Joseph Paul Robinson

Due to the prevalence of food fraud and its associated risks to human health, food safety and quality assurance systems must become more comprehensive, rapid, and accurate. Food fraud often involves contamination, which refers to the presence of harmful substances in food, including biological, chemical, and physical adulterants. Traditional detection methods, such as microbiological and chemical testing, are limited in sensitivity, are time-consuming, and often lack comprehensive analysis capabilities. This report presents the development of a hybrid Raman and laser-induced breakdown spectroscopy (LIBS) system, referred to as Hy-R-LIBS, for the quantitative analysis of food contaminants on fruit surfaces. In this work, the term hybrid denotes a system in which both spectroscopic modalities are co-housed within a single optical chassis, share a common optical path, and perform automated sequential acquisition on the same sample spot, enabling either independent or joint analysis of the resulting spectra. Raman spectroscopy provides molecular fingerprinting capability, while LIBS enables elemental analysis at low concentrations. The miniaturization of the combined system is feasible because Raman and LIBS share similar optical pathways. To overcome the inherently weak Raman scattering cross-section that limits sensitivity in portable systems, a surface-enhancement strategy based on silver nanoparticles (AgNPs) was employed. The study demonstrated that AgNP-assisted surface enhancement improves both LIBS and Raman signals, as confirmed by the quantitative analysis of food contaminants on fruit peel. Pesticide and heavy-metal controls were used, and the limit of detection (LOD) for various contaminants was estimated. The results indicate the feasibility of the hybrid system and the enhancement achieved through surface-enhancement techniques. Overall, the Hy-R-LIBS system offers a promising approach for the quantitative analysis of food contaminants. The combination of these optical spectroscopic techniques enables comprehensive investigations of complex food matrices contaminated with both organic chemicals and metallic residues, providing valuable insights for food safety assurance and public health.

The genus Vibrio contains over 150 species of aquatic origin, some of which are important foodborne pathogens, while others cause diseases in finfish, shellfish and/or coral. Accurate identification of these bacteria is important, but highly challenging given their taxonomic diversity. Here we used a range of methods to identify strains from New Zealand oysters initially identified as Vibrio vulnificus. The API20E kit either misidentified, or failed to identify the strains depending on the database used; comparisons of the 16S rRNA gene sequences could not distinguish them from over 50 exemplars of other Vibrio spp. or Allocatenococcus thiocycli. Elastic light scatter analysis showed clear differences between each of the novel isolates and reference strains of Vibrio parahaemolyticus and V. vulnificus, but limits of the in-house database precluded identification. Our whole-genome sequencing (WGS) pipeline allowed genomes and plasmids to be effectively sequenced to completion, enabling in silico DNA-DNA hybridization analysis with 162 Vibrio spp., which identified the strains as either V. campbellii, or one that may represent a novel species closely related to V. rotiferianus. Genome analysis also revealed a variety of genes found in aquacultural and human pathogens, including V. cholerae. Furthermore, resistance to multiple antibiotic classes was indicated both genetically and phenotypically. The implications for New Zealand aquaculture and public health are discussed. WGS is an effective identification strategy for taxonomically complex bacteria such as Vibrio spp.

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R² = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 10⁴ CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 10³ CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications.

Flow cytometry is a versatile analytical technology for measuring physical and molecular characteristics of individual cells or particles in suspension. The technology has had its greatest impact in immunology, enabling the identification and quantification of rare cell populations within complex mixtures, but applications span diverse biological systems including hematopoietic cells, microorganisms, cultured cells, plant cells, gametes, and disaggregated tissues. Target molecules are typically identified using fluorophore‐conjugated antibodies, though alternative labeling strategies exist. A key advantage of flow cytometry is the ability to physically isolate cells of interest for downstream applications such as culture, genomic analysis, or functional studies. The field has undergone substantial evolution from conventional filter‐based polychromatic systems to spectral cytometry platforms that capture full emission spectra, enabling higher‐parameter analyses and more flexible panel design. This review examines current capabilities and limitations of flow cytometry technology, with emphasis on recent advances in spectral detection, quantitative standardization, and computational analysis. We discuss remaining technical challenges and explore emerging opportunities for innovation in excitation systems, detector technology, and integration with artificial intelligence‐based analysis platforms. Addressing these challenges will be essential for cytometry to continue driving biological discovery and clinical applications in the coming decades.

Over the past 6 decades, cytometry has evolved from a niche experimental method into a cornerstone of modern biomedical research. The development of the first cell sorter by Mack Fulwyler laid the groundwork for technologies that now define single‐cell analysis. From the early challenges of instrument operation and laser alignment to today's era of automated, high‐parameter systems, the field has undergone continual transformation. We now enter what may be termed the “diamond age” of cytometry, an era of exceptional sensitivity, resolution, and analytical depth, driven by innovations such as spectral flow cytometry. Here, we reflect on the historical milestones that shaped the discipline, the cultural and educational shifts within the community, and the future challenges of standardization and quantitative rigor.

The application of flow cytometry for sorting and analyzing pathogenic organisms poses significant challenges, not only due to the difficulty of maintaining a sterile environment but, more critically, because of improper handling and disposal of pathogen-containing waste. If viable pathogens remain in the waste stream, they can present serious environmental risks. Conventional practice typically relies on adding chemical disinfectants, which in turn creates complications for subsequent autoclaving. In this work, we demonstrate an alternative approach using UV-C light for bacterial disinfection prior to waste collection. A UV-C device was integrated just before the flow cytometer’s waste chamber, ensuring that liquid waste flowing through the flow cytometry waste tubing was continuously exposed to UV-C irradiation. To validate this approach, we analyzed three bacterial species: Escherichia coli, Salmonella sp., and Klebsiella sp. to determine the efficacy of using such a device in a flow cytometer. Samples from the waste stream were collected both before and after UV-C exposure and plated on agar overnight. Our results showed complete inactivation (100% kill rate) of two bacterial species (E.coli and Klebsiella sp.) and near-complete inactivation of Salmonella sp. following UV-C treatment. This method provides a simple, chemical-free, and effective approach to enhance biosafety and ensure safe waste disposal in laboratories performing flow cytometry with pathogenic organisms.

Lateral flow assays (LFAs) are extensively utilized in point-of-care diagnostics due to their affordability, simplicity, and rapid time-to-results. However, their low sensitivity remains a significant limitation, particularly for detecting foodborne pathogens at concentrations below regulatory thresholds. This study evaluated two distinct sensing modalities—photothermal speckle imaging and colorimetric line intensity analysis—for their potential to enhance the sensitivity of commercially available LFAs. Photothermal imaging quantified refractive index shifts induced by plasmonic heating of gold nanoparticles, while colorimetric analysis used smartphone-acquired images processed with machine learning. The photothermal method achieved a limit of detection (LOD) of 2.13 × 10⁵ CFU/mL, while the colorimetric approach, using a logistic regression model with LASSO regularization, achieved an LOD of 10⁵ CFU/mL. While both approaches demonstrated detection thresholds comparable to traditional visual interpretation, the colorimetric method provided an added advantage by enabling quantitative prediction of bacterial concentration through regression modeling. With further optimization of each sensing method, these findings demonstrate the feasibility of improving unmodified commercial LFAs through optical and computational enhancements, offering a promising pathway toward the development of portable biosensing systems for real-time food safety monitoring.

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony’s unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. Key points: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes

Application of flow cytometry to microbiology has been limited due to inadequate availability of bacterial-specific stains, expensive antibody-based fluorophores, ineffective stain cell permeability and challenges in differentiating bacterial cells from cell debris due to their similarity and their small size. In addition, staining cells demands multiple washing steps which limits the sensitivity of detection due to the cell volume that is up to two orders smaller than typical eukaryotic cells. Further, most flow cytometers are not equipped to handle pathogenic organisms. Autofluorescence-based detection of cells can be a useful method for bacterial detection as multiple washing steps can be avoided and it also reduces the time and cost of using stains. Multiple studies have shown that the autofluorescence in bacterial cells are mainly linked to specific proteins, enzymes, or enzyme cofactors such as Flavin Adenine Dinucleotide (FAD) and Nicotinamide Adenine Dinucleotide (NAD) which are involved in bacterial metabolism. In this report, we present a novel method for differentiation between clinically isolated antibiotic resistant and non-resistant bacteria by utilizing their autofluorescence spectral signatures. We utilized a spectral cytometer known as Bigfoot which is equipped with an integrated biosafety cabinet allowing easy handling of pathogenic organisms unlike any other flow cytometers. Bigfoot also has 9 lasers and 54 fluorescence detectors which we utilize to capture the bacterial spectral autofluorescence signatures. As a proof of principle, we initially stressed different types of bacteria ( E . coli and Salmonella sp . ) using gentamicin antibiotics by collecting spectral autofluorescence over different time points. The spectral signatures were compared with the non-stressed bacteria. We observed that the stressed bacteria showed an increase in autofluorescence at distinct excitation (Ultraviolet, Violet, and blue color) and emission wavelengths whereas the non-stressed did not. The same experiments were repeated to compare the autofluorescence signatures between Methicillin-resistant and methicillin susceptible staphylococcus aureus (MRSA and MSSA) which were stressed with oxacillin antibiotics. MSSA showed an increase in autofluorescence between 4 – 6 h after exposure to oxacillin. MRSA on the other hand showed no increase in autofluorescence and the autofluorescence between stressed and non-stressed MRSA had similar signatures. This demonstrated that the antibiotic resistant or susceptible strain can be detected by observing the change in autofluorescence signature at specific wavelengths in a few hours. This label-free, quantitative, and resistant-specific autofluorescence spectral signatures from the Bigfoot spectral flow cytometer could potentially be utilized for rapid detection of antibioticresistant strain.