Comparative investigation of two different self-organizing map-based wavelength selection approaches for analysis of binary mixtures with strongly overlapping spectral lines

Purpose: To demonstrate the ability and investigate the performance of two different wavelength selection approaches based on self-organizing map (SOM) technique in partial least-squares (PLS) regression for analysis of pharmaceutical binary mixtures with strongly overlapping spectra.Methods: Two different variable selection methods were compared, namely, SOM1-PLS and SOM2- PLS. The main difference between these methods involved the structure of neurons in input layer and the algorithm for variable selection. Adjustable parameters for each technique were optimized for better comparison. The performance of these methods was statistically verified for predictive ability using both synthetic mixtures and a real combination product of sulfamethoxazole (SMX) and trimethoprim (TMP), which exhibited strongly overlapping of spectral lines.Results: The results obtained indicate that SOM2-PLS was more efficient than SOM1-PLS technique with 30 and 6 % improvement in predictive ability for SMX and TMP, respectively. Furthermore, the mean difference between the results obtained from SOM2-PLS method and those from the official method was not statistically significant as p-value was more than 0.01.Conclusion: Although, SOM2-PLS method is more efficient than SOM1-PLS method for the analysis of pharmaceutical binary mixtures with severely overlapping spectra, some problems associated with SOM2-PLS technique include difficult computations of some parameters.Keywords: Co-trimoxazole, Self-organizing map, Wavelength selection, Pharmaceutical analysis, Overlapping spectral line

เกมการศึกษาด้านการแพทย์และสุขภาพ Education Game in Health and Medicine

Learning through electronic media has become popular nowadays and it isalso widely used for teaching in Thailand. The learning process isdeveloped by including contents in the game which makes the class funand the student knowledge is gained. Moreover, the student learning canbe enhanced with this media. In medical and health area, learning throughgames was used for practice-based learning from professional experiences,simulated situations, and case studies. The benefit of learning throughgames is that the low budget implement since the student can be playingmany times without risk of harm and they are also educated according tolearning objective by the interaction among the players in the game. Thus,learning games in the field of medicine and health have been the mostpopular and widespread in development. Furthermore, learning throughgames is used to disseminate knowledge to the general public and to helpmedical staffs to repeat practicing and learning patient care. This isparticularly useful when faced with real patients. The staffs will be able toprovide appropriate care with no damage to life and health of the patient.Keywords: game, education, health, medicin

Physicochemical analysis and determination of di- and tri-saccharide content in Longan, Litchi and Siam weed honeys of Thailand

<p>Honeys produced by bees from the nectars of longan, litchi and Siam weed growing in Thailand were analyzed for physicochemical profiles (moisture, Brix%, soluble solids, density, viscosity, pH, free acid, electrical conductivity, HMF, DN, proline, color, flavonoid and antioxidant activities) and their carbohydrates quantified as monosaccharides, disaccharides and trisaccharides. All physicochemical parameters met the regulation requirements under the Codex Alimentarius Standard, EU Council and directives legislation of the Ministry of Public Health of Thailand, except the HMF value of one sample. The conductivity, color [Abs]₄₅₀ and antioxidant activity were parameters that showed that there are significant differences in means between the groups of honey samples based on one way analysis of variance (ANOVA) (<i>p</i> < 0.05). The amounts of palatinose and isomaltose in the honey samples were statistical significantly different (ANOVA, <i>p</i> < 0.05). The differences between longan, litchi and Siam weed honeys by principle component analysis visible from the di- and trisaccharide profiles from an Rtx-65TG column were shown to be different from those from an SPB-1 column. The PC1 from the Rtx-65TG column profile was a linear combination that summarized 54.0% of the cumulative proportion of variance. The differences between the honey samples appeared to stem from many parameters and quantification compositions of the saccharides. The palatinose and isomaltose in longan honey were suggested to be markers for its authentication.</p

Artificial Neural Network for Solid Dosage Form Applications-โครงข่ายประสาทเทียมสำหรับการประยุกตใ์ช้ในเภสัชภัณฑ์รูปแบบของแข็ง

The artificial neural network (ANN) is a mathematical structure designed to mimic the information processing functions of a network of neurons in the brain, which can learn from experience. Briefly, the general structure of ANN has input layer, hidden layer, and output layer. Each layer has a few units corresponding to neurons. The units in neighboring layers fully interconnected with links between two units are called weights. ANNs usually learn or train through experience with back propagation algorithm. The ANN model has predicted and formulated optimization. The ANN models can be used to predict the response for new experimental conditions after the models are trained. ANN technique can be used for optimizing tablet formulation effectively. Keywords: artificial neural network, solid dosage formบทคัดย่อโครงข่ายประสาทเทียม (Artificial neural network) คือ โมเดลทางคณิตศาสตร์ที่ออกแบบเพื่อเลียนแบบการประมวลผลข้อมูลของเครือข่ายเซลล์ประสาทในสมองมนุษย์โดยเรียนรู้จากประสบการณ์และการฝึกฝน โครงข่ายประสาทเทียมประกอบด้วยชัน้ นำสัญญาณเข้า (input layer) ชัน้ ซ่อน (hidden layer) และชัน้ นำสัญญาณออก (output layer) โหนด (node) ในแต่ละชั้นเท่ากับตัวแปรที่ต้องการศึกษา จำนวนชั้นและโหนดของชั้นซ่อนขึ้นกับความซับซ้อนของปัญหา ชั้นนำสัญญาณเข้าเชื่อมโยงกับค่าน้ำหนัก (weight) โครงข่ายประสาทเทียมอาศัยการเรียนรู้และฝึกฝนผ่านประสบการณ์ ซึ่งมีขัน้ ตอนการฝึกฝน (training algorithm)มากมาย แต่ที่ใช้มากที่สุดคือ ขัน้ การฝึกอบรมแบบย้อนกลับ (back propagationlearning) โครงข่ายประสาทเทียมสามารถทำนายและใช้ทำออฟติไมเซชัน่ เพื่อพัฒนาตำรับเภสัชภัณฑ์ นอกจากนี้ ยังใช้ทำนายปัจจัยตอบสนองสำหรับสภาวะการทดลองใหม่ ๆ หลังจากที่โมเดลผ่านการฝึกฝนแล้ว เทคนิคนี้นำมาใช้ได้อย่างมีประสิทธิภาพสำหรับเภสัชภัณฑ์รูปแบบของแข็งชนิดรับประทานคำสำคัญ: โครงข่ายประสาทเทียม, เภสัชภัณฑ์รูปแบบของแข็