Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Summary

This article describes a set of methods for measuring the suppressive ability of sniffing alcoholic beverages on the wasabi-elicited stinging sensation.

Abstract

The commercial wasabi pastes commonly used for food preparation contain a homologous compound of chemosensory isothiocyanates (ITCs) that elicit an irritating sensation upon consumption. The impact of sniffing dietary alcoholic beverages on the sensation of wasabi spiciness has never been studied. While most sensory evaluation studies focus on individual food and beverages separately, there is a lack of research on the olfactory study of sniffing liquor while consuming wasabi. Here, a methodology is developed that combines the use of an animal behavioral study and a convolutional neural network to analyze the facial expressions of mice when they simultaneously sniff liquor and consume wasabi. The results demonstrate that the trained and validated deep learning model recognizes 29% of the images depicting co-treatment of wasabi and alcohol belonging to the class of the wasabi-negative liquor-positive group without the need for prior training materials filtering. Statistical analysis of mouse grimace scale scores obtained from the selected video frame images reveals a significant difference (P < 0.01) between the presence and absence of liquor. This finding suggests that dietary alcoholic beverages might have a diminishing effect on the wasabi-elicited reactions in mice. This combinatory methodology holds potential for individual ITC compound screening and sensory analyses of spirit components in the future. However, further study is required to investigate the underlying mechanism of alcohol-induced suppression of wasabi pungency.

Introduction

Wasabia japonica, commonly known as wasabi, has gained recognition in food preparation^1,2. The intense sensory experience it elicits upon consumption, characterized by tearing up, sneezing, or coughing, is well-known. This distinctive pungency of wasabi can be attributed to a homologous compound of chemosensory isothiocyanates (ITCs). They are volatile organosulfur phytochemicals that can be categorized into ω-alkenyl and ω-methylthioalkyl isothiocyanates³. Among these compounds, allyl isothiocyanate (AITC) is the most predominated natural ITC product found in plants belonging to the Cruciferae family, such as horseradish and mustard⁴. Commercial wasabi pastes are commonly prepared from horseradish, making AITC a chemical marker used for quality control of these commercial products⁵.

Pairing dietary alcoholic beverages with wasabi-infused dishes can be considered an example of cultural disposition⁶. Subjectively, this combination may complement the spiciness and heat between wasabi and the spirit, enhancing the overall culinary experience. Animal qualitative behavioral assessment (QBA) is a comprehensive whole-animal methodological approach that examines behavioral changes in subjects in response to short-term or long-term external stimuli using numerical terms⁷. This method encompasses pain tests, motor tests, learning and memory tests, as well as emotion tests specifically designed for rodent models⁸. However, studies investigating the synergistic sensory evaluation of gustation together with olfaction remain scarce in the literature until now^9,10. Most of the studies on chemesthetic sensation are confined to examining individual food and beverage consumption separately¹¹. Consequently, there is a dearth of research on the taste-smell interaction involving the act of sniffing liquor while consuming wasabi.

As the wasabi-induced stinging sensation is believed to be a form of nociception¹², animal behavioral assessments are well-suited for evaluating the nociceptive sensory responses in rodent animals^8,13,14. A method for assessing nociception in mice, known as the mouse grimace scale (MGS) scoring was developed by Langford et al.^15,16. This behavioral study method is a pain-related assessment approach, relying on the analysis of facial expressions exhibited by the experimental mice. The experimental setup is straightforward, involving a transparent cage and 2 cameras for video recording. By incorporating advanced technologies^17,18,19 for automatic data capture, quantitative and qualitative behavioral measures can be obtained, enhancing animal welfare during behavioral monitoring²⁰. Consequently, the MGS has the potential to be applied in studying the effects of various external stimuli on animals in an uninterrupted and ad libitum manner. However, the scoring process only involves selecting a few (less than 10) video frame images for evaluation by the panelists, and prior training is necessary. Scoring a large number of sample images can be labor-intensive. To overcome this time-consuming challenge, several studies have employed machine learning techniques for predicting the MGS score^21,22. Yet, it is important to note that the MGS is a continuous measure. Therefore, a multi-class classification model would be more suitable for evaluating a logical and categorical problem, such as determining whether the images of mice simultaneously ingesting wasabi and sniffing liquor resemble those of normal mice.

In this study, a methodology for investigating the taste-smell interaction in mice was proposed. This methodology combines animal behavioral studies with a convolutional neural network (CNN) to analyze the facial expressions of the mouse subjects. Two mice were observed thrice under normal behavioral conditions, during the experience of wasabi-induced nociception and while sniffing liquor in a specifically designed cage. The facial expressions of the mice were video-recorded, and the generated frame images were utilized to optimize the architecture of a deep learning (DL) model. The model was then validated using an independent image dataset and deployed to classify the images acquired from the experimental group. To determine the extent of wasabi pungency suppression when the mice simultaneously sniffed liquor during wasabi consumption, the insights provided by artificial intelligence were further corroborated through cross-validation with another data analysis method, the MGS scoring¹⁶.

Protocol

In this study, two 7-week-old ICR male mice weighing between 17-25 g were utilized for the animal behavioral assessment. All housing and experimental procedures were approved by the Hong Kong Baptist University Committee on the Use of Human and Animal Subjects in Teaching and Research. The animal room was maintained at a temperature of 25 °C and a room humidity of 40%-70% on a 12-h light-dark cycle.

1. Cage design

1. Prepare acrylonitrile butadiene styrene bricks in 3 different dimensions for cage construction: 8 mm x 8 mm x 2 mm, 16 mm x 16 mm x 6 mm, and 32 mm x 16 mm x 6 mm.
2. Prepare an acrylonitrile butadiene styrene plate (312 mm x 147 mm x 2 mm) as the cage base.
3. Prepare a 239 mm x 107 mm non-transparent acrylic plate with a thickness of 2 mm to be used as the bottom plate.
4. Prepare a 239 mm x 107 mm transparent acrylic plate with a thickness of 5 mm to be used as the top plate.
5. Prepare a 107 mm x 50 mm transparent acrylic plate with a thickness of 7 mm to be used as the terminal plate.
6. Construct 2 opaque side walls by stacking bricks to a height of 54 mm.
7. Embed the acrylic plates into the acrylonitrile butadiene styrene-based cage, as illustrated in Figure 1A.
8. Prepare a chows' chamber that is constructed by five 90 mm x 50 mm transparent acrylic plates with a thickness of 2 mm, as illustrated in Figure 1B. Among the 5 transparent acrylic plates, use 2 plates for the sides, 1 plate for the top, 1 plate for the bottom, and 1 plate for the terminal.
9. Prepare a 60 mm x 50 mm 2 mm transparent acrylic plate as a chow introduction plate, and place it in the chow's chamber.

2. Animal behavioral assessment

1. House littermates of 2 7-week-old ICR male mice together in a regular cage.
2. Provide the littermates of 2 mice with free access to food pellets and tap water for a 1-week adaptation period.
3. After 1 week, introduce the littermates of 2 mice with a bottle of ethanol (~40% v/v).
   NOTE: Make sure that they are only allowed to sniff or inhale the provided aqueous ethanol on an ad libitum basis while drinking is restricted.
4. Conduct behavioral experiments using the 9-10-week-old mouse model and the transparent cubicle cage that is depicted in Figure 1A.
5. Disassemble all the acrylic plates and acrylonitrile butadiene styrene plates and clean them thoroughly. Start by rinsing them with ultrapure water at least 3 times and then dry them using paper towels. Next, spray them with 75% ethanol, followed by cleaning them with lens paper. Finally, allow them to air dry for at least 15 min.
6. Weigh the mice and record their body weights before each replication of the behavioral experiment.
7. Freshly prepare a mixture of wasabi and peanut butter by weighing 1 g of commercial wasabi and 4.5 g of peanut butter. Mix them in a zip plastic bag.
   NOTE: Due to the volatility of the isothiocyanates in wasabi, it is important to store the commercial wasabi in a -20 °C freezer.
8. Weigh and provide either two 0.5 g pastes of peanut butter or a mixture of wasabi and peanut butter on the chow introduction plate, as illustrated in Figure 1B, C.
9. Place the prepared chow introduction plate in the chows' chamber, as illustrated in Figure 1B, C, to allow the 2 mice to have ad libitum access to food during each video recording session.
10. Fill the groove underneath with 30 mL of liquid, either pure water or liquor (~42% v/v ethanol), to facilitate concurrent inhalation, as indicated in Figure 1B, C.
11. Begin recording using the cameras on 2 smartphones placed on the phone stands at each terminal.
    NOTE: The specifications of the videos are as follows: frame width, 1920; frame height, 1080; data rate, 20745 kbps; frame rate, 30.00 frames per second (fps).
12. Carefully place the 2 trained littermates of mice into the designed animal behavioral study platform from the top and promptly secure the cage with the top plate.
    NOTE: Make sure this step is completed within 15 s.
13. Record each video for 2-3 min.
    NOTE: Make sure the entire duration of the experiment, from the preparation of the peanut butter-wasabi mixture to the completion of the video recording, is limited to 5 min.
14. Repeat the whole experiment 3 times.
    NOTE: Make sure each replicate of the experiment is separated for at least 6 h.
15. Mimic different scenarios.
    NOTE: For example, in this work, a pair of mice was used in 4 groups with 4 different scenarios that were mimicked by using the experimental setting described above. These scenarios include Scenario A for the background study, Scenario B for the positive control study, Scenario C for the wasabi-alcohol taste-smell interaction study, and Scenario D for the negative control study. Table 1 provides a summary of these scenarios.

3. Image recognition

Similar to many studies on image processing^23,24,25, a classification model was attained by training a CNN. The script for DL operations was written in Python v.3.10 on Jupyter Notebook (anaconda3). It is available on the following GitHub repository: git@github.com:TommyNHL/imageRecognitionJove.git. To construct and train the CNN, open-source libraries were used, including, numpy v.1.21.5, seaborn v.0.11.2, matplotlib v.3.5.2, cv2 v.4.6.0, sklearn v.1.0.2, tensorflow v.2.11.0, and keras v.2.11.0. These libraries provided the necessary tools and functionality to develop and train CNN for image recognition.

1. Export a series of video frame images from the collected video clips to generate a data set for model training by using the provided Jupyter Notebook, named Step1_ExtractingAndSavingVideoFrameImages.ipynb.
2. Only select the images with at least 1 mouse consuming the provided paste. Examples of the selected images are provided in Supplementary Figure 1, Supplementary Figure 2, Supplementary Figure 3, Supplementary Figure 4, Supplementary Figure 5, Supplementary Figure 6, and Supplementary Figure 7.
3. Perform data augmentation by horizontally flipping the generated images by implementing the script provided in the Jupyter Notebook, named Step2_DataAugmentation.ipynb.
4. Reserve the image data from each second replicate for external independent CNN model validation. Use the images from each first and third replicates for internal model training and testing.
5. Preprocess the image data used in CNN modeling by running the script in the Jupyter Notebook, named Step3_CNNmodeling_TrainTest.ipynb, including image resizing, black color conversion, and image signal normalization.
6. Split the training materials into internal training and testing datasets in an 8:2 fashion randomly.
7. Initialize the architecture for CNN. Design the number of outputs of CNN based on the number of scenarios to be examined.
   NOTE: For example, in this study, the neural network was designated to classify 3 classes. Make sure the script for handling data imbalance on class weight is compiled.
8. Find the hyperparameter combination that yields minimal loss on the internal test samples for CNN construction.
9. Adopt the optimal hyperparameters combination for constructing CNN architecture.
10. Open the provided Jupyter Notebooks Step4_CNNmodel_ExternalValOriginal.ipynb and Step5_CNNmodel_ExternalValFlipped.ipynb. Validate the attained model using the independent (original and flipped) images from the second replicate of the animal behavioral experiment.
11. Deploy the attained and validated model for classifying the video frame images generated from the experimental group using Jupyter Notebook Step6_CNNmodel_Application.ipynb.
    NOTE: For example, it is scenario C in this work.

4. Manual mouse grimace scale scoring

NOTE: To validate the insights provided by the CNN model prediction, another method previously developed and validated by Langford et al. was applied¹⁶. This method involves scoring the MGS based on the 5 specific mouse facial action units (AUs): orbital tightening, nose bulge, cheek bulge, ears tightening outward, and whisker change. Each AU is assigned a score of 0, 1, or 2, indicating the absence, moderate presence, or obvious presence of the AU, respectively. This scoring system allows for the quantification and scaling of each AU to assess the level of nociception or discomfort experienced by the mice.

1. Capture 3 video frame images of the littermates ingesting the paste for each video clip. Ensure that each frame is separated by at least 3 seconds.
2. Blind code and randomly reorder images from different classes of scenarios in sequence by using the provided template file named "shuffleSlides.pptm" (Supplementary File 1) and running the embedded Macro code.
3. Invite at least 10 panelists to score the sample images.
4. Train the panelists to score image samples using the MGS. Provide the panelists with training materials that include the original article regarding MGS and its manual^15,16.
5. Calculate the MGS score of each animal subject in a captured frame by averaging all the score points of the corresponding 5 facial AUs. Present the MGS score as the mean ± standard error of measurement (SEM).
6. Determine whether statistically significant differences exist among different classes of scenarios by one-way analysis of variance (ANOVA) with Bonferroni's multiple comparison post-hoc test.
   NOTE: A value of P < 0.05 is considered statistically significant.

Results

The main objective of this study is to establish a robust framework for investigating the taste-smell interaction in mice. This framework incorporates the use of artificial intelligence and QBA to develop a predictive classification model. Additionally, the insights obtained from DL are cross-validated with a quantitative MGS assessment for an internal independent analysis. The primary application of this methodology is to examine the extent of suppression of the wasabi-invoked nociception when mice sniff dietary alcohol...

Discussion

The proposed method for studying taste-smell interaction in this work is based on the original method of behavioral coding for facial expression of pain in mice, which was developed by Langford et al.¹⁶. Several recently published articles have introduced CNN for automatic mouse face tracking and subsequent MGS scoring^21,26,27,28. Applying CNNs offers an advantage over t...

Materials

Name	Company	Catalog Number	Comments
Absolute ethanol (EtOH)	VWR Chemicals BDH	CAS# 64-17-5
Acrylonitrile butadiene styrene bricks	Jiahuifeng Flagship Store	https://shop.paizi10.com/jiahuifeng/chanpin.html
Acrylonitrile butadiene styrene plates	Jiahuifeng Flagship Store	https://shop.paizi10.com/jiahuifeng/chanpin.html
Allyl isothiocyanate (AITC)	Sigma-Aldrich	CAS# 57-06-7
Anhydrous dimethyl sulfoxide	Sigma-Aldrich	CAS# 67-68-5
Chinese spirit	Yanghe Qingci	https://www.chinayanghe.com/article/45551.html
Commercial wasabi	S&B FOODS INC.	https://www.sbfoods-worldwide.com
Formic acid (FA)	VWR Chemicals BDH	CAS# 64-18-6
GraphPad Prism 5	GraphPad	https://www.graphpad.com
HPLC-grade acetonitrile (ACN)	VWR Chemicals BDH	CAS# 75-05-8
HPLC-grade methanol (MeOH)	VWR Chemicals BDH	CAS# 67-56-1
Microsoft Excel 2016	Microsoft	https://www.microsoft.com
Microsoft PowerPoint 2016	Microsoft	https://www.microsoft.com
Milli-Q water system	Millipore	https://www.merckmillipore.com
Mouse: ICR	Laboratory Animal Services Centre (The Chinese University of Hong Kong, Hong Kong, China)	N/A
Peanut butter	Skippy	https://www.peanutbutter.com/peanut-butter/creamy
Python v.3.10	Python Software Foundation	https://www.python.org
Transparent acrylic plates	Taobao Store	https://item.taobao.com/item.htm?_u=32l3b7k63381&id=60996545797 0&spm=a1z09.2.0.0.77572e8dFPM EHU