&#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

<ol>
	<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. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning+and+machine+vision+for+food+processing:+A+survey.">Deep learning and machine vision for food processing: A survey.</a> Current Research in Food Science. 4, 233-249 (2021).</li><li>Donhowe, D. P., 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=Recrystallization+of+ice+in+ice+cream+during+controlled+accelerated+storage.">Recrystallization of ice in ice cream during controlled accelerated storage.</a> International Dairy Journal. 6 (11-12), 1191-1208 (1996).</li><li>Vel&#225;squez-Cock, J., 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=Influence+of+cellulose+nanofibrils+on+the+structural+elements+of+ice+cream.">Influence of cellulose nanofibrils on the structural elements of ice cream.</a> Food Hydrocolloids. 87, 204-213 (2019).</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=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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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.

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

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

This research aims to propose a computer vision system procedure to study one of the most important features of ice cream, which is the melting behavior. Usually, melting behavior is studied by a gravimetric approach, giving information about the initial time of melting and the rate of melting. However, the aspect of the product during melting is also very important, and this method calculates a couple of indexes related to the shape and size retention of ice cream during the meltdown.The advantage of using a computer vision system in the ice cream melting evaluation relies on the increased sensitivity in detecting small variations compared to the gravimetric method, and on the possibility to elaborate data related to the visual appearance of the product during meltdown. The computer vision system approach here proposed can be easily applied to other food products for which the melting rate and shape retention index are important, such as mousses and foams like whipped milk cream and egg albumin. These can lead to a better understanding of the meltdown mechanism, which is related to different factors.

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...

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

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

Experimental Setup and Procedure for Ice Cream Melting Trials

To begin, select transparent cups with fixed volume and shape, each equipped with a lid. Cut two strips of baking paper, each about two-centimeters wide. Using paper tape, attach these strips inside the cup walls, forming a cross at the bottom.Fill the cup with ice cream using a spatula. Once full, smooth the surface of the ice cream with the spatula, and seal the cup with its lid. Store the prepared sample at 30 degrees Celsius for at least 24 hours.Then condition the sample at 16 degrees Celsius for 24 hours before analysis. For the melting trial, set a thermostatic cabinet to approximately 20 degrees Celsius. Place a digital scale inside the cabinet.Connect the scale to a computer with software to record weight as a function of time. Next, position a graduated cylinder on the scale and reset the weight. Place a hanging funnel over the cylinder to collect melted ice cream.Set up a camera on a tripod in front of the cabinet, ensuring the best framing for sample images. Then prepare a metal wire mesh screen with a size reference. Remove the ice cream cup from the freezer and detach the ice cream from the cup walls Using a spatula, keeping the baking paper attached gently place it on the wire mesh screen, then remove the baking paper strips.Position the screen with the ice cream on the funnel inside the cabinet. Capture the initial image with the open cabinet door, including the size reference. Record data for 90 minutes, and take pictures of the ice cream every 15 minutes.Using this protocol, melting trials were performed for the three ice cream samples with different sweeteners, such as sucrose, sucromalt, and erythritol. While all samples showed good shape retention after 90 minutes, the lowest melting rate was observed for the ice cream made with erythritol.

Melting Index Calculation and Gravimetric Analysis of Ice Cream Melting 

To begin, download the images of melting ice cream captured by the digital camera setup to a computer. Then, in the image analysis software, click on File and Open to open the ice cream images. Start processing the first image and click on Edit and Rotate to adjust its rotation if necessary.Then, zoom into the size reference and click on Measure, then Calibration, followed by Spatial. Next, click on Image to spatially calibrate the image. Move the scale over the size reference and calibrate the length to convert pixels to millimeters.Next, click Edit, NEW AOI, and rectangular to select a rectangular area of interest that includes the ice cream sample avoiding the edge of the metal wire mesh screen. Crop this area and convert it to grayscale by clicking Edit, then Convert To and selecting Gray Scale. To apply the best fit filter, click on Enhance, then Equalize and Best Fit to automatically adjust brightness and contrast.Then, open the Measure and Count/Size window and set the parameters of Area, Box Height and Box Width. Choose manual measurement in the Count/Size window and click on Select Ranges to set the histogram values to segment the bright shape of the ice cream. After closing the Segmentation window, click on Count in the Count/Size window of the software to measure the parameters.View the results by clicking on View and Measurement Data. Then click on File and Data to Clipboard to copy these results and paste them into a data management software spreadsheet To evaluate the melting index, use this equation on the H and W data at different melting times to calculate the shape retention index. Refer R and A data at each time point to the corresponding index at time zero, and plot these results over time according to these equations.For gravimetric data analysis, save the spreadsheet with the weight of the melted sample per minute. At the end of the 90-minute melting trial, open this spreadsheet in data management software to create a melting curve showing the melted weight over time. Select data from the linear portion of the melting curve to calculate the least squares regression line and the regression coefficient.The slope of this line represents the ice cream melting rate in grams per minute. Calculate the starting time of melting as the x-intercept where y is zero. Using this protocol, melting behavior was analyzed for ice creams made with different sweeteners such as sucrose, sucromalt, and erythritol.Shape retention curves showed that ice cream made with sucrose lost its shape faster than the others, whereas the ice cream made with erythritol kept its shape until the end of the trial. Similarly, area retention curves showed that ice cream containing erythritol showed a lower meltdown rate compared to ice creams with sucromalt and sucrose. Further, gravimetric analysis showed that the ice cream containing erythritol showed the highest melting starting time and lowest melting rate out of the three samples.

948 Views.  Università degli Studi di Milano.  Here, we present a protocol based on a computer vision system (CVS) to determine the melting behavior of multi-phase food systems.

Studying Ice Cream Melting Behavior Using a Computer Vision System

Podsumowanie