Software and Hardware Design Specifications for Quantifying Carbohydrate Contents in Food

Abstract

Analysis and detection of carbohydrate contents in food have been achieved in the past using different methods. However, these methods require pre-treatment and chemical reaction, which tamper with the sample composition, thereby making it unhealthy for consumption afterwards. A non- destructive method that will preserve the composition of the food sample– while giving an estimated result– is crucial. In this paper, a spectrometer was designed and built that can detect light spectrum penetrating through food samples. The goal is to assist diabetic patients and the general public to identify the amount of carbohydrate in their food for proper medical ration. The paper adopted the experimental research approach. An optical approach was employed using Near-Infrared LED to design a spectrometer that scans through food samples to obtain a spectrum using the TCD1304CD image sensor. The output was extracted using Arduino Uno Microcontroller which runs through a supervised machine learning algorithm, first to train the algorithm, and later to make the prediction using the algorithm. We trained the machine learning model using standard food samples with known composition. The system was tested using standard food samples which are not in the training dataset. The test result obtained was evaluated in comparison to the nutritional data of the testing samples. More so, it shows an average deviation of 46.87% of the expected values provided in the nutritional data of the sample.

Keywords

• spectrometry
• near-infrared radiation
• carbohydrate
• machine learning
• multiple linear regression
• feature scaling
• diabetes mellitus