Food Protection Trends - November/December 2020 - 458

STE technology
Unlike the AR and VR technologies, which use a headmounted display, smartphone, tablet, or computer to view
the virtual or augmented scenes of an environment, simulated
task environment (STE) technology utilizes a video theatre
design. The video theatre comprises high-resolution flat panel
displays (capable of higher resolution than typical consumergrade televisions) or rear projection media panels to recreate
a computer-synthesized depiction of an environment in
which the viewer occupies the space within the video theatre
(24, 62) (Fig. 1). Sensors communicate user movement and
interaction within the video theatre and maintain appropriate
real-time rendering of environment scales during exploration.
STE setup often includes allied props and multisensory
cues to increase presence and context (65). Because the real
environment can be too complex to provide experimental
control, variables in an STE can be controlled, potentially
making it easier to study behavior in an STE than in the
actual task environment (45). Consequently, researchers can
study behavior in a task environment that is appropriate to
the research question of interest (45, 75).

FIGURE 1. Example of STE technology at the Perceptual Experience
Laboratory (Cardiff Metropolitan University). The research facility
comprises of a 2.5-m-tall high-resolution cylindrical rear-projection
screen that wraps 200° around the participant. The screen can be
configured to represent virtually any environment in terms of sight,
sound, smell, temperature, air movement, and physical objects (117).

STE application for food safety training
Global companies are using STE and VR technologies
to better understand consumer purchasing behaviors (53),
to improve the design of products and packaging, and thus
strengthen brand competitiveness. The use of STEs to test
packaging designs concurs with Young's (123) opinion
that product testing is more valid when participants are
in an appropriate shopping context with realistic product
surroundings. Inclusion of multisensory cues in an STE (e.g.,
olfactory cues, temperature control, air movement, auditory

458

Food Protection Trends November/December

effects, and props) facilitates presence and engagement (65)
and has successfully provided real-life context for product
testing across several research areas (42, 43, 58, 59). Given
the technical setup and running costs for STE facilities,
this technology is used exclusively for in-house testing by
companies with large research, development, and marketing
budgets. Consequently, dissemination of commercial
research activity and such valuable insights are not available
in the public domain, and small and medium-size businesses
may not have access to such valuable technology.
STEs could be used to expand upon the training and
education capabilities of AR and VR by delivering interactive
group training relating to areas that are not easily accessible
and enabling peer-to-peer learning. Knowledge-based
training alone may not improve food safety performance,
whereas behavior-based training approaches are often
more effective for improving food safety performance and
frequency (124). The use of simulated tasks in an STE facility
may provide a knowledge and behavioral training approach
to enhancing food safety.
Eye tracking
Eye-tracking technologies project near-infrared light onto
an individual's eyes, producing reflections that are captured
by sensors and then processed by algorithms that infer
gaze points (where the user is looking), allowing metric
calculations of eye movement data (108). Eye-tracking data
can be captured with either wearable eye-tracking technology
(e.g., eye-tracking glasses (115)) or unobtrusive screen-based
eye-tracking technology (e.g., eye-tracking bar placed on a
laptop (114) or a laptop webcam (113)).
Eye tracking provides noninvasive and comprehensive
data on brain function and cognition (30). Eye-tracking
technology allows the assessment of an individual's visual
attention and behavior while performing tasks within a
desired setting. Eye-tracking data are initially observed
using attention-based heat maps and gaze path visualization.
Detailed investigations rely on demarcating areas of interest
for quantitative analysis (111). These unique insights have
resulted in widespread use of eye-tracking technology
throughout the research, development, and marketing stages
for new and existing products from global manufacturing
companies (107).
The ability to capture an unbiased visual record of a
participant's subconscious during tasks (implicit research)
robustly supports behavioral and attention insights and
removes the uncertainty (lack of candor) associated with
response data collected with traditional research methods
such as questionnaires, in which responses can be subject to
biases (110, 112). Eye-tracking technology has significantly
improved in its usability, accuracy, and speed while
decreasing in cost (28), resulting in increased availability
and use in research (82, 107). Research exploring visual
reaction to stimuli has been conducted in psychology,



Food Protection Trends - November/December 2020

Table of Contents for the Digital Edition of Food Protection Trends - November/December 2020

Characterizing Microbial Cross-Contamination on Large Surfaces Using a Traditional “Cloth and Bucket” Disinfection Method
Sanitation Monitoring of Stainless Steel Surfaces with a Test for Total Adenylates
Variability in the Microbial Profile of Retail Cricket Powders in the U.S. Retail Market
Exploring Food Truck Food Safety Training and Practices in the United States: A Qualitative Study
Use of the Theory of Planned Behavior to Determine Food Safety Behavioral Intentions among Child Nutrition Employees
Produce Safety Alliance Train-the-Trainer Course: Developing Trainers to Support Fruit and Vegetable Growers
Beyond the Bio Manpreet Singh
PDG Highlight Food Packaging Professional Development Group
General Interest Paper Exploring Novel Technologies to Enhance Food Safety Training and Research Opportunities
General Interest Paper Food Safety and Quality Data Management Using Artificial Intelligence
Industry Products
Coming Events
Food Protection Trends - November/December 2020 - Cover1
Food Protection Trends - November/December 2020 - Cover2
Food Protection Trends - November/December 2020 - 385
Food Protection Trends - November/December 2020 - 386
Food Protection Trends - November/December 2020 - 387
Food Protection Trends - November/December 2020 - 388
Food Protection Trends - November/December 2020 - 389
Food Protection Trends - November/December 2020 - 390
Food Protection Trends - November/December 2020 - 391
Food Protection Trends - November/December 2020 - Characterizing Microbial Cross-Contamination on Large Surfaces Using a Traditional “Cloth and Bucket” Disinfection Method
Food Protection Trends - November/December 2020 - 393
Food Protection Trends - November/December 2020 - 394
Food Protection Trends - November/December 2020 - 395
Food Protection Trends - November/December 2020 - 396
Food Protection Trends - November/December 2020 - 397
Food Protection Trends - November/December 2020 - 398
Food Protection Trends - November/December 2020 - 399
Food Protection Trends - November/December 2020 - 400
Food Protection Trends - November/December 2020 - 401
Food Protection Trends - November/December 2020 - Sanitation Monitoring of Stainless Steel Surfaces with a Test for Total Adenylates
Food Protection Trends - November/December 2020 - 403
Food Protection Trends - November/December 2020 - 404
Food Protection Trends - November/December 2020 - 405
Food Protection Trends - November/December 2020 - 406
Food Protection Trends - November/December 2020 - Variability in the Microbial Profile of Retail Cricket Powders in the U.S. Retail Market
Food Protection Trends - November/December 2020 - 408
Food Protection Trends - November/December 2020 - 409
Food Protection Trends - November/December 2020 - 410
Food Protection Trends - November/December 2020 - 411
Food Protection Trends - November/December 2020 - 412
Food Protection Trends - November/December 2020 - Exploring Food Truck Food Safety Training and Practices in the United States: A Qualitative Study
Food Protection Trends - November/December 2020 - 414
Food Protection Trends - November/December 2020 - 415
Food Protection Trends - November/December 2020 - 416
Food Protection Trends - November/December 2020 - 417
Food Protection Trends - November/December 2020 - 418
Food Protection Trends - November/December 2020 - 419
Food Protection Trends - November/December 2020 - 420
Food Protection Trends - November/December 2020 - 421
Food Protection Trends - November/December 2020 - 422
Food Protection Trends - November/December 2020 - 423
Food Protection Trends - November/December 2020 - Use of the Theory of Planned Behavior to Determine Food Safety Behavioral Intentions among Child Nutrition Employees
Food Protection Trends - November/December 2020 - 425
Food Protection Trends - November/December 2020 - 426
Food Protection Trends - November/December 2020 - 427
Food Protection Trends - November/December 2020 - 428
Food Protection Trends - November/December 2020 - 429
Food Protection Trends - November/December 2020 - 430
Food Protection Trends - November/December 2020 - 431
Food Protection Trends - November/December 2020 - 432
Food Protection Trends - November/December 2020 - 433
Food Protection Trends - November/December 2020 - 434
Food Protection Trends - November/December 2020 - Produce Safety Alliance Train-the-Trainer Course: Developing Trainers to Support Fruit and Vegetable Growers
Food Protection Trends - November/December 2020 - 436
Food Protection Trends - November/December 2020 - 437
Food Protection Trends - November/December 2020 - 438
Food Protection Trends - November/December 2020 - 439
Food Protection Trends - November/December 2020 - 440
Food Protection Trends - November/December 2020 - 441
Food Protection Trends - November/December 2020 - 442
Food Protection Trends - November/December 2020 - 443
Food Protection Trends - November/December 2020 - 444
Food Protection Trends - November/December 2020 - 445
Food Protection Trends - November/December 2020 - 446
Food Protection Trends - November/December 2020 - 447
Food Protection Trends - November/December 2020 - 448
Food Protection Trends - November/December 2020 - 449
Food Protection Trends - November/December 2020 - 450
Food Protection Trends - November/December 2020 - 451
Food Protection Trends - November/December 2020 - Beyond the Bio Manpreet Singh
Food Protection Trends - November/December 2020 - 453
Food Protection Trends - November/December 2020 - 454
Food Protection Trends - November/December 2020 - PDG Highlight Food Packaging Professional Development Group
Food Protection Trends - November/December 2020 - General Interest Paper Exploring Novel Technologies to Enhance Food Safety Training and Research Opportunities
Food Protection Trends - November/December 2020 - 457
Food Protection Trends - November/December 2020 - 458
Food Protection Trends - November/December 2020 - 459
Food Protection Trends - November/December 2020 - 460
Food Protection Trends - November/December 2020 - 461
Food Protection Trends - November/December 2020 - 462
Food Protection Trends - November/December 2020 - 463
Food Protection Trends - November/December 2020 - General Interest Paper Food Safety and Quality Data Management Using Artificial Intelligence
Food Protection Trends - November/December 2020 - 465
Food Protection Trends - November/December 2020 - 466
Food Protection Trends - November/December 2020 - 467
Food Protection Trends - November/December 2020 - Industry Products
Food Protection Trends - November/December 2020 - 469
Food Protection Trends - November/December 2020 - 470
Food Protection Trends - November/December 2020 - 471
Food Protection Trends - November/December 2020 - Coming Events
Food Protection Trends - November/December 2020 - Cover3
Food Protection Trends - November/December 2020 - Cover4
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