Inguruwatte M Premachandra

Publications (3)8.49 Total impact

• Article: Design of optimal food-based complementary feeding recommendations and identification of key "problem nutrients" using goal programming.

  Elaine L Ferguson, Nicole Darmon, Umi Fahmida, Suci Fitriyanti, Timothy B Harper, Inguruwatte M Premachandra
  [show abstract] [hide abstract]
  ABSTRACT: The WHO is urging countries to promote improved complementary feeding practices to ensure optimal health, growth, and development of young children. To help achieve this, a rigorous 4-phase approach for designing optimal population- specific food-based complementary feeding recommendations (CFRs) was developed and is illustrated here. In phase I, an optimized diet is selected, using goal programming (Model #1), which aims to provide a desired nutrient content with respect to habitual diet patterns and cost. Based on its food patterns, a set of draft CFRs is designed. In phase II, their success for ensuring a nutritionally adequate diet is assessed via linear programming (Model type #2) by sequentially minimizing and maximizing the level of each nutrient (i.e., worst and best-case scenarios) while respecting the CFRs. For nutrients that are <70% of desired levels, the best food sources are identified via linear programming in phase III (Model #3). Different combinations of these foods are incorporated into the original draft of the CFRs to produce alternative CFRs, which are then compared on the basis of their cost, flexibility, and "worst-case scenario" nutrient levels (Model type #2) to select, in phase IV, a final set of CFRs. A hypothetical example is used to illustrate this approach. Outcomes include a set of optimal, population-specific CFRs and practical information regarding key "problem nutrients" in the local diet. Such information is valuable for nutrition promotion, as well as nutrition program planning and advocacy, to help achieve global initiatives for improving the complementary feeding practices of young children living in disadvantaged environments.
  Journal of Nutrition 10/2006; 136(9):2399-404. · 3.92 Impact Factor
• Article: A new approach to improve TMJ morphological information from plain film radiographs.

  George J Dias, Inguruwatte M Premachandra, Patricia M Mahoney, Jules A Kieser
  [show abstract] [hide abstract]
  ABSTRACT: The hard tissues of the temporomandibular joint (TMJ) can be assessed through radiographic imaging to provide information to assist in diagnosis and treatment. However, the quality of information gathered from such imaging is often less than desired due to the small size of the TMJ, the widely varying fossa and condylar morphology, and the surrounding dense osseous structures. These make a clear and undistorted radiographic image of the hard tissue of the joint technically difficult. It is postulated that, if the degree of inaccuracy of a given radiograph is known quantitatively and taken into account, the clinician will be able to make a better informed qualitative assessment of TMJ morphology. The aim of this study is: 1. to improve the qualitative information that can be acquired from routine clinical plain film radiographs of the TMJ; 2. to use quantitative data to test the novel Neural Network (NN) model; and 3. to statistically show the discrepancies between routine radiographic images and the actual joint. Linear measurements of excised TMJs and their radiographic images were used to train a multilayer perceptron (MP) type NN model to predict joint dimensions more accurately. Such a neural network, developed using a statistical software package such as SPSS (SPSS, Inc. Chicago, IL), functions as a computer software program and predicts joint dimensions with increased accuracy when radiographic measurements are entered into the program. The NN model described here predicts the actual linear distances of the TMJ more closely than radiographic measurements and is more reliable in assessing the TMJ morphology.
  Cranio: the journal of craniomandibular practice 02/2005; 23(1):30-8. · 0.66 Impact Factor
• Article: Food-based dietary guidelines can be developed and tested using linear programming analysis.

  Elaine L Ferguson, Nicole Darmon, André Briend, Inguruwatte M Premachandra
  [show abstract] [hide abstract]
  ABSTRACT: Effective food-based dietary guidelines (FBDGs) are required to combat micronutrient deficiencies. This study aimed to develop a rigorous approach for designing population-specific FBDGs. A 4-phase approach based on linear programming analysis was used to design, test, and refine the FBDGs. This was illustrated for Malawian children. In phase I, the objective function minimized the difference in the energy contributed by different food groups between modeled and observed diets for 16 observed diet types, while preferentially selecting foods most often consumed. Constraints ensured nutrient adequacy and diet palatability. In phase II, the meal/snack patterns of the phase I modeled diets were examined to develop season-specific FBDGs. In phase III, the robustness of these FBDGs, for ensuring a nutritionally adequate diet, was tested. The objective function, in this analysis, minimized selected nutrient levels in the modeled diets (i.e., chose the "worst-case scenario"), while respecting the FBDGs, palatability, and energy constraints. The FBDGs were refined in phase IV. In the Malawian example used to illustrate our approach, the FBDGs promoted daily consumption of maize flour, small dry fish (>or=20 g), leaf relish, and 2-3 snacks. The last mentioned included mangoes, in the food-shortage season, and pumpkin in the food-plenty season. In addition, legume relish was recommended in the food-shortage season. The approach presented here can be used to design and then test the robustness of FBDGs for meeting nutrient recommendations.
  Journal of Nutrition 04/2004; 134(4):951-7. · 3.92 Impact Factor