INTRODUCTION
Slowly, but surely the non-invasive sensory techniques (NIST) are gaining attention in the field of sensory science. With tight race in the food market, industries spare no effort to understand the consumer needs and that is where the growth of NIST comes to place. Normally, NIST is used in the medical field to retrieve information from the surface of a human body without penetrating inside. These techniques are mainly used for collecting brain signals (EEG – Electroencephalography, fMRI – Functional Magnetic Resonance, MEG – Magnetoencephalography), active muscle fibres electric signals (MEG – electromyography), and heartbeat rates (ECG – electrocardiogram). Nevertheless, adapting NIST in sensory analysis retrieves more sensory perception details from consumers which are otherwise impossible to obtain through verbal feedback. For a better understanding of this concept, we should first know how a food product is perceived by the consumers. The food products are perceived based on the inputs of five basic senses called vision (eyes), taste (tongue), smell (nose), sound (ear), and touch (mouth & skin). Hence, one food product cannot be rated high until it persuades all these sensory organs. In industries, using the traditional hedonic rating method sensory panels give scores to the food items based on their perceived sensory response. But, when the industry wants to understand the consumer acceptance of food products, a market survey will be done using questionnaires.
But, the question is “Will these questionnaires provide actual consumer sensory perception of a food product?” – the answer would be ‘NO’.
Because the consumers are not trained sensory panels to provide accurate feedback for the questionnaires by interpreting their perceived sensory responses. At the same time, the sensory panels also cannot be considered as a true representation of consumers, as the sensory perception varies from person to person. To overcome this, we need technology to extract the sensory data from a group of targeted audience for a specific food product.
Though, the consumers have less knowledge of sensory analysis, the sensory perception mechanism in the human body remains the same for all. Therefore, by measuring the biochemical changes in the body, it is possible to analyze a product’s sensory perception. These biochemical changes occur as the result of sensory organ’s signals to different brain regions through neural networks. With the help of bio-sensing gadgets, these changes can be measured in the source point (sensory organ) or at the perception point (brain).
Among the NIST, appearance, taste, aroma and flavours causing biochemical changes can be measured at the perception point with the help of brain imaging techniques such as EEG, fMRI and MEG; while texture measurement can be done at the source point with the use of EMG.
Sensory cues signal transaction
• Appearance: Eyes capture the visual of food and send the signal to activate specific brain regions (orbitofrontal cortex, lateral occipital complex and left middle insula) by a neural signal transaction. Based on the experience and memories stored in the brain, the activation in brain areas will be intense for food than that of non-food product cues.
• Taste: Tongue taste buds contain taste cells that detect different taste stimuli (sweet, sour, salt, bitter, umami) and produce an electric signal to pass through tongue nerves. These signals will then activate brain taste cortex regions.
• Aroma: Odorant interaction with olfactory receptor which causes the olfactory bulb to send the aroma signal to brain olfactory cortex region.
For all the above-mentioned signal transactions, the biochemical changes produce measurable changes in the electrical activity or oxygen consumption of the specific brain regions.
• Texture: The texture of a food is mainly perceived during the mastication process and thus a food product nature will be decided based on the force given by masticatory muscles. Depending on the force, the muscle fibres activation generates an electric signal.
Non-invasive techniques signal detection mechanism
Electroencephalography: In EEG, electrical signals produced in the brain are received via metal electrodes from the surface of subject (consumer) scalp. The received signal will be in the form of fluctuating voltage with potential peaks at different time intervals. By interpreting these time and peak potential values, the specific taste stimuli and its pleasantness can be measured.
Functional Magnetic Resonance (fMRI): When the brain regions are activated due to the food stimuli, there will be a rise in blood O2 level. In fMRI imaging, these activated brain regions will ‘light up,’ which is a sign of response that can be interpreted as sensory perception data. In recent times, fMRI technique is gaining more popularity among other non-invasive techniques, as it produces spatial images from received signals.
Magnetoencephalography (MEG): Flow of electric current through an object creates magnetic induction. Likewise, the electrochemical current flow between the neurons caused by food stimuli induces magnetic field in specific brain regions. By utilizing these magnetic signals, MEG can provide valuable data for sensory studies.
Electromyography (EMG): Mastication of food produces an electric current in muscle fibres as a result of ion flow between cell membranes. The intensity of current varies based on the force required for masticating the particular food item. By fixing the EMG on the jaw masticatory muscles surface, the textural nature of the food can be interpreted from the recorded electric signals.
NIST computer interface
NIST computer interface consists of 3 major units called signal sensing unit, processing unit and data interpretation unit. At first, the sensing unit records signals at the source point or at the perception point which are then converted into accessible data form by the processing unit under the three steps.
• Preprocessing – enhances the received data for precise data retrieval to improve or enhance the signal for precise data detection
• Feature extraction – removes the noises from the signals
• Signal classification – Suitable software (i.e., MATLAB) and translation algorithm. the signals can be classified based on the frequency or shape, etc.
Finally, the data interpretation unit converts the signals into specific sensory output and displays them on the device screen.
Applications
• Tools for market survey
• Consumer feedback collection
• Design and development of new food products for targeted audience.
• Food products sensory analysis
Advantages and challenges
• NIST can be used as an analyzing tool for the product oriented parameters effect (cost, brand, package, color) on the consumer perception of food.
• Under NIST supervision, fortified and low calorific food products can be compared with the existing products original sensory perception.
• This non-verbal technique can retrieve sensory information from infants which can be used for baby food product designs.
• NIST does not necessarily need a sensory expert, instead the sensory data can be extracted from an untrained panel.
• NIST are limited by certain factors such as age, gender, health and habit. However, the influence of these factors can be used for developing food products for targeted audience (different age, gender, health groups).
• NIST are experimented only in few food products (i.e., wine and milk shakes)
• Since, the NIST instruments are not solely manufactured for sensory studies, cost of the instruments might be a concern.
Conclusion
Adaptability of new techniques always brings convenience in day-to-day life. Similarly, introducing NIST in sensory science can extract valuable information from consumers that helps a food manufacturer in understanding the subconscious need of the consumer for a product. The collected data from NIST can only be experienced while ingesting a food stimulus but cannot be expressed through verbal communications. NIST can showcase the consumer connection and trust towards a specific product or brand which also brings the emotional and appetitive response for a food item. In addition, these techniques make no room for biasness in sensory analysis and also provide an opportunity to use untrained panels for the analysis. With further research in this area, NIST can emerge as an invaluable method in the field of sensory science.
References
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