Jinorose, Maturada

Loading...
Thumbnail Image
Preferred name
Jinorose, Maturada
Alternative Name
Jinorose, M.
Main Affiliation
Email
maturada.ji@kmitl.ac.th

Settings

Now showing 1 - 1 of 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Development of machine learning enhanced low-cost spectrophotometer for pesticide prediction
    (2025-05-15)
    Murathathunyaluk, S.
    ;
    Jinorose, M.
    ;
    Janpetch, K.
    ;
    Chanthapanya, N.
    ;
    Sombatsri, W.
    Conventional analytical methods for measuring pesticide concentrations, such as chromatography, offer high accuracy but require expensive instrumentation, prompting the investigation of cost-effective alternatives like smartphone-based spectrophotometers. Despite their potential, these methods face challenges related to assembly and precision, often requiring human intervention to select appropriate images for analysis. This study presents a novel, affordable spectrophotometer designed for integration with machine learning algorithms. The device captures images of two spectral bands and employs a six-step image processing methodology to prepare images for analysis. A machine learning model trained on four algorithms with feature selection and cross-validation demonstrates high accuracy in predicting chemical concentrations of coloured solutions. The approach achieves 98.5 % accuracy for KMnO<inf>4</inf> and 96.7 % for Carbosulfan solutions, comparable to high-end spectrophotometry devices. The design eliminates the need for human intervention, reducing biased selection and result manipulation. However, concentration estimation of non-coloured compounds remains inaccurate, indicating areas for further refinement.