Near-Infrared Reflectance Spectroscopy

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Journal of Environmental Quality

Reviews and Analyses

Review of the Application of Near-Infrared Spectroscopy Technology to Determine the Chemical Composition of Animal Manure Longjian Chen, Li Xing, and Lujia Han*

B

ecause animal manure contains a variety of plant

Animal manure contains a variety of chemical constituents that are highly valuable to agriculture, including nitrogen, phosphorus, potassium, and metal micronutrients. Although appropriately applied manure has numerous positive attributes, the excessive application of manure may lead to pollution of the atmosphere, water, or soil. To reconcile precision agriculture and the potential negative environmental influences of animal manure, it is necessary to develop rapid and robust methods to evaluate the chemical composition of animal manure. This paper summarizes recent advances in nearinfrared reflectance spectroscopy (NIRS) in predicting moisture, dry matter, organic matter, nitrogen, phosphorus, carbon, and metal content in animal manure. The results indicate the high potential of NIRS as an efficient tool for monitoring the chemical composition of animal manure. Future prospects and needs related to increasing the feasibility of the industrial application of NIRS and improving NIRS prediction precision in determining the chemical composition of animal manure are discussed.

nutrients, it is widely applied as an organic fertilizer in the production of human food and livestock feed (Araji et al., 2001). Although appropriately applied manure has numerous positive attributes, the excessive application of manure may lead to pollution of groundwater and soil (Xiong et al., 2010). Therefore, a reliable method for quantifying the chemical composition of animal manure is required. Traditional wet chemical analysis is highly accurate but is time- and resource intensive. Due to the heterogeneous nature of animal manure and the volatility of certain constituents in animal manure, a rapid, low-cost, and accurate analysis at the time of application is needed (Reeves, 2007). There are two rapid evaluation methods: physicochemical modeling and near-infrared reflectance spectroscopy (NIRS). The former method relates animal manure chemical composition to its physicochemical properties (e.g., specific gravity, electrical conductivity, and pH) (Chen et al., 2008; Chen et al., 2009a; Martinez-Suller et al., 2008; Moral et al., 2005). The latter is based on light absorption at near-infrared wavelengths by samples to determine chemical composition (Huang et al., 2008; Reeves and Van Kessel, 2000). Compared with physicochemical modeling, the NIRS method has the advantage of rapid analysis without generating chemical waste (Burns and Ciurczak, 1992). The NIRS method requires little or no sample preparation and can evaluate several components simultaneously (Burns and Ciurczak, 1992). Therefore, NIRS is increasingly being applied to determine the chemical composition of animal manure (Fujiwara and Murakami, 2007; Fujiwara et al., 2009; Malley et al., 2005; Millmier et al., 2000; Sørensen et al., 2007). This study reviews the major advances of the NIRS method, as applied to determining the chemical composition of animal manure, including noncomposted and composted samples.

Near-Infrared Reflectance Spectroscopy Characterization of Animal Manure Near infrared reflectance (NIR) spectra are a consequence of vibrational energy transitions of molecular bonds in substances Copyright © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

L. Chen and L. Han, P.O. Box 232, College of Engineering, China Agricultural Univ. (East campus), 17 Qing-Hua-Dong-Lu, Hai-Dian District, Beijing 100083, P.R. China; L. Xing, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, P.R. China. Assigned to Associate Editor Keith Goyne. Abbreviations: AN, ammoniacal-N; ANN, artificial neural network; DA, diode array; DM, dry matter; MLR, multiple linear regression; NIR, near-infrared reflectance; NIRS, near-infrared reflectance spectroscopy; OM, organic matter; ON, organic N; PLS, partial least squares; PLS1, individual constituent partial least squares; PLS2, group constituent partial least squares; TC, total carbon; TDP, total dissolved phosphorus; TOC, total organic carbon.

J. Environ. Qual. 42:1015–1028 (2013) doi:10.2134/jeq2013.01.0014 Received 14 Jan. 2013. *Corresponding author ([email protected]; [email protected]).

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(Burns and Ciurczak, 2008). The overtone and combination bands are the main molecular vibration forms in the NIR region (McClure, 2003). Compared with the overtone and combination bands of other molecular bonds, those of hydrogen bonds (C-H, O-H, and N-H) in the NIR region have stronger absorptivity and thus dominate the NIR spectrum. Each substance has special characteristics in these hydrogen bonds and hence has a unique NIR spectrum. These special characteristics are the spectral basis for predicting chemical composition in animal manure using the NIRS method. Animal manure is organic material including water and other nutrients such as nitrogen (N), phosphorus (P), potassium (K), and carbon (C). Two types of chemical constituents in animal manure can be accurately predicted (Saeys et al., 2005a). The first type consists of chemical constituents containing chemical bonds contributing to the NIR spectral absorption. For example, moisture, organic matter (OM), dry matter (DM), C, and N may be directly assigned to main NIR absorption bands such as N-H, C-H, and O-H. Most previous studies have also presented satisfactory predictions of the NIRS technique as applied to these constituents in animal manure (Reeves and Van Kessel, 2000; Reeves et al., 2002; Xing et al., 2008). The second type consists of constituents that are not spectrally active but correlate well with the first type of chemical constituents. This property was observed in a study by Saeys et al. (2005a) in which phosphorus (P) and magnesium (Mg) without spectral absorption bonds could be quantitatively analyzed using the NIRS method due to their correlations with DM content in swine manure. For spectral analysis of animal manure, reflectance and transflectance modes are applied. In reflectance mode, a fraction of the incident light is backscattered at the sample surface and directed to the detection optics. In transflectance mode, the signal collected by the detector is comprised of two parts. Part of the incident light can be transmitted through the sample and back to the detector thereafter. The second part of the signal comes from the backscattered light at the sample surface. For liquid samples with few particles, the transmission of light would be dominant, and the use of a transflectance mode of analysis

may be appropriate. However, the transflectance measurement is difficult for a liquid sample with a high concentration of particles because the suspended particles reduce transmission of light through the sample. Different types of NIR spectrometers are also applied in the NIR analysis of animal manure. Based on the optical configuration, the NIR spectrometers for animal manure analysis can be categorized into filter type, grating dispersion type, and Fourier transform type. The pros and cons of the three types of NIR spectrometers have been summarized previously (Table 1) (Siesler et al., 2008). The choice of an NIR spectrometer is dependent on the intended purpose. For example, grating dispersion-type spectrometers with an acousto-optical element and multichannel Fourier transform-type spectrometers with an array detector may be suitable for field measurements, whereas Fourier transform-type spectrometers are considered to be optimal for laboratory use due to greater precision.

Near-Infrared Reflectance Research on the Chemical Composition of Animal Manure Model Evaluation

Certain statistics, such as the coefficient of determination (r2), the coefficient of correlation (r), the root mean square error (RMSE), the root mean squared deviation (RMSD), the standard error of prediction (SEP), and the ratio of the standard deviation (SD) of the reference data in the validation set to the SEP (RPD) (Williams, 1987), have often been applied to evaluate model performance in studies (Malley et al., 2004; Viscarra Rossel et al., 2006). Viscarra Rossel et al. (2006) used r2 and RMSE statistics to evaluate NIRS calibration models for various soil properties. Due to different ranges among the soil properties, the NIRS models of some soil properties had high r2 values, but they also had high RMSE values. To overcome these discrepancies, the RPD considering the range information was widely proposed (Chang et al., 2001; Dunn et al., 2002; Pirie et al., 2005). Chang et al. (2001) and Pirie et al. (2005) divided the NIRS models of soil properties into three categories, where calibrations with RPD > 2 are considered as excellent, those with RPD = 1.4 to 2.0

Table 1. Comparison among different types of near-infrared spectroscopy instruments for animal manure analysis.† Near-infrared spectrometer

Instrument type filter type

NIR-OT (Varilab AB) NIR-R (MM55 instrument, NDC Infrared Engineering Ltd.) FOSS-NIRSystems model 6500 spectrometer (FOSSgrating NIRSystems Inc.) dispersion type Diode array spectrometer (Mouazen et al., 2005) Combination of diode array and scanning monochromator spectrometer (Mouazen et al., 2005) Varian Cary 5G UV/visible/NIR (Varian Inc.) Zeiss Corona 45 spectrometer (Carl Zeiss) Fourier transform spectrometer (Mouazen et al., Fourier 2005) transform type Spectrum One NTS NIRS system (PerkinElmer) Antaris NIR spectrometer (Thermo Nicolet Corp.) InfraAlyzer 500 spectrometer (Bran+Luebbe) FT-NIR spectrometer (MPA, Bruker Optik GmbH)

Resolution I‡

Elimination of highOptical Scanning Dimension Cost order diffraction throughput speed S S I S S

G

I

I

I

I

G

S

S

S

S

I

I

† Data from Siesler et al. (2008). ‡ G, good; I, inferior; S, superior. 1016


as acceptable, and calibrations with RPD < 1.4 as unacceptable. Dunn et al. (2002) suggested that when using NIRS for the analysis of soils for site-specific agricultural, suitable limits for RPD may be: 2.0, excellent. Although no critical levels of RPD have been set for the NIRS analysis of animal manure, acceptable values depend on the intended applications of the predicted values. The NIRS predictions for the chemical composition of animal manure may be applied for precise fertilization. Therefore, we applied the higher evaluation criteria proposed by Malley et al. (2004), where prediction precision has been divided into five levels based on the r2 and RPD values. Values of r2 > 0.95 and RPD > 4 represent an excellent calibration. A successful calibration

is defined as 0.90 ≤ r2 ≤ 0.95 and 3 ≤ RPD ≤ 4. A moderately successful calibration is defined as 0.8 ≤ r2 < 0.90 and 2.25 ≤ RPD < 3. A moderately useful calibration is defined as 0.7 ≤ r2 < 0.8 and 1.75 ≤ RPD < 2.25. Therefore, a calibration model with r2 < 0.7 and RPD < 1.75 is considered less reliable.

Moisture and Dry Matter Water is one of the most important constituents of animal manure and affects its nutritive value (Vergnoux et al., 2009). Therefore, the monitoring of moisture content is important for animal manure used as fertilizer (Table 2). Yang et al. (2006) collected 108 samples of swine manure in China and scanned the samples from 833 to 2500 nm using an Antaris NIRS apparatus

Table 2. Summary of near-infrared spectroscopy applications for evaluation moisture and dry matter contents in animal manure.† Sample description Swine manure, fresh (n = 194) Swine manure, fresh (n = 195) Swine manure, fresh (n = 195) Swine manure, fresh (n = 195) Poultry manure, fresh (n = 207) Dairy manure, fresh (n = 107)

Composted swine manure, fresh (n = 135) Swine manure, fresh (n = 584) Swine manure, fresh (n = 169) Poultry manure, fresh (n = 136) Composted samples from cattle, swine, and poultry manures, fresh and dried (n = 120) Swine manure, fresh (n = 108) Egg-laying poultry manure, fresh (n = 91) Cattle and swine manures, fresh (n = 342)

Near-infrared spectrometer FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm diode array spectrometer, 300–1700 nm combination of diode array and scanning monochromator spectrometer, 350–2500 nm Fourier transform spectrometer, 1000–2500 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 1100–2498 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm

Multivariate calibration‡

Calibration

PLS PLS

Model statistics§ Reference Validation 2 reflectance mode: DM (rcv = 0.75, Saeys et al., 2005b RPD = 1.99); transflectance mode: DM (rcv2 = 0.86, RPD = 2.71) DM (rcv2 = 0.91, RPD = 3.38) Mouazen et al., 2005

PLS

DM (rcv2 = 0.79, RPD = 2.19)

Mouazen et al., 2005

PLS

DM (rcv2 = 0.76, RPD = 2.04)


PLS

DM (rc2 = 0.98, RMSD = 0.016)

PLS

Reeves, 2001 visible-NIR: moisture (rcv2 = 0.91, RMSD = 1.33); NIR: moisture (rcv2 = 0.90, RMSD = 1.41); visible-SWNIR: moisture (rcv2 = 0.68, RMSD = 2.48)

InfraAlyzer 500 (Bran+Luebbe), 1100–2500 nm

MLR/PLS

Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss), 426–1683 nm Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss), 426–1683 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm

PLS

DM (rv2 = 0.91, RPD = 3.22)

Saeys et al., 2005a

PLS

DM (rv2 = 0.58, RPD = 1.54)

Saeys et al., 2004

PLS

moisture (rv2 = 0.98, RPD = 7.48)

Huang et al., 2007

Antaris NIR spectrometer (Thermo Nicolet Corp.), 833–2500 nm Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm

PLS

moisture (RPD = 2.18)

Yang et al., 2006

PLS

moisture (rv2 = 0.86, RPD = 2.68)

Xing et al., 2008

FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 1200–2400 nm

PLS

DM (RPD = 6.20)

Sørensen et al., 2007

PLS

moisture (rc = 0.97)

Reeves and Van Kessel, 2000

Nam and Lee, 2000

DM (rc2 = 0.98)

Reeves et al., 2002

† The reflectance mode is used if not denoted. Validation model statistics are presented when available; otherwise, calibration statistics are shown. ‡ MLR, multiple linear regression; PLS, partial least squares. § DM, dry matter; rc, coefficient of correlation in calibration; rc2, coefficient of determination in calibration; rcv2, coefficient of determination in crossvalidation; RMSD, root mean squared deviation; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in validation;rv2, coefficient of determination in validation. www.agronomy.org • www.crops.org • www.soils.org

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equipped with InGaAs detectors (Thermo Nicolet Corp.). The NIRS calibration models of moisture were developed using the leave-one-out cross-validation procedure using the partial least squares (PLS) method. The results indicated that the NIR technique is a potentially useful method to determine moisture content with a RMSE of prediction (RMSEP) of 1.75% and a RPD of 2.18. Xing et al. (2008) investigated the use of the NIRS technique to predict the moisture content of egg-laying poultry manure. In total, 91 samples were collected and scanned from 1000 to 2500 nm using a Spectrum One NTS NIRS system (PerkinElmer). The NIR model of moisture content based on the modified PLS method provided moderately successful predictions, with a coefficient of determination in validation (rv2) of 0.86 and a RPD of 2.68. Reeves and Van Kessel (2000) further examined the influence of various NIR spectral ranges on predicting the moisture content of dairy manure. These researchers collected 107 dairy manure samples in the northeastern United States and scanned the samples from 400 to 2498 nm using a Model 6500 scanning monochromator equipped with a sample transport device (FOSS-NIRSystems). Three spectral ranges, including visible-NIR (400–2498 nm), NIR (1100–2498 nm), and visible-shortwave NIR (visibleSWNIR, 400–1098 nm), were used in the moisture content analysis. The results were similar for the visible-NIR (coefficient of determination in cross-validation [rcv2] = 0.91, RMSD = 1.33) and NIR (rcv2 = 0.90, RMSD = 1.41) wavelengths, whereas the third spectral range failed to generate satisfactory estimations (visible-SWNIR: rcv2 = 0.68, RMSD = 2.48). Calibration for moisture content of composted manure samples was also presented by Huang et al. (2007), where 120 composted samples from 22 provinces in China were scanned (1000–2500 nm) using a Spectrum One NTS NIRS system (PerkinElmer). Compared with the moisture content in noncomposted samples of animal manure, the moisture content in composted samples was generally lower. The mean moisture content of composted samples including cattle, swine, and poultry manures in Huang et al. (2007) was about 22%, whereas that of noncomposted samples in Reeves and Van Kessel (2000) was higher than 60% (Yang et al., 2006; Xing et al., 2008). The NIR model in Huang et al. (2007) gave a rv2 of 0.98 and a RPD of 7.48. The excellent predictability of moisture using the NIRS method is expected due to the existence of moisture O-H bands. Because the O-H vibration of water presents high absorption intensity in the NIR region, the moisture content is expected to be easily quantified. Due to the inverse correlation between DM and moisture, NIRS also provides satisfactory predictions on the DM content of animal manure (Table 2). For example, Reeves (2001) analyzed poultry manure samples using a FOSS-NIRSystems model 6500 scanning monochromator and used the NIRS model for excellent predictions of DM content (coefficient of determination in calibration [rc2], 0.98 and RMSD = 0.016). In a subsequent study by Reeves et al. (2002), the NIRS model based on slightly larger sample size of poultry manure (n = 136) produced excellent results (rc2 = 0.98, RMSD < 1%). The variation in DM content affected the spectra of animal manure (Saeys et al., 2005a) for a rather exceptional situation in which an increase in DM content (less moisture content) resulted in more pronounced water peaks. A possible explanation was that the solid particles act as reflectors. When more solid particles 1018

are present, more light is scattered and, consequently, reflected back to the detectors, resulting in a lower overall absorption. To decrease the negative influence of reflectance on predicting chemical composition, Saeys et al. (2005b) later proposed a relationship between DM and the NIR spectra obtained in transflectance mode. More positive prediction results of DM content were obtained using transflectance spectra than with reflectance spectra (transflectance model: rcv2 = 0.86, RPD = 2.71; reflectance model: rcv2 = 0.75, RPD = 1.99).

Organic Matter Organic matter is essential for maintaining soil quality by increasing the stability of aggregates that reduce erosion and decrease soil bulk density to improve soil drainage, aeration, root penetrability, and porosity (Termorshuizen et al., 2004). If current agricultural practices are maintained, an increase in soil OM is obtained only through application of external organic amendments. Due to the vast amount and the rich OM content of animal manure, animal manure is one of the most reliable soil OM supplements. Quantification analysis of OM in animal manure using the NIRS technique has been explored by previous researchers (Table 3). Due to the good correlation between DM and OM as the main component of DM in animal manure, it is expected that the NIRS method would provide satisfactory predictions on the animal manure OM content. Saeys et al. (2004) scanned 169 swine manure samples in the visible-NIR range (400–1710 nm) using a Zeiss Corona 1.7 VISNIR diode array instrument equipped with an OMK diffuse reflection measuring head (Carl Zeiss AG). Three types of NIRS calibration models, including individual constituent PLS (PLS1), group constituent PLS (PLS2), and individual constituent multiple linear regression (MLR), were constructed to predict OM content. The results indicated that there were no significant differences among these calibration models (PLS1: rcv2 = 0.57, RPD = 1.52; PLS2: rcv2 = 0.57, RPD = 1.53; MLR: rcv2 = 0.58, RPD = 1.55). Although the prediction results were less reliable based on model evaluation criteria (r2 < 0.7, RPD < 1.75), this study was useful in establishing the feasibility of using a mobile and cost-effective diode array instrument for the rapid on-site analysis of animal manure. Saeys et al. (2005a) extended the sample database by adding 420 samples and explored the potential for on-site (i.e., the NIR analyzer is located in the production area, and the samples are brought to the analyzer for analysis by area operators rather than sending the sample to the laboratory) and on-line (i.e., the NIR analyzer is placed in the production process, and the NIR measurement, sample handling, and sample preparation are controlled automatically) analysis of swine manure using the same diode array configuration. A better prediction for OM (rv2 = 0.90, RPD = 3.00) was obtained than when using the model developed and presented in Saeys et al. (2004). These researchers also noted that the Zeiss Corona 1.7 VISNIR only measured the spectral region of 400 to 1710 nm, whereas additional information could be observed in the NIR region beyond 1800 nm. In a follow-up study by Saeys et al. (2005b), the visible and full-NIR band range (400–2498 nm) was studied; nevertheless, the calibration model based on the fullNIR region did not present better predictions (rcv2 = 0.85, RPD = 2.55) than did the model based on the partial-NIR region. Physical and compositional variations of the calibration datasets Journal of Environmental Quality

Table 3. Summary of near-infrared spectroscopy applications for evaluation organic matter content in animal manure.† Sample description Swine manure, fresh (n = 194) Swine manure, fresh (n = 195) Swine manure, fresh (n = 195) Swine manure, fresh (n = 195) Swine manure, fresh (n = 584) Swine manure, fresh (n = 169)

Model statistics§ Multivariate calibration‡ Calibration Validation FOSS-NIRSystems model 6500 spectrometer PLS reflectance: OM (rcv2 = 0.73, (FOSS-NIRSystems Inc.), 400–2498 nm RPD = 1.92); transflectance: OM (rcv2 = 0.85, RPD = 2.55) Near-infrared spectrometer

diode array spectrometer, 300–1700 nm

PLS

Saeys et al., 2005b

OM (rcv2 = 0.89, RPD = 3.00)


OM (rcv2 = 0.77, RPD = 2.07)


OM (rcv2 = 0.73, RPD = 1.92)


OM (rv2 = 0.90, RPD = 3.00)

Saeys et al., 2005a

PLS1: OM (rcv2 = 0.57, RPD = 1.53); PLS2: OM (rcv2 = 0.57, RPD = 1.52); MLR: OM (rcv2 = 0.58, RPD = 1.55)

Saeys et al., 2004

PLS

OM (RPD = 2.28)

Yang et al., 2006

PLS

OM (RPD = 2.91)

Xing et al., 2008

combination of diode array and scanning PLS monochromator spectrometer, 350–2500 nm Fourier transform spectrometer, PLS 1000–2500 nm Zeiss Corona 45 VISNIR 1.7 fiber diode array PLS instrument (Carl Zeiss), 400–1710 nm Zeiss Corona 45 VISNIR 1.7 fiber diode array PLS1/PLS2/ instrument (Carl Zeiss), 400–1710 nm MLR

Swine manure, fresh Antaris NIR spectrometer (Thermo Nicolet (n = 108) Corp.), 833–2500 nm Egg-laying poultry Spectrum One NTS NIRS system (PerkinElmer), manure, fresh (n = 91) 1000–2500 nm

Reference

† The reflectance mode is used if not denoted. Validation model statistics are presented when available; otherwise, calibration statistics are shown. ‡ MLR, multiple linear regression; PLS, partial least squares; PLS1, individual constituent partial least squares; PLS2, group constituent partial least squares. § OM, organic matter; rcv2, coefficient of determination in cross-validation; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in validation; rv2, coefficient of determination in validation.

in the two studies may exist, as demonstrated by the higher OM standard error in Saeys et al. (2005b) (SD = 29.15 g L-1) than that in Saeys et al. (2005a) (SD = 23.97 g L-1). To clarify the effect of the NIR region, Mouazen et al. (2005) further analyzed the same calibration set of swine manure studied by Saeys et al. (2005a) using three NIR spectrometers, including a diode array (DA) analysis (300–1700 nm), a combination of DA and scanning monochromator analysis (350–2500 nm), and a Fourier transform (FT) (750–2500 nm). The DA spectrometer was clearly the most suitable instrument studied for evaluating OM content (DA: rcv2 = 0.889, RPD = 2.995; DA and scanning monochromator analysis: rcv2 = 0.766, RPD = 2.069; FT: rcv2 = 0.729, RPD = 1.917). In addition, several similar NIRS studies examining the prediction of OM content in egg-laying poultry manure (Xing et al., 2008) and swine manure compost (Nam and Lee, 2000) have been conducted. Theoretically, OM is primarily composed of C, H, N, and O atoms, which give characteristic signals in the NIR region. Therefore, good predictability of NIRS for OM content in animal manure is typically observed (Mouazen et al., 2005; Saeys et al., 2005a).

Nitrogen In animal manure, N is a valuable resource for crop growth, but its efficient management plays an important role in environmental impacts. Nitrogen in animal manure can be present as ammoniacal N (AN), organic N (ON), and nitrate-N (Sørensen and Rubæk, 2012; Sommer et al., 2006). Compared with AN and ON, nitrate N accounts for a small fraction of N and is generally not measured. The AN can be subdivided the ammonium (NH4+) and ammonia (NH3) forms. Ammonia-N can be readily lost to the atmosphere via volatilization and can adversely affect air quality (Chadwick et al., 2011). Ammonium-N is a charged molecule that can be adsorbed to soil, remain in soil www.agronomy.org • www.crops.org • www.soils.org

solution, or be absorbed by plant roots. Ammonium-N usually does not leach from soil, although it does leach from sandy land; nevertheless, it is typically converted in the soil to nitrate-N, which can be susceptible to leaching and lead to groundwater contamination (Vervoort et al., 1998). Organic N in animal manure with a high solids content is the most abundant form of N and is not available to plants until it has been decomposed to NH4+–N by mineralization processes (Yan et al., 2013). There is no reference method for measuring ON directly, and the ON concentration is calculated by the following equation: ON = TN - AN - nitrate-N. Due to various N forms and their different roles in animal manure as fertilizer, there are a number of studies on the quantification analyses of total nitrogen (TN), AN, and ON using the NIRS method (Table 4). Ye et al. (2005) collected various types of manure samples, including 111 egg-laying poultry solid manures, 95 broiler poultry manures, 72 beef cattle manures, 39 swine solid manures, 85 swine slurry samples, and 88 swine liquid lagoon samples. The NIR spectra data (1100–2500 nm) of these manure samples were obtained using a FOSS-NIRSystems model 6500 spectrometer and were correlated with the TN content using PLS. The results indicated that the rcv2 and RPD for TN were in the range of 0.80 to 0.92 and 3.32 to 4.87, respectively, for all manure samples. Although it is difficult to directly compare Ye et al.’s (2005) study with other published results due to the variable nature of manure, good predictions by the NIRS technique for evaluating the TN content of animal manure have been presented (Millmier et al., 2000; Reeves, 2001; Reeves and Van Kessel, 2000; Xing et al., 2008). For AN, most previous reports have indicated that the NIRS technique can provide good quantitative predictions (rc2 = 0.97, RPD = 5.76 [Malley et al., 2002]; rcv2 = 0.69, RPD = 1.79 [Saeys et al., 2004]; rc2 = 0.76, RPD = 2.06 [Saeys et al., 2005b]; rcv2 = 0.67–0.71, RPD = 1.75–1.91 [Mouazen et al., 2005]; rcv2 1019

Table 4. Summary of near infrared spectroscopy applications for evaluation nitrogen-related constituents in animal manure.† Sample description

Near-infrared spectrometer

Noncomposted and Field-portable Corona 45 VIScomposted samples NIR spectrometer (Carl Zeiss), from beef cattle 360–1690 nm manures, dried (n = 179) Egg-laying poultry FOSS-NIRSystems model 6500 manure, fresh (n = 111) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Broiler poultry manure, FOSS-NIRSystems model 6500 fresh (n = 95) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine solid manure, fresh FOSS-NIRSystems model 6500 (n = 39) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Beef cattle manure, fresh FOSS-NIRSystems model 6500 (n = 72) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine slurry manure, fresh FOSS-NIRSystems model 6500 (n = 85) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine liquid lagoon, fresh FOSS-NIRSystems model 6500 (n = 88) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine, turkey, and cattle NIR-OT (Varilab AB), 1935, 2100, manures, fresh (n = 45) 2180, and 2230 nm Turkey and cattle NIR-R (MM55 instrument, NDC manures, fresh (n = 30) Infrared Engineering Ltd.), 1935, 2050, 2100, 2180, and 2220 nm Swine manure, fresh FOSS-NIRSystems model 6500 (n = 194) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm

Swine manure, fresh (n = 88) Composted samples from cattle and swine manures, dried (n = 100) Noncomposted and composted samples from poultry manure, dried (n = 90) Swine manure, fresh (n = 195) Swine manure, fresh (n = 195) Swine manure, fresh (n = 195) Poultry manure, fresh (n = 207)

Swine liquid pit manure, fresh (n = 174)

PCA/PLS

Model statistics§ Calibration Validation TN (rc2 = 0.74, RPD = 2.14)

Reference Malley et al., 2005

PLS

TN (rcv2 = 0.92, RPD = 4.87); AN (rcv2 = 0.91, RPD = 6.23)

Ye et al., 2005

PLS


Ye et al., 2005

PLS


Ye et al., 2005

PLS


Ye et al., 2005

PLS


Ye et al., 2005

PLS


Ye et al., 2005

PLS PLS

TN (rc2 = 0.77); AN (rc2 = 0.82) TN (rc2 = 0.86); AN (rc2 = 0.96)

PLS

Kemsley et al., 2001 Kemsley et al., 2001 reflectance: TN (rcv2 = 0.89, Saeys et al., RPD = 3.00), 2005b 2 AN (rcv = 0.77, RPD = 2.10); transflectance: TN (rcv2 = 0.92, RPD = 3.46), AN (rcv2 = 0.76, RPD = 2.06)

Varian Cary 5G UV/visible/NIR (Varian Inc.), 250–2500 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2500 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm

MLR

TN (rv2 = 0.89, RPD = 2.43)

MLR

TN (rv = 0.95, RPD = 2.80)

MLR

TN (rv = 0.92, RPD = 2.60)

Fujiwara and Murakami, 2007

diode array spectrometer, 300– 1700 nm combination of diode array and scanning monochromator spectrometer, 350–2500 nm Fourier transform spectrometer, 1000–2500 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 1100–2498 nm

PLS

TN (rcv2 = 0.86, RPD = 2.66); AN (rcv2 = 0.67, RPD = 1.75) TN (rcv2 = 0.83, RPD = 2.43); AN (rcv2 = 0.68, RPD = 1.78)

Mouazen et al., 2005 Mouazen et al., 2005


Mouazen et al., 2005 Reeves, 2001

FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Swine lagoon effluent, FOSS-NIRSystems model 6500 fresh (n = 100) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Beef solid manure, fresh FOSS-NIRSystems model 6500 (n = 100) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Dairy manure, fresh FOSS-NIRSystems model 6500 (n = 107) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Composted swine manure, InfraAlyzer 500 (Bran+Luebbe), fresh (n = 135) 1100–2500 nm

1020


PLS PLS PLS

Dagnew et al., 2004 Fujiwara et al., 2009

PLS

AN (rc2 = 0.78, RMSD = 0.105); ON (rc2 = 0.89, RMSD = 0.325); TN (rc2 = 0.88, RMSD = 0.345) TN (rc = 0.90); AN (rc = 0.79)

PLS

TN (rc = 0.83); AN (rc = 0.79)

Millmier et al., 2000

PLS

TN (rc = 0.82); AN (rc = 0.98)


PLS MLR/PLS


AN (rcv2 = 0.83, RMSD = 0.034); TN (rcv2 = 0.90, RMSD = 0.051) TN (rc = 0.96); ON (rc = 0.76)

Reeves and Van Kessel, 2000 Nam and Lee, 2000


Table 4. Continued. Multivariate calibration‡

Model statistics§ Validation TN (rv2 = 0.86, RPD = 2.63); AN (rv2 = 0.76, RPD = 2.00)

Saeys et al., 2005a


Saeys et al., 2004

Reference

Sample description


Swine manure, fresh (n = 584)

Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss), 426–1683 nm Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss), 426–1683 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm

PLS

PLS

fresh: TN (rv2 = 0.97, RPD = 6.11); dried: TN (rv2 = 0.97, RPD = 6.95)

Huang et al., 2007

Antaris NIR spectrometer (Thermo Nicolet Corp.), 833–2500 nm Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm FT-NIR spectrometer (MPA, Bruker Optik GmbH), 830–2600 nm

PLS

TN (RPD = 1.50); AN (RPD = 2.42)

Yang et al., 2006

PLS

TN (rv2 = 0.88, RPD = 2.75); AN (rv2 = 0.88, RPD = 2.62) TN (rcv2 = 0.80, RPD = 2.22)

Xing et al., 2008 Galvez-Sola et al., 2010

Swine manure, fresh (n = 169) Poultry manure, fresh (n = 136) Swine manure, fresh (n = 64) Composted manure samples from cattle, swine, and poultry manures, fresh and dried (n = 120) Swine manure, fresh (n = 108)

Egg-laying poultry manure, fresh (n = 91) Composted samples from cattle, poultry, and sheep manures, fresh (n > 300) Cattle and swine manures, FOSS-NIRSystems model 6500 fresh (n = 342) spectrometer (FOSS-NIRSystems Inc.), 1200–2400 nm

Calibration

PLS PLS

AN (rc2 = 0.90)

Reeves et al., 2002

PLS

AN (rc2 = 0.97, RPD = 5.76)

Malley et al., 2002

PLS

PLS

TN (RPD = 4.30); AN (RPD = 3.80) Sørensen et al., 2007

† The reflectance mode is used if not denoted. Validation model statistics are presented when available; otherwise, calibration statistics are shown. ‡ MLR, multiple linear regression; PCA, principal component analysis; PLS, partial least squares. § AN, ammoniacal nitrogen; ON, organic nitrogen; rc, coefficient of correlation in calibration; rc2, coefficient of determination in calibration; rcv2, coefficient of determination in cross-validation; RMSD, root mean squared deviation; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in validation; rv2, coefficient of determination in validation; TN, total nitrogen.

= 0.88–0.92, RPD = 3.75–6.23 [Ye et al., 2005]; RPD = 2.42 [Yang et al., 2006]; RPD = 3.80 [Sørensen et al., 2007]; and rv2 = 0.88, RPD = 2.62 [Xing et al., 2008]). Kemsley et al. (2001) compared three spectroscopic techniques (Fourier transform mid-infrared spectroscopy [FT-MIR], NIR optothermal photoacoustic [NIR-OT], and NIR reflectance [NIR-R]) for the determination of AN in composted manure samples. Compared with NIR-OT (coefficient of correlation in calibration [rc] = 0.82) and FT-MIR (rc = 0.90), NIR-R had the best performance (rc = 0.96). According to previous studies, the ON content in noncomposted and composted samples of animal manure can be accurately predicted using the NIRS technique. Reeves (2001) proposed the NIRS model for determining ON content in poultry manure with rc2 = 0.89 and RMSD = 0.325. Based on 62 compost samples from cattle, swine, and poultry manure, an ON NIRS prediction model with rv > 0.80 was presented (Nakatani et al., 1996). Apparently, the NIRS predictions of N-related constituents were mainly contributed to by the N-H bonds, such as the N-H stretch first overtone at 1500 to 1530 nm, the N-H deformation second overtone at 2168 to 2180 nm, and the N-H stretching vibration at 2050 to 2060 nm. To clarify the presence of the N-H bond in AN, Sørensen et al. (2007) performed a fortification experiment using NH4HCO3. In their study, the AN NIRS calibration model based on cattle and swine slurries gave almost constant 2.2 g kg-1 predicted differences of AN content in 15 www.agronomy.org • www.crops.org • www.soils.org

samples before and after fortification when 2.2 g kg-1 NH4+–N was added.

Carbon Carbon in animal manure is an important energy source for microorganisms in the soil ecosystem and relates with plant nutrient cycles such as N and P. Nevertheless, the improper application of animal manure may elevate the emission of greenhouse gases, which may pose a threat due to the increased atmospheric concentration of greenhouse gases (Ginting et al., 2003). The studies for predicting C-related constituents are listed in Table 5. Reeves and Van Kessel (2000) used 107 fresh dairy manure samples to develop a NIRS prediction model for total C (TC) content with successful results (rcv2 = 0.90, RMSD = 0.602). In a subsequent study (Reeves and Van Kessel, 2002), the same dairy manure samples studied by Reeves and Van Kessel (2000) were dried and used to develop a NIRS calibration model. Although the prediction of the TC content was less successful than in the previous study, the NIRS method provided a good determination of the TC content (rc2 = 0.83, RMSD = 1.61). Sørensen et al. (2007) conducted a similar study by investigating the feasibility of the NIRS method for predicting the TC content of cattle and swine slurries with a total solids content from 300) Cattle and swine manures, fresh (n = 342) Composted samples from cattle, swine, and poultry manures, fresh and dried (n = 120)



field-portable Corona 45 VIS-NIR spectrometer (Carl Zeiss), 360–1690 nm

PCA/PLS

FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm InfraAlyzer 500 (Bran+Luebbe), 1100–2500 nm

PLS

Model statistics§ Calibration Validation TC (rc2 = 0.91, RPD = 3.30); TOC (rc2 = 0.91, RPD = 3.44) TC (rcv2 = 0.90, RMSD = 0.602)

Reference Malley et al., 2005


MLR/PLS

TC (rc = 0.95)

Nam and Lee, 2000

PLS

TC (rc2 = 0.83, RMSD = 1.61)


FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm FT-NIR spectrometer (MPA, Bruker Optik GmbH), 830–2600 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 1200–2400 nm Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm

PLS

TOC (rcv2 = 0.92, RPD = 3.55)

Galvez-Sola et al., 2010

PLS

C (RPD = 5.40)

Sørensen et al., 2007

PLS

Fresh: TOC (rv2 = 0.91, RPD = 3.17); dried: TOC (rv2 = 0.85, RPD = 2.56)

Huang et al., 2007

† The reflectance mode is used if not denoted. Validation model statistics are presented when available; otherwise, calibration statistics are shown. ‡ MLR, multiple linear regression; PCA, principal component analysis; PLS, partial least squares. § rc, coefficient of correlation in calibration; rc2, coefficient of determination in calibration; rcv2, coefficient of determination in cross-validation; RMSD, root mean squared deviation; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in validation;rv2, coefficient of determination in validation; TC, total carbon; TOC, total organic carbon.

by NIRS was achieved by Huang et al. (2007), who developed calibration models for total organic C (TOC) content in fresh and dried manure compost. Huang et al. (2007) observed higher efficiency in the TOC estimations of fresh manure samples (dried manure: rv2 = 0.85, RPD = 2.56; fresh manure: rv2 = 0.91, RPD = 3.17). Malley et al. (2005) developed NIRS calibration using principal component analysis and PLS regression for the TC (rc2 = 0.91, RPD = 3.30) and TOC contents (rc2 = 0.91, RPD = 3.44) in cattle manure compost using a field-portable NIR spectrometer. Galvez-Sola et al. (2010) collected more than 300 composted samples from several agro-industrial activities cocomposting animal manures or urban wastes and developed a successful calibration for TOC content (rcv2 = 0.92, RPD = 3.55) using PLS regression. Robust and superior performances of quantitative analysis for C and C-related constituents in animal manure can be attributed to the presence of C-H bonds in the NIR region.

Phosphorus Phosphorus in animal manure exists in inorganic and organic forms. Inorganic P can be available for plant uptake, whereas organic P must be decomposed into inorganic P through mineralization before plant uptake. The excessive P applied to soil can be leached deeper into the soil profile and the ground water. Phosphorus can also be lost from the soil in runoff water as sediment bound and dissolved P. The loading of surface and ground waters with P can result in water eutrophication (Del Campillo et al., 1999; Heckrath et al., 1995; Park, 2009). The studies for predicting phosphorus-related constituents are listed in Table 6. Phosphorus in animal manure comes from two major sources: undigested phytate and excess dietary phosphate (Malley et al., 1022

2002). Previous studies on the predictability of P content using the NIRS method have obtained inconsistent results. Several studies have presented less reliable predictions on P content in poultry manure (rc2 = 0.59, RMSD = 0.50 [Reeves, 2001]), swine manure (rv2 = 0.79, RPD = 1.93 [Dagnew et al., 2004]; rcv2 = 0.59, RPD = 1.57 [Saeys et al., 2004]; RPD = 1.37 [Yang et al., 2006]; rv2 = 0.75, RPD = 1.78 [Saeys et al., 2005a]), dairy manure (rcv2 = 0.34, RMSD = 0.09 [Reeves and Van Kessel, 2000]), and the noncomposted and composted samples of beef cattle manure (rc2 = 0.61, RPD = 1.59 [Malley et al., 2005]). The lack of a direct relationship between P and C-H-O-N bonds is regarded as the main reason for the inability to predict P content (Reeves, 2001). Nevertheless, other studies have achieved the prediction of P content in poultry manure (rv2 = 0.80, RPD = 2.01 [Xing et al., 2008]), swine manure (rcv2 = 0.81, RPD = 2.33 [Saeys et al., 2005b]); rcv2 = 0.85, RPD = 2.62 [Mouazen et al., 2005]; rc2 = 0.99, RPD = 8.46 for total dissolved P and rc2 = 0.94, RPD = 4.00 for suspended P [Malley et al., 2002], cattle and swine combined manure (RPD = 3.60 [Sørensen et al., 2007]), and animal manure compost (rv2 = 0.77, RPD = 2.12 [Huang et al., 2007]). Several researchers attribute these successful predictions to two reasons (Malley et al., 2002). The first reason is that P bonds are spectrally active (Clark et al., 1987; Saiga et al., 1989). The second reason is related to the correlation between P and DM. Considerable fractions of P in animal manure exist in solid forms, such as insoluble calcium phosphates (Saeys et al., 2005a), and good linear relationships between P and DM in animal manure have been observed (Marino et al., 2008; MartinezSuller et al., 2008; Zhu et al., 2004). The DM content in animal manure commonly can be accurately predicted by the NIRS method due to the presence of O-H and C-H bonds. Therefore, Journal of Environmental Quality

Table 6. Summary of near-infrared spectroscopy applications for evaluation phosphorus-related constituents in animal manure.† Manure sample description


Composted samples from FT-NIR spectrometer (MPA, Bruker animal manures, fresh (n > Optik GmbH), 830–2600 nm 300) Cattle and swine manures, fresh FOSS-NIRSystems model 6500 (n = 342) spectrometer (FOSS-NIRSystems Inc.), 1200–2400 nm Noncomposted and composted Field-portable Corona 45 VIS-NIR samples from cattle manures, spectrometer (Carl Zeiss), 360–1690 dried (n = 179) nm Egg-laying poultry manure, FOSS-NIRSystems model 6500 fresh (n = 111) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Broiler poultry manure, fresh FOSS-NIRSystems model 6500 (n = 95) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine solid manure, fresh FOSS-NIRSystems model 6500 (n = 39) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Beef cattle manure, fresh FOSS-NIRSystems model 6500 (n = 72) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine slurry manure, fresh FOSS-NIRSystems model 6500 (n = 85) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine liquid lagoon, fresh FOSS-NIRSystems model 6500 (n = 88) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm Swine manure, fresh FOSS-NIRSystems model 6500 (n = 194) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Swine manure, fresh Varian Cary 5G UV/visible/NIR (Varian (n = 88) Inc.), 250–2500 nm Swine manure, fresh diode array spectrometer, 300–1700 (n = 195) nm Swine manure, fresh combination of diode array (n = 195) and scanning monochromator spectrometer, 350–2500 nm Swine manure, fresh Fourier transform spectrometer, (n = 195) 1000–2500 nm Poultry manure, fresh FOSS-NIRSystems model 6500 (n = 207) spectrometer (FOSS-NIRSystems Inc.), 1100–2498 nm Swine liquid pit manure, fresh FOSS-NIRSystems model 6500 (n = 174) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Swine lagoon effluent, fresh FOSS-NIRSystems model 6500 (n = 100) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Beef solid manure, fresh FOSS-NIRSystems model 6500 (n = 100) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Dairy manure, fresh (n = 107) FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Composted samples from swine InfraAlyzer 500 (Bran+Luebbe), manure, fresh (n = 135) 1100–2500 nm Swine manure, fresh (n = 584) Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss), 400–1710 nm Swine manure, fresh (n = 169) Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss), 400–1710 nm Swine manure, fresh (n = 64) FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm

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Multivariate calibration‡ PLS

Model statistics§ Calibration Validation P (rcv2 = 0.85, RPD = 2.56)

PLS PCA/PLS

P (RPD = 3.60) P (rc2 = 0.61, RPD = 1.59)

Reference Galvez-Sola et al., 2010 Sørensen et al., 2007 Malley et al., 2005

PLS

P (rcv2 = 0.76, RPD = 2.42)

Ye et al., 2005

PLS

P (rcv2 = 0.50, RPD = 1.70)

Ye et al., 2005

PLS

P (rcv2 = 0.74, RPD = 2.21)

Ye et al., 2005

PLS

P (rcv2 = 0.91, RPD = 4.09)

Ye et al., 2005

PLS

P (rcv2 = 0.90, RPD = 4.72)

Ye et al., 2005

PLS

P (rcv2 = 0.91, RPD = 4.85)

Ye et al., 2005

PLS

reflectance: P (rcv2 = 0.67, Saeys et al., 2005b RPD = 1.73); transflectance: P (rcv2 = 0.81, RPD = 2.33) P (rv2 = 0.79, RPD = 1.93) Dagnew et al., 2004

MLR PLS

P (rcv2 = 0.85, RPD = 2.62)


PLS

P (rcv2 = 0.71, RPD = 1.85)


PLS

P (rcv2 = 0.67, RPD = 1.74)


PLS

P (rc2 = 0.59, RMSD = 0.50)

Reeves, 2001

PLS

P (rc = 0.69)


PLS

P (rc = 0.78)


PLS

P (rc = 0.76)


PLS MLR/PLS

P (rcv2 = 0.34, RMSD = 0.09) P (rc = 0.88)


PLS

P (rv2 = 0.75, RPD = 1.78)

Saeys et al., 2005a

PLS

P (rcv2 = 0.59, RPD = 1.57)

Saeys et al., 2004

PLS

TDP (rc2 = 0.99, RPD = 8.46); SUP (rc2 = 0.94, RPD = 4.00)

Malley et al., 2002

1023

Table 6. Continued. Manure sample description Composted samples from cattle, swine, and poultry manures, fresh and dried (n = 120) Swine manure, fresh (n = 108) Egg-laying poultry manure, fresh (n = 91)



Model statistics§ Calibration Validation fresh: P (rv2 = 0.77, RPD = 2.12); dried: P (rv2 = 0.67, RPD = 1.52)

Reference

Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm

PLS

Antaris NIR spectrometer (Thermo Nicolet Corp.), 833–2500 nm Spectrum One NTS NIRS system (PerkinElmer), 1000–2500 nm

PLS

P (RPD = 1.37)

Yang et al., 2006

PLS

P (rv2 = 0.80, RPD = 2.01)

Xing et al., 2008

Huang et al., 2007

† The reflectance mode is used if not denoted. Validation model statistics are presented when available; otherwise, calibration statistics are shown. ‡ MLR, multiple linear regression; PCA, principal component analysis; PLS; partial least squares. § rc, coefficient of correlation in calibration; rc2, coefficient of determination in calibration; rcv2, coefficient of determination in cross-validation; RMSD, root mean squared deviation; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in validation;rv2, coefficient of determination in validation; SUP, suspended phosphorus; TDP, total dissolved phosphorus.

the NIRS method may provide indirect or surrogate prediction for P content in animal manure.

Metals Animal manure contains a number of metal constituents, such as potassium (K), calcium (Ca), zinc (Zn), copper (Cu), iron (Fe), magnesium (Mg), sodium (Na), and manganese (Mn). A number of elements are essential for plant growth. For example, the application of K in animal manure could replace all or part of the use of K fertilizer, with cost savings to agricultural producers. Calcium and Mg are secondary nutrients, and Fe and Zn are micronutrients (Huang et al., 2008; Thompson et al., 2002). These metals may be accumulated due to repeated application of animal manure, and this becomes a very important problem from an agricultural and environmental standpoint, especially for heavy metals. Hence, it is essential to evaluate the concentration of these metals in animal manure (Table 7). Theoretically, there are no absorption bands for metal species in the NIR region. This property has also been demonstrated by less reliable predictions of animal manure metal constituents in most studies, such as Reeves (2001) (rcv2 = 0.40–0.80 for Zn, Mg, Cu, Mn, K, and Ca), Saeys et al. (2004) (rcv2 = 0.36–0.69, RPD = 1.24–1.79 for Na, Ca, Mg, and K), Saeys et al. (2005a) (rv2 = 0.59–0.80, RPD = 1.30–1.84 for Ca, K, and Mg), Sørensen et al. (2007) (RPD = 1.20–2.20 for Na, Zn, K, Mg, and Cu), and Xing et al. (2008) (rv2 = 0.48–0.62, RPD = 1.38–1.87 for Cu, Fe, K, Mg, and Na). Only a limited number of studies have obtained better results, especially for animal manure compost. In Huang et al.’s (2008) study, the Fe (rv2 = 0.84, RPD = 2.27) and Ca (rv2 = 0.78, RPD = 2.12) contents in dried manure compost samples were moderately well predicted. In Galvez-Sola et al.’s (2010) study, good predictions were presented for Fe (rcv2 = 0.87, RPD = 2.73), Cu (rcv2 = 0.81, RPD = 2.29), Mn (rcv2 = 0.86, RPD = 2.71), and Zn (rcv2 = 0.88, RPD = 2.49) in different agroindustrial materials cocomposted with animal manures or urban wastes. There are two reasons for the results found in composted samples. First, the metal constituents in compost samples have higher concentrations due to the loss of mass during the composting process compared with those in noncomposted manure samples (Gomez, 1998). For example, the mean values of the metal constituents of composted samples in Huang et al. (2008) were 12.46 g kg-1 for K, 46.21 g kg-1 for Ca, 7.63 g kg-1 for Mg, and 7.77 g kg-1 for Fe on a fresh weight basis. Conversely, 1024

some metal constituents closely relate with organic functional groups or the organic matrix of animal manure (Huang et al., 2008). Saeys et al. (2005a) reported high correlations between Ca, Mg, and DM in swine manure. Almost all mineral Ca and Mg in animal manure are in the form of struvite (Mg2+ + NH4+ + PO4-3 + 6H2O « NH4MgPO4·6H2O) and apatite (5Ca2+ + 3PO4-3 + OH- « Ca5(PO4)3OH) (Bril and Salomons, 1990; Hunger et al., 2008). Therefore, Saeys et al. (2005a) proposed that the correlation between Ca, Mg, and DM may be partly attributable to the precipitation of struvite and apatite in animal manure. However, the opinion requires further validation.

Summary and Future Perspectives A number of applications of NIRS for the analyses of chemical composition in animal manure have been summarized and evaluated, including for moisture, DM, OM, N, P, C, and metal contents. Most of the chemical constituents of animal manure can be predicted accurately, whereas P and metals require further research to improve their related prediction precision. Although these studies demonstrate the potential of NIRS for the analysis of the chemical composition of animal manure, the applications of NIRS in monitoring animal manure warrant further research. A major challenge is that most of the previous studies were performed in the laboratory with stationary NIR instruments that provide discontinuous spectral information for animal manure. The production and use of animal manure, including composted products, are continuous and real-time procedures. There is a need to develop on-line NIRS systems for the analysis of animal manure. Therefore, more studies should be conducted to close the gap between the stationary-state NIR technique at the laboratory level and the on-line dynamic-state NIR technique at the industrial level. The factors complicating the development of on-line NIR techniques include sample inhomogeneity, sample instability, and complex environmental parameters. Animal manure is nonhomogeneous with solid and liquid fractions. Animal manure samples can be mixed thoroughly to ensure homogeneity and stability in laboratory NIR analyses, whereas the homogeneity and stable properties of animal manure are difficult to achieve in on-line NIR analysis due to rapid and dynamic-state processes. In addition, complex environmental properties, such as the varied temperature of animal manure samples, have adverse effects on on-line NIR spectral stability. Journal of Environmental Quality

Table 7. Summary of near infrared spectroscopy applications for evaluation metal contents in animal manure.† Manure sample description Composted samples from cattle, poultry, and sheep manures, fresh (n > 300)

Near-infrared spectrometer FT-NIR spectrometer (MPA, Bruker Optik GmbH), 830–2600 nm

Cattle and swine manures, FOSS-NIRSystems model 6500 fresh (n = 342) spectrometer (FOSS-NIRSystems Inc.), 1200–2400 nm Noncomposted and Field-portable Corona 45 VIScomposted samples from NIR spectrometer (Carl Zeiss), beef cattle manures, dried 360–1690 nm (n = 179) Egg-laying poultry manure, FOSS-NIRSystems model 6500 fresh (n = 111) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm

Multivariate calibration‡ PLS

PLS PCA/PLS

Model statistics§ Reference Validation K (rcv2 = 0.71, RPD = 1.86); Fe (rcv2 = Galvez-Sola 0.87, RPD = 2.73); Cu (rcv2 = 0.81, RPDet al., 2010 = 2.29); Mn (rcv2 = 0.86, RPD = 2.71); Zn (rcv2 = 0.88, RPD = 2.49) Ca (RPD = 3.50); Mg (RPD = 2.20); Sørensen et K (RPD = 1.60); Cu (RPD = 2.20); Na al., 2007 (RPD = 1.20); Zn (RPD = 1.50) K (rc2 = 0.83, RPD = 2.43) Malley et al., 2005 Calibration

PLS

Ca (rcv2 = 0.75, RPD = 2.25); K (rcv2 = 0.71, RPD = 2.23); Na (rcv2 = 0.71, RPD = 2.16); Zn (rcv2 = 0.81, RPD = 2.81); Cu (rcv2 = 0.71, RPD = 2.53)

Ye et al., 2005

Broiler poultry manure, fresh (n = 95)


PLS


Ye et al., 2005

Swine solid manure, fresh (n = 39)


PLS


Ye et al., 2005

Beef cattle manure, fresh (n = 72)


PLS


Ye et al., 2005

Swine slurry manure, fresh FOSS-NIRSystems model 6500 (n = 85) spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm

PLS


Ye et al., 2005

Swine liquid lagoon, fresh (n = 88)

PLS

Ca (rcv2 = 0.90, RPD = 4.22); K (rcv2 = Ye et al., 0.73, RPD = 2.19); Na (rcv2 = 0.80, RPD 2005 = 2.63); Zn (rcv2 = 0.79, RPD = 2.61) reflectance: Ca (rcv2 = 0.58, RPD = Saeys et al., 1.54), Mg (rcv2 = 0.74, RPD = 1.96), 2005b K (rcv2 = 0.84, RPD = 2.60), Na (rcv2 = 0.49, RPD = 1.39); transflectance: Ca (rcv2 = 0.70, RPD = 1.82), Mg (rcv2 = 0.83, RPD = 2.42), K (rcv2 = 0.83, RPD = 2.50), Na (rcv2 = 0.57, RPD = 1.53)


FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 1100–2500 nm FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm

PLS

Composted samples from Spectrum One NTS NIRS system cattle, swine and poultry (PerkinElmer), 1000–2500 nm manures, fresh and dried (n = 120)

PLS


Varian Cary 5G UV/visible/NIR (Varian Inc.), 250–2500 nm

MLR


diode array spectrometer, 300– 1700 nm

PLS


combination of diode array and scanning monochromator spectrometer, 350–2500 nm

PLS


Fourier transform spectrometer, 1000–2500 nm

PLS

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fresh: K (rv2 = 0.69, RPD = 1.83), Ca Huang et al., (rv2 = 0.46, RPD = 1.33), Mg (rv2 = 2008 0.54, RPD = 1.42), Fe (rv2 = 0.86, RPD = 2.55), Zn (rv2 = 0.65, RPD = 1.71); dried: K (rv2 = 0.68, RPD = 1.79), Ca (rv2 = 0.78, RPD = 2.12), Mg (rv2 = 0.72, RPD = 1.81), Fe (rv2 = 0.84, RPD = 2.27), Zn (rv2 = 0.57, RPD = 1.52) K (rv2 = 0.68, RPD = 1.14)

Dagnew et al., 2004

K (rcv2 = 0.71, RPD = 1.84); Ca (rcv2 = Mouazen et 0.76, RPD = 2.05); Mg (rcv2 = 0.87, al., 2005 RPD = 2.74); Na (rcv2 = 0.52, RPD = 1.44) K (rcv2 = 0.73, RPD = 1.91); Ca (rcv2 = Mouazen et 0.59, RPD = 1.57); Mg (rcv2 = 0.78, al., 2005 RPD = 2.15); Na (rcv2 = 0.46, RPD = 1.36) K (rcv2 = 0.83, RPD = 2.38); Ca (rcv2 = Mouazen et 0.58, RPD = 1.54); Mg (rcv2 = 0.76, al., 2005 RPD = 2.02); Na (rcv2 = 0.52, RPD = 1.41)

1025

Table 7. Continued. Manure sample description Poultry manure, fresh (n = 207)



Model statistics§ Calibration Validation K (rc2 = 0.63, RMSD = 0.323); Ca (rc2 = 0.80, RMSD = 0.827); Mg (rc2 = 0.46, RMSD = 0.136); Mn (rc2 = 0.54, RMSD = 64.8); Zn (rc2 = 0.40, RMSD = 95.5); Cu (rc2 = 0.52, RMSD = 119)

Reference


PLS

FOSS-NIRSystems model 6500 spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Swine lagoon effluent, fresh FOSS-NIRSystems model 6500 (n = 100) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Beef solid manure, fresh FOSS-NIRSystems model 6500 (n = 100) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Dairy manure, fresh FOSS-NIRSystems model 6500 (n = 107) spectrometer (FOSS-NIRSystems Inc.), 400–2498 nm Composted samples form InfraAlyzer 500 (Bran+Luebbe), swine manure, fresh 1100–2500 nm (n = 135) Swine manure, fresh Zeiss Corona 45 VISNIR 1.7 fiber (n = 584) diode array instrument (Carl Zeiss), 426–1683 nm

PLS

K (rc = 0.89)


PLS

K (rc = 0.84)


PLS

K (rc = 0.91)


Swine liquid pit manure, fresh (n = 174)

PLS

Reeves, 2001

K (rcv2 = 0.57, RMSD = 0.086)


PLS

Ca (rv2 = 0.59, RPD = 1.30); Mg (rv2 = 0.80, RPD = 1.84); K (rv2 = 0.69, RPD = 1.68)

Saeys et al., 2005a

Ca (rcv2 = 0.54, RPD = 1.42); Mg (rcv2 = 0.69, RPD = 1.59); K (rcv2 = 0.69, RPD = 1.79); Na (rcv2 = 0.36, RPD = 1.24)

Saeys et al., 2004

MLR/PLS

Cu (rc = 0.96); K (rc = 0.95); Na (rc = 0.79)


Zeiss Corona 45 VISNIR 1.7 fiber diode array instrument (Carl Zeiss, Germany), 426–1683 nm

PLS



PLS


Antaris NIR spectrometer (Thermo Nicolet Corp.), 833– 2500 nm

PLS

K (RPD = 2.52); Mg (RPD = 1.24); Fe Yang et al., (RPD = 1.41); Cu (RPD = 1.84); 2006 Zn (RPD = 1.79)

PLS

K (rv2 = 0.58, RPD = 1.51); Cu (rv2 = Xing et al., 0.48, RPD = 1.38); Fe (rv2 = 0.55, RPD 2008 = 1.47); Mg (rv2 = 0.60, RPD = 1.51); 2 Na (rv = 0.62, RPD = 1.87)

Egg-laying poultry manure, Spectrum One NTS NIRS system fresh (n = 91) (PerkinElmer), 1000–2500 nm

Na (rc2 = 0.95, RPD = 4.59); K (rc2 = 0.87, RPD = 2.78); Ca (rc2 = 0.80, RPD = 2.25); Mg (rc2 = 0.98, RPD = 5.15)

Malley et al., 2002

† The reflectance mode is used if not denoted. Validation model statistics are presented when available; otherwise, calibration statistics are shown. ‡ MLR, multiple linear regression; PCA, principal component analysis; PLS, partial least squares. § rc, coefficient of correlation in calibration; rc2, coefficient of determination in calibration; rcv2, coefficient of determination in cross-validation; RMSD, root mean squared deviation; RPD, ratio of the standard error of prediction to the standard deviation of the reference data in validation;rv2, coefficient of determination in validation.

Other challenges exist for the on-farm use of NIRS techniques (Reeves, 2007). Near-infrared reflectance spectroscopy instruments remain expensive for the average farmer to purchase and use. Moreover, the NIRS method requires calibrations relating the sample composition to the spectral data. These calibrations require periodic maintenance due to raw material variations. These calibrations are expensive and technically difficult, which impedes on-farm use. Therefore, studies on the use of cheap NIR instruments and low-cost calibration maintenance will be important for increasing on-farm use of NIRS techniques. Another area for future research is the application of advanced chemometrics methods to improve the prediction precision of the chemical composition of animal manure. The NIR spectral information used in an analysis is encoded as an electrical signal 1026

from the spectrometer. In addition to desirable information, the signal usually contains an undesirable component, termed “noise,” that can interfere with the accurate extraction and interpretation of the analytical data. The noise contained in the spectral information is one of key factors significantly influencing the prediction of NIRS analysis. Chemometrics, which involves spectra preprocessing and multivariate calibration methods, is an efficient tool to eliminate or reduce spectra noise and improve NIRS prediction (Azzouz et al., 2003; Vidal et al., 2010). In most previous studies, the most common preprocessing methods, such as standard normal variate, multiplicative scatter correction, derivative, and smoothing, were usually used. Nevertheless, several advanced preprocessing methods may have a greater ability to address the noise effects.


Recently, our laboratory investigated the influences of common preprocessing methods, including standard normal variate, multiplicative scatter correction, smoothing, derivatives, and one advanced method, known as direct orthogonal signal correction, on the predictions of nutrient contents in poultry manure (Chen et al., 2010). The results indicated that the direct orthogonal signal correction method not only gave the best predictions but also produced the simplest PLS calibration models by removing spectra information that was orthogonal to nutrient contents and concentrating the information into fewer principal components. Similar to the preprocessing method, the multivariate calibration plays an important role in improving NIRS prediction. Previous studies have mainly used multivariate statistical methods, such as principal component analysis and PLS regressions, to develop the calibration model between the chemical composition and animal manure NIR spectral information. Due to the heterogeneous and complex properties of animal manure, its properties of scattered light or intrinsic nonlinearity in the absorption bands can result in substantial nonlinearity and complexity of the response. Such a result was observed by Saeys et al. (2005a), where solid particles acting as reflectors resulted in a nonlinear relationship between measured absorbance values and constituent concentrations in swine manure. Our laboratory evaluated the use of nonlinear regression methods (e.g., artificial neural networks [ANN]) for developing NIRS calibration models to predict nutrient contents in poultry manure (Chen et al., 2009b). It was observed that the ANN models for four types of nutrient contents consistently provided better predictions than did the PLS models. The RPD values of 2.62 (AN), 1.51 (K), 2.75 (TN), and 2.01 (P) of the PLS models were improved to 3.02 (AN), 1.74 (K), 3.41 (TN), and 2.71 (P) with the corresponding ANN models. These results indicate that the application of advanced chemometrics methods, including data preprocessing and multivariate calibration, have the potential to further improve NIRS predictability for determining the chemical composition of animal manure.

Acknowledgments This study is supported by the Specialized Research Fund for the Doctoral Program of Higher Education (Project No. 20100008120030), the Scientific Research Foundation for the Returned Overseas Chinese Scholars State Education Ministry (Project No. 2010-1561), the Program for New Century Excellent Talents in University (Project No. NCET-11-0477), the Program for Changjiang Scholars and Innovative Research Team in University (Project No. IRT1293), the Special Fund for Agro-Scientific Research in the Public Interest (Project No. 201003063), and the Chinese Universities Scientific Fund (Project No. 2013RC018).

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