Application of mid-infrared photoacoustic spectroscopy in monitoring carbonate content in soils

Du, Changwen; Ma, Zhao-yang; Zhang, Jianmin; Goyne, Keith W.

doi:10.1016/j.snb.2013.08.023

Cited by 33 publications

(12 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compare the performances of different calibration models, the same calibration and validation sets were used to test all of the models. Previous studies have assessed the accuracy and the estimating performance of different models in terms of absolute prediction accuracy (RMSE), the coefficient of determination (R 2 ), and the residual prediction deviation (RPD) (Saeys, Mouazen & Ramon, 2005; Viscarra Rossel, McGlynn & McBratney, 2006; Vasques, Grunwald & Sickman, 2008; Mouazen et al, 2010; Kinoshita et al, 2011; Hu, 2013; Du et al, 2013; Mirzaie et al, 2014; Gomes et al, 2017). In this study, the performance of all models was evaluated by the following indices: the coefficients of determination of calibration (R 2 cal ) and validation (R 2 val ), the root mean square errors of calibration (RMSE c ) and validation (RMSE p ), and the residual prediction deviations of calibration (RPD cal ) and validation (RPD val ).…”

Section: Methodsmentioning

confidence: 99%

Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera

Liu

Shi

et al. 2019

PeerJ

View full text Add to dashboard Cite

The anthocyanin content in leaves can reveal valuable information about a plant’s physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450–600 nm) in Prunus cerasifera and then the performance of these models was compared for three multivariate calibrations. Certain principal components (PCs) and latent variables (LVs) were used as input for the back-propagation neural network (BPNN) model. The results showed that the best PCR and PLSR models were obtained by standard normal variate (SNV), and BPNN models outperformed both the PCR and PLSR models. The coefficient of determination (R2), the root mean square error of prediction (RMSEp), and the residual prediction deviation (RPD) values for the validation set were 0.920, 0.274, and 3.439, respectively, for the BPNN-PCs model, and 0.922, 0.270, and 3.489, respectively, for the BPNN-LVs model. Visible spectroscopy combined with BPNN was successfully applied to determine leaf anthocyanin content in P. cerasifera and the performance of the BPNN-LVs model was the best. The use of the BPNN-LVs model and visible spectroscopy showed significant potential for the nondestructive determination of leaf anthocyanin content in plants.

show abstract

Section: Methodsmentioning

confidence: 99%

Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera

Liu

Shi

et al. 2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…However, the last decade has seen a proliferation of calibration work for soil properties other than total C and N. DRIFTS has been used to predict alkyl, carbonyl, and aromatic C in soil and litter samples quantified via NMR (Ludwig et al, 2008). Recent work shows that we can potentially calibrate for other parameters in soil such as metals, carbonates (inorganic C), enzymes, potential nitrification, and pH (Du et al, 2013b;McCarty et al, 2002;Mimmo et al, 2002;Reeves et al, 2001;Siebielec et al, 2004). Because DRIFTS contains information related to organic and inorganic components that relate to texture and particle size distribution, calibrations can also be developed for soil physical attributes including moisture retention, bulk density, or hydraulic conductivity (Janik et al, 2007;Minasny et al, 2008;Tranter et al, 2008).…”

Section: Quantitative Analysis Of Soil Carbon and Nitrogenmentioning

confidence: 99%

Soil Chemical Insights Provided through Vibrational Spectroscopy

Parikh

Goyne

Margenot

et al. 2014

Advances in Agronomy

Self Cite

216

163

View full text Add to dashboard Cite

“…where n is the number of repeated measurements; x j is the spectral intensity of jth repeated measurement;x is the average intensity of n repeated spectra; y i andŷ i refer to the measured value and the corresponding estimated value, respectively;ŷ is the average of the estimated value; N denotes the number of observations; andȳ is the average of the measured value. The RPD V value in soil science is considerably lower than in most other fields because of the complicated interaction among soil components, which influences the distribution of specific soil properties (Du, Ma, Zhou, & Goyne, 2013). Thus, RPD V values < 1.4…”

Section: Partial Least Squares Regression and Model Evaluationmentioning

confidence: 97%

Optimization of measuring procedure of farmland soils using laser‐induced breakdown spectroscopy

et al. 2020

Soil Science Soc of Amer J

Self Cite

View full text Add to dashboard Cite

Laser‐induced breakdown spectroscopy (LIBS) is an emerging multi‐elemental analytical technique offering fast and simultaneous quantification of soil properties with minimal sample preparation and effective cost. Due to soil heterogeneity, spectral variation however limits the quantitative robustness. In this study, 348 soil samples were collected and prepared for acquisition of LIBS spectra. Influences of shot layer and number on LIBS quality were evaluated by spectral intensity and relative standard deviation (RSD). Effects of shot layer and number and five normalization procedures on LIBS ability to measure soil organic matter (SOM), total nitrogen (TN), and total soluble salt content (TSC), were evaluated using partial least squares regression (PLSR). Increasing shot number reduced LIBS spectral variance, thereby improving the quantitative accuracy of selected soil properties. Deep shot layers (4th or 5th shot layers) reduced the intensities of soil spectra and thereby decreased the quantitative accuracy for TSC. However, deep shot layers improved the SOM and TN prediction performances. Among the normalization approaches, the method based on the correction of Si line (DS) showed superior performance for improving quantitation of SOM and TN. The arithmetic average method (AA) was best for TSC prediction. Optimization of shot layer, number and normalization procedures of LIBS spectra resulted in fair prediction of SOM (residual prediction deviation of validation set, RPDV = 1.608), good prediction of TN (RPDV = 1.836), and very good quantitative analysis of TSC (RPDV = 2.456). Therefore, our findings illustrate very good potential for improving the quantitative accuracy of the LIBS soil spectra.

show abstract

Application of mid-infrared photoacoustic spectroscopy in monitoring carbonate content in soils

Cited by 33 publications

References 50 publications

Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera

Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera

Soil Chemical Insights Provided through Vibrational Spectroscopy

Optimization of measuring procedure of farmland soils using laser‐induced breakdown spectroscopy

Contact Info

Product

Resources

About