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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Development of functional near infrared spectroscopy (fNIRS) technologies has advanced quantification signal using multiple wavelength and detector to investigate hemodynamic response in human muscle. These non-invasive technologies have been widely used to solve the propagation of light inside the tissues including the absorption, scattering coefficient and to quantify the oxygenation level of haemoglobin and myoglobin in human muscle. The goal of this paper is to optimize the measurement of muscle oxygen saturation during isometric exercise using functional near infrared spectroscopy (fNIRS). The experiment was carried out on 15 sedentary healthy male volunteers. All volunteers are required to perform an isometric exercise at three assessment of muscular fatigue’s level on flexor digitalis (FDS) muscle in the human forearm using fNIRS. The slopes of the signals have been highlighted to evaluate the muscle oxygen saturation of regional muscle fatigue. As a result, oxygen saturation slope from 10% exercise showed steeper than the first assessment at 30%-50% of fatigues level. The hemodynamic signal response showed significant value (p=0.04) at all three assessment of muscular fatigue’s level which produce a p-value (p<0.05) measured by fNIRS. Thus, this highlighted parameter could be used to estimate fatigue’s level of human and could open other possibilities to study muscle performance diagnosis. [ABSTRACT FROM AUTHOR]

Monte Carlo Simulation has advanced their quantification based on number of the photon counting to solve the propagation of light inside the tissues including the absorption, scattering coefficient and act as preliminary study for functional near infrared application. The goal of this paper is to identify the optical properties using Monte Carlo simulation for non-invasive functional near infrared spectroscopy (fNIRS) evaluation of penetration depth in human muscle. This paper will describe the NIRS principle and the basis for its proposed used in Monte Carlo simulation which focused on several important parameters include ATP, ADP and relate with blow flow and oxygen content at certain exercise intensity. This will cover the advantages and limitation of such application upon this simulation. This result may help us to prove that our human muscle is transparent to this near infrared region and could deliver a lot of information regarding to the oxygenation level in human muscle. Thus, this might be useful for non-invasive technique for detecting oxygen status in muscle from living people either athletes or working people and allowing a lots of investigation muscle physiology in future. [ABSTRACT FROM AUTHOR]