<meta charset="utf8"></head><body>Studiare il comportamento di scioglimento del gelato utilizzando un sistema di visione artificiale

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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>Autore in primo piano: Analisi innovativa del comportamento di fusione del gelato attraverso un sistema di visione artificiale

Un sistema di visione artificiale per la valutazione del comportamento di fusione del gelato

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

1.2K Views.  Università degli Studi di Milano. Questa ricerca ha lo scopo di proporre una procedura di computer vision system per studiare una delle caratteristiche più importanti del gelato, ovvero il comportamento di fusione. Di solito, il comportamento di fusione viene studiato con un approccio gravimetrico, fornendo informazioni sul tempo iniziale di fusione e sulla velocità di fusione. Tuttavia, anche l'aspetto del prodotto durante la fusione è molto importante, e questo metodo calcola un paio di ...

Video: Autore in primo piano: Analisi innovativa del comportamento di fusione del gelato attraverso un sistema di visione artificiale

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.