Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants

Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review

Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.

Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring

Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.

Differentiation of Listeria monocytogenes serotypes using near infrared hyperspectral imaging

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.

Rapid Detection of Broad-Spectrum Pathogenic Bacteria Based on Highly Sensitive Proton Response of the Nucleic Acid Amplification SPQC Platform

Pathogenic bacteria significantly contribute to elevated morbidity and mortality rates, highlighting the urgent need for early and precise detection. Currently, there is a paucity of effective broad-spectrum methods for detecting pathogenic bacteria. We have developed an innovative proton-responsive series piezoelectric quartz crystal (PR-SPQC) platform for the broad-spectrum identification of pathogenic bacteria. This was achieved by retrieving and aligning sequences from the NCBI GenBank database to identify and validate 16S rRNA oligonucleotide sequences that are signatures of pathogenic bacteria but absent in humans or fungi. The hyperbranched rolling circle amplification, activated exclusively by the screened target, exponentially generates protons that are detected by SPQC through a 2D polyaniline (PANI) film. The PR-SPQC platform demonstrates broad-spectrum capabilities in detecting pathogenic bacteria, with a detection limit of 2 CFU/mL within 90 min. Clinical testing of blood samples yielded satisfactory results. With its advantages in miniaturization, cost efficiency, and suitability for point-of-care testing, PR-SPQC has the potential to be extensively used for the rapid identification of diverse pathogenic bacteria within clinical practice and public health sectors.

Microbial food spoilage: impact, causative agents and control strategies

Microbial food spoilage is a major contributor to food waste and, hence, to the negative environmental sustainability impacts of food production and processing. Globally, it is estimated that 15-20% of food is wasted, with waste, by definition, occurring after primary production and harvesting (for example, in households and food service establishments). Although the causative agents of food spoilage are diverse, many microorganisms are major contributors across different types of foods. For example, the genus Pseudomonas causes spoilage in various raw and ready-to-eat foods. Aerobic sporeformers (for example, members of the genera Bacillus, Paenibacillus and Alicyclobacillus) cause spoilage across various foods and beverages, whereas anaerobic sporeformers (for example, Clostridiales) cause spoilage in a range of products that present low-oxygen environments. Fungi are also important spoilage microorganisms, including in products that are not susceptible to bacterial spoilage due to their low water activity or low pH. Strategies that can reduce spoilage include improved control of spoilage microorganisms in raw material and environmental sources as well as application of microbicidal or microbiostatic strategies (for example, to products and packaging). Emerging tools (for example, systems models and improved genomic tools) represent an opportunity for rational design of systems, processes and products that minimize microbial food spoilage.

Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology

To meet the demand for rapid bacterial detection in clinical practice, this study proposed a joint determination model based on spectral database matching combined with a deep learning model for the determination of positive-negative bacterial infection in directly smeared urine samples. Based on a dataset of 8124 urine samples, a standard hyperspectral database of common bacteria and impurities was established. This database, combined with an automated single-target extraction, was used to perform spectral matching for single bacterial targets in directly smeared data. To address the multi-scale features and the need for the rapid analysis of directly smeared data, a multi-scale buffered convolutional neural network, MBNet, was introduced, which included three convolutional combination units and four buffer units to extract the spectral features of directly smeared data from different dimensions. The focus was on studying the differences in spectral features between positive and negative bacterial infection, as well as the temporal correlation between positive-negative determination and short-term cultivation. The experimental results demonstrate that the joint determination model achieved an accuracy of 97.29%, a Positive Predictive Value (PPV) of 97.17%, and a Negative Predictive Value (NPV) of 97.60% in the directly smeared urine dataset. This result outperformed the single MBNet model, indicating the effectiveness of the multi-scale buffered architecture for global and large-scale features of directly smeared data, as well as the high sensitivity of spectral database matching for single bacterial targets. The rapid determination solution of the whole process, which combines directly smeared sample preparation, joint determination model, and software analysis integration, can provide a preliminary report of bacterial infection within 10 min, and it is expected to become a powerful supplement to the existing technologies of rapid bacterial detection.

Multicomponent hyperspectral grade evaluation of ilmenite using spectral-spatial joint features

Obtaining a comprehensive understanding of ore grade information is of significant importance for evaluating the value of ore. However, the real-time detection of multicomponent grade needs more effective online methods. This study proposes a novel approach utilizing hyperspectral imaging (HSI) to evaluate the grade information of nine major ilmenite components by integrating spectral and spatial data. Four multivariate input-output models were developed to mitigate variable interference to predict each component's grade. The results demonstrated that the backpropagation neural network (BPNN) model built from iPLS-VCPA-IRIV feature selection spectral data worked best (RP2 = 0.9935, RMSEP = 0.1364, RPD = 12.8986, and RPIQ = 21.4871, with a computational time of approximately 0.8 s). Furthermore, applying the best optimal combination algorithm for multicomponent grade inversion yielded highly accurate results, in which 97% of the component inversion residuals were less than 1. This investigation affirms that HSI enables rapid and accurate prediction and inversion of the multicomponent grade of ilmenite, thereby presenting a promising alternative to online analysis in the mineral field.

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000-1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

Hyperspectral imaging through vacuum packaging for monitoring cheese biochemical transformation caused by Clostridium metabolism

This study developed a novel method for monitoring cheese contamination with Clostridium spores non-invasively using hyperspectral imaging (HSI). The ability of HSI to quantify Clostridium metabolites was investigated with control cheese and cheese manufactured with milk contaminated with Clostridium tyrobutyricum, Clostridium butyricum and Clostridium sporogenes. Microbial count, HSI and SPME-GC-MS data were obtained over 10 weeks of storage. The developed method using HSI successfully quantified butyric acid (R² = 0.91, RPD = 3.38) a major compound of Clostridium metabolism in cheese. This study creates a new venue to monitor the spatial and temporal development of late blowing defect (LBD) in cheese using fast and non-invasive measurement.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a R_cv² in the range of 0.74-0.92 and standard error of cross validation (SECV) in the range of 0.61-0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

Rapid Identification of Infectious Pathogens at the Single-Cell Level via Combining Hyperspectral Microscopic Images and Deep Learning

Identifying infectious pathogens quickly and accurately is significant for patients and doctors. Identifying single bacterial strains is significant in eliminating culture and speeding up diagnosis. We present an advanced optical method for the rapid detection of infectious (including common and uncommon) pathogens by combining hyperspectral microscopic imaging and deep learning. To acquire more information regarding the pathogens, we developed a hyperspectral microscopic imaging system with a wide wavelength range and fine spectral resolution. Furthermore, an end-to-end deep learning network based on feature fusion, called BI-Net, was designed to extract the species-dependent features encoded in cell-level hyperspectral images as the fingerprints for species differentiation. After being trained based on a large-scale dataset that we built to identify common pathogens, BI-Net was used to classify uncommon pathogens via transfer learning. An extensive analysis demonstrated that BI-Net was able to learn species-dependent characteristics, with the classification accuracy and Kappa coefficients being 92% and 0.92, respectively, for both common and uncommon species. Our method outperformed state-of-the-art methods by a large margin and its excellent performance demonstrates its excellent potential in clinical practice.

Petri-plate, bacteria, and laser optical scattering sensor

Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21^st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.

Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: a literature review

BACKGROUND

Hyperspectral imaging (HSI) is a promising imaging modality that uses visible light to obtain information about blood flow. It has the distinct advantage of being noncontact, nonionizing, and noninvasive without the need for a contrast agent. Among the many applications of HSI in the medical field are the detection of various types of tumors and the evaluation of their blood flow, as well as the healing processes of grafts and wounds. Since tumor perfusion is one of the critical factors in oncology, we assessed the value of HSI in quantifying perfusion changes during interventions in clinical oncology through a systematic review of the literature.

MATERIALS AND METHODS

The PubMed and Web of Science electronic databases were searched using the terms "hyperspectral imaging perfusion cancer" and "hyperspectral imaging resection cancer". The inclusion criterion was the use of HSI in clinical oncology, meaning that all animal, phantom, ex vivo, experimental, research and development, and purely methodological studies were excluded.

RESULTS

Twenty articles met the inclusion criteria. The anatomic locations of the neoplasms in the selected articles were as follows: kidneys (1 article), breasts (2 articles), eye (1 article), brain (4 articles), entire gastrointestinal (GI) tract (1 article), upper GI tract (5 articles), and lower GI tract (6 articles).

CONCLUSIONS

HSI is a potentially attractive imaging modality for clinical application in oncology, with assessment of mastectomy skin flap perfusion after reconstructive breast surgery and anastomotic perfusion during reconstruction of gastrointenstinal conduit as the most promising at present.

Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications

As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.

Design and Validation of a Custom-Made Laboratory Hyperspectral Imaging System for Biomedical Applications Using a Broadband LED Light Source

Hyperspectral imaging (HSI) is a promising optical modality that is already being used in numerous applications. Further expansion of the capabilities of HSI depends on the modularity and versatility of the systems, which would, inter alia, incorporate profilometry, fluorescence imaging, and Raman spectroscopy while following a rigorous calibration and verification protocols, thus offering new insights into the studied samples as well as verifiable, quantitative measurement results applicable to the development of quantitative metrics. Considering these objectives, we developed a custom-made laboratory HSI system geared toward biomedical applications. In this report, we describe the design, along with calibration, characterization, and verification protocols needed to establish such systems, with the overall goal of standardization. As an additional novelty, our HSI system uses a custom-built broadband LED-based light source for reflectance imaging, which is particularly important for biomedical applications due to the elimination of sample heating. Three examples illustrating the utility and advantages of the integrated system in biomedical applications are shown. Our attempt presents both the development of a custom-based laboratory HSI system with novel LED light source as well as a framework which may improve technological standards in HSI system design.