FIELD OF INVENTION

The present invention relates to determine food quality produced inside commercial kitchens. More specifically the invention relates to determine a processed food quality based on certain predefined parameters, so as to ensure the processed food follows certain minimum quality standards.

BACKGROUND OF INVENTION

The foods are prepared in commercial kitchens by heating the ingredients, or by baking them, or by skinning and cutting them, or by blending them, or squeezing them, or mixing them or by applying any other food processing techniques.

Quality control of these processed foods is critical from the perspective of consumer experience, safety and cost. These processed foods need to match the quality requirements of that specific processed food. Some of the quality standards may relate to packing or serving the processed food at the proper temperature so as to ensure that the food is safe from any disease-causing microorganisms, with appropriate weight, size and appearance.

Accordingly, the quality control metrics may include size, weight, appearance, temperature, freshness, microbial content, solid/liquid ratio, presence or absence of accompanying dishes, number of units of food/drink items in a package etc. Maintaining of such quality standards in processed food enhances the customer experience, and reduce costs in the kitchen due to unnecessary wastage of resources.

One mechanism for determining quality standards of cooked food is disclosed in the US Publication No. EP2813764A2, which discloses cooking devices having inspection systems including a distance sensor and a digital optical recognition device. The distance sensor detects the position of the food product placed in the cooking device and the digital optical recognition device captures a series of images for the purpose of food product recognition. Once the food product is recognized, the operator is provided with the correct cooking cycle/program for the position and type of food product placed in the cooking device. The inspection systems also ensure that the food product has been properly cooked at the end of the cooking cycle/program. The inspection systems ensure: (1) the food product is correctly recognized; (2) the cooking cycle/program is correctly selected; (3) the correct cooking cycle/program is followed to completion and (4) the quality of the cooked food product meets expected standards. The technique used here is mostly able to identify certain attributes of the quality parameters due to limitations of image-based techniques when used alone.

Another mechanism is disclosed in Publication No. RU2613319C2, which discloses a method of monitoring the quality of the plurality of food products moving in the dynamic production system, based on the assessment of the food product colour. Wherein the method complies the stages of: capturing the images of a plurality of moving food products; said image analysis for determining the intensity variable of at least one colour responsible for the defect; the assessment of the plurality of moving food products as a group based on the colour percentage of at least one colour responsible for the defect, defining thereby the overall group appearance assessment; the assessments of each food product based on the image analysis, thereby obtaining a plurality of individual assessments of the product; comparing the plurality of individual assessments of the product with the desired product colour characteristic. Moreover, the device for the quality monitoring of the plurality of moving food products is disclosed. This method has a limitation as it just takes in consideration of food color which gives limited information for establishing quality parameters for a processed food.

US Patent Publication No. US6609078B2 discloses a food quality and safety monitoring system and method for evaluating food characteristic management for improving the safety of perishable food products for human consumption and the shelf life of perishable food product. In a first embodiment, a temperature sample is taken for each product group within a refrigeration case that is used to calculate either a food safety index or a food quality index. Alternatively, the food product temperature for each food product group within a refrigeration case may be continuously monitored to calculate a food characteristic index. The food characteristic index can be monitored over time to evaluate the food characteristic management of a particular store or group of stores. The mechanism disclosed uses only temperature of the food product group for determining safety or quality criteria for whole food group, and do not evaluate quality parameters for individual food products. Also, the purpose of this methodology is to determine degradation of quality of the perishable food product over a period of time, and do not relate to identifying quality standards of a processed food before delivery to the customer. In furtherance, only considering temperature criteria has its limitation in determining a food products quality.

None of the prior arts mention about mechanisms which can efficiently checks a processed food product against maximum possible quality parameters, at least specific quality parameters with respect to appropriate temperature and appearance. These parameters are the primary one’s which appeals most to consumers senses before even customer starts eating the food. Hence, there is a need for such mechanisms which can efficiently check quality of processed food against maximum possible parameters before being served or delivered to consumer.

OBJECTIVE OF INVENTION

The objective of the invention is to provide a system for determining quality of food products which can enhance customer’s experience.

SUMMARY OF INVENTION

The objective of the invention is achieved by a system for determining and managing quality of a processed food according to claim 1.

The system includes an image sensor which capture an image of the processed food, a temperature sensor which senses temperature of the processed food, and generates a temperature data of the processed food. The system also includes a quality processor which receives and processes the image of the processed food and generates at least one of a visual parameter of the processed food defining at least one of a shape, a size, a colour, or a texture, or combination thereof, of the processed food. Further, the quality processor receives a quality data related to the processed food and the temperature data, compares the visual parameter and the temperature data with the quality data, and determines a quality of the processed food. The quality data defines visual parameters and temperature for the processed food acceptable for further using of the processed food. This embodiment helps to satisfy at least basic requirements of processed food quality and accordingly the customer experience, i.e., maintaining an appropriate appearance and temperature of the food. According one embodiment of the system, the system includes a weight sensor adapted to generate a weight data of the processed food. The quality processor receives the weight data along with the visual parameter and the temperature data of the processed food, and compares the weight data, the visual parameter and the temperature data with the quality data, and to determine the quality of the processed food. The quality data defines visual parameters, weight, and temperature for the processed food acceptable for further using of the processed food. In furtherance on quality perspective, the appropriate weight of the dish also plays important role. A lesser amount of dish may make the customer feels cheated. Hence, this embodiment further enhances quality checking to weight parameters which further enhances customer experience.

According to another embodiment of the system, wherein the processed food is inside a packaging and the packaging bears an identification code. The system further includes a code reader which reads the identification code. The quality processor receives the identification code along with the visual parameter and the temperature data of the processed food, and optionally the weight data, and compares the visual parameter and the temperature data, optionally along with the weight data, with respect to the quality data, and determines the quality of the processed food bearing the identification code. This invention is having the utility specifically where the food is packed and the quality of each packed food can be determined and tracked.

According to yet another embodiment of the system, wherein more than one food items are packed in a packaging, and the packaging has multiple compartments and each of the compartment holds one or more processed foods, wherein one or more food items is to be packed at different temperature. The image sensor captures the image of each of the processed food. The temperature sensor senses temperature of each of the processed food placed in each of the compartments, and generates the temperature data of each of the processed food. The weight sensor generates a weight data of the processed food placed inside the packaging. The quality processor generates at least one of the visual parameters of the processed food defining at least one of the shape, the size, or the texture of each of the processed food, and further receives the visual parameter of each of the processed food, the temperature data of each of the processed food, and optionally the weight data, and compares the visual parameter of each of the processed food, the temperature data of each of the processed food, and optionally the weight data, with the quality data, and determines the quality of the packaging having multiple processed foods. The quality data defines visual parameters, temperature of each of the processed food in the packaging and, optionally the weight of the food inside the packaging which is acceptable for further using of the packaging. The embodiment is beneficial to determine quality of a complex packaging like a meal box which includes various processed food, and quality parameters of each of the food items individually, and overall composite quality parameters of all the food items are relevant to be determined, so as to satisfy consumer experience.

According to one embodiment of the system, the system includes an update processor which is in communication coupling to more than one quality processors and receives the visual parameters related to one or more processed foods checked for quality from more than one quality processors, and processes the visual parameters related to one or more processed foods checked for quality from more than one quality processors based on a set of rules, and updates the quality data. During a course of time certain quality parameters related to visual appearance of certain food products may change due to change in availability of the ingredients. For example, certain vegetables colour or size may change during a particular time period. If the quality standards considered remains same, many such processed foods using such ingredients may fail on those quality standards. Accordingly, an optimization is required for optimizing the quality standards for visual parameters if there is change in visual appearance of certain ingredients used in a particular processed food across one particular season. Another such change may be due to certain seasonal ingredients becomes unavailable during a particular time frame. Hence, such change should not be linked to change in visual parameters of the processed food, leading to degrading of quality of the processed food. This embodiment is helpful in optimizing quality standards according to any such changes occurring during a time frame or in a geographical area, or both.

According to another embodiment of the system, wherein the set of rules are based on one of a deep neural network based algorithm. This embodiment provides for efficient means for processing segregating images to provide accurate visual parameters, which are useful in efficient determination of the quality standards of a processed food.

According to yet another embodiment of the system, wherein the quality processor processes the visual parameter, the temperature data and optionally the weight data to identify the processed food and receives the quality data related to the processed food based on the identification of the processed food. This embodiment is helpful for reducing manual efforts to enter identification of the processed food, and the parameters sensed or generated by the system elements are helpful in identification of the processed food.

According to one embodiment of the system, wherein the temperature sensor is a thermal image sensor or an infrared sensor. This embodiment is helpful to provide efficient means for temperature measurement of the processed food. According to another embodiment of the system, wherein the quality processor determines deficiency in specific quality parameters of the processed food including visual parameters, temperature parameters, and optionally the weight parameters of the processed food, as part of determination of the quality of the processed food. This embodiment is specifically helpful to identify specific reasons for the failing of the processed food on quality standards, and accordingly the staff can make changes to the processed food for bringing at par to the quality standards. One such change can be made by reheating the processed food, or replacing the processed food, or adding some more portion of the processed food, etc. Such rectification is helpful in reducing the wastage, as instead of outright rejection of the order, the order can be rectified to pass the desired quality standards.

According to yet another embodiment of the system, wherein the quality processor processes the visual parameters of the processed food to categorize the processed food based on a level of cooking related to appropriate heating of the food while processing of the food, or a level of freshness of the processed food, or a liquid ratio in the processed food, or combination thereof, and the quality processor processes the categorization of the processed food, the visual parameters, the temperature data, optionally the weight data, and the quality data to determine the quality of the processed food. The quality data comprises a characterization related information for the processed food acceptable for further using of the processed food. Such categorization of the processed food based on visual parameters further fine tunes the quality standards, and accordingly enhances the user experience if the processed food further passes such quality standards based on visual categorization. According to one embodiment of the system, wherein the quality processor processes the visual parameters, the temperature data, optionally the weight data and determines a quantity deficiency related to missing of one or more of the processed foods from the packaging having multiple compartments or missing of a part of the processed food. A missing item from a composite meal degrades the user experience substantially, and the consumer sometimes may feel cheated because of non-presence of an item in a meal. This embodiment helps in reducing any such incidences.

According to another embodiment of the system, the system includes a microbial sensor which senses microbial activity on a surface of the food item, and generates a microbial data. The quality processor processes the microbial data, the visual parameters, the temperature data, optionally the weight data, and the quality data to determine the quality of the processed food. The quality data includes a microbial information of the processed food acceptable for further using of the processed food. This embodiment further helps in identifying if the food is fit to eat or not. A processed food may be good in visuals, temperature wise or weight wise, however still the processed food may have turned unfit for eating. This embodiment is helpful in determining the quality standards even for any such processed food which may have turned unfit for eating.

BRIEF DESCRIPTION OF DRAWINGS

Fig. 1 illustrates a schematic diagram of a system for determining and managing quality of a processed food according to an exemplary embodiment.

Fig. 2 illustrates a schematic diagram of another exemplary embodiment of the system which illustrate additional features of the embodiment illustrated in Fig. 1. Figs. 3 and 4 illustrates different views of a schematic representation of a device implementing the system for determining and managing quality of a processed food according to yet another exemplary embodiment.

The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments illustrated herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION

The best and other modes for carrying out the present invention are presented in terms of the embodiments, herein depicted in drawings provided. The embodiments are described herein for illustrative purposes and are subject to many variations. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but are intended to cover the application or implementation without departing from the spirit or scope of the present invention. Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of the description and should not be regarded as limiting. Any heading utilized within this description is for convenience only and has no legal or limiting effect.

The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other, sub-systems, elements, structures, components, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

The invention envisages a computer implemented system which identifies whether each and every food product leaving a commercial kitchen conforms to a specific standard of quality, ensuring that only consistent and superior quality food product leaves the kitchen, thereby elevating consumer satisfaction levels. This helps in ensuring the health and safety of the consumer, by only providing them with consistently high-quality products. Such a system provides cost-benefits to commercial kitchens, wherein consistent products will lead to lesser wastage of resources and may also lead improvement in sales due to consistent standards and high consumer satisfaction. This computer implemented system can be implemented in a clientserver system where some processing and data storage is carried out at the client side, while some processing and storage happens at server side, or can be implemented as part of standalone computer implemented system.

Fig 1. illustrates a schematic diagram of a computer implemented system 10 for determining and managing quality of a processed food. The system includes various sensory elements 11, 13, 19, 28 which captures data from the processed food, and sends to a quality processor 15 which processes these data by comparing these data with a quality data 17 and determines a quality 18 of the processed food. The processor 15 is one or more microprocessors or any computing processors which are configured to perform in a fashion as described in the Fig. 1, 2, and 3.

The processed food is placed inside a packaging 23 which bears an identification code 21, and the sensory elements which captures the data when the processed food is placed inside the packaging.

These sensory elements include image sensor 11, the temperature sensor 13, the weight sensor 19, and the microbial sensor 29. The image sensor 11 captures an image 12 of the processed food. The temperature sensor 13 senses temperature of the processed food, and generates a temperature data 14 of the processed food. The temperature sensor can be a thermal image sensor or an infrared sensor. The thermal image sensor provides a complete thermal map of the food product being inspected. This has the benefit of obtaining full surface coverage, including for multi-component food products where some of the components are at high temperature and some are at low temperature, for example, a meal combo where the rice and curry is ‘hot’ and the yogurt / raita is ‘cold’. The weight sensor 19 generates a weight data 20 of the processed food placed inside the packaging 23. The weight sensor 19 can be a regular weighing scale or any other kind of specific weighing means specifically designed for the current system 10. The microbial sensor 28 senses microbial activity on a surface of the food item, and generates a microbial data 29. The microbial sensor are specialized transducers which are coupled to microorganisms. The microbial sensors can be Quartz Crystal Microbalance (QCM) that are based on extremely sensitive mass balance that measures nanogram to microgram level changes in mass per unit area. Any other kind of microbial sensor can be used for carrying out the functionality required by the microbial sensor 28 of the current system 10.

The temperature data 14 is significant for foods, as the processed foods are palatable when they are packed or served at a particular temperature. Higher or lower temperature for the processed food reduces consumer’s impression while he is consuming food. In furtherance, when a particular food is packaged, it needs to be kept at a temperature, such that when it reaches to its consumer, it’s temperature should have reached to a particular level which is an appropriate temperature required for providing best palatability for that particular processed food.

Weight data 20 has a significance, as any deviation in weight of the processed food inside packaging shall again create an impression in the consumer’s mind that something is not right. Even a slightest deviation in weight creates such an impression, and may lead him/her to think that he/she has not received the right amount of food for which he/she has paid for. Hence, an accurate weight of the processed food needs to be maintained to enhance user’s experience. However, measurement of such weight can be separately carried out, and in certain scenario, weight measurement may not be significant, for example when the quality of the processed food is being checked even before packaging, hence in such scenario weight sensor 19 shall be not be required, and hence shall not be included as part of the system 10.

Microbial data 29 is generally utilized for scenario where the processed food is not freshly prepared, rather it is kept stored for a period of time. Such stored processed food needs to be checked before serving or packaging to the consumers. The microbial data 29 shall point out whether the processed food is fit for eating or not. An unfit food may just not bring bad taste in consumer’s mouth, rather it can also lead to food poisoning. However, in scenario where the processed food is freshly prepared with ingredients, such quality checks related to microbial activity of the processed food may not be required, and accordingly the microbial sensor 28 may not be required as part of the system 10.

The quality processor 15 receives and processes the image 12 of the processed food and generates atleast one of a visual parameter 16 of the processed food defining at least one of a shape, a size, a colour, or a texture, or combination thereof. All these visual parameters 16 has a significance in terms of quality standards and play an important role in consumer satisfaction. For example, the consumer is used to seeing and having the processed food of a particular size, and any variations in food size generally reduces his appeal to the food. Further, even the size is relevant to the palatability of the consumer. The food of a bigger size may lead to keeping a bigger food amount in mouth, and may lead to difficulty in chewing, and also a too smaller size may not be felt by the consumer enough mouthful while keeping the processed food item in the mouth. Similarly, the shape also plays an important role in creating an appeal to the consumer’s mind. If the processed food is of irregular size, it creates a negative impression in the mind of the consumer, and may also reduce his willingness to further eat the food. In similar way, improper texture, for example a processed food is too dry, or is having less smooth surface, or too much watery may again hugely impact impression of the consumer to the processed food, and further reduces his willingness to consume the food. In the same line, an irregular colour to a particular processed food, which is not it’s usual colour, or even the minor variations in the processed food’s colour from standard colour, shall again creates a negative impression in consumers mind, and again reduce his willingness to consume the food. Hence, extraction of some or all of these features can be relevant here for measuring the processed food quality. It is to be noted that all the visual parameters mentioned shape, size, colour, or texture may not be relevant for every processed food, for example something which is liquid like a beverage, only colour or texture maybe important in those case, while for a flat bread may be shape, size and colour may be important. Hence, the visual parameters shall be generated based on a particular processed food.

The quality processor 15 further processes the visual parameters 16 of the processed food to provide a categorization 26 the processed food based on any one or all of the below mentioned parameters:

• A level of cooking related to appropriate heating of the food while processing of the food. One of such level of cooking can be based on if the food is burnt, undercooked or overcooked. All the three level of cooking have different colour and texture, and the quality processor 15 is able to determine the level of cooking based on colour and texture of the processed food. Some of the foods are cooked with user’s preference, like steak is cooked as well done, medium well done, medium rare, and rare, and all these types of steaks requires different level of cooking and have difference in either colour or texture, or both. Hence, such categorization may have a significance in certain scenario when the food is cooked, and not just squeezed in juice, or pureed, etc., which do not require any heating. While the processed food which are heated has this categorization as a significance, and this significance is further enhances where the food is served with different level of cooking as per user’s preference. In cooked food, the level of heating changes user’s experience largely. In some of the scenario, like steak, the user does not even have to eat the food rather just looking at it will make an impression in his mind, whether the food is cooked as per his/her preference or not.

• A level of freshness of the processed food. The food which are not cooked, rather they are either just peeled or cut, or which are served with minimal level of processing, the freshness of the food is of utmost important, and the colour and texture clearly shows whether the food is fresh or not. Hence, the quality processor 15, based on visual parameters’ 16 can identify the level of freshness of the food, such as whether the food is stale or not, and further add up into the consumer’s experience. Skin texture and colour of fruits substantially changes if they are not fresh or a fruit salad made from them is stored for a long period. Hence, this categorization is useful especially when the food is minimally processed.

• A liquid ratio in the processed food. This categorization is also relevant and sometimes has user’s preference involved. For example, a Chinese processed food can be served with no gravy, with a thicker gravy, and a thinner liquid type gravy. A user can set his preference for the dish at any of these levels. And, if the Chinese processed food is received by the consumer without considering his gravy preference, his experience gets subpar, and reduces his satisfaction towards the food. In such scenario, where a liquid portion may or may not involve, or where the dish is like liquid type, such as juice, raita, kheer, etc., such categorization, and quality check around such categorization is significant. Categorization 26 based on visual parameters 16 may not be used in some embodiments of the system 10, rather macro level quality check based on visual parameters

16 may itself can be carried out.

The quality processor 15 further receives a quality data 17 related to the processed food from the first memory unit, the temperature data 14, the weight data 20, and the microbial data 29. Further, the quality processor 15 compares the visual parameter 16, the categorization 26, the temperature data 14, the weight data 20, and the microbial data 29, and compares them with the quality data 17, and determines a quality 18 of the processed food regarding whether it follows the minimum quality standards, and is also fit for consumption by the consumer. In scenarios, as discussed above where the weight sensors 19, and the microbial sensors 28 are not used, the quality processor 15 shall not use the weight data 20, and the microbial data 29 while comparing with the quality data 17. In certain scenarios where macro level quality checks based on visual parameters 16 are carried out, the categorization 26 of the processed food is not generated by the quality processor 15, and is not further compared with quality data 17.

The quality data 17 defines visual parameters, weight, microbial information, categorization related information and temperature for a particular processed food acceptable for further using of the processed food. In case, if the packaging has a composite of multiple processed food making it a meal, then the quality data 17 shall defines visual parameters, weight, microbial information, categorization related information and temperature for every processed food packed in the packaging or a meal box. In certain scenario, the quality parameters only defines visual parameters and temperature for the processed food acceptable for further using of the processed food, other parameters such as weight, microbial information and the categorization related information may optionally be present as per the configuration of the system 10 considering whether, the system 10 is configured to include weight sensor 19, the microbial sensor 28, or the quality processor 15 is further configured to generate the categorization data 26 or not.

The quality processor 15 receives the temperature data 14, the weight data 20, the microbial data 29, and also receives the quality data 17 related to the particular processed food from the first memory unit 30 and compares the quality data 17 with the visual parameter 16, the categorization, the temperature data 14, the weight data 20, and the microbial data 29 and determines a quality 18 of the processed food. In some scenarios, one or both of the microbial sensor 28, and the weight sensor 19 may not be present, and accordingly, the quality processor 15 is not configured to receive the microbial data 29, and/or the weight data 20, and accordingly, the quality data may not include the microbial information and/or the weight information of the processed food, and further quality processor 15 only uses temperature data 14, and the visual parameters, and optionally the categorization 26 while comparing them with quality data 17 of the processed food. In certain scenario, where all the sensors 11, 13, 19, 28, and the quality processor 15 is configured to generate the categorization 26, the quality processor 15 may only receive relevant data required for quality comparison, and accordingly for that specific processed food, the quality data 17 may only include the relevant quality parameters for a processed food item. Hence, it is to be considered that the system 10 is highly configurable with respect to every processed food to be examined for the quality.

For identification of specific order of the processed food, the packaging 23 also bears an identification code 21, and the system 10 includes a code reader 22 which reads the identification code 21. The quality processor 17, while comparing the sensed data 14, 20, 29, the visual parameters 16, and the categorization 26 with the quality data 17 shall also link the quality 18 determined for the processed food to the identification code 21. This helps to manage a proper log of quality of each order of the processed food which is shipped out of the commercial kitchen. This data can be further used for any audit purpose which may be linked to a vendor contract, or may help in resolving consumer complaints where a customer executive can easily get into the quality log, and provide a resolution to the consumer. In the example given for the vendor’s contract, say for example, if a particular processed food has similar quality issues in multiple orders which would have used ingredients from a same lot shall definitely point towards the quality in the ingredients used from that specific lot of ingredients, and accordingly a proper audit of the quality log can be shown to the vendor, and as per the vendor’s contract a warning, penalty, or a replacement can be issued to the vendor.

In the scenario, where more than one food items are packed in a packaging 23, and the packaging 23 has multiple compartments and each of the compartment holds one or more processed foods, wherein one or more food items is to be packed at different temperature, the image sensor 11 captures the image 12 of each of the processed food, the temperature sensor 13 senses temperature of each of the processed food placed in each of the compartments, and generate the temperature data 14 of each of the processed food, the weight sensor 19 generates the weight data 20 of the processed food placed inside the packaging 23, and the quality processor 15 generates at least one of the visual parameters 16 of the processed food defining at least one of the shape, the size, or the texture of each of the processed food, and further receives the temperature data 14 of each of the processed food, and optionally the weight data 20, and compares the visual parameter 16 of each of the processed food, the temperature data 14 of each of the processed food, and optionally the weight data 20, with the quality data 17, and determine the quality 18 of the packaging 23 having multiple processed foods. In such scenario, the quality data 17 defines visual parameters, temperature of each of the processed food in the packaging and, optionally the weight of the food inside the packaging which is acceptable for further using of the packaging. In one additional embodiment of this scenario, the quality processor 15 may also generate categorization 26 of each of the processed food, and the quality data 17 also defines categorization of each of the processed food, and while making comparison with the quality data 17, the quality processor 26 also used the categorization 26 along with other sensed data or generated data.

In scenario, where the system 10 is configured to check quality for variety of processed food, and in such scenario identification of the processed food is important for fetching the relevant quality data 17 which is specifically related to the specific processed food. Such identification information can be entered by a user of the system 10 through an input interface or device, or can automatically identified by the system. The automatic identification is carried out by the quality processor 17 which processes the visual parameter 16, the temperature data 14 and optionally the weight data 20 and identify the processed food, and receives the quality data 17 related to the processed food from the first memory unit 30 based on the identification of the processed food.

In one the embodiments, the quality processor 15 can further be configured to determine deficiency in specific quality parameters of the processed food including visual parameters, categorization deficiency, temperature parameters, and optionally the weight parameters of the processed food, as part of determination of the quality 18 of the processed food. The quality processor 15 is adapted to determine a quantity deficiency 27 as part of determining the quality of the processed food. The quantity deficiency 27 is related to missing of one or more of the processed foods from the packaging 23 having multiple compartments or missing of a part of the processed food.

Fig. 2 illustrates a schematic diagram of another exemplary embodiment of the system 10 which illustrate additional features of the embodiment illustrated in Fig. 1. The system 10 further includes a update processor 24 which is in communication coupling to more than one quality processors 15 and receives the visual parameters 16 related to one or more processed foods checked for quality from more than one quality processors 15, and processes the visual parameters 16 related to one or more processed foods checked for quality from more than one quality processors 15 based on a set of rules 25 stored in a second memory unit 31, and based on such processing, the update processor 24 generates an updated quality data 17, and send to the connected quality processors 15 to be stored inside the first memory unit 30, and further utilized by the quality processors 15 for further checking the quality of the processed foods. In one embodiment, the set of rules 25 are based on one of a deep neural network based algorithm. This deep neural network (DNNs) algorithm is used for segmentation and classification of food products. Training dataset is being created with in-house data generation pipelines setup in multiple locations. The DNN will be developed by evaluating suitable models (either pre-trained or new) based on performance on our dataset. It will be fine-tuned and modified for our use case accordingly. The model will keep getting updated with improved versions after deployment. The model will be deployed over cloud servers wherein comparison values are returned to the computer for further processing. In an alternative embodiment, the model will be deployed in the on-board computer and the image processing will take place in the system itself. It is to be noted that similar set of rules based on DNNs may also be used by the quality processor 15 for generating the visual parameters 16 from the image 12, and for generating the categorization 26 using the visual parameters 16.

Figs. 3 and 4 illustrates different views of a schematic representation of a device implementing the system for determining and managing quality of a processed food according to yet another exemplary embodiment. The device is having a cabinet like structure 200. The setup primarily comprises of a weighing scale 1 operated by a Programmable Logic Controller (PLC) placed inside the Weighing Scale Controller 5 and provides input in the form of a digital signal to the computer 3, which may be a touchscreen or any other type of computer, including but not limited to a desktop or laptop setup. For the sake of convenience, the term “computer” shall include both a computer monitor 3 and a controller box/processing unit 7. An optical scanner 2 adapted to read bar code / QR code, which also feeds data into the computer 3, 7, a Temperature Sensor and Camera Module 100 comprising of non-contact temperature sensors 10 detects and measure infrared frequencies and feed such data into the computer 3, 7 and an imaging device in the form of a digital camera attached to the computer 3, 7 which captures the image of the food product and displays it on the computer display 3. The particular embodiments in question also comprise of a Microwave Oven 4, which can be utilised to heat or reheat food products. However, an alternate embodiment may not have the said Microwave Oven. Another embodiment of the device uses a Thermal Camera instead of an infrared sensor, said camera providing a complete thermal map of the food product being inspected. This has the benefit of obtaining full surface coverage, including for multi-component food products where some of the components are at high temperature and some are at low temperature, for example, a meal combo where the rice and curry is ‘hot’ and the yogurt / raita is ‘cold’. If the input data falls within a particular range from the standard metric, having taken into account the error limits, then the processing unit 7 will return a value of “1”, and will reflect on the computer display monitor 3, that the food product is within acceptable limits and therefore is good to be shipped to the consumer. If the input data falls beyond the acceptable range from the standard quality control metric, then the processing unit 7 will return a value of “0”, and will reflect on the computer display monitor 3 that the food "product is not up to the standards and should not be shipped to the consumer. The processing unit 7 can be adapted to calculate deviations in metrics which may reflect on the computer display monitor 3 and this system may also be adapted to recommend actions which can rectify the food product, if the value returned post comparison is 0. The system is adapted in a manner which makes it capable of using Machine Learning (ML) to capture appearance of the food products over a large number of image captures from multiple locations over a period of time, and refine the appearance criteria to accurately detect deviations from the acceptable range.

The device also includes a Spike Guard 6 which is placed nearby to prevent damage 10 to the setup due to surge in load. The system power can be toggled through the ON / OFF switch provided at the back of the setup. In an alternate embodiment, the processing unit 7 and spike guard 6 may be incorporated with the computer display 3.

In an alternate embodiment, the cabinet 200 can be enclosed within a cabinet shell, which covers the four faces of the cabinet, while leaving open provision for placement of micro wave oven. In another alternate embodiment, the device can be a table-top setup with short leg stands, which can be placed on counter tops, comprising of the weighing scale 1, Bar code/QR code scanner 2, temperature sensor, camera 9 and the computer 3, 7. The food product shall be placed on the weighing scale. The bar code / QR code scanner scans the tag on the food package and identifies the type of food product. The infrared sensors measure the temperature of the food product and the camera enclosed within the same module along with infrared sensors, captures the image and uploads to the computer which in turn feeds the collected input to a cloud-based quality control database, wherein quality control metrics are retrieved and comparison is made post which the system returns a binary value based on predetermined algorithms based on the concept discussed above.

In another alternate embodiment, the device is a table setup, wherein the table-top setup discussed above is incorporated with long leg stands.

While specific language has been used to describe the invention, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

List of Reference Numerals:

1 Weighing scale

2 Barcode reader/ QR code reader

3 Computer with Touch screen display

4 Micro wave

5 Weighing scale controller

6 Spike Guard

7 Controller Box / Processing Unit

10 System

11 Image sensor

12 Image

13 Temperature sensor

14 Temperature data

15 Quality processor

16 Visual parameter

17 Quality data

18 Quality

19 Weight Sensor

20 Weight data

21 Identification code

22 Code reader

23 Packaging

24 Update processor

25 Set of rules

26 Categorization

27 Quantity deficiency 8 Microbial sensor 9 Microbial data

30 First memory unit

31 Second Memory unit 100 Thermal Sensor and Camera Module

200 QC system cabinet