1. Lab of Plant Genetics and Breeding
Application of NIR for the estimation of nutritive values in forage samples of soybean recombinant inbred lines for quantitative trait loci analysis
Lab of Plant Genetics and Breeding
Sovetgul Asekova
KABS Seminar

2. I. Determination of forage quality by near-infrared reflectance spectroscopy in soybean
Introduction I.

3. I. Introduction Background
Soybean [Glycine max (L.) Merr.] is a productive annual legume crop for human and animal consumptions
Although presently grown almost entirely as an oil-seed crop, the early use of soybean was for forage (USDA, 1940)
Soybean forage could be successfully used in ruminant diets because it has less impact on the environment through reduced methane emissions with an acceptable energy content and nutrient digestibility 3

4. Literature Review
Limited amount of research has been conducted to determine the nutritional quality of forage soybean (Hintz et al., 1992a, 1994b; Sheaffer et al., 2001)
Adapted grain cultivars were the most suitable for forage production (Sheaffer et al., 2001; Chang et al., 2012)
Soybean dry matter yields increase as developmental stages progress from R1 to R7 (Hintz et al., 1992; Munoz et al., 1983).
The forage yield and quality for soybean lines derived from G. soja x G. max were evaluated and these inter-specific cross would be an excellent method to develop forage soybean with good yield and quality (Lee et al., 2014) 4

5. Literature Review
Forage quality can be determined by measuring the crude protein (CP), crude fat (CF), acid detergent fiber (ADF), neutral detergent fiber (NDF), total digestible nutrients (TDN), and relative feed value (RFV)
However the traditional methods are labor intensive and inefficient
Near-infrared reflectance spectroscopy (NIRS) is an indirect and efficient method has a unique near infrared absorption properties of the major chemical components of a sample
No information was presented on measuring forage quality of forage soybean by using NIR, validly 5

6. Courtesy by Ye, W. Lorimor, J. C. . Zhang, Hattey H. J.

7. I. Introduction Objective
The objective of this study was to develop calibration equations for NIR determination of soybean forage quality parameters CP, CF, NDF, and ADF 7

8. Materials & Methods
II.

9. II. MATERIALS AND METHODS Plant materialsX
181 RILs derived from PI (G. soja)  Hutcheson (G. max) (Ha et al., 2013)
104 cultivated soybeans (G. max)
randomly selected from a soybean germplasm collection
at Kyungpook National University
68 wild soybeans (G. soja) that were collected in Korea
Total of 353 forage soybean samples were used for this study 9

10. II. Materials and Methods
A single soybean plant from each plot was collected at the R6 growth stage
Samples were dried in a forced-air oven at (60°C) until they reached constant weight
Grounded in a Cyclotec mill to pass through a 1-mm sieve
The moisture content for each sample was determined from 1g of forage powder by using a Sartorius moisture analyzer MA35
Populations were planted on 4th of July, 2012 at the Gyeongsangbuk-do Agricultural Research Service farm, Daegu, Republic of Korea 10

11. II. Materials and Methods
Proximate analysis
Crude protein for soybean forage was analyzed by using the Kjeldahl method AOAC (1990)
The crude fat content was determined by the auto-soxhlet method with Soxtherm apparatus (Gerhardt, Bonn, Germany).
- The ADF and NDF were determined by using a Fiber Analyzer (ANKOM2000 Fiber Analyzer, Ankom Technology, Macedon,NY) 11

12. II. Materials and Methods
NIRS application
All the samples were scanned over a wavelength range of 400 to 2,498 nm at every 2 nm to give total of 1,050 data points on a XDS-NIRS Rapid Content Analyzer (FOSS Analytical, Slangerupgade, Denmark)
Each sample was subsequently scanned and the average spectrum was collected to process calibration, cross-validation, and external validation 12

14. II. Materials and Methods
NIRS application
Calibration
Calibration models were developed using WinISI software, version 1.50.
Calibration was performed using recommended modified partial least squares (MPLS) as well as partial least squares (PLS) and principal component regression (PCR) in developing compatible calibrations for soybean forage components
Prior to MPLS regression, multiple scatter corrections (MSC) algorithm was applied 14

15. II. Materials and Methods
NIRS application
Calibration
Two mathematical treatments, 1,4,4,1, which was from the first derivative (D1), and 2,5,5,1, which was from the second derivative (D2), were used to maximize the calibration results 15

16. NIR: Statistical indicators
II. Materials and Methods
NIR: Statistical indicators
Cross-validation
R², coefficient of determination for calibration, >0.9 is considered to have excellent accuracy
RER (range/SEP) (> 10)
RPD (SD/SEP) (>3.0)
SEC, standard error of calibration
SECV, standard error of cross-validation
SEP, standard error of prediction
SEP(C), standard error of prediction corrected for bias
1-VR, coefficient of determination for cross-validation [one minus the variance ratio (1-VR)]
The RER should be more than 10 and is often more than 20. Williams and Norris (2001) also stated that an RPD value more than 2.4 is desirable for good calibration equations, whereas equations with RPD of less than 1.5 are unusable. 16

17. NIR: Statistical indicators
II. Materials and Methods
NIR: Statistical indicators
External validation
One hundred samples from the original sample set were selected to check the NIRS calibration equations independently
The coefficient of determination in validation (r2); the standard error of performance (SEP); the standard error of prediction corrected for bias [SEP(C)]; relative predictive determinant RPDv and the and the range to error ratio (RER) [Range/SEP(C)] were used to evaluate the predictive ability of the models 17

18. RESULTS AND DISCUSSION
III

19. III. Results and Discussion
Table 1. Descriptive statistics of soybean forage quality parameters used in calibration and validation sets.
Groups
CP (%)
CF (%)
NDF (%)
ADF (%)
Range
Mean ± SD
Calibration
10.9–25.7
19.6 ± 2.2
0.7–10.5
4.4 ± 1.7
37.4–66.6
50.7 ± 4.6
22.6–38.1
29.4 ± 2.8
(n = 353)
Validation
(n = 100)
14.4–24.8
19.5 ± 2.2
1.1–10.5
3.9 ± 2.1
37.4–58.8
48.9 ± 4.1
22.6–37.6
28.8 ± 2.8 19

20. III. Results and Discussion
Table 2. Equation development statistics by using modified partial least squares (MPLS) and multiple scatter correction MSC (D2g) (2,5,5,1) to predict forage quality by using near-infrared reflectance spectroscopy (NIRS). a
Groups N
Mean SD
Calibration
Cross-validation
RPDc
SEC R2
1 - VR
SECV
Crude protein
336
19.62
2.17
0.608
0.922
0.911
0.650
3.34
Crude fat
319
4.41
1.61
0.387
0.942
0.916
0.467
3.45 20

21. III. Results and Discussion
Table 3. Equation development statistics by using modified partial least squares (MPLS) and multiple scatter correction (MSC; D1) (1,4,4,1) to predict forage quality by using near-infrared reflectance spectroscopy (NIRS). a
Groups
　Na
Mean
　SD
Calibration
Cross-validation
RPDc
SEC R2
1 - VR
SECV
NDF
332
50.64
4.3
1.677
0.848
0.818
1.836
2.34
ADF
336
29.19
2.6
1.192
0.789
0.749
1.32
1.97 21

22. III. Results and Discussion
Galvez-Sola et al. (2010) also suggested a guideline for describing the performance of calibrations for environmental samples as follows:
- excellent calibrations r2 > 0.95, RPD > 4;
- successful, r2 > 0.9–0.95, RPD 3–4;
- moderately successful, r2 > 0.8–0.9, RPD 2.25–3;
- and moderately useful, r2 > 0.7–0.8, RPD 1.75–2.25.
Some calibrations with r2 > 0.7 may be useful for screening purposes
Using this criterion successful calibration equations were obtained for CP and CF model, and moderately useful calibration equations for NDF and ADF model in this study 22

23. III. Results and Discussion
Table 4. Monitoring statistics of eternal validation set (modified partial least squares [MPLS] and multiple scatter correction [MSC] ‘2,5,5,1’) to predict forage quality by using near-infrared reflectance spectroscopy (NIRS). a
Groups N
Mean
Range SD
SEP r2
SEP(C)
bias
slope
RPDv
RER
Crude protein 93
19.34
2.11
0.912
0.909
0.649
0.644
1.067
3.25
16.02
Crude fat
3.70
1.91
0.537
0.934
0.497
-0.208
1.040
3.85
18.91 23

24. III. Results and Discussion
Table 5. Monitoring statistics of external validation set (modified partial least squares [MPLS] and multiple scatter correction [MSC] ‘1,4,4,1’) to predict forage quality by using near-infrared reflectance spectroscopy (NIRS). a
Groups N
Mean
Range SD
SEP r2
SEP(C)
bias
slope
RPDv
RER
NDF 98
49.00
4.00
2.053
0.767
1.934
-0.715
0.978
2.07
11.07
ADF 96
28.83
2.80
1.557
0.748
1.419
-0.658
1.085
1.97
10.57 24

25. IV. Conclusion
Our results showed that NIR could constitute a feasible technique to quantify several essential soybean forage quality parameters such as CP, CF, NDF, and ADF and use for screening purposes in soybean breeding program
This research will be the first step towards the using NIR equation on chemical constituents of whole forage type soybeans and developing additional chemometric models 25

26. II. QTL mapping for forage quality and yield of whole type soybeans
The loci controlling quantitative traits are commonly referred to as quantitative trait loci (QTL)
A recombinant inbred line (RIL) is a suitable population designed for studying QTL analysis, since it is genetically homozygous, stable and can be reproduced identically

27. II. Materials and Methods
planted on 7th of June, 2013 and at the Affiliation field of KNU, Gun-wi
190 RILs (Hutcheson x PI483463) :
RCBD with three replications and a check cvr. Williams 82 (1 x 1.5 m apart)
R6 stage
Dry in forced air oven (60ºC)
Grinding 1mm sieve
Identification of Quantitative Trait Loci (QTL) of forage quality and yield of forage soybean 27

30. 감사합니다

31. III. Results and Discussion
In general, test samples and calibration samples should be as similar as possible. 31

32. III. Results and Discussion32