Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification

Joy Sim; Yash Dixit; Cushla Mcgoverin; Indrawati Oey; Russell Frew; Marlon M Reis; Biniam Kebede

doi:10.1016/j.foodres.2023.113518

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification

Food Res Int. 2023 Dec;174(Pt 1):113518. doi: 10.1016/j.foodres.2023.113518. Epub 2023 Sep 28.

Authors

Joy Sim¹, Yash Dixit², Cushla Mcgoverin³, Indrawati Oey⁴, Russell Frew⁵, Marlon M Reis², Biniam Kebede⁶

Affiliations

¹ Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand. Electronic address: simjo010@student.otago.ac.nz.
² AgResearch, Grasslands Research Centre, Palmerston North, New Zealand.
³ Department of Physics, University of Auckland, Auckland 1010, New Zealand; The Dodd-Walls Centre for Photonic and Quantum Technologies, Auckland 1010, New Zealand.
⁴ Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand; Riddet Institute, Palmerston North, New Zealand.
⁵ Oritain Global Limited, Dunedin, New Zealand.
⁶ Department of Food Science, University of Otago, PO BOX 56, Dunedin 9054, New Zealand. Electronic address: biniam.kebede@otago.ac.nz.

PMID: 37986508
DOI: 10.1016/j.foodres.2023.113518

Abstract

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, and δ³⁴S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ¹⁸O, δ²H, and δ³⁴S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R²_predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.

Keywords: Green coffee bean; Non-destructive; Origin traceability; Prediction; Stable Isotopes; Support vector regression; Trace elements.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Hyperspectral Imaging
Isotopes
Spectroscopy, Near-Infrared
Trace Elements* / analysis

Substances

Trace Elements
Isotopes