Paper Spray Mass Spectrometry as an Effective Tool for Differentiating Coffees Based on Their Geographical Origins

With the rising trend of valuing flavor complexity of coffees, means to distinguish the properties of individual coffee sources is vital to the sustainable growth of the coffee industry. Herein, paper spray mass spectrometry (PS-MS), a simple technique with little sample preparation, was used to collect mass data from aqueous extracts of coffees from various sources. Thereafter, principal component analysis and linear discriminant analysis were used to successfully classify coffee samples (with 80-100 % accuracy) from various studies including the differentiations of Arabica and Robusta coffees, Arabica coffees from different countries, Robusta coffees from different geographical locations, and Arabica coffees from different locations within the same province in Thailand. With further insight from significant test via Fisher weight determination, this method was proved to be practical for differentiating coffees based on types and geographical origins, thus paving the way for broader applications.

Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry

With increasing demands for more rapid and practical analyses, various techniques of ambient ionization mass spectrometry have gained significant interest due to the speed of analysis and abundance of information provided. Herein, an ambient ionization technique that utilizes corona discharge was applied, for the first time, to analyze and categorize whole seeds of black and white peppers from different origins. This setup requires no solvent application nor gas flow, thus resulting in a very simple and rapid analysis that can be applied directly to the sample without any prior workup or preparation. Combined with robust data pre-processing and subsequent chemometric analyses, this analytical method was capable of indicating the geographical origin of each pepper source with up to 98% accuracies in all sub-studies. The simplicity and speed of this approach open up the exciting opportunity for onsite analysis without the need for a highly trained operator. Furthermore, this methodology can be applied to a variety of spices and herbs, whose geographical indication or similar intellectual properties are economically important, hence it is capable of creating tremendous impact in the food and agricultural industries.

MCR-ALS with sample insertion constraint to enhance the sensitivity of surface-enhanced Raman scattering detection

The multivariate curve resolution-alternative least squares (MCR-ALS) algorithm was modified with sample insertion constraint to deconvolute the overlapping peaks in SERS spectra. The developed method was evaluated by the spectral data simulated using a Gaussian distribution function to generate two independent peaks corresponding to a capping agent and an analyte. The spectra were generated with different overlapping levels and various intensity ratios of the analyte to the capping agent. By using MCR-ALS with the sample insertion constraint, the peak of the capping agent was completely excluded to obtain a calibration model of the analyte with R² > 0.95 under all conditions. Furthermore, our developed method was later applied to a real SERS measurement to quantify carbofuran (analyte) using the azo-coupling reaction with p-ATP (capping agent) on silver nanoparticles as a SERS substrate. A calibration model of derivative carbofuran phenol was generated with R² = 0.99 and LOD = 28.19 ppm. To assess the performance of the calibration model, the model was used to estimate the concentration of carbofuran in an external validation set. It was found that the RMSE of prediction was only 2.109 with a promising R² = 0.97.