This project was funded, in part, by ConAgra Foods (Omaha, NE, USA), the Virginia Agricultural Experiment Station, the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Virginia Tech Water INTERface Interdisciplinary Graduate Education Program.

Ethics Statement: This study was pre-approved by Virginia Tech Institutional Review Board (IRB) (IRB 14-229) prior to starting the project.
Caution: Human subject research requires informed consent prior to participation. In addition to IRB approval, consent for use of still or video images is also required prior to releasing any images for print, video, or graphic imaging. Additionally, food allergens are disclosed prior to testing. Participants are asked prior to panel start if they have any intolerance, allergies or other concerns....

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AFEA application in literature related to food and beverage is very limited1-11. The application to food is new, creating an opportunity for establishing methodology and data interpretation. Arnade (2013)7 found high individual variability among individual emotional response to chocolate milk and white milk using area under the curve analysis and analysis of variance. However, even with participant variability, participants generated a happy response longer while sad and disgusted had shorter time r...

Automated facial expression analysis (AFEA) is a prospective analytical tool for characterizing emotional responses to beverages and foods. Emotional analysis can add an extra dimension to existing sensory science methodologies, food evaluation practices, and hedonic scale ratings typically used both in research and industry settings. Emotional analysis could provide an additional metric that reveals a more accurate response to foods and beverages. Hedonic scoring may include participant bias due to failure to record reactions1.
AFEA research has been used in many research applications including computer gaming, user behavior, education/pedagogy, and psychology studies on empathy and deceit. Most food-associated research has focused on characterizing emotional response to food quality and human behavior with food. With the recent trend in gaining insights into food behaviors, a growing body of literature reports use of AFEA for characterizing the human emotional response associated with foods, beverages, and odorants1-12.
AFEA is derived from the Facial Action Coding System (FACS). The facial action coding system (FACS) discriminates facial movements characterized by action units (AUs) on a 5-point intensity scale13. The FACS approach requires trained review experts, manual coding, significant evaluation time, and provides limited data analysis options. AFEA was developed as a rapid evaluation method to determine emotions. AFEA software relies on facial muscular movement, facial databases, and algorithms to characterize the emotional response14-18. The AFEA software used in this study reached a &#34;FACS index of agreement of 0.67 on average on both the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Amsterdam Dynamic Facial Expression Set (ADFES), which is close to a standard agreement of 0.70 for manual coding&#34;19. Universal emotions included in the analysis are happy (positive), sad (negative), disgusted (negative), surprised (positive or negative), angry (negative), scared (negative) and neutral each on a separate scale of 0 to 1 (0=not expressed; 1=fully expressed)20. In addition, psychology literature includes happy, surprised, and angry as &#34;approach&#34; emotions (toward stimuli) and sad, scared, and disgusted as &#34;withdrawal&#34; emotions (away from aversive stimuli)21.
One limitation of the current AFEA software for characterizing emotions associated with foods is interference from facial movements associated with chewing and swallowing as well as other gross motor motions, such as extreme head movements. The software targets smaller facial muscular motions, relating position and degree of movement, based on over 500 muscle points on the face16,17. Chewing motions interfere with classification of expressions. This limitation may be addressed using liquefied foods. However, other methodology challenges can also decrease video sensitivity and AFEA analysis including data collection environment, technology, researcher instructions, participant behavior, and participant attributes.
A standard methodology has not been developed and verified for optimal video capture and data analysis using AFEA for emotional response to foods and beverages in a sensory evaluation laboratory setting. Many aspects can affect the video capture environment including lighting, shadowing due to lighting, participant directions, participant behavior, participant height, as well as, camera height, camera angling, and equipment settings. Moreover, data analysis methodologies are inconsistent and lack a standard methodology for assessing emotional response. Here, we will demonstrate our standard operating procedure for capturing emotional data and processing data into meaningful results using beverages (flavored milk, unflavored milk and unflavored water) for evaluation. To our knowledge only one peer reviewed publication, from our lab group, has utilized time series for data interpretation for emotions analysis8; however, the method has been updated for our presented method. Our aim is to develop an improved and consistent methodology to help with reproducibility in a sensory evaluation laboratory setting. For demonstration, the objective of the study model is to evaluate if AFEA could supplement traditional hedonic acceptability assessment of flavored milk, unflavored milk and unflavored water. The intention of this video protocol is to help establish AFEA methodology, standardize video capture criteria in a sensory evaluation laboratory (sensory booth setting), and illustrate a method for temporal emotional data analysis of a population.

<table border="0" cellpadding="0" cellspacing="0" width="1087">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">2% Reduced Fat Milk</td>
			<td style="width:436px;">Kroger Brand, Cincinnati, OH or DZA Brands, LLC, Salisbury, NC</td>
			<td style="width:119px;">na</td>
			<td style="width:316px;">for solutions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Drinking Water</td>
			<td>Kroger Brand, Cincinnati, OH</td>
			<td style="width:119px;">na</td>
			<td>for solutions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Imitation Clear Vanilla Flavor</td>
			<td>Kroger Brand, Cincinnati, OH</td>
			<td style="width:119px;">na</td>
			<td>for solutions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Iodized Salt</td>
			<td>Kroger Brand, Cincinnati, OH</td>
			<td style="width:119px;">na</td>
			<td>for solutions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">FaceReader 6</td>
			<td style="width:436px;">Noldus Information Technology, Wageningen, The Netherlands</td>
			<td style="width:119px;">na</td>
			<td>For Facial Analysis</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Sensory Information Management System (SIMS) 2000</td>
			<td style="width:436px;">Sensory Computer Systems, Berkeley Heights, NJ</td>
			<td style="width:119px;">Version 6</td>
			<td>For Sensory Data Capture</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Rhapsody</td>
			<td style="width:436px;">Acuity Brands Lighting, Inc., Conyers, GA</td>
			<td style="width:119px;"></td>
			<td>For Environment Illumination</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">R Version&#160;</td>
			<td style="width:436px;">R Core Team 2015</td>
			<td style="width:119px;">3.1.1</td>
			<td>For Statistical Analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Microsoft Office</td>
			<td style="width:436px;">Microsoft</td>
			<td style="width:119px;">na</td>
			<td>For Statistical Analysis</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">JMP</td>
			<td style="width:436px;">Statistical Analysis Software (SAS) Version 9.2, SAS Institute, Cary, NC</td>
			<td style="width:119px;">na</td>
			<td>For Statistical Analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Media Recorder 2.5</td>
			<td style="width:436px;">Noldus Information Technology, Wageningen, The Netherlands</td>
			<td style="width:119px;">na</td>
			<td>For capturing participants sensory evaluation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Axis M1054 Camera</td>
			<td style="width:436px;">Axis Communications, Lund, Sweden</td>
			<td style="width:119px;">na</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Beverage</td>
			<td style="width:436px;"></td>
			<td style="width:119px;">na</td>
			<td>Beverage or soft food for evaluation</td>
		</tr>
	</tbody>
</table>

protocol for data collection and analysis applied to automated facial expression analysis technology and temporal analysis for sensory evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

The method proposes a standard protocol for AFEA data collection. If suggested protocol steps are followed, unusable emotional data output (Figure 1) resulting from poor data collection (Figure 2: A; Left Picture) may be limited. Time series analysis cannot be utilized if log files (.txt) predominantly contain &#34;FIT_FAILED&#34; and &#34;FIND_FAILED&#34; as this is bad data (Figure 1). Furthermore, the method includes a protocol for dir...

Watch this Scientific Journal Video about Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation at JoVE.com

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

The overall goal of this methodology is to capture and analyze emotional responses to beverages and liquefied foods in a sensory evaluation laboratory using automated facial analysis software. This method can help answer key questions in the sensory science field such as how to measure a participant's emotional response to beverages and soft foods. The main advantage of this protocol is that it shows us standardized data capture methodology using automated facial expression analysis software.It also describes the method for analyzing automated facial expression output. The implications of this technique extend towards standardizing methodology of automated facial expression analysis as it often varies between laboratories. Although this method can provide insight to an individual's implicit emotional response, it can also be applied to other systems such as packaging, marketing, and labels.Lester will serve as a representative participant. In preparation for the experiment, create sample solutions by pouring half ounce aliquots of each original solution into two ounce transparent plastic sample cups, and then cap with color-coded lids. Next, escort the participant to the sensory booth and have them sit straight up against the back of the chair.Instruct the participant about the experimental process and standard operating procedures. Adjust the position of the chair and the camera angle so that the participants face is captured in the center of the video recording with no shadows on the chin or around the eyes. Instruct the participant to focus on the monitor display, follow the directions on the screen, and refrain from any sudden movements for 30 seconds after each sample is consumed.Demonstrate an impromptu practice session by first instructing the participant to hold up the associated identification card. Then instruct them to open the sample container, consume the entire beverage sample, and swallow. After consumption, instruct the participant to quickly move the sample cup away from the chin and place it on the table to avoid facial occlusion.Remind them to keep looking toward the monitor. Remind the participant that water will be provided on the serving tray to cleanse their palate in between samples when prompted by the monitor. Finally, instruct the participant to follow the instructions as they appear on the touch screen monitor.Confirm that the video camera is directed toward the participant's face by viewing the computer monitor on which the video capture is displayed. Then begin recording by clicking the record button on the computer monitor. Prompt the participant, via the touch screen monitor, to sip water to cleanse their palate.Place a unique-colored index card on top of each sample, allowing for identification of each treatment sample. Next, the touch screen monitor prompts the participant to hold up the associated index card pre-consumption for sample identification in the video, and then have them place the card back on the tray. Provide only one sample at a time on the serving tray, starting with a baseline sample of unflavored water.Have the participant consume the sample while facing the camera, and then wait approximately 30 seconds, a time enforced by the touch screen monitor. After 30 seconds, prompt the participant, via the touch screen monitor, to enter their hedonic acceptability score. Finally, have the participant rinse their mouth with drinking water before moving on to the next sample.Use facial analysis software to analyze the experimental videos. Add participants to the project by clicking the add participants icon. Press the batch analysis icon, which is the red and black target-like symbol, to analyze the project videos.Export the AFEA data output as log files for further analysis. Using the AFEA software, manually review each video and identify post-consumption time zero for each sample, which is defined as when the sample cup is below the participant's chin and longer occludes the face. Finally, record the timestamp in the data spreadsheet, and save this spreadsheet as a reference for extracting relevant data from videos.Unflavored milk, unflavored water, and vanilla extract flavor in milk were not different in mean acceptability scores. And salty flavor in milk had a lower mean hedonic acceptability score. However, AFEA time series analysis indicated unflavored milk generated less disgusted, surprised, sad, and neutral responses than did unflavored water.Furthermore, the vanilla extract flavor in milk introduced more happy and less sad expressions when compared to unflavored water. Salty flavor in milk generated more disgusted and less neutral responses when compared to unflavored water. Once mastered, this technique can be done in 30 minutes if it is performed properly.While attempting this procedure, it's important to remember to give the participant complete instructions and expectations. Following this procedure, other evaluations can be performed in order to answer additional questions like what is the influence on behavior and emotional response? After its development, this technique paved the way for researchers in the field of sensory science to explore implicit emotional responses in human participants to beverages and soft foods.After watching this video, you should have a good understanding of how to standardize the sensory lab environment, and how to instruct participants in order to capture the best data and results, as well as analyze automated facial expression analysis output. Don't forget that working with human participants can be sensitive. It is important to discuss ingredients prior to performing this procedure.

protocol-for-data-collection-analysis-applied-to-automated-facial

Research

JoVE Journal

Behavior

9.3K visualizações.  Virginia Tech. The overall goal of this methodology is to capture and analyze emotional responses to beverages and liquefied foods in a sensory evaluation laboratory using automated facial analysis software. This method can help answer key questions in the sensory science field such as how to measure a participant's emotional response to beverages and soft foods. The main advantage of this protocol is that it shows us standardized data capture methodology using automated facial expression analysis software....

Vídeo: Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation