Judul: Prediction of chemical compositions of calophyllum inophyllum l. Seeds by Near Infrared (NIR) method
Abstrak: The objective of this study was to apply NIR method to predict chemical composition of Calophyllum inophyllum L. seeds accurately (moisture, fat, acid number, free fatty acid contents). The reflectance of seeds at the wavelength ranges from 1000 to 2500 nm (4000-10000 cm-1 at intervals of 4 cm-1) were measured by NIRFlex Solids Petri Apparatus. Calibration method which used in this study are principal component regression (PCR) and partial least squares (PLS). Data treatment on the reflectance and absorbance spectrum are used that is: smooth average 3 points, second derivative Savitzky-Golay 9 points, normalization 0-1, combination both of smooth average 3 points and second derivative Savitzky-Golay 9 points, and combination of all data treatment. A number of 70 Calophyllum inophyllum L. seeds were used as samples. Samples were divided into two parts: ± 45 samples (2/3 of total samples) for developing calibration equation and ± 25 samples (1/3 of total samples) for performing validation. NIR data analysis shows that PLS method with NIR reflectance data and the combination both of smooth average 3 points and second derivative Savitzky-Golay 9 points is the best method of calibration and data treatment to predicting moisture contents of Calophyllum inophyllum L. seeds was also prediction with standard error of prediction (SEP) of 0.45% and coefficient of variability (CV) of 0.81%. Prediction of fat contents of Calophyllum inophyllum L. seeds best obtained with the PLS method, the reflectance data, and the combination both of smooth average 3 points and second derivative Savitzky-Golay 9 points was also prediction with standard error of prediction (SEP) of 0.37% and coefficient of variability (CV) of 0.62%. Prediction of acid number contents of Calophyllum inophyllum L. seeds best obtained with the PLS method, the absorbance data, and the second derivative Savitzky-Golay 9 points data treatment was also prediction with standard error of prediction (SEP) of 0.04% and coefficient of variability (CV) of 0.09%. And prediction of free fatty acid contents of Calophyllum inophyllum L. seeds best obtained with the PLS method, the reflectance data, and the combination was all data treatments was also prediction with standard error of prediction (SEP) of 0.04% and coefficient of variability (CV) of 0.18%.
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Judul: Prediction of moisture, protein and carbohydrate content of sorghum by using near infrared (nir) method
Abstrak: Sorghum is local resources that can be used as a substitution of corn on feed. Moisture, protein and carbohydrate contents of sorghum can be predicted using near infrared method. The aim of this research was to apply NIR method to analyze moisture, protein and carbohydrate contents of sorghum. The calibration methods that used in this research are principal component regression (PCR) and partial least square (PLS). Some data treatments are applied on absorbance and reflectance spectrum. They are smooth average 3 points, normalization between 0-1, second derivative Savitzky-Golay 9 points, combination of smooth average 3 points and second derivative Savitzky-Golay 9 points, and combination of smooth average 3 points, normalization between 0-1, and second derivative Savitzky-Golay 9 points. PLS method with absorbance data and combination of smooth average 3 points, normalization between 0-1, and second derivative Savitzky-Golay 9 points is the best calibration method and data treatment to predict moisture content of sorghum. The best calibration method to predict protein content is PLS with absorbance data and second derivative Savitzky-Golay 9 points data treatment. PLS method with absorbance data and second derivative Savitzky-Golay is the best calibration method and data treatment to predict carbohydrate content. Standard error of prediction (SEP) and coefficient of variability (CV) respectively were 0.005% and 0.04% for moisture content prediction, 0.01% and 0.18% for protein content prediction, 0.01% and 0.02% for carbohydrate content prediction.
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Judul: Penyelesaian Masalah Pelabelan Graf vertex Magic pada Graf Cycle Sederhana
Abstrak: Graf vertex magic adalah suatu graf memiliki nilai bilangan magic yang sama pada masing-masing vertex yang berbeda. Graf cycle sederhana adalah suatu walk tertutup yang mengandung setidaknya tiga vertex, semua vertex-nya berbeda dan setiap vertex terhubungkan oleh satu edge.
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