# The Determination of Total N, Total P, Cu and Zn in Chicken Manure Using Near Infrared Reflectance Spectroscopy

Yiwei Dong, Yongxing Chen, Dazhou Zhu, Yuzhong Li, Chunying Xu, Wei

Bai, Yanan Wang, Qiaozhen Li

► To cite this version:

Yiwei Dong, Yongxing Chen, Dazhou Zhu, Yuzhong Li, Chunying Xu, et al.. The Determination of Total N, Total P, Cu and Zn in Chicken Manure Using Near Infrared Reflectance Spectroscopy. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.92-98, 10.1007/978-3-642-18354-6_13 . hal-01563466

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# The determination of total N, total P, Cu and Zn in chicken manure using near infrared reflectance spectroscopy

Yiwei Dong¹,³, Yongxing Chen², Dazhou Zhu³, Chunying Xu², Wei Bai², Yanan Wang¹,
Qiaozhen Li¹, Yuzhong Li¹,*

1. Institute of Environment and Sustainable Development in Agriculture, the Chinese Academy of Agricultural Sciences, Beijing, 100081, P. R. China

2. Key Laboratory of Agro-Environment & Climate Change, Ministry of Agriculture, Beijing, 100081, P. R. China

3. National Research Center of Intelligent Equipment for Agriculture, Beijing, 100081, P. R. China

E-mail: liyz@ieda.org.cn

## Abstract

In this study, the 74 chicken manure samples from a chicken farm were used to take the diffuse reflection spectra from 12500 to 4000 cm⁻¹ by FT-NIR spectrometer. The total N, total P, Cu and Zn of chicken manure samples were predicted by NIR spectra. For the samples of each component, an ascending order was arranged and they were divided into calibration set and prediction set according to their content. Partial least square regression (PLSR) method was applied to construct the calibration model. Results showed that the correlation coefficients of the calibration model for total N was 0.69, the root mean square error of calibration (RMSEC) was 0.66, the root mean square error of prediction (RMSEP) was 0.80. For the models of total P, Cu and Zn, the results were r=0.86, RMSEC=0.29, RMSEP=0.34; r=0.95, RMSEC=3.46, RMSEP=5.71; r=0.94, RMSEC=14.13, RMSEP=25.21, respectively. The results indicated that the NIR spectroscopy was useful to non-destructively determine the content of total N, total P, Cu and Zn of chicken manure. As a complementary detecting method to the conventional analysis, NIR spectroscopy could significantly improve the detecting efficiency.

Key words: near infrared spectroscopy, chicken manure, total N, total P, Cu, Zn

## 1. Introduction

In the last decades, intensive livestock operations produced considerable amounts of animal manure that

may cause serious environmental problems [1]. China is one of the largest producers of animal manure in the world, with an annual output of more than 3 billion tonnes[2]. Composting is an appealing solution for sustainable management of animal manure especially the chicken manure[3]. Use of composting results in a stabilized, mature, deodorized and hygienic product, free of pathogens and rich in humic substances, which is environmentally friendly and marketable as an organic amendment or fertilizer[4,5].

The parameters established by the legislation for marketable composts are electrical conductivity (EC), total organic matter (TOM), total organic carbon (TOC), total nitrogen(TN) and C/N ratio, macronutrient contents (N, P, K) and potentially pollutant element concentrations (Fe, Cu, Mn and Zn)[6]. The determination of these important parameters of composts required numerous reagents, skilled labors and expensive analytical equipments. Consequently, a more convenient and reliable method is necessary.

Near-infrared spectroscopy (NIR) (800-2500 nm), which reflects the overtones and combinations of fundamental vibrations of C–H, O–H and N–H bonds, has been widely used for quantitative and qualitative analysis for organic components in petrochemical, agricultural and pharmaceutical field[7]. Previous study have also indicated that one of the mechanism for measuring heavy metals by NIR was their inter correlation with organic compounds[8]. In recent years, NIR was also applied as an inexpensive tool for rapid analysis of important component in animal manures [9,10,11,12].
The purpose of this study was to explore the
feasibility of analyzing organic and inorganic
components of chicken manure using NIR
simultaneously.

**2. Materials and methods**

**2.1 Sample Preparation and chemical analysis**

In this study, the chicken manure samples were
collected from a chicken farm from Heishan county,
Liaoning Province. The samples were dried at 65 °C in
a forced-air drier to a constant weight and ground to
pass a 1 mm screen for latter analysis. In total, 74
samples were prepared.

**2.2 NIR Spectra Collection**

NIR spectra were recorded by a Fourier transformed
near-infrared spectrometer (IFS 28/N, BRUKER,
Germany) with integral sphere accessory. The ground
chicken manure was filled into a cylindrical quartz cup
with 20 mm depth to insure that light did not permeate
the sample. The cylindrical cup was rotated slowly to
assure fine sample distribution. And the reflectance
spectra were recorded within the band between 12500
and 4000 cm⁻¹(800-2500 nm), at 16 cm⁻¹ interval. For
each sample, the spectra were scanned 64 times and the
average spectrum was obtained.

**2.3 Measurement of Total Nitrogen, Total Phosphorus, Cu and Zn**

The chemical analyses were performed by the
Analysis and Test Center, Institute of Environment and
Sustainable Development in Agriculture, Chinese
Academy of Agricultural Science. The total nitrogen
(TN) was determined by dry combustion at 975 °C
using a PE-2400 C-N Elemental Analyzer
(PerkinElmer Corp., USA). The total Phosphorus (TP)
was determined according to the method described in
NY525-2002, which is the standard of Ministry of
Agriculture. The content of Cu and Zn were measured
using an acid digestion method with inductively
coupled plasma-atomic emission spectroscopy (ICP-
AES, PerkinElmer 3300DV) [13].

**2.4 Data Analysis**

The spectra were not being pre-processed since the
quality of original spectra was relatively high. Principle
component analysis (PCA) was used to explorer the
spectra distribution in the latent space. The calibration
model between reference values and spectra were
constructed by partial least square regression (PLSR).
The outliers for each model were eliminated according
to the plot of COOK value vs. leverage value [14]. For
each model, the reference value was sorted in an
ascending order. Every third sample was selected as
calibration set, while the remaining part the prediction
set. The calibration set was used for the model
construction, while the prediction set was used for the
model validation. The optimal number of PLS factors
was selected by leave-one-out (LOO) cross validation.
The corresponding PCs were selected when the
prediction residual error sum of square (PRESS) was
the lowest. The predictive performance of the
calibration model was evaluated by the correlation
coefficient (r) for the calibration set, the standard
deviation of calibration (SEC), the standard error of
cross-validation (SECV), the standard deviation of
prediction (SEP). A good model are supposed to have
small SEC, small SEP and high r. The PCA calculation
was carried out with the software Unscrambler 9.1
(Camo Corp., Norway), and the PLSR was conducted
by MATLAB 7.0 (The Math Works, Inc., Natick, MA,
USA).

**3. Results and discussion**

**3.1 The Characteristics of the components of chicken manure**

Table 1 shows the composition statistics in
calibration sets and validation sets for total N, total P,
Cu and Zn, respectively, which include the number of
samples, the mean, standard deviation and range value.
It could be seen that the components of prediction set
full in the scope of calibration set, which assured the
representative of the calibration models.

**3.2 NIR Spectra and the PCA plots of chicken manure**

The full wavelength range of the spectra was 800-
2500 nm. The original spectra were shown in **Figure 1**.
The spectra had several absorbance peaks at the
wavelength of about 1135 nm, 1340 nm, 1540 nm,
1657 nm, 1870 nm, 2010 nm, 2235 nm, 2416 nm,
indicating rich information was contained in the spectra.
The possible water absorption peaks was not obvious
because the samples were already dried at 65 °C to a constant weight.

**Figure 2** showed the result of PCA analysis. It can be seen that the samples constituted a sample set that

<figure><img src="image_2.png" /><figcaption>Fig 1. The NIR spectra of chicken manure</figcaption></figure>

has some extent of representative. In addition, the first and second principle scores involved 99% variation of the original spectra.

<figure><img src="image_3.png" /><figcaption>Fig. 2. The PCA score plot of chicken manure</figcaption></figure>

<table><caption>Table 1 The statistic characteristics of calibration set and prediction set for chicken manure</caption><thead><tr><th></th><th></th><th>Number of samples</th><th>Min value</th><th>Max value</th><th>Mean value</th><th>Standard deviation</th></tr></thead><tbody><tr><td rowspan="2">N(%)</td><td>Calibration</td><td>50</td><td>3.51</td><td>7.55</td><td>5.36</td><td>0.91</td></tr><tr><td>Prediction</td><td>24</td><td>3.55</td><td>6.79</td><td>5.29</td><td>0.85</td></tr><tr><td rowspan="2">P(%)</td><td>Calibration</td><td>42</td><td>1.73</td><td>4.12</td><td>2.86</td><td>0.56</td></tr><tr><td>Prediction</td><td>21</td><td>1.85</td><td>3.88</td><td>2.86</td><td>0.55</td></tr><tr><td rowspan="2">Cu(mg/Kg)</td><td>Calibration</td><td>47</td><td>23.41</td><td>64.22</td><td>41.93</td><td>10.85</td></tr><tr><td>Prediction</td><td>23</td><td>23.80</td><td>57.24</td><td>41.44</td><td>10.53</td></tr><tr><td rowspan="2">Zn(mg/Kg)</td><td>Calibration</td><td>44</td><td>144.63</td><td>291.98</td><td>222.83</td><td>39.91</td></tr><tr><td>Prediction</td><td>22</td><td>145.16</td><td>291.42</td><td>222.44</td><td>40.97</td></tr></tbody></table>

### 3.3 The Calibration Models for chicken manure

The calibration models of total N, total P, Cu and Zn were shown in Table 2. It can be seen that the total P, Cu and Zn model had high calibration accuracy, with the correlation coefficient (r) of 0.8577, 0.9479 and 0.9352, respectively. Figure 3 shows that the NIR predicted value of total P, Cu and Zn and its reference value were closely arranged with the 45° line, indicating the prediction error was low. The above result suggested that total P, Cu and Zn of chicken manure could be measured by NIRS. The measurement of Cu and Zn were the indirect relation between metal elements and NIR spectra. Previous study have

indicated that one of the mechanism for measuring heavy metals by NIR was their inter correlation with organic compounds[8]. For the Zn model of compost of pig manure on fresh and dried basis, the R² and r² values were about 0.69 and 0.6 (Huang et al., 2008). The current Zn model had higher calibration accuracy than that of previous study [15].

The calibration model of total N, however, showed relatively lower accuracy. In the calibration set, NIR spectra and total N had some certain relationship, with the correlation coefficient (r) of 0.6923, which was much lower than that of total P, Cu and Zn model.
According the scatter plot, the NIR predicted total N and its reference value were also distributed aside the 45° line (see Figure 3). However, they were not as close as that of Cu and Zn, indicating that the prediction error was relatively high. The calibration results suggested that although NIR spectra had some extent of relation with total N, the prediction accuracy could not satisfy the requirement of quantitative measurement. Future research may focus on how to improve the accuracy of total N model.

<table><caption>**Table 2. The statistic results of calibration models for chicken manure**</caption><thead><tr><th></th><th>PLS factors</th><th>r</th><th>SECV</th><th>SEC</th><th>SEP</th></tr></thead><tbody><tr><td>N</td><td>4</td><td>0.6923</td><td>0.7532</td><td>0.6552</td><td>0.8047</td></tr><tr><td>P</td><td>6</td><td>0.8577</td><td>0.3822</td><td>0.2888</td><td>0.3418</td></tr><tr><td>Cu</td><td>5</td><td>0.9479</td><td>4.3503</td><td>3.4595</td><td>5.7103</td></tr><tr><td>Zn</td><td>8</td><td>0.9352</td><td>23.4447</td><td>14.1337</td><td>25.2114</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig 3. The scatter plots of reference values and NIR predicted values for the components of chicken manure**</figcaption></figure>

## 4. Conclusions

The current study explored the simultaneous evaluation of organic and inorganic components of chicken manure using NIRS. The results showed that

the NIRS technique could be a complementary detecting method to the conventional analysis. However, further research is needed to improve the prediction precision of calibration models by enlarging the number and range of samples. The present study only constructed the linear calibration models for the
four components of chicken manure. In order to
improve the precision of models, future studies may
apply non-linear methods such as artificial neural
networks to build the non-linear relation between
spectra and components.

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**Acknowledgements**

This work was supported by Major projects on
control and rectification of water body
pollution (2008ZX07425-001)

UK. Appl. Geochem(Suppl. 2). 51--56.

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