Accuracy and stability enhanced honey authenticity visual tracing method via false positive-eradicating PCR assisted nucleic acid-capturing lateral flow strip

Low-cost signal enhanced colorimetric and SERS dual-mode paper sensor for rapid and ultrasensitive screening of mercury ions in tea

Mercury ions (Hg2+) are highly toxic heavy metals that are commonly found in natural environments. Owning to their non-biodegradability and accumulation in the food chain, the precise detection of trace amounts of Hg2+ is essential for preventing chronic accumulation and ensuring food safety. In this study, we present a dual-mode paper sensor for simultaneous colorimetric and Surface-Enhanced Raman Spectroscopy (SERS) detection of Hg2+ in tea, achieving ultrasensitive, rapid, and on-site screening. 4-Mercaptopyridine (4-MPY) was effectively chemisorbed onto the gold nanoparticles (AuNPs), acting as a signal probe for colorimetric methods. Moreover, it can produce plasmonic hot spots for SERS by interacting with the pyridine ring. To enhance the signal intensity of both colorimetry and SERS, a silver shell is in-situ grown on the surface of AuNPs captured on the paper sensor by reduction of Ag+, achieving signal amplification. The visual limit of detection (LOD) for the colorimetric biosensor is 2.5 pM, while the LOD of SERS is 0.48 pM with this dual-mode paper sensor. The sensitivity of both the colorimetric method and SERS was improved by approximately 200 and 500 times, respectively, with the designed signal amplification strategy. The system allows for multiple parallel screening of the same sample, ensuring accurate results without any false-positive or false-negative. This study provides a valuable platform for the accurate detection of various other heavy metal ions and provides effective strategies for improving the performance of colorimetric methods.

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.