Rosdisham Endut
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Rosdisham Endut
Official Name
Rosdisham, Endut
Alternative Name
Endut, Rosdisham
Endut, R.
Main Affiliation
Scopus Author ID
57189347166
Researcher ID
ABC-3290-2020

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Nitrate (NO3-) prediction in soil analysis using near-infrared (NIR) spectroscopy

2020-01-08 , Sabri, Mohd Shafiq Amirul , Rosdisham Endut , Mohd Rashidi Che Beson , Syed Alwee Aljunid Syed Junid , Norshamsuri Ali @ Hasim , Laili M.H. , Laili A.R. , Ismail M.N.M.

Nutrient composition in soil analysis is investigated by using nitrogen (N) in form of nitrate (NO3-) as a representative factor correlated with NIR spectroscopy spectral absorbance. NIR spectroscopy method of sampling has been tested to overcome time consuming, complex chemical analysis procedure and invasive sampling method in order to identify nitrate content in soil samples. Spectral absorbance data from range 950 nm to 1650 nm correlated with nitrate reading then tested through few pre-processing techniques. Five techniques have been listed as top performer, which are Multiplicative Scatter Correction using Common Offset (MSCCO), Multiplicative Scatter Correction (MSC), Range Normalization (RN), Mean Normalization (MN) and Reduced (R) technique. Data calibration and prediction of both data is evaluated using Partial Least Square Regression (PLSR) model. In the final analysis, R technique has achieved as top performer pre-processing technique for both calibration and prediction results, with the coefficient of determination (R2) values of 0.9991 and root mean square error (RMSE) values of 0.0886 for prediction. Overall, the correlation of NIRS absorbance data and nitrate can be obtained using PLSR model with R pre-processing technique. Henceforth, we can conclude that the NIRS method of sampling can be used to identify nitrate content in soil analysis by using time saving, non-invasive and less laborious method of sampling.

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Prediction of soil macronutrient (nitrate and phosphorus) using near-infrared (NIR) spectroscopy and machine learning

2020-01-08 , Laili A.R. , Rosdisham Endut , Norshamsuri Ali @ Hasim , Laili M.H. , Amirul M.S. , Syed Alwee Aljunid Syed Junid , Mohd Rashidi Che Beson , Ismail M.N.M.

Determination of basic soil macronutrients such as nitrogen (N), phosphorus (P) and potassium (K) that dissolve from organic matter (OM) prior to the plantation of fruit and vegetable corps is one of the important process of soil preparation towards precision farming. In this paper comparative analysis is performed for detection algorithm on OM, (N) and (P) sample using near infrared spectroscopy (NIRS) spectrometer in reflective mode with an effective range of 900nm to 1700nm. In pre-processing we execute data dimension reduction by combining multiple feature selection such as data normalization, permutation feature importance, principle component analysis, fisher linear discriminant and filter-based feature selection. Pre-processing able to reduce 50% data dimension. For prediction model development we combine with multiple classification algorithm such as multiclass decision jungle, decision forest, logistic regression and neural network to come out with highest accuracy of N and P detection. We conclude that near infrared spectroscopy combines with feature selection and multiclass classification able to determine nitrogen and phosphorus.

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Estimation of Harumanis (Mangifera indica L.) Sweetness using Near-Infrared (NIR) Spectroscopy

2020-03-20 , Sabri, Mohd Shafiq Amirul , Rosdisham Endut , Mohd Rashidi Che Beson , Syed Alwee Aljunid Syed Junid , Norshamsuri Ali @ Hasim , Laili M.H. , Laili A.R. , Ismail M.N.M.

Harumanis mango quality demanded by consumers is depending on the sweetness level of the fruit. The sweetness is evaluated by brix percentage using refractometer as a representative factor correlated with near-infrared (NIR) spectroscopy spectral absorbance. NIR spectroscopy method of sampling have been tested to overcome the time consuming, complex chemical analysis more importantly invasive sampling methods in order to determine the sugar content in mangoes. Spectral absorbance data from range 941 nm to 1685 nm of mango skin is correlated with Brix reading then tested through five pre-processing techniques. Data calibration and prediction of both data is evaluated using Partial Least Square Regression (PLSR) model. In the final analysis, Unit vector normalization (UVN) technique has achieved as a best pre-processing technique for predicting results, with the coefficient of determination (R2) values of 0.9836 and root mean square error (RMSE) values of 0.3131. Overall, the correlation of NIRS absorbance data and Brix data can be obtained using PLSR model with UVN pre-processing technique. Henceforth, we can conclude that the NIRS method of sampling can be used to identify sugar content in Harumanis mango by using time saving, non-invasive and less laborious method of sampling.

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