Recently, the use of Near Infrared (NIR) spectral sensor in agricultural process is getting much attention, particularly for fruit quality evaluation. The sensor requires a spectrometer to produce some sufficient information called spectrum as interaction between physical matters of the sample with the electromagnetic spectrum. In fact, the presence of experimental error or/and measurement error due to the heterogeneous particle size, moisture content variability, sample density, the instrument noise and pretreatment experience are often cannot be avoided. These would damage the spectra collected which results to decrease the performance in model selection, and increases the prediction error as the harmful influence of possible outlier and leverage points in dataset. To encounter these, a robust pretreatment of NIR spectral data is needed to correct the spectra before it is used for post-processing using any statistical method. In this paper, several different classical pretreatment methods were evaluated and a new robust Generalized Multiplicative Scatter Correction (GMSC) algorithm was proposed to correct the additive and/or multiplicative baseline effects in the spectral data. A dataset of NIR spectral on oil palm (Elaeis guineensis Jacq.) fruit bunch was used in the simulation. In the simulation, a number of repetitions using the single and double cross validation with robust partial least square are also applied. The Desirability Indices as statistical measures are presented for evaluating the methods.

The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.

Heat and mass transfer effects of Casson fluid in the entrance of concentric annuli with moviment of inner wall was analyzed here. The problem analysis concerns the simultaneous development of thermal boundary layers and hydrodynamic in concentric walls, one ring is isothermal and the other wall being adiabatic. With the assumption that the inner ring rotates with a fixed angular velocity, also the outer ring is at rest. The finite difference technique is applied to find the velocity Profiles, variation of pressure in the radial coordinate direction and temperature changes in the same direction. Calculation results are obtained for different annular gap values, Casson number and Prandtl's number. The comparison of the results for different special cases was made and observed.

In practice, the collected spectra are very often composes of complex overtone and many overlapping peaks which may lead to misinterpretation because of its significant nonlinear characteristics. Using linear solution might not be appropriate. In addition, with a high-dimension of dataset due to large number of observations and data points the classical multiple regressions will neglect to fit. These complexities commonly will impact to multicollinearity problem, furthermore the risk of contamination of multiple outliers and high leverage points also increases. To address these problems, a new method called Kernel Partial Diagnostic Robust Potential (KPDRGP) is introduced. The method allows the nonlinear solution which maps nonlinearly the original input X matrix into higher dimensional feature mapping with corresponds to the Reproducing Kernel Hilbert Spaces (RKHS). In dimensional reduction, the method replaces the dot products calculation of elements in the mapped data to a nonlinear function in the original input space. To prevent the contamination of the multiple outlier and high leverage points the robust procedure using Diagnostic Robust Generalized Potentials (DRGP) algorithm was used. The results verified that using the simulation and real data, the proposed KPDRGP method was superior to the methods in the class of non-kernel and some other robust methods with kernel solution.

In the survival data analysis, commonly, it is presumed that all study subjects will eventually have the event of concern. Nonetheless, it tends to be unequivocally expected that a fraction of these subjects will never expose to the event of interest. The cure rate models are usually used to model this type of data. In this paper, we introduced a maximum likelihood estimates analysis for the four-parameter exponentiated Weibull exponential (EWE) distribution in the existence of cured subjects, censored observations, and predictors. Aiming to include the fraction of unsusceptible (cured) individuals in the analysis, a mixture cure model, and two non-mixture cure models—bounded cumulative hazard model, and geometric non-mixture model with EWE distribution—are proposed. The mixture cure model provides a better fit to real data from a Melanoma clinical trial compared to the other two non-mixture cure models.