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Human Taste-Perception: Brain Computer Interface (BCI) and Its Application as an Engineering Tool for Taste-Driven Sensory Studies

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Abstract

Sensory satisfaction is the key to consumer acceptance which also decides the success of any food products in the market. Though different sensory parameters like appearance, odor, and texture are considered for deciding the overall acceptability of food, taste plays a major role. As sensory panels cannot be a true representation of consumer’s taste perception, industries focus on market surveys. In reality, consumers taste perception varies according to the product cost, brand, and their age and health condition. The process of food tasting starts from tongue, where different taste receptors respond to various taste stimuli and pass the signals to the cortex of the brain region. These signals cause the electric current to flow through the brain neural networks and increase oxygen-containing blood utilization in specific brain areas. Using non-invasive gadgets such as electroencephalography, magnetoencephalography, functional MRI, and brain computer interface (BCI) technique, these signals can be sensed and decoded into useful sensory data. This review explains the taste recognition pathways of different taste stimuli and the basic steps involved in BCI techniques for detecting and discriminating them. In addition, it also explores the BCI-related taste-driven sensory studies and the limiting factors associated with them to emerge as a future sensory method.

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Abbreviations

BCI:

Brain computer interface

EEG:

Electroencephalography

MEG:

Magnetoencephalography

fMRI:

Functional MRI

PET:

Positron emission tomography

CN:

Cranial nerves

CALHM1:

Calcium homeostasis modulator 1

ENaC:

Epithelial sodium channel

ATP:

Adenosine triphosphate

TRPM5:

Transient receptor potential cation channel subfamily M member 5 /transient receptor potential channel 5

G-Protein or GPCR:

G protein-coupled receptor

PLCβ-2:

Phospholipase Cβ-2

PIP2:

Phosphatidylinositol 4,5-bisphosphate

IP2:

Inositol bisphosphate

IP3:

Inositol triphosphate

DAG:

Diacylglycerol

IP3R3:

IP3 receptors 3

CALHM1/3:

Calcium homeostasis modulator 1/3

P2X2/3:

Purinergic receptor-channel P2X2/3

OTOP1:

Otopetrin 1

Kir2.1 :

Inward rectifier K+ channel

FFT:

Fast Fourier transform

SPM:

Statistical Parametric Mapping

CHR:

Canonical hemodynamic response function

AR:

Autoregressive model

DCM:

Dynamic causal modelling

GLM:

General linear model

EPI:

Echo-planar imaging

BOLD:

Blood oxygenation level dependent

EPSP:

Excitatory postsynaptic potential

IPSP:

Inhibitory postsynaptic potential

ERP:

Event-related potential

EOG:

Electrooculogram

MVPA:

Multivariate pattern approach/analysis

ANN:

Artificial neural network

AGT:

Adaptive Gabor transform

GFP:

Global-field-potential

MSG:

Monosodium glutamate

NaCl:

Sodium chloride

AgCl:

Silver chloride

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  1. National Institute of Food Technology, Entrepreneurship and Management - Thanjavur (NIFTEM-T), 613005, Thanjavur, India

    R Anbarasan & R Mahendran

  2. Universidad de Granada, Jerez de La Frontera, Andalusia, Spain

    Diego Gomez Carmona

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Anbarasan, R., Gomez Carmona, D. & Mahendran, R. Human Taste-Perception: Brain Computer Interface (BCI) and Its Application as an Engineering Tool for Taste-Driven Sensory Studies. Food Eng Rev 14, 408–434 (2022). https://doi.org/10.1007/s12393-022-09308-0

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