Details

Autor(en) / Beteiligte

• Lawlor, J. Ben,
• A. McEwan, Jean,
• Labbe, David,

Titel

Digital sensory science : applications in new product development

Auflage

1st ed

Ort / Verlag

San Diego : Elsevier Science & Technology,

Erscheinungsjahr

2023

Beschreibungen/Notizen

• Front Cover -- Digital Sensory Science -- Copyright Page -- Contents -- List of contributors -- Foreword -- 1 Introduction -- 1.1 Introduction -- 1.1.1 Why this book now? -- 1.2 Organisation and macro summary of content -- 1.2.1 Setting the scene -- 1.2.2 How digital is creating new opportunities for sensory science -- 1.2.3 Digitalisation in instrumental, neurological, psychological and behavioural methods: current applications and opportu... -- 1.2.4 Immersion technologies, context and sensory perception -- 1.2.5 How to tell powerful digital sensory stories -- 1.3 Final words -- 1 Setting the scene -- 2 The emergence of digital technologies and their impact on sensory science -- 2.1 The beginning -- 2.2 Photocopiers -- 2.2.1 Digital consumers -- 2.2.2 Scanning and digitising -- 2.2.3 Digitising -- 2.2.4 Paper ballots -- 2.2.5 Sample identity -- 2.2.6 Direct data entry -- 2.2.7 The light pen -- 2.2.8 Personal digital assistants -- 2.2.9 Touchscreens -- 2.3 Statistics unleashed -- 2.4 Temporal methods -- 2.5 Summary -- 2.6 Quo Vadis -- References -- 3 The arrival of digitisation in sensory research and its further development in the use of artificial intelligence -- 3.1 Digitisation is already old -- 3.2 State-of-the-art sensory analysis -- 3.3 Digitised furniture for sensory evaluation -- 3.4 Room management - smart networks via KNX-control -- 3.5 High-performance sensory tables: intelligent, digitised and modular for sensory evaluation -- 3.6 The software -- 3.7 Performance characteristics of sensory analysis software -- 3.8 Target-oriented sensory analysis -- 3.9 Agile elements in sensory analysis -- 3.10 Flexibilisation of research -- 3.11 Artificial intelligence: how does it work? -- 3.12 Data warehouse - a fundamental prerequisite -- 3.13 Artificial intelligence: what can be expected from artificial intelligence in sensory analysis?.
• 3.14 Conclusion -- 2 How digital is creating new opportunities for sensory science -- 4 Digital toolbox - ways of increasing efficiency in a descriptive panel -- 4.1 Introduction -- 4.2 Excursus: descriptive analysis under Covid-19 restrictions -- 4.3 isiSensorySuite -- 4.3.1 Panel training -- 4.3.2 Business case study: a training tool for sensory profiling -- 4.3.3 Data collection -- 4.3.4 Business case study: sensory profiling programme -- 4.3.5 Panellist management -- 4.3.6 Business case study: panel management tool -- 4.3.6.1 Panellist profile -- 4.3.6.2 Project management -- 4.3.6.3 Recruitment -- 4.3.6.4 Booking planner -- 4.3.7 Business case study: isiSensorySuite -- 4.4 Status Quo and a glimpse into the future -- References -- 5 'Hybridisation' of physical and virtual environments in sensory design -- 5.1 Introduction -- 5.1.1 Setting the scene - moving from physical, to a mix of both physical and digital, environments in sensory design -- 5.1.2 The phygital concept and learnings for sensory design -- 5.2 Case studies - exemplification of hybridisation of physical and digital environments in concrete situations of sensory ... -- 5.2.1 Designing a digital sensory training with physical stimuli -- 5.2.2 Projects' team tasting: engaging all senses in a remote meeting -- 5.2.3 Running remote sensory descriptive in-house panels -- 5.3 Conclusions -- References -- 6 Sensory evolution: deconstructing the user experience of products -- 6.1 What kept us in the laboratory? -- 6.1.1 Sensory laboratories and booths -- 6.1.2 Social -- 6.2 What is a product? -- 6.2.1 Putting the 'human' back into sensory research -- 6.3 Kicked out of the laboratory -- 6.3.1 The elephant in the (sensory) room -- 6.3.2 Elevating the sensory outputs -- 6.3.3 Beyond classic sensory -- 6.4 The paradigm shift: using digital sensory to meet the new industry challenges.
• 6.4.1 Tech adjustment -- 6.4.2 Protocol adjustment - environment -- 6.4.3 The human touch -- 6.4.4 Digital innovation -- 6.5 Future for sensory scientists -- 6.5.1 Overall challenges and opportunities -- 6.5.2 Challenges and opportunities moving towards digital-trained user panel approach -- 6.5.2.1 How to put the human at the heart of the process -- 6.5.2.2 Remote screening -- 6.5.2.3 Digital platforms for data collection -- 6.5.2.4 Create the outlet for user experience sharing - motivation -- 6.5.2.5 Amplify the sensory power and beyond -- 6.6 What does it mean for young sensory scientists about to enter the digital sensory era? -- 6.6.1 Deconstruct beyond the obvious -- 7 Using digital tools to understand individual differences in flavour perception which impact on food preferences -- 7.1 Introduction -- 7.2 Differences in flavour perception -- 7.3 Implications of differences in flavour perception -- 7.3.1 Case study 1: detection and control of off-notes in whisky samples -- 7.3.2 Case study 2: flavour profiling of mature whiskies during new product development -- 7.4 Conclusions and future perspectives -- References -- 8 Sensory self-service - digitalisation of sensory central location testing -- 8.1 Introduction -- 8.2 Digital project -- 8.2.1 Project planning -- 8.2.2 Fieldwork -- 8.2.3 Analysis and reporting -- 8.3 Business case: a digital study-management platform -- 8.3.1 Background -- 8.3.2 Objectives -- 8.3.3 Setting up the platform -- 8.3.4 Hosting the platform -- 8.3.5 Summary -- 8.3.6 Outlook -- 8.4 Status quo and a look into the future -- References -- 3 Digitalization in instrumental, neurological, psychological and behavioural methods: Current applications and opp... -- 9 Predicting sensory properties from chemical profiles, the ultimate flavour puzzle: a tale of interactions, receptors, mat... -- 9.1 Introduction.
• 9.2 Computing strategies -- 9.3 Importance of data quality -- 9.3.1 Chemical composition of flavours -- 9.3.2 Alternative flavour profile measurements -- 9.3.3 Data from sensory analysis -- 9.4 Case histories -- 9.4.1 Tea -- 9.4.2 Trends in current research publications -- 9.4.3 DREAM project -- 9.4.4 Odorify -- 9.4.5 Commercial platforms -- 9.4.6 Compositional-sensory relationships in a real food -- 9.5 Conclusions and further perspectives -- References -- 10 Electronic noses and tongues: current trends and future needs -- 10.1 Introduction -- 10.2 Machine learning methods for e-tongue and e-nose technologies -- 10.2.1 Feature extraction and learning -- 10.2.2 Qualitative analysis -- 10.2.3 Quantitative analysis -- 10.2.4 Compensation of sensor variability -- 10.2.5 Statistical evaluation -- 10.2.6 Reliability and agreement among sensory ratings -- 10.3 Case studies for e-tongue and e-nose applications in sensory evaluation -- 10.3.1 Case study 1: food safety -- 10.3.2 Case study 2: food innovation -- 10.4 Future perspectives -- 10.5 Conclusion -- References -- 11 Leveraging neuro-behavioural tools to enhance sensory research -- 11.1 Introduction -- 11.2 Digital neuro-behavioural methods -- 11.2.1 Physiological measures -- 11.2.1.1 Peripheral measures -- 11.2.1.2 Central measures -- 11.2.1.3 Wearables -- 11.2.2 Psychological tools -- 11.2.3 Behavioural tools -- 11.2.4 Digital technologies -- 11.2.4.1 Smart devices -- 11.2.4.2 Immersive technologies -- 11.3 Future directions and challenges in digitalising neuro-behavioural research -- References -- 12 Emerging biometric methodologies for human behaviour measurement in applied sensory and consumer science -- 12.1 Introduction -- 12.1.1 Explicit versus implicit measures -- 12.1.2 Eye-tracking -- 12.1.2.1 Visual attention -- 12.1.2.2 The technology -- 12.1.3 Electrodermal activity.
• 12.1.3.1 Emotional arousal -- 12.1.3.2 The technology -- 12.1.4 Facial expression analysis -- 12.1.4.1 Emotional valence -- 12.1.4.2 The technology -- 12.1.5 Electroencephalography -- 12.1.5.1 Cognitive load -- 12.1.5.2 Reward processing -- 12.1.5.3 The technology -- 12.2 Research cases -- 12.2.1 Eye-tracking to investigate bottom-up effects of visual attention -- 12.2.2 Electrodermal activity and facial expression analysis to study emotional responses of food expectation and acceptance -- 12.2.3 Electroencephalography and electrodermal activity to explore the neurophysiology of food cravings -- 12.3 Concluding remarks -- 12.3.1 Theoretical and practical considerations -- 12.3.2 Future perspectives -- Funding -- Conflicts of interest -- References -- 13 Added value of implicit measures in sensory and consumer science -- 13.1 Introduction -- 13.1.1 Why implicit measures? -- 13.1.2 Factors related to the use of implicit measures in academic and industrial research -- 13.1.3 Are implicit measures primarily used in academic research? -- 13.1.4 How do results from implicit measures compare to results from explicit measures? -- 13.1.5 The complexity of real-life food experiences -- 13.1.6 Testing in real-life -- 13.2 The case studies -- 13.2.1 Case study 1 -- 13.2.2 Case study 2: effects of recreated consumption contexts on facial expressions -- 13.3 What are the learnings from these case studies? -- 13.4 The road towards user-friendly implicit measures that can be used at any place and at any time and by everybody -- 13.5 In conclusion and future perspectives -- References -- 4 Immersion technologies, context and sensory perception -- 14 Using virtual reality as a context-enhancing technology in sensory science -- 14.1 Introduction -- 14.1.1 The role of context in the sensory evaluation of food -- 14.1.2 Immersive virtual reality technologies.
• 14.2 Creating context through an immersive virtual reality environment: case studies with beef and chocolate.
• Description based on: online resource; title from pdf title page (ScienceDirect, viewed on April 15, 2024).