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.
A RFID web-based infotracing system for the artisanal Italian cheese quality traceability
A RFID web-based infotracing system for the artisanal Italian cheese quality
Patrizia Papetti a
, Corrado Costa b,*, Francesca Antonucci b
, Simone Figorilli b
, Silvia Solaini b
Paolo Menesatti b
Department of Economics, University of Cassino, Via Marconi 10, 03043 Cassino (FR), Italy
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
Received 7 February 2012
Received in revised form
16 March 2012
Accepted 24 March 2012
Tracing web-based architecture
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.
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: email@example.com (C. Costa).
Contents lists available at SciVerse ScienceDirect
journal homepage: www.elsevier.com/locate/foodcont
0956-7135/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved.
Food Control 27 (2012) 234e241
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
and an optical ﬁbre bundle consisting of approx. 30 quartz glass; (2)
an optical entrance with input round: 70 mm Â 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
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,
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
List of pre-processing algorithms applied on PLS and PLSDA dataset.
Abs Takes the absolute value of the data
Autoscale Centres columns to zero mean and scales to
Baseline Baseline (weighted least squares)
Centring Multiway centre
Detrend Remove a linear trend
Diff1 Differences between adjacent variables (approximate
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
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
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,
The proposed system is divided in 4 phases (Fig. 2):
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.
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
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
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).
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
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).
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
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
2-Way ANOVA results Month Signiﬁcance Position
External Median Internal Mean
Source d. f. Mean sq. Prob F a a a
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
2-Way ANOVA results Month Signiﬁcance Position
External Median Internal Mean
Source d. f. Mean sq. Prob F a b b
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
2-Way ANOVA results Month Signiﬁcance Position
External Median Internal Mean
Source d. f. Mean sq. Prob F a b ab
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
P. Papetti et al. / Food Control 27 (2012) 234e241238
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.
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
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)
LV 15 12 19 7
Baseline sg Diff1 sg
Pre-processing Y-block Median
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
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
% 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
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
- 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
- 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.
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.).
Authors would like to thank Ms Giuliana Laureti of Cheese
Factory San Lorenzo in Valle e Amaseno (Latina e Italy) for the kind
hospitality and the willingness to provide extensive information
and testing material.
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