Same

Technical Field

The present invention pertains to the thermodynamics of frying food items in oil. In particular, the present invention pertains to continuous monitoring of the frying process and calculating its energy balance, heat flow and thermo -physical effects in the processed food items. Accordingly, the invention provides a method and system for an automatic optimal frying process of various food items in deep oil.

Background

Typical items that can be fried are potato chips, onion rings, chicken breast, chicken wings, chicken nuggets, Tampura coating dishes, samosa dishes and many more items customarily fried in various kitchens. Typical to all these dishes is that they have a certain pre-cooking process, which they have to go through, including cooling, soaking in water or other types of liquids, and coating with flour or other mixes, before they are put into hot oil that covers the items to be fried for a pre-defined period of time. The dishes are typically considered well cooked if they do not soak too much oil, their outer part is crunchy and crispy, colored in brown, and their inner part is moist and fluffy. Of course, the inner part should also be well cooked to prevent poisoning such as with un-cooked poultry dishes. The procedure of frying in oil can go wrong very easily if the proper time or temperature are not used in the process, or if the pre-cooking process is not well monitored and kept as needed by the recipe.

It is, therefore, an object of the present invention to provide means and method for monitoring throughout the frying of food items in deep oil along the different steps, and ensure that the results are optimal and consistent regardless of the frying device that is used.

Summary

In one aspect, the present invention provides a method for monitoring the thermodynamics of frying of food items in oil. Essentially, the monitoring comprises continuously tracking the differential, instantaneous changes in temperature of the oil bath, oil medium and frying food in the oil medium in the process and visual aspects of the food surface. Combined with visual indication, sensory means for identifying thermo-physical effects and a mathematical model for calculating the instantaneous temperature and state of the food, energy balance and heat flow between the fried food and oil medium are calculated. This in turn allows evaluating and identifying the cooking state of the food, elevating or lowering the cooking temperature and terminating the frying process.

Therefore, in one particular embodiment, the present invention provides the following method for monitoring food frying in an oil medium:

- providing a food fryer and filling a container of said fryer with an oil reservoir;

- providing a scanning optical head above the oil reservoir;

- providing visible light camera and sensory means in direct communication with the fryer, particularly with the oil reservoir;

- elevating temperature of the oil reservoir and introducing food items into the oil reservoir;

- scanning and monitoring visible and sensory thermo-physical effects in the fryer, oil reservoir and food items and at an interface between the oil reservoir and outer surface of the food items;

- progressively calculating and measuring temperature, energy balance and heat flow in frying the food items;

- setting visible and numerical target values for identifying termination of frying the food items; and

- terminating frying of the food items once achieving optimal point.

In one particular embodiment, the sensing means is a sensing head which is configured to monitor temperature of the oil reservoir and food items, filling level of the oil, size, shape and estimated weight of the food items, air bubble level at the interface of the oil with the food items, time of frying and appearance of the thermo-physical effects, foam and food residues in the oil, smoke, fire and burned food pieces and post-tracking activities and temperature of serving of the fried items.

In particular, the thermo-physical effects comprise texture and color of the food items, concentration and intensity of the air bubbles and level of moisture in the food item and/or oil reservoir. In another particular embodiment, the method also comprises setting time for replacing the oil, when it is overheated or overused for a selected period of time, and accordingly replacing it.

In still another aspect, the present invention provides an apparatus for monitoring the thermodynamics of frying food items in oil medium, where the apparatus comprises thermal and visual means for measuring the temperature of the oil medium and food items, sensory means for receiving indication on thermo-physical effects of the fried food and a mathematical model for calculating the instantaneous status of the food, advancement of cooking and setting termination point of cooking.

Accordingly, in one particular embodiment, the apparatus of the present invention comprises:

- a fryer;

- scanning and monitoring means for obtaining visible and sensory thermo-physical effects of frying of food items in said fryer;

- a CPU in communication with said scanning and monitoring means for receiving real time data on said frying of said food items, calculating temperature, energy balance and heat flow in said frying, evaluating advancement of frying according to measured thermo-physical effects in said food items and oil reservoir in said fryer, terminating said frying and alerting on termination of said frying of said food items.

In still another particular embodiment, the apparatus is configured for automatic control of deep oil frying process, thereby reducing the need for human intervention in the frying processes, saving manpower and improving cooking results.

The calculating and measuring temperature, energy balance and heat flow is carried out with the set of equations as detailed in the following description.

Brief Description of the Drawings

Fig. 1 schematically illustrates a deep oil fryer of the present invention with a monitoring sensing module above it.

Fig- 2 shows a frying process in progress in a deep frying device. Fig. 3 schematically illustrates the monitoring, sensing and data processing module of the present invention.

Detailed Description of the Drawings

Fig- 1 describes the scheme of the deep oil fryer 300 of the present invention with a monitoring sensing module 100 above it. The sensing module 100 is located above the deep oil fryer 300 at any given distance or angle that suits the user. The sensing module can measure in real time very accurately the temperature of the oil as the frying process takes place. The sensing module 300 can also measure the temperature and texture of the incoming items and can verify the type of items to be cooked, before they go into the oil. While cooking, the sensing module can measure the filling level, color, texture and mixing level of the items, and measure the moisture reduction rate of the food being cooked. The CPU, 115 in Fig. 3, of the sensing module 100 processes these parameters, which allows it to calculate the core temperature of the food that is being cooked and recommend a proper end time to take out the items from the fryer in an automatic way. Once the fried items are taken out, the sensing module 100 measures the readiness level of the items and recommends further frying or alert on readiness of the items for serving. Accordingly, the apparatus of the present invention is configured for automatic control of deep oil frying processes, thereby reducing the need for human intervention in such frying processes, saving manpower and improving cooking results.

Apparatus - Sensing Parameters by the Sensing Head

There are several functions and parameters that the sensing module 100 supports, as part of the overall automatic frying system and process suggested in this application. Below are detailed the main functions: a. Temperature - the sensing module 100 is capable of measuring various real time temperatures in various locations and environments, including, but not limited to, oil temperature, oil container temperature, surrounding and ambient temperatures, precooked food temperature and temperature of the outer surface of the food when cooked, and the temperature of the food after taken out of the oil. The overall system that includes the optical sensing head, the software and algorithms and the processor can, as explained above, accurately estimate the temperature of the fried food, and therefore assure that proper core temperature is achieved. This is especially important for health reasons to ensure disinfection of bacteria carrying food items. For example, poultry items must be cooked properly to prevent food poisoning risks. At the same time, the sensing head alerts on optimal time to stop frying and take the food out of the oil for optimal results. b. Filling level of the oil - using the 3D sensor within the sensing head, filling level of the oil can be measured before and during the frying process. This parameter is important for calculating and solving Eq. 1 (see the Method chapter below), also allowing alerting on lack of oil in the process that can create fire and/or safety hazards due to non-optimal frying conditions. c. Size, shape and estimated weight of the food, prior to cook and after the cooking process - all are supported by the 3D and visible sensing modules in the sensing head. These parameters are crucial for solving Eq. 1, and important also to assure that the cooking recipe that is selected for the fryer is correct, and that the outcome after frying is adequate and meets the quality standards of the cooking process selected. d. Bubble level - this parameter reflects the change in the moisture level of food being fried. It is part of Eq. 1 and therefore need to be timely controlled and measured. The visible sensor of the sensing head with support from the 3D sensor will be responsible for these measurements. As explained earlier, the measurement involves monitoring bubbles density in real time, and rate of generation and destruction of bubbles with proper image processing tools, e.g., round edge detection algorithm and Neural Network tool to teach bubble density 400 in an oil fryer 300. e. Time - the sensing head and overall control system monitor the various processes over time and control the proper time of bubbling, cooking and other phases that are part of the cooking process. f. Color of the oil and food items - this is monitored by the visible sensor and helps identifying food items. Common mistakes that should be prevented including selecting wrong recipes for cooking for a particular food item, alerting on oil quality and the need to filter or replace it, controlling the visibility of the end product after frying and assuring proper look before serving, and more such functions that are related to the appearance of the food before serving. g. Foam and food residues in the oil - are monitored by the visible sensor of the head and use a special algorithm to detect excess foam and/or burnt leftover food parts that can damage the frying process going forward. h. Smoke, fire, burned pieces - are detected by the visible and IR sensors, whether in the oil or in the fryer container or its vicinity. i. Visible tracking of post-processing activities done on the food items - such as salting, adding spices for the food - the visible sensor applies automatic control algorithms that detect such activities and alert the user if this phase is completed or not. This prevents mistakes in post processing of the food before serving. j. Monitor temperature of items just before serving - to prevent food from being returned by customers and dissatisfaction of service.

The scanning optical head

The scanning optical head or module 100 that is located above the frying tool has the role of measuring in real time all the relevant parameters that are important for solving the heat power Eq. 1 and temperature evolution model described above. One suggested structure of such an optical head is given in Fig. 3. The design allows placing the optical module 100 at any distance and angle above the cooking tool 300 with oil baths or reservoirs 310. The system performs well as long as it has clear line of sight 200 to the oil and the food items being cooked. The system can automatically calibrate its area of interest for monitoring the cooking process by using depth, range and visible image data of the area that is monitored. The system contains a processor 115, memory and communication units that accumulate the data produced by the sensors, 105, 110, 145, in real time, run the heat power model, solve it in real time, and combine various images from the various sensors into one unified database that can help take decisions regarding the cooking process being monitored.

The scanning head contains the following components:

1. An Infra Red chip 140 which is sensitive to the thermal heat that is radiated from the food and the hot oil, as well as other parts surrounding the cooking area. The chip has its own unique optics 155 to set its field of view, focal range and resolution, and can include a variable focal point, zoom, aperture and any other optical parameter used in such a type of sensors. Given the emissivity of the object, this IR chip 140 and optics 155 can measure the temperature of surface of the observed object with high accuracy and very high resolution, as well as with high frame rate of at least 25 Hz.

2. A visible range chip 125 with its own optics 130 - that is sensitive to the visible range of the spectrum and can detect, monitor and measure objects with color information in real time at a rate of at least 60Hz and HD resolution. A high resolution Lidar or 3D sensor 185 combining a transmitter 105 and/or receiver 110 that can measure the range and depth of any object with depth resolution of better than 2mm, and horizontal and vertical resolution of the same accuracy. A laser or LED device 145 with different color options that has a collimated beam and can illuminate an object inside the cooking tool or on any surface in its vicinity, and mark by different color and/or different flash rate the object, in order to visually signal information about the status of the object or the surface (hot, cold, above certain temperature, ready, remove from oil, shake and/or flip, etc). A set of mirrors 160, dichroic mirrors, 135, 150, and low emissivity glass windows that are aligned along the optical path of all the sensors described above and allow to combine all the sensors with a line of sight into a single axis with no parallax between the sensors and optimal efficiency. This set of mirrors and reflectors allows having one single aperture to the system, through which all the sensors operate, and the images that are created by all the sensors are fully aligned with no need for spatial calibration. A two-dimensional scanner 165, 170, with two scanning mirrors, 175, 180, or another known scanning mechanism that is positioned right before the main aperture of the optical head and can scan the optical axis of the system. This allows for the various sensors included in the optical head to see variable directions in space and cover large field of view while still keeping the high resolution of the sensor and the image it creates. The scanner can operate at various speeds and scan rates and serve as a scanner for various functions. There are several functionalities for the scanner: a. Serving as an individual scanner for one of the sensors while the others are idled - scanning the field of view of one sensor with its optimal scan rate and scanning a pattern where the other sensors are either shut down or blocked in order to avoid misreading of their image. b. Serving as a scanner for all the sensors together - where in this case the scanner scans the field of view for all the sensors that are aligned along a single optical axis. In this mode the scan rate and scan pattern are optimized for all the sensors and not to one specific sensor. c. Calibration mode - the scan unit moves in small steps to detect the corners of the required scan area, or moves to certain points in space to allocate a reasonable area to be monitored by the optical head - for instance - allocate the corners of an oil reservoir and set this area as the system monitoring area for the cooking process. d. High resolution mode - scanning a specific area with very small steps to accumulate multiple images of same area - and in this way get higher pixel resolution for a specific area on the cooking surface. e. Large field of view mode - Moving the scanning unit at a lower speed and accumulate multiple images of the various sensors to create one larger image from each sensor. The new image has much larger cover area with similar resolution to the instantaneous image of each one of the sensors. f. Mark mode - The 2D scanner is configured to direct the laser or LED light which is part of the optical head in a way that creates clear light markers on the cooking surface or on and around the cooked food - laser beam manipulation by mirror scanning is well known in areas like laser shows and similar techniques known in the literature can be used here to mark specific items and functions with light. A CPU unit 115 is integrated in the optical head or connected to its components through a communication line. This unit can be based on, for example, an nVIDIA Jetson Nano GPU processor, that is specifically designed to run advanced Al and neural networks algorithms on large images of various types, and can connect to multiple cameras and sensors, acquire their images in real time and perform strong image processing and object classification in between frames. This CPU unit controls, communicates and sends commands to multiple external sensors and devices, as required in the system described above. The following functions of the CPU are to be mentioned: a. The CPU unit connects to the optical head sensors, acquires the images in real time and processes them based on various algorithms. b. Through running the power distribution model described above, and by using the stored data from all the sensors, the CPU can calculate and monitor the core temperature of the cooked food and core temperature of the oil and items in its vicinity. c. By using Al algorithms the CPU verifies the quality of cooking through the whole cooking process (by identifying temperature, color, shape, smoke level, size and shape changes and more). d. The CPU communicates with external monitors and/or database through a communication line - either physical or wireless. e. The CPU collects and stores data for future use - including improving the learning process of the algorithms, monitoring the performance of kitchen teams while cooking dishes, and other use cases for the data. f. The CPU is capable of running strong Al algorithms, including such that are based on Neural Networks, to decide in real time on the cooking quality, change the energy flow in the cooking tool to optimize the cooking process, and save energy consumption (gas, electricity etc). g. The CPU also sends data to a server in the cloud, where such data include, for example, images of dishes while being cooked, power consumption data, gas reserve values, and other relevant data points. h. The CPU can send alarms in various ways - sound, light, calls in a fixed line or mobile phone etc - in order to alert on hazardous situations such as smoke, fire, hot parts in front of people using the fryer, spilled liquids on surfaces, overcooking or undercooking of food items, etc. i. The CPU connects to other sensors such as temperature, smoke, CO (Carbon Monoxide) and other hazardous gases, earthquake sensors etc. Connecting to these sensors, the CPU alerts on various situations that need human intervention in the process or hazardous situations. j. The CPU connects the user via a communication fixed line, ethernet line, Wifi, Bluethooth, NFC, RF, or other remote control available wireless solutions.

Method

The method of optimizing frying of items in deep oil is based on accurately modeling and sensing the thermodynamics of several processes that the oil and the food items inside the oil go through, while cooked in the hot oil. The overall heating and cooking process is set as an energy balance between the input power and output power. The input power includes the heat supplied to the oil, for example with an electrical or gas based heater in Watts. The output power is the heat also in Watts that that goes for heating the oil, its surroundings and food items inside it. The thermo-physical effect of this power input-output comprises letting the latent moisture inside the food items evaporate and leave out to the oil. These energy balance and thermo-physical effects, such as the texture and color of the food item and its intended level of moisture, set the required cooking time and required end result. Calculating these elements can give a powerful tool to better control the frying process and achieve an optimal end result. The general model of the invention allows estimating the power (energy over time) which is invested directly in the food item, and therefore the temperature it reaches while being cooked. The general heat balance equation of the model is presented here:

!■ Qif Qfr ~ Qoil ~ Qmo ~ Qsur where -

Qif is the heating power that goes directly into the food item while being cooked in the oil; Q _r is the input heat power from the heater and the heat power Q _oil is the heat invested in heating the oil;

Q _mo is the heat that goes into evaporating water off of the fried food item; and the Q _sur is the heat that is transferred into the surrounding of the oil.

Qif is the balance between these four heat components.

Q _rad is another heat component that is the heat power (or total energy) that is radiated outside to the surrounding atmosphere by the hot oil. It can be taken into account in the total balance of heat in the above heat equation, but will be neglected at this stage. The only measurable heat component in the heat balance equation (1) above is Qf _r that represents the heat power that the fryer heater supplies by to the oil in the container. This parameter can be measured and is usually well characterized by the manufacturer of the oil fryer. Other variables in the equation need to be calculated based on the sub-components that affect their behavior. To calculate each element in this equation, we need to further detail and model the following relations: where -

D _oil is the density of the frying oil measured in Kg/m ³ ;

C _po n is the heat capacity of the frying oil measured in J/kg °C;

V ₀ n is the volume of the oil in the fryer reservoir; and represents the absorbed heat that is translated into change in the oil temperature with time. This equation represents the heat power (J/sec) that the oil accumulates in the fryer in real time, due to the heating of the oil reservoir. D and C _p can be measured prior to the cooking process and basically characterize the type of oil used. The parameter V can be measured by measuring the filling level of the oil reservoir during frying, using the height sensor integrated into the sensing head in the sensing unit illustrated in Fig. 3.

Knowing the area and depth of the fryer, one can accurately calculate V. can be accurately measured by the thermal sensor that is part of the sensing unit shown in Fig- 3 A detailed explanation of the measurement process of this parameter is given further in the description. Eq. 1.2 Q _mo = m _f h _fg - ^d ^ where - m is the mass of the food item; hf _g is the latent heat of water evaporation measured in J/kg; and is the rate of change of the dry-basis moisture content in the food over time.

This equation expresses the thermo-physical effect of the heat power that is absorbed in the food item, which causes moisture to evaporate out of the food in the cooking process, m can also be estimated quite accurately with the sensing unit, for example by visually identifying the type of food with the camera and size and volume with an embedded 3D sensor of the sensing unit. We assume in this model that the moisture change rate is linearly correlated with the rate of formation of moisture carrying air bubbles and their volume density, which are observed around the food in the oil surrounding it. The bubbles reflect the explosion, which is created locally by water molecules that evaporate and leave the food in a burst into the oil. These burst level and density accurately reflect the moisture reduction level in the food item. We, therefore, propose the following relation: a. Eq. 1.3

“ where -

Ba is the bubble density measured by the sensing unit above the oil fryer; and a and [J are constant correlation parameters, which are obtained by experimental setup prior to cooking and may be characteristic for each food type with no need to measure them for each cooking cycle; Ba can be measured directly with the visible sensor integrated into the sensing head. Using area classification and edge count algorithm that allows focusing on the bubble area in the oil and calculate the bubble density in real time. Sample image of the effect is shown in Fig. 2. The bubble density 400 surrounding the frying food in the oil is tracked and measured over time using the visible sensor and the 3D sensor in the sensing head above the oil fryer 300.

3. E . 1.4

This equation describes the heat power that is absorbed over the surface area of the oil container and any other part that is in contact with the hot oil. where -

^surface is the total effective surface area that is in contact with the oil reservoir and can be measured and calculated for each oil fryer. This effective surface area can also be updated in real time in the sensing head above the fryer after measuring the filling level of the oil; y is the overall heat transfer coefficient for the surface of the fryer measured in W/m ^{2 o} C. y should be measured prior to the cooking process and reflect the thermal isolation/conduction characteristics of the oil container that is used.

The best way to measure y is to heat up oil in the container without food items inside and keep its temperature constant by supplying heating power over time. The amount of heat loss, which is reflected in the additional power that is needed to keep the temperature of the oil constant, is the power absorbed in the oil surface. Once y and A _Sur ^ _ace are measured and known prior to cooking, the rest of the equation, that is T _oil and T _sur face can be directly measured by the sensing head in real time during the cooking process, and therefore Q _sur can be accurately measured and calculated.

4. Eq. 1.5 where -

Cpfood is the heat capacity of fried food measured in J/kg °C; ^m food is the mass of food in kg; and ^dT _g ^f °° ^d is the temperature change over time of the cooked food. This term can be calculated from equations 1 and 1.1 through 1.4.

This equation represents the heating power that is absorbed by the food items, which are cooked. This is the actual value that the present invention models and predicts over time. By calculating the temperature change of the food when fried in oil, we can optimize the cooking time and termination time for stopping frying and optimizing cooking quality.

Summarizing the above equations and relations, the present invention enables accurately calculating the temperature gradient that develops over time inside the fried food in the hot oil. The essential means for achieving this combine sensory means and computational power. Particularly, the invention provides a smart sensing head and the equations 1 to 1.5 detailed above. The sensing head has a proper processing power that measures proper parameters of the oil, food and surroundings in real time. Combined with solving equations 1 to 1.5 based on experimentally obtained values of variables brings optimized cooking. The combination of initial temperature, cooking time and temperature gradient of the food in real time gives good prediction of the level of cooking of the food, particularly if its cooking is complete and ready to be taken out from the oil. This may also support automatic cooking recipes in standard deep oil fryers as well as specially designed new tools.

Based on the above description, the present invention provides a method for monitoring thermodynamics of frying of food items in oil. This method comprises the following steps:

- providing a food fryer and filling a container of the fryer with an oil reservoir;

- providing a scanning optical head above the oil reservoir;

- providing visible light camera and sensory means in direct communication with the fryer, particularly with the oil reservoir;

- elevating the temperature of the oil reservoir and introducing food items into the oil reservoir;

- scanning and monitoring visible and sensory thermo-physical effects in the fryer, oil reservoir and food items and at the interface of the oil reservoir and outer surface of the food items;

- progressively calculating and measuring temperature, energy balance and heat flow in frying the food items; - setting visible and numerical target values for identifying termination of frying the food items; and

- terminating frying of the food items once achieving optimal point.