Controlled release urea (CRU) is considered to enhance crop yields while alleviating negative environmental problems caused by the hazardous gas emissions that are associated with high concentrations of ammonium (NH₄⁺) and nitrate (NO₃⁻) in black soils. Short-term effects of sulfur-coated urea (SCU) and polyurethane-coated urea (PCU), compared with conventional urea, on NO₃⁻ and NH₄⁺ in black soils were studied through the buried bag experiment conducted in an artificial climate chamber. We also investigated nitrogen (N) release kinetics of CRU and correlations between the cumulative N release rate and concentrations of NO₃⁻ and NH₄⁺. CRU can reduce concentrations of NO₃⁻ and NH₄⁺, and PCU was more effective in maintaining lower soil NO₃⁻/NH₄⁺ ratios than SCU and U. Parabolic equation could describe the kinetics of NO₃⁻ and NH₄⁺ treated with PCU. The Elovich equation could describe the kinetics of NO₃⁻ and NH₄⁺ treated with SCU. The binary linear regression model was established to predict N release from PCU because of significant correlations between the cumulative N release rate and concentrations of NO₃⁻ and NH₄⁺. These results provided a methodology and data support for characterizing and predicting the N release from PCU in black soils.

To promote the green development of agriculture by returning biochar to farmland, it is of great significance to simultaneously detect heavy and nutritional metals in agricultural biochar. This work aimed first to apply laser-induced breakdown spectroscopy (LIBS) for the determination of heavy (Pb, Cr) and nutritional (K, Na, Ca, Mg, Cu, and Zn) metals in agricultural biochar. Each batch of collected biochar was prepared to a standardized sample using the separating and milling method. Two types of univariate analysis model were developed using peak intensity and integration area of the sensitive emission lines, but the performance did not satisfy the requirements of practical application because of the poor correlations between the measured values and predicted values, as well as large relative standard deviation of the prediction (RSDP) values. An ensemble learning algorithm, adaboost backpropagation artificial neural network (BP-Adaboost), was then used to develop the multivariate analysis models, which had a more robust performance than traditional univariate analysis, partial least squares regression (PLSR), and backpropagation artificial neural network (BP-ANN). The optimized RSDP values for K, Ca, Mg, and Cu were less than 10%, while the RSDP values for Pb, Cr, Zn, and Na were in the range of 10–20%. Moreover, the pairwise t-test of its prediction set showed that there was no significant difference between the measurements of LIBS and ICP-MS. The promising results indicate that rapid and simultaneous detection of major heavy and nutritional metals in agricultural biochar can be achieved using LIBS and reasonable chemometric algorithms.

Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims first to apply laser-induced breakdown spectroscopy (LIBS) for the quantitative detection of Cr in biochar. Learning from the principles of traditional matrix effect correction methods, calibration samples were divided into 1–3 classifications by an unsupervised hierarchical clustering method based on the main elemental LIBS data in biochar. The prediction samples were then divided into diverse classifications of calibration samples by a supervised K-nearest neighbor (KNN) algorithm. By comparing the effects of multiple partial least squares regression (PLSR) models, the results show that larger numbered classifications have a lower averaged relative standard deviations of cross-validation (ARSDCV) value, signifying a better calibration performance. Therefore, the 3 classification regression model was employed in this study, which had a better prediction performance with a lower averaged relative standard deviations of prediction (ARSDP) value of 8.13%, in comparison with our previous research and related literature results. The LIBS technology combined with matrix effect classification regression model can weaken the influence of the complex matrix effect of biochar and achieve accurate quantification of contaminated metal Cr in biochar.

For the reasonable and effective collection of Ophiocordyceps sinensis, a new method of on-site identification was attempted using a portable multispectral imaging (MSI) technique. Three dimensional (3D) data-cubes of representative Ophiocordyceps sinensis and weeds samples were acquired and pre-processed with standard normal variate transformation (SNV). Principal component analysis (PCA) and simulated annealing particle swarm optimisation (SAPSO) algorithms were used to extract characteristic images and develop the support vector classification (SVC) models. Results show that the fused feature model of SAPSO-SVC has the best performance, resulting in a recognition accuracy of the prediction set of 96.30%. Moreover, on-site distribution map of Ophiocordyceps sinensis and weeds was created using the spectral feature model of SAPSO-SVC, and the target could be easily identified from the distribution map. This work demonstrates the potential for on-site identification of Ophiocordyceps sinensis in the Qinghai–Tibet Plateau using a portable MSI technique combined with the SAPSO-SVC algorithm. Keywords: Ophiocordyceps sinensis, MSI, SAPSO-SVC, On-site distribution map DOI: 10.25165/j.ijabe.20201306.5425 Citation: Duan H W, Tong X, Cui R X, Han L J, Huang G Q. On-site identification of Ophiocordyceps sinensis using multispectral imaging and chemometrics. Int J Agric & Biol Eng, 2020; 13(6): 166–170.