Low-cost VIS/NIR range hand-held and portable photospectrometer and evaluation of machine learning algorithms for classification performance

Abstract

In this study, the electronic design of a low-cost and portable spectrophotometer device capable of analyzing in the visible-near infrared region was established. The design of C#.NET-based user-friendly device control software and the development of machine learning algorithms for data classification as well as the comparison of the results were presented. When the spectrophotometer design and implementation studies are reviewed in the literature, two groups of subjects become prominent: (i) a new device fabrication, (ii) solution approaches to current problems by combining commercial portable spectrometer systems and devices with artificial intelligence applications. This work encompasses both groups, and a supportive approach has been followed on how to transform the theoretical knowledge into practice in device development and supportive software with the help of machine learning approaches from design to production. Three commercial spectral sensors, each with six photodiode arrays, were adopted in the spectrophotometer. Thus, 18 features belonging to each sample were acquired in the optical spectral region in the 410 nm to 940 nm band range. The spectral analyses were conducted with 9 different food types of powder or flake structures. A Support Vector Machines (SVM) and Convolutional Neural Network (CNN) approaches were employed for data classification. As a result, SVM and CNN achieved 97% and 95% accuracies, respectively. Moreover, we provided the spectral measurement data, the electronic circuit designs, the API files containing the artificial intelligence algorithms and the graphical user interface (GUI).