Rapid detection and quantification of milk adulterants using a nanoclusters-based fluorescent optical tongue.

A paper-based sensor array consisting of eight nanoclusters (NCs) combined with multivariate analysis was used as a rapid method for the determination of animal sources of milk; goat, camel, sheep and cow. It was also used to detect and quantify three adulterants including sodium hypochlorite, hydrogen peroxide and formaldehyde in milk. The changes in fluorescence intensity of the NCs were quantified using a smartphone when the sensor array was immersed in the milk samples. The device generated a specific colorimetric signature for milk samples from different animals and for different adulterants. This allowed simultaneous identification of animal and adulterant sources with 100% accuracy. The device was found to be capable of accurately measuring the level of contaminants with a detection limit as low as 0.01% using partial least squares regression. In conclusion, a paper-based optical tongue device has been developed for the detection of adulterants in milk with point-of-need capability.

Colorimetric determination of peroxide value in vegetable oils using a paper based analytical device.

Peroxide value (PV) is one of the most typically used quality parameters to monitor lipid oxidation. Here, a simple paper-based analytical device (PAD) has been developed to determine PV in vegetable oils. The analysis is based on setting up the iodometric titration, where hydroperoxides in the oil are reacted with excess iodide ions to generate iodine molecules, on the paper substrate. The device is composed of two paper layers acting as reaction and detection zones, aligned in a metallic mold. A well-defined inverse logarithmic calibration curve was established between the measured PV by the official iodometric method (ISO;3960, 2017) and the color intensity of PAD. It offered a working range of 0.01-30.0 meq/Kg. The limit of detection of 0.015 meq/Kg demonstrated enough sensitivity of the method to estimate peroxide value in edible oils. On-site and visual detection, low cost, simplicity, and less solvent consumption are advantages of the proposed device.

A poly(arylene ethynylene)-based microfluidic fluorescence sensor array for discrimination of polycyclic aromatic hydrocarbons.

Polycyclic aromatic hydrocarbons (PAHs) are persistent contaminants in the environment. Several of them have carcinogenic properties. There is considerable interest in their sensitive low-cost detection and monitoring. We present a simple paper-based microfluidic sensor for the rapid detection of PAHs. Craft punch patterning generated multiple detection zones inhabited by fluorescent poly(arylene ethynylene)s (PAEs). Changes in fluorescence image and/or intensity of the sensor array were recorded using a smartphone camera. The RGB color values of the photographed images were extracted through ImageJ software. 10 different PAHs were correctly identified using Principal Component Analysis and discrimination analysis (PCA-DA). 100% classification accuracy was achieved for model training, whereas validating the PCA-DA model by cross-validation resulted in 93% classification accuracy for 5.0 mg L-1 analyte.