ER-R: Improving regression by deep learning and prior knowledge utilization for fluorescence analysis

Sergey Sinitsa*, Nir Sochen, Mikhail Borisover, Nadia Buchanovsky, David Mendlovic, Iftach Klapp

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Linear regression is a dominant estimation technique in chemometrics, where there is a need for inexpensive and reliable sensors for water monitoring. However, most problems are nonlinear, such as the estimation of concentration in solution from an emitted fluorescence spectrum (EFS). Even if an estimation method gives desirable results, at some point it will be used under field conditions, where poor signal quality and less control over environmental effects are expected, leading to poor performance. In this study, we overcome these problems by implementing deep neural network (DNN) models and transfer learning technique for EFS analysis. The proposed models, R (Regression module) and ER (Encoder-Regression), outperformed linear methods and a naive DNN approach for high-quality laboratory-sampled data with a maximum mean relative error of ∼11%, vs. a minimum mean relative error of 184% for the linear methods. In the case of low-quality data, which were simulated based on a real-use case, the lowest error of the linear methods climbed to 263%, whereas the proposed ER model error remained at 9%. At low concentrations, ER gave the best results for all datasets: ∼3.46 ppb in the high-quality datasets, and 2.4 ppb in the low-quality datasets.

Original languageEnglish
Article number104785
JournalChemometrics and Intelligent Laboratory Systems
StatePublished - 15 May 2023


FundersFunder number
Ministry of Agriculture and Rural Development20-12-0018


    • Chemometrics
    • Deep learning
    • Organic contamination
    • Regression
    • Transfer learning
    • Water quality


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