1
Prediction of Dry Matter and Crude Protein Content in Fresh Grass Silage by Near
Infrared Spectroscopy
M. Vranić*, M. Knežević*, Zs.Seregély**, K. Bošnjak*, J. Leto*, G. Perčulija*
*Department of Crop, Forage and Grass Production, Faculty of Agriculture, The University of
Zagreb, Croatia
** Corvinus University of Budapest, H1118 Budapest, Ménesi út 45., Hungary
Introduction
Grass silage varies greatly in terms of chemical and biological composition due to the impact
of factors such maturity stage at harvesting, sward botanical composition, level of
fertilisation, climate and ensiling techniques on the fermentation process in the silo. The
feeding value of silage for animal production depends on the concentration of nutrients and
the voluntary intake (Gordon and Murdoch, 1978), which are mainly affected by digestibility,
and DM concentration (Steen et al., 1998).
Highly digestible silage alone provides nutrients for maintenance and the production of only
14.3 kg of milk per day (Castle et al., 1980). This is the reason the silage is rarely used as the
sole food for highly yielding dairy cows and has to be supplemented with other feeds,
normally concentrates, that are high in energy (McKee et al., 1996). Supplementation
represents a substantial part of the variable on-farm costs.
Analytical procedures are slow and expensive while the rapid analysis of fresh grass silage
samples (a very heterogeneous, high moisture forage) on DM and CP would reduce the cost
of analysis and provide essential data for rationing purposes.
It has been shown (Steen et al., 1995) that NIRS can accurately predict the voluntary intake of
grass silage by cattle. It also has the potential to enhance understanding of grass silage
digestion and cell wall degradation (Wilman et.al., 2000), and it is the most precise approach
available among laboratory methods to predict in vivo organic matter digestibility of grass
silage (Barber et.al., 1990).
There are advantages in analysing fermented forages in their natural state due to avoidance of
analytical errors caused by the loses of volatile compounds by oven-drying.
The objective of this study was to determine the accuracy of near infrared reflectance
spectroscopy (NIRS) for measuring DM (g kg-1) and CP (gkg-1DM and %) contents of fresh
grass silage samples.
2
Materials and methods
The silages were harvested at five different maturity stages in 2002 from the leafy to the late
blooming stage at 6-10 days intervals.
The sward was fertilised with 36 kg ha-1 nitrogen (N), 117 kg ha-1 phosphorus (P2O5) and 117
kg ha-1 potassium (K2O) (NPK 8:26:26) in early spring. Thirty-five days prior to harvesting
40.5 kg ha-1 N was applied in the form of calcium ammonium nitrate (CAN) (150 kg ha-1).
Green and dry matter yield (t ha-1) was determined at mowing by calculating the weight of
forage taken by a square frame (0.25x0.25m). Botanical composition was determined from the
same samples by manual separation of sward components (grass, clover, other forage plants).
The herbage was wilted for 8 hours before baling with a round baler. Bales were ensiled on
each maturity date (wrapped in 4 layers of 500 mm-wide white plastic film). After 60 days, all
bales were cored through twice using a motorised sampler fitted with a 50-mm diameter, 500
mm long steel tube. The cored material from each bale was then bulked, mixed and subsampled prior to scanning.
Without any pre-treatment, three independent scans were recorded from each of 103 samples,
using an NIRSystem Model 6500 spectrometer system (Foss-NIRsystem, Sweden) fitted with
a sample transport module and a natural product sample cup (56 x 240 mm). Samples were
scanned (32 scans co-aded) using the ISI SCAN Version 1.0 (Infrasoft International, Port
Matilda, PA, USA) from 1100 to 2498 nm in reflectance mode (R mode: PbS detector). Data
were collected every 2 nm (700 data points per spectrum). The mean spectral value of each
sample was calculated using the WIN ISI III Version 1.5.
After scanning the the fresh samples, the DM contents of the same samples was determined
immediately by oven drying to a constant weight at 60°C in a forced-draught oven. The
samples were dried and ground to pass a 1mm screen. Total nitrogen (TN) concentrations
were determined using a Gerhardt nitrogen analyser and expressed as crude protein (CP g kg-1
DM) (TN x 6.25) and percent on a fresh basis.
Data were analysed using mixed model procedures (SAS, 1999). Mean separation was
calculated using the least significant difference (LSD) values if the F-test was significant at
P=0.05.
In performing measurements with the NIRSystem Model 6500 spectrometer, hundreds of data
are generated for each sample and it is obvious that some data reduction method is needed to
facilitate data interpretation. The user is interested in the compositional data of the samples
rather than in the spectral data. Thus, there is a need for data processing methods that
transform the measured spectral data into the sample properties (concentration of the
constituents) of interest. For producing such models that determine the equations describing
the relationship between spectral data and constituent values, three different methods (MLR,
PCR and PLS) were used. MLR uses a multiterm linear polynomial to describe this
relationship using only several spectral data measured as “characteristic” wavelengths. With
OCA, all spectral data are used, reducing the dimensionality of the data set by looking for
orthogonal directions in spectral data space along which the variance of the data set is
maximised. These directions are called principal components. Thus, the first principal
component (PC1) is determined as the direction in spectral data space that corresponds to the
3
largest variation in the data set. The second principal component (PC2) is calculated as the
direction perpendicular to Pc1 along which the remaining variation in the data set is the
largest. This procedure is repeated until no variation is left in the data set. PLS uses not only
the spectral information in the data set but also incorporates information about sample
properties, e.g. concentrations to determine the most useful orthogonal directions in spectral
space.
Results
The sward consisted of 80.6% grass, 11.69% clover and 7.71% of other forage. The yields of
herbage green mass (t ha-1) were 32.18; 32.52; 29.91; 25.79 and 24.36 from early to late cut
respectively. The yields of herbage dry matter (t ha-1) were 4.842; 5.429; 6.466; 7.026 and
7.255 from early to late cut respectively.
The latest-cut silage had the highest DM concentration (P<0.05) compared with earlier-cut
silage. Because the DM concentration in the silage was high (Table 1), polluting effluent
production from the silage was not observed.
Table 1. Dry matter and crude protein content of samples
as determined by laboratory reference methods
Cut
n
DM
g kg-1
min
mean
1
2
3
4
5
Total
22
22
25
19
15
103
bc
472.60
439.14 cd
432.36 d
481.11 b
540.93a
467.21
345
326
368
216
280
216
max
597
504
520
560
629
629
mean
a
6.05
5.29 b
4.40 cd
4.02 d
4.79 c
4.93
CP
%
min
4.48
3.62
3.42
1.88
2.74
1.88
max
7.50
6.90
5.12
4.4
6.06
7.50
CP
g kg-1 DM
min
mean
a
128.40
120.49 b
102.50 c
83.85 d
88.99 d
106.47
100.1
108.6
86.14
76.22
72.40
72.40
max
150.1
151.8
116.6
89.72
103.4
151.8
Means with a different letters within columns are significantly different (P<0.05).
As anticipated, the concentration of crude protein (CP) declined from the early to fourth-cut
silage, but the latest cut showed higher CP content (P<0.05) than the fourth-cut silage (Table
1).
For quantitative evaluation methods multiple linear regression (MLR), principal component
regression (PCR) and partial least squares regression (PLS) were used to for data modelling
and for predicting the investigated chemical constituents. For model validation, full crossvalidation was used; each case was predicted by the model derived from all other remaining
spectra. The results of full cross-validation is the average of the standard error of prediction
values produced during each cross-validation step. The recorded log (1/R) spectra were
smoothed (5 points) and transformed to second derivative (7 points) before the analysis.
Optimum models were achieved using 9 orthogonal factors with the PLS method and 5 PCs
with PCR, whilst for MLR only the spectral values measured at two wavelengths were used.
4
Figure 1, 4, and 7 show the relationship between chemical (LAB) data and data calculated
from near infrared (NIR) spectral data for dry matter (g/kg fresh sample) for the 103 fresh
silage samples investigated. These were determined using PLS, PCR, and MLR methods,
respectively, for evaluation. The results of the calibration procedures are summarised in the
upper figure, while the cross-validated results are presented in the lower figures. It can be
seen in the figures that the smallest value for prediction error (SEP = 29,7 dry matter g/kg
fresh sample) with the highest multiple correlation coefficient (r = 0,91) was provided by the
PLS method. The application of PCR using the whole wavelength region (1100-2500 nm, 700
data) requiring scanning type spectrometers as with PLS, resulted in an almost twice as high
prediction error. The same order of magnitude could be obtained for SEP value by using
MLR. Compared to PCR and PLS the MLR model uses only two characteristic wavelengths
for the calculation, creating the conditions for constructing cheaper single-purpose filter
instruments (In reality – using second derivative – 6 spectral data point are needed).
Similarly as in figures 1, 4, and 7, the relationship between chemical (LAB) data and data
calculated from near infrared (NIR) spectral data for crude protein (% FW) of 103 fresh silage
samples, determined by the above three methods are shown in Figures 2, 5 and 8, while for
crude protein content (g/kg DM) the results are shown in Figures 3, 6 and 9. By studying the
figures presented, the same conclusion can be drawn as in case of the investigation of dry
matter content discussed in the previous paragraph. PLS provided the highest correlations and
the smallest prediction errors.
5
Computed Value (NIR data)
800
600
dry matter (g/kg fresh
sample)
Number of elements:
103
Standard error of calibration:
22,2
Multiple correlation
coefficient:
0,95
400
200
200
400
500
Constituent (LAB data)
600
700
400
500
Constituent (LAB data)
600
700
300
800
Computed Value (NIR data)
dry matter (g/kg fresh
sample)
600
Number of samples:
103
Standard error of prediction:
29,7
Multiple correlation
coefficient:
0,91
400
200
200
300
Figure 1 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for dry matter (g/kg fresh sample) of 103 fresh silage samples,
determined using PLS method for evaluation. Calibration results above, cross-validated
results below. Wavelength range: 1100-2500 nm. The log(1/R) spectra were smoothed (5
points) and transformed to second derivative (7 points)
6
Computed Value (NIR data)
8
6
crude protein %
Number of elements:
103
Standard error of
calibration:
0,24
Multiple correlation
coefficient:
0,97
4
2
2
3
4
6
5
Constituent (LAB data)
7
8
7
8
8
Computed Value (NIR data)
crude protein %
6
Number of samples:
103
Standard error of prediction:
0,31
Multiple correlation
coefficient:
0,95
4
2
2
3
4
5
Constituent (LAB data)
6
Figure 2 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for crude protein % of 103 fresh silage samples, determined using
PLS method for evaluation. Calibration results above, cross-validated results below.
Wavelength range: 1100-2500 nm. The log(1/R) spectra were smoothed (5 points) and
transformed to second derivative (7 points)
7
Computed Value (NIR data)
150
120
crude protein content
(g/kg DM)
Number of elements:
103
Standard error of
calibration:
5,86
Multiple correlation
coefficient:
0,96
90
60
60
Computed Value (NIR data)
150
120
80
120
100
Constituent (LAB data)
140
160
120
100
Constituent (LAB data)
140
160
crude protein content
(g/kg DM)
Number of elements:
103
Standard error of prediction:
6,65
Multiple correlation
coefficient:
0,94
90
60
60
80
Figure 3 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for crude protein content (g/kg DM) of 103 fresh silage samples,
determined using PLS method for evaluation. Calibration results above, cross-validated
results below. Wavelength range: 1100-2500 nm. The log(1/R) spectra were smoothed (5
points) and transformed to second derivative (7 points)
8
800
Computed Value (NIR data)
dry matter (g/kg fresh
sample)
600
Number of elements:
103
Standard error of calibration:
50,4
Multiple correlation
coefficient:
0,69
400
200
200
300
400
500
Constituent (LAB data)
600
700
600
700
800
Computed Value (NIR data)
dry matter (g/kg fresh
sample)
600
Number of samples:
103
Standard error of prediction:
51,7
Multiple correlation
coefficient:
0,66
400
200
200
300
400
500
Constituent (LAB data)
Figure 4 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for dry matter (g/kg fresh sample) of 103 fresh silage samples,
determined using PCR method for evaluation. Calibration results above, cross-validated
results below. Wavelength range: 1100-2500 nm. The log(1/R) spectra were smoothed (5
points) and transformed to second derivative (7 points)
9
Computed Value (NIR data)
8
6
crude protein %
Number of elements:
103
Standard error of
calibration:
0,60
Multiple correlation
coefficient:
0,79
4
2
2
3
4
5
6
7
8
6
7
8
Constituent (LAB data)
Computed Value (NIR data)
8
6
crude protein %
Number of elements:
103
Standard error of
prediction:
0,64
Multiple correlation
coefficient:
0,76
4
2
2
3
4
5
Constituent (LAB data)
Figure 5 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for crude protein % of 103 fresh silage samples, determined using
PCR method for evaluation. Calibration results above, cross-validated results below.
Wavelength range: 1100-2500 nm. The log(1/R) spectra were smoothed (5 points) and
transformed to second derivative (7 points)
10
Computed Value (NIR data)
150
120
crude protein content
(g/kg DM)
Number of elements:
103
Standard error of
calibration:
7,94
Multiple correlation
coefficient:
0,91
90
60
60
Computed Value (NIR data)
150
120
80
120
100
Constituent (LAB data)
140
160
120
100
Constituent (LAB data)
140
160
crude protein content
(g/kg DM)
Number of elements:
103
Standard error of
prediction:
8,99
Multiple correlation
coefficient:
0,89
90
60
60
80
Figure 6 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for crude protein content (g/kg DM) of 103 fresh silage samples,
determined using PCR method for evaluation. Calibration results above, cross-validated
results below. Wavelength range: 1100-2500 nm. The log(1/R) spectra were smoothed (5
points) and transformed to second derivative (7 points)
11
Calibration (dry matter g/kg)
The standard error of calibration
= 47,4
The multiple correlation coefficient = 0,73
Computed Value (NIR data)
dry matter (g/kg fresh
sample)
Number of elements:
103
Constituent (LAB data)
Cross Validation (dry matter g/kg)
The standard error by cross validation = 48,4
The multiple correlation coefficient = 0,71
Figure 7 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for dry matter (g/kg fresh sample) of 103 fresh silage samples
determined using MLR method for two wavelengths. Calibration results above, crossvalidated results below. Wavelength range used for selecting the two characteristic
wavelengths: 1100-2500 nm. The log(1/R) spectra were smoothed (5 points) and transformed
to second derivative (7 points)
12
Calibration (crude protein %)
The used two wavelengths: 1948, 2166 nm
The standard error of calibration
= 0,58
The multiple correlation coefficient = 0,81
crude protein %
103
Computed Value (NIR data)
Number of elements:
Constituent (LAB data)
Cross Validation (crude protein %)
The standard error by cross validation = 0,59
The multiple correlation coefficient = 0,79
Figure 8 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for crude protein % of 103 fresh silage samples determined using
MLR method for two wavelengths. Calibration results above, cross-validated results below.
Wavelength range used for selecting the two characteristic wavelengths: 1100-2500 nm. The
log(1/R) spectra were smoothed (5 points) and transformed to second derivative (7 points)
13
Calibration (crude protein g/kg DM)
The used two wavelengths: 1142, 2080 nm
The standard error of calibration
= 8,80
The multiple correlation coefficient = 0,89
Computed Value (NIR data)
crude protein content
(g/kg DM)
Number of elements:
103
Constituent (LAB data)
Cross Validation (crude protein g/kg DM)
The standard error by cross validation = 8,96
The multiple correlation coefficient = 0,89
Figure 9 The relationship between chemical (LAB) data and data calculated from near
infrared (NIR) spectral data for crude protein content (g/kg DM) of 103 fresh silage samples
determined using MLR method for two wavelengths. Calibration results above, crossvalidated results below. Wavelength range used for selecting the two characteristic
wavelengths: 1100-2500 nm. The log (1/R) spectra were smoothed (5 points) and transformed
to second derivative (7 points)
14
Discussion
Samples selected for calibration (Table 1) comprised a wide range from 216 to 629 g kg-1 for
dry matter (DM), from 1.88 to 7.5 % for crude protein (CP) and from 72.4 to 151.8 for CP g
kg-1DM.
DM content increases and CP decreases with advanced maturity (Castle et al., 1980;
Cushnahan et al., 1996). This is confirmed by a certain instance in our study as fifth cut silage
shows the highest mean DM value, but the last cut silage had higher CP content compared
with the third cut silage that was probably caused by higher ammonium concentration.
PLS regression analysis of the spectral data with DM content resulted in the highest
prediction correlation coefficients (R) compared with PCA and MLR method (Table 2). Also,
differences in standard error of full cross-validation (SECV) for DM prediction show the
SECV achieved by PLS (29.74) was more than 40% lower compared with PCR (51.64 g kg-1)
or MLR methods (48.74 g kg-1).
Table 2. Comparison of the accuracy of the LAB data with the results achieved by near
infrared spectroscopy using three different methods for evaluation
PLS
PCR
MLR
LAB
data
R
SEP
R
SEP
R
SEP
SEL
dry matter (g/kg
0,90
29,74
0,66
51,64
0,71
48,74
6.79
fresh sample)
crude protein %
0,95
0,31
0,75
0,64
0,79
0,59
0.095
crude
protein
(g/kg DM)
0,94
6,64
0,89
8,99
0,89
8,96
1.91
The SECV values for CP of 6.64, 8.99 and 8.96 (on DM basis) for PLS, PCR and MLR,
respectively, are comparable with Murray’s (1986) results that was 8.4 and with the data
reported by Flinn and Murray (1987) being 10.5 and 11.1.
The SECV for CP of 6.64; 8.99 and 8.96 g kg-1DM gained by PLS, PCR and MLR methods,
respectively, is comparable with the range of 6.0-13.4 g kg-1 reported by Norris et al. (1976),
Marten et al. (1983) and Murray (1986).
The two wavelengths chosen for calibration by the MLR method were 1366 and 2418 nm for
DM (g kg-1); 1142 and 2080 nm for CP (g kg-1DM), and 1948 and 2166 for CP (% FW). They
are not within the range of 2100-2164 quoted by Redshaw et al. (1986) and Murray (1986).
The MLR evaluation method for CP (gkg-1DM) indicates a strong relationship (R2 of 0.88)
with spectroscopic data.
Several authors (Windham and Flinn, 1992; Rosental and Williams, 1996; Blanco et al.,
1998) compared results obtained by MLR and PLS in different products and concluded PLS
and MLR give nearly the same prediction errors. In our study, PLS was more accurate than
MLR method. One of the reasons might be that 9 components were used for PLS compared
15
with 5 PC’a used for PCR and 2 wavelengths used for MLR. In our paper the SEP values
were all higher than SEC results, similar to the ones reported by Stimson et al. (1991).
The lowest SEP achieved for CP (%) content among the methods applied may be due to the
fact the samples were scanned fresh and CP content in % was recalculated to the fresh weight
while DM (gkg-1DM) and CP (gkg-1DM) were expressed on DM basis.
Differences between SEC and SEP values may be due to the limited number of samples. This
clearly reflects the problem of obtaining representative samples in practice. Also, grass silage
samples, which are very heterogeneous, were scanned fresh without any pre-treatment while
for wet chemistry analysis the same samples were dried and ground.
Although errors are often slightly higher for samples scanned in their natural state than for
dried and milled samples, this is balanced by the ability to scan much larger samples, the
avoidance of compositional losses and changes due to oven drying and a major reduction in
analysis time and cost due to no sample preparation being necessary.
The findings indicate spectroscopic data evaluated by PLS method were strongly related to
reference values and had lower SECV values compared with PCA and MLR methods.
CP (gkg-1) content in fresh grass silage samples determined by MLR method (R2=0.88)
suggests that this trait may be also accurately estimated by inexpensive filter instruments.
Conclusion
The NIRS estimation of forage chemical composition is a relatively inexpensive, a rapid and a
reliable method compared with reference methods. It requires a relatively small quantity of
sample and predicts several concentrations of components simultaneously. An important
advantage of NIRS is its ability to analyse samples without chemical treatment, hence costs
and chemical wastes can be reduced by the use of accurate NIRS equation. The success of
NIRS analysis depends almost entirely on the reliability of the primary, reference data, used at
calibration.
NIRS can predict DM and CP content in fresh grass silage samples without costly or lengthy
pre-treatment as shown in this paper.
Nevertheless, a satisfactory accuracy with an average standard error of prediction by PLS
method of 29.74, 0.31 and 6.64 for DM, CP (%) and CP (gkg-1), respectively, was obtained.
The comparison of validation statistics (R2 and SECV) among PLS, PCR and MLR equations
(with two wavelengths per variable) showed PLS to be the most accurate. (However, it does
use 9 factors compared with 5 for PCA and 2 for MLR.)
The MLR evaluation method for CP (gkg-1DM) has the potential to be used for industry
requirements, as it needs less sophisticated and cheaper instrumentation using only a few
wavelengths.
Acknowledgments
The authors express their sincere thanks to Professor Ian Murray from SAC for introducing
them to the NIR technology and Professor Károly Kaffka from Corvinus University of
Budapest, Hungary for providing helpful comments on the results. Thank also goes to Dr. Ian
16
Lane from ULG Consultants, UK for his technical assistance in establishing NIR unit within
the Faculty of Agriculture, the University of Zagreb.
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