Abstract: A method of predicting a blood compound concentration of a target may include receiving, by a system, spectral data associated with a region of the target, using near-infrared (NIR) spectroscopy. The method may include classifying, by the system, each of the plurality of data instances of the spectral data to one of a plurality of labelled classes. The method may include obtaining, by the system, one or more best fit models from a plurality of prediction models based on the classification. The method may include determining, by the system, blood compound concentration values corresponding to each of the one or more best fit models. The method may include predicting, by the system, the blood compound concentration of the target using the blood compound concentration values predicted using the best fit models.

Abstract: A method of estimating concentration of a blood compound may include: removing a baseline drift from Near-Infrared (NIR) spectroscopy data to obtain drift-free spectral features; obtaining a set of global features based on the drift-free spectral features; and estimating the concentration of the blood compound by regression using the set of global features.

Abstract: A method for obtaining blood glucose concentration using near infrared spectroscopy (NIR) data is provided. The method includes obtaining, by an independent component analysis (ICA) temporal module, orthogonal pure spectra from human NIR spectra; performing, by a processing module, one or more preprocessings and drift removal on the human NIR spectra and the orthogonal pure spectra to obtain preprocessed spectra; and obtaining, by a regression block, the blood glucose concentration from the preprocessed spectra.

Abstract: A method for obtaining blood glucose concentration using near infrared spectroscopy (NIR) data is provided. The method includes obtaining, by an independent component analysis (ICA) temporal module, orthogonal pure spectra from human NIR spectra; performing, by a processing module, one or more preprocessings and drift removal on the human NIR spectra and the orthogonal pure spectra to obtain preprocessed spectra; and obtaining, by a regression block, the blood glucose concentration from the preprocessed spectra.

Abstract: A method includes processing near infrared spectroscopy (NIR) spectra and pure spectra, to obtain a preprocessed NIR spectra, wherein the preprocessed NIR spectra includes any one or any combination of a training data of a plurality of subjects, a calibration data of a test subject and a validation data of the test subject, extracting a dominant feature set from the preprocessed NIR spectra, wherein the dominant feature set includes at least one preprocessed NIR spectrum corresponding to each of the training data, the calibration data and the validation data, and determining a blood glucose concentration of the test subject, using the training data of the plurality of subjects and based on the extracted dominant feature set.

Abstract: A method of predicting a blood compound concentration of a target may include receiving, by a system, spectral data associated with a region of the target, using near-infrared (NIR) spectroscopy. The method may include classifying, by the system, each of the plurality of data instances of the spectral data to one of a plurality of labelled classes. The method may include obtaining, by the system, one or more best fit models from a plurality of prediction models based on the classification. The method may include determining, by the system, blood compound concentration values corresponding to each of the one or more best fit models. The method may include predicting, by the system, the blood compound concentration of the target using the blood compound concentration values predicted using the best fit models.

Abstract: A method of extracting glucose feature, a method for monitoring glucose using near-infrared (NIR) spectroscopy and a glucose monitoring device are provided. The method comprising: removing noise from a near-infrared (NIR) data; extracting a glucose signal from the NIR data; and removing temporal drift components from the extracted glucose signal.

Abstract: A method for predicting a blood glucose level using a near-Infrared (NIR) spectrometer is provided. The method may include obtaining a feature set from an NIR glucose spectra; and predicting glucose values from the feature set based on a binary classification of the NIR glucose spectra and an in-class prediction of glucose using Machine Learning Regression.

Abstract: A method includes processing near infrared spectroscopy (NIR) spectra and pure spectra, to obtain a preprocessed NIR spectra, wherein the preprocessed NIR spectra includes any one or any combination of a training data of a plurality of subjects, a calibration data of a test subject and a validation data of the test subject, extracting a dominant feature set from the preprocessed NIR spectra, wherein the dominant feature set includes at least one preprocessed NIR spectrum corresponding to each of the training data, the calibration data and the validation data, and determining a blood glucose concentration of the test subject, using the training data of the plurality of subjects and based on the extracted dominant feature set.

Abstract: A method of estimating concentration of a blood compound may include: removing a baseline drift from Near-Infrared (NIR) spectroscopy data to obtain drift-free spectral features; obtaining a set of global features based on the drift-free spectral features; and estimating the concentration of the blood compound by regression using the set of global features.

Abstract: A method for obtaining blood glucose concentration using near infrared spectroscopy (NIR) data is provided. The method includes obtaining, by an independent component analysis (ICA) temporal module, orthogonal pure spectra from human NIR spectra; performing, by a processing module, one or more preprocessings and drift removal on the human NIR spectra and the orthogonal pure spectra to obtain preprocessed spectra; and obtaining, by a regression block, the blood glucose concentration from the preprocessed spectra.

Abstract: A method for predicting a blood glucose level using a near-Infrared (NIR) spectrometer is provided. The method may include obtaining a feature set from an NIR glucose spectra; and predicting glucose values from the feature set based on a binary classification of the NIR glucose spectra and an in-class prediction of glucose using Machine Learning Regression.

Abstract: A method of extracting glucose feature, a method for monitoring glucose using near-infrared (NIR) spectroscopy and a glucose monitoring device are provided. The method comprising: removing noise from a near-infrared (NIR) data; extracting a glucose signal from the NIR data; and removing temporal drift components from the extracted glucose signal.