Sparse NIR optimization method (SNIRO) to quantify analyte composition with visible (VIS)/near infrared (NIR) spectroscopy (350 nm-2500 nm)

Yonatan Peleg, Shai Shefer, Leon Anavy, Alexandra Chudnovsky, Alvaro Israel, Alexander Golberg, Zohar Yakhini*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Visual-Near-Infra-Red (VIS/NIR) spectroscopy has led the revolution in high-throughput phenotyping methods used to determine chemical and structural elements of organic materials. In the current state of the art, spectrophotometers used for imaging techniques are either very expensive or too large to be used as a field-operable device. In this study we developed a Sparse NIR Optimization method (SNIRO) that selects a pre-determined number of wavelengths that enable quantification of analytes in a given sample using linear regression. We compared the computed complexity time and the accuracy of SNIRO to Marten's test, to forward selection test and to LASSO all applied to the determination of protein content in corn flour and meat and octane number in diesel using publicly available datasets. In addition, for the first time, we determined the glucose content in the green seaweed Ulva sp., an important feedstock for marine biorefinery. The SNIRO approach can be used as a first step in designing a spectrophotometer that can scan a small number of specific spectral regions, thus decreasing, potentially, production costs and scanner size and enabling the development of field-operable devices for content analysis of complex organic materials.

Original languageEnglish
Pages (from-to)32-40
Number of pages9
JournalAnalytica Chimica Acta
StatePublished - 21 Mar 2019


FundersFunder number
Israel Ministry of Energy and Israel Ministry of Science and Technology
Microsoft Azure
Microsoft Corporation
Technion-Israel Institute of Technology
research groups of Alex Golberg in Tel Aviv University


    • Chemometrics
    • Diesel octane number
    • Imaging
    • Multivariate analysis
    • Seaweeds
    • Sparse linear regression
    • Ulva sp.
    • VIS/NIR spectroscopy


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