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PySpectro: A modular 3D printed, machine learning assisted optical device for recognition and quantification of

Cristian Grazioli1, Michele Abate1, Nicolò Dossi1

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Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a modular, 3D-printed instrument for absorbance and reflectance measurements using a light sensor and LED. The device enables rapid sample identification and quantification via machine learning, offering a portable solution for colorimetric detection.

Keywords:
3D printingFood dyes detectionMachine learningOptical analytical devicesPaper-based devicesPhosphate detection

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Area of Science:

  • * Materials Science and Engineering
  • * Analytical Chemistry
  • * Informatics and Machine Learning

Background:

  • * Advancements in materials, manufacturing, and informatics drive the development of new analytical devices.
  • * Existing analytical instruments can be complex and limited in portability.
  • * There is a need for accessible, versatile tools for rapid chemical analysis.

Purpose of the Study:

  • * To describe a modular, 3D-printed instrument for absorbance and reflectance spectroscopy.
  • * To demonstrate the device's capability for rapid sample identification and quantification.
  • * To showcase the application of machine learning for data analysis in a portable analytical device.

Main Methods:

  • * Development of a modular instrument using a 3D-printed chassis, AS7262 light sensor, LED, and Arduino Nano.
  • * Implementation of a Python-based software for data acquisition, handling, and analysis.
  • * Utilized a recursive regression methodology for quantitative analysis and machine learning algorithms for sample identification.

Main Results:

  • * The instrument successfully performed absorbance and reflectance measurements, with adjustable modes via 3D-printed components.
  • * Accurate sample quantification was achieved over 2.5 orders of magnitude with errors typically below 10%.
  • * Demonstrated efficacy on homogeneous solutions, Paper-based Analytical Devices (PADs), food dyes, and phosphate assays.

Conclusions:

  • * The 3D-printed spectroscopic instrument offers a versatile and cost-effective solution for colorimetric detection.
  • * The device integrates hardware and software, including machine learning, for efficient data analysis.
  • * Potential applications include field testing, rapid preliminary analysis, and educational purposes, extending beyond traditional laboratory settings.