Prediction of organic carbon and total nitrogen contents in organic wastes and their composts by Infrared spectroscopy and partial least square regression

Sisouane, M.; Cascant-Vilaplana, Mari Merce; Tahiri, S.; Garrigues, Salvador; Krati, M. El; Boutchich, G.El Kadiri; Cervera, M. Luisa; Guárdia, Miguel de la

doi:10.1016/j.talanta.2017.02.034

Cited by 28 publications

(12 citation statements)

References 31 publications

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“…The spectral pretreatment methods include Savitzky-Golay smoothing, standard normal variate (SNV), detrend and Savitzky-Golay 1st derivative. 27,32 Leave-one-out cross validation was used to establish the calibration model to prevent over-fitting. The evaluation indicators of the calibration model included determination coefficient (R 2 ), determination coefficient of prediction (r 2 P ), root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), ratio of standard deviation to root mean square error of prediction (RPD) and relative standard deviation (RSD).…”

Section: Methodsmentioning

confidence: 99%

Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer

Huang

Sun

Liao

et al. 2020

Journal of Near Infrared Spectroscopy

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Producing organic fertilizer by aerobic composting is an effective way to solve the livestock manure pollution problem and to achieve economic utilization of the valuable resource. To control the composting process effectively and ensure the quality of such organic fertilizers, it is necessary to quantify the key parameters and provide timely feedback of their changes during the composting process. In the industrial field, the traditional laboratory analysis is being transferred into process analysis. This study explored the application of real-time and field monitoring of the key parameters in the industrial trough composting process using handheld near infrared (NIR) spectroscopy and evaluated its ability to accurately predict these changes. The results showed that the handheld NIR could accurately detect moisture content (MC), total nitrogen (TN), total carbon (TC), the carbon/nitrogen (C/N) ratio, organic matter (OM) and electrical conductivity (EC) during the trough composting process, with excellent predictions for MC, good predictions for TN and OM, approximate predictions for TC, C/N ratio and EC. Changes in NIR-predicted values and measured values were consistent as the composting process progressed. The handheld NIR sensor shows good potential for real-time and field monitoring of the composting process and organic fertilizer quality assurance.

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Section: Methodsmentioning

confidence: 99%

Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer

Huang

Sun

Liao

et al. 2020

Journal of Near Infrared Spectroscopy

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“…La concentración de CO y NT en el suelo son propiedades que poseen una amplia base teórica, respecto al desarrollo de modelos de predicción (Barthès et al, 2010;Macías et al, 2015;Terra et al, 2015;Sisouane et al, 2017). Sin embargo, cuando las ecuaciones de calibración han sido desarrolladas, estas deben ser validadas, constantemente, con muestras independientes, pero dentro del intervalo considerado en el modelo inicial.…”

Section: Discussionunclassified

Carbono orgánico y nitrógeno total en suelos forestales de México mediante espectroscopia VIS-NIR

Esquivel-Valenzuela

Cueto-Wong

Cruz-Gaistardo

et al. 2018

RMCF

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La sostenibilidad del uso del recurso suelo y la correcta toma de decisiones sobre su manejo depende del conocimiento de las propiedades del mismo. Los métodos de análisis de suelo convencionales son laboriosos, con alto costo y generan gran cantidad de residuos químicos. Debido a la necesidad de tener métodos que permitan el rápido y confiable análisis de sus propiedades, el objetivo de este trabajo fue desarrollar un modelo de predicción para el contenido de carbono orgánico (CO) y nitrógeno total (NT), mediante espectroscopia en las regiones Visible e Infrarrojo Cercano (VIS-NIR). Se llevó a cabo el análisis convencional de carbono total (CT) y NT por combustión seca y del carbono inorgánico (CI) por el método del calcímetro de Bernard, de 599 muestras de suelos forestales de diversas regiones de México. Las ecuaciones de predicción fueron desarrolladas en un equipo FOSS NIR System 6500. Los modelos generados en el proceso de calibración presentaron valores de R2 = 0.93 y 0.88 para CO y NT, respectivamente. La relación entre el error estándar de predicción y la desviación estándar de las muestras (RPD, por sus siglas en inglés) para ambas propiedades fueron superiores a 2. En el proceso de validación, los valores de R2 fueron mayores a 0.9 y RPD a 2, en ambas propiedades. Los resultados muestran que la espectroscopia VIS-NIR es una técnica alternativa a los métodos de análisis convencionales de carbono orgánico y nitrógeno total del suelo.

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“…Calibration requires conventionally analyzed samples prior to predicting properties of unknown samples. In a partial least squares regression (plsr) approach, the authors of [27] used benchtop MIRS (bMIRS) to predict organic carbon and total nitrogen in compost and organic waste products, and the authors of [28] used bMIRS to predict humic acids as well as respiration activity to determine compost quality. Calibrated models provided convenient results for these parameters.…”

Section: Introductionmentioning

confidence: 99%

Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines

2021

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Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc’s) of >4 mm, 2–4 mm, 0.5–2 mm, and <0.5 mm. A universal SVM model was then developed for all OA and psc’s (n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts.

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Prediction of organic carbon and total nitrogen contents in organic wastes and their composts by Infrared spectroscopy and partial least square regression

Cited by 28 publications

References 31 publications

Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer

Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer

Carbono orgánico y nitrógeno total en suelos forestales de México mediante espectroscopia VIS-NIR

Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines

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