Tata Consultancy Services Limited

Parijat Dilip Deshpande

Beena Rai

Jayita Dutta

Debankita De

Arpan Pal

Mahesha Gunmi

Mohan Bhaskara

Satish Murlidhar Chaudhari

Anirban Dutta Choudhury

Shirish Subhash Karande

Manasi Samarth Patwardhan

Shankar Balajirao Kausley

Arijit Sinharay

Rohan Banerjee

Ranjan Dasgupta

Shreyasi Datta

Kayapanda Muthana Mandana

Ramu Reddy Vempada

Shashank Madhukar Deshmukh

Tapas Chakravarty

Sakyajit Bhattacharya

Dibyendu Roy

Raj Kumar Rakshit

Deb Kumar Ghosh

Deepan Das

Soumyadipta Maiti

Naga Neehar Dingari

Venkata Muralidhar Kanamarlapudi

Nital Shah

Vijay Anil Date

Shrikant Arjunrao Kapse

Priya Kedia

Shashank Deshmuk

Debkumar Ghosh

Food diagnostic device

State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in a container. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting food quality in a non-invasive manner. A Color Changing Indicator (CCI) in a biosensor strip forming a component of the enclosed package in which the liquid food item is packed, changes color when came in contact with the liquid food item. For different quality of the liquid food item the CCI has different color. Based on the color of the CCI, and ambient temperature and relative humidity at the time the color of the CCI is determined, a machine learning model determines rate of deterioration of the liquid food item, and then predicts remaining shelf life, which in turn provided as output to a user.

System and method for non-invasive real-time prediction of liquid food quality within enclosed package

State of the art milk quality estimation techniques fail to determine quality of the milk non-invasively. This disclosure relates generally to a system and method for non-invasive estimation of milk quality within targeted packaging. A voltage signal is applied at a first capacitor plate amongst a plurality of capacitor plates placed on either side of a targeted packaging containing milk, wherein the targeted packaging is placed inside an electrode receptacle. The resulting voltage signal is measured at a second capacitor plate of the plurality of capacitor plates. A capacitance value is calculated from the measured voltage signal and a value of pH corresponding to the calculated capacitance value is then determined using a regression-based model. A correlation of the determined value of pH with one or more pre-defined quality indices is performed and the quality of milk within the targeted packaging estimated based on the correlation.

System and method for non-invasive estimation of milk quality within targeted packaging

Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.

Apparatus and method for multimodal sensing and monitoring of perishable commodities

Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.

Method and system for real-time shelf-life prediction of food items

State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in an enclosed package. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting liquid food quality in a non-invasive manner. A near infra-red (NIR) radiation is transmitted through a semi-transparent opening configured on an enclosed package containing a liquid food item and the resulting NIR reflection spectra is collected. The quality of the liquid food item is estimated by correlating a plurality of features derived from the NIR reflection spectra with the concentration of the biomarker contained in the liquid food item, using a trained machine learning model and the remaining shelf life of the liquid food item is estimated based on the concentration of the biomarker.

There is a demand for low-cost robust method to detect corrosion for estimating corrosion inhibitor (CI) concentration sensing. This disclosure herein relates to method and system for estimating corrosion inhibitor (CI) concentration using a multi-electrode array sensor. The method initially obtains a plurality of electrochemical signals using the multi-electrode array sensor from the corroding environment. Further, the plurality of electrochemical signals are analyzed to obtain a plurality of parameters. Further, the method analyses a plurality of features from the plurality of parameters for estimating the corrosion inhibitor (CI) concentration using a trained machine learning model. The method is capable of estimating the corrosion inhibitor concentration of any unknown liquid using the regression model and the classification model.

Method and system for estimating corrosion inhibitor concentration using a multi-electrode array sensor

This disclosure relates generally to a system and method for real-time non-invasive estimation of food quality within enclosed package. Existing works utilize invasive methods that require direct contact of the food item with the sensors. In the present disclosure, a potential is applied over a plurality of frequencies through the food item contained the enclosed package which includes a plurality of polyethylene layers and a conducting layer arranged between two adjacent polyethylene layers using electrochemical impedance spectroscopy. Values of electrical voltages and the electrical impedances of the food item are then obtained. A plurality of features is derived from the obtained values of the electrical voltages and the electrical impedances using a trained model. The present disclosure estimates the quality of the food item in real-time by co-relating the plurality of derived features with the quality of the food item contained inside the enclosed package.

System and method for real-time non-invasive estimation of food quality within enclosed package

This disclosure relates generally to detection of concentration of micro and nano particles in a fluid environment. An acoustic transmitter array is selective coated with polymer and receiver array is deployed at a random location in a conduit. The acoustic transmitter array on the conduit is insonified at a predetermined frequency to obtain a plurality of reflected signals. A plurality of key features pertinent to the conduit are extracted from the plurality of reflected signals to obtain a plurality of acoustic signals. A correlation model is configured by inputting, at least one feature associated with the pre-processed acoustic signals. A known concentrations of nano and micro particles are trained with an artificial neural network algorithm and calibrated with ground truth data. The location of the transmitter array and receiver array and the correlation model are finalized for detecting concentration of the particular micro and nano particles in the fluid environment.

System and method for detection of concentration of micro and nano particles in a fluid environment

This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.

System and method for monitoring and quality evaluation of perishable food items

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Pune, India

Parijat Dilip Deshpande