Abstract: A system may include one or more sensors which analyze a sample of human milk to be fed to a specific infant. The system may further include an artificial intelligence server including one or more processors implementing several artificial intelligence processes. The artificial intelligence server may receive sensor data generated by one or more sensors and analyze the sensor data to identify constituent elements of macronutrients and micronutrients in the sample of milk. The artificial intelligence server may further compare the constituent elements of macronutrients and or micronutrients in the sample of milk with nutritional guidelines and with nutritional protocols obtain from historical clinical data from infants with a clinical profile similar to that of the specific infant. The artificial intelligence server may further identify one or more disease risk scores for the specific infant. Methods implemented by the system are also disclosed.

Abstract: A system includes a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer and a cloud server including a server processor. The server processor receives the spectrometer data generated by the spectrometer from the processor, analyzes the spectrometer data, identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample and provides to the processor data representative of a graphical display, which includes an indication of whether or not the scanned sample includes the one or more unique characteristics of the spectrometer data.

Abstract: A cuvette includes a first body portion and a second body portion. The first body portion and the second body portion may be connected by a hinge. The cuvette may include a first slide and a second slide.

Abstract: Obtaining two spectra from the same sample under two different conditions at about the same time for comparison, where at least one of the spectra measures magnitudes of electromagnetic radiation on at least four different ranges or weightings of wavelengths or frequencies. Classifying a sample using these spectra obtained by a user, and using spectra obtained from different samples by different users to identify the sample. Computing correlations between data related to food and ingredient consumption by one or more users over time, and data related to passive personal log data, user entered feedback, user interaction data or personal information related to those users, and detecting: foods or ingredients to which a user may be allergic or intolerant; a possible medical condition of a user; a possible link between food and ingredient consumption and a medical or health condition; or a similarity between at least two such users.

Abstract: Obtaining two spectra from the same sample under two different conditions at about the same time for comparison, where at least one of the spectra measures magnitudes of electromagnetic radiation on at least four different ranges or weightings of wavelengths or frequencies. Classifying a sample using these spectra obtained by a user, and using spectra obtained from different samples by different users to identify the sample. Computing correlations between data related to food and ingredient consumption by one or more users over time, and data related to passive personal log data, user entered feedback, user interaction data or personal information related to those users, and detecting: foods or ingredients to which a user may be allergic or intolerant; a possible medical condition of a user; a possible link between food and ingredient consumption and a medical or health condition; or a similarity between at least two such users.

Abstract: Obtaining two spectra from the same sample under two different conditions at about the same time for comparison, where at least one of the spectra measures magnitudes of electromagnetic radiation on at least four different ranges or weightings of wavelengths or frequencies. Classifying a sample using these spectra obtained by a user, and using spectra obtained from different samples by different users to identify the sample. Computing correlations between data related to food and ingredient consumption by one or more users over time, and data related to passive personal log data, user entered feedback, user interaction data or personal information related to those users, and detecting: foods or ingredients to which a user may be allergic or intolerant; a possible medical condition of a user; a possible link between food and ingredient consumption and a medical or health condition; or a similarity between at least two such users.