&#8203;1. Experimental setup and procedures for melting trials
<ol>
	<li>Ice cream sample preparation
	<ol>
		<li>Choose transparent cups of fixed volume and shape, with lids. Cut two long stripes of baking paper about 2 cm in width, and, with the help of paper tape, fix them to the inside walls of the cup to form a cross at the bottom. Start to fill the cup with the ice cream sample by using a spatula. 
		NOTE: Before starting, be sure that the ice cream has a temperature in the range of -7 &#176;C to -12 &#176;C so that it can be spread inside the cup. Pay attention not to create empty spaces during the cup filling by regularly checking through the cup transparency.</li>
		<li>When the cup is full, gently pull away the excess of ice cream with the spatula, creating a smooth and flat surface. Close the cup with its lid and store the sample at -30 &#176;C for at least 24 h.</li>
		<li>Before the analysis, condition the sample at -16 &#176;C for 24 h.</li>
	</ol>
	</li>
	<li>Setup of the melting trial and image acquisition
	<ol>
		<li>Set a thermostatic cabinet at 20 &#177; 1 &#176;C. Insert a digital scale in the cabinet and connect it to a computer with software for the registration of weight as a function of time.</li>
		<li>Place a graduated cylinder on the digital scale and reset the weight. Over the cylinder, place a hanging funnel to help collect the melted ice cream. The setup of the melting trial is shown in Figure 2. 
		NOTE: The funnel should be suspended over the cylinder to avoid exceeding the full scale of the digital scale. To avoid instability of the funnel during the sample positioning at the beginning of the analysis, it should be fixed to the cabinet shelf over the cylinder.</li>
		<li>Set a camera with a tripod in front of the cabinet door at a defined height and distance to have the best reliable framing of the sample. 
		NOTE: Be sure that the camera is well aligned with the ice cream sample to avoid parallax errors when processing the image.</li>
	</ol>
	</li>
	<li>Melting trial
	<ol>
		<li>Prepare a metal wire-mesh screen equipped with a size reference. Take the ice cream cup from the freezer, remove the lid, and start gently to put a spatula between the ice cream and the cup walls to detach the sample from the container. 
		NOTE: The ice cream cup must be removed from the freezer only when the previous steps (until step 1.2.3) are completed to avoid meltdown before data registration.</li>
		<li>While detaching the ice cream sample from the cup walls, keep the baking paper stuck on the surface of the ice cream.</li>
		<li>When the whole ice cream sample has been detached from the cup walls, gently pull the baking paper ends to extract the ice cream and lay it down on the metal wire-mesh screen along with the baking paper stripes. Then, carefully remove the baking paper stripes from the ice cream surface. 
		NOTE: Pay attention not to alter the ice cream shape when performing steps 1.3.1-1.3.3.</li>
		<li>Place the metal wire mesh screen with the ice cream sample on the funnel in the cabinet. Using the digital camera on the tripod, take the first picture (t0) of the ice cream sample with the cabinet door open, paying attention to also shoot the size reference. Close the cabinet door and start recording the gravimetric data every minute with the software connected to the scale. 
		NOTE: Do not use flash to avoid shadows in the image, and use a very contrasting background to improve ice cream segmentation. Check the focus of the acquired picture; if the image is out of focus, take immediately a new one.</li>
		<li>Record the gravimetric data for 90 min (one registration per minute) and take a picture of the ice cream sample (see step 1.3.4) every 15 min (for a total of 7 pictures).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66114/66114fig02.jpg" /> 
Figure 2: Melting test setup. The figure shows how to set up the melting trial in the thermostatic cabinet: Put a graduated glass cylinder on a digital scale to collect and weigh the melted ice cream. The ice cream sample is laid on a metal wire mesh screen on a funnel suspended over the cylinder. <a href="https://www.jove.com/files/ftp_upload/66114/66114fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Image processing for melting indexes&#39; calculation
<ol>
	<li>Image processing
	<ol>
		<li>Download the digital images from the camera memory card and save them as .tiff or .jpg files without compression.</li>
		<li>Use the File &#62; Open commands of the image analysis software to open the ice cream images. Start processing the first image (t0), correcting the rotation if needed (commands: Edit &#62; Rotate).</li>
		<li>Use the size reference acquired in each picture (see steps 1.3.1. and 1.3.4.) to spatially calibrate the image (commands: Measure &#62; Calibration &#62; Spatial &#62; Image), converting pixels in millimeters (Supplementary Figure 1).</li>
		<li>Select the rectangular area of interest (AOI), including the ice cream sample&#160;and avoiding the edge of the metal wire mesh screen (commands: Edit &#62; New AOI &#62; Rectangular). Crop the AOI and convert it into a grey scale (color depth, 8) using the commands Edit &#62; Covert to &#62; Grey Scale 8 (Supplementary Figure 2).</li>
		<li>Apply the Best Fit filter that automatically adjusts brightness and contrast (commands: Enhance &#62; Equalize &#62; Best Fit).</li>
		<li>Go to the Measure &#62; Count/Size window of the software and open the Measure window to select the following parameters: Area, Box Height, and Box Width. Then click OK to close this window (Supplementary Figure 3).</li>
		<li>Select the manual measurement (flag Manual in the Count/Size window) and click on Select Ranges to set the histogram values to segment the bright shape of the ice cream exactly. Close the Segmentation window (Supplementary Figure 4).</li>
		<li>Click on Count in the Count/Size window of the software to measure the three parameters selected in step 2.1.6. If bright objects other than the ice cream sample are counted, use an area threshold to filter the objects (commands: Measure &#62; Select measurements &#62; Area; adjust start and end ranges). View the results of measurement by clicking View &#62; Measurement Data (Supplementary Figure 5). Use File &#62; Data to Clipboard to copy the measurement results and paste them into a spreadsheet of data management software.</li>
		<li>Repeat steps from 2.1.2 to 2.1.8 for each image collected during the ice cream meltdown.</li>
	</ol>
	</li>
	<li>Melting indexes evaluation 
	NOTE: From now on, the size parameters measured from the ice cream images will be indicated as follows: A, area; H, box height; W, box width (Supplementary Figure 6).
	<ol>
		<li>By using the H and W data calculated in the previous step 2.1 at the different times t during melting (0 min, 15 min, 30 min, 45 min, 75 min, and 90 min), calculate the shape retention index (Rt) according to equation 1: 
		<img alt="Equation 1" src="/files/ftp_upload/66114/66114eq01.jpg" style="margin: auto;" />&#160; &#160; &#160;(1)</li>
		<li>According to equation 2 and equation 3, refer R and A data calculated at each time to the corresponding index at time 0 (R0 and A0) and plot the obtained results as a function of time as shown in Figure 3A, B. Trend of At as a function of time is related to the melting rate of ice cream. 
		<img alt="Equation 2" src="/files/ftp_upload/66114/66114eq02.jpg" style="margin: auto;" />&#160; &#160; &#160;(2) 
		<img alt="Equation 3" src="/files/ftp_upload/66114/66114eq03.jpg" style="margin: auto;" />&#160; &#160; &#160;(3)</li>
	</ol>
	</li>
</ol>
Supplementary Figure 1: Image spatial calibration. (A)&#160;Go to the window&#160;Measure &#62; Calibration &#62; Spatial of the image analysis software. Select New, then flag Image to open the Scaling window. The reference length in the unit to convert pixels (e.g., millimeters) is indicated. (B)&#160;Carefully overlap the green bar with the reference portion corresponding to the indicated length and click&#160;OK. <a href="https://www.jove.com/files/ftp_upload/66114/Figure 1S.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Cropping AOI and converting it into a grayscale. (A) Conversion of the Area of Interest (AOI) into the grayscale and (B) the resulting image. <a href="https://www.jove.com/files/ftp_upload/66114/Figure 2S.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Selection of the parameters to be measured. In the Select Measurement window, the parameters to be measured can be selected; for the ice cream meltdown evaluation, area, box width, and box height must be selected. <a href="https://www.jove.com/files/ftp_upload/66114/Figure 3S.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: Segmentation of the ice cream sample. In the &#8220;Segmentation&#8221; window it is possible to select the histogram ranges to be considered to cover exactly the area of the ice cream shape. <a href="https://www.jove.com/files/ftp_upload/66114/Figure 4S.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: Filtering objects and the Count function. The red lines highlight the recognized bright objects. By applying the count function and opening the &#8220;View, Measurement data&#8221; window, the results of the selected parameters will be shown (A). To filter only the ice cream shape, it is possible to select a minimum and a maximum area range in the &#8220;Select Measurement&#8221; window, thus counting only the parameters of one object (B). <a href="https://www.jove.com/files/ftp_upload/66114/Figure 5S.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: Shape retentionindex (R). Box height (Ht, red dotted line) and box width (Wt, black solid line) used for the shape retention index (R) calculation are shown. <a href="https://www.jove.com/files/ftp_upload/66114/Figure 6S.zip" target="_blank">Please click here to download this File.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66114/66114fig03.jpg" /> 
Figure 3: Shape and area retention curves. Example of an ice cream (A) shape and (B) area retention curves, in which Rt/R0 and At/A0 average values are plotted over time; error bars correspond to the standard deviation values obtained by the analysis replicates. <a href="https://www.jove.com/files/ftp_upload/66114/66114fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Elaboration of gravimetric data
<ol>
	<li>At the end of the melting trial (90 min), save the spreadsheet with the weight of the melted sample (grams) per each minute of the analysis obtained by the software connected to the digital scale.</li>
	<li>Open the weight spreadsheet in a data management software to create a plot of the melted weight (grams) as a function of time (minutes), thus obtaining the melting curve of the ice cream sample.</li>
	<li>Select data in the linear portion of the melting curve and calculate the least squares regression line, registering equation 4 and the regression coefficient (R2). 
	<img alt="Equation 4" src="/files/ftp_upload/66114/66114eq04.jpg" style="margin: auto;" />&#160; &#160; &#160;(4)</li>
	<li>The slope value of the regression line (m) is the ice cream melting rate (grams/minute). Calculate the starting time of melting (ts; minute) as the x-intercept (when y = 0) as follows: 
	<img alt="Equation 5" src="/files/ftp_upload/66114/66114eq05.jpg" style="margin: auto;" />&#160; &#160; &#160;(5)</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66114/66114fig04.jpg" /> 
Figure 4: Gravimetric curve. Example of an ice cream melting curve obtained by the gravimetric method. The original curve is shown in red; the selected series of data in the linear portion are shown in green; the calculated regression line is shown in black dots. The equation and the coefficient of determination (R2) of the regression line are also shown. <a href="https://www.jove.com/files/ftp_upload/66114/66114fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
NOTE: To have reliable results to be statistically analyzed, replicate the whole procedure of melting trial and image processing at least three times for each sample.

Studying Ice Cream Melting Behavior Using a Computer Vision System

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	<li>Marshall, R. T., Arbuckle, W. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Ice Cream. , (1996).</li><li>Goff, H. D., Hartel, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Ice Cream. , (2013).</li><li>Metilli, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Investigating+the+microstructure+of+soft,+microporous+matter+with+synchrotron+X-ray+tomography.">Investigating the microstructure of soft, microporous matter with synchrotron X-ray tomography.</a> Materials Characterization. 180, 111408 (2021).</li><li>Liu, X., Sala, G., Scholten, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+and+functional+differences+between+ice+crystal-dominated+and+fat+network-dominated+ice+cream.">Structural and functional differences between ice crystal-dominated and fat network-dominated ice cream.</a> Food Hydrocolloids. 138, 108466 (2023).</li><li>Alamprese, C., Foschino, R., Rossi, M., Pompei, C., Savani, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survival+of+Lactobacillus+johnsonii+La1+and+influence+of+its+addition+in+retail-manufactured+ice+cream+produced+with+different+sugar+and+fat+concentrations.">Survival of Lactobacillus johnsonii La1 and influence of its addition in retail-manufactured ice cream produced with different sugar and fat concentrations.</a> International Dairy Journal. 12 (2-3), 201-208 (2002).</li><li>Alamprese, C., Foschino, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Technology+and+Stability+of+Probiotic+and+Prebiotic+Ice+Cream.">Technology and Stability of Probiotic and Prebiotic Ice Cream.</a> Probiotic and Prebiotic Foods: Technology, Stability and Benefits to Human Health. , (2011).</li><li>Golnabi, H., Asadpour, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+application+of+industrial+machine+vision+systems.">Design and application of industrial machine vision systems.</a> Robotics and Computer-Integrated Manufacturing. 23 (6), 630-637 (2007).</li><li>Patel, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DBGC:+Dimension-based+generic+convolution+block+for+object+recognition.">DBGC: Dimension-based generic convolution block for object recognition.</a> Sensors. 22 (5), 1780 (2022).</li><li>Bhatt, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CNN+variants+for+computer+vision:+History,+architecture,+application,+challenges+and+future+scope.">CNN variants for computer vision: History, architecture, application, challenges and future scope.</a> Electronics. 10 (20), 2470 (2021).</li><li>Zhu, L., Spachos, P., Pensini, E., Plataniotis, K. 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E., Alamprese, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Honey,+trehalose+and+erythritol+as+sucrose-alternative+sweeteners+for+artisanal+ice+cream.+A+pilot+study.">Honey, trehalose and erythritol as sucrose-alternative sweeteners for artisanal ice cream. A pilot study.</a> LWT-Food Science &amp; Technology. 75, 329-334 (2017).</li><li>Loffredi, E., Moriano, M. E., Masseroni, L., Alamprese, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+different+emulsifier+substitutes+on+artisanal+ice+cream+quality.">Effects of different emulsifier substitutes on artisanal ice cream quality.</a> LWT-Food Science &amp; Technology. 137, 110499 (2021).</li><li>Loffredi, E., Alamprese, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimisation+of+a+blend+of+emulsifier+substitutes+for+clean-label+artisanal+ice+cream.">Optimisation of a blend of emulsifier substitutes for clean-label artisanal ice cream.</a> LWT-Food Science &amp; Technology. 173, 114338 (2023).</li><li>Moriano, M. E., Alamprese, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organogels+as+novel+ingredients+for+low+saturated+fat+ice+creams.">Organogels as novel ingredients for low saturated fat ice creams.</a> LWT-Food Science &amp; Technology. 86, 371-376 (2017).</li></ol>

The authors have nothing to disclose.

The proposed CVS allows the calculation of the shape and area retention indexes of ice cream samples during melting, besides visualizing the melting process. It can be coupled with the traditional gravimetric method applied to assess the melting behavior of ice cream5, to obtain results related to the aspect of the ice cream. This is very important because consumers evaluate its quality also based on the visual appearance of the product, and the ability to keep the shape during melting is linked t...

Ice cream is a multi-phase system in which liquid, solid, and gas phases are strictly connected. The continuous liquid phase envelops air bubbles and ice crystals, and it contains partially crystallized fats, colloidal proteins, salts, sugars (eventually crystallized), and stabilizers. The ice cream composition varies depending on the local market requests and possible regulations. Although the processing technology affects the characteristics of the final ice cream, each constituent plays an important role in defining the product quality1. Melting behavior is one of the most important quality indices of ice cream, considering phenomena occurring both during consumption and in the mouth. With heat penetration into the ice cream, ice crystals melt, and water diffuses and mixes with the serum phase, which can drain through the remaining structure2. A fast-melting product is undesirable for comfortable eating but also for guaranteeing a higher heat-shock resistance. However, slow-melting products also indicate some defects in the formulation1. It is known that ice cream microstructure is responsible for melting properties3, but contrasting results have been published so far, showing that knowledge about the influence of microstructural factors on melting is still limited4. Thus, more studies are necessary to elucidate the meltdown mechanism, which is crucial also in the design of new formulations3.
Melting behavior is usually evaluated by gravimetric methods and expressed in terms of starting time and rate of melting5. A given portion of ice cream is placed on a wire mesh in a controlled temperature cabinet, and the weight of the melted product is registered. From the weight-time curve, three phases can be highlighted: the lag phase during which heat penetration occurs, the fast-melting phase with the dilute serum phase flowing through the ice cream structure at the maximum rate, and the stationary phase, where most of the product has dripped out2.
With the gravimetric method, slow- and fast-melting products can be recognized; however, the aspect of ice cream during meltdown is important too, because shape retention is linked to a good composition and structure of the product6. Thus, a procedure based on a computer vision system (CVS) can support the already existing gravitational methodology by allowing the study of the product&#39;s appearance during melting. CVSs can acquire numerous food attributes3 (e.g., size, weight, shape, texture, and color) with accurate details that cannot be observed by the human eye. Such systems are usually made of digital cameras and image processing software7. Indeed, a protocol based on CVSs includes two main steps: 1) image acquisition and 2) image processing. Various levels of image processing can be applied7, from the simplest to the more complex, such as deep-learning methods for Artificial Intelligence development8,9. Great attention has been recently paid to CVSs in the food sector, and a high number of applications have been developed for food safety inspection, food processing monitoring, foreign object detection, and other fields. They are fast, efficient, and non-destructive, thus representing valid tools to provide consumers with safe foods of high quality10.
In the field of ice cream, an image analysis method was suggested to study ice recrystallization by optical microscopy11. More recently, X-ray computed tomography images were processed to analyze the 3D microstructure of soft-porous matters, including ice cream3. However, the elaboration of simple digital Charge Coupled Device (CCD) images can present several advantages in terms of easiness of acquisition and rendering of the ice cream aspect as perceived by consumers. Some Authors show images of ice cream during melting12, but, to the best of our knowledge, the extraction of numerical indices from the images was reported for the first time by Moriano and Alamprese13.
Therefore, the protocol here proposed, based on the work by Moriano and Alamprese13, illustrates a simple CVS that can be applied to support the already existing gravitational methodology for the study of ice cream melting behavior. A block diagram of the proposed method is illustrated in Figure 1. The use of such a system allows the calculation of two melting indices related to shape retention and the melting rate. In particular, the paper describes for the first time the detailed experimental setup and procedure for digital image acquisition during ice cream meltdown and the image processing steps. Besides, the results obtained from ice creams produced with different sweeteners (i.e., sucrose, sucromalt, and erythritol) are reported to show the potential of the method.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66114/66114fig01.jpg" /> 
Figure 1: Block diagram of the proposed methodologies. Summary of the general steps for the proposed Computer Vision System and the gravimetric method for studying ice cream melting behavior. <a href="https://www.jove.com/files/ftp_upload/66114/66114fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>Cabinet</td><td>Cavallo s.r.l.</td><td>FTX700</td><td>Location for the melting test</td></tr><tr><td>Digital camera</td><td>Sony Group Corp</td><td>DSC-S650</td><td></td></tr><tr><td>Digital scale</td><td>Gibertini Elettronica</td><td>EU-C 4002 LCD</td><td></td></tr><tr><td>ImagePro Plus 7.0</td><td>Media Cybernetics, Inc</td><td>N/A</td><td>Image analysis elaboration software</td></tr><tr><td>Microsoft Excel</td><td>Microsoft</td><td>N/A</td><td>Data and graphical elaboration</td></tr><tr><td>Scalecom</td><td>Gibertini Elettronica</td><td>N/A</td><td>Digital scale software acquisition</td></tr><tr><td>Tripod</td><td>Manfratto</td><td>#055</td><td></td></tr></tbody></table>

a computer vision system for the assessment of ice cream melting behavior

Melting behavior is one of the most important quality indices of ice cream. It is usually evaluated by gravimetric methods and expressed in terms of starting time and rate of melting. However, the aspect of ice cream during melting is also important because shape retention is linked to a good structure of the product. The protocol proposed here illustrates a computer vision system (CVS) that can be used to support the already existing gravitational methodology to calculate two new melting indices related to shape retention and melting rate. Pictures of ice cream during melting are taken every 15 min for a total of 90 min. Afterward, digital images are elaborated using a purposely developed image processing method to calculate ice cream area, height, and width. The ratio between height and width at each melting time, referred to&#160;the ratio at time 0 (Rt/R0), is an index of the shape retention of ice cream, while the area at the different melting times referred to the area at time 0 (At/A0) is related to the melting rate. This computer vision system allows the obtaining of highly sensitive and reliable results, and it can be applied not only to ice cream but also to different food matrices, such as whipped milk cream or egg albumen.

As an example of the proposed CVS outputs, results of meltdown analyses for three different ice cream formulations are shown, compared with data obtained from the gravimetric method. In particular, the melting behavior of ice creams made with different sweeteners (i.e., sucrose, sucromalt, and erythritol) was studied.
Table 1 and Figure 5A show the results of the shape retention index (Rt/R0) for the three ice cream samples d...

Author Spotlight: Innovative Ice Cream Melting Behavior Analysis Through a Computer Vision System

Here, we present a protocol based on a computer vision system (CVS) to determine the melting behavior of multi-phase food systems.

A Computer Vision System for the Assessment of Ice Cream Melting Behavior

Melting behavior is one of the most important quality indices of ice cream. It is usually evaluated by gravimetric methods and expressed in terms of ...

a-computer-vision-system-for-assessment-ice-cream-melting

Research

JoVE Journal

Engineering

2.2K Views.  Università degli Studi di Milano. Dit onderzoek heeft tot doel een procedure voor een computervisiesysteem voor te stellen om een van de belangrijkste kenmerken van ijs te bestuderen, namelijk het smeltgedrag. Gewoonlijk wordt het smeltgedrag bestudeerd door een gravimetrische benadering, die informatie geeft over de begintijd van het smelten en de smeltsnelheid. Het aspect van het product tijdens het smelten is echter ook erg belangrijk, en deze methode berekent een aantal indexen die verban...

Video: Auteur in de schijnwerpers: innovatieve analyse van het smeltgedrag van ijs door middel van een computervisiesysteem

LabVIEW-operated Novel Nanoliter Osmometer for Ice Binding Protein Investigations

Ice-binding proteins (IBPs), including antifreeze proteins, ice structuring proteins, thermal hysteresis proteins, and ice recrystallization inhibition proteins, are found in cold-adapted organisms and protect them from freeze injuries by interacting with ice crystals. IBPs are found in a variety of organism, including fish1, plants2, 3, arthropods4, 5, fungi6, and bacteria7. IBPs adsorb to the surfaces of ice crystals and prevent water molecules from joining the ice lattice at the IBP adsorption location. Ice that grows on the crystal surface between the adsorbed IBPs develops a high curvature that lowers the temperature at which the ice crystals grow, a phenomenon referred to as the Gibbs-Thomson effect. This depression creates a gap (thermal hysteresis, TH) between the melting point and the nonequilibrium freezing point, within which ice growth is arrested8-10, see Figure 1. One of the main tools used in IBP research is the nanoliter osmometer, which facilitates measurements of the TH activities of IBP solutions. Nanoliter osmometers, such as the Clifton instrument (Clifton Technical Physics, Hartford, NY,) and Otago instrument (Otago Osmometers, Dunedin, New Zealand), were designed to measure the osmolarity of a solution by measuring the melting point depression of droplets with nanoliter volumes. These devices were used to measure the osmolarities of biological samples, such as tears11, and were found to be useful in IBP research. Manual control over these nanoliter osmometers limited the experimental possibilities. Temperature rate changes could not be controlled reliably, the temperature range of the Clifton instrument was limited to 4,000 mOsmol (about -7.5 &deg;C), and temperature recordings as a function of time were not an available option for these instruments.
We designed a custom-made computer-controlled nanoliter osmometer system using a LabVIEW platform (National Instruments). The cold stage, described previously9, 10, contains a metal block through which water circulates, thereby functioning as a heat sink, see Figure 2. Attached to this block are thermoelectric coolers that may be driven using a commercial temperature controller that can be controlled via LabVIEW modules, see Figure 3. Further details are provided below. The major advantage of this system is its sensitive temperature control, see Figure 4. Automated temperature control permits the coordination of a fixed temperature ramp with a video microscopy output containing additional experimental details.
To study the time dependence of the TH activity, we tested a 58 kDa hyperactive IBP from the Antarctic bacterium Marinomonas primoryensis (MpIBP)12. This protein was tagged with enhanced green fluorescence proteins (eGFP) in a construct developed by Peter Davies' group (Queens University)10. We showed that the temperature change profile affected the TH activity. Excellent control over the temperature profile in these experiments significantly improved the TH measurements. The nanoliter osmometer additionally allowed us to test the recrystallization inhibition of IBPs5, 13. In general, recrystallization is a phenomenon in which large crystals grow larger at the expense of small crystals. IBPs efficiently inhibit recrystallization, even at low concentrations14, 15. We used our LabVIEW-controlled osmometer to quantitatively follow the recrystallization of ice and to enforce a constant ice fraction using simultaneous real-time video analysis of the images and temperature feedback from the sample chamber13. The real-time calculations offer additional control options during an experimental procedure. A stage for an inverted microscope was developed to accommodate temperature-controlled microfluidic devices, which will be described elsewhere16.
The Cold Stage System
The cold stage assembly (Figure 2) consists of a set of thermoelectric coolers that cool a copper plate. Heat is removed from the stage by flowing cold water through a closed compartment under the thermoelectric coolers. A 4 mm diameter hole in the middle of the copper plate serves as a viewing window. A 1 mm diameter in-plane hole was drilled to fit the thermistor. A custom-made copper disc (7 mm in diameter) with several holes (500 &mu;m in diameter) was placed on the copper plate and aligned with the viewing window. Air was pumped at a flow rate of 35 ml/sec and dried using Drierite (W.A. Hammond). The dry air was used to ensure a dry environment at the cooling stage. The stage was connected via a 9 pin connection outlet to a temperature controller (Model 3040 or 3150, Newport Corporation, Irvine, California, US). The temperature controller was connected via a cable to a computer GPIB-PCI card (National instruments, Austin, Texas, USA).

Ice binding proteins (IBPs), also known as antifreeze proteins, inhibit ice growth and are a promising additive for use in the cryopreservation of tissues. The main tool used to investigate IBPs is the nanoliter osmometer. We developed a home-designed cooling stage mounted on an optical microscope and controlled using a custom-built LabVIEW routine. The nanoliter osmometer described here manipulated the sample temperature in an ultra-sensitive manner.

Ice-binding proteins (IBPs), including antifreeze proteins, ice structuring proteins, thermal hysteresis proteins, and ice recrystallization inhibition ...

Chemistry

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

In 2010 approximately 68,720 melanomas will be diagnosed in the US alone, with around 8,650 resulting in death 1. To date, the only effective treatment for melanoma remains surgical excision, therefore, the key to extended survival is early detection 2,3. Considering the large numbers of patients diagnosed every year and the limitations in accessing specialized care quickly, the development of objective in vivo diagnostic instruments to aid the diagnosis is essential. New techniques to detect skin cancer, especially non-invasive diagnostic tools, are being explored in numerous laboratories. Along with the surgical methods, techniques such as digital photography, dermoscopy, multispectral imaging systems (MelaFind), laser-based systems (confocal scanning laser microscopy, laser doppler perfusion imaging, optical coherence tomography), ultrasound, magnetic resonance imaging, are being tested. Each technique offers unique advantages and disadvantages, many of which pose a compromise between effectiveness and accuracy versus ease of use and cost considerations. Details about these techniques and comparisons are available in the literature 4.
Infrared (IR) imaging was shown to be a useful method to diagnose the signs of certain diseases by measuring the local skin temperature. There is a large body of evidence showing that disease or deviation from normal functioning are accompanied by changes of the temperature of the body, which again affect the temperature of the skin 5,6. Accurate data about the temperature of the human body and skin can provide a wealth of information on the processes responsible for heat generation and thermoregulation, in particular the deviation from normal conditions, often caused by disease. However, IR imaging has not been widely recognized in medicine due to the premature use of the technology 7,8 several decades ago, when temperature measurement accuracy and the spatial resolution were inadequate and sophisticated image processing tools were unavailable. This situation changed dramatically in the late 1990s-2000s. Advances in IR instrumentation, implementation of digital image processing algorithms and dynamic IR imaging, which enables scientists to analyze not only the spatial, but also the temporal thermal behavior of the skin 9, allowed breakthroughs in the field.
In our research, we explore the feasibility of IR imaging, combined with theoretical and experimental studies, as a cost effective, non-invasive, in vivo optical measurement technique for tumor detection, with emphasis on the screening and early detection of melanoma 10-13. In this study, we show data obtained in a patient study in which patients that possess a pigmented lesion with a clinical indication for biopsy are selected for imaging. We compared the difference in thermal responses between healthy and malignant tissue and compared our data with biopsy results. We concluded that the increased metabolic activity of the melanoma lesion can be detected by dynamic infrared imaging.

We demonstrated that malignant pigmented lesions with increased metabolic activity generate quantifiable amounts of heat and the measurement of the transient thermal response of the skin to a cooling excitation allows quantitative identification of melanoma and other skin cancers (vs. non-proliferative nevi) at an early stage of the disease.

In 2010 approximately 68,720 melanomas will be diagnosed in the US alone, with around 8,650 resulting in death 1. To date, the only effective treatment ...

Medicine

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.

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 ...

Behavior

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor compression based cooling process. Nickel-Titanium (Ni-Ti) based alloy systems, especially, show large elastocaloric effects. Furthermore, exhibit large latent heats which is a necessary material property for the development of an efficient solid-state based cooling process. A scientific test rig has been designed to investigate these processes and the elastocaloric effects in SMAs. The realized test rig enables independent control of an SMA&#39;s mechanical loading and unloading cycles, as well as conductive heat transfer between SMA cooling elements and a heat source/sink. The test rig is equipped with a comprehensive monitoring system capable of synchronized measurements of mechanical and thermal parameters. In addition to determining the process-dependent mechanical work, the system also enables measurement of thermal caloric aspects of the elastocaloric cooling effect through use of a high-performance infrared camera. This combination is of particular interest, because it allows illustrations of localization and rate effects &#8212; both important for efficient heat transfer from the medium to be cooled.
The work presented describes an experimental method to identify elastocaloric material properties in different materials and sample geometries. Furthermore, the test rig is used to investigate different cooling process variations. The introduced analysis methods enable a differentiated consideration of material, process and related boundary condition influences on the process efficiency. The comparison of the experimental data with the simulation results (of a thermomechanically coupled finite element model) allows for better understanding of the underlying physics of the elastocaloric effect. In addition, the experimental results, as well as the findings based on the simulation results, are used to improve the material properties.

Experimental methods for investigation of solid state cooling processes and characterization of elastocaloric material properties of Shape Memory Alloys (SMA) are presented. A custom-built test rig has been designed for controlling and comprehensive monitoring of elastocaloric cooling processes. Furthermore, it provides a validation platform for thermomechanically coupled modeling approaches.

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor ...

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle activities during food intake and other physical activities. We fabricated a 3D-printed frame of the glasses and a load cell-integrated printed circuit board (PCB) module inserted in both hinges of the frame. The module was used to acquire the force signals, and transmit them wirelessly. These procedures provide the system with higher mobility, which can be evaluated in practical wearing conditions such as walking and waggling. A performance of the classification is also evaluated by distinguishing the patterns of food intake from those physical activities. A series of algorithms were used to preprocess the signals, generate feature vectors, and recognize the patterns of several featured activities (chewing and winking), and other physical activities (sedentary rest, talking, and walking). The results showed that the average F1 score of the classification among the featured activities was 91.4%. We believe this approach can be potentially useful for automatic and objective monitoring of ingestive behaviors with higher accuracy as practical means to treat ingestive problems.

This study presents a protocol of designing and manufacturing a glasses-type wearable device that detects the patterns of food intake and other featured physical activities using load cells inserted in both hinges of the glasses.

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle ...

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

An Automated Assessment of Visual Acuity in Infants and Toddlers

An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Visual acuity measurement is an important visual function test to perform in infancy and childhood. However, accurate visual acuity measurement in infants is difficult because of deficiencies in their communication ability. This paper presents a novel automated method to assess visual acuity in children (5-36 months old). This method, the automated acuity card procedure (AACP), uses a webcam for eye tracking and recognizes children's watching behaviors automatically. A two-choice preferential-looking test is performed when the tested child watches the visual stimuli shown on a high-resolution digital display screen. When the tested child watches the stimuli, their facial pictures are recorded by the webcam. These pictures are used by the set computer program to analyze their watching behavior. With this procedure, the child's eye movement responses to different stimuli are measured, and their visual acuity is assessed without communication. By comparing the results with grating acuity obtained by Teller Acuity Cards (TACs), AACP performance is deemed comparable to that of TACs.

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System  

In this paper, we present a novel automated method for assessing visual acuity in infants and toddlers by using an eye-tracking system.

Visual acuity measurement is an important visual function test to perform in infancy and childhood. However, accurate visual acuity measurement in infants ...

A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization

An accurate mechanistic description of water crystallization is challenging and requires a few key elements: superb temperature control to allow the formation of single microscopic crystals and a suitable microscopy system coupled to the cold stage. The method described herein adds another important feature that includes exchanging solutions around ice and clathrate hydrate crystals. The described system comprises a combination of unique and home-developed instruments, including microfluidics, high-resolution cold stages, and fluorescence microscopy. The cold stage was designed for microfluidic devices and allows for the formation of micron-sized ice/hydrate crystals inside microfluidic channels and the exchange of solutions around them. The temperature resolution and stability of the cold stage is one millikelvin, which is crucial for controlling the growth of these small crystals. This diverse system is used to study the different processes of ice and hydrate crystallization and the mechanism by which the growth of these crystals is inhibited. The protocol describes how to prepare microfluidic devices, how to grow and control microscopic crystals in the microfluidic channels, and how the utilization of the flow of liquids around ice/hydrate crystals affords new insights into the crystallization of water.

The present protocol describes the crystallization of microscopic ice crystals and clathrate hydrates in microfluidic devices, enabling liquid exchange around the formed crystals. This provides unparalleled possibilities to examine the crystallization process and binding mechanisms of the inhibitors.

An accurate mechanistic description of water crystallization is challenging and requires a few key elements: superb temperature control to allow the ...

Alcohol Diminishes Wasabi Reactions: Behavioral and AI Analysis in Mice

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

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 &lt; 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.

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

The commercial wasabi pastes commonly used for food preparation contain a homologous compound of chemosensory isothiocyanates (ITCs) that elicit an ...

Evaluating the Heat Transfer of a Spin-and-Chill

Source: Michael G. Benton and Kerry M. Dooley, Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA
The Spin-and-Chill uses heat transfer and fluid flow fundamentals to chill beverages from room temperature to 38 &#176;F in as little as 2 min. It would take a refrigerator approximately 240 min and an ice chest approximately 40 min to achieve an equivalent temperature change. This is accomplished Spin and Chill by spinning a can or bottle at up to 500 rpm, which creates little or no foaming.
In this experiment, the efficacy of spinning a cylinder (i.e., soda can) at high speeds to cool a soft drink will be evaluated. Operational parameters, such as rpm and spin time, will be varied to assess their effect on heat transfer, and the heat transfer coefficient will be calculated using a lumped parameter model.

Convective Spin-and-Chill Heat Transfer: Lumped Parameter Model

Source: Michael G. Benton and Kerry M. Dooley, Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA
The Spin-and-Chill uses ...

A Computer Vision System for the Assessment of Ice Cream Melting Behavior

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