic Spectroscopy (FTIR-PAS) to predict macro- and micro-nutrients in a diverse set of manures and digestates. Furthermore, the prediction capabilities of FTIR-PAS were assessed using a novel error tolerance-based interval method in view of the accuracy required for application in agricultural practices. Partial Least-Squares Regression (PLSR) was used to correlate the FTIR-PAS spectra with nutrient contents. The prediction results were then assessed with conventional assessment methods (root mean square error (RMSE), coefficient of determination R, and the ratio of prediction to deviation (RPD)). The results show the potential of FTIR-PAS to be used as a rapid analysis technique, with promising prediction results (R > 0.91 and RPD >2.5) for all elements except for bicarbonate-extractable P, K, and NH-N (0.8 < R < 0.9 and 2 < RPD <2.5). The results for nitrogen and phosphorus were further evaluated using the proposed error tolerance-based interval method. The probability of prediction for nitrogen within the allowed limit is calculated to be 94.6 % and for phosphorus 83.8 %. The proposed error tolerance-based interval method provides a better measure to decide if the FTIR-PAS in its current state could be used to meet the required accuracy in agriculture for the quantification of nutrient content in manure and digestate.
在本研究中,我们评估了使用傅里叶变换红外光声光谱法(FTIR-PAS)预测多种粪便和沼渣中常量和微量营养素的可行性。此外,鉴于农业实践应用所需的准确性,使用一种基于新型误差容限的区间方法评估了FTIR-PAS的预测能力。采用偏最小二乘回归(PLSR)将FTIR-PAS光谱与养分含量相关联。然后用传统评估方法(均方根误差(RMSE)、决定系数R和预测偏差比(RPD))评估预测结果。结果表明,FTIR-PAS有潜力用作快速分析技术,除了碳酸氢盐可提取的磷、钾和铵态氮(0.8 < R < 0.9且2 < RPD < 2.5)外,对所有元素都有良好的预测结果(R > 0.91且RPD > 2.5)。使用所提出的基于误差容限的区间方法进一步评估了氮和磷的结果。计算得出,在允许限度内氮的预测概率为94.6%,磷为83.8%。所提出的基于误差容限的区间方法为判断当前状态下的FTIR-PAS是否可用于满足农业中粪便和沼渣养分含量量化所需的准确性提供了更好的衡量标准。