Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry

Rapid differentiation of cystic fibrosis-related bacteria via reagentless atmospheric pressure photoionisation mass spectrometry

Breath analysis is an area of significant interest in medical research as it allows for non-invasive sampling with exceptional potential for disease monitoring and diagnosis. Volatile organic compounds (VOCs) found in breath can offer critical insight into a person's lifestyle and/or disease/health state. To this end, the development of a rapid, sensitive, cost-effective and potentially portable method for the detection of key compounds in breath would mark a significant advancement. Herein, we have designed, built and tested a novel reagent-less atmospheric pressure photoionisation (APPI) source, coupled with mass spectrometry (MS), utilising a bespoke bias electrode within a custom 3D printed sampling chamber for direct analysis of VOCs. Optimal APPI-MS conditions were identified, including bias voltage, cone voltage and vaporisation temperature. Calibration curves were produced for ethanol, acetone, 2-butanone, ethyl acetate and eucalyptol, yielding R2 > 0.99 and limits of detection < 10 pg. As a pre-clinical proof of concept, this method was applied to bacterial headspace samples of Escherichia coli (EC), Pseudomonas aeruginosa (PSA) and Staphylococcus aureus (SA) collected in 1 L Tedlar bags. In particular, PSA and SA are commonly associated with lung infection in cystic fibrosis patients. The headspace samples were classified using principal component analysis with 86.9% of the total variance across the first three components and yielding 100% classification in a blind-sample study. All experiments conducted with the novel APPI arrangement were carried out directly in real-time with low-resolution MS, which opens up exciting possibilities in the future for on-site (e.g., in the clinic) analysis with a portable system.

Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning

Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was developed. Specifically, volatile compounds in coffee aroma were detected using self-aspiration corona discharge ionization mass spectrometry (SACDI-MS), and the acquired MS data were processed using a customized deep learning algorithm to perform origin authentication automatically. To facilitate high-throughput analysis, an air curtain sampling device was designed and coupled with SACDI-MS to prevent volatile mixing and signal overlap. An accuracy of 99.78% was achieved in the classification of coffee samples from six origins at a throughput of 1 s per sample. The proposed approach may be effective in preventing coffee fraud owing to its straightforward operation, rapidity, and high accuracy and thus benefit consumers.

Cyanostilbene-based fluorescent paper array for monitoring fish and meat freshness via amino content detection

The detection of biogenic amines released from degraded meats is an effective method for evaluating meat freshness. However, existing traditional methods like titration are deemed tedious, while the use of sophisticated analytical instruments is not amenable to field testing. Herein, a cyanostilbene-based fluorescent array was rapidly fabricated using macroarray synthesis on a cellulose paper surface to detect amines liberated from spoiled beef, fish, and chicken. The fluorescence changes of immobilized molecules from the interaction with gaseous amines were used to monitor changes in freshness levels. Thanks to the high-throughput nature of macroarray synthesis, a set of highly responsive molecules such as pyridinium and dicyanovinyl moieties were quickly revealed. Importantly, this method offers flexibility in sensing applications including (1) sensing by individual sensor molecules, where the fluorescence response correlated well with established titration methods, and (2) collective sensing whereby chemometric analysis was used to provide a cutoff of freshness with 73-100% accuracy depending on meat types. Overall, this study paves the way for a robust and cost-effective tool for monitoring meat freshness.

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.