Modeling Consumer Overall Acceptance for Traditional Spice-Based Ready-to-Drink Using Artificial Neural Network and Kansei Engineering

Measuring consumer acceptance of food products is challenging, primarily because the process is susceptible to bias. Several studies have reported that this challenge can be addressed through Kansei engineering through verbal and nonverbal response measurements. Therefore, this study aimed to predict consumer overall acceptance using artificial neural networks (ANN) and Kansei engineering. A total of 30 respondents participated in this study to test nine different samples of traditional spice-based ready-to-drink (RTD). The overall acceptance score and Kansei responses, including verbal and nonverbal, were then measured. Each sample was served cold in a 60-mL cup labeled with a three-digit random code, and the panelists were successfully presented with the nine drinks. All participants were asked to rank each Kansei word scale based on the intensity of their feelings during the assessment. The heart rate (HR) and skin temperature (ST) were also measured as nonverbal responses in real time. The results showed that Kansei’s responses and respondent background best predicted overall acceptance. The optimal model architecture had ten input neurons, two hidden neurons, and one output neuron (10–2–1). The training, validation, and testing data showed that the performance of ANN was satisfactory, with a low error rate (RMSE) and a high coefficient of correlation value (R2). Based on the findings, the developed model could inspire and motivate further studies and development in industries to develop appropriate products for potential consumers, thereby revolutionizing the food industry. IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.