Application of TLC-Spectrodensitometric and Chemometric Methods for Determination of Momenta® Cream: A Comparative Study Applied on Ternary Mixture

A comparative study for the validation and advancement of two analytical approaches applied for the simultaneous determination of Mometasone Furoate (MF), Miconazole Nitrate (MIC) and Gentamicin (GM) formulated in Momenta® cream. The first approach was TLC-spectrodensitometric method, which was advanced by separating the three components on TLC aluminum plates coated with silica gel 60 F254 using chloroform: methanol: formic acid (4:0.3:0.15, v/v/v) as a mobile phase, then scanned at 254nm using Camage TLC scanner 3 operated in reflectance-absorbance mode. The second approach was the chemometric method using two models: Partial Least Squares (PLS) and Principle Component Regression Model (PCR). The proposed approaches were validated according to ICH guidelines and were applied for the determination of the ternary mixtures in their analytical mixtures and pharmaceutical preparation.

Facile Detection of Oil Adulteration using UV-Visible Spectroscopy Coupled with Chemometrics Analysis

Engine and machine oils, better known as lubricant, is a fast-moving part within the motorcycle and automobile industries. Due to its high demand, these oils are often counterfeited by irresponsible people to get more profit. The thing most often done to adulterate oil is by mixing it with other low-quality or used oil. Here, we propose a simple analytical method to identify oil adulteration by using UV-Visible spectroscopy. A number of 425 genuine and adulterated oils were used as samples. After appropriate dilution using n-hexane, the samples were analyzed by UV-Visible spectrophotometer followed by Principle Component Analysis (PCA) and Principle Component Regression (PCR) as part of the chemometrics analysis. The results show that prediction samples were accurately classified into their corresponding groups with PCA scores of 49% and 27% for principal component 1 and 2, respectively. PLS model achieved a good prediction to detect lubricant oil adulteration, with R-Square of predicted and reference samples were 0.9257 and 0.9204, respectively. The proposed method shows a promising alternative to the conventional chemical method using a more sophisticated instruments such as GC-MS and HPLC for oil or other organic compound identification.

Prediksi Kadar Air dan Kafein Biji Kopi Hijau Menggunakan Near Infrared Spectroscopy

Abstrak. Kopi (Coffea sp.) merupakan salah satu hasil komoditi unggulan perkebunan yang memiliki nilai ekonomis yang cukup tinggi dan sangat potensial diantara tanaman perkebunan lainnya di Indonesia. Menjadikan Indonesia sebagai eksportir kopi terbesar keempat di dunia yang diharuskan untuk menjaga kualitasnya. Untuk menjaga kualitas green bean kopi perlu diperhatikan beberapa karakteristik bahan diantaranya adalah kadar air dan kafein. Penentuan kadar air dan kafein green bean kopi dapat dilakukan dengan menggunakan NIRS (Near Infrared Spectroscopy) yang bersifat Non Destruktif Test (NDT). Tujuan dari penelitian ini adalah untuk mengaplikasikan Partial Least Square (PLS) dan Principle Component Regression (PCR) dalam menduga kadar air dan kafein dengan membandingkan data hasil uji laboratorium. Penelitian ini menggunakan data akuisisi spektrum green bean kopi lokal yang berjumlah 20 sampel serta data uji laboratorium kadar air dan kafein (Adnan,2013). Dengan analisa data spektrum menggunakan De-trending, Extended Multiplicative Scatter Correction (EMSC), dan Kombinasi. Hasil penelitian ini menunjukkan panjang gelombang kadar air berkisar 1400-1415 nm dan 1881-1910 nm serta panjang gelombang kafein berkisar 1920-1947 nm. EMSC sebagai pretreatmentterbaik dalam prediksi kadar air dan kafein.Prediction Moisture Content and Cafein Green Coffee Bean Using Near Infrared SpectroscopyAbstract. Coffee (Coffea sp) is one of the main commodities of plantation which has high economic value and is very potential among other plantation crops in Indonesia. Making Indonesia the fourth largest coffee exporter in the world that is required to maintain its quality. To maintain the quality of green beans, coffee needs to be considered some of the characteristics of the material including water content and caffeine. Determination of water content and caffeine of green bean can be using NIRS ( Near Infrared Spectroscopy) which is Non Destructive Test (NDT). The purpose of this study was to apply Partial Least Square (PLS) and the Principle Component Regression (PCR) in estimating water and caffeine content by comparing laboratory test data. This study used data acquisition of the green bean spectrum of the local totaling 20 samples and test data laboratory water content and caffeine (Adnan,2013). With spectrum data analysis using De-trending, Extended Multiplicative Scatter Correction (EMSC), and Combination. The results of this studiy incate wavelengths of water content ranging from 1440-1450 nm and 1881-1919 nm and caffeine ranging from 1920-1947 nm. EMSC is the best pretreatment in predicting water and caffeine levels.

Comparison of Non-linear and Linear Models of Single Channel EEG in patients and normal subjects

This article examines the possibility of using non-linear models(Support Vector Regression) to model the single channel EEG signals from psychiatric patients and a group of normal participants, to predict psychology trait ratings, like attention, anxiety, alertness, fatigue, sleepiness and depression. It used linear models as benchmarks, and the results showed non-linear models outperformed the benchmarks, as well as more advanced linear methods, like principle component regression. It is thus concluded that using single channel in practical situations to monitor these traits would be possible.