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A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

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A machine learning-assisted fluorescent sensor array utilizing silver nanoclusters for coffee discrimination.

Yidan Mo¹, Jinming Xu¹, Huangmei Zhou¹

¹State Key Laboratory of Precision Spectroscopy, East China Normal University, No.500, Dongchuan Rd., Shanghai 200241, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|July 3, 2024

Summary

This summary is machine-generated.

A new fluorescent sensor array using silver nanoclusters detects organic acids and identifies coffee origins and types with 100% accuracy. This technology aids in coffee quality control and detecting counterfeit products.

Keywords:

Coffees Fluorescent sensor array Organic acids Principal component analysis Random forest Silver nanoclusters

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Area of Science:

Analytical Chemistry
Materials Science
Food Science

Background:

Coffee is a major global commodity with significant commercial value.
Accurate detection and identification methods are crucial for coffee quality control and authenticity verification.
Existing methods may lack the specificity or efficiency for complex coffee sample analysis.

Purpose of the Study:

To develop a novel fluorescent sensor array for detecting organic acids and identifying coffee samples.
To assess the sensor array's capability in distinguishing coffees based on processing, roast degree, geographical origin, and mixtures.
To explore the potential of this sensor array in quality control and counterfeit coffee detection.

Main Methods:

Construction of a fluorescent sensor array using two types of polymer-templated silver nanoclusters (AgNCs).
Utilizing unique fluorescence response patterns generated by AgNC interactions with organic acids.
Application of principal component analysis (PCA) and random forest (RF) algorithms for data analysis and classification.

Main Results:

The sensor array demonstrated good qualitative and quantitative capabilities for organic acids.
Achieved 100% recognition accuracy in distinguishing coffees by processing methods and roast degrees.
Successfully identified 40 coffee samples from 12 geographical origins and classified mixtures and other beverages.

Conclusions:

A novel, highly accurate fluorescent sensor array for coffee analysis has been developed.
The sensor array shows significant potential for practical applications in coffee quality control and authentication.
This method offers a promising approach for identifying fake blended coffees and verifying product origin.