Acoustic techniques to detect eye formation during ripening of Emmental type cheese
Introduction
Cheese production involves a series of steps, in which proper control determine the quality of the product. For pressed and cooked-curd cheeses these steps include coagulation, cut of the curd, cooking of the curd, pressing, brining, cold temperature pre-ripening and warm temperature ripening. During ripening, sensory properties such as texture, flavour, colour and aroma develop. Ripening can take from 3 weeks to roughly 2 years depending on the type of cheese and its size (Fox, Guinee, Cogan, & McSweeney, 2017). Therefore, it is relevant to determine the end of ripening stage, which influences quality and selling price of the product. On one hand, if cheese ripening is stopped prematurely, the outcome is a poor quality, low-value product. On the other hand, if ripening is longer than required, the product might not satisfy consumers quality demands, wasting time and resources of the manufacturer.
Eye forming cheeses (e.g. Emmental, Gouda, Maasdam) are semi-hard cheeses, with long ripening times. They start ripening as homogeneous pieces. Later on, eye development (as gas cavities) takes place as one of the most significant changes. When ripening is longer than needed, gas cavities unite and cheese structure may collapse. When this happens, the final product will not comply with specifications and must be sold in other presentations as grated, spread or processed, or as an ingredient.
Acoustic techniques as a non-destructive monitoring method of the ripening stage are used by cheesemakers in traditional cheese making. For semi-hard and hard cheeses, the cheesemaker strikes the wheel and, according to its sound, decides whether to stop or continue the ripening. The cheesemaker may also detect the presence of defects such as gas blowing or slits from the acoustic response of the wheel to the impact (de Roest, 2000). Acoustic techniques as a non-destructive method for cheese properties evaluation have been studied. Most of such work involves cheeses without eyes such as Manchego (Benedito, Simal, Clemente, & Mulet, 2006; Conde, Cárcel, García-Pérez, & Benedito, 2007) and Comté (Nassar et al., 2010). Benedito et al. studied ultrasound application to evaluate cheese texture (Benedito et al., 2006), whereas Conde et al. studied acoustic impacts to determine cheese textural properties (Conde et al., 2007). Nassar et al., focused on the use of acoustic techniques to detect internal defects in Comté cheese (Nassar et al., 2010). Though acoustic response method has been used for a long time in traditional cheesemaking, few references are found related to analysis of acoustic signals (Benedito et al., 2006; Conde et al., 2007; Nassar et al., 2010). A systematic study of the acoustic response of different cheese types is needed to incorporate this information in a quantitative way, or to include it in a decision system.
For eye forming cheeses, it is of interest to detect when eyes start forming, their size and distribution inside the cheese. Few articles focus on developing a non-destructive technique to distinguish eyes and slits in cheese. Non-destructive techniques such as X-Rays (Guggisberg et al., 2013), MNR (Huc et al., 2014; Musse, Challois, Huc, Quellec, & Mariette, 2014) and CT (Lee et al., 2012) might be useful for the detection of eyes in cheese. However, they are high in cost (around hundreds of thousand US dollars) and require trained personnel to operate the equipment and interpret the information. Ultrasound was proposed as a cheaper, faster and simpler alternative to evaluate the presence of eyes and defects (Eskelinen, Alavuotunki, Hæggström, & Alatossava, 2007; Nassar et al., 2010). Eskelinen et al. studied ultrasound to detect eyes in Finnish Emmental cheese (Eskelinen et al., 2007). Eskelinen et al. results were promising, as ultrasonic scanning can give information about cheese internal structure, but signal attenuation does not allow inspection beyond few centimetres. Since attenuation increases with frequency, using lower frequencies may help in overcoming this hurdle. The acoustic technique explored in this article works at lower frequencies, and it is the systematization of the impact response traditionally made by the cheesemaker using the analysis of the frequency spectrum.
The aim of this work was to develop an acoustic technique that could be used in non-destructive monitoring of the ripening process of semi-hard, eye-forming cheeses. The main hypothesis of this work was that eyes are a major structural change that will alter the acoustic response of cheese. Image processing techniques will help in the understanding of the information of the acoustic responses.
Section snippets
Ripening conditions and data acquisition schedule
Ripening of eight cheese wheels, Emmental type provided by a local manufacturer after pre-ripening, was studied for 30 days. This period of time was suggested by the manufacturer due to the size of the cheese wheels. At the beginning of ripening cheese wheels were 15 cm in height and 25 cm in diameter, and weighed on average 8 kg. This cheese type starts its ripening as a homogeneous cylinder, and after a proper nucleation, eyes appear as a product of the fermentation process inside the cheese.
Eye formation evolution
When cheese wheels were cut, eyes were registered on photographs that were used to assess eye formation qualitatively. Fig. 3A shows an example of photographs registered along ripening. Photographs were binarized to segment eyes, as described in Section 2.2.3, and total eye area relative to cheese section (Aeye) was calculated. Results of Aeye are shown on Fig. 3B.
From Fig. 3B it can be seen that eye formation begins between day 10 and day 15, when there is an inflection point in Aeye tendency.
Conclusions
In this work, the analysis of the acoustic response, which proved to be able to detect eye formation during cheese ripening, was presented. Experimental results showed that relevant information about eye formation can be obtained from the analysis of the impulse response in the 0–500 Hz band. Moreover, two different acoustic parameters, first order momentum of the spectrum (FOM) and cross-correlation (CCmax) of the acoustic signal, were related to different stages of eye formation. FOM for low
Acknowledgements
Authors gratefully acknowledge Espacio Interdisciplinario-Universidad de la República for the financial support of this research work.
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