In this proof of concept study (open access) the authors used machine learning to build a classification model for saffrom authentication using mid infrared spectroscopy (MIR). MIR is a portable technique.
111 authentic saffron samples (2023 harvest) were directly sourced from farmers in 6 different regions of Iran.
Adulterant were prepared in-house by blending 1 – 30% of saffron style, safflower, madder, or calendula.
After grinding into a uniform powder and then sieved in accordance with ISO 3632 standard, the samples were washed, dried, ground and then extracted into ultrapure water. Extracts were filtered, then analysed by a solvent MIR spectrometer.
The authors report that data-driven soft independent modeling of class analogy (DD-SIMCA) successfully differentiated between authentic and adulterated samples, achieving 100 % sensitivity and specificity. PLS-DA and RSDE were then employed to identify the type and level of adulterants, with RSDE clearly outperforming PLS-DA, achieving accuracy above 94.0 %, as compared to PLS-DA's accuracy of over 90.0 %. They were also able to differentiate between the 6 Iranian growing regions. The authors do not report if they challenged or validated their model with samples independent of the reference set.
In conclusion, they conclude that the combination of solvent-based MIR spectroscopy and modern chemometric techniques shows great potential as a reliable tool for saffron quality control at the point of need.
