<meta charset="utf8"></head><body>Estudio del comportamiento de derretimiento del helado mediante un sistema de visión artificial

<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>

<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

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>

<meta charset="utf8"></head><body>Autor destacado: Análisis innovador del comportamiento de fusión del helado a través de un sistema de visión por computadora

Un sistema de visión artificial para la evaluación del comportamiento de fusión del helado

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.

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

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

995 vistas.  Università degli Studi di Milano. Esta investigación tiene como objetivo proponer un procedimiento de sistema de visión artificial para estudiar una de las características más importantes del helado, que es el comportamiento de fusión. Por lo general, el comportamiento de fusión se estudia mediante un enfoque gravimétrico, que proporciona información sobre el tiempo inicial de fusión y la velocidad de fusión. Sin embargo, el aspecto del producto durante la fusión también es muy im...

Video: Autor destacado: Análisis innovador del comportamiento de fusión del helado a través de un sistema de visión por computadora

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.

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

Investigación

Ingeniería

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.

Configuración experimental y procedimiento para ensayos de fusión de helados

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.