&#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. 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. 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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.

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

1.9K 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.

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

Implementation of a Reference Interferometer for Nanodetection

A thermally and mechanically stabilized fiber interferometer suited for examining ultra-high quality factor microcavities is fashioned. After assessing its free spectral range (FSR), the module is put in parallel with a fiber taper-microcavity system and then calibrated through isolating and eliminating random shifts in the laser frequency (i.e. laser jitter noise). To realize the taper-microcavity junction and to maximize the optical power that is transferred to the resonator, a single-mode optical fiber waveguide is pulled. Solutions containing polystyrene nanobeads are then prepared and flown to the microcavity in order to demonstrate the system&#8217;s ability to sense binding to the surface of the microcavity. Data is post-processed via adaptive curve fitting, which allows for high-resolution measurements of the quality factor as well as the plotting of time-dependent parameters, such as resonant wavelength and split frequency shifts. By carefully inspecting steps in the time-domain response and shifting in the frequency-domain response, this instrument can quantify discrete binding events.

A reference interferometer technique, which is designed to remove undesirable laser jitter noise for nanodetection, is utilized for probing an ultra-high quality factor microcavity. Instructions for assembly, setup, and data acquisition are provided, alongside the measurement process for specifying the cavity quality factor.

A thermally and mechanically stabilized fiber interferometer suited for examining ultra-high quality factor microcavities is fashioned. After assessing ...

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators

Mechanically underdamped electrostatic fringing-field MEMS actuators are well known for their fast switching operation in response to a unit step input bias voltage. However, the tradeoff for the improved switching performance is a relatively long settling time to reach each gap height in response to various applied voltages. Transient applied bias waveforms are employed to facilitate reduced switching times for electrostatic fringing-field MEMS actuators with high mechanical quality factors. Removing the underlying substrate of the fringing-field actuator creates the low mechanical damping environment necessary to effectively test the concept. The removal of the underlying substrate also a has substantial improvement on the reliability performance of the device in regards to failure due to stiction. Although DC-dynamic biasing is useful in improving settling time, the required slew rates for typical MEMS devices may place aggressive requirements on the charge pumps for fully-integrated on-chip designs. Additionally, there may be challenges integrating the substrate removal step into the back-end-of-line commercial CMOS processing steps. Experimental validation of fabricated actuators demonstrates an improvement of 50x in switching time when compared to conventional step biasing results. Compared to theoretical calculations, the experimental results are in good agreement.

The robust device design of fringing-field electrostatic MEMS actuators results in inherently low squeeze-film damping conditions and long settling times when performing switching operations using conventional step biasing. Real-time switching time improvement with DC-dynamic waveforms reduces the settling time of fringing-field MEMS actuators when transitioning between up-to-down and down-to-up states.

Mechanically underdamped electrostatic fringing-field MEMS actuators are well known for their fast switching operation in response to a unit step input ...

Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging (ECCI) in a scanning electron microscope (SEM). ECCI allows for imaging of defects and crystallographic features under specific diffraction conditions, similar to that possible via plan-view transmission electron microscopy (PV-TEM). A particular advantage of the ECCI technique is that it requires little to no sample preparation, and indeed can use large area, as-produced samples, making it a considerably higher throughput characterization method than TEM. Similar to TEM, different diffraction conditions can be obtained with ECCI by tilting and rotating the sample in the SEM. This capability enables the selective imaging of specific defects, such as misfit dislocations at the GaP/Si interface, with high contrast levels, which are determined by the standard invisibility criteria. An example application of this technique is described wherein ECCI imaging is used to determine the critical thickness for dislocation nucleation for GaP-on-Si by imaging a range of samples with various GaP epilayer thicknesses. Examples of ECCI micrographs of additional defect types, including threading dislocations and a stacking fault, are provided as demonstration of its broad, TEM-like applicability. Ultimately, the combination of TEM-like capabilities &#8211; high spatial resolution and richness of microstructural data &#8211; with the convenience and speed of SEM, position ECCI as a powerful tool for the rapid characterization of crystalline materials.

The use of electron channeling contrast imaging in a scanning electron microscope to characterize defects in III-V/Si heteroexpitaxial thin films is described. This method yields similar results to plan-view transmission electron microscopy, but in significantly less time due to lack of required sample preparation.

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging ...

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

The setup of a planar Frequency Mixing Magnetic Detection (p-FMMD) scanner for performing Magnetic Particles Imaging (MPI) of flat samples is presented. It consists of two magnetic measurement heads on both sides of the sample mounted on the legs of a u-shaped support. The sample is locally exposed to a magnetic excitation field consisting of two distinct frequencies, a stronger component at about 77 kHz and a weaker field at 61 Hz. The nonlinear magnetization characteristics of superparamagnetic particles give rise to the generation of intermodulation products. A selected sum-frequency component of the high and low frequency magnetic field incident on the magnetically nonlinear particles is recorded by a demodulation electronics. In contrast to a conventional MPI scanner, p-FMMD does not require the application of a strong magnetic field to the whole sample because mixing of the two frequencies occurs locally. Thus, the lateral dimensions of the sample are just limited by the scanning range and the supports. However, the sample height determines the spatial resolution. In the current setup it is limited to 2 mm. As examples, we present two 20 mm &#215; 25 mm p-FMMD images acquired from samples with 1 &#181;m diameter maghemite particles in silanol matrix and with 50 nm magnetite particles in aminosilane matrix. The results show that the novel MPI scanner can be applied for analysis of thin biological samples and for medical diagnostic purposes.

A scanner for imaging magnetic particles in planar samples was developed using the planar frequency mixing magnetic detection technique. The magnetic intermodulation product response from the nonlinear nonhysteretic magnetization of the particles is recorded upon a two-frequency excitation. It can be used to take 2D images of thin biological samples.

The setup of a planar Frequency Mixing Magnetic Detection (p-FMMD) scanner for performing Magnetic Particles Imaging (MPI) of flat samples is presented. ...

Ice Generation and the Heat and Mass Transfer Phenomena of Introducing Water to a Cold Bath of Brine

We demonstrate a method for the study of the heat and mass transfer and of the freezing phenomena in a subcooled brine environment. Our experiment showed that, under the proper conditions, ice can be produced when water is introduced to a bath of cold brine. To make ice form, in addition to having the brine and water mix, the rate of heat transfer must bypass that of mass transfer. When water is introduced in the form of tiny droplets to the brine surface, the mode of heat and mass transfer is by diffusion. The buoyancy stops water from mixing with the brine underneath, but as the ice grows thicker, it slows down the rate of heat transfer, making ice more difficult to grow as a result. When water is introduced inside the brine in the form of a flow, a number of factors are found to influence how much ice can form. Brine temperature and concentration, which are the driving forces of heat and mass transfer, respectively, can affect the water-to-ice conversion ratio; lower bath temperatures and brine concentrations encourage more ice to form. The flow rheology, which can directly affect both the heat and mass transfer coefficients, is also a key factor. In addition, the flow rheology changes the area of contact of the flow with the bulk fluid.

Here, we present a protocol to demonstrate the generation of ice when water is introduced to a cold bath of brine, as a secondary refrigerant, at a range of temperatures well below the freezing point of water. It can be used as an alternative way of producing ice for industry.

We demonstrate a method for the study of the heat and mass transfer and of the freezing phenomena in a subcooled brine environment. Our experiment showed ...

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy

Vacuum induction melting is a popular method for refining high purity metal and alloys. Traditionally, standard process control in metallurgy involves several steps, include drawing samples, cooling, cutting, transport to the laboratory, and analysis. The whole analysis process requires more than 30 minutes, which hinders on-line process control. Laser-induced breakdown spectroscopy is an excellent on-line analysis method that can satisfy the requirements of vacuum induction melting because it is fast and noncontact and does not require sample preparation. The experimental facility uses a lamp-pumped Q-switched laser to ablate melted liquid steel with an output energy of 80 mJ, a frequency of 5 Hz, a FWHM pulse width of 20 ns, and a working wavelength of 1,064 nm. A multi-channel linear charge coupled device (CCD) spectrometer is used to measure the emission spectrum in real time, with a spectral range from 190 to 600 nm and a resolution of 0.06 nm at a wavelength of 200 nm. The protocol includes several steps: standard alloy sample preparation and an ingredient test, smelting of standard samples and determination of the laser breakdown spectrum, and construction of the elements concentration quantitative analysis curve of each element. To realize the concentration analysis of unknown samples, the spectrum of a sample also needs to be measured and disposed with the same process. The composition of all main elements in the melted alloy can be quantitatively analyzed with an internal standard method. The calibration curve shows that the limit of detection of most metal elements ranges from 20-250 ppm. The concentration of elements, such as Ti, Mo, Nb, V, and Cu, can be lower than 100 ppm, and the concentrations of Cr, Al, Co, Fe, Mn, C, and Si range from 100-200 ppm. The R2 of some calibration curves can exceed 0.94.

During vacuum induction melting, laser-induced breakdown spectroscopy is used to perform real-time quantitative analysis of the main-ingredient elements of a molten alloy.

Vacuum induction melting is a popular method for refining high purity metal and alloys. Traditionally, standard process control in metallurgy involves ...

A Rapid Method for Modeling a Variable Cycle Engine

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft engines. However, developing VCE requires high costs. Thus, it is essential to build a mathematical model when developing an aircraft engine, which may avoid a large number of real tests and reduce the cost dramatically. Modeling is also crucial in control law development. In this article, based on a graphical simulation environment, a rapid method for modeling a double bypass variable cycle engine using object-oriented modeling technology and modular hierarchical architecture is described. Firstly, the mathematical model of each component is built based on the thermodynamic calculation. Then, a hierarchical engine model is built via the combination of each component mathematical model and the N-R solver module. Finally, the static and dynamic simulations are carried out in the model and the simulation results prove the effectiveness of the modeling method. The VCE model built through this method has the advantages of clear structure and real-time observation.

Here, we present a protocol to build a component-level mathematical model for a variable cycle engine.

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft ...

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with slopes of up to 84&#176;. The manufacturing method is valid for the fast pneunet bending actuators in general and might, therefore, be interesting for newcomers to the field of actuator manufacturing. The control of the robot is achieved by means of a pneumatic control box that can provide arbitrary pressures and can be built by only using purchased components, a laser cutter, and a soldering iron. For the walking performance of the robot, the pressure-angle calibration plays a crucial role. Therefore, a semi-automated method for the pressure-angle calibration is presented. At high inclines (&#62; 70&#176;), the robot can no longer reliably fix itself to the walking plane. Therefore, the gait pattern is modified to ensure that the feet can be fixed on the walking plane.

This protocol provides a detailed list of steps to be performed for the manufacturing, control and evaluation of the climbing performance of a gecko-inspired soft robot.

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with ...

3D Printing - Evaluating Particle Emissions of a 3D Printing Pen

Three-dimensional (3D) printing as a type of additive manufacturing shows continuing increase in application and consumer popularity. The fused filament fabrication (FFF) is an inexpensive method used most frequently by consumers. Studies with 3D printers have shown that during the printing process particulate and volatile substances are released. Handheld 3D printing pens also use the FFF method but the consumer&#8217;s proximity to the 3D pens gives reason to higher exposure compared to a 3D printer. At the same time, 3D printing pens are often marketed for children who could be more sensitive to the printing emission. The aim of this study was to implement a low cost method to analyze the emissions of 3D printing pens. Polylactide (PLA) and acrylonitrile butadiene styrene (ABS) filaments of different colors were tested. In addition, filaments containing metal and carbon nanotubes (CNTs) were analyzed. An 18.5 L chamber and sampling close to the emission source was used to characterize emissions and concentrations near the breathing zone of the user.
Particle emissions and particle size distributions were measured and the potential release of metal particles and CNTs investigated. Particle number concentrations were found in a range of 105 - 106 particles/cm3, which is comparable to previous reports from 3D printers. Transmission electron microscopy (TEM) analysis showed nanoparticles of the different thermoplastic materials as well as of metal particles and CNTs. High contents of metal were observed by inductively coupled plasma mass spectrometry (ICP-MS).
These results call for a cautious use of 3D pens due to potential risk to the consumers.

This protocol presents a method to analyze the emission of 3D printing pens. Particle concentration and particle size distribution of the released particle is measured. Released particles are further analyzed with transmission electron microscopy (TEM). Metal content in filaments is quantified by inductively coupled plasma mass spectrometry (ICP-MS).

Three-dimensional (3D) printing as a type of additive manufacturing shows continuing increase in application and consumer popularity. The fused filament ...

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...