A processor implemented method , comprising: transmitting , via one or more hardware processors , a near infra-red radiation, NIR, signal over a range of frequencies using a near infra-red radiation, NIR, device through a semi-transparent window configured on an enclosed package containing a liquid food item; receiving, via the one or more hardware processors , data related to near infra-red radiation, NIR, reflection spectra inside the enclosed package containing the liquid food item using the NIR device ; scanning the NIR reflection spectra by a user via a mobile application to obtain the data related to the NIR reflection spectra inside the enclosed package containing the liquid food item; collecting , via the one or more hardware processors , the data related to NIR reflection spectra inside the enclosed package containing the liquid food item using an electronic device ; deriving , using a machine learning model included in the electronic device via the one or more hardware processors , a plurality of features from the NIR reflection spectra so collected, wherein the machine learning model is trained on NIR reflection spectra obtained over a range of frequencies to characterize the liquid food item and correlate with a concentration of a biomarker contained in the liquid food item indicative of quality; performing , via the one or more hardware processors , a correlation of the plurality of derived features with the concentration of the biomarker contained in the liquid food item, and estimating a remaining shelf-life of the liquid food item based on the concentration of the biomarker; estimating , via the one or more hardware processors , the quality of the liquid food item based on the correlation and the estimated remaining shelf-life; and displaying to the user on the mobile application, the quality of the liquid food item based on the correlation and the estimated remaining shelf-life. transmitting , via one or more hardware processors , a near infra-red radiation, NIR, signal over a range of frequencies using a near infra-red radiation, NIR, device through a semi-transparent window configured on an enclosed package containing a liquid food item; receiving, via the one or more hardware processors , data related to near infra-red radiation, NIR, reflection spectra inside the enclosed package containing the liquid food item using the NIR device ; scanning the NIR reflection spectra by a user via a mobile application to obtain the data related to the NIR reflection spectra inside the enclosed package containing the liquid food item; collecting , via the one or more hardware processors , the data related to NIR reflection spectra inside the enclosed package containing the liquid food item using an electronic device ; deriving , using a machine learning model included in the electronic device via the one or more hardware processors , a plurality of features from the NIR reflection spectra so collected, wherein the machine learning model is trained on NIR reflection spectra obtained over a range of frequencies to characterize the liquid food item and correlate with a concentration of a biomarker contained in the liquid food item indicative of quality; performing , via the one or more hardware processors , a correlation of the plurality of derived features with the concentration of the biomarker contained in the liquid food item, and estimating a remaining shelf-life of the liquid food item based on the concentration of the biomarker; estimating , via the one or more hardware processors , the quality of the liquid food item based on the correlation and the estimated remaining shelf-life; and displaying to the user on the mobile application, the quality of the liquid food item based on the correlation and the estimated remaining shelf-life. <EOS>