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A RFID web-based infotracing system for the artisanal Italian cheese quality
traceability
Patrizia Papetti a
, Corrado Cos...
in food logistics and in the supply chain management processes
(Jedermann, Ruiz-Garcia, & Lang, 2009). This system uses ra...
and an optical fibre bundle consisting of approx. 30 quartz glass; (2)
an optical entrance with input round: 70 mm  2500 m...
productive information which follow the product shelf-life from
producer to consumer, providing web-based tools for each c...
system more portable, handy at maintenance, handy at updating
the two tiers, more flexibility and greater scalability.
The ...
The pH average values of the different portions during the
ageing time reported in Table 2 didn’t show statistically signi...
resistant to mechanical damage in relation to handling, cutting, and
compression. The main issue in order to avoid the tag...
Costa, C., Aguzzi, J., Menesatti, P., Mànuel, A., Boglione, C., Sarriá, D., et al. (2011).
Versatile application of RFID t...
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A RFID web-based infotracing system for the artisanal Italian cheese quality traceability

The aim of this study is the integration of an electronic tracing system with a non-destructive quality analysis system for single product of a typical Italian cheese, prepared with buffalo milk and called “Caciottina massaggiata di Amaseno”, a typical diary product of Lazio Region. The tracing and quality information are combined on a web platform to obtain a complete procedure to develop what we define as an “infotracing system”. Quality analyses (chemical, sensorial and spectrophotometric) were carried out on a total of 23 cheese wheels (8 with TAGs) and for three cheese maturation classes (3, 6 or 9 months after production). Two typologies of RFID tags were tested. Results were screened by Partial Least Squares regressions (PLS) on reflectance values for the prediction of chemical content, while classifica- tion of cheese maturation classes (3, 6 or 9 months) was carried out by Partial Least Squares Discriminant Analysis (PLSDA) on reflectance values. The RFID system turned out as effective, reliable and compatible with the production process tool. A good estimation of maturation degree by spectral and chemical analysis was obtained. Moreover an infotracing web-based system was designed to acquire and link basic information that can be made available to the final consumer or to different food chain actors before or after purchasing, using the RFID code to identify the single and specific cheese product. The projected web-based tracing system could improve the products commerce by increasing the information trans- parency for the consumer.

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A RFID web-based infotracing system for the artisanal Italian cheese quality traceability

  1. 1. A RFID web-based infotracing system for the artisanal Italian cheese quality traceability Patrizia Papetti a , Corrado Costa b,*, Francesca Antonucci b , Simone Figorilli b , Silvia Solaini b , Paolo Menesatti b a Department of Economics, University of Cassino, Via Marconi 10, 03043 Cassino (FR), Italy b CRA-ING (Agricultural Engineering Research Unit of the Agriculture Research Council), Via della Pascolare 16, 00015 Monterotondo Scalo, Rome, Italy a r t i c l e i n f o Article history: Received 7 February 2012 Received in revised form 16 March 2012 Accepted 24 March 2012 Keywords: RFID Spectrophotometry Tracing web-based architecture PLS Chemical analyses Buffalo milk cheese a b s t r a c t The aim of this study is the integration of an electronic tracing system with a non-destructive quality analysis system for single product of a typical Italian cheese, prepared with buffalo milk and called “Caciottina massaggiata di Amaseno”, a typical diary product of Lazio Region. The tracing and quality information are combined on a web platform to obtain a complete procedure to develop what we define as an “infotracing system”. Quality analyses (chemical, sensorial and spectrophotometric) were carried out on a total of 23 cheese wheels (8 with TAGs) and for three cheese maturation classes (3, 6 or 9 months after production). Two typologies of RFID tags were tested. Results were screened by Partial Least Squares regressions (PLS) on reflectance values for the prediction of chemical content, while classifica- tion of cheese maturation classes (3, 6 or 9 months) was carried out by Partial Least Squares Discriminant Analysis (PLSDA) on reflectance values. The RFID system turned out as effective, reliable and compatible with the production process tool. A good estimation of maturation degree by spectral and chemical analysis was obtained. Moreover an infotracing web-based system was designed to acquire and link basic information that can be made available to the final consumer or to different food chain actors before or after purchasing, using the RFID code to identify the single and specific cheese product. The projected web-based tracing system could improve the products commerce by increasing the information trans- parency for the consumer. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Quality can be defined as the possession by a product of the conditions that make it suitable to meet the expressed or potential needs of its users (Giusti, Bignetti, & Cannella, 2008). In this definition both the consumers and the producers needs are considered: the first is interested in health, safety, organoleptic characteristic and utiliza- tion modalities, the second in parameters more related to the market. Consumers and other stakeholders are increasingly concerned about the continuing sequence of food frauds and transparency is strongly requested in this sector: tracking and tracing systems are considered as efficient tools for early warning in case of a possible emerging problem (Beulens, Broens, Folstar, & Hofstede, 2005). Another important field of application for tracking and tracing systems is the niche products market for the valorisation of food with particular quality characteristics and a strong local identity (Ilbery & Kneafsey, 1999). Regulation (EC) No. 178/2002 of the European Parliament and of the Council of 28th January 2002 sets the general principles and requirements of food law and it defines traceability as “the ability to trace and follow a food, feed, food-producing animal or substance intended to be, or expected to be incorporated into a food or feed, through all stages of production, processing and distribution” (European Commission, 2002). The focus on traceability today is based on innovation, in order to allow the maximum of information flow management. Xiong et al. (2007) developed a practical application platform consisting of a bar-code based data identification system for pork products, a data-record keeping system, correlated databases, and a data query interfaceinorder to monitortheproduct qualityfrom the farm to the final consumer. An evolution of bar-codes systems is represented by innovative devices, such as advanced data handling systems based on RFID (radio frequency identification) and WSN (wireless sensor networks) (Ruiz-Garcia, Steinberger, & Rothmund, 2010) These systems can be used on food also during its processing without interfering or being damaged by the transformation opera- tions, as in the case of presence of liquids or conservation solutions. Moreover theycan store a high amount of information andin a remote way. InparticularRFID is an emerging technology increasingly utilized * Corresponding author. Tel.: þ39 0690675214; fax: þ39 0690625591. E-mail address: corrado.costa@entecra.it (C. Costa). Contents lists available at SciVerse ScienceDirect Food Control journal homepage: www.elsevier.com/locate/foodcont 0956-7135/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodcont.2012.03.025 Food Control 27 (2012) 234e241
  2. 2. in food logistics and in the supply chain management processes (Jedermann, Ruiz-Garcia, & Lang, 2009). This system uses radio waves to receive and transmit data stored into tags, consisting in a silicon chip with an antenna where information are stored under a unique serial number. The exchange of information between reader and e antenna e tag is codified and transmitted to a database (Brofman-Epelbaum & Kluwe Aguiar, 2007). RFID technology is not only based on the presence of tags and readers but it requires other software and hardware specifications in order to manage the infor- mation load through space and time (Costa et al., 2011; Sarriá et al., 2009). Products information can be associated to each step of the food chain (producers, raw material, food processors, transporters, retailers) and conveyed to Internet on a web platform in order to become available to the following different end users: to multiple- or to the single-final buyer or to the different actors of the food chain (Lammers & Hasselmann, 2007). The information on product quality, available for the different stakeholders, can derive from traditional, chemical and destructive systems. In these cases it is not possible to monitor each single product in the whole batch of goods, but only some samples that will be destroyed for the analysis. Another possible application is based on spectrophotometric analytical systems, based on non-destructive, opto-electronic technologies (Menesatti et al., 2010; Woodcock, Fagan, O’Donnell, & Downey, 2008). These tech- nologies are characterized by a high analytical capacity, a high speed information acquisition, a total non-destructivity of measurements and, in particular conditions, the possibility of operating on a single products, also on the processing line (Downey et al., 2005; Lee, Jeon, & Harbers, 1997; Martín-del-Campo, Picque, Cosío-Ramírez, & Corrieu, 2007a,b; Rodriguez-Saona, Koca, Harper, & Alvarez, 2006). The infor- mation on quality parameters can be associated with the electronic traceability, as in the case of experiments carried out on Parmesan cheese (Kahn, 2005; Regattieri, Gamberi, & Mancini, 2007; Zanasi, Nasuelli, Buccolini, & Pulga, 2008). The aim of the present work was the experimental testing of a system which integrates an electronic tracing system with a non- destructive quality analysis system for each single unit of cheese product. This system records the two typologies of information on a web platform. This was carried out in order to obtain a complete procedure of quality tracing and information and to develop an “infotracing system”. For the present research activity, an info- tracing system was developed on a dairy niche product called “Caciottina massaggiata di Amaseno”, a typical Italian cheese produced in Lazio Region and prepared with buffalo milk. 2. Materials and methods 2.1. Cheese samples Cheese wheels of “Caciottina massaggiata di Amaseno” were produced in the cheese factory “San Lorenzo in Valle” located in Amaseno (LT, Italy). The product, according to the production protocol, was periodically subjected to manipulations by hand with a mixture of olive oil and wine and to overturning until the end of seasoning, which has a minimum duration of 60 days. 2.2. Identification devices Two different RFID tags were used for the present experimen- tation: TAG cheese HF2009 and TAG cheese arrow screw (Fig. 1). Four Tags of each type were inserted in four cheese samples, for a total of eight tested wheels. Tag were inserted at the time of production (T0) and the tags’ reading was performed at time 0 and 3, 6, 9 months (maturation times: T3, T6 and T9) after T0. The tags’ reader was CPR.MR50-USB Multi ISO (ISO14443-A/B, ISO15693 & NFC). A specific reading and database software was realized using the open source Python Programming Language. The range of the antenna was 10 cm. 2.3. Quality analyses Chemical and spectrophotometric analyses were carried out on a total of 23 cheese wheels (8 with RFID tags), according to the following scheme: i. chemical analyses were carried out at T0 on 3 cheese wheels without RFID tags; ii. chemical and spectrophoto- metric analyses were carried out at T3 and T6 on 3 cheese wheels without RFID tags; iii. chemical and spectrophotometric analyses were carried out at T9 on 8 cheese wheels with RFID tags. On each cheese wheel 6 slices were cut along the length: two samples (internal part) were obtained from the central slice, two samples (median part) were obtained cutting 2 slices at 2 cm from the central one, the last two samples (external part) were obtained from the remaining part. These sampling methods were chosen in order to obtain analytical parameters of reference to be used for the evaluation of cheese with unusual fermentations or defects, which frequently occur in limited areas within the whole cheese wheel. 2.3.1. Chemical analyses The following chemical analyses were performed: dry matter by drying in oven at 102 C (International standard FIL-IDF 4/A: 1982); fat (3433:1975 e Determination of fat content e Van Gulik method); pH by potentiometric measurement by direct insertion of the electrode in the cheese wheel; sodium chloride by titration with AgNO3 (International Standard FIL-IDF 88/A: 1988). 2.3.2. Spectrophotometric analyses For the VISeNIR measurements, a (portable) single channel spectrophotometer was used. The system is composed of five parts: (1) a Hamamatsu S 3904 256Q spectrograph in a special housing; a customized illumination system realized by a 20 W halogen lamp Fig. 1. RFID tag typologies used in the experimentation: TAG cheese HF2009 and TAG cheese arrow screw. P. Papetti et al. / Food Control 27 (2012) 234e241 235
  3. 3. and an optical fibre bundle consisting of approx. 30 quartz glass; (2) an optical entrance with input round: 70 mm  2500 mm and diameter 0.5 mm NA ¼ 0.22 mounted in SubMiniature version A- coupling; (3) specific probes with quartz optical fibre of connec- tion; (4) a transmission device for transmitted or absorbed light for thin solids or liquid with variable optical length; (5) a notebook equipped with specific software to acquire, calibrate and elaborate spectral data. The Hamamatsu spectrograph has the following characteristics: grating: flat-field, 366 line/mm (centre); spectral range: 310e1100 nm; wavelength accuracy absolute: 0.3 nm; temperaturedinduced drift: 0.02 nm/K; resolution (Rayleigh- criterion): DlRayleigh 10 nm; sensitivity: 1013 Counts/Ws (with 14-Bit-conversion); straylight: 0.8% with halogen lamp and 16 bit A/D converter. For spectral acquisition, the ‘pen’ probe was used to measure the spectral reflectance response on each sample for three repetitions (spot area z 10 mm2 ). To avoid high signal noise typical of the tails of the spectral range, only values between 400 and 800 nm were considered. 2.4. Statistical analysis A two-way analysis of variance (Two-Way ANOVA) was used to assess the statistically significant differences among the chemical variables (pH, moisture, chlorides and fat content) for different maturation times and for the three different cheese portions. A multi-comparison between factors’ means was performed by a Least Significant Difference (LSD) test. Predictions of chemical content were performed by Partial Least Squares regressions (PLS) on reflectance values. Classification of the cheese maturation times was carried out by Partial Least Squares Discriminant Analysis (PLSDA) on reflectance values. These two multivariate supervised methods are based on Partial Least Squares (PLS) (Wold, Sjostrom, Erikssonn, 2001) and are widely used to find the correlations between the output signals of a multi-channel device and the information enclosed in a certain number of measures. The model operates through a specific algo- rithm (SIMPLS; De Jong, 1993) for two types of analysis: i) quanti- tative predictions (PLS, see Menesatti et al., 2010 for more details) and ii) classifications or modelling (PLSDA, see Menesatti et al., 2008 for more details). The degree of estimation accuracy in quantitative prediction must be inferred by the direct comparison between the measured and the estimated response variable, by calculating different parameters of the prediction efficiency: coefficient of correlation (r) between measured and predicted values, RMSE (Root Mean Square Error); SEP (standard error of prediction). The PLSDA analysis provides the percentage of correct classifi- cation of each class. This analysis expresses also the statistical parameters indicating the modelling efficiency indicated by sensi- tivity and specificity parameters. The sensitivity is the percentage of the species of a category accepted by the class model. The specificity is the percentage of the species of the categories different from the modelled one, rejected by the class model (Costa et al., 2008). In order to grant a higher strength and ability of generalization to the modelling analysis, the strategy used of both PLS and PLSDA multivariate analysis was as follows: 1. Repartition of the entire dataset (DS) into two parts: a dataset for the training or model (DM), including the 75% of DS; b dataset for the validation or test (DT) including the remaining 25% of the entire DS. 2. The repartition was carried out by: c partitioning algorithm that takes into account the vari- ability in both X- and Y-spaces called sample set partition- ing based on joint x-y distances (SPXY; Harrop Galvao et al., 2005) for PLS; d extraction function based on distances and on KennardeStone algorithm for PLSDA (Kennard Stone, 1969). The y- and x-block variables were pre-processed with different algorithms in order to limit or to enhance the scale effects, drifts and noises. In Table 1, the list of utilized algorithms is indicated. The DM was used for the development of different models derived from the factorial combination of: increasing number of Latent Variables (LV, from 1 to 20), different types of pre- processing for X-block (14) and different types of pre-processing for Y-block (4) e only for PLS models. About 30,000 models and tests were classified in terms of their ability of transferability and robustness (Brown, 2009), using a combined parameter (RPD) that provides a standardization of the SEP or of the RMSE. The RPD is the ratio between the standard deviation of the measured laboratory (reference) data (Ystd) and the SEP (Williams, 2001) or between the Ystd and the RMSE (Viscarra-Rossel, Taylor, McBratney, 2007). The RPD was calcu- lated on both the training and the validation set. The optimum models were selected using the RPD based on RMSE. For the RPD performance evaluation, the classification proposed by Viscarra- Rossel et al. (2007) is the following: RPD 1.0 indicates a very poor model; 1.0 to 1.4 indicates a poor model; 1.4 to 1.8 indicates a fair model; 1.8 to 2.0 indicates a good model; 2.0 to 2.5 is very good and 2.5 is excellent. The modelling iteration was developed through an appropriate software routine, realized in Matlab 7.1 and PLS toolbox 4.0 environment. 2.5. Infotracing web-based system The aim of the web-based tracing system refers to the improvement of the products logistic management by increasing its quality and information transparency for the consumer. This objective was carried out by collecting a set of scientific and Table 1 List of pre-processing algorithms applied on PLS and PLSDA dataset. Label Description Abs Takes the absolute value of the data Autoscale Centres columns to zero mean and scales to unit variance Baseline Baseline (weighted least squares) Centring Multiway centre Detrend Remove a linear trend Diff1 Differences between adjacent variables (approximate derivatives) GLS weighting Generalized least squares weighting Groupscale Group/block scaling Log 1/R Transformation of reflectance in absorbance following log (1/R) formula Log 10 Log 10 Logdecay Log decay scaling Mean centre Centre columns to have zero mean Median centre Centre columns to have zero median Msc (mean) Multiplicative scatter correction with offset, the mean is the reference spectrum None No pre-processing Normalize Normalization of the rows Osc Orthogonal signal correction Scaling Multiway scale Sg SavitskyeGolay smoothing and derivatives Snv Standard normal variate Sqmnsc Scale each variable by the square root of its mean P. Papetti et al. / Food Control 27 (2012) 234e241236
  4. 4. productive information which follow the product shelf-life from producer to consumer, providing web-based tools for each cate- gory. The categories involved in this system are divided into manufacturers, wholesalers, resellers, retailers and consumers who contribute separately, according to their level of member- ship, to provide a set of data related to each product. All the collected data will enter into a centralized database. Computer companies, specialized in CED (Center Data Elaboration), will manage this database by providing web hosting and backup services. This management structure guarantees the uniqueness and the centrality of the acquired data maintaining a controlled access for each system part in order to ensure their integrity. In this work we will consider only those companies able to respect the standards provided in ISO/IEC 27001:2005 (ISO/IEC 27001, 2005). The proposed system is divided in 4 phases (Fig. 2): 1) Manufacturer The product is identified by RFID technology (personal computer and RFID tags) and its quality information, chemical and spectrophotometric analyses developed are stored into the centralized database through a web application. Moreover infor- mation on milk, producer and animals (farming type, animal feeding), cheese producer, manipulation and processing treat- ments, hygienic and sanitary controls performed during time, micro-climatic characteristics of conservation and maturation of the product will be stored. 2) Wholesalers, resellers and retailers The categories involved in this phase can monitor the supply chain of each product through the centralized database and improve their tracing by adding quality information into the system through a web application. 3) Consumers The consumers can control the supply chain of each product using the RFID readers provided by the resellers and/or retailers and by the web (browser personal computer) and smartphone applications (APP) inserting the RFID tag code. Also the consumers can improve the product tracing by adding feedback information on the quality of the product into the system through a web services as blog and forum. 4) Research institutions and statistics Through the centralized database all the research institutions can use the quality information collected by the categories, to statistics and marketing scopes. The implemented web software is structured to provide various services to all the categories thanks to the API (Applica- tion Programming Interface). This allows to the manufacturer, wholesalers, resellers etc., the possibility to implement the acquisition and/or writing system following their needs and available technologies and ensuring uniformity of data to consult or send. This Infotracing system can provide also a reference web inter- face to access to the product info card displaying all information and data released as feedback by the manufacturer, wholesaler, reseller, retailer and consumer. 2.5.1. Software architecture The selected software architecture is the Three-Tier (software module implementing one or more conceptual layers) that allows to create a client/server architecture to integrate different systems (within a LAN, Over Internet) in which the clients are independent from each others (Ramakrishnan Gehrke, 1999). It is possible to obtain different levels of presentation client specific. All the application logic relies within intermediate state and ensures to the Fig. 2. Infotracing web-based system: flowchart of the architecture of the different phases. P. Papetti et al. / Food Control 27 (2012) 234e241 237
  5. 5. system more portable, handy at maintenance, handy at updating the two tiers, more flexibility and greater scalability. The Internet-client is the applicative layer that refers to clients over Internet (personal computer, workstations and smartphones). The presentation-layer, composed by the web server, manages the communication with the independent external clients. The application-layer processes data and produces the results to forward tothepresentation-layer. The resource-layeristhe layerthat manages the data required for the system functioning. Into the Internet-client- layer there are the different categories which access to the system by web. The presentation-, application- and resource-layers are imple- mented into the CED structure and provide management services for the various categories through the web server. The language implemented for the configuration application of the local central system was Python while Java language was implemented to allow the simplification of the application writings of network, permitting a system interoperability and implementing an applicative logic for the different technologies and not for the different devices. For example, for the mobile technology only a software independent of the mobile device (iPhone, Andorid, Windows Mobile, Symbian) has been configured. In terms of deployment a database management system (DBMS) relational was chosen supporting stored procedures and triggers (ability to handle part of the application logic of the server). 3. Results The production information concerning cheese samples equip- ped with RFID tags and used in the present research activity were inserted in the infotracing web-based system to be used as production reference for RFID tracing and indirect quality charac- terizations of the final product. All the RFID tags performed a correct reading at all monitored production stages. Despite the continuous handling due to the cheese manipulation at T9 no tags moved away: they were all perfectly in service and inserted in the cheese wheels. Only the samples with “Cheese arrow screw” tags showed at T3 some ruptures in the rind that became more and more evident during the maturation, leading to the formation of moulds after 9 months. On the other hand, tags HF2009 remained in service and well visible also at T9, without showing particular problems for cheese sale and consumption. In Table 2 the mean value for the chemical parameters (pH, moisture, chlorides and fat content), of the internal, median and external cheese wheel parts, during the 9 months of the experi- ment were reported together with the two-way ANOVA results. For the maturation time results, all the parameters (pH, mois- ture, chlorides content and fat content) presented statistically significant differences between all the maturation classes (p 0.05). Table 2 Mean values and statistical analysis (ANOVA and LSD multi-comparison between factors) of the chemical parameters measured for different maturation months and position on cheese wheels. For the significance, different letters indicate significant differences for P 0.05. 2-Way ANOVA results Month Significance Position External Median Internal Mean Source d. f. Mean sq. Prob F a a a pH Month 3 0.62338 0.031923 0 a 5.22 5.12 5.12 5.15 Position 2 0.011852 0.94126 3 b 5.59 5.58 5.58 5.59 Month position 6 0.013005 0.99874 6 ab 5.42 5.35 5.35 5.37 Error 48 0.19554 9 a 5.12 5.18 5.18 5.16 Total 59 2-Way ANOVA results Month Significance Position External Median Internal Mean Source d. f. Mean sq. Prob F a a a Moisture Month 3 2265.8 0 0 a 58.70 53.06 53.06 54.94 Position 2 17.007 0.25641 3 b 39.42 38.86 38.86 39.05 Month position 6 22.176 0.11386 6 c 29.79 30.42 30.42 30.21 Error 48 12.145 9 c 27.20 29.60 29.60 28.80 Total 59 2-Way ANOVA results Month Significance Position External Median Internal Mean Source d. f. Mean sq. Prob F a b b Chlorides content Month 3 0.56134 0 0 a 1.37 1.29 1.29 1.31 Position 2 0.31923 1.02E-10 3 b 2.12 1.56 1.56 1.75 Month position 6 0.11275 5.82E-09 6 c 1.64 1.56 1.56 1.59 Error 48 0.008273 9 d 1.40 1.36 1.36 1.37 Total 59 2-Way ANOVA results Month Significance Position External Median Internal Mean Source d. f. Mean sq. Prob F a b ab Fat content Month 3 75.279 0 0 a 21.09 19.23 19.23 19.85 Position 2 2.9004 0.064135 3 a 18.83 18.53 18.53 18.63 Month position 6 3.8298 0.003292 6 b 21.67 21.00 21.00 21.22 Error 48 0.99666 9 c 24.10 23.90 23.90 23.96 Total 59 P. Papetti et al. / Food Control 27 (2012) 234e241238
  6. 6. The pH average values of the different portions during the ageing time reported in Table 2 didn’t show statistically significant differences, although the central part showed a lower pH compared to the external portion. In Table 2, the average moisture trend was shown for the three different cheese sections (external, median and internal), according to ageing time. During the cheese maturation, moisture decrease by evaporation led to a progressive weight decline and afterwards there was a progressive dehydration during the following matu- ration phase of 35e40%. Moisture content decreased in a statisti- cally significant way in the first maturation time, until the sixth month, while, later, moisture decrease is less relevant in percentage. An appreciable difference between the cheese centre and periphery remained: the internal portion had a higher mois- ture content than the external, of about 2 units. Concerning chlorides content, the salt diffusion into the cheese e from the external to the central portion e starts immediately after the production and continues during the maturation time. Concerning the interaction month/position, only for chlorides and fat content a statistically significant difference was reported. The chemical parameters resulted significantly independent from the samples position measurement. Table 3 reports the characteristics of the four best models selected (one for each quality parameter) basing on the highest RPD value of the test set. For all selected models, y-block was pre- processed with the Matlab ‘median center’ algorithm. The pH model was based on 15 LV and characterized by a ‘baseline’ pre- processing for x-block. The moisture model was based on 12 LV, with a ‘baseline’ pre-processing for x-block. The chlorides content model was based on 19 LV and characterized by a ‘Diff1’ first pre- processing for x-block followed by a ‘Log 10’ second pre- processing for x-block. Finally, the selected fat content model used 7 LV and a ‘sg’ pre-processing for x-block. All the models presented high values of the correlation coefficient (r) (ranging from 0.73 to 1), slightly higher in the training test compared to the validation phase. In the training set phase, the RMSE, which has the same measurement unit as the quantity being estimated, results lower for the chlorides content and the pH models, while it is higher for the moisture content model. For the fat content model, RMSE has the highest values of all the four models. Concerning the validation set, the lowest values of RMSE are those of pH and chlorides content models, while higher values result respectively for moisture and fat content models. On the basis of the Viscarra- Rossel et al. (2007) RPD classification, the 4 models showed that the pH (RPD ¼ 1.5) can be considered as a fair model, the water and chlorides content (respectively RPD ¼ 1.8 and 1.9) are good models, but the fat content model presents a RPD ¼ 1.4, showing a quite fair model performance. These results are supported also by the eval- uation of the correlation between observed and estimated values. All the parameters presented quite good coefficients of correlation in validation, being the lower value equal to 0.73 for Fat content and the highest 0.87 for Chlorides content. Several PLSDA models were generated in order to discriminate between three different cheese maturation times. Table 4 reports the characteristics of the resulting best model, based on 6 LV. The model percentage of correct classification (89.5%) and of validation test (89.5%) showed high values and also the class prediction (88.9%). The model presented also very high values of sensitivity (92.6%) and specificity (91.5%). The confusion matrix of the 25% of the individuals of the model was reported in Table 5; it is possible to observe that the efficiency of classification results very high, considering that the random percentage was 25% and that only 2 cases out of 19 (10.5%) were wrongly classified. Moreover, the error of classification was between two classes close one to the other (T3 vs T6), while the model correctly estimated classes with a wider extension of time. 4. Discussion The present work showed an integrated application of an RFID system together with qualitative analyses (chemical and spectro- photometric) and an infotracing system on a traditional Italian dairy product. Other experiences in literature reported that RFID has been successfully applied to food logistics and supply chain management processes because of its ability to identify, categorize, and manage the flow of goods (Ruiz-Garcia, Lunadei, Barreiro, Robla, 2009). Fukatsu and Nanseki (2009) proposed a farm opera- tion monitoring system using “Field Servers” and a wearable device equipped with an RFID reader and motion sensors in order to monitor crop growth, field environment, and farming operations. Regattieri et al. (2007) developed a traceability system on Parmesan cheese based on an integration of alphanumerical codes and RFID technologies with positive consequences for both manufacturers and for consumers. The reading system is portable and of easy utilization and configuration (Dolgui Proth, 2008). The use of an open source programming language (Lwoga Chilimo, 2006), as we did when using Python software, repre- sents an advantage in terms of costs, flexibility and possibility of diffusion of the technology also to promote local cheese producers (Dubeuf, Ruiz Morales, Castel Genis, 2010). As also observed by Pérez-Aloe et al. (2007) both tested RFID tags showed a perfect degree of readings, but, in our case, the cheese arrow screw one caused ruptures. Moreover, both tags were extremely robust and Table 3 Results of Partial Least Squares (PLS) multivariate analysis to predict four different independent variables (pH, moisture, chlorides and fat). In the table are reported: number of Latent Vectors (LV), correlation coefficient (r), Standard Error of Predic- tion (SEP), Root Mean Squares Error (RMSE) and Ratio of Percentage Deviation (RPD). pH Moisture Chlorides Fat Model (training set) N LV 15 12 19 7 First pre-processing X-block Baseline sg Diff1 sg Second pre-processing X-block Log 10 Pre-processing Y-block Median centre Median centre Median centre Median centre r (observed vs predicted) 0.9558 0.7879 1 0.7421 SEP 0.053 3.153 0.0004 1.94 RMSE 0.053 3.128 0.0004 1.924 RPDTrain 1.5572 1.8493 1.9552 1.3846 TEST (validation set) r (observed vs predicted) 0.777 0.8551 0.8661 0.7333 SEP 0.122 3.194 0.038 2.225 RMSE 0.121 3.138 0.038 2.207 RPDtest 1.5371 1.8166 1.9445 1.3737 Table 4 Results of Partial Least Squares Discriminant Analysis (PLSDA) for the four matu- ration times (T0, T3, T6 and T9) obtained with spectrophotometric measurements. n. LV is the number of latent vectors. Random probability (%) is the probability of random assignment of an individual into a unit. Pre-processing X-block Baseline Pre-processing Y-block None n. LV 6 Sensitivity 0.926 Specificity 0.915 % Random probability 25.000 % Class prediction 88.889 % Correct classification model (75%) 89.474 % Correct classification independent test (25%) 89.474 P. Papetti et al. / Food Control 27 (2012) 234e241 239
  7. 7. resistant to mechanical damage in relation to handling, cutting, and compression. The main issue in order to avoid the tag’s ingestion is to increase its visibility. This could be enhanced with vivid colours and by positioning it just below the cheese rind. Varese, Buffagni, and Percivale (2008) indicated that the positioning of the tags did not affect readability. Concerning the chemical analyses, the obtained data seem to confirm that during maturation, pH reaches a maximum level around the third month, while afterwards equilibrium is estab- lished, as a result of the opposed proteolysis effect from which ammonia is produced and lipolysis leads to a fatty acids release (Zapparoli Duroni, 1997). Moisture into the analysed sections (external, median and internal) regularly decreased during time, as described in previous works (Resmini, Volonterio, Annibaldi, Ferri, 1997). After six months the salt concentration in the external area resulted higher in a statistically significant way than that in the internal portion, as already evidenced by Fossa, Sandri, Scotti, and Malacarne (2007). Average sodium chloride values (see Table 2) were comparable with those reported in literature (Tosi, Sandri, Tedeschi, Fossa, Franceschi, 2007; Tosi, Sandri, Tedeschi, Malacarne, Fossa, 2008). The fat amount into the “Caciottina massaggiata di Amaseno” is mainly related to the milk fat/casein ratio: this parameter depends on the fat amount in the starting milk and on the transformation technology (Fossa et al., 2007). Fat content showed a significant decrease during the maturation period. The uneven moisture distribution and the pH variation explain also the differences in fat amounts (see Table 2). Results on spectrophotometric analysis confirm what reported by Curda and Kukackováb (2004) for the determination of the processed cheese composition (dry matter, fat content and pH) by NIR spectroscopy techniques. A portable VISeNIR system was used directly in the production location, as reported by Antonucci et al. (2010). As this is a non-destructive technique, it is possible to hypothesize a continuous monitoring of cheese chemical charac- teristics during time of maturation and an always accessible web- based reporting system. The estimation of maturation degree by spectral analysis is a very important, as reported by Downey et al. (2005) and it could be used in the cheese factory during the product maturation to characterize the ripening stages, as performed by Martín-del- Campo et al. (2007a,b). The RFID system turned out as effective, reliable and compatible with the production process tool. The spectral-based quality esti- mation of pH, water, chlorides and fat content provided good results that could allow an effective monitoring of the characteristic for each, single cheese wheel. Also a good estimation of maturation degree by spectral analysis was obtained. Concerning the infotracing on the web site, our system could be soon tested on a large scale, in order to enable the different stakeholders to access to the information on production and on the characteristics of cheese producer and farmer (concerning milk origin), entering the RFID code or even a bar-code in casein. In addition, the stakeholders can have access to the information on maturity degree and on quality characteristics, identified by non- destructive spectral systems. This is finalized to increase trans- parency and to ensure product quality, with important impacts on the consumer and on the producer. Moreover, the utilization of a web-based system allows the integration of production and traceability information with all the other information concerning the inspection and documentation requirements, as it is often required to obtain the quality marks. The integration of ICT (Information and Communication Technology), realized in this experimental work, is considered of high importance in order to maximize the following promotion and guarantee keyfactors of local food products: - full traceability, from the producer of raw materials to the single final product; - guarantee of quality control at all the stages of the supply chain; - information transparency, available for the consumer, but also for third inspection bodies and research institutes); - direct product promotion without direct mediation and promotion of its specific characteristics (e.g. organic milk, cruelty free products); - direct interaction channel between consumer and producer (e.g. blog); - direct marketing and online shopping. The producer was interested in such application and also the consumers at marketplaces were curious about this innovative technology. These facts stress the efficacy in traceability and all the potential informative development of such system. 5. Conclusions The consumer or other involved stakeholders can access to the whole production history and to the quality characteristics of every single product, by entering the RFID code on the web screen. Gandino, Montrucchio, Rebaudengo, and Sanchez (2007) adopted a traceability system based on RFID technology in a fruit warehouse in order to match traceability with other benefits, such as supply chain management, commodity value addition and brand management. There are web platforms that allow the access to static or dynamic product information for different stakeholders (Breyer, Daubresse, Sneyers, 2007; Samad, Murdeshwar, Hameed, 2010). A Classical example is represented by the web systems of main courier services enterprises (FedEx, UPS, DHL, etc.). 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