The menace of saffron adulteration: Low-cost rapid identification of fake look-alike saffron using Foldscope and machine learning technology - Husaini, Amjad M.; Haq, Syed Anam Ul; Shabir, Asma; Wani, Amir B.; Dedmari, Muneer A. (Frontiers in Plant Science, 2022)
Saffron authenticity is an important issue for the saffron industry and the consumers, food industry, and regulatory agencies. Herein we describe a combo of two novel methods to distinguish genuine saffron from fake in a user-friendly manner and without sophisticated instruments. Foldscope, an origami-based low-cost optical microscope, was used for the first time to visualize characteristic features and distinguish ‘genuine’ saffron from ‘fake’. Coupled with a smartphone, it can be used by saffron dealers to showcase the authenticity of saffron to their customers efficiently. Furthermore, destaining and staining agents were used to study the staining patterns. Toluidine blue staining pattern was distinct and easier to use as it stained the papillae and the margins deep purple, while its stain is lighter yellowish green towards the central axis. Further to automate the process, we tested and compared different machine-learning-based classification approaches for performing the automated saffron classification into genuine or fake. We demonstrated that the deep learning-based models are efficient in learning the morphological features and classifying samples either fake or genuine, making it much easier for end-users. This approach performed much better than conventional Machine Learning approaches (Random Forest and SVM), and the model achieved an accuracy of 99.5 % and a precision of 99.3% on the test dataset. The process has increased the robustness and reliability of authenticating saffron samples.