Determination of water content in corn stover silage using near-infrared spectroscopy

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November, 2019 Int J Agric & Biol Eng Open Access at https://www.ijabe.org Vol. 12 No.6 143
Determination of water content in corn stover silage using near-infrared
spectroscopy
Maoqun Zhang1,2, Chao Zhao1*, Qianjun Shao3, Zidong Yang1, Xuefen Zhang1,
Xiaofeng Xu1, Muhammad Hassan4
(1. National Engineering Research Center for Wood-based Resource Utilization, School of Engineering, Zhejiang A&F University,
Lin’an, Zhejiang 311300, China; 2. Zhejiang Collaborative Center of Efficient Utilization of Bamboo Resources, Zhejiang 311300, China;
3. Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo, Zhejiang 315211, China;
4. US-Pakistan Centre for Advanced Studies in Energy, National University of Science and Technology, Islamabad 44000, Pakistan)
Abstract: The aim of this study was to evaluate the feasibility of utilizing near-infrared spectroscopy to determine the water
content of corn stover silage across a wide range. The water contents of 208 samples were measured, and their corresponding
near-infrared spectra were simultaneously collected. The effects of different preprocessing methods, such as derivation,
standard normal variety (SNV), multiplicative scatter correction (MSC), and non-preprocessing methods for the obtained
near-infrared spectra on the performance of calibration models were compared. The calibration models were established by
modified partial least squares (MPLS) regression. The results showed that the calibration model developed from the
successive preprocessing of MSC and first-order derivation (1-D) achieved the optimal performance. The correlation
coefficients of the calibration and validation subset were 0.974 and 0.949, respectively, and the standard errors of the
calibration and cross validation were 4.249% and 4.256%, respectively. External validation was performed on 60 samples.
The correlation coefficient between the measured and predicted values of the calibration model was 0.973 and the prediction
model’s relative percent deviation was 4.317. This indicated that the mathematical model of near-infrared spectroscopy
predicted the water content in corn stover silage with high accuracy. The study showed that the near-infrared spectroscopy
technology can be used for rapid and non-destructive testing across a wide range of water contents in the corn stover silage.
Keywords: near-infrared spectroscopy, water, non-destructive measurement, corn stover silage
DOI: 10.25165/j.ijabe.20191206.4914
Citation: Zhang M Q, Zhao C, Shao Q J, Yang Z D, Zhang X F, Xu X F, et al. Determination of water content in corn stover
silage using near-infrared spectroscopy. Int J Agric & Biol Eng, 2019; 12(6): 143–148.
1 Introduction
Corn (Zea mays L.) is one of the high and stable-yield crops in
the world, and corn stover is one of the three major agricultural
residues in China which accounts for 36.81% of China’s total straw
production[1,2]. Although still contains rich nutrients suitable for
livestock, corn stover is usually incinerated or ploughed back in the
soil. Silage is a common preserved feed in many countries, and
corn stover silage is mainly used as animal forage[3]. At present,
many dairy farms adopt the corn stover silage to raise cows, so as
to ensure the stable quality of roughage in the diet structure and
improve the milk yield. Therefore, the ensiling of fresh corn
stover is an attractive raw material for forage production due to its
high content of carbohydrates and easy degradability. Water,
Received date: 2019-01-14 Accepted date: 2019-10-21
Biographies: Maoqun Zhang, Master candidate, research interests:
near-infrared spectroscopy and its application, Email: 2465144243@qq.com;
Qianjun Shao, PhD, Professor, research interests: biomass property detection,
Email: shaoqianjun@nbu.edu.cn; Zidong Yang, PhD, Professor, research
interests: nondestructive detection, Email: yzd66@126.com; Xuefen Zhang,
Master, research interests: biomass property detection, Email: 404365047@
qq.com; Xiaofeng Xu, PhD candidate, research interests: biomass property
detection, Email: penguinuaa@sina.com; Muhammad Hassan, PhD, research
interests: biomass and bioenergy, Email: engrhasan74@yahoo.com.
*Corresponding author: Chao Zhao, PhD, Associate Professor, research
interests: biomass property detection. Mailing address: 666# Wusu street, Lin’an
district, Hangzhou 311300, Zhejiang, China. Tel: +86-571-63746877, Email:
zhaochao@zafu.edu.cn.
temperature, and lactic acid bacteria have great influence on the
quality of forage during ensiling process[4]. Of which, the water
content of the corn stover silage is the most fundamental and
important parameter[5,6]. For ensiling process, it is very important
to realize the fast determination of water content, and then
adjusting it for better forage quality. However, the conventional
chemical analysis method for determining water content is
time-consuming and labor-intensive[7,8] and could not meet the
requirements of online analysis. Therefore, research on rapid and
non-destructive testing of water content in corn stover silage could
improve the detection efficiency, reduce the analysis cost,
provide an efficient analysis platform, and information on straw
utilization[9,10].
Generally, near-infrared spectroscopy provides the vibrational
information of O–H, C–O, and other groups inside a substance.
When combined with chemometric methods, near-infrared
spectroscopy could be used to perform qualitative or quantitative
analysis of the groups mentioned above[11-14] . As a new and
non-destructive testing technology, near-infrared spectroscopy has
been widely utilized in agriculture, pharmacy, and chemical
industry[15-17]. In recent years, advanced non-destructive testing
techniques, such as terahertz spectroscopy[18] and laser-induced
breakdown spectroscopy[19], have appeared. Multi-component
parameters in fruits, grains and food could be determined by
near-infrared spectral information, especially the rapid and
non-destructive detection of water content. Extensive studies on
straw analysis have been conducted using near-infrared

144 November, 2019 Int J Agric & Biol Eng Open Access at https://www.ijabe.org Vol. 12 No.6
spectroscopy. Huang et al.[20,21] successfully applied this
technology to determine of the water content and calorific value of
rice straw and wheat straw. Liu et al.[22] established prediction
models for water and ash content of corn stover using near-infrared
spectroscopy. Wu et al.[23] analyzed the cellulose content of corn
stover using near-infrared spectroscopy. Some researchers
constructed an analytical model to analyze the feed quality of corn
stover, such as dry matter digestibility, acid detergent fiber, neutral
detergent fiber and soluble sugars[24,25]. Even the decomposition
of corn stover has been studied using near-infrared spectroscopy[26].
To the best of our knowledge, the detection of water content using
near-infrared spectroscopy for corn stvoer silage has rarely been
reported.
Many researchers have claimed that the changes in sample
composition affect the accuracy of near-infrared calibrations[11].
Investigations by Huang et al.[20] suggested that the variations in
moisture and calorific value of wheat straw had a significant
influence on the prediction of the calibration model. To solve this
wide range of testing parameter, they divided the test samples into
three subsets according to the chemical values (labeled as high,
middle and low) using a local algorithm. A calibration model for
the rapid determination of neutral detergent fiber (NDF) and acid
detergent fiber (ADF) in corn stover silage was obtained by
near-infrared spectroscopy in a recent study, in which the NDF and
ADF ranges 43.39%-84.56% and 25.61%-57.13%, respectively[25].
The wide variations in NDF and ADF were caused by the different
varieties, different growth stages, and different parts of the samples.
This indicates that a parameter with a wide range could also be
predicted using near-infrared spectroscopy. Furthermore, the
reported studies had mainly focused on the quality of straw feed in
terms of cellulose content, calorific value, ash content, and even
decomposition process, where the detection of water is almost
based on the safe water content of straw, in which the highest water
content of straw is 20.44%[20]. To the best of our knowledge, the
detection of water content higher than 70% using near-infrared
spectroscopy for straw has rarely been reported.
In this study, corn stover silage with a wide-range of water
content (9.82%-71.09%) was used as the research object and
near-infrared spectroscopy was utilized to determine the water
content of this silage. A prediction model for water content in the
corn stover silage was established and the validation of the model
was evaluated.
2 Materials and methods
2.1 Sample collection and preparation
The corn stover silage was harvested in July 2012 from
Quanjiao Yangzhuang Farm (32.10°N; 118.23°E), Anhui Province,
China. The corn variety was Xin’an 5#, and it took about 85 d
from sowing to harvesting. After cutting the upper corn ear and
lower root, the corn stalk and leaf were used as corn stover silage.
These raw materials were then cut into strips of about 5-10 mm,
evenly mixed, and placed in a laboratory environment for water
analysis and spectral acquisition.
2.2 Chemical analysis
The water content of corn stover silage was determined based
on wet basis in this study. Only one water content value of the
initial corn stover silage could be obtained. To obtain a wide
range of water content values of the experimental materials, the
initial corn stover silage was put in an electric blast drying oven
(DHG series 9075A, Shanghai Yiheng Scientific Instrument Co.,
Ltd. China) and a sample was taken at 10-min intervals until the
silage was dried below the safe water content. The near-infrared
spectrum was recorded, and water content measured for each
sample. The near-infrared spectrum of each sample was recorded
as described in Section 2.3. Next, the sample were marked and
filled into 3 aluminum boxes (diameter = 80 cm, height = 50 cm,
weight = 15 g) to measure their water contents, and the average
value was recorded as the water content of the sample. Each
aluminum box was randomly filled with corn stover silage. The
initial weight of each corn stover silage sample varied from 3.6 g to
12.1 g depending on the water content. The water content was
determined on a wet basis according to GB/T 14095-2007[27].
Samples were dried to a constant quality (without quality change),
and then their water contents were calculated. This ensured that
the spectral information corresponded to their water content.
Overall, 208 samples, with water contents ranging from 9.82% to
71.09%, were obtained in this experiment.
2.3 Collection of near-infrared spectra
The near-infrared spectra of the samples were obtained on a
near-infrared spectrometer (Infraxact Lab, Foss Instruments,
Denmark) that used a monochromator-based NIR reflectance and
transflectance analyzer. The spectral scanning range was
570-1850 nm, with increments of 2 nm, and the data acquisition
frequency was 3 s. Figure 1 shows the schematic of the
near-infrared spectra collection process of corn stover silage. The
light source from a tungsten halogen lamp was dispersed by a
holographic grating and then split into single-wavelength light.
The dispersed light entered the sample and interacted with the
sample molecules. Si (570-1100 nm) and InGaAs (1100-1850 nm)
detectors collected the spectra in the NIR region. The spectral
acquisition and storage were performed by using ISI scan operating
software. The samples were scanned by a small sample cup
arrangement in the reflectance mode. During the spectral
acquisition process, the ambient temperature was maintained at
26°C. The bandwidth accuracy was less than 0.1 nm and the
exposure time was 400 ms. Each sample was registered three
times and scanned separately, and the average value was recorded
as the near-infrared spectrum of the sample[5].
2.4 Establishment of near-infrared calibration model
To establish the calibration model, the measured water content
values of the samples were input into the modeling program.
Notably, each measured water content was related to its
corresponding scanning spectrum. Samples of 208 were
randomly divided into a calibration set and a cross validation set in
a ratio of 3:1, and the calibration and validation sets were used for
model establishment and reliability evaluation, respectively.
When establishing the calibration model, the obtained
spectrum was first smoothed by four data interval window points
(every 8 nm) according to GB/T 18868-2002 to eliminate the
influences of noise[28]. Then, different spectral preprocessing
methods were tested to reduce unwanted variation due to sources
not related to water properties. The preprocessing methods were
first-order derivation (1-D), second-order derivation (2-D),
standard normal variation (SNV), multiplicative scatter correction
(MSC), and the combination of the above mentioned methods[5].
According to a previous study, smoothing mainly removes the
interference of high-frequency noise on the signal. Derivation
could effectively eliminate background-induced interference and
baseline drift or rotation. SNV eliminates the effects of the
changes due to the optical-path or dilution. MSC could eliminate
the spectral baseline shift due to the difference in size and shape of
samples[15,16].

November, 2019 Zhang M Q, et al. Determination of water content in corn stover silage using near-infrared spectroscopy Vol. 12 No.6 145
Figure 1 Schematic of collection process of near-infrared spectra for corn stover silage
The calibration model was established by the modified partial
least squares (MPLS) method. The optimal factor number
adopted by the model was determined by a cross validation set,
including root mean square error of calibration (RMSEC),
calibration correlation coefficient (R), root mean square error of
cross validation (RMSECV) and cross validation correlation
coefficient (RCV). The optimal model was usually with good
enough compromise between low RMSEC and RMSECV, and high
R and RCV[9].
RMSEC, RMSECV and the root mean square error of
prediction (RMSEP) were calculated as follows[5]:
2
1
( )
RMSEC 1
I
i i
im m
I


 (1)
2
1
( )
RMSECV
l
I
i i
i
l
m m
I

 (2)
2
1
( )
RMSEP
t
I
i i
i
t
m m
I

 (3)
where, mi is the water content of sample i, %;
i
m
is the water
content of model prediction for sample i, %; I is the number of
calibration samples; Il is the number of samples in the cross
validation set; and It is the number of prediction samples.
2.5 Evaluation of the calibration model
To examine the accuracy of the model, a cross verification set
was performed. The accuracy of the model’s prediction was
judged by comparing the difference between the predicted and the
measured water content values. The accuracy of the model was
evaluated by the ratio of the performance to deviation (RPD). If
RPD ≥ 3, the established calibration model could be well fitted to
the measured results. If 2.5 < RPD < 3, the prediction accuracy
needs to be further improved, and RPD ≤ 2.5, indicates that it is
difficult to predict the quantitative analysis of the established
calibration model[5, 20]. It should be noted that it was necessary to
expand the coverage of the calibration samples when the water
content to be determined exceeded the range of the existing
calibration model. RPD was calculated as follows:
SD
RPD
RMSEP
 (4)
where, SD is the standard deviation of the validation set.
3 Results and Discussion
3.1 Analysis of the water content measurement results
Table 1 shows that the water contents of the 208 samples
ranged from 9.82% to 71.09%, much higher than the coverage of
water content in the general near-infrared spectrum water detection
calibration set. Liu et al. reported that the water content of corn
stover ranged from 3.15% to 6.75%[22], while Huang et al. reported
the water content of wheat/rice straw to range from 5.13% to
20.44%[21] and the water content of soybean was predicted to range
from 6.92% to 13.71%[9]. Water contents in these studies varied
by less than 15%. In an interesting study, water uptake while
barley steeping was monitored the by near-infrared spectroscopy
over a large scale range of water content (28.5%-97.9%)[29]. The
samples were randomly divided into a calibration set and a
verification set in a 3:1 ratio. The results of the sample
partitioning are also listed in Table 1. These results suggest that
the water content ranges of the calibration and the verification sets
were both widely distributed and were consistent with the overall
samples. The water content range of verification set was in that of
calibration set.
Table 1 Water content measurement results and sample set
partition
Sample Set Sample
Number
Max/% Min/% Average
/%
Standard
Deviation
Total 208 71.09 9.82 38.28 18.67
Calibration set
156 71.09 9.82 38.80 18.84
Validation set
52 67.64 12.11 36.72 17.97
3.2 Characteristics of near-infrared spectra
Figure 2 shows the near-infrared spectra of 5 corn stover silage
samples. It was observed that different samples exhibited almost
similar spectral morphology over the scanned wavelength range,

146 November, 2019 Int J Agric & Biol Eng Open Access at https://www.ijabe.org Vol. 12 No.6
and this was similar to previous reported observations[9,30]. There
were multiple absorption peaks in the spectra over the entire
wavelength range, which reflected the composition of water and
organic matter (mainly fibrous matter) in the silage. The spectra
showed strong absorption peaks around 670 nm and 1450 nm, and
the absorption peaks also appeared at around 970 nm and 1190 nm.
According to previous studies, the strongest absorption peak at
around 670 nm was due to the out-of-plane bending vibration of the
C–H bond associated with cis olefin[15]. The absorption peaks
around 1450 nm are associated with either C–H combinations
(aromatic groups) or the O–H first overtone (water)[29]. The
absorption peaks around 970 nm and 1190 nm are attributed to the
hydroxyl stretching vibration of either water molecule or the H–OH
variable angle vibration. These results are in agreement with
previous studies. Many researchers have reported that the typical
water regions in near-infrared spectra are at 1351-1667 nm and
1852-2128 nm[15,23]. Therefore, the near-infrared spectra obtained
herein covered the multiplier and frequency spectrum band of
water molecule vibration. All the above-mentioned findings
indicated that the spectra of the samples could provide the
information necessary for determination of water content.
Figure 2 Near-infrared spectra of corn stover silage samples
3.3 Establishment and optimization of near-infrared
calibration model
The quantitative relationship between the spectra and water
content for each sample was established by using the MPLS
method in the whole spectra. When establishing the calibration
model, the obtained spectra were first smoothed by four data
interval window points (every 8 nm) according to GB/T
18868-2002[28]. Signal smoothing was one of the most common
ways to eliminate the interference of high-frequency noise. Then,
processing was performed by first order derivation (1-D), second
order derivation (2-D), standard normal variate (SNV) and
multiplicative scatter correction (MSC). The calibration models
established from the four different preprocessing methods are
shown in Table 2. Compared with the modeling results of the raw
spectra, the model established by the SNV method had the worst
parameters of the calibration and the verification sets, while the
other preprocessing methods were found to significantly improve
the stability and predictability of the model. This indicated that
there was almost no optical path change in spectral response. The
correlation coefficients of both the calibration and verification sets
based on derivation (1-D and 2-D) preprocessing were above 0.9,
indicating that derivation could effectively eliminate
background-induced interference and baseline drift, and it had a
strong characteristic extraction ability from near-infrared spectra.
However, the RMSECV of the verification set based on the 2-D
preprocessing method was slightly higher than that based on the
1-D preprocessing method, whereas the RCV of verification set
based on the 2-D preprocessing method was slightly lower than that
based on the 1-D preprocessing method. This suggested that
second order derivation caused the noise to be over-amplified, and
hence, the model performance was degraded. The RCV of the
verification set based on the MSC preprocessing method was 0.936,
which indicated that the surface scattering characteristics of the
sample had significant influence on the spectrum. This may be
due to the different size and shape of the corn stalk and leaf in the
sample. In summary, there was almost no optical path change in
spectral response, while surface scattering characteristics of the
sample greatly influenced on the spectra under the described
experimental conditions. Derivation had strong characteristic
extraction ability from the near-infrared spectra. Different
pretreatment methods were utilized to eliminate noise and
interference from the spectra. According to reported studies,
some spectra only need derivation (either the first or the second
order)[29], but some spectra need additional processing methods,
such as either SNV or MSC which are closely related to the
experimental environment and the tested object[20,21,24].
Table 2 Results of MPLS models for water content based on
the spectra of corn stover silage
Processing
method
Calibration set Validation set
RMSEC/% R RMSECV/% RCV
Raw spectra 10.265 0.838 10.316 0.698
1-D 5.617 0.955 5.637 0.911
2-D 5.679 0.953 5.719 0.909
SNV 12.465 0.759 12.474 0.573
MSC 4.762 0.968 4.780 0.936
MSC + 1-D 4.249 0.974 4.256 0.949
Note: 1-D: the first-order derivation; 2-D: the second-order derivation; SNV:
standard normal variate; MSC: multiplicative scatter correction.
According to the results of Table 2, it concluded that MSC and
derivation had strong characteristic extraction ability from
near-infrared spectra. To obtain a more accurate calibration
model, a combination of MSC and 1-D preprocessing was
performed. The results showed that the calibration model derived
from the combined method gave the lowest RMSECV (4.256%) and
the highest RCV (0.949), thus demonstrating the best performance.
That’s to say, the optimal calibration model for determination of
water content after selecting from different preprocessing methods,
was obtained by successively preprocessing using MSC and 1-D.
The experimental result was consistent with that of the optimal
calibration model for predicting acid ADF content of corn stover
silage over a wide range (25.61%-57.13%)[25]. According to
published reports, almost all the optimal calibration models were
obtained by the combined preprocessing of the spectrum. Huang
et al. suggested that the optimal calibration models for moisture
content and calorific value of corn stover were adopted from
combined SNV and derivation preprocessing[21]. The optimal
calibration models for determining feed quality of corn stover were
adopted from combined derivation and MSC preprocessing[24], and
that for composition of corn stover was adopted from the
combination of derivation and Karl Norris filter preprocessing[22].
3.4 External validation of the calibration model
An external verification set was performed to examine the
accuracy of the calibration model. Sixty samples, which were not
involved in the calibration, were tested using the calibration
equation. Table 3 gives the external validation statistics of the
near-infrared spectroscopy model. The correlation coefficient
between the measured and predicted sample water content was
0.973, which indicated that there was good correlation between the

November, 2019 Zhang M Q, et al. Determination of water content in corn stover silage using near-infrared spectroscopy Vol. 12 No.6 147
near-infrared predicted value and the chemically measured value.
The RMSEP (4.037%) was very close to the previous RMSEC
(4.249%), suggesting good reliability of the prediction equation.
The ratio of the performance to deviation (RPD) of the prediction
model was greater than 3 (4.317). The water contents predicted
from the near-infrared spectroscopy model were in good agreement
with the measured values (Figure 3). Therefore, the calibration
model had good prediction accuracy and could be used for actual
detection.
Table 3 External validation statistics of the calibration
model
Test parameters
Rp Bias Slope RMSEP/% RPD
Value 0.973 –0.178
1.004 4.037 4.317
Figure 3 Correlations between measured and predicted values of
corn stover silage
3.5 Comparison of the near-infrared spectroscopy models of
water content
Table 4 lists the different near-infrared spectroscopy models of
water content. In addition to straw[20,22], the near-infrared
spectroscopy models of water content have been established in
fresh leaf[31], seeds[9,29], freeze-dried insulin[11], and so on.
According to the previous studies, the water change of sample is
usually less than 20%, while water content in this study ranged
from 9.82% to 71.09%. When referring to the optimal processing
method, SNV and derivative are important processing method for
spectral data in the establishment of water prediction model. All
the optimal processing method includes one or both. The optimal
processing method in this study was obtained by successive
processing of MSC and 1-D. MSC could eliminate the spectral
baseline shift due to the difference in size and shape of samples.
From sample preparation, the difference in shape and size was
found in corn stover silage, whereas not in other samples. In
terms of research object, the Golden pothos leaf and corn stover
silage were the most similar[31]. The RMSEC and correlation
coefficient (R) of water calibration model for Golden pothos leaf
were 3.36% and 0.80, while those for corn stove silage were
4.249% and 0.974. The accuracy of the near-infrared model for
water content of corn stover silage has been greatly improved. In
terms of wide range of water change, the barley and corn stover
silage were the most similar[29] . The RMSEP and correlation
coefficient (Rp) of water prediction model for barley were 5.36%
and 0.90, while those for corn stover silage were 4.037% and 0.973.
The accuracy of the near-infrared model for water content of corn
stover silage has been slightly improved.
Table 4 Comparison of the near-infrared spectroscopy models of water content
Number
Water
range/% Sample Optimal processing
method
Calibration set Validation set Prediction set
Reference
RMSEC/%
R RMSECV/% RCV RMSEP/%
RP
1 5.13-20.44
Wheat/Rice straw SNV + 1-D - - 0.630 - 0.692 0.9331
[20]
2 3.15-6.75 Corn stover 1-D + Karl Norris - - 0.547 0.861
1.325 0.9317
[22]
3 6.92-13.71
Soybean 1-D + SNV 0.451 0.983
0.203 0.965
- 0.966 [9]
4 28.5-97.9 Barley 2-D - - 6.36 0.94 5.36 0.90 [29]
5 0.70-2.85 Freeze-dried insulin SNV - - 0.15 0.98 0.19 0.907 [11]
6 32.8-61.7 Golden pothos leaf SNV 3.36 0.80 3.77 0.75 - - [31]
7 9.82-71.09
Corn stover silage MSC + 1-D 4.249 0.974
4.256 0.949
4.037 0.973 This study
Note: 1-D: the first-order derivation; 2-D: the second-order derivation; SNV: standard normal variate; MSC: multiplicative scatter correction.
RMSEC: the root mean square error of calibration; RMSECV: the root mean square error of cross validation; RMSEP: the root mean square error of prediction.
R: the calibration correlation coefficient; RCV, the cross validation correlation coefficient; RP the prediction correlation coefficient.
4 Conclusions
(1) In this study, corn stover silage samples were measured for
water content and their corresponding near-infrared spectroscopy
information were collected. The results show that the water
content in the samples ranged from 9.82% to 71.09%, and the
different samples exhibited nearly similar spectral morphology.
The absorption peak of the spectrum contained the multiplier and
frequency spectrum band of water molecule vibration, which could
provide the spectral information required for water detection.
(2) The calibration model for water content prediction was
established by MPLS. In this experiment, derivation-based
processing could effectively eliminate background-induced
interference and baseline drift or rotation. The SNV and MSC
preprocessing methods suggested that there was almost no optical
path change in spectral response and the surface scattering
characteristics of the sample had significant influence on the
spectrum.
(3) The optimal calibration model for water content
determination was successively preprocessed by MSC and 1-D
after smoothing by four data interval window points (every 8 nm).
The correlation coefficients of the calibration and verification sets
were 0.974 and 0.949, respectively. The parameters of the
calibration and verification sets corresponding to the calibration
model were optimal.
(4) Sixty samples were externally tested via the calibration
equation. The results showed that the correlation coefficient
between the measured and predicted sample water content was
0.973, suggesting that the calibration model had good prediction
accuracy. It was concluded that near-infrared spectroscopy
technology could be used for rapid and non-destructive testing of
water content in corn stover silage. Further studies could explore
the application of the near-infrared model for different varieties of
corn stover silage.

148 November, 2019 Int J Agric & Biol Eng Open Access at https://www.ijabe.org Vol. 12 No.6
Acknowledgements
This work was supported by Commonwealth Project of
Science and Technology Agency of Zhejiang Province, (No.
2017C32068, LGN18F030001); the Major Project of Zhejiang
Science and Technology Department (2016C02G2100540), China.
[References]
[1] Zhao C, Shao Q, Li B, Ding W. Comparison of hydrogen peroxide and
ammonia pretreatment of corn Stover: Solid Recovery, composition
changes, and enzymatic hydrolysis. Energy & Fuels, 2014; 28(10):
6392–6397.
[2] Qiao X, Zhao C, Shao Q, Hassan M. Structural characterization of corn
stover lignin after hydrogen peroxide presoaking prior to ammonia fiber
expansion pretreatment. Energy & Fuels, 2018; 32(5): 6022–6030.
[3] Xu Z, He H, Zhang S, Kong J. Effects of inoculants Lactobacillus brevis
and Lactobacillus parafarraginis on the fermentation characteristics and
microbial communities of corn stover silage. Scientific Reports, 2017;
7(1): 13614.
[4] Sun Y, Gong X, Wang Z, Huang C, Ma X, Wang M. Two-step
pretreatment of corn stover silage using non-ionic surfactant and ferric
nitrate for enhancing sugar recovery and enzymatic digestibility of
cellulose. Applied Biochemistry Biotechnology, 2019; 189(1): 65–75.
[5] Sans S, Ferré J, Boqué R, Sabaté J, Casals J, Simó J. Determination of
chemical properties in 'calçot' (Allium cepa L.) by near infrared
spectroscopy and multivariate calibration. Food Chemistry, 2018; 262:
178–183.
[6] Li B, Long Y, Yang H. Measurements and analysis of water content in
winter wheat leaf based on terahertz spectroscopy. Int J Agric & Biol Eng,
2018; 11(3): 178–182.
[7] Büning-Pfaue H. Analysis of water in food by near infrared spectroscopy.
Food Chemistry, 2003; 82(1):107–115.
[8] Czarnik-Matusewicz B, Pilorz S, Hawranek J P. Temperature-dependent
water structural transitions examined by near-IR and mid-IR spectra
analyzed by multivariate curve resolution and two-dimensional correlation
spectroscopy. Analytica Chimica Acta, 2005; 544(1-2): 15–25.
[9] Zhu Z, Chen S, Wu X, Xing C, Yuan J. Determination of soybean routine
quality parameters using near-infrared spectroscopy. Food Science &
Nutrition, 2018; 6(4): 1109–1118.
[10] Qin J, Kim M S, Chao K, Bellato L, Schmidt W F, Cho B, Huang M.
Inspection of maleic anhydride in starch powder using line-scan
hyperspectral Raman chemical imaging technique. Int J Agric & Biol Eng,
2018; 11(6): 120–125.
[11] Grohganz H, Gildemyn D, Skibsted E, Flink J M, Rantanen J. Towards a
robust water content determination of freeze-dried samples by near-infrared
spectroscopy. Analytica Chimica Acta, 2010; 676(1-2): 34–40.
[12] Manley M, du Toit G, Geladi P. Tracking diffusion of conditioning water
in single wheat kernels of different hardnesses by near infrared
hyperspectral imaging. Analytica Chimica Acta, 2011; 686(1-2): 64–75.
[13] Zhao C, Qiao X, Cao Y, Shao Q. Application of hydrogen peroxide
presoaking prior to ammonia fiber expansion pretreatment of energy crops.
Fuel, 2017; 205(10): 184–191.
[14] Zhao C, Shao Q, Ma Z, Li B, Zhao X. Physical and chemical
characterizations of corn stalk resulting from hydrogen peroxide
presoaking prior to ammonia fiber expansion pretreatment. Industrial
Crops and Products, 2016; 83(5): 86–93.
[15] Bär D, Debus H, Brzenczek S, Fischer W, Imming P. Determining
particle size and water content by near-infrared spectroscopy in the
granulation of naproxen sodium. Journal of Pharmaceutical and
Biomedical Analysis, 2018; 151(3): 209–218.
[16] Huang Y, Lu R, Chen K. Prediction of firmness parameters of tomatoes
by portable visible and near-infrared spectroscopy. Journal of Food
Engineering, 2018; 222(4): 185–198.
[17] Liu C, Han Y, Min S, Jia W, Meng X, Liu P. Rapid qualitative and
quantitative analysis of methamphetamine, ketamine, heroin, and cocaine
by near-infrared spectroscopy. Forensic Science International, 2018;
290(9): 162–168.
[18] Yang Y, Zhou S, Song J, Li G, Zhu S. Feasibility of terahertz
spectroscopy for hybrid purity verification of rice seeds. Int J Agric &
Biol Eng, 2018; 11(5): 65–69.
[19] Zhang H, Zhu Q, Huang M, Guo Y. Automatic determination of optimal
spectral peaks for classification of Chinese tea varieties using laser-induced
breakdown spectroscopy. Int J Agric & Biol Eng, 2018; 11(3): 154–158.
[20] Huang C, Han L, Liu X, Ma L. Rapid measurement for moisture and
calorific value of straw based on near infrared spectroscopy and local
algorithm. Journal of Infrared and Millimeter Wave, 2009; 28(3):
184–187.
[21] Huang C, Han L, Liu X, Yang Z. Proximate analysis of straw by near
infrared spectroscopy (NIRS). Spectroscopy and Spectral Analysis, 2009;
29(04): 960–963. (in Chinese)
[22] Liu L, Chen H. Prediction of maize stover components with near infrared
reflectance spectroscopy. Spectroscopy and Spectral Analysis, 2007;
27(02): 275–278. (in Chinese)
[23] Wu J, Bai Q, Su S, Chen S, Meng Q, Yan Y. Near-Infrared reflectance
spectroscopy analysis of cellulose content in corn stalk. Chinese Journal
of Analytical Chemistry, 2005; 33(10): 1421–1423. (in Chinese)
[24] Tai S, Zhang R, Shi J, Xue J, Zhang X, Ma G, Lu H. Prediction of forage
quality of maize stover by near infrared spectroscopy. Transactions of the
CSAE, 2009; 25(12): 151–155. (in Chinese)
[25] Bai Q, Chen S, Dong X, Meng Q, Yan Y, Dai J. Prediction of NDF and
ADF concentrations with near infrared reflectance spectroscopy (NIRS).
Spectroscopy and Spectral Analysis, 2004; 24(11): 1345–1347. (in
Chinese)
[26] Wu J, Xi S, Zeng G, Wang D, Jiang Y, Wang M. Study on decomposed
corn straw residue by Fourier transform infrared spectroscopy. Acta
Pedologica Sinica, 1999; 34(1): 91–100. (in Chinese)
[27] GB/T 14095-2007. Drying technology of agricultural
products-Terminology, 2007.
[28] GB/T 18868-2002. Method for determination of moisture, crude protein,
crude fat, crude fibre, lysine and methinione in feeds-Near-infrared
reflectance spectroscopy, 2002.
[29] Cozzolino D, Roumeliotis S, Eglinton J. Monitoring water uptake in
whole barley (Hordeum vulgare L.) grain during steeping using near
infrared reflectance spectroscopy. Journal of Food Engineering, 2013;
114(4): 545–549.
[30] Templeton D W, Sluiter A D, Hayward T K, Hames B R, Thomas S R.
Assessing corn stover composition and sources of variability via NIRS.
Cellulose, 2009; 16(4): 621–639.
[31] Higa S, Kobori H, Tsuchikawa S. Mapping of leaf water content using
near-infrared hyperspectral imaging. Applied Spectroscopy, 2013; 67(11):
1302–1307.