Herbs and spices play an important role as flavorants, colorants, preservatives and bioactive compounds in medical, food and cosmetic applications. Food fraud in herbs and spices is an important topic, which has led to new technologies being studied as potential tools for fraud identification. At the present study, the chemical characterisation of Iranian herbs and spices was carried out based on spectral fingerprint of UV-Vis, FT-IR and cyclic voltammetry (CV). The fingerprint approach has become one of the most effective tools for quality assessment of herbal medicines, spice, food and food supplements. Due to the complexity spectral fingerprints the chemometric approach include principal components analysis (PCA), K Nearest Neighbour (KNN) and partial least square discriminant analysis (PLS-DA) is employed to analytical evaluation of quality, adulteration, variety and geographical origin. Initially, the spectra of fingerprints of samples obtained from the three techniques were analyzed individually and then the data fusion strategy was proposed for classification of data sets. Meanwhile, the latent variables (LVs) of three sets were extracted by partial least square discriminant analysis (PLS-DA) and three data sets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by proposed chemometric techniques. The classification results showed that the data fusion strategy can improve the classification performance effectively in comparison with single data sets. The fusion strategy and PLS-DA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity. Also, PLS-DA of fusion matrix seem to be promising tools for determining the presence of adulterants, and contaminants in herbs and spices. Biological synthesis of nanoparticles using biological agents has attracted much attention in the field of nanotechnology due to its cost effectiveness, non toxicity and environmental compa