US 12,241,841 B2
System and method for non-invasive real-time prediction of liquid food quality within enclosed package
Jayita Dutta, Pune (IN); Parijat Deshpande, Pune (IN); and Beena Rai, Pune (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Oct. 19, 2021, as Appl. No. 17/504,823.
Claims priority of application No. 202021045758 (IN), filed on Oct. 20, 2020.
Prior Publication US 2022/0120693 A1, Apr. 21, 2022
Int. Cl. G01N 21/78 (2006.01); G01N 33/02 (2006.01); G06Q 10/087 (2023.01)
CPC G01N 21/78 (2013.01) [G01N 33/02 (2013.01); G06Q 10/087 (2013.01)] 3 Claims
OG exemplary drawing
 
1. A method for non-invasive real-time prediction of quality of a liquid food item within enclosed package, comprising:
capturing, via one or more hardware processors, an image of a Color Changing Indicator (CCI) in a bio-sensor strip forming a component of the enclosed package, when the liquid food item comes in contact with the CCI, wherein the CCI comprises:
a transparent poly-di-methyl-siloxane (PDMS) substrate;
a thin film layer of bio-edible and bio-compatible color changing pigments, wherein the bio-edible and bio-compatible color changing pigments change color by interacting with one or more chemical components of the liquid food item, wherein a plurality of physio-thermal properties of each of the one or more chemical components vary with degradation of the liquid food item; and
an optical device, wherein the color change of the color changing pigments is visible through a transparent lens of the optical device;
determining, via the one or more hardware processors, a color of the CCI;
processing, via the one or more hardware processors using a machine learning data model, information on a) the determined color of the CCI, b) a measured ambient temperature inside the enclosed package while determining the color of the CCI and c) a measured relative humidity inside the enclosed package while determining the color of the CCI;
determining, via the one or more hardware processors, a value of a remaining shelf life of the liquid food item by:
determining a current quality of the liquid food item for the determined color of the CCI, the measured ambient temperature inside the enclosed package, and the measured relative humidity inside the enclosed package, in comparison with training data, wherein the training data used for training the machine learning data model comprises information on quality of the liquid food item corresponding to a plurality of combinations of a) a color of CCI, b) a value of the measured ambient temperature, and c) a value of the measured relative humidity;
determining a rate of deterioration of the liquid food item, based on the determined current quality of the liquid food item, and time expired, wherein the time expired is measured based on packaging date of the liquid food item; and
determining the value of the remaining shelf life of the liquid food item, based on the determined rate of deterioration; and
generating, via the one or more hardware processors, a result indicating the determined value of the remaining shelf life of the liquid food item.