A sensorial platform for mozzarella cheese characterization and ...

2015 XVIII AISEM Annual Conference

A Sensorial Platform For Mozzarella Cheese Characterization And Authentication M. Santonico, A. Sudano, G. Pennazza, D. Accoto, S. Grasso, F. Genova, A. Finazzi Agrò

F. R. Parente, G. Ferri Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila, Italy

CIR – Center for Integrated Research University Campus Bio-Medico of Rome Rome, Italy

A. D’Amico Department of Electronic Engineering University of Rome Tor Vergata Rome, Italy Abstract—Mozzarella cheese is one of the most counterfeited dairy product worldwide, with implications concerning either economical and health issues. In the context of food quality monitoring and authentication, a non-destructive multi-sensorial analytic approach is proposed. A minimally invasive evaluation of the mechanical properties of the mozzarella cheese as well as of its preserving whey has been performed by means of a mechanical and a liquid sensor, respectively. Data fusion of both approaches allowed a good and easy discrimination between mozzarella cheese samples having different brand provenance and milk composition. Keywords—mozzarella cheese; food authentication; food quality monitoring; non-destructive analysis; electronic sensorial platform.

I.

B. The liquid sensor For the analysis of the whey a voltammetric technique has been used (Fig. 1). It takes advantage of a single output curve (V, I) obtained by each SPE (Working: Gold, Reference: Silver, Counter: Platinum; DropSens S.L.), which is treated as a multidimensional dataset. Indeed, the response of the physical sensor is composed of n=1/fo virtual sensors’ response current values relative to the voltage input curve of frequency fo [4]. In this study, cyclic voltammetry in the range from −1 to 1 Volt was performed using a triangular function at 10 mHz and a sampling interval of 1 second.

INTRODUCTION

Food quality monitoring and authentication is a timeliness research field which aims at developing non-destructive analysis methods with a sensory evaluation near to consumer perception. In this context, mozzarella cheese urgently needs innovative, cheap and easy techniques. Actually, it is perhaps the most imitated and often counterfeited dairy product, especially a Protected Designations of Origin (DOP) product like Buffalo mozzarella [1-3]. Here a non-destructive multisensorial characterization is proposed, by measuring the whey with a Screen Printed Electrodes and the mechanical properties of the mozzarella cheese via an electro-dynamic probe. II.

MATERIAL AND METHODS

A. Mozzarella cheese During preliminary tests, 13 samples of mozzarella cheese have been analyzed: 7 samples of brand A (3 of cow milk, 4 of buffalo milk); 6 samples of brand B (3 of cow milk, 3 of buffalo milk). All the mozzarella cheese samples were purchased at the local store and were analyzed in the same day.

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Fig. 1. Liquid sensor analysis. The SPE is plunged in a vial containing the mozzarella cheese preserving whey while it is performing the electrochemical characterization.

2015 XVIII AISEM Annual Conference

C. The mechanical sensor An electro-dynamic probe has been developed to determine the mechanical properties of the mozzarella cheese in a minimally invasive way (Fig. 2). The probe is capable of applying a force step to the surface of the mozzarella cheese and to read its response in terms of contact point speed. The stimulated surface is sufficiently small so that boundary effects, which depend on the unknown shape of the mozzarella cheese, can be considered negligible. The transducer is a linear voice coil device, mounted in a vertical configuration. The stator, which comprises the coil, is connected to the frame, while the moving part is free to fall under the action of its own weight. The tip of the transducer, fixed to the moving part of the device, comes into contact with the surface of the mozzarella cheese. The speed of the probe is proportional to the counterelectromotive force generated in the solenoid actuator, and is measured through a custom data acquisition system. For each mozzarella cheese sample, 80 consecutive responses have been measured over 10 different areas of its surface. III.

RESULTS AND DISCUSSION

A. Preliminary results Thirteen samples of mozzarella cheese having different milk composition and brand provenance were analyzed through a multi-sensorial approach. The measure system employed in this study is composed by a taster (the voltammetric sensor) and a squeezer (the mechanical sensor). A gold SPE (Aux.: Pt; Ref.: Ag), controlled by an electronic interface granting on the reference electrode a voltage input and converting the out current value from the working electrode in an output voltage one, was used as a liquid sensor.

Fig. 3. Scores plot of the first two principal components deriving from the data fusion of liquid and mechanical sensors reposnses.

An array of 100 virtual sensor responses was obtained from a single physical sensor applying a 10 mHz triangular function as a voltage input signal and recording one output value per second. A volume of 3 mL of whey was taken from each mozzarella cheese package and evaluated through five consecutive independent measures. After liquid analyses were accomplished, mozzarella cheese samples were separated from the whey, left at room temperature for 30 minutes and then analyzed for their mechanical properties. At the end of the analyses, an overall dataset has been realized with a data fusion process between liquid sensors and mechanical sensors. Finally, a Principal Component Analysis (PCA) model was built on the whole dataset. The score plot of the first two Principal Components (PCs) shows the ability of the system to sharply discriminate between mozzarella cheese samples having different brand provenance and milk composition. As can be seen from figure 3, mozzarella cheese samples clustered in four separate regions part of the first two principal components plane. In particular, the two brands are distinguished along the Principal Component 1 (PC1), which accounts for 97.86 % of the explained variance, while along the PC2 is possible to discriminate between the milk compositions (cow’s milk versus buffalo’s milk). B. Test results To evaluate the system predictive performances, further measurements on samples of buffalo’s and cow’s mozzarella cheese were carried out. The analysis was performed taking into account only the preserving whey, after that mozzarella cheese samples were correctly classified with the electromechanical sensor. Classification was performed using partial Least Squares Discriminant Analysis (PLS-DA). A model was built using 80 measurements, including 45 samples and 35 samples of cow’s and buffalo’s mozzarella cheese, respectively. The results are shown in the confusion matrix reported below (Table 1). The percentage of correct classification was of 100%.

Fig. 2. Mechanical sensor analysis. The electro-dynamic probe is capable of applying a force step to the surface of the mozzarella cheese and to read its response in terms of contact point speed.

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2015 XVIII AISEM Annual Conference TABLE I. PREDICTED CLASSIFICATION RATE OF THE CLASSIFIER IDENTIFYING SAMPLES OF BUFFALO’S MOZZARELLA CHEESE FROM COW’S MOZZARELLA CHEESE ONES

Cow’s mozzarella cheese

Buffalo’s mozzarella cheese

Cow’s mozzarella cheese

45

0

Buffalo’s mozzarella cheese

0

35

In conclusion, the multi-sensorial system has demonstrated to distinguish between mozzarella cheese samples of different brands and to recognize the type of milk at the origin of the dairy product. Although these are only preliminary results, here it has been demonstrated the analytical potential of a multisensorial system that is able to operate in a noninvasive fashion. The possibility to analyze mozzarella cheese easily and without damaging the dairy product represents an important innovation in the field of food analysis. Indeed, in a landscape where food counterfeits represent a serious risk for health and economy, it is desirable to create easy-to-use devices that consumers may use autonomously at home. REFERENCES [1]

[2]

[3]

[4]

M.A. Brescia, M. Monfreda, A. Buccolieri, C. Carrino, “Characterisation of the geographical origin of buffalo milk and mozzarella cheese by means of analytical and spectroscopic determinations.”, Food Chem., vol. 89, pp. 139-147, January 2005. F. Gasperi, G. Gallerani, A. Boschetti, F. Biasioli, A. Monetti, E. Boscaini, A. Jordan, W. Lindinger, S. Iannotta, “The mozzarella cheese flavour profile: a comparison between judge panel analysis and proton transfer reaction mass spectrometry.”, J. Sci. Food Agric., vol 81, pp. 357-363, February 2001. E. Pagliarini, E. Monteleone, I. Wakelin, “Sensory profile description of mozzarella cheese and its relationship with consumer preference.”, J. Sens. Stud., vol. 12, pp. 285-301, December 1997. M. Santonico, G. Pennazza, S. Grasso, A. D’Amico, M. Bizzarri, “Design and Test of a Biosensor-Based Multisensorial System: A Proof of Concept Study”, Sensors, vol. 13, pp. 16625-16640, December 2013.

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