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Abstract Numerous novel methods to detect foodborne pathogens have been extensively developed to ensure food safety. Among the important foodborne bacteria, Bacillus cereus was identified as a pathogen of concern that causes various food illnesses, leading to interest in developing effective detection methods for this pathogen. Although a standard method based on culturing and biochemical confirmative test is available, it is time- and labor-intensive. Alternative PCR-based methods have been developed but lack high-throughput capacity and ease of use. This study, therefore, attempts to develop a robust method for B. cereus detection by leveraging the highly specific pyrrolidinyl peptide nucleic acids (PNAs) as probes for a bead array method with multiplex and high-throughput capacity. In this study, PNAs bearing prolyl-2-aminocyclopentanecarboxylic acid (ACPC) backbone with groEL, motB, and 16S rRNA sequences were covalently coupled with three sets of fluorescently barcoded beads to detect the three B. cereus genes. The developed acpcPNA-based bead array exhibited good selectivity where only signals were detectable in the presence of B. cereus, but not for other species. The sensitivity of this acpcPNA-based bead assay in detecting genomic DNA was found to be 0.038, 0.183 and 0.179 ng for groEL, motB and 16S rRNA, respectively. This performance was clearly superior to its DNA counterpart, hence confirming much stronger binding strength of acpcPNA over DNA. The robustness of the developed method was further demonstrated by testing artificially spiked milk and pickled mustard greens with minimal interference from food metrices. Hence, this proof-of-concept acpcPNA-based bead array method has been proven to serve as an effective alternative nucleic acid-based method for foodborne pathogens.

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.

In the past few decades, chemistry has evolved to interact with various disciplines to synergistically help tackle global challenges. This, in turn, requires that newer generations of chemistry students are trained to be more flexible in accepting and coordinating new concepts. In this experiment, pineapple was used as a key model to allow for the incorporation of multiple important concepts into the laboratory. Using carotenoid separation as a main goal, column chromatography, UV-vis spectroscopy, thin-layer chromatography, high-performance liquid chromatography, mass spectrometry, and principal component analysis can be included in a cohesive laboratory experiment. On the other hand, these activities were also designed to be modular, thus allowing instructors to add, remove, or modify the contents in a highly customizable manner. This makes it highly versatile and amenable to uncertain situations like unexpected university closure due to COVID-19related lockdown. Overall, this laboratory experiment serves as a practical example of how chemistry can help solve real-world problems while also allowing high flexibility in teaching management.