Development of Colorimetric Sensor Array Based on Metal Ion Complexes for Discrimination and Adulteration Detection of Lime Juice Samples

The Quality assurance of beverages are important growing need of both industry and consumers. Lime, is one of the most important citrus species cultivated. Lime fruits are rich in nutrients such as vitamins, fiber, minerals, carotenoids, phenolic compounds, flavanones, hesperidin and eriocitrin [1]. Thus, lime juice quality control is importance because of synthetic product effects consumers’ health. Until now, several analytical methods have been used to find a method to detect adulteration in lime, lemon and other Citrus juice. Gas chromatography–mass spectrometry (GCMS), Liquid chromatography–mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), solid-phase microextraction followed by GC-MS (SPME-GCMS), HPLC coupled with diode-array detection, electrospray ionization tandem mass spectrometry (HPLC-DAD-MS/MS), Near-infrared spectroscopy (NIR) and Fourier-transform infrared spectroscopy (FTIR) are common methods for detection of adulteration. However, they are expensive, time consuming, destructive, need professional operator, and don’t have point-ofneed capabilities [2-3]. In this work a simple colorimetric sensor array made of 32 metal ion complexes including 4 chemical responsive dyes and 4 metal ions in 2 buffered and non-buffered environments, was prepared by dropping the solutions of metal ion complexes on a hydrophobic paper. The colorimetric sensor array responses to volatile organic compounds (VOCs) in the head-space of 11 hand-made lime juice samples, 12 commercial samples of different brands and 15 adulterated samples, were evaluated using sensor images recorded by an ordinary flatbed scanner. The developed sensor array produced color difference map patterns as a unique fingerprint, for different types of the lime juice samples. Multivariate RGB responses of the designed sensor were analyzed with statistical unsupervised pattern recognition methods including principal component analysis (PCA) and hierarchical clustering analysis (HCA) without any preprocessing. The sensor array was provided an acceptable discrimination between commercial, natural and adulterated lime juice samples. Besides, analysis of the sensor array data by discriminant analysis method resulted in correct identification of the commercial sample brands. In addition, the array could discriminate natural lime juices prepared in three consecutive years. This sensor array can be used up to 1 month after production with a small deviation of 5.25% with fresh sensor responses and provide reproducible responses.