EP1971840A2

EP1971840A2 - Device for monitoring a product degradation

Info

Publication number: EP1971840A2
Application number: EP06847160A
Authority: EP
Inventors: Renaud Vaillant
Original assignee: Cryolog SA
Current assignee: Cryolog SA
Priority date: 2005-12-28
Filing date: 2006-12-28
Publication date: 2008-09-24
Also published as: WO2007074247A2; WO2007074247A3; AU2006329759A1; CN101336367A; BRPI0620936A2; US20090222235A1; JP2009522545A

Abstract

The invention relates to a device (10) for monitoring a perishable product degradation which is arrangeable near said product and comprises a time measuring module (12) such as a clock and at least one sensor (12) for measuring at least one product intrinsic variable representative for the product storage conditions, such as a temperature, moisture degree, atmosphere composition, a program memory (16) for recording a program displaying a degradation model specific for the monitored product, a processor (18) for calculating, by using a program representing a product time-dependent degradation and the intrinsic variable values measured by the sensor, a data memory for storing the product intrinsic parameters such as the pH and/or texture, and/or water activity, and/or organic acid content, and/or heat transfer factor, and/or content of limiting floras, and/or enzymatic degradation products, and/or redox potential, wherein the evolution of said intrinsic parameters is taken into account in the degradation model in such a way that the degradation calculation carried out by the processor is dependent only on the intrinsic variable and the time.

Description

DEVICE FOR MONITORING THE DEGRADATION OF A PRODUCT

The present invention relates to a device for monitoring the degradation of a product, in particular perishable products such as foods.

In the agri-food industry, and more particularly in the field of fresh and frozen products, the monitoring of the respect of the cold chain is essential for food safety. For a long time, manufacturers have been required by law to include on the packaging of many products a deadline for consumption (D.L.C). These dates are determined under the responsibility of the industrialists with more or less important technical margins to take into account the disparities of the conditions of conservation according to the different pathways of the product of the manufacture to the place of consumption. The DLCs are thus determined according to theoretical conditions of product conservation and therefore do not take into account the actual state of degradation of each product.

As a result, the product status information provided by the DLC is almost always wrong. Indeed, if the actual conditions of conservation have been optimal, the product will be able to be consumed although the DLC is exceeded. Conversely, if the actual conditions of conservation have been worse than the theoretical conditions for determining the DLC, then the product will no longer be able to be consumed although the DLC has not yet been reached.

It is therefore interesting for both manufacturers and consumers to be able to take into account the actual state of degradation of each product. This eliminates the risks for both the industrialist and the consumer. Indeed, for manufacturers, knowing the actual state of degradation of a product makes it possible to simplify the logistics management of the products conveyed, in particular the transfer of responsibility between its distributor and him.

For the consumer, it avoids any health risk due to the consumption of an unfit product, because of conditions of conservation more unfavorable than those envisaged within the framework of the determination of the limit date of consumption of the products concerned.

To be able to precisely know the state of degradation of a fresh product, a method consists of measuring the temperature and the time so as to obtain the history of temperature variations over time. It is imperative to monitor these two quantities because, if the cold chain is broken between manufacturing and consumption, it must be possible to assess the importance of any exceeding of the maximum permitted temperature, as well as the duration of this break. Knowing this history, it is then possible to determine, according to each product, using calculation models derived from the predictive microbiology, whether the product is able to be consumed or not.

However, if it is easy to envisage knowing the temperature history of a storage warehouse, it is technically more difficult when it is desired to monitor products in a completely individual way, or a set of identical packaged products. together (eg on a pallet), especially that an individual monitoring device must involve a very low additional cost compared to the product being monitored. It is known from the international application WO2005 / 106813 a compact monitoring device, in the form of an "RFID tag" intended to be fixed on the packaging of a perishable product, and to trace the history temperature over time. Such a device is equipped with a calculation function, making it possible to send back information relating to the state of freshness of the monitored product from the temperature history. The device described in the aforementioned patent proposes to use calculation methods based on the Arrhenius model that is not adaptable finely to each product and, in addition, requires significant computing power.

However, in order to minimize the energy required by such a device and its cost, it is advantageous to use a calculation method allowing the best compromise between the relevance of the result obtained and the computing power required.

Thus, the invention relates to a device for monitoring the degradation of a perishable product, this device being intended to be arranged close to the product, this device comprising:

a time measuring module, such as a clock, and at least one sensor measuring at least one extrinsic variable of the product representative of the storage conditions of this product, such as the temperature, the hygrometric degree, the composition of the atmosphere,

a program memory for storing a program representing a degradation model specific to the monitored product; a processor calculating, by the implementation of the program representing the degradation model, the state of degradation of the product as a function of time and values; the extrinsic variable measured by the sensor,

a data memory for storing intrinsic parameters of the product, the intrinsic parameters of the product being its pH, and / or its texture, and / or its activity in water, and / or the amount of organic acid it contains, and / or its heat transfer coefficient, and / or the limiting flora that it contains, and / or the enzymatic degradation products, and / or the redox potential, the evolution of the intrinsic parameters being taken into account in the degradation model, so that the calculation of the degradation carried out by the processor is a function of only the extrinsic variables and time. Thus, a very fine surveillance is carried out because totally adapted to the product by taking into account the various intrinsic parameters, while using a model involving simple calculations and thus requiring a relatively low computing power. In one embodiment, the program memory can store one or more additional programs.

In one embodiment, the program memory stores a measurement management program.

In one embodiment, the measurement management program determines the frequency of measurements of the extrinsic variable.

In one embodiment, if, between two measurements, the variation of the extrinsic variable is less than a predetermined threshold, the measurement management program determines a lower measurement frequency and / or instructs the processor not to perform a new calculation of the state of degradation.

In one embodiment, the device comprises radiofrequency type communication means.

In one embodiment, the device provides, in response to the interrogation of a suitable reader, a signal representing information relating to the state of degradation of the product.

In one embodiment, the information provided further comprises: an identifier of the product, and / or a measured use-by date, and / or the differential between the measured use-by date and the theoretical expiry date.

In one embodiment, the information provided includes the history of variations of the extrinsic variable since the start of product monitoring.

In one embodiment, the device includes a rechargeable battery for powering the processor (18) and / or the program memory (16) and / or the data memory (20).

In one embodiment, the battery can be recharged during use of the device (10).

In one embodiment, the device is reusable after consumption or degradation of the monitored product.

A detailed example of an embodiment of the invention is described below, in connection with the figures, among which:

- Figure 1 shows a primary model of the growth of a microorganism;

FIGS. 2 and 3 show a cardinal model as a function of temperature; FIGS. 4 and 5 represent different evolutions of the population of a microorganism modeled according to the cardinal model of FIG. 3;

FIG. 6 represents a device according to the invention; FIG. 7 represents the internal architecture of the device according to the invention.

The state of degradation of a perishable product, especially food products is mainly related to the presence and development of microorganisms, whether pathogenic or alteration. To know the state of degradation of a food product, it is sufficient to determine which is the limiting flora (s), that is to say the microorganism (s) whose quantity and / or or the growth will have a preponderant action on the degradation of the product among all the microorganisms contained in the product. A Once it has been determined which microorganisms have a preponderant influence, it is sufficient to know their respective quantities in order to deduce the state of degradation.

Thus, for each type of microorganism is set a maximum threshold beyond which the product is considered no longer consumable. The prediction of the degradation of a product therefore consists of modeling the population evolution of each limiting flora contained in the product.

A first approach to this modeling, shown in Figure 1, is a model of primary type that can determine the growth of a bacterium at constant temperature, pH and water activity. Figure 1 shows the evolution of the population of the bacteria as a function of time. This evolution is represented on the one hand by the curve obtained by means of the model, and on the other hand by point values obtained experimentally.

The model used in this case is represented by the following equation:

if t <= a lag <& * = n 0 di

In which :

N = number of cells Nmax = maximum number of cells μ = maximum specific growth rate The advantage of this model is to clearly visualize the 3 successive phases of microbial development: lag phase (delay), growth phase (after rupture ), stationary phase (plateau). However, it can not be an interesting forecasting model since it takes into account only one factor, time. To describe a growth linked to more than two factors, we use models, called secondary models. These make it possible to precisely evaluate the degradation of a product by describing the evolution of the parameters of the primary models (latency time, maximum growth rate, maximum cell concentration), with respect to the environmental conditions, represented by the intrinsic parameters defined above.

Among these secondary models, polynomial models and cardinal models are distinguished.

In the case of a polynomial model, growth is defined by an equation of the following form:

Growth = ax + by + cz + dx ² + eY ² + .... + Fz ⁿ , where x, y, ... z are environmental factors. Polynomial models provide acceptable forecasts in the field in which they are established. The cardinal models are based on the cardinal values of the parameters influencing the growth of the microorganisms considered, in particular the cardinal values of temperatures (T _1111n , T _opt , TW _x ), of pH (pH _1111n , pH _op t, pH _ma χ), of activity in water (aw), etc. For example, the "CTMI" model, represented in the figure

2, (Model of Cardinal Temperatures with Inflection Point) expresses the rate of growth as a function of temperature: U _m a _x = maximum growth rate

U _opt = growth rate under optimum conditions, that is to say under the conditions favoring the maximum development of microorganisms.

T _m1n = low temperature limit at which growth is observed. Below this temperature, growth is zero T _max = high temperature limit at which growth is observed. Above this temperature, growth is zero.

T _op t = temperature at which growth is maximal. In these models, the cardinal values of temperature, pH, etc. are specific for a species of microorganism, or even a strain.

These models give a good precision of adjustment, for calculations of a relative simplicity. They also have the advantage of an obvious biological significance of the parameters (temperature, pH, aw ...). Finally, they are evolutionary models, thus presenting wide possibilities for improving forecasts.

An example illustrating the impact of cardinal values on growth simulations is described below. This is to predict the growth of a Listeria, whose cardinal temperatures are 45 ⁰ C, 1 ° C, and 33 ⁰ C. Figure 3 shows the calculation of the growth rates forecast by the model according to the temperatures, and FIG. 4 shows the evolution of the microbial population obtained respectively at the temperature of 10 ° C. for curve 42, and at the temperature of 12 ^° C. for curve 44, over a period of 200 hours. FIG. 5 also shows the evolution of the microbial population obtained respectively at the temperature of 10 ^° C. for the curve 52, and at the temperature of 8 ° C. for the curve 54. The following values are obtained here:

T _m in = low temperature limit at which growth is observed; in the example, T _nUn = 1 ° C

T _m ax = high temperature limit at which growth is observed; T _m3x = 45 ⁰ C

T _O pt = temperature at which growth is maximal; T _opt = 33 ⁰ C

By modifying the storage temperature by plus or minus 20 ^° C., the growth estimate at 4 days is decreased (curve 44, FIG. 4) or increased (curve 54, FIG. 5) by a power of 10 (1 log ).

As previously described, the first calculations use primary models: estimation of growth rate and latency (model proposed by ROSSO). Secondary models then make it possible to integrate the effects of the environment on the parameters of the primary models. Secondary models are polynomial or modular models; polynomial models are not very extrapolable and, in food, modular models are more widely used. The effects taken into account by these models are: temperature, pH and organic acids, water activity, inhibitors. Each of these factors is described by a function, to which is added an interaction function between these factors. In addition, the characteristics of the food are taken into account by the optimal growth rate of the microorganism in the food (challenge tests are conducted for this). Finally, the growth rate of a microorganism in a food depends on 5 factors and its optimal growth rate in this food:

M _O pt'it 'IfpH i aw'I AH Vim: t Thus, predict the development of a microorganism in a food, requires knowledge: specific growth characteristics microorganism parameters: temperature, pH and a _w cardinal and MICs of inhibitors or organic acids; - the characteristics of the food / microorganism pair: optimum growth rate, minimum latency time and maximum population; the environmental factors of the microorganism in the food, three intrinsic factors (pH, a _w and organic acid) and a single extrinsic factor: temperature.

For use in the device according to the invention of the calculations presented here, it is not necessary to embark the entire database in the chip but a limited number of data, which reduces the power of necessary calculation. To simplify the calculation, one can ignore the confidence intervals, for example by systematically taking the worst case. The calculation can be incremented as the temperature is taken (it must not be redone completely at each take).

The cardinal models make it possible to take into account as many extrinsic or intrinsic variables as desired. The main extrinsic variables that influence the growth of microorganisms include:

• the storage temperature

• the hygrometric degree

• Atmospheric pressure

• the composition of the atmosphere, ie the relative content of O ₂ , CO ₂ , N ₂ , NH ₃ , ethylene

Among the intrinsic parameters whose evolution is taken into account are:

• pH

• the activity in water, or aw • the texture of the food which intervenes on several levels: diffusion, aw, thermal transfer)

• Quantity of organic acids

• Redox potential

• Enzymatic degradation products: they can correspond to degradation products relating to hydrolysis / proteolysis and aminopeptidase activities, which lead to the formation of volatile bases (including biogenic amines) and ultimately to the formation of NH ₃ . It may also be the oxidation of fat, or lipase and lipolytic activities. In more general ways, it may be possible to add concentration in substrates / metabolites and waste.

• Physiological state of the strain considered (stationary phase, lag phase, ...) • Initial microbial load • Interactions and products of interactions within and between microbial species

• Temperature gradient within the product.

An analysis of the system architecture was carried out, taking into account the product use cycle, service functions and constraints.

FIG. 6 represents a diagram of the device 10 according to the invention, in an embodiment adapted to the surveillance of fresh or frozen food products. The device 10 is in the form of a card, or chip, comprises a clock 12, a temperature sensor 14. A processor 18 makes it possible to calculate the degradation state of the monitored product, thanks to a degradation model contained in a program memory 16. This degradation model takes into account the intrinsic parameters of the product and their evolution, their values being stored in a data memory 20. Such a device is intended to be interrogated remotely by a reading device, via a device. radiofrequency communications protocol. For this purpose, the device comprises an RFID antenna.

Figure 7 shows the detailed architecture of a device according to the invention. This includes an onboard power source for powering the components of the chip. A study was carried out concerning the choice of components for the realization of the demonstrator.

On the player side, the demonstration will be based on a 15693 RFID reader.

Side chip, a module RTC + temperature sensor, reference DSl629 "digital thermometer and real time clock" was selected

For the demonstration, a memory I ² C of 512K is used for data storage.

A low-power microcontroller was chosen, implementing the calculation of the use-by date and the management of the real-time clock and the temperature sensor. Regarding the RFID interface, two solutions can be envisaged: the first is to use an RF head developed at Leti, and a remote-compatible component to be programmed to support the protocol

Base Station T ° Memory

Programmable Component (Microcontroller / FPGA) RF Head

Remote powered component RTC

Drivers (I2C, SPI, ...) Calculation Algorithm Interface Interface

RF Storage Head Driver

Time / Temperature Calculation DLC Exchange of Information RF Head

PC interface

RF Head Driver Antenna Onboard Power Source

Antenna

RFID15693. The second solution is to look for a component of the trade.

The RF head is tasked with retrieving the commands from the player and transmitting them to the microphone, which will be responsible for executing them and sending a response to the reader via the RF head. The RF exchange will follow the standard 15693

The protocol is based on a request from the reader to the chip, and a response of the chip or chips. The data transmitted between the RF head and the microcontroller may be initialization / parameterization data for the proper functioning of the device, as well as information relating to the temperature monitoring of the product.

The following describes the parameterization data that can be used:

Coefficients for the heat transfer model implanted in the chip. The heat transfer model corresponds to the temperature change inertia of a product according to parameters such as the food itself, the nature of its packaging, the safety margin requested by the customer, etc. The parameters of this heat transfer model must be loaded when the chip is activated.

- Cardinal stem values: coefficients of the equation of the prediction of microbiological evolution. These values depend on the nature of the product.

- The personalization of the chip, approaching a conventional use of traceability: loading the batch number, the product number, the theoretical DLC.

- The parameters for triggering the writing of the time / temperature pair in memory. Indeed, all the measured values are not necessarily written in memory. This may depend on the temperature delta chosen for memorization between two measurements. It is not necessary to store two identical successive values, in order to limit the memory size.

- Choice of a sampling model, which will vary the time between two measurements according to the temperature of the previous sample, the frequency having to accelerate with the approach of the critical temperatures. (Parameterizable data during the life of the product)

- Initialization of the start time of the chip. - Sensor calibration may be required. The data returned by the chip include:

- Identifier of the chip.

- The measured DLC, or residual life of the product.

- The Theoretical DLC.

- The state of the product being plotted, according to the acceptance parameter (difference between the theoretical DLC / measured DLC)

Reading data in memory (time / temperature pair).

A first card was made, including 1 microcontroller, 1 temperature sensor, 1 real time clock (RTC), 1 EEPROM memory and an RS232 link.

Firstly, all commands for controlling the card (loading parameters, start, stop, PSTN programming, etc.) are performed by serial link. A second card is in evaluation including an RFID interface

ISO15693.

The map operations are: - Calculation based on real time temperature measurements,

- Memorization of the measured temperature value only if the temperature is different from the previous sample. Test conditions: measurement frequencies programmed every 5 seconds.

The temperature monitoring is acquired in real time on the food product. From the models of predictive microbiology, and the physiological characteristics of the main bacterial species of alteration, it is possible to simulate the speed of development of microorganisms, and to deduce the remaining freshness capital. The time remaining before the consumption limit of the product is then re-estimated in real time, depending on the temperature at which the food is subjected during its storage. Mathematical models have been simplified to a minimum to reduce computing time. The calculation of the remaining freshness capital must be updated at regular intervals. To define this time interval, tests were performed on actual temperature records.

A comparative study was thus conducted to measure the impact of the time interval between two information processes (measurement of temperature followed by a re-estimation of the freshness capital). The shorter the time interval, the more reliable the overall calculation. Below is the error percentage of the different time intervals tested, compared to the 5 minute reference interval.

Time interval tested% error 5 min: 0%

30 min: 0.8%

1 h: 1.9%

2 hours: 3%

4 h: 4.8% 4 h lagged 2h: 7.7%

Two main applications are considered here: pharmaceuticals and agribusiness. We will describe below a cycle of use of the monitoring device according to the invention. Before use, it is necessary to impose a system recharge function by activating the battery. Depending on the degree of monitoring required, the chip can be placed either on a pallet of identical products, or on an intermediate packaging of such a pallet, or on each individual product, being obvious that surveillance will be most effective in the latter. case.

However, an interesting compromise is to place the chip on an intermediate packaging because they generally contain only one batch of products having the same DLC. The question then arises as to whether the temperature is homogeneous within the intermediate packaging. The temperature measured by the chip is outside the packaging: it should therefore take into account a coefficient of heat exchange between the outside and inside, including possible differences depending on the environment. This heat transfer model must also take into account the nature of the intermediate packaging (cardboard, plastic crate, polystyrene): this data must be mentioned during the activation. One can also take into account the positioning of the intermediate packaging within the pallet.

Once the chip is positioned, the first action to perform is to switch on the battery charge. The charge verification can be done by viewing on the charger in a binary mode (loaded / unloaded).

When activating the chip after the battery charge, several data are needed to customize the chip:

• identification of the product to be traced (batch number, type), • microbiological parameters of the model (cardinal values of the strain, information on heat transfers, specific product data of the pH / Aw / μ _op t type) t

• initial date of recording (absolute date supplied by the system) and sampling frequency, • calibration to be defined after n cycles.

Mathematical models that allow the calculation of degradation can be integrated during the design of the chip or during its activation. They understand :

• the model of predictive microbiology determining the DLC of the product,

• the temperature management model to speed up or slow down the sampling frequency as a function of temperature (the model will have to be set so that alerts are set up if the system is outside the desired temperatures) ). All data recorded during activation is important. For reasons of confidentiality or to compensate for the bad handling of the actors of the chain, it is necessary to manage the rights of access to this information. The memory can thus be protected after the input of the information. If mishandling makes it necessary to modify the recorded information, the only possibility to return to the original data would be as for the recycling of the chip with a complete cleaning of the data then a new recording. To facilitate handling, the write function during the use of the chip must be managed by the chip independently.

In the case where the monitoring device according to the invention is to be affixed to an intermediate product packaging within a pallet, it may be in the form of a credit card having a certain rigidity. This can be placed inside the packaging if it does not present electromagnetic discomfort, but an attachment to the outside is preferable to facilitate handling. The device must be firmly secured with a simple system to recover it for recycling. For example, the device can be slipped into a self-adhesive transparent envelope (of the type slip) and will be put in a new envelope after each recycling.

The sampling frequency must be modifiable when moving to different links in the supply chain. This parameter must be described on the chip by the various stakeholders. Access to the data contained in the chip must be controlled. The data is readable to all users but, once the activation is done, there is no possibility of external writing: only the chip uses the write function for storing temperatures. The life of the chip must be defined according to the DLC of the products to be traced. In agribusiness, the DLC of fresh products can vary from a few hours to 42 days, even 60 days. However, as the chip can be placed on the intermediate packaging, the average life would then correspond to the duration of a logistic period for this type of packaging, or 20 days. In the health sector, the life span of products can be up to 2 years: the management of energy chip can be problematic, unless the recharge of batteries could be done during storage by an antenna.

The reading of the data contained in the chip could be done by a manual gun or by passing through a gantry. The first solution is impractical in the case of reading a large number of chips. The second solution involves equipping all the docks with storage areas so that the pallets can pass through the gantries entering and leaving. In addition, the reading of the chips must be parallel to the gantry; in order to avoid having to turn the pallets on themselves to read the chips positioned perpendicularly to the gantry, it would be necessary to use 3D reading antennas.

The reading of the data must be able to provide information on: • the identifier, • the state of the products traced via simple language ("everything is fine" or "problem") by comparison between the theoretical DLC and the measured DLC,

• the measured DLC. The state of the products traced characterized by the mention

The "problem" must be set by the customer who defines the acceptable margin between the theoretical DLC and the actual tolerated DLC. In addition, if the customer needs more information, then the complete reading could be done manually.

The calculation of the measured DLC can be performed:

• in masked time during the reading of the identifier and the theoretical DLC,

• in real time at each temperature write in the chip and not at each measurement.

Indeed, to save battery, all the measures defined by the internal clock are not written in the chip. The writing can be triggered as soon as there is a significant temperature change, for example of plus or minus 0.5 ^° C.

Because of the cost of implementation, the results could be visualized on devices intended only for the pharmaceutical sector. The data could be readable directly or via a color code describing the states "everything is fine" or "problem" (this color change could be visualized for example at the level of a polymer antenna). Charging by inductive coupling (or other energies: solar) could be triggered at the time of reading. The end of the cycle of use takes place at the last link of the supply chain (the store) which will have to ensure the recycling of the chip. This last actor will have the role of stopping the recording of the data and returning the chip to the supplier of the chip. This provider will then have to retrieve the data and put it on a server accessible to the different actors in the chain. He will then reset the chip to zero and return it to the actors in the chain.

The maximum life of the chip depends on its life on the product and the number of returns to the supplier. It can be estimated at 2 years: average life on the product of 20 days with 20 to 30 cycles of recycling.

The device according to the invention has the following advantages: - STANDARD: the device communicates with its environment by radio, via the RFID standard

- PORTABILITY: the device adapts to the various packaging used on the logistics circuit.

Applied to the logistic units to be traced, and no longer in the product environment, it carries out continuous monitoring of the entire chain.

REAL TIME: The portability, the communication standard used and the precision of the analysis allow the invention to transmit in real time information on the state of conservation of the product.

Claims

1. Device (10) for monitoring the degradation of a perishable product, this device being intended to be arranged close to the product, this device comprising:

a time measuring module (12), such as a clock, and at least one sensor (14) measuring at least one extrinsic variable of the product representative of the storage conditions of this product, such as the temperature, the hygrometric degree, , the composition of the atmosphere,

a program memory (16) for storing a program representing a degradation model specific to the monitored product,

a processor (18) calculating, by the implementation of the program representing the degradation model, the state of degradation of the product as a function of time and the values of the extrinsic variable measured by the sensor,

a data memory (20) for storing intrinsic parameters of the product, the intrinsic parameters of the product being its pH, and / or its texture, and / or its activity in water, and / or the quantity of organic acid that it contains, and / or its heat transfer coefficient, and / or the limiting floras it contains, and / or the enzymatic degradation products, and / or the redox potential, the evolution of the intrinsic parameters being taken into account in the degradation model, so that the calculation of the degradation performed by the processor is a function of only the extrinsic variables and time.

Device (10) according to claim 1, wherein the program memory can store one or more additional programs.

3 Device (10) according to claim 2 the program memory stores a measurement management program.

Apparatus (10) according to claim 3, wherein the measurement management program determines the frequency of measurements of the extrinsic variable. Apparatus (10) according to claim 4, wherein, if, between two measurements, the variation of the extrinsic variable is less than a predetermined threshold, the measurement management program determines a lower measurement frequency and / or control at the same time. processor not to recalculate the state of degradation.

6 Device (10) according to one of claims 1 to 5, comprising means of radio-frequency type communication.

7 Device (10) according to claim β, providing, in response to the interrogation of a suitable reader, a signal representing information relating to the state of degradation of the product.

Apparatus (10) according to claim 7, wherein the information provided further comprises: a product identifier, and / or a measured measured consumption end date, and / or the differential between the measured use-by date and the date theoretical consumption limit.

Device (10) according to claim 7 or 8, wherein the information provided comprises the history of the variations of the extrinsic variable since the beginning of the surveillance of the product.

Device (10) according to one of the preceding claims, comprising a rechargeable battery for powering the processor (18) and / or the program memory (16) and / or the data memory (20).

Device (10) according to claim 10, wherein the battery can be recharged during use of the device (10).

12 Device (10) according to one of the preceding claims, the device being reusable after consumption or degradation of the monitored product.