No abstract

The losses in taste and smell that occur with advancing age can lead to poor appetite, inappropriate food choices, as well as decreased energy consumption. Decreased energy consumption can be associated with impaired protein and micronutrient status and may induce subclinical deficiencies that directly impact function. Most nutritional interventions in the elderly do not compensate for taste and smell losses and complaints. For example, cancer is a medical condition in which conventional nutritional interventions (that do not compensate for taste and smell losses) are ineffective. Evidence is now emerging that suggests compensation for taste and smell losses with flavor-enhanced food can improve palatability and/or intake, increase salivary flow and immunity, reduce chemosensory complaints in both healthy and sick elderly, and lessen the need for table salt.

No abstract

Sensitivity, repeatability, and discernment are three major issues in any classification problem. In this study, an electronic nose with an array of 32 sensors was used to classify a range of odorous substances. The collective time response of the sensor array was first partitioned into four time segments, using four smooth time-windowing functions. The dimension of the data associated with each time segment was then reduced by applying the Karhunen-Loéve (truncated) expansion (KLE). An ensemble of the reduced data patterns was then used to train a neural network (NN) using the Levenberg-Marquardt (LM) learning method. A genetic algorithm (GA)-based evolutionary computation method was used to devise the appropriate NN training parameters, as well as the effective database partitions/features. Finally, it was shown that a GA-supervised NN system (GANN) outperforms the NN-only classifier, for the classes of the odorants investigated in this study (fragrances, hog farm air, and soft beverages).