ES2563679B1 - Procedure and control system for sterilizing food in an autoclave - Google Patents

Abstract

Procedure and control system for food sterilization in an autoclave. The procedure comprises: # - periodically obtaining the temperature in the cold spot (T {sub, cold}) of the autoclave (1) during the sterilization process; # - periodically calculating the accumulated lethality and the remaining time (t {sub, rest }) of sterilization to meet a specific target lethality (F {sub, 0}); # - calculate, for a set of setpoint temperatures (T {sub, with}) of the autoclave (1), degradation values of at less a compound of the food to be sterilized estimated after the sterilization process; # - select, based on the estimated degradation values for the different setpoint temperatures, a setpoint temperature (T {sub, with}) in order to minimize the degradation of the food; # - apply the selected setpoint temperature (T {sub, with}) to the autoclave. # The procedure allows control of the sterilization process based on the lethality and deterioration of the organoleptic and nutritional properties of the pr product, thus minimizing product degradation.

Description

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Procedure and control system for sterilizing food in an autoclave

DESCRIPTION

Field of the invention

The present invention falls within the field of autoclave control systems.

Background of the invention

An autoclave is a sealed chamber in which temperature and pressure conditions are applied on a series of elements during a certain time. One of the applications of an autoclave is the sterilization of food containers (canned food and canned food). The sterilization process can be carried out in different ways: by direct steam, by recirculation or by foundation. Thus, there are currently different types of autoclaves: flood, static shower and rotary shower.

The sterilization process in an autoclave is usually divided into three distinct phases: heating phase, sterilization phase and cooling phase. In the heating phase the autoclave is heated until the setpoint temperature is reached. In the sterilization phase, the setpoint temperature is maintained for a sufficient sterilization time to ensure the elimination of possible pathogenic microorganisms in canned food and canned foods. In the cooling phase, the setpoint temperature is no longer applied to the autoclave and cold water is introduced until the product is cooled to a certain temperature, normally slightly higher than the ambient temperature, between 37 ° C and 43 ° C.

Pressure and temperature variables are controlled throughout the cycle by the autoclave control system, depending on an autoclave work program. The process control is usually carried out by a PLC that executes a control algorithm so that the autoclave reaches certain known and controlled theoretical conditions of pressure and temperature, by means of the adjustment and utilization of the autoclave inputs and outputs.

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The sterilization process is mainly based on three interrelated parameters: time, temperature and lethality:

• During a certain time, and reaching a certain temperature, a certain lethality is reached.

• For a certain time, for a desired lethality, a certain temperature needs to be reached.

• For a certain temperature to be reached, and a desired lethality, a certain process time is needed.

This means that:

• For a temperature, there are infinite time-lethality combinations.

• For a time, there are infinite temperature-lethality combinations.

• For lethality, there are infinite time-temperature combinations.

The process is usually defined with an achievable or known autoclave temperature and an objective lethality F0, which guarantees food safety at a certain level normally legislated and, with it, the destruction of all microorganisms harmful to humans at the end of sterilization. of the product. With these variables, only time remains as a parameter to be calculated theoretically and to be applied in the process. Lethality calculations are made taking into account normally the Clostridium Botulinum bacteria, as it is the most resistant. The reference temperature used for the Clostridium Botulinum is 121.1 ° C. During sterilization, the control program calculates the cumulative lethality, and depending on the same, widens or decreases the remaining sterilization time to meet the lethality target F0 set.

However, autoclaves are complex devices, with a multitude of physical and logical inputs and outputs, and independent subsystems, which together allow for an operation as critical as sterilization. This complexity causes a series of errors in which sterilization conditions cannot be predicted in a static way, which causes the process control to be lost. An example is that the boiler that provides water at a certain temperature is not able to provide the desired temperature at a certain time in the process.

In these cases, although the process control uses PID algorithms that adjust the inputs and outputs of the autoclave to reach the desired temperature before a large

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Parameter variability, theoretical calculations of sterilization time are no longer useful, since theoretical conditions are not maintained. At this point, the execution of the sterilization using the initial theoretical time would cause a different lethality due to the variation of the theoretical conditions.

Thus, in case the temperature of the process is different from the theoretical one, even if it is slightly, and the theoretical time is not modified, the lethality to be obtained in the process will vary inexorably. That is, in case of an unexpected variation of the temperature, it will be necessary to modify the sterilization time to obtain the initially planned lethality. This time must be communicated to the process control, so that the process is dynamically adjusted.

The autoclave performs static and dynamic calculations of sterilization time. The static calculation assumes that the process is carried out by executing a standard heating, so that the load is assumed to pass through a series of known temperatures. In a way, a prediction is made that the process will occur in that way. The dynamic calculation, on the other hand, does not make a prediction, but simply adds the cumulative lethality reached at each moment of the process. Therefore, this method is powerful in the face of temperature variations, which is precisely the scenario of heating errors. An autoclave therefore has the ability to theoretically predict the variables of a sterilization process, but it can also be adapted to variations in conditions by means of a lethality counter provided, among other things, by the autoclave sensorization analysis.

It should also be taken into account that the autoclave is a large-sized device where large loads are sterilized in each sterilization process. The loads are normally sterilizable hermetic containers, plastic containers, student bags or metal containers that are distributed in cages, trying to take advantage of the maximum amount of space, so that each process is as profitable as possible. However, not all packages receive the same amount of temperature, nor do they reach the same temperatures at the same time. It is important to emphasize that the theoretical prediction is also not fully effective with regard to the composition and density of the products to be sterilized, since many are not completely homogeneous (for example, a soup or sauce), which will sterilize in a different way.

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This causes that, for a given load configuration, there will always be a point where the temperature is lower than in the rest of the autoclave, which is called the cold point. For any calculation concerning the lethality of the sterilization, and therefore at the time of the same, this cold point is always taken as the reference, since it is considered the worst case within the autoclave.

However, although the cold point will have a certain lethality, the rest of the autoclave points will always accumulate more lethality, because its temperature is higher. This over-sterilization is one more measure of safety in a process designed to ensure the safety of food products.

With respect to the automatic autoclave control system, patent document WO0027227-A1 discloses a controller for a rotary sterilizer that allows to handle in real time the temperature deviations produced during the sterilization process. The controller uses a simulation model for finite differences to simulate the temporary variations of the cold point temperature when a temperature deviation occurs.

However, autoclave sterilization processes lead to a loss of the nutritional qualities of the product to be sterilized. First, this loss is caused by over-sterilization in the rest of the autoclave points other than the cold point. In addition, it must be taken into account that in the current autoclave sterilization processes, despite alleviating the process setbacks by means of a dynamic calculation of the time-temperature-lethality variables, not all time-temperature combinations to reach lethality are equal , since each one has a different effect on the quality of the product, and on the degradation of the compounds present in the load to be sterilized.

Therefore, in the current autoclaves the nutritional degradations of the food product derived from the sterilization itself are not taken into account at any time during the sterilization process, for example, in order to reach the target lethality, the product has deteriorated at the end of the sterilization process to an unacceptable limit.

The present invention solves this problem, allowing control in the autoclave sterilization process based not only on the marked lethality, but

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also depending on the deterioration of the organoleptic and nutritional properties of the product provided before and / or during the sterilization process, so that the food safety of the product at the end of sterilization is not compromised, thus minimizing the degradation of the products in the sterilization process.

Bibliographic references:

Ball, C. O. and Olson, F. C. W., 1957, "Sterilization in Food Technology", McGraw-Hill, London

Berteli, M.N., Berto, M.I., Vitali, A.A .: Applicability of the Ball method for or calculating the lethality of sterilization processes or steam autoclaves unwanted by water. Brazilian Journal of Food Technology vol. 16 (3), p. 243-252 (2013).

Bigelow, W. D., 1921. Logarithmic nature of thermal death time curves. Journal of Infectious Diseases 29, 528.

Cameron, M. S., Leonard, S. J. and Barrett, E. L., 1980. Effect of moderately acidic pH on heat resistance of Clostridium sporogenes spores in phosphate buffer and in buffered pea puree. Appl. Environ. Microbiol., 39, 043-949

Casolari, A., 1988. ‘Microbial Death’, in Physiological Models in Microbiology, Vol. II, p. 1-44; M.J. Basin and J.I. Prosser, Ed., CRC Press Inc., Boca Raton FL.

Casolari, A., 1994. About basic parameters of food sterilization technology. Food Microbiology 11, 75-84

Esty, J. R. & Meyer, K. F., 1922. The heat resistance of the spores of B. Botulinus and allied anaerobes. XI J. Infestious Diseases 31, 650-663

Federal Register, 1979. Part 113. Thermally processed low-acid foods packaged in hermetically sealed containers. Federal Register 44, No. 53, 16215-16238. U.S. Gov. Printing office

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Greenberg, R. A., Tompkin, R. B., Bladel, B. O., Kittaka, R. S. and Anellis, A., 1966. Incidence of mesophilic spores in raw pork, beef and chicken in processing plant in the United States and Canada. Appl. Microbiol., 14, 789-793

Holdsworth, S.D .: Optimization of Thermal Processing - A review. Journal of Food Engineering 4, p. 89-116 (1985).

Padron, J.A., Reyes, M.E., Gandolff, C.E .: Validation of sterilization cycles in autoclaves. Memories V Congress of the Cuban Society of Bioengineering, Havana, Cuba (2003).

Pflug, I. J., 1987a.Factors important in determining the heat process value, FT, for low-acid canned foods. Journal Food Protection 50 (6), 528-533

Pflug, I. J., 1987b. Calculating FT-values for heat preservation of shelf-stable, low-acid canned foods using the straight-line semilogarithmic model. J. Food Protection 50 (7), 608620

Pflug, I. J. & Odlaug, T. E., 1978. A review of z and F values used to ensure the safety of low-acid canned foods. Food Technology 32, 63-70

Stumbo, C. R., 1973, "Thermobacteriology in Food Processing", Academic Press, London

Stumbo, C. R., Purohit, K. S. and Ramakrishnan, T. V., 1975. Thermal process lethality guide for low-acid foods in metal containers. J. Food Science, 40, 1316-1323

Townsend, C. T., Esty, J. R. & Baselt, F. C., 1938. Heat resistance studies on spores of putrefactive anaerobes in relation to determination of safe processes for canned foods. Food Research 3, 323-346

Tucker, G., Featherstone, S .: Essentials of Thermal Processing. Wiley-Blackwell (2011). Description of the invention

The present invention relates to an intelligent autoclave that uses algorithms to control the sterilization cycle of products. Integrates into your control system the

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Ball method for sterilization cycle control. In addition, it uses algorithms to know the sensory and nutritional degradations derived from sterilization (in terms of vitamin degradation, and organoleptic attributes: texture, color, taste, smell, etc.).

The present invention allows a control of the sterility of the product without compromising the quality of the product before the fall of the system variables (P, T), taking into account during the sterilization the nutritional and organoleptic properties of the product, so that Your food safety is compromised. The autoclave has a control system based on neural networks that allows responding efficiently to deviations in the thermal cycle and controlling the deterioration of organoleptic and nutritional properties during the sterilization process. In this way, in the autoclave, a sterilization process time management is carried out on the one hand to a desired configuration, and on the other hand based on the actual activity of the process.

An automaton is the element in charge of taking active control of the autoclave, while a computer control system records data collected in the autoclave and processes them using intelligent algorithms that are responsible for:

- Estimation of the remaining process time, taking into account the accumulated lethality and the instantaneous temperature.

- Estimation of how various compounds are being degraded during sterilization, and how they will be terminated if the estimated remaining time is applied.

- Estimation of how the quality of the product will be at the end of the process through a neural network, taking into account the estimated time and the estimation of degradation performed.

For the realization of these estimates, and although the automaton collects dozens of autoclave data, the only parameter collected from the autoclave that is used is the temperature at the cold point. The rest of the data necessary for the estimation are stored in the computer control system after having been previously configured (data related to products, food, degradation of compounds, specific program parameters, autoclave, sensors, etc.).

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For the computer control system to change the behavior of the autoclave, you must modify some register of the automaton. The computer control system writes the estimate of the remaining time of the process in a computer register. Thus, said estimation would be used to lengthen or shorten the process. The rest of the information generated with the intelligent algorithms can be used to support decision-making by a technician, that is, it would serve as information to assess whether or not the process should be modified. Thus, the technician could also vary the setpoint temperature of the autoclave to minimize the degradation of certain compounds.

It is also contemplated that the computer control system automatically modify other parameters of the autoclave, such as the setpoint temperature of the autoclave, taking into account estimates related to degradation and product quality. Thus, for example, the computer control system can determine degradation thresholds of compounds from which it would write certain information in the automaton to modify the process in order to preserve the compounds in the desired amount. Therefore, the control system can modify not only the process time, but also the temperature or the steps to follow in the sterilization cycle. In the same way it can be done with the estimation of quality, in case the neural network predicts at some point that the quality to be achieved is not the desired one.

A first aspect of the present invention relates to a method of controlling the sterilization of food in an autoclave. The procedure includes:

- Periodically obtain the temperature in the cold spot of the autoclave during the sterilization process.

- Periodically calculate the cumulative lethality and the remaining sterilization time to meet a specific target lethality.

- Calculate, for a set of setpoint temperatures of the autoclave, degradation values of at least one compound of the food to be sterilized estimated after the sterilization process.

- Select, based on the estimated degradation values for the different setpoint temperatures, a setpoint temperature in order to minimize food degradation.

- Apply the selected setpoint temperature to the autoclave.

In a preferred embodiment the procedure comprises obtaining different proposals for

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according to the degradation, these setpoint proposals including different setpoint temperatures; show the setpoint proposals in a user interface; and select the appropriate setpoint temperature through the user interface.

In another preferred embodiment, the process comprises iterating different setpoint temperature proposals and automatically selecting the setpoint temperature that minimizes food degradation.

The method may also include predicting, by means of a prediction algorithm based on artificial intelligence and for a set of setpoint temperatures of the autoclave, the quality of the food after the sterilization process, where the estimated degradation values are input variables of the algorithm of prediction; where the selection of the setpoint temperature of the autoclave is also carried out based on the predicted quality prediction for the different setpoint temperatures. In this case, the prediction of quality can be carried out at the beginning of the sterilization process, using the Ball method for the prediction of the time used in said sterilization process, or during the sterilization process, using the temperature at the cold point.

Similarly, the calculation of the degradation values can be performed before starting the sterilization process, using the Ball method for predicting the time used in said sterilization process, or during the sterilization process, using the temperature at the cold spot

A second aspect of the present invention relates to a food sterilization control system in an autoclave. The system includes:

- A temperature probe to periodically obtain the temperature in the cold spot of the autoclave during the sterilization process.

- A first module responsible for periodically calculating the cumulative lethality and the remaining sterilization time to meet a specific target lethality.

- A second module responsible for calculating, for a set of setpoint temperatures of the autoclave, degradation values of at least one compound of the food to be sterilized estimated after the sterilization process.

The system is configured to obtain, depending on the degradation values

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Estimates for the different setpoint temperatures, a setpoint temperature in order to minimize food degradation; and apply the selected setpoint temperature to the autoclave.

The system may comprise a third module responsible for obtaining different setpoint proposals according to the degradation, said setpoint proposals including different setpoint temperatures; and a user interface to display the setpoint proposals and allow the manual selection of the appropriate setpoint temperature.

Alternatively, the system may comprise a third module responsible for iterating different setpoint temperature proposals and automatically selecting the setpoint temperature that minimizes food degradation.

The system preferably also includes a fourth module in charge of predicting, by means of a prediction algorithm based on artificial intelligence and for a set of autoclave set temperatures, the quality of the food after the sterilization process, where the estimated degradation values are variable input prediction algorithm; where the selection of the setpoint temperature of the autoclave is also carried out based on the predicted quality prediction for the different setpoint temperatures.

The fourth module may be configured to perform the quality prediction at the beginning of the sterilization process, using the Ball method for the prediction of the time used in said sterilization process, or alternatively it may be configured to perform the quality prediction during the sterilization process, using the temperature in the cold spot. The system may additionally comprise a training module of the neural network of quality prediction.

Similarly, the second module can be configured to calculate the degradation values before starting the sterilization process, using the Ball method for predicting the time used in said sterilization process; or it can be configured to calculate the degradation values during the sterilization process, using the temperature at the cold point.

The system may comprise an automaton responsible for establishing in the autoclave the

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setpoint temperature and remaining sterilization time; and a communication interface with the automaton.

Brief description of the drawings

A series of drawings that help to better understand the invention and that expressly relate to an embodiment of said invention that is presented as a non-limiting example of this is described very briefly below.

Figure 1 represents a diagram of the sterilization process control system in an autoclave according to a first preferred embodiment of the present invention.

Figure 2 shows a temperature approximation line for the remaining time prediction made with the Ball method.

Figure 3 shows a table with theoretical values of compound degradation.

Figure 4 shows a table with the amounts of compound per food.

Figures 5A-5E show different graphs of degradation prediction of

Compounds for a first reference.

Figures 6A-6E show different graphs of degradation prediction of

compounds for a second reference.

Figures 7A and 7B show, respectively, the conservation of compounds for the first and second reference, in percentage.

Figure 8 shows, by way of example, different instances of a knowledge base of the neural network for predicting product quality.

Figure 9 represents a diagram of the sterilization process control system in an autoclave according to a second embodiment.

Detailed description of the invention

Figure 1 shows a schematic diagram of the process control system of

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sterilization performed in an autoclave 1, according to a preferred embodiment of the present invention. The autoclave 1 is controlled by an automaton 2, which is responsible for establishing, among other variables, the setpoint temperature Tcon of the autoclave and the remaining time trest of sterilization.

The control system has a first module 11 in charge of calculating sterilization time, a second module 12 in charge of calculating the degradation of compounds during sterilization, a third module 13 in charge of obtaining the setpoint proposals according to degradation and a fourth Module 14 for predicting product quality through a neural network.

In addition, the control system has a communication interface 10 with the automaton 2, to configure at least the setpoint temperature parameters Tcon of the autoclave and remaining time trest of sterilization, and a user interface 16 through which the The user can interact with the control system to, for example, monitor the sterilization process, configure different parameters of the sterilization process (definition of the autoclave and automaton used, definition of the product to be sterilized, etc.) and even perform a manual control of the sterilization process (eg change of setpoint temperature Tcon or sterilization time trest).

The control system has access to a database 17 to obtain relevant information from the autoclave 1, the product to be sterilized and its compounds, information that will be used by the different modules. The control system has a module 18 for training the neural network of quality prediction (for the training of the fourth module 14).

The first module 11 of the control system performs a calculation of the sterilization time, which includes the static calculation and the dynamic calculation of the time. The sterilization process time management is done on the one hand, to a desired configuration, and on the other hand, based on the actual activity of the process. The management based on a configuration is a static management of the process time, since it is only an initial prediction based on theoretical calculations, while the management based on the activity of the process is a dynamic management, since it supposes a continuous and real-time update of the remaining sterilization time, and adapts to each specific case of sterilization based on data captured by sensors installed in the autoclave 1.

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The sterilization time management includes a set of calculations that lead the system to know at all times the time necessary to reach a certain level of lethality desired within previously configured conditions. That is, the autoclave sensorization is analyzed during the process and the results are used for decision making, which results in the modification of the process to reach the desired lethality result.

In the static management of the sterilization time a prediction of the time is assumed assuming theoretical conditions, using the Ball method. Ball's method is a simulation of what sterilization will be, based on a large set of parameters that model the behavior of the autoclave and the load during the process. The simulation calculates how the temperature in the load is increased while reaching the target lethality, and with it the time it would take to achieve it.

For the calculation of time according to the Ball method, a series of data are required:

- fh: It corresponds to the heating rate. It is the time in minutes for

that the heat penetration line goes through a logarithmic cycle. It is expressed in minutes, and is a value that differs for each reference.

- jh: Is the lag heating factor, or thermal induction time for a

specific reference.

- CUT: It is the time (in minutes) for the autoclave to reach the temperature for the

That has been scheduled. According to the original statement of the Ball method, for calculations only 42% of the CUT is added. This is the main cause of divergences between the Ball method and the general method, which begins to add the accumulated lethality from the first moment (and not from 58% of the CUT, as does the Ball method).

- Initial product temperature: Expressed in both ° C and ° F, depending

of the system, is the temperature at which the product is before starting the sterilization.

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- F0: It is the target lethality value to be reached at the end of sterilization.

- Z: Z value for the Clostridium Botulinum, which in this case is 18 ° F or 10 ° C. The

Lethality calculations are made taking into account this bacterium, as it is the most resistant.

- Reference temperature for the Clostridium Botulinum: 250 ° F is used or

121.1 ° C (its equivalence in ° C).

With this data a series of operations are carried out that lead to the time necessary for sterilization at a given reference temperature, to reach a target F0 lethality: U = F0 * 10 (Tref "Tprod) / z is the time required for , at the temperature of the treatment device, the same amount of microbial destruction is carried out, equivalent to the F value of the process.

Subsequently, the Operation Log (fh / U) is performed. If the result of fh / U is less than 0.6, the following equation is used:

Log (g) = (0.71 * (fh / U) -1) / (fh / U)

Otherwise, the following operation should be performed:

Log (g) = 0.042808 * log (fh / U) 5-0.35709 * log (fh / U) 4 + 1.1929 * log (fh / U) 3-2.1296 * log (fh / U ) 2 + 2.4847 * log (fh / U) -0.28274

With the result of Log (g) the temperature difference that should be between the autoclave and the cold point can be obtained as follows: g = 10Log (g). This result will be in ° F, which is transformed to ° C by dividing by 1.8.

The next calculation is Log (jh (T1-T0)), where T1 is the operating temperature of the autoclave and T0 is the starting temperature of the product.

To end the final equation, which indicates the process time, it is necessary to perform the following operation:

Ball time = fh * ((Log (jh (T1-T0)) - Log (g))

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With this, the process time is obtained according to Ball, to which 42% of the CUT must be added.

On the other hand, in the dynamic management of the sterilization time a sum of the accumulated lethality is made, using what is traditionally called as a general method. In the general method, a series of previous definitions are used:

- Temperature in the cold point (Cold): It is obtained by means of a specific temperature probe 3 located in said cold point, which depends on each load configuration. Although it is not a frequent practice to have said probe due to the complexity of its use and installation, it is an essential requirement to provide the necessary calculation capacity for this general method.

- Working temperature of the autoclave: The working temperature of the autoclave during sterilization.

- Z: Parameter Z corresponds to the number of degrees necessary to reduce parameter D of the compound in question in a logarithmic cycle, which means leaving parameter D in one tenth. Generally the parameter D of the bacterium Clostridium Botulinum is used, which is the cause of botulism in preserves and is the objective of sterilization. Based on this, it is used as parameter Z = 18 ° F or Z = 10 ° C (the transformation being between the Celsius and Farenheit scales of 1.8).

Once these data are available, the instant lethality is calculated using the following formula:

L = 10 (T "Tref) / z

This calculation allows to obtain the lethality for an instant of 1 minute, which will be accumulated to the one obtained in the previous sensing, thus knowing how much a product has been sterilized. Seen in another way, the remaining lethality is the objective minus the cumulative one:

F REMAINING = F OBJ - LUMPED

Knowing this data, the remaining F is obtained, which corresponds to the minutes needed to reach the desired target at the reference temperature for the

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Clostridium Botulinum, which in this case is 250 ° F or 121.1 ° C, in which a lethality of 1 is generated.

For the lethality that is currently being generated at this time at the temperature recorded in the temperature probe 3, the resulting F must be divided by lethality, obtaining the result in minutes of the time it would take for the lethality current, and knowing the accumulated L, you can reach the target F:

t = F remaining / Lactual

This gives the theoretical time necessary to reach the target F and thus complete the sterilization. If the theoretical conditions are maintained in the autoclave, the dynamic and static methods will give a very even, almost equivalent result.

The static and dynamic algorithms for calculating the sterilization time allow to know, from both points of view, the time needed to reach a certain lethality.

A sterilization can be seen as a time-temperature-lethality triangle, where setting one of the variables (for example, marking an objective lethality) there are infinite possible combinations of the other variables, time and temperature, to reach the target lethality. But not all time-temperature combinations to achieve a certain lethality are the same, since each combination has a different effect on the quality of the product and on the degradation of the compounds present in the load to be sterilized.

The sterilization of a food in an autoclave, despite being carried out in a sealed container, has irreparable consequences on the food at the biochemical level. The temperature and pressure to which it is subjected causes the different compounds that form it to degrade, becoming new compounds that modify the nutritional, physical and chemical properties.

The present invention allows to predict and measure the degradation to design the best sterilization program. For this, the scientific literature (in a theoretical way) and the analyzes developed by the producers (in a practical way) provide degradation rates of a series of compounds for a series of foods, for some

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set temperatures These degradation formulas are used to allow a better understanding of the sterilization process, no longer from the point of view of sterilization time, but from the point of view of compound degradation.

The objective is to have:

- a predictor of degradation prior to sterilization.

- a degradation counter at the end of sterilization.

- a counter + predictor hybrid during sterilization.

In this way the process is better known, allowing decision making in extreme situations. An example is a loss of temperature in the boiler during sterilization. The extra sterilization time to reach the target lethality can be known, but it is currently unknown, in real time, what effects this extra time will cause in terms of food degradation. The algorithms described below will allow it.

For the calculation of the degradation of the organic compounds of the product during a sterilization, the temperatures through which the container has passed must be known, or the temperatures that it will pass must be predicted, depending on whether an a priori or subsequent calculation of Degradation.

In the case of a priori calculation, time is predicted by the Ball method. This method, unlike the general one, predicts a time based on a hypothetical warming. Since the Ball method does not provide a measure for each unit of time (which is necessary for the calculation of the degradation), it is necessary, by means of the temperature heating line, to calculate what the temperature of the container is (at the cold point ) for a series of intermediate points that allow to calculate the degradation.

Knowing the initial temperature of the product (Ta product) and the operating temperature of the autoclave, with this it is obtained that the temperature of the container (Ta) is:

Ta = ((Ta autoclave - Ta product) / total time) x time + Ta product

With this equation of the first degree a line is obtained that simulates quite well the trajectory of the heating curve (see example of Figure 2), making the

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result between the measurement of each point and the theory do not differ much. An approximate difference between 1% and 3% has been proven, which makes the first degree equation ideal for a prediction towards degradation without an excessive processing load.

For the calculation of the degradation, as in the calculation of the sterilization time, the calculation is made by taking values per unit of time, applying a correction factor in case you would like to make measurements on a larger scale or less than what is normally used, set to 1 minute.

The formula used for the punctual survival of a compound is:

Nt = No x 10 -t / D

Where Nt is the amount of the component that survives a period of time t, N0 is the initial amount of the component before the heat treatment and D is the decimal reduction time for a Ta, which has to be recalculated to adapt it to the different temperatures through which the product passes, since a component does not behave the same at 55 ° as at 120 °.

To recalculate D at a certain temperature T the following formula is used:

Dt = Dtref x 10 (Tref-T) / Zc

In which, Dtref is the decimal reduction time that is used as a reference for that component in that food, Tref is the temperature at which Dtref is calculated, T is the new Temperature for which you want to calculate Dt and Zc is the number of degrees [° C, ° F] necessary to achieve a tenfold change in Dt values.

Adding the different surviving units for each minute, a degraded amount is obtained for the entire sterilization. By knowing the initial amount of the component in the food, the degraded percentage and, consequently, the saved percentage can be calculated.

The difference between the degradation taking as temperature data those provided by the Ball method and the general method lies in the validity of the measurements obtained.

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While the Ball method (a priori calculation) provides an estimate, the general method (a posteriori calculation) provides the actual measurements, each method being useful for different times of sterilization:

- At the beginning, using Ball, a theoretical degradation is obtained with the temperatures expected by Ball, and the remaining time of Ball.

- During sterilization, and having added the actual degradation, the remaining degradation can be estimated by the time estimated by the general method to reach the target lethality, at the current temperature.

- At the end of the sterilization, the actual degradation can be calculated by means of the sensing collected during the entire sterilization process.

Figure 3 shows by way of example a table with theoretical values of degradation of compounds (vitamin A, vitamin B1, vitamin C, catalase and chlorophyll) by food (carrot, spinach and peas) [Tucker et al].

To obtain the proportions of vitamins per food, composition tables are used per food (
www.nutriguia.com/alimentos/) in which the amount of each compound per food in mg / 100g scale is indicated, results that have been passed to mg / g and subsequently multiplied by the initial amount of each food to know the initial amount of each component. Figure 4 shows a table with the amounts of compound (vitamins A, B1, C) by food from the example of Figure 3.

Below is an example of prediction of degradation of compounds using the Ball method for an autoclave temperature range and for a first reference. For the tests with this first reference the following data have been used:

fh = 149.94887 min. jh = 1,94059 Fo = 6.5

Taautoclave = It is the temperature of the range, between 105 ° C and 130 ° C CUT = 23 min.

Z = 10 ° C

Initial T Product = 55.64 C

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The foods that have been used are carrots and peas, which exist in a proportion of 5% in a can of meatballs taken as a reference, which means that there are 21.5 g per can.

The concentrations of the compounds for this assumption are the following:

• Vitamin B1 in Peas = 0.074375mg

• Vitamin B1 in Carrot = 0.01.625mg

• Vitamin C in peas = 5.7375mg

• Vitamin A in Carrot = 0.1581 mg

Having these values and making the calculations detailed above, the following graphs were obtained for the first reference:

- Time-temperature graph (Figure 5A): This graph shows that the more temperature you enter, the less time is needed to reach the target lethality of 6.5.

- Graph of the resulting amount of vitamin B1 depending on the temperature of the autoclave (Figure 5B): This graph presents, for a series of simulated programs whose temperature is simply indicated on the x-axis, the value of vitamin B1 remaining for each of the programs (y axis, in mg). It is appreciated that there is an optimum around 112 ° C in which the amount of conserved B1 is maximized. At very high temperatures the compound degrades excessively.

- Graphs of the resulting amount of vitamin A (Figure 5C), vitamin C (Figure 5D) and chlorophyll in% (Figure 5E) depending on the temperature: These three graphs indicate the same behavior for vitamin A, vitamin C and chlorophyll for the same programs, verifying that for each of them a different degradation is reproduced due to the indices of each component, its presence in the reference foods, and the quantities of the foods in said reference.

To verify the success of the use of the line in the prediction of the temperatures through which the program subsequently passes, the final results are verified with a certain temperature used in the known data of a real test in an autoclave:

• Vitamin B1 in peas remains at 57.73% in the calculation of the general method, while in the theoretical line the result is 56.252%.

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• Vitamin A in carrots is maintained 42.44% with the general method, while the theoretical line indicates 39.759%.

• Vitamin B1 in carrots remains 81.15% in the general method, while 79.279% is maintained in the theoretical line

• Vitamin C in peas is maintained at 94.78% in the general method, while in the theoretical line it is maintained at 93.83%

Next, another example of prediction of compound degradation is performed using the Ball method for an autoclave temperature range and for a second reference. For the tests with this second reference the following data have been used:

fh = 166.6666667min. jh = 1,978206679 F0 = 6.5

Taautoclave = It is the temperature of the range, between 105 ° C and 130 ° C CUT = 23 min.

Z = 10 ° C

Initial T Product = 55.64 C

The foods that have been used are carrots, spinach and peas that exist in a fictitious proportion in a can of food, which means that there is 35.0 g per can of spinach and carrots and 27.0 g of peas.

The concentrations of the compounds for this assumption are the following:

• Vitamin B1 in Peas = 0.0945 mg

• Vitamin B1 in Carrot = 0.0175 mg

• Vitamin B1 in Spinach = 0.028 mg

• Vitamin C in Peas = 7.29 mg

• Vitamin C in Spinach = 10.5 mg

• Vitamin A in Carrot = 0.259 mg

With these values the following graphs were obtained for the second reference:

- Time-temperature graph (Figure 6A).

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- Graphs of the resulting amount of vitamin B1 (Figure 6B), A (Figure 6C), vitamin C (Figure 6D) and chlorophyll in% (Figure 6E) depending on the temperature. In these results, catalase has been omitted because its value always gives 0, since it is very little resistant to temperature.

For both references, global degradation graphs have been generated (Figure 7A for the first reference and Figure 7B for the second reference), which allow to visualize all the compounds at the same time, so that it is easier to choose the most appropriate program (that is , the autoclave temperature) depending on the target compounds to be maximized. In this case, and to allow consistent results, all compounds are indicated in percent conserved, the place of mg. preserved. In these figures it can be seen that the degradation of global compounds for the second reference is more chaotic than in the first reference.

Before starting the sterilization process, the technician in charge of selecting the autoclave program is able to obtain the prediction of degradation of the product compounds or reference to be sterilized for different programs and, once obtained, evaluate which is the autoclave program optimal to minimize the degradation of the compounds. For this, the user must select, through user interface 16:

- A specific reference preloaded in the system. The reference consists of foods, said compound foods, and said compounds of a degradation rate at certain temperatures. All this information (available references, foods included in each reference, compounds included in each food, degradation index of the compounds at certain temperatures) can be obtained by accessing database 17, where it is stored.

- A target F0 in sterilization.

- A range of temperatures to simulate (initial, final temperature and interval).

For each F0-Temperature combination, the first module 11 calculates the corresponding Ball time. Taking this time into account, and the compounds present in the selected reference, the second module 12 calculates how much each of the compounds will be degraded for each program.

The third module 13 obtains the setpoint proposals according to the degradation. To facilitate the user to visualize the different setpoint proposals, the third module 13

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it presents in the user interface 16 an orderable table by columns, presentable by percentage conserved or by milligrams conserved, indicating the maximum and the minimum of each compound, and highlighting the program that has more conserved said compound. On the other hand, the visualization in the user interface 16 of a series of global graphs and by compound is also enabled to facilitate the analysis of the data to the technician, so that it is easily observable to determine which program is the most appropriate to maximize a given compound.

The technician in charge of selecting the autoclave program (either before starting the sterilization or during the sterilization process itself), is able to evaluate all the information displayed in the user interface 16 to determine the optimal autoclave program (for example, set the temperature setpoint Tcon of the autoclave) that produces less aggressive degradation in the reference foods to be sterilized. Thus, the technician can select a specific program at the beginning of sterilization, based on the degradation predictions shown. But you can also modify the autoclave program during the sterilization process, depending on the evolution of the degradation of compounds that have occurred (which will be evaluated based on the temperature records at the cold point Tfrio) and the estimated degradation for the remaining time.

On the other hand, the fourth module 14 allows to obtain a prediction of the quality of the product using a neural network. This module provides valuable information that supports the technician during sterilization for the decision making of the autoclave program selection.

During sterilization, the autoclave sensing is recorded periodically (temperature in the cold point Tfrio according to the measurements of the temperature probe 3), and the modules perform different tasks each time such data is received:

- As for the remaining sterilization time, the target F0 to be reached is known, so it is possible to calculate the accumulated F0 by reading the temperature at the cold point Tfrio. Therefore, the first module 11 calculates the remaining F0 and estimates, depending on the instantaneous temperature, the recommended remaining time to complete the process.

- As for the degradation, an objective degradation is known for several compounds (that is, the expected degradation for a given compound as

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result of the simulation of the a priori program) and how these are degraded depending on the temperature (values stored in database 17). Therefore, the second module 12 calculates the accumulation of degradation, and taking into account the newly recommended remaining time, it is predicted how they would degrade at the instant temperature during said recommended time, that is, its final state after sterilization.

- Regarding the quality of the product, quality is defined as a unique index of organoleptic quality obtained by a weighted average of the different assessments made in organoleptic tastings, scoring (for example, from 1 to 10, from 1 to 5, of very bad to very good, etc.) different attributes such as color, aroma, flavor and texture. In each case, and depending on the type of product, a given attribute will be given more weight / value (for example in a pate 30% texture, 20% color and 50% flavor). The fourth module 14 periodically predicts after each temperature reading in the cold point Tfrio what quality will be obtained at the end of the sterilization, by means of a prediction based on pattern recognition supervised by artificial intelligence, whose input is the final degradation values calculated in every moment.

With this data, the process technician has data during the process to assess the actions to be performed, especially how to react to a process deviation, by means of the variation in the sterilization parameters, or to assess that it is worth discarding the load , in case it does not meet a certain minimum level of quality.

A prediction system based on pattern recognition allows, from a knowledge base formed by representative data, to make predictions about new cases, by comparing them with the knowledge base data.

The objective pursued with the creation of a knowledge base is the realization of data mining on a known data set to extract information, and finally feed artificial intelligence algorithms (classifier / predictor), for example a neural network. For this, the knowledge base collects representative knowledge through a set of characteristics. The occurrences or instances are labeled, so that the "class" or type of each is known. This is known as supervised pattern recognition.

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Subsequently, the fourth module 14 uses prediction algorithms that, through cross-validation procedures based on knowledge, maximize the percentage of success and correlation with the knowledge base.

Once developed, the predictor receives an unknown occurrence, represented by the same characteristics as the occurrences of the knowledge base (or a subset of them), so that the predictor predicts the class or type in which it best fits.

The following parameters are defined in the prediction system:

- The knowledge base. It is constructed from known data, in which input and output data are known. For this, various tests are carried out in which the same product is sterilized at different temperatures, reaching different F0. For each case, and after sterilization, the degradation index of a series of compounds is analyzed, and an expert labels the quality of each sample using an organoleptic quality index.

- The instance. An instance is each case of the knowledge base. The instance is formed by a vector of characteristics, which define the input data, and a class, which is the output data. In this system, the feature vector is the result of degradation of a series of compounds, as well as the temperature T reached during sterilization, the process time t and the F0 reached.

- The class, that is, the result of the prediction. The class is the organoleptic quality index, which is a discrete value that can be numeric (quantitative from 1 to 10) or textual (qualitative: "I like it" - "I don't like it"; "Good" - "Bad")

- The classification algorithm. It is an artificial neural network (RNA), which is one of the most suitable predictors to represent expert knowledge whose abstraction is direct to represent in rules. A neural network is an algorithm that simulates the behavior of the human brain, by modeling a series of interconnected neurons, there is a weight in each interconnection, in which knowledge falls on the weights between neurons.

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Through the presentation of known cases (knowledge base), the network learns (modifies the weights between the interconnections), so that the output neuron or neurons provides the expected result. In this way, module 18 of training of the neural network is responsible for training or preparing the network so that, in the face of unknown new instances, a result consistent with the knowledge base data will return.

The knowledge base is created from tests with the following characteristics: on the same reference-product, on the same autoclave, using the same placement of containers in the autoclave, using the same sterilization technique, and with time conditions -temperature that vary as much as possible to cause as different as possible outputs of degradation, and therefore of quality. This shows the artificial neural network the extremes to predict. In addition, quality must be evaluated for all processes by the same expert, under the same conditions, knowing the minimum amount of process data to avoid its influence. This expert is representative, with the criteria that you want to enter in the system predictor.

As an example, there is a knowledge base formed by known cases with the form illustrated in the table in Figure 8. Each row represents a different instance. At every moment of the process, and after calculating the remaining time (first module 11) and degradation (second module 12), there are new, unknown data that need to be analyzed:

 Vit A

 Vit C Omega3 Catalase T t Fo

 66.6

 12.8 49.2 91.1 118.8 133.5 7.1

These data are presented to the neural network (fourth module 14), which predicts the corresponding class:

Class

Evil

This information is presented to the technician (through user interface 16) as support in

the decision making of the selection of the autoclave program, and is registered for

subsequent analysis. Thus, the technician has not only information related to the degradation of

compounds obtained by the second module 12, but also information related to the

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prediction of product quality obtained by the fourth module 14, which will be used for decision making.

The neural network captures expert knowledge in a software algorithm, so that it is possible to use that knowledge unattended and in real time, to provide information during the sterilization process. This information is used:

- before the process, to predict the quality of each sterilization program

- during the process, to predict the quality at the end of the process, at every moment.

- after the process, to record the quality at the end, so that it is possible to trace that information after the product is shipped.

During the sterilization process the control system therefore measures lethality, degradation and quality. The control system can modify in autoclave 1, through the automaton 2, not only the remaining time but also the temperature to be applied. A possible realization for the automatic calculation of the remaining time and the temperature to be applied according to the lethality, degradation and quality measurements is explained below.

For each concept to be monitored, a metric is created:

Metric = Prediction - Objective

Thus, for lethality it must be fulfilled that the predicted F0 is the same as the objective. That is to say:

Metric = F0 predicted - F0 target = 0.

This metric is used for different variables or "excesses":

• excess_degradation = predicted degradation - objective degradation (for example, a degradation of 90% is desired and a degradation of 92% is predicted, resulting in a 2% excess degradation)

• excess_quality = objective quality - predicted quality (for example, we want to obtain a quality of 7 out of 10 and it is estimated to obtain a quality of 5.5 out of 10, which means we have a 1.5 excess; Quality is calculated backwards, objective - prediction, so that a good grade is negative).

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• excess_letality = predicted F0 - target F0 (for example, you want to sterilize at 6.5 but predicted to sterilize at 7.2, which means you have a 0.7 excess)

With respect to the variable excess_degradation, obviously there is not a single excess of degradation, but several (since there are several compounds), but a similar function can be implemented that combines the various compounds, so that a single excess degradation is obtained. For example, in case three compounds are monitored in a program (B1, C and catalase) obtained from several foods of the reference to be sterilized (it is indifferent that it comes from several foods; finally we have three degradation counters of the three compounds) , there would be 3 excess_degradation of each of the compounds (excess_degradation = predicted degradation - objective degradation) and a function that combines them with the style (where wi are the weights given to the different excesses of degradation for the different compounds):

excess_degradation = I diwdi = excess_degradation0 • w0 + ... + excess_degradation

Wn

And, for example, in that case it could be:

excess_degradation = excess_B1 • 0.6 + excess_C • 0.3 + excess_catalase • 0.1

In this case it is assumed that the degradation of vitamin B1 is much more important than the others. Each program is executed on a reference (product), which already uses certain compounds, so it is expected that the program itself defines these important weights of each of the compounds.

At the temperature at each moment, and for a certain remaining process time, the control system calculates a function (where wi are the weights given to the different excesses):

total_excess = I xiwi = excess_degradation • w1 + excess_quality • w2

The control system is responsible for minimizing this function, with the additional condition that:

excess lethality = 0

The objective is to iterate various temperatures to apply until you find one that minimizes the total excess_function. For each temperature to be iterated Ti, the remaining time ti that satisfies the lethality condition is calculated (excess_ lethality = 0). From these values (Ti, ti) the different degradations of the products and the final quality of the product 5 can be estimated and, from them, the value of the variable excess_total. The temperature for which said total_excess variable has the lowest value will be the selected setpoint temperature Tcon. The control system will write the remaining time trest and the setpoint temperature Tcon selected in the autoclave 2 automaton 2.

10 Figure 9 shows a schematic diagram of the control system, where the third module 13 is responsible for iterating the different setpoint temperature proposals Tcon and selecting the one that minimizes the total_excess variable. Therefore, the setpoint temperature Tcon can be set manually by the operator through the user interface 16 (Figure 1) or automatically by the third module 13 of the control system (Figure 9).

Claims (17)

5 10 fifteen twenty 25 30 35 1. Procedure for controlling the sterilization of food in an autoclave, characterized in that it comprises: - periodically obtain the temperature in the cold spot (Cold) of the autoclave (1) during the sterilization process; - periodically calculate the cumulative lethality and the remaining time (trest) of sterilization to meet a certain target lethality (F0); - calculate, for a set of setpoint temperatures (Tcon) of the autoclave (1), degradation values of at least one compound of the food to be sterilized estimated after the sterilization process; - select, based on the estimated degradation values for the different setpoint temperatures, a setpoint temperature (Tcon) in order to minimize food degradation; - apply the selected setpoint temperature (Tcon) to the autoclave. 2. Method according to claim 1, characterized in that it comprises: - obtain different setpoint proposals according to the degradation, said setpoint proposals including different setpoint temperatures (Tcon); - show the setpoint proposals in a user interface (16); - select the appropriate setpoint temperature (Tcon) through the user interface (16). 3. Method according to claim 1, characterized in that it comprises iterating different setpoint temperature proposals (Tcon) and automatically selecting the setpoint temperature (Tcon) that minimizes food degradation. 4. Method according to any of the preceding claims, characterized in that it comprises predicting, by means of a prediction algorithm based on artificial intelligence and for a set of set temperatures (Tcon) of the autoclave (1), the quality of the food after the process of sterilization, where the estimated degradation values are input variables of the prediction algorithm; and because the selection of the setpoint temperature (Tcon) of the autoclave is also carried out based on the predicted quality prediction for the different setpoint temperatures. 5 10 fifteen twenty 25 30 35 5. Method according to claim 4, characterized in that the prediction of quality is made at the start of the sterilization process, using the Ball method for the prediction of the time used in said sterilization process. 6. Method according to claim 4, characterized in that the quality prediction is made during the sterilization process, using the temperature at the point fno (cold). 7. Method according to any of the preceding claims, characterized in that the calculation of the degradation values is carried out before starting the sterilization process, using the Ball method for predicting the time used in said sterilization process. Method according to any one of claims 1 to 6, characterized in that the calculation of the degradation values is carried out during the sterilization process, using the temperature in the cold point (Cold). 9. Food sterilization control system in an autoclave, characterized in that it comprises: - a temperature probe (3) to periodically obtain the temperature in the cold point (Cold) of the autoclave (1) during the sterilization process; - a first module (11) responsible for periodically calculating the cumulative lethality and the remaining time (trest) of sterilization to meet a certain target lethality (Fo); - a second module (12) responsible for calculating, for a set of setpoint temperatures (Tcon) of the autoclave (1), degradation values of at least one compound of the food to be sterilized estimated after the sterilization process; and because the system is configured to obtain, based on the estimated degradation values for the different setpoint temperatures, a setpoint temperature (Tcon) in order to minimize the degradation of the food; and apply the selected setpoint temperature (Tcon) to the autoclave. 10. System according to claim 9, characterized in that it comprises: - a third module (13) responsible for obtaining different setpoint proposals according to the degradation, said setpoint proposals including different temperatures of 5 10 fifteen twenty 25 30 35 setpoint (Ton); - a user interface (16) to display the setpoint proposals and allow the manual selection of the appropriate setpoint temperature (Tcon). 11 System according to claim 9, characterized in that it comprises a third module (13) responsible for iterating different setpoint temperature proposals (Tcon) and automatically selecting the setpoint temperature (Tcon) that minimizes food degradation. 12. System according to any of claims 9 to 11, characterized in that the system additionally comprises a fourth module (14) responsible for predicting, by means of a prediction algorithm based on artificial intelligence and for a set of setpoint temperatures of the autoclave (1 ), the quality of the food after the sterilization process, where the estimated degradation values are input variables of the prediction algorithm; where the selection of the setpoint temperature (Tcon) of the autoclave is also carried out based on the predicted quality prediction for the different setpoint temperatures. 13. System according to claim 12, characterized in that the fourth module (14) is configured to perform the prediction of quality at the start of the sterilization process, using the Ball method for predicting the time used in said sterilization process. 14. System according to claim 12, characterized in that the fourth module (14) is configured to perform the prediction of the quality during the sterilization process, using the temperature in the cold point (Cold). 15. System according to any of claims 12 to 14, characterized in that the system additionally comprises a module (18) for training the neural network of quality prediction. 16. System according to any of claims 9 to 15, characterized in that the second module (12) is configured to calculate the degradation values before starting the sterilization process, using the Ball method for the prediction of time used in said sterilization process. 17. System according to any of claims 9 to 15, characterized in that the second module (12) is configured to calculate the degradation values during the sterilization process, using the temperature at the cold point (Cold). 5 18. System according to any one of claims 9 to 17, characterized in that it comprises an automaton (2) responsible for establishing in the autoclave (1) the setpoint temperature (Tcon) and the remaining time (trest) of sterilization; and a communication interface (10) with the automaton (2). 10