<p>Predictive models is crucial in near-infrared (NIR) spectroscopic analysis. Partial least square - artificial neural network (PLS-ANN) is a hybrid method that may improve the performance of prediction in NIR spectroscopic analysis. This study investigates the advantage of PLS-ANN over the well-known modelling in spectroscopy analysis that is partial least square (PLS) and artificial neural network (ANN). The results show that ANN that coupled with first order SG derivatives achieved the best prediction with root mean square error of prediction (RMSEP) of 0.3517 gd/L and coefficient of determination ( ) of 0.9849 followed by PLS-ANN with RMSEP of 0.4368 gd/L and  of 0.9787, and PLS with RMSEP of 0.4669 gd/L and  of 0.9727. This suggests that the spectrum information may unable to be totally represented by the first few latent variables of PLS and a nonlinear model is crucial to model these nonlinear information in NIR spectroscopic analysis.</p>

Abstract-Monitoring laboratory equipment record is important to ensure every item is always in place. Generally, in and out equipment is handled manually by technician by writing down the equipment information, including time and date in equipment circulation form. To automate the process, Radio Frequency Identification (RFID) is one of the most practical and applicable in real implementation in-line with the nature where most of the systems are made computerized. In this paper, a solution has been provided for the problem encountered in laboratory equipment monitoring system using RFID technology. This project consist four main parts: the tag, tag reader, system development and networking system. The RFID tag is tagged on the laboratory equipment where the tag contains laboratory equipment information and RFID reader is located at the door of each laboratory room. This monitoring system enables the head of laboratory and technician to monitor in-out equipment in actual environment and also increase the efficiency in managing equipment in the laboratory. Benefits of the system include enhancement of the safety of University asset and reduce losses of assets and enhancement of the laboratory inventory control of equipment.

Monitoring blood hemoglobin level is essential to diagnose anaemia disease. This study aims to evaluate the capability of an artificial neural network (ANN) and Savitzky Golay (SG) pre-processing in predicting the blood hemoglobin level based on the near-infrared spectrum. The effects of the hidden neuron number and different SG pre-processing strategies were examined and discussed. ANN coupled with first order SG derivative and five hidden neurons achieved better prediction performance with root mean square error of prediction of 0.3517 g/dL and coefficient determination of prediction of 0.9849 compared to the previous studies. Results depict that ANN that coupled with first order SG derivative could improve near-infrared spectroscopic analysis in predicting blood hemoglobin level, and the proposed nonlinear model outperforms linear models without variable selections. This finding suggests that the modelling strategy is promising in establishing a better relationship between the blood hemoglobin and near-infrared spectral data.

Abstract. Near infrared spectroscopy (NIRS) is a reliable technique that widely used in medical fields. Partial least square was developed to predict blood hemoglobin concentration using NIRS. The aims of this paper are (i) to develop predictive model for near infrared spectroscopic analysis in blood hemoglobin prediction, (ii) to establish relationship between blood hemoglobin and near infrared spectrum using a predictive model, (iii) to evaluate the predictive accuracy of a predictive model based on root mean squared error (RMSE) and coefficient of determination . Partial least square with first order Savitzky Golay (SG) derivative preprocessing (PLS-SGd1) showed the higher performance of predictions with RMSE = 0.7965 and = 0.9206 in K-fold cross validation. Optimum number of latent variable (LV) and frame length (ƒ) were 32 and 27 nm, respectively. These findings suggest that the relationship between blood hemoglobin and near infrared spectrum is strong, and the partial least square with first order SG derivative is able to predict the blood hemoglobin using near infrared spectral data.