{'fraction_non_alphanumeric': 0.034482758620689655, 'fraction_numerical': 0.0034482758620689655, 'mean_word_length': 5.84433962264151, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Use of chemical profiling to differentiate geographic growing origin of raw pistachios.

The objective of this study was to demonstrate the feasibility of chemical profiling methods combined with multivariate methods to differentiate the geographical growing regions of pistachios (Pistachia vera). Elemental analysis (Ba, Be, Ca, Cu, Cr, K, Mg, Mn, Na, V, Fe, Co, Ni, Cu, Zn, Sr, Ti, Cd, and P) of pistachios samples was performed using inductively coupled plasma atomic emission spectrometry. Analysis of inorganic anions and organic acids (selenite, bromate, fumarate, malate, selenate, pyruvate, acetate, phosphate, and ascorbate) of pistachio samples was performed using capillary electrophoresis. Bulk carbon and nitrogen isotope ratios were performed using stable isotope MS. There were nearly 400 pistachio samples analyzed from the three major pistachio growing regions: Turkey, Iran, and California (United States). A computational evaluation of the trace element data sets was carried out using statistical pattern recognition methods including principal component analysis, canonical discriminant analysis, discriminant analysis, and neural network modeling. Several linear discriminant function models classified the data sets with 95% or higher accuracy. We report the development of a method combining elemental analysis and classification techniques that may be widely applied to the determination of the geographical origin of foods.