TY - JOUR

T1 - Design of experiments meets immersive environment

T2 - Optimising eating atmosphere using artificial neural network

AU - Kantono, Kevin

AU - How, Muhammad Syahmeer

AU - Wang, Qian Janice

N1 - Funding Information: This study was supported by a Carlsberg Foundation Young Researcher Fellowship ( CF19-0587 ) awarded to QJW. Publisher Copyright: © 2022 Elsevier Ltd

PY - 2022

Y1 - 2022

N2 - Design of experiments (DOE) is a family of statistical tools commonly used in food science to optimise recipes and facilitate new food development. In a novel cross-disciplinary twist, we propose to adapt DOE approach to the optimisation of restaurant atmosphere. In this study, an artificial neural network (ANN) with particle swarm optimisation algorithm (PSO; hereafter ANN-PSO) was selected and compared with classical Response Surface Method (RSM) as ANN-PSO has been reported to yield better reliability and predictability compared to RSM. Recent research has increasingly demonstrated that perceived food quality, enjoyment, and willingness to pay are influenced by contextual factors such as lighting, decoration, and background noise/music. Moreover, virtual reality (VR) technology, which has become increasingly accessible, sophisticated, and widespread over the past years, presents a new way to study scenarios which may be otherwise too expensive/implausible to test in real life this includes delivering immersive environment. We hereby demonstrate a novel proof-of-concept study by varying the degree of illumination and of background sound level in an immersive restaurant setup. Participants (N = 283) watched immersive 360° videos while rating situational appropriateness and food wanting for two different dishes in various ambient conditions as determined by DOE's Central Composite Design (CCD). Participants did not actually consume the foods but rather only viewed them. Optimal restaurant lighting and sound levels were then estimated using ANN-PSO model which was found to be at 289 lux and −21.38 Loudness Unit Full Scale (LUFS) for burger and 186.9 lux and −30 LUFS for pizza. While the results of our study are of obvious interest to those in the hospitality industry, this work further highlights the transferability of methods across different disciplines and the applicability of time-tested methods to new emerging areas.

AB - Design of experiments (DOE) is a family of statistical tools commonly used in food science to optimise recipes and facilitate new food development. In a novel cross-disciplinary twist, we propose to adapt DOE approach to the optimisation of restaurant atmosphere. In this study, an artificial neural network (ANN) with particle swarm optimisation algorithm (PSO; hereafter ANN-PSO) was selected and compared with classical Response Surface Method (RSM) as ANN-PSO has been reported to yield better reliability and predictability compared to RSM. Recent research has increasingly demonstrated that perceived food quality, enjoyment, and willingness to pay are influenced by contextual factors such as lighting, decoration, and background noise/music. Moreover, virtual reality (VR) technology, which has become increasingly accessible, sophisticated, and widespread over the past years, presents a new way to study scenarios which may be otherwise too expensive/implausible to test in real life this includes delivering immersive environment. We hereby demonstrate a novel proof-of-concept study by varying the degree of illumination and of background sound level in an immersive restaurant setup. Participants (N = 283) watched immersive 360° videos while rating situational appropriateness and food wanting for two different dishes in various ambient conditions as determined by DOE's Central Composite Design (CCD). Participants did not actually consume the foods but rather only viewed them. Optimal restaurant lighting and sound levels were then estimated using ANN-PSO model which was found to be at 289 lux and −21.38 Loudness Unit Full Scale (LUFS) for burger and 186.9 lux and −30 LUFS for pizza. While the results of our study are of obvious interest to those in the hospitality industry, this work further highlights the transferability of methods across different disciplines and the applicability of time-tested methods to new emerging areas.

KW - Appropriateness

KW - Artificial neural network

KW - Design of experiments

KW - Immersive environment

KW - Multisensory

KW - Particle swarm optimisation

KW - Wanting

U2 - 10.1016/j.appet.2022.106122

DO - 10.1016/j.appet.2022.106122

M3 - Journal article

C2 - 35675873

AN - SCOPUS:85132423982

VL - 176

JO - Appetite

JF - Appetite

SN - 0195-6663

M1 - 106122

ER -