a rfid web-based infotracing system for the artisanal italian cheese quality traceability

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Food Control 27 (2012) 234e241

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Food Control

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

Patrizia Papetti a, Corrado Costa b,*, Francesca Antonucci b, Simone Figorilli b, Silvia Solaini b,Paolo Menesatti b

aDepartment of Economics, University of Cassino, Via Marconi 10, 03043 Cassino (FR), ItalybCRA-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 2012Received in revised form16 March 2012Accepted 24 March 2012

Keywords:RFIDSpectrophotometryTracing web-based architecturePLSChemical analysesBuffalo milk cheese

* Corresponding author. Tel.: þ39 0690675214; faxE-mail address: [email protected] (C. Costa

0956-7135/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.foodcont.2012.03.025

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 qualityanalysis 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 qualityinformation are combined on a web platform to obtain a complete procedure to develop what we defineas an “infotracing system”. Quality analyses (chemical, sensorial and spectrophotometric) were carriedout on a total of 23 cheese wheels (8 with TAGs) and for three cheese maturation classes (3, 6 or 9months after production). Two typologies of RFID tags were tested. Results were screened by Partial LeastSquares 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 DiscriminantAnalysis (PLSDA) on reflectance values. The RFID system turned out as effective, reliable and compatiblewith the production process tool. A good estimation of maturation degree by spectral and chemicalanalysis was obtained. Moreover an infotracing web-based systemwas designed to acquire and link basicinformation that can be made available to the final consumer or to different food chain actors before orafter purchasing, using the RFID code to identify the single and specific cheese product. The projectedweb-based tracing system could improve the products commerce by increasing the information trans-parency for the consumer.

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1. Introduction

Quality can be defined as the possession by a product of theconditions that make it suitable to meet the expressed or potentialneeds of its users (Giusti, Bignetti, & Cannella, 2008). In this definitionboth the consumers and the producers needs are considered: the firstis interested in health, safety, organoleptic characteristic and utiliza-tionmodalities, the second inparametersmore related to themarket.Consumers and other stakeholders are increasingly concerned aboutthe continuing sequence of food frauds and transparency is stronglyrequested in this sector: tracking and tracing systems are consideredas efficient tools for early warning in case of a possible emergingproblem (Beulens, Broens, Folstar, & Hofstede, 2005). Anotherimportant field of application for tracking and tracing systems is theniche products market for the valorisation of food with particularquality characteristics and a strong local identity (Ilbery & Kneafsey,1999). Regulation (EC) No. 178/2002 of the European Parliament and

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of the Council of 28th January 2002 sets the general principles andrequirements of food law and it defines traceability as “the ability totrace and follow a food, feed, food-producing animal or substanceintended to be, or expected to be incorporated into a food or feed, throughall stages of production, processing and distribution” (EuropeanCommission, 2002). The focus on traceability today is based oninnovation, in order to allow the maximum of information flowmanagement. Xiong et al. (2007) developed a practical applicationplatform consisting of a bar-code based data identification system forpork products, a data-record keeping system, correlated databases,andadataquery interface inorder tomonitor theproductquality fromthe farm to the final consumer. An evolution of bar-codes systems isrepresented by innovative devices, such as advanced data handlingsystems 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 processingwithout interfering or being damaged by the transformation opera-tions, as in the case of presence of liquids or conservation solutions.Moreover theycan store ahigh amountof informationand ina remoteway. InparticularRFID is anemerging technology increasinglyutilized

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P. Papetti et al. / Food Control 27 (2012) 234e241 235

in food logistics and in the supply chain management processes(Jedermann, Ruiz-Garcia, & Lang, 2009). This system uses radiowaves to receive and transmit data stored into tags, consisting ina silicon chip with an antenna where information are stored undera unique serial number. The exchange of information between readerand e antenna e tag is codified and transmitted to a database(Brofman-Epelbaum & Kluwe Aguiar, 2007). RFID technology is notonly based on the presence of tags and readers but it requires othersoftware 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 thefood chain (producers, raw material, food processors, transporters,retailers) and conveyed to Internet on a web platform in order tobecome available to the following different end users: to multiple- orto 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 tomonitor each single product in the whole batch of goods, but onlysome samples thatwill bedestroyed for the analysis. Another possibleapplication is based on spectrophotometric analytical systems, basedon 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 speedinformation acquisition, a total non-destructivity of measurementsand, in particular conditions, the possibility of operating on a singleproducts, also on the processing line (Downeyet 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 electronictraceability, as in the case of experiments carried out on Parmesancheese (Kahn, 2005; Regattieri, Gamberi, & Mancini, 2007; Zanasi,Nasuelli, Buccolini, & Pulga, 2008).

The aim of the present work was the experimental testing ofa systemwhich integrates an electronic tracing systemwith a non-destructive quality analysis system for each single unit of cheeseproduct. This system records the two typologies of information ona web platform. This was carried out in order to obtain a completeprocedure 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 cheeseproduced in Lazio Region and prepared with buffalo milk.

2. Materials and methods

2.1. Cheese samples

Cheese wheels of “Caciottina massaggiata di Amaseno” wereproduced in the cheese factory “San Lorenzo in Valle” located in

Fig. 1. RFID tag typologies used in the experimentation:

Amaseno (LT, Italy). The product, according to the productionprotocol, was periodically subjected to manipulations by hand witha mixture of olive oil and wine and to overturning until the end ofseasoning, 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, fora total of eight tested wheels. Tag were inserted at the time ofproduction (T0) and the tags’ reading was performed at time 0 and3, 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 usingthe open source Python Programming Language. The range of theantenna was 10 cm.

2.3. Quality analyses

Chemical and spectrophotometric analyses were carried out ona total of 23 cheese wheels (8 with RFID tags), according to thefollowing scheme: i. chemical analyses were carried out at T0 on 3cheese wheels without RFID tags; ii. chemical and spectrophoto-metric analyses were carried out at T3 and T6 on 3 cheese wheelswithout RFID tags; iii. chemical and spectrophotometric analyseswere carried out at T9 on 8 cheese wheels with RFID tags.

On each cheese wheel 6 slices were cut along the length: twosamples (internal part) were obtained from the central slice, twosamples (median part) were obtained cutting 2 slices at 2 cm fromthe central one, the last two samples (external part) were obtainedfrom the remaining part.

These sampling methods were chosen in order to obtainanalytical parameters of reference to be used for the evaluation ofcheese with unusual fermentations or defects, which frequentlyoccur in limited areas within the whole cheese wheel.

2.3.1. Chemical analysesThe 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 Gulikmethod); pH by potentiometric measurement by direct insertion ofthe electrode in the cheese wheel; sodium chloride by titrationwith AgNO3 (International Standard FIL-IDF 88/A: 1988).

2.3.2. Spectrophotometric analysesFor 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

TAG cheese HF2009 and TAG cheese arrow screw.

Table 1List of pre-processing algorithms applied on PLS and PLSDA dataset.

Label Description

Abs Takes the absolute value of the dataAutoscale Centres columns to zero mean and scales to

unit varianceBaseline Baseline (weighted least squares)Centring Multiway centreDetrend Remove a linear trendDiff1 Differences between adjacent variables (approximate

derivatives)GLS weighting Generalized least squares weightingGroupscale Group/block scalingLog 1/R Transformation of reflectance in absorbance

following log (1/R) formulaLog 10 Log 10Logdecay Log decay scalingMean centre Centre columns to have zero meanMedian centre Centre columns to have zero medianMsc (mean) Multiplicative scatter correction with offset, the mean is the

reference spectrumNone No pre-processingNormalize Normalization of the rowsOsc Orthogonal signal correctionScaling Multiway scaleSg SavitskyeGolay smoothing and derivativesSnv Standard normal variateSqmnsc Scale each variable by the square root of its mean

P. Papetti et al. / Food Control 27 (2012) 234e241236

and an optical fibre bundle consisting of approx. 30 quartz glass; (2)an optical entrance with input round: 70 mm � 2500 mm anddiameter 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 forthin solids or liquid with variable optical length; (5) a notebookequipped with specific software to acquire, calibrate and elaboratespectral data. The Hamamatsu spectrograph has the followingcharacteristics: grating: flat-field, 366 line/mm (centre); spectralrange: 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 and16 bit A/D converter.

For spectral acquisition, the ‘pen’ probewas used tomeasure thespectral reflectance response on each sample for three repetitions(spot area z 10 mm2).

To avoid high signal noise typical of the tails of the spectralrange, only values between 400 and 800 nm were considered.

2.4. Statistical analysis

A two-way analysis of variance (Two-Way ANOVA) was used toassess the statistically significant differences among the chemicalvariables (pH, moisture, chlorides and fat content) for differentmaturation times and for the three different cheese portions. Amulti-comparison between factors’ means was performed bya Least Significant Difference (LSD) test.

Predictions of chemical content were performed by Partial LeastSquares regressions (PLS) on reflectance values. Classification of thecheese maturation times was carried out by Partial Least SquaresDiscriminant Analysis (PLSDA) on reflectance values.

These two multivariate supervised methods are based on PartialLeast Squares (PLS) (Wold, Sjostrom, & Erikssonn, 2001) and arewidely used to find the correlations between the output signals ofa multi-channel device and the information enclosed in a certainnumber 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 predictionmust be inferred by the direct comparison between the measuredand the estimated response variable, by calculating differentparameters of the prediction efficiency: coefficient of correlation (r)between measured and predicted values, RMSE (Root Mean SquareError); SEP (standard error of prediction).

The PLSDA analysis provides the percentage of correct classifi-cation of each class. This analysis expresses also the statisticalparameters indicating the modelling efficiency indicated by sensi-tivity and specificity parameters. The sensitivity is the percentageof the species of a category accepted by the class model. Thespecificity is the percentage of the species of the categoriesdifferent from the modelled one, rejected by the class model (Costaet al., 2008).

In order to grant a higher strength and ability of generalizationto the modelling analysis, the strategy used of both PLS and PLSDAmultivariate 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% ofDS;

b dataset for the validation or test (DT) including theremaining 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 onKennardeStone algorithm for PLSDA (Kennard & Stone,1969).

The y- and x-block variables were pre-processed with differentalgorithms in order to limit or to enhance the scale effects, driftsand noises. In Table 1, the list of utilized algorithms is indicated.

The DM was used for the development of different modelsderived from the factorial combination of: increasing numberof Latent Variables (LV, from 1 to 20), different types of pre-processing for X-block (14) and different types of pre-processingfor Y-block (4) e only for PLS models.

About 30,000 models and tests were classified in terms of theirability of transferability and robustness (Brown, 2009), usinga combined parameter (RPD) that provides a standardization of theSEP or of the RMSE. The RPD is the ratio between the standarddeviation of the measured laboratory (reference) data (Ystd) andthe 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 optimummodels were selected using the RPD based on RMSE. For the RPDperformance evaluation, the classification proposed by Viscarra-Rossel et al. (2007) is the following: RPD <1.0 indicates a verypoormodel; 1.0 to<1.4 indicates a poormodel; 1.4 to<1.8 indicatesa fair model; 1.8 to <2.0 indicates a good model; 2.0 to <2.5 is verygood and >2.5 is excellent. The modelling iteration was developedthrough an appropriate software routine, realized in Matlab 7.1 andPLS toolbox 4.0 environment.

2.5. Infotracing web-based system

The aim of the web-based tracing system refers to theimprovement of the products logistic management by increasingits quality and information transparency for the consumer. Thisobjective was carried out by collecting a set of scientific and


productive information which follow the product shelf-life fromproducer to consumer, providing web-based tools for each cate-gory. The categories involved in this system are divided intomanufacturers, wholesalers, resellers, retailers and consumerswho contribute separately, according to their level of member-ship, to provide a set of data related to each product. All thecollected data will enter into a centralized database. Computercompanies, specialized in CED (Center Data Elaboration), willmanage this database by providing web hosting and backupservices. This management structure guarantees the uniquenessand the centrality of the acquired data maintaining a controlledaccess for each system part in order to ensure their integrity. Inthis work we will consider only those companies able to respectthe 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 (personalcomputer and RFID tags) and its quality information, chemical andspectrophotometric analyses developed are stored into thecentralized database through a web application. Moreover infor-mation on milk, producer and animals (farming type, animalfeeding), cheese producer, manipulation and processing treat-ments, hygienic and sanitary controls performed during time,micro-climatic characteristics of conservation and maturation ofthe product will be stored.

2) Wholesalers, resellers and retailers

The categories involved in this phase can monitor the supplychain of each product through the centralized database andimprove their tracing by adding quality information into the systemthrough a web application.

Fig. 2. Infotracing web-based system: flowchart

3) Consumers

The consumers can control the supply chain of each productusing the RFID readers provided by the resellers and/or retailersand by the web (browser personal computer) and smartphoneapplications (APP) inserting the RFID tag code. Also the consumerscan improve the product tracing by adding feedback information onthe quality of the product into the system through aweb services asblog and forum.

4) Research institutions and statistics

Through the centralized database all the research institutionscan use the quality information collected by the categories, tostatistics and marketing scopes.

The implemented web software is structured to providevarious 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 theacquisition and/or writing system following their needs andavailable technologies and ensuring uniformity of data to consultor send.

This Infotracing system can provide also a reference web inter-face to access to the product info card displaying all informationand data released as feedback by the manufacturer, wholesaler,reseller, retailer and consumer.

2.5.1. Software architectureThe selected software architecture is the Three-Tier (software

module implementing one or more conceptual layers) that allowsto create a client/server architecture to integrate different systems(within a LAN, Over Internet) in which the clients are independentfrom each others (Ramakrishnan & Gehrke, 1999). It is possible toobtain different levels of presentation client specific. All theapplication logic relies within intermediate state and ensures to the

of the architecture of the different phases.


system more portable, handy at maintenance, handy at updatingthe two tiers, more flexibility and greater scalability.

The Internet-client is the applicative layer that refers to clientsover Internet (personal computer, workstations and smartphones).The presentation-layer, composed by the web server, manages thecommunication with the independent external clients. Theapplication-layer processes data and produces the results to forwardto thepresentation-layer. The resource-layer is the layer thatmanagesthe data required for the system functioning. Into the Internet-client-layer there are the different categories which access to the system byweb. The presentation-, application- and resource-layers are imple-mented into the CED structure and providemanagement services forthe various categories through the web server.

The language implemented for the configuration application ofthe local central system was Python while Java language wasimplemented to allow the simplification of the applicationwritingsof network, permitting a system interoperability and implementingan applicative logic for the different technologies and not for thedifferent devices. For example, for the mobile technology onlya software independent of the mobile device (iPhone, Andorid,Windows Mobile, Symbian) has been configured. In terms ofdeployment a databasemanagement system (DBMS) relational waschosen supporting stored procedures and triggers (ability to handlepart of the application logic of the server).

Table 2Mean values and statistical analysis (ANOVA and LSD multi-comparison between factors)on cheese wheels. For the significance, different letters indicate significant differences fo

2-Way ANOVA results Month

Source d. f. Mean sq. Prob > F

pHMonth 3 0.62338 0.031923 0Position 2 0.011852 0.94126 3Month position 6 0.013005 0.99874 6Error 48 0.19554 9Total 59



MoistureMonth 3 2265.8 0 0Position 2 17.007 0.25641 3Month position 6 22.176 0.11386 6Error 48 12.145 9Total 59



Chlorides contentMonth 3 0.56134 0 0Position 2 0.31923 1.02E-10 3Month position 6 0.11275 5.82E-09 6Error 48 0.008273 9Total 59



Fat contentMonth 3 75.279 0 0Position 2 2.9004 0.064135 3Month position 6 3.8298 0.003292 6Error 48 0.99666 9Total 59

3. Results

The production information concerning cheese samples equip-ped with RFID tags and used in the present research activity wereinserted in the infotracing web-based system to be used asproduction reference for RFID tracing and indirect quality charac-terizations of the final product.

All the RFID tags performed a correct reading at all monitoredproduction stages. Despite the continuous handling due to thecheese manipulation at T9 no tags moved away: they were allperfectly in service and inserted in the cheese wheels. Only thesamples with “Cheese arrow screw” tags showed at T3 someruptures in the rind that becamemore andmore evident during thematuration, leading to the formation of moulds after 9 months. Onthe other hand, tags HF2009 remained in service and well visiblealso at T9, without showing particular problems for cheese sale andconsumption.

In Table 2 the mean value for the chemical parameters (pH,moisture, chlorides and fat content), of the internal, median andexternal 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 statisticallysignificant differences between all thematuration classes (p< 0.05).

of the chemical parameters measured for different maturation months and positionr P < 0.05.

Significance Position

External Median Internal Mean

a a a

a 5.22 5.12 5.12 5.15b 5.59 5.58 5.58 5.59ab 5.42 5.35 5.35 5.37a 5.12 5.18 5.18 5.16



a a a

a 58.70 53.06 53.06 54.94b 39.42 38.86 38.86 39.05c 29.79 30.42 30.42 30.21c 27.20 29.60 29.60 28.80



a b b

a 1.37 1.29 1.29 1.31b 2.12 1.56 1.56 1.75c 1.64 1.56 1.56 1.59d 1.40 1.36 1.36 1.37



a b ab

a 21.09 19.23 19.23 19.85a 18.83 18.53 18.53 18.63b 21.67 21.00 21.00 21.22c 24.10 23.90 23.90 23.96


The pH average values of the different portions during theageing time reported in Table 2 didn’t show statistically significantdifferences, although the central part showed a lower pH comparedto the external portion.

In Table 2, the average moisture trend was shown for the threedifferent cheese sections (external, median and internal), accordingto ageing time. During the cheesematuration, moisture decrease byevaporation led to a progressive weight decline and afterwardsthere 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 sixthmonth, while, later, moisture decrease is less relevant inpercentage. An appreciable difference between the cheese centreand 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 cheesee from the external to the central portion e starts immediatelyafter the production and continues during the maturation time.Concerning the interaction month/position, only for chlorides andfat content a statistically significant difference was reported.

The chemical parameters resulted significantly independentfrom the samples position measurement.

Table 3 reports the characteristics of the four best modelsselected (one for each quality parameter) basing on the highest RPDvalue of the test set. For all selected models, y-block was pre-processed with the Matlab ‘median center’ algorithm. The pHmodel 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 contentmodel 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 modelused 7 LV and a ‘sg’ pre-processing for x-block. All the modelspresented high values of the correlation coefficient (r) (rangingfrom 0.73 to 1), slightly higher in the training test compared to thevalidation phase. In the training set phase, the RMSE, which has thesame measurement unit as the quantity being estimated, resultslower for the chlorides content and the pH models, while it ishigher for the moisture content model. For the fat content model,RMSE has the highest values of all the four models. Concerning thevalidation set, the lowest values of RMSE are those of pH andchlorides content models, while higher values result respectivelyfor moisture and fat content models. On the basis of the Viscarra-

Table 3Results of Partial Least Squares (PLS) multivariate analysis to predict four differentindependent 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), RootMean Squares Error (RMSE) and Ratio of Percentage Deviation (RPD).

pH Moisture Chlorides Fat

Model (training set)N� LV 15 12 19 7First pre-processing

X-blockBaseline sg Diff1 sg

Second pre-processingX-block

Log 10

Pre-processing Y-block Mediancentre

Mediancentre

Mediancentre

Mediancentre

r (observed vs predicted) 0.9558 0.7879 1 0.7421SEP 0.053 3.153 0.0004 1.94RMSE 0.053 3.128 0.0004 1.924RPDTrain 1.5572 1.8493 1.9552 1.3846

TEST (validation set)r (observed vs predicted) 0.777 0.8551 0.8661 0.7333SEP 0.122 3.194 0.038 2.225RMSE 0.121 3.138 0.038 2.207RPDtest 1.5371 1.8166 1.9445 1.3737

Rossel et al. (2007) RPD classification, the 4 models showed thatthe pH (RPD¼ 1.5) can be considered as a fair model, the water andchlorides content (respectively RPD¼ 1.8 and 1.9) are goodmodels,but the fat content model presents a RPD¼ 1.4, showing a quite fairmodel 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 correlationin validation, being the lower value equal to 0.73 for Fat content andthe highest 0.87 for Chlorides content.

Several PLSDA models were generated in order to discriminatebetween three different cheese maturation times. Table 4 reportsthe characteristics of the resulting best model, based on 6 LV. Themodel percentage of correct classification (89.5%) and of validationtest (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% ofthe individuals of themodel was reported in Table 5; it is possible toobserve that the efficiency of classification results very high,considering that the random percentage was 25% and that only 2cases out of 19 (10.5%) were wrongly classified. Moreover, the errorof classification was between two classes close one to the other (T3vs T6), while the model correctly estimated classes with a widerextension of time.

4. Discussion

The present work showed an integrated application of an RFIDsystem together with qualitative analyses (chemical and spectro-photometric) and an infotracing system on a traditional Italiandairy product. Other experiences in literature reported that RFIDhas been successfully applied to food logistics and supply chainmanagement 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 awearable deviceequipped with an RFID reader and motion sensors in order tomonitor crop growth, field environment, and farming operations.Regattieri et al. (2007) developed a traceability system onParmesan cheese based on an integration of alphanumerical codesand RFID technologies with positive consequences for bothmanufacturers and for consumers. The reading system is portableand 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 ofdiffusion of the technology also to promote local cheese producers(Dubeuf, Ruiz Morales, & Castel Genis, 2010). As also observed byPérez-Aloe et al. (2007) both tested RFID tags showed a perfectdegree of readings, but, in our case, the cheese arrow screw onecaused ruptures. Moreover, both tags were extremely robust and

Table 4Results 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 ofrandom assignment of an individual into a unit.

Pre-processing X-block BaselinePre-processing Y-block Nonen. LV 6Sensitivity 0.926Specificity 0.915% Random probability 25.000% Class prediction 88.889% Correct classification model (75%) 89.474% Correct classification independent test (25%) 89.474

Table 5Confusion matrix.

Predicted T3 T6 T9 Total observed

T3 4 2 0 6T6 0 6 0 6T9 0 0 7 7


resistant tomechanical damage in relation to handling, cutting, andcompression. The main issue in order to avoid the tag’s ingestion isto increase its visibility. This could be enhanced with vivid coloursand by positioning it just below the cheese rind. Varese, Buffagni,and Percivale (2008) indicated that the positioning of the tags didnot affect readability.

Concerning the chemical analyses, the obtained data seem toconfirm that during maturation, pH reaches a maximum levelaround the third month, while afterwards equilibrium is estab-lished, as a result of the opposed proteolysis effect from whichammonia 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, asdescribed in previous works (Resmini, Volonterio, Annibaldi, &Ferri, 1997). After six months the salt concentration in theexternal area resulted higher in a statistically significant way thanthat 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 milkfat/casein ratio: this parameter depends on the fat amount in thestarting milk and on the transformation technology (Fossa et al.,2007). Fat content showed a significant decrease during thematuration period. The uneven moisture distribution and the pHvariation explain also the differences in fat amounts (see Table 2).

Results on spectrophotometric analysis confirm what reportedby �Curda and Kuka�ckováb (2004) for the determination of theprocessed cheese composition (dry matter, fat content and pH) byNIR spectroscopy techniques. A portable VISeNIR systemwas useddirectly in the production location, as reported by Antonucci et al.(2010). As this is a non-destructive technique, it is possible tohypothesize 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 isa very important, as reported by Downey et al. (2005) and it couldbe used in the cheese factory during the product maturation tocharacterize the ripening stages, as performed by Martín-del-Campo et al. (2007a,b).

The RFID system turned out as effective, reliable and compatiblewith the production process tool. The spectral-based quality esti-mation of pH, water, chlorides and fat content provided goodresults that could allow an effectivemonitoring of the characteristicfor each, single cheese wheel. Also a good estimation of maturationdegree by spectral analysis was obtained.

Concerning the infotracing on the web site, our system could besoon tested on a large scale, in order to enable the differentstakeholders to access to the information on production and on thecharacteristics of cheese producer and farmer (concerning milkorigin), entering the RFID code or even a bar-code in casein. Inaddition, the stakeholders can have access to the information onmaturity 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 onthe consumer and on the producer. Moreover, the utilization of

a web-based system allows the integration of production andtraceability information with all the other information concerningthe inspection and documentation requirements, as it is oftenrequired to obtain the quality marks. The integration of ICT(Information and Communication Technology), realized in thisexperimental work, is considered of high importance in order tomaximize the following promotion and guarantee keyfactors oflocal food products:

- full traceability, from the producer of raw materials to thesingle final product;

- guarantee of quality control at all the stages of the supplychain;

- information transparency, available for the consumer, but alsofor third inspection bodies and research institutes);

- direct product promotion without direct mediation andpromotion 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 theconsumers at marketplaces were curious about this innovativetechnology. These facts stress the efficacy in traceability and all thepotential informative development of such system.

5. Conclusions

The consumer or other involved stakeholders can access to thewhole production history and to the quality characteristics of everysingle product, by entering the RFID code on the web screen.Gandino, Montrucchio, Rebaudengo, and Sanchez (2007) adopteda traceability system based on RFID technology in a fruit warehousein order to match traceability with other benefits, such as supplychain management, commodity value addition and brandmanagement. There are web platforms that allow the access tostatic or dynamic product information for different stakeholders(Breyer, Daubresse, & Sneyers, 2007; Samad, Murdeshwar, &Hameed, 2010). A Classical example is represented by the websystems ofmain courier services enterprises (FedEx, UPS, DHL, etc.).

Acknowledgements

Authors would like to thank Ms Giuliana Laureti of CheeseFactory San Lorenzo in Vallee Amaseno (Latinae Italy) for the kindhospitality and the willingness to provide extensive informationand testing material.

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