• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1032-1032
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• 2001

Artificial Neural Network (ANN) calibration techniques have been used commercially for agricultural applications since the mid-nineties. Global models, based on transmission data from 850 to 1050 nm, are used routinely to measure protein and moisture in wheat and barley and also moisture in triticale, rye, and oats. These models are currently used commercially in approx. 15 countries throughout the world. Results concerning earlier European ANN models are being published elsewhere. Some of the findings from that study will be discussed here. ANN models have also been developed for coarsely ground samples of compound feed and feed ingredients, again measured in transmission mode from 850 to 1050 nm. The performance of models for pig- and poultry feed will be discussed briefly. These models were developed from a very large data set (more than 20,000 records), and cover a very broad range of finished products. The prediction curves are linear over the entire range for protein, fat moisture, fibre, and starch (measured only on poultry feed), and accuracy is in line with the performance of smaller models based on Partial Least Squares (PLS). A simple bias adjustment is sufficient for calibration transfer across instruments. Recently, we have investigated the possible use of ANN for a different type of NIR spectrometer, based on reflectance data from 1100 to 2500 nm. In one study, based on data for protein, fat, and moisture measured on unground compound feed samples, dedicated ANN models for specific product classes (cattle feed, pig feed, broiler feed, and layers feed) gave moderately better Standard Errors of Prediction (SEP) compared to modified PLS (MPLS). However, if the four product classes were combined into one general calibration model, the performance of the ANN model deteriorated only slightly compared to the class-specific models, while the SEP values for the MPLS predictions doubled. Brix value in molasses is a measure of sugar content. Even with a huge dataset, PLS models were not sufficiently accurate for commercial use. In contrast an ANN model based on the same data improved the accuracy considerably and straightened out non-linearity in the prediction plot. The work of Mr. David Funk (GIPSA, U. S. Department of Agriculture) who has studied the influence of various types of spectral distortions on ANN- and PLS models, thereby providing comparative information on the robustness of these models towards instrument differences, will be discussed. This study was based on data from different classes of North American wheat measured in transmission from 850 to 1050 nm. The distortions studied included the effect of absorbance offset pathlength variation, presence of stray light bandwidth, and wavelength stretch and offset (either individually or combined). It was shown that a global ANN model was much less sensitive to most perturbations than class-specific GIPSA PLS calibrations. It is concluded that ANN models based on large data sets offer substantial advantages over PLS models with respect to accuracy, range of materials that can be handled by a single calibration, stability, transferability, and sensitivity to perturbations.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.4
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• pp.350-357
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• 2017

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.45-51
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• 2008

A single layer-latticed dome is advantageous for large span structures because it is very stiff despite the light weight of the structure itself. However, this structure becomes easily unstable during erection due to its large size. The Block method is popular with the large span structures. A partial block of the dome is fabricated on the ground and lifted by crane to a designated location of structures. The lifting point selection is very important to create a stable erection and to avoid buckling of members during the erection. The purpose of this study is to analyze the structural behaviors and buckling characteristics according to the lifting point of single-layer latticed domes with triangle network in order to take materials about the safe and economic erection. The conclusions are obtained as follow. 1) The buckling strength of the block part varies with the location of lifting points when it is erected. In case, the height of the dome is lower, the effort of buckling strength of the structure is higher. 2) In buckling strength, the effect of the lifting rope length is smaller than it of the lifting points change.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.10
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• pp.1-43
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• 1971

Experiment I: The objective of this study was to know variation in some selected agronomic characters of sweet sorghum when planted in several growing seasons. The 17 different sweet sorghum varieties having various maturities, and plant, syrup and sugar types were used in this study which had been carried out for the period of two years from 1968 to 1969 at Industrial Crops Division of Crop Experiment Station in Suwon. These varieties were planted at an interval of 20 days from April 5 to August 25 both in 1968 and 1969. The experimental results could be summarized as follows: 1. As planting was made early, the number of days from sowing to germination was getting prolonged while germination took place early when planted at the later date of which air temperature was relatively higher. However, such a tendency was not observed beyond the planting on August 25. In general, a significant negative correlation was found between the number of days from sowing to germination and the average daily temperature but a positive correlation was found between the former and the total accumulated average temperature during the growth period. 2. The period from sowing to heading was generally shortened as planting was getting delayed. The average varietal difference in number of days from sowing to heading was as much as 30.2 days. All the varieties were grouped into early-, medium and late-maturing groups based upon a difference of 10 days in heading. The average number of days from sowing to heading was 78.5$\pm$4.5 days in the early-maturing varieties, 88.5$\pm$4.5 days in the medium varieties and 98.5$\pm$4.5 days in the late-maturing varieties, respectively. The early-maturing varieties had the shortest period to heading when planted from July 15 to August 5, the medium varieties did when planted before July 15 and the late-maturing varieties did when planted before June 5. 3. The relationship between the sowing date (x) and number of days from sowing to heading could be expressed in an equation of y=a+bx. A highly positive correlation was found between the coefficient of the equation(shortening rate in heading time) and the average number of days from sowing to heading. 4. The number of days from sowing to heading was shortened as the daily average temperature during the growth period was getting higher. Early-maturing varieties had the shortest period to heading at a temperature of 24.2$^{\circ}C$, medium varieties at 23.8$^{\circ}C$ and late-maturing varieties at 22.9$^{\circ}C$, respectively. In other words, the number of days from sowing to heading was shortened rapidly in case that the average temperature for 30 days before heading was 22$^{\circ}C$ to $25^{\circ}C$. It prolonged relatively when the temperature was lower than 21$^{\circ}C$. 5. There was a little difference in plant height among varieties. In case of early planting, no noticeable difference in the height was observed. The plant height shortened generally as planting season was delayed. Elongation of plant height was remarkably accelerated as planting was delayed. This tendency was more pronounced in case of early-maturing varieties rather than late-maturing varieties. As a result, the difference in plant height between the maximum and the minimum was greater in late-maturing varieties than in early-maturing varieties. 6. Diameter of the stalk was getting thicker as planted earlier in late-maturing varieties. On the other hand, medium or early-maturing varieties had he thickest diameter when they were planted on April 25. 7. In general, a higher stalk yield was obtained when planted from April 25 to May 15. However, the planting time for the maximum stalk yield varied from one variety to another depending upon maturity of variety. Ear]y-maturing varieties produced the maximum yield when planted about April 25, medium varieties from April 25 to May 15 and late-maturing varieties did when planted from April 5 to May 15 respectively. The yield decreased linearly when they were planted later than the above dates. 8. A varietal difference in Brix % was also observed. The Brix % decreased linearly when the varieties were planted later than May 15. Therefore, a highly negative relationship between planting date(x) and Brix %(y) was detected. 9. The Brix % during 40 to 45 days after leading was the highest at the 1st to the 3rd internodes from the top while it decreased gradually from the 4th internode. It increased again somewhat at the 2nd internode from the ground level. However, it showed a reverse relationship between the Brix % and position of internode before heading. 10. Sugar content in stalk decreased gradually as planting was getting delayed though one variety differed from another. It seemed that sweet sorghum which planted later than June had no value as a sugar crop at all. 11. The Brix % and sugar content in stalk increased from heading and reached the maximum 40 to 45 days after heading. The percentage of purity showed the same tendency as the mentioned characters. Accordingly, a highly positive correlation was observed between. percentage of purity and Brix % or sugar content in stalk. 12. The highest refinable sugar yield was obtained from the planting on April 25 in late-maturing varieties and from that on May 15 in early-maturing varieties. The yield rapidly decreased when planted later than those dates. Such a negative correlation between planting date(x) and refinable sugar yield(y) was highly significant at 1% level. 13. Negative correlations or linear regressions between delayed planting and the number of days from sowing to germination. accumulated temperature during germination period, number of days to heading, accumulated temperature to heading, plant height, stem diameter, stalk weight, Brix %. sugar content, refinable sugar yield or Purity % were obtained. On the other hand, highly positive correlations between the number of days from sowing to heading(x) and Brix %, sugar content, purity %, refinable sugar yield, plant height or stalk yield, between Brix %(x) and purity %, refinable sugar yield or stalk yield, between sugar content(x) and purity% or refinable sugar yield(y), between purity %(x) and refinable sugar yield and between daylength at heading(x) and Brix %. number of days from sowing to heading, sugar content, purity % or refinable sugar yield (y), were found, respectively. Experiment II: The 11 varieties were selected out of the varieties used in Experiment I from ecological and genetic viewpoints. Complete diallel cross were made among them and the heading date, stalk length, stalk yield, Brix %, syrup yield, combining ability and genetic behavior of F$_1$ plants and their parental varieties were investigated. The results could be summarized as follows: 1. In general, number of days to heading showed a partial dominance over earliness or late maturity or had a mid-value, though there were some specific combinations showing a complete dominance or transgressive segregation in maturity. Some combinations showed relatively high general or specific combining abilities in maturity. Therefore, a 50 to 50 segregation ratio in heading date could be estimated in this study and it might be positive to have a selection in early generation since heritability of the character was relatively high. 2. A vigorous hybrid vigor was observed in stalk length. A complete or partial dominant effect of long stalk was obtained. The general combining ability and specific combining ability of stalk length were generally high. Long and short stalks segregated in a ratio of 50:50 and its heritability was relatively low. 3. Except for several specific combinations, high stalk yield seemed to be partial dominant over the low yield. Some varieties demonstrated relatively high general as well as specific combining abilities. It was assumed that several recessive genes were involved in expression of this character. The interaction among regulating recessive genes was also obtained. Accordingly, the heritability of stalk yield seemed to be rather low. 4. The Brix % of hybrid plants located around mid-parental value though some of them showed much higher or lower percentage. It could be explained by the fact that such behavior might be due to partial dominance of Brix %. The varieties with, relatively higher Brix % were high both in general. and specific combining abilities. Therefore, it could be recommended to use the varieties having higher sugar content in order to develop higher-sugar varieties. 5. The syrup yield seemed to be transgressively segregated or completely dominant over low yield. Hybrid vigor of syrup yield was relatively high. No-consistent relationship between general combining ability and specific combining ability was observed. However, some cases demonstrated that the varieties with relatively higher general combining ability had relatively lower specific combining ability. It was assumed that the frequencies of dominant and recessive alleles were almost same.