Honey is a natural sweetener, which is consumed in a variety of sweet products. It is
considered as healthy food because it contains nutrients such as carbohydrate,
protein, vitamins and mineral. The presence of adulterated honey in the market is
worrying the consumers since it is difficult to distinguish between pure and adulterated
honey due to similar appearance and texture of both type honeys. Chemometric
analysis combined with spectroscopic data is a powerful technique that has been used
to discriminate different type of honey. Samples of pure honey are collected from
beekeepers at Ayer Keroh, Melaka and Cameron Highland, Pahang. The adulterants
used to prepare adulterated honey are sugar and corn syrup with the concentration of
the adulterants added to the pure honey ranging from 10% to 90% by weight of
adulterant. All the samples are treated with heat at 40o
C to ensure the adulterant and
pure honey are mixed well. Fourier transforms infrared spectroscopy (FTIR) is used to
generate the spectra of the honey and subsequently subjected to chemometric
analysis. The spectra data is then analysed by using Principal Component Analysis (PCA)
technique using SOLO+Mia software. In this study, all honeys have been successfully
discriminated according to their origins and purity as well as types of adulterants used.
Consequently, the developed model can potentially be used as a screening tool to
determine the purity of honey in the market.
Recently, research has been critically focused on finding new compounds with antirepellent
activity due to the rising of new types of mosquito-borne diseases. Mosquito
repellents are the safer and cleaner alternative to fight the anthropods from bitten
human skins, hence reduce the spread of diseases. This study investigated the
relationships between biological activity and structure of carboxamides by using
Quantitative Structure-Activity Relationship (QSAR) analysis. The data set used in this
study comprised of 40 carboxamide compounds taken from the literature with their
activities expressed as log PT (protection time). These compounds were split into
training set for model building and test set for external validation using activity-based
ranking method. The training set contained approximately 75% of the compounds
while the remaining compounds were then used as the validation set to verify the
accuracy of the model. DRAGON software was employed to generate molecular
descriptors. The important relevant descriptors were further selected and reduced by
using Genetic Algorithm (GA) as variable selection method. Two QSAR models were
developed by combining GA method with two different modelling techniques that are
multiple linear regressions (MLR) and partial least square (PLS). All the models are
robust with good correlation coefficient (r2) greater than 0.6 and external validation
r2test more than 0.5. Statistics of the GA-MLR model are r2 = 0.779 and r2test = 0.646.
Whereas, the second model generated from GA and PLS shows good r2 with value of
0.775 and r2test = 0.563. These results could be useful in finding new, safe, and more
effective repellents against Aedes Aegypti in a short time by providing guidance for
further laboratory work as well as prediction of external compounds and help to
understand the factors affecting their activity.
Management is consistently facing fast-flowing and lots of changes in business, including in the inventory management. Especially for fast-moving inventories, the correct stocking, controlling, checking and safety stock calculation is highly needed to have an exquisite inventory management and to reduce the possibility of running out of inventory which leads to unavailability to meet the demand. One of the ways to overcome this is by doing an excellent and appropriate forecasting. Therefore, the objective of this concept paper is to analyse and recommend tools to improve inventory management using the appropriate time-series forecasting method. The firm studied in this study is serving its employees as customers that demand the routine items including stationeries and other routine products to support their job as auditors and consultants for its client. However, there are occasions when there is out-of-stock situation for fast-moving items, especially in the peak season period. Furthermore, the firm is only applying replenishment based on the used inventories from the previous month. Therefore, this study suggests to eliminate out-of-stock items situation by applying precaution initiatives such as time-series forecasting. This study is planned to employ 10 time-series forecasting methods such as moving average, exponential smoothing, regression analysis, Holt-Winters analysis, Seasonal analysis and Autoregressive Integrated Moving Average (ARIMA) using Risk Simulator Software. By simulating those methods, the most appropriate method is selected based on the forecasting accuracy measurement.