Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging

Benchmarking classification abilities of novel optical photothermal IR spectroscopy at the single-cell level with bulk FTIR measurements

Fourier-transform infrared (FTIR) spectroscopy is a simple, fast and inexpensive method with a history of use for bacterial analysis. However, due to the limitations placed on spatial resolution inherent to infrared wavelengths, analysis has generally been performed on bulk samples, leading to biological variance among individual cells to be buried in averaged spectra. This also increases the bacterial load necessary for analysis, which can be problematic in clinical settings where limiting incubation time is valuable. Optical photothermal-induced resonance (O-PTIR) spectroscopy is a novel method aiming to bypass this limitation using a secondary lower wavelength laser, allowing for infrared measurements of a single bacterium. Here, using Staphylococcus capitis, Staphylococcus epidermidis and Micrococcus luteus strains as a model and FTIR as a benchmark, we examined O-PTIR's ability to discriminate single-cell samples at the intergenetic, interspecific and intraspecific levels. When combined with chemometric analysis, we showed that O-PTIR is capable of discriminating different between genera, species and strains within species to a degree comparable with FTIR. Furthermore, small variations in the amide bands associated with differences in the protein structure can still be seen in spite of smaller sample sizes. This demonstrates the potential of O-PTIR for single-cell bacterial analysis and classification.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Using staining as reference for spectral imaging: Its application for the development of an analytical method to predict the presence of bacterial biofilms

At present, although spectral imaging is known to have a great potential to provide a massive amount of valuable information, the lack of reference methods remains as one of the bottlenecks to access the full capacity of this technique. This work aims to present a staining-based reference method with digital image treatment for spectral imaging, in order to propose a fast, efficient, contactless and non-invasive analytical method to predict the presence of biofilms. Spectral images of Pseudomonasaeruginosa biofilms formed on high density polyethylene coupons were acquired in visible and near infrared (vis-NIR) range between 400 and 1000 nm. Crystal violet staining served as a biofilm indicator, allowing the bacterial cells and the extracellular matrix to be marked on the coupon. Treated digital images of the stained biofilms were used as a reference. The size and pixels of the hyperspectral and digital images were scaled and matched to each other. Intensity color thresholds were used to differentiate the pixels associate to areas containing biofilms from those ones placed in biofilm-free areas. The model facultative Gram-negative bacterium, P. aeruginosa, which can form highly irregularly shaped and heterogeneous biofilm structures, was used to enhance the strength of the method, due to its inherent difficulties. The results showed that the areas with high and low intensities were modeled with good performance, but the moderate intensity areas (with potentially weak or nascent biofilms) were quite challenging. Image processing and artificial neural networks (ANN) methods were performed to overcome the issues resulted from biofilm heterogeneity, as well as to train the spectral data for biofilm predictions.

Optical Immunosensor for the Detection of Listeria monocytogenes in Food Matrixes

In this paper, simple imine-based organic fluorophore 4-amino-3-(anthracene-9 yl methyleneamino) phenyl (phenyl) methanone (APM) has been synthesized via a greener approach and the same was used to construct a fluorescent immunoassay for the detection of Listeria monocytogenes (LM). A monoclonal antibody of LM was tagged with APM via the conjugation of the amine group in APM and the acid group of anti-LM through EDC/NHS coupling. The designed immunoassay was optimized for the specific detection of LM in the presence of other interfering pathogens based on the aggregation-induced emission mechanism and the formation of aggregates and their morphology was confirmed with the help of scanning electron microscopy. Density functional theory studies were done to further support the sensing mechanism-based changes in the energy level distribution. All photophysical parameters were measured by using fluorescence spectroscopy techniques. Specific and competitive recognition of LM was done in the presence of other relevant pathogens. The immunoassay shows a linear appreciable range from 1.6 × 10⁶-2.7024 × 10⁸ cfu/mL using the standard plate count method. The LOD has been calculated from the linear equation and the value is found as 3.2 cfu/mL, and this is the lowest LOD value reported for the detection of LM so far. The practical applications of the immunoassay were demonstrated in various food samples, and their accuracy obtained was highly comparable with the standard existing ELISA method.

Exploring the identification of multiple bacteria on stainless steel using multi-scale spectral imaging from microscopic to macroscopic

This work investigates non-contact reflectance spectral imaging techniques, i.e. microscopic Fourier transform infrared (FTIR) imaging, macroscopic visible-near infrared (VNIR), and shortwave infrared (SWIR) spectral imaging, for the identification of bacteria on stainless steel. Spectral images of two Gram-positive (GP) bacteria (Bacillus subtilis (BS) and Lactobacillus plantarum (LP)), and three Gram-negative (GN) bacteria (Escherichia coli (EC), Cronobacter sakazakii (CS), and Pseudomonas fluorescens (PF)), were collected from dried suspensions of bacterial cells dropped onto stainless steel surfaces. Through the use of multiple independent biological replicates for model validation and testing, FTIR reflectance spectral imaging was found to provide excellent GP/GN classification accuracy (> 96%), while the fused VNIR-SWIR data yielded classification accuracy exceeding 80% when applied to the independent test sets. However, classification within gram type was far less reliable, with lower accuracies for classification within the GP (< 75%) and GN (≤ 51%) species when calibration models were applied to the independent test sets, underlining the importance of independent model validation when dealing with samples of high biological variability.