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The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria
... Interpretation of the chemometrics involved plays a major role in the final prediction or results. For example, Foca et al. (2016) used PCA for detecting LAB in a Petri dish and sliced ham. Preprocessing with a combination of detrending and column mean-centering allowed the best discrimination for background and Petri dish. ...
... Further, PC2 allowed the distinction of two different bacterial strains. This separation could be attributed to preprocessing, which eliminated any background effects and thus enhanced the relevant spectral bands contributing to the detection of the two microbial species with PCA (Foca et al., 2016). Since PCA loadings were not provided in the study, relevant wavelengths could not be interpreted. ...
... The spectral signals associated with the components of the food matrices might severely interfere with the detection of microorganisms in food. For example, a study by Foca et al. (2016) investigated the use of HSI and spectral techniques to detect lactic acid bacteria (LAB). The two species of L. sakei and L. curvatus on De Man, Rogosa, and Sharpe (MRS) plates were distinguishable using PCA constructed using the spectral variables between 955 nm and 1700 nm. ...
Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf‐stable food products. The conventional culture‐based methods for microbial detection are time and labor‐intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity.
... Several recent studies used bacteria grown on Petri dishes to build identification methods. [5][6][7] After an internal study successfully assessing the feasibility of NIR spectroscopy analysis coupled to classification models on agar-based samples, Levasseur-Garcia et al. 5 ...
... Spectra were processed by multiplicative scatter correction (MSC) 8 prior to discrimination. Foca et al. 6 pointed out the spectral differences between two Lactobacillus species with a FT-NIR spectrometer and a NIR hyperspectral camera. For both series of spectral acquisitions, they used a combination of spectral pretreatments, such as Standard Normal Variate (SNV) and Savitzky-Golay derivatives followed by a PCA. ...
... [9][10][11] More noise was still observed on the whole spectral range than in other bacterial analysis studies, which might be due to the lower resolution of the Bruker MPA used in this study (1 cm À1 ) compared to the spectrometers used in these works. [1][2][3][4][5][6][7] Series of assays for discriminating between bacteria Raw spectra. From the 40 strains under study, 240 spectra were acquired (three replicates per strain and two locations on Petri dishes measured per replica). ...
Fast diagnostic tools such as near infrared spectroscopy have recently gained interest for bacterial identification. To avoid a process involving microbial pellet or suspension preparation from Petri dishes for NIR analysis, direct screening from agar in Petri dishes was explored. This two-step study proposes a new procedure for bacterial screening directly on agar plates with minimal nutrient medium bias. Firstly, principal component analyses showed optimal discrimination between the genera Lactobacillus, Pseudomonas and Brochothrix on different culture media, in transmission mode and with the bottom of Petri dishes facing the light source. The repeatability of spectra in these conditions was assessed with an average coefficient of variation inferior to 5% in the 12,500–3680 cm ⁻¹ range. Secondly, 40 strains of Lactococcus and Enterococcus species were grown on Bennett agar and measured over a series of five assays. Principal component analyses highlighted better clustering according to genera and species and lower external bias while retaining the 8790–3680 cm ⁻¹ spectral range and applying an extended multiplicative scatter correction with an average agar spectrum as a reference, in comparison to raw data and standard multiplicative scatter correction.
... Various experiments were performed on different food samples in order to obtain the total viable counts ranging from 5 to 9 Log 10 CFU/g with the standard error of prediction of 0.3 Log 10 CFU/g (Gowen et al., 2015). Moreover, the potential of hyperspectral technique was investigated to detect bacterial surface contamination on slice cooked ham food samples (Foca, Ferrari et al., 2016). In another study, Yoon and his coworkers developed a noninvasive and noncontact hyperspectral imaging technique to detect various species of Campylobacter on agar plates. ...
... It is difficult, to sum up all the available spectral and hyperspectral imaging techniques for the detection of bacteria. Three techniques are documented up-till now, operating in the range of NIR, FT-NIR and FT-MIR region of IR spectrum (Foca et al., 2016). Integrating modern imaging techniques with the conventional spectroscopy, hyperspectral imaging can give the spectral and spatial information regarding target sample and can be applied as a visual and smart technology for microbial analyses in agricultural and food products though this technique is complex and expensive. ...
Background
Continuous transformation and development of new detection tools for bacteria has converted the laborious scientific work into smart apparatus in recent years. The journey had begun with the culture-based plate enumeration, and now it has evolved into several culture-independent techniques. Polymerase chain reaction (PCR) is on the top of the list that is now a routinely used biological approach to detect bacterial cells. Instrumental techniques are also helpful in this regard, as they are more sensitive for detection of various microbes.
Scope and approach
In this review, we described new trends and their practical application in the fields of detection microbiology and food technology. This study provides a brief overview of conventional and modern detection techniques which includes nucleic-acid sequence based techniques to non-destructive imaging techniques.
Key findings and conclusions
Besides the availability of antibiotics and clinical treatments, bacterial infections significantly increase the mortality rate. It is necessary to detect apparent infectious agents beforehand. Therefore, the detection methods for microorganisms should be more rapid, smart and reliable in response to the need. Conventional detection techniques are slow and time-consuming but more accurate and reliable than the modern detection techniques. By combing the mentioned techniques, scientists can achieve better results.
... A hyperspectral reflectance imaging protocol is the available spectral and hyperspectral imaging technique for detecting bacteria. The methods operate in the range of three central regions near-infrared (NIR), mid-infrared (MIR), and far infrared (FIR) spectrum [130]. Traditional spectroscopy and hyperspectral imaging techniques, when compared to modern imaging techniques, can provide spectral and spatial information detecting target samples and can be used as a visual and smart technology for pathogen analyses in food products. ...
Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results.
... Commonly reported bacterial detection techniques include polymerase chain reaction detection [2,3], surface plasmon resonance optical sensor [4], immunological detection [5], FT-IR spectroscopy [6] and fluorescence spectroscopy [7,8], etc. Among them, fluorescence technique has the advantages of high selectivity, fast detection, non-invasion and low cost [9]. ...
In this work, a novel type of vesicles (VT/G) composed of Gemini cationic surfactant (G12-8-12) and TPE-2OH, an AIEgen fluorescence probe, were prepared by methanol evaporation method. Herein, G12-8-12 has good bacteriostatic function. TPE-2OH with aggregation-induced emission feature resided in the interlayer of VT/G. The average size of VT/G was about 1.2–1.4 μm measured by dynamic light scattering. Zeta potential studies indicated that VT/G could bind to bacteria like S. aureus and E. coli under electrostatic interaction, increasing the size of the complex of VT/G and bacteria to 4.5–6 μm. Because VT/G interlayer was compressed by external bacteria, the aggregation degree of TPE-2OH was increased, and the fluorescence intensity was increased by 3 times compared with VT/G alone. The linear increase in fluorescence intensity with the increase of bacteria content implied that it is possible to sense bacteria. After the bacteria were co-incubated with VT/G, almost no bacteria growth was observed with the light irradiation for 30 min, indicating that VT/G showed good bactericidal effect. This can be ascribed to the synergistic effect of the inherent bactericidal property of G12-8-12 and the oxygen-active substances generated from TPE-2OH. This new strategy integrating bacteria detection and killing showed good potential application.
... Hyperspectral imaging technique, as rapid and nondestructive method integrates computer vision and spectral analysis techniques to acquire spectral information of each pixel location in a two-dimensional (2D) objective image, generating a multi-dimensional data (one-dimensional spectral dimension and two-dimensional spatial information) [15,16]. Each strain colony with unique ingerprints spectrum has been identiied successfully by taking full advantage of one-dimensional spectral information [17][18][19]. Although hyperspectral imaging technology is very useful for strain identiication during mixed fermentation, there are still exist a major issue: rapid and automated *Corresponding author. ...
Hyperspectral imaging technology with chemometrics was used for identifying and counting each species in microbial community during mixed fermentation. Hyperspectral images of microbial community of Enterobacter sp, Acetobacter pasteurianus , and Lactobacillus paracasei colonies were obtained and the spectra of strain colonies were extracted. Identification models were developed using linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) by using 23 variables selected by genetic algorithm. The optimal LS-SVM model with identification rate of 96.67 % was used to identify colonies and prepare colony distribution maps in color for strains counting. The counting results by hyperspectral imaging technology agree with that of the manual counting method with average relative error of 3.70 %. The developed counting method has been successfully used to identify and count the specific strain from the mixed strains simultaneously. The hyperspectral imaging technology has a great potential to monitor changes in the microbial community structure.
... In recent decades, advancements in micro-electro-mechanical systems (MEMS) and micro-electro-opto-mechanical systems (MEOMS) have enabled the development of miniaturized spectroscopic devices that can be used for analysis at all points along the food supply chain, from farm fields to distribution centers to retail markets. Hyperspectral imaging (HSI) combines spectroscopy and imaging to enable evaluation of an object's spectroscopic composition at a high spatial resolution, thus providing a more comprehensive evaluation tool for any given sample [7][8][9]. As the scale and complexity of food supply networks continues to grow, there is an ever increasing need for low-cost, portable, analytical devices to combat the corresponding growth in vulnerability of food products to adulteration, contamination, and fraud [10]. ...
Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements.
... HSI has seen application for the determination of the Campylobacter species or Shiga toxin-producing E. coli. (STEC) serogroup of bacterial colonies grown on their respective selective nutrient enriched agar plates [5][6][7]. Anderson et al. [8] discovered that an HMI system could differentiate between spectral patterns of viable and non-viable Bacillus anthraces spores damaged from contact with hydrogen peroxide. Previously, our laboratory's research has shown that bacterial species can be differentiated through HMI, as well as serotypes of the same species, by using a single cell-based mean pixel intensity pattern, and early detection was possible in times of 8 h or less [9]. ...
Hyperspectral microscope images (HMIs) have been previously explored as a tool for the early and rapid detection of common foodborne pathogenic bacteria. A robust unsupervised classification approach to differentiate bacterial species with the potential for single cell sensitivity is needed for real-world application, in order to confirm the identity of pathogenic bacteria isolated from a food product. Here, a one-class soft independent modelling of class analogy (SIMCA) was used to determine if individual cells are Salmonella positive or negative. The model was constructed and validated with a spectral library built over five years, containing 13 Salmonella serotypes and 14 non-Salmonella foodborne pathogens. An image processing method designed to take less than one minute paired with the one-class Salmonella prediction algorithm resulted in an overall classification accuracy of 95.4%, with a Salmonella sensitivity of 0.97, and specificity of 0.92. SIMCA’s prediction accuracy was only achieved after a robust model incorporating multiple serotypes was established. These results demonstrate the potential for HMI as a sensitive and unsupervised presumptive screening method, moving towards the early (<8 h) and rapid (<1 h) identification of Salmonella from food matrices.
... Optical methods have gained popularity and become good candidates and viable options to be implemented for on-line applications in the food industry (ElMasry, Nassar, Wang & Vigneault, 2008;Kamruzzaman, ElMasry & Nakauchi, 2015). Considerable amount of research endeavours have been directed in the past two decades towards using optical techniques and spectral imaging methods for different quality assessment scenarios in food science and technology, such as reagent-less determination of chemical composition of raw and processed meat (Craigie et al., 2017;Reis et al., 2018;Vel asquez, Cruz-Tirado, Siche, & Quevedo, 2017), seed quality evaluation (Dumont et al., 2015;ElMasry, Mandour, Al-Rejaie, Belin, & Rousseau, 2019;ElMasry, Mandour, Wagner, et al., 2019;Wakholi et al., 2018), quality estimation of fruits and vegetables (ElMasry, Wang, ElSayed, & Ngadi, 2007;ElMasry, Wang, Vigneault, Qiao & ElSayed, 2008;Pathmanaban, Gnanavel, & Anandan, 2019), determination of food safety, authentication and microbiological evaluation (Barbin, ElMasry, Sun, Allen, & Morsy, 2013;Crichton et al., 2017;ElMasry & Sun, 2010;Foca et al., 2016;Siripatrawan, 2018). ...
Sliced dry-cured ham arranged in ready-to-eat packages is a convenient and widely consumed commodity characterised by heterogeneity in composition not only among different industrial batches but also through their horizontal and vertical profiles, making precise nutrition labelling of the packages a difficult task. Hyperspectral imaging techniques can serve as a steadfast solution not only to predict the overall composition of the major constituents of dry-cured ham but also to visualise their distributions. The main aim of this study was to define the optimal protocol for pretreating hyperspectral images and selecting representative hyperspectral data for developing accurate predictive models in excessively heterogeneous samples, using sliced dry-cured ham as a case study. Hyperspectral images (400–1000 nm) were acquired for heterogeneous sliced dry-cured ham and homogeneous unsliced dry-cured muscles. Partial least squares (PLS) regression models to predict fat, water, salt and protein contents were developed and tested in an independent dataset. The PLS predictive models developed from the whole surface of sliced dry-cured ham were the most accurate ones for predicting fat, water, salt and protein contents with a determination coefficient in prediction (Rp2) of 0.89, 0.85, 83 and 0.63 and standard error in prediction (SEP) of 1.43, 1.21, 0.51 and 1.57%, respectively. The chemical images resulting from the models gave advantages of hyperspectral imaging technique over traditional chemical methods to visualise the spatial distribution of different constituents within the packaged ham slices.
... Seo et al. [18] developed classification models for Staphylococcus five species, using hyperspectral microscopic imaging, and highlighted the use of HSI in the presumptive screening of foodborne pathogenic bacteria. Foca et al. [19] evaluated spectral and hyperspectral techniques the bacterial contamination by Lactobacillus curvatus and L. sakei in ham and observed that the techniques used can be effective for recognizing bacterial contamination and still recognize species to which the bacteria belong. ...
... The effectiveness of this approach induced us to adapt it also to the analysis of large datasets of hyperspectral images, leading to the hyperspectrograms approach, which allowed to obtain satisfactory results in various applications [19,[29][30][31][32][33]. ...
Colourgrams GUI is a graphical user-friendly interface developed in order to facilitate the analysis of large datasets of RGB images through the colourgrams approach. Briefly, the colourgrams approach consists in converting a dataset of RGB images into a matrix of one-dimensional signals, the colourgrams, each one codifying the colour content of the corresponding original image. This matrix of signals can be in turn analysed by means of common multivariate statistical methods, such as Principal Component Analysis (PCA) for exploratory analysis of the image dataset, or Partial Least Squares (PLS) regression for the quantification of colour-related properties of interest.
Colourgrams GUI allows to easily convert the dataset of RGB images into the colourgrams matrix, to interactively visualize the signals coloured according to qualitative and/or quantitative properties of the corresponding samples and to visualize the colour features corresponding to selected colourgram regions into the image domain. In addition, the software also allows to analyse the colourgrams matrix by means of PCA and PLS.
... A recent advancement in current microscopy techniques is enhanced-darkfield hyperspectral imaging (ED-HSI) microscopy by CytoViva (whose model is applied in this study) and Photon Etc. Multiple researchers have found ED-HSI to be a promising tool for characterization and semi-quantitation of biofilms in both natural and technical systems (26)(27)(28). The main advantage of this microscope is that wet samples and real-time interactions in the samples can be visualized at high resolution without staining. ...
... However, HSI can obtain a spectrum of each pixel of the whole tested object; thus, it is also able to provide the spatial distribution of chemical composition (Huang, Zhou, Meng, Wu, & He, 2017;. HSI has exhibited great utility and wide application potential in food safety and quality inspection (Foca et al., 2016;Liu, Pu, & Sun, 2017;Shi et al., 2017;Xing et al., 2017). Under the conditions when adulterants are concentrated in a small region, that is, only one or several pixels, HSI can allow better detectability and attain lower detection limits than conventional spectroscopy technology (Forchetti & Poppi, 2017). ...
Keywords: Near-infrared hyperspectral imaging Low-level contamination Peanut powder Whole wheat flour Visualisation Near-infrared hyperspectral imaging (HSI) was used for detecting low levels of peanut powder contamination in whole wheat flour, with concentrations of 0.01e10% (w/w). Two types of whole wheat flours, i.e. spring wheat flour (WFS) and winter wheat flour (WFW), were used. Minimum noise fraction combined with n-Dimensional visualiser tool was applied on light intensity calibrated hyperspectral images for preliminary discrimination. Competitive adaptive reweighted sampling (CARS) was applied for optimal wavelength selection. Partial least squares regression (PLSR) models with standard normal variate followed by SavitzkyeGolay first derivatives had the best performance, with coefficients of determination of prediction (R 2 p) of 0.993 and 0.991, and root mean square error of prediction (RMSEP) of 0.251% and 0.285%, respectively for contaminated WFS and WFW samples. Prediction maps based on PLSR models permitted visualising spatial variations in the concentration of peanut contamination. The results indicated that near-infrared HSI has the potential to detect low-level peanut contamination in whole wheat flour.
... used macroscopic and microscopic hyperspectral imaging systems to detect two species of lactic acid bacteria (LAB) on the surface of cooked ham and agar plates [21]. Results found species differentiation of the two LABs possible on the agar and cooked ham. ...
Salmonella is an organism of importance to the poultry industry with increasingly stringent government regulatory standards. Real-time polymerase chain reaction (RT-PCR) and plating procedures on nutrient enriched growth media have been the standard detection methods of Salmonella from broiler chicken carcasses for years. These methods are proven, but offer disadvantages in the amount of time or reoccurring sample cost. Here, we propose the use of a hyperspectral microscope imaging system (HMI) for comparison to standard detection methods. Broiler chicken carcasses were rinsed and plated on Salmonella selective agar. Colonies from plates were picked and RT-PCR was used as a confirmation test to verify plating results, while HMI was collected from the same colonies. Spectral signatures of cells were extracted between 450 and 800 nm from HMI collected with 100x objective. A quadratic discriminant analysis (QDA) was used to classify cells as either Salmonella positive or negative (n = 341). Spectra preprocessing minimized the influence of cellular shape on the spectra, increasing the initial classification accuracy of 81.8–98.5%, yielding a sensitivity of 1.0, and a specificity of 0.963. Results showed the potential as an initial investigation of HMI as a microbial confirmation tool, compared to RT-PCR.
... Additionally, Grewal, Jaiswal & Jha (2015) also used FTIR spectroscopy coupled with chemometric analysis for the detection of poultry meat specific bacteria and concluded that spectral windows in the regions of 4,000-575 cm −1 , 3,000-2,500 cm −1 and 1,800-1,200 cm −1 have the potential to classify poultry meat based on the presence of different pathogenic bacteria and level of contamination. Moreover, Foca et al. (2016) applied different spectral (Fourier transform mid infrared spectroscopy (FTMIR) and Fourier transform near infrared spectroscopy) and hyperspectral techniques for detection of lactic acid bacteria in sliced cooked ham and revealed that FTMIR spectroscopy in the region of 4,000-675 cm −1 can be used in combination with multivariate analysis to get information regarding bacterial contamination in food samples. FTIR has also been used for the determination of molds in different food products (Shapaval et al., 2017). ...
Background Use of traditional methods for determining meat spoilage is quite laborious and time consuming. Therefore, alternative approaches are needed that can predict the spoilage of meat in a rapid, non-invasive and more elaborative way. In this regard, the spectroscopic techniques have shown their potential for predicting the microbial spoilage of meat-based products. Consequently, the present work was aimed to demonstrate the competence of Fourier transform infrared spectroscopy (FTIR) to detect spoilage in chicken fillets stored under aerobic refrigerated conditions. Methods This study was conducted under controlled randomized design (CRD). Chicken samples were stored for 8 days at 4 + 0.5 °C and FTIR spectra were collected at regular intervals (after every 2 days) directly from the sample surface using attenuated total reflectance during the study period. Additionally, total plate count (TPC), Entetobacteriaceae count, pH, CTn (Color transmittance number) color analysis, TVBN (total volatile basic nitrogen) contents, and shear force values were also measured through traditional approaches. FTIR spectral data were interpreted through principal component analysis (PCA) and partial least square (PLS) regression and compared with results of traditional methods for precise estimation of spoilage. Results Results of TPC (3.04–8.20 CFU/cm2), Entetobacteriaceae counts (2.39–6.33 CFU/cm2), pH (4.65–7.05), color (57.00–142.00 CTn), TVBN values (6.72–33.60 mg/100 g) and shear force values (8.99–39.23) were measured through traditional methods and compared with FTIR spectral data. Analysis of variance (ANOVA) was applied on data obtained through microbial and quality analyses and results revealed significant changes (P
... Simultaneously, the technology is susceptible to water vapor, and is hard to apply to watery or thicker samples and appear overlapping absorption peaks in the mixture of various components. 40,41 2.2.2 SPR. Point-of-care (POC) diagnostics have been expanded from traditional approaches to respond to the challenge of infectious disease. ...
Bacterial infections have severely affected human health, leading to substantial amount of human deaths. Inevitably direct and indirect contacts of medical devices, food packings and daily supplies with harmful bacteria are among the major transmission routes of bacterial infections. Effective bacterial theranostic systems that combine diagnostic functions and therapeutic effects have received extensive attention owing to their excellent features including high efficiency, real-time performance and low drug resistance. In this review article, we summarize the recent advance of antibacterial materials including natural antibacterial compounds, quaternary ammonium polymers, and inorganic or hybrid nanoparticles with dual function of detection and imaging. In particular, multiple imaging modal and antibacterial modal were systematically discussed in order to serve the better understanding of modern theranostics.
... Cells grown on solid media can be harvested directly from the media, suspended in the water and subjected to the analysis, while cultures from the liquid media first need to be centrifuged and washed to remove the medium. The most frequently employed FTIRS technique is transmission, where the sample is placed on an IR transparent ZnSe crystal ( Amiel et al., 2000;Oust et al., 2004;Bosch et al., 2006;Luginbühl et al., 2006;Dziuba et al., 2007;Nicolaou et al., 2011); the use of reflectance on different optical plates has also been reported (Savić et al., 2008;Foca et al., 2016). Although spectroscopic equipment is rela- tively expensive, no additional costs are needed for analysis. ...
This chapter reviews the importance of proper and reliable enumeration and classification of probiotic and lactic acid bacteria (LAB) starter cultures by the use of different approaches based on either phenotypic and/or genotypic methods. Methods for the identification and enumeration of probiotics and LAB starter cultures can be systematically classified in various ways on the basis of properties inherent to each method. The conventional microbiological methods for bacterial identification are based on morphological and physiological characteristics, such as Gram staining, cell shape, spore formation, enzyme production and the fermentation of different carbohydrates. The most commonly used PCR-based methods for LAB and probiotic bacteria identification and/or enumeration are polymerase chain reaction (PCR), reverse-transcribed (RT)-PCR, quantitative PCR (qPCR) and propidium monoazide (PMA)-ethidium monoazide (EMA) qPCR. Fluorescence in situ hybridisation is a popular technique for research into probiotics and dairy starter cultures, although few publications have described its use for the enumeration of dairy microbes.
... Therefore, HSI systems have had a significant influence on the outcome to the combined questions "where and how much of what," providing a comprehensive characterization of a sample. [73] Figure 2a. shows a typical laboratory experimental setup of the HSI system. ...
Responding to the ever growing concern about safe foods and security, the food industries are forced to seek an emerging technology capable of detecting and quantifying contaminations, especially those of biological origin. Amongst the different emerging technologies, hyperspectral imaging (HSI) is considered a good alternative as it can be easly applied at all steps of the food production process and is a non-destructive technique. This paper reviews targeted analytical applications of HSI in monitoring biological contaminants in food. First, traditional techniques for detection of biological contaminants in foods are presented, where disadvantages for practical applications are highlighted and explained in detail in their respective sections. Second, prominent applications of HSI from the last decade to food safety and quality assessment are reviewed, especifically focusing on both deteriorative and pathogenic microorganisms, microbial toxins and parasites; whether acting individually or collectively spoil food products and/or represent a health risk to the consumers. Finally, relevant current and future challenges, advantages and disadvantages of HSI applications are briefly examined.
... Additionally, Grewal, Jaiswal & Jha (2015) also used FTIR spectroscopy coupled with chemometric analysis for the detection of poultry meat specific bacteria and concluded that spectral windows in the regions of 4,000-575 cm −1 , 3,000-2,500 cm −1 and 1,800-1,200 cm −1 have the potential to classify poultry meat based on the presence of different pathogenic bacteria and level of contamination. Moreover, Foca et al. (2016) applied different spectral (Fourier transform mid infrared spectroscopy (FTMIR) and Fourier transform near infrared spectroscopy) and hyperspectral techniques for detection of lactic acid bacteria in sliced cooked ham and revealed that FTMIR spectroscopy in the region of 4,000-675 cm −1 can be used in combination with multivariate analysis to get information regarding bacterial contamination in food samples. FTIR has also been used for the determination of molds in different food products (Shapaval et al., 2017). ...
The contagious nature of certain biological agents and the difficulty in treating infections render them a significant threat to public health and safety. In situations involving chemical, biological, radiological, and nuclear (CBRN) agents, effective detection is of paramount importance to prevent the undetected spread of these agents and enable swift and targeted responses tailored to the specific threat. Although portable detection tools are effective for chemical and nuclear detection, current biological detection methods face several challenges, including limited mobility, extended processing times, and varying accuracy. In the context of biological threats, where agents such as anthrax can be rapidly dispersed due to environmental factors and human activities, rapid detection is of paramount importance. It is imperative to develop field-applicable detection devices that are highly selective, capable of differentiating between biological and non-biological agents, as well as benign and harmful microorganisms. This study examines the potential of near-infrared (NIR) spectroscopy in conjunction with machine learning as a rapid in-situ biological detection method. The objective is to distinguish between biological agents and common white powders that are used as confounding agents in suspect letters. The non-pathogenic surrogates employed are safe and representative of typical biological warfare agents. The near-infrared (NIR) spectra of lyophilized bacterial and fungal surrogates, along with common white powders, were subjected to analysis and processing through the application of principal component analysis (PCA) and hierarchical clustering analysis (HCA). This resulted in the successful classification of the samples into distinct groups. The classification model demonstrated high accuracy in its prediction, thereby emphasizing the potential of the method for field detection of solid biological agents. These promising results suggest that NIR spectroscopy combined with machine learning could be further investigated as a rapid in-situ tool for biological detection in CBRN contexts.
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.
Assessment of lactic acid bacteria (LAB) activity plays a key role in the fermented food industry. Fluorescence imaging method based on dye is facile to detect LAB viability. However, it is difficult to obtain stable fluorescence, non-toxic and low-cost dyes. In this study, we prepare P- and N-doped carbon dots (PN-CDs) via microwave-assisted hydrothermal synthesis. The properties of high quantum yield (60.36%) and excitation dependence allowed for multicolor imaging of LAB (Lactobacillus plantarum [L.p] and Streptococcus thermophilus [S.t]). The abundant functional groups and positive charges (+2.34 mV) on the surface of PN-CDs facilitated their quickly integrated into cell wall of live LAB with obvious fluorescence or into dead cells. As a result, PN-CDs can not only be used to rapidly and efficiently monitor bacterial viability (one minute), but can also be used to visualize LAB division using fluorescence imaging. Importantly, the PN-CDs have potential to rapidly detect LAB activity in LAB-fermented juices.
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.
Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.
Screening for microbial contaminants in fresh produce is a lengthy process relative to their short shelf-life. The aim of this study is to develop a comprehensive assay which employs FTIR and spectral classification algorithm for detection of bacterial contamination of fresh produce.
The procedure starts by soaking a sample of the fresh produce in broth for 5 hours. Then, magnetic nanoparticles are added to capture bacteria which are then collected and prepared for FTIR scanning. The generated FTIR spectra are compared against an in-house database of different bacterial species (n=6). The ability of the database to discriminate contaminated and uncontaminated samples and to identify the bacterial species was assessed. The compatibility of the FTIR procedures with subsequent DNA extraction and PCR was tested. The developed procedure was applied for assessment of bacterial contamination in fresh produce samples from the market (n=78). Results were compared to the conventional culture methods.
The generated FTIR database coupled to spectral classification was able to detect bacterial contamination with overall accuracy exceeding 90%. The sample processing did not alter the integrity of the bacterial DNA which was suitable for PCR. On application to fresh produce samples collected from the market, the developed method was able to detect bacterial contamination with 94% concordance with the culture method.
In conclusion, the developed procedure can be applied for fast detection of microbial contamination in fresh produce with comparable accuracy to conventional microbiological assays and is compatible with subsequent molecular assays.
Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The objectives of this study were to evaluate the feasibility of using hyperspectral imaging (HSI) and deep learning approaches, firstly to identify dead and live forms of C. sporogenes spores and secondly, to estimate the concentration of spores on culture media plates and ready-to-eat mashed potato (food matrix). C. sporogenes spores were inoculated by either spread plating or drop plating on sheep blood agar (SBA) and tryptic soy agar (TSA) plates and by spread plating on the surface of mashed potato. Reflectance in the spectral range of 547-1701 nm from the region of interest was used for principal component analysis (PCA). PCA was successful in distinguishing dead and live spores and different levels of inoculum (10² to 10⁶ CFU/ml) on both TSA and SBA plates, however, was not efficient on the mashed potato (food matrix). Hence, deep learning classification frameworks namely 1D- convolutional neural networks (CNN) and random forest (RF) model were used. CNN model outperformed the RF model and the accuracy for quantification of spores was improved by 4 % and 8 % in the presence and absence, respectively of dead spores. The screening system used in this study was a combination of HSI and deep learning modelling, which resulted in an overall accuracy of 90-94 % when the dead/inactivated spores were present and absent, respectively. The only discrepancy detected was during the prediction of samples with low inoculum levels (< 10² CFU/ml). In summary, it was evident that HSI in combination with a deep learning approach showed immense potential as a tool to detect and quantify spores on nutrient media as well as on specific food matrix (mashed potato). However, the presence of dead spores in any sample is postulated to affect the accuracy and would need replicates and confirmatory assays.
Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.
The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples' surface. It is also shown that the PCA loadings obtained from a set of training samples can be applied to hyperspectral data from new test samples to detect the presence of mould on these. For both the agar and cheeselets, the first three principal components contained more than 99
%
of the total variance. The spatial projection of the second principal component highlights the presence of mould on cheeselets. The proposed analysis methods can be adopted in industry to detect mould on cheeselets at an early stage and with further testing this application may also be extended to other food products.
This chapter will cover a range of optical detection technologies, which either detect the intrinsic properties of the microorganisms themselves or exploit external labels. Optical microscopy of viruses is not possible due to their small size, although it is possible that future developments in microscopy may enable this. Bacteria and parasites can be observed using light microscopy, either with or without fluorescent labels. The use of fluorophores, coupled to specific antibodies or which react with specific intracellular components, eases identification, in some cases to the species level and/or providing viability determination. The existing fluorescence microscopy technology based on manual counting by lab technicians has been detailed in Chapter 3.
This research aims to verify the feasibility of developing an improved and efficient reduced spectrum model for quantitative tracking of foodborne pathogens. Rapid monitoring of bacteria foodborne pathogen (Escherichia coli O157 and Staphylococcus aureus) contamination of fresh longissimus pork muscles was implemented by employing visible near-infrared (Vis-NIR) hyperspectral imaging spectra and partial least squares regression algorithm (PLSR). Six (6) wavelength variables selection algorithms were applied to the full spectral information to determine the wavelength variables of the collected HSI spectra that provides essential and relevant information about the concentration of bacterial foodborne pathogen. Commonly used algorithms based on model population analysis (MPA) (2), Intelligent Optimization Algorithms (2), and Hybrid variable selection methods (HVSM) (2) were utilised to select characteristic wavelengths. Compared to other strategies, variable combination population analysis with genetic algorithm (VCPA – GA), and variable combination population analysis with iteratively retaining informative variables (VCPA – IRIV) considerably bettered the predictive efficiency of the model, suggesting that the updated VCPA step is a very efficient way to remove unrelated variables. Vcpa-based hybrid strategy is an effective and reliable approach for variable selection of visible near-infrared (vis-NIR) spectra. Visualising bacterial foodborne pathogen distribution map on the pork samples provided a more insightful and detailed evaluation of the bacterial contamination at each pixel, offering a novel approach for evaluating bacterial contamination of agricultural products.
In this study, the major maize ear rot pathogens were differentiated from one another, on growth media, with near-infrared (NIR) hyperspectral imaging. Fungal isolates of four pathogens commonly associated with maize grain (Fusarium verticillioides, F. graminearum s.s., F. boothii and Stenocarpella maydis) were plated on potato dextrose agar, in triplicate, and incubated at 25°C for 5 days. Images were collected with a SisuChema short-wave infrared (SWIR) pushbroom hyperspectral imaging system ranging from 1000 to 2500 nm. Pixel and object wise classification algorithms were compared to determine the best approach. These were evaluated with principal component analysis and partial least squares discriminant analysis. The pathogens were distinguished using three-way classification models that were validated with independent images. The object wise approach proved to be more effective in distinguishing ear rot pathogens with a higher average overall classification accuracy (93.75%). The object wise models achieved two 100% classification accuracies from the four pathogen models, while none of the pixel wise classification models were error free. The specificity and sensitivity further highlighted the superiority of the object wise approach. The high accuracy of object wise classification (> 86%) can be attributed to the representation of the mean spectra per object. NIR hyperspectral imaging can thus accurately distinguish between the major maize ear rot pathogens, with object wise classification proving to be the optimal approach.
A simultaneous evaluation of various quality attributes of packaged bratwurst using hyperspectral imaging (HSI) was developed. Changes in physicochemical (L*, a*, b* color values, pH and thiobarbituric acid (TBA)), microbiological (total viable counts (TVC) and lactic acid bacteria (LAB)) and sensory (color, odor and overall acceptability) characteristics of the packaged sausages were monitored during storage at 4 ± 1 °C. Reflectance spectra covering a wavelength range of 400-1000 nm of the samples were acquired using HSI. The relationships between the quality attributes and the spectroscopic reflectance were investigated using canonical correlation analysis. Among all quality attributes, L* color value, TBA, TVC, LAB, odor and overall acceptability appeared to be highly associated with the reflectance. To facilitate the HSI for rapid image acquisition and data processing, partial least squares regression (PLSR) analysis was employed for selection of optimal wavelengths. The selected wavelengths were then assembled into multispectral data and used as input variables to optimize the PLSR and artificial neural network models for the prediction of quality attributes of the sausage samples. The HSI technique can be used for rapid and nondestructive evaluation of the product's quality and shelf life.
A rapid method to evaluate and visualize quality of packaged dry-cured sausages was developed using hyperspectral imaging (HSI) spectroscopy. Changes in physicochemical, microbiological and sensory attributes of the packaged dry-cured sausages were monitored during storage at 20 °C. Discriminant factor analysis (DFA) was employed to classify samples into different groups by taking physicochemical, microbiological and organoleptic properties into consideration. The quality deterioration index (QDI) indicating the assembled qualities of the packaged dry-cured sausages was signified and assigned to the individual DFA classified groups. HSI spectroscopy was used to collect both spectral data in the wavelength range of 380–1000 nm and spatial data of 68 × 68 pixels. Partial least squares (PLS) regression model was used to establish the relationship between spectral data and the QDI. Distribution maps of QDI were depicted to visualize the sample quality attributes which facilitate the result interpretation. The developed technique can be implemented in wide varieties of quality inspection of food products without additional laborious chemical analysis and sensory evaluation.
This paper presents a method for measuring and quantitatively analyzing the fluorescent whitening agent in soybean milk based on image techniques. After collecting the fluorescent images of the soybean milk samples, the top seven wavelet moment invariants are selected according to the sample training and experimental comparison. Then calculate the standard templates of the 49 classes of calibration samples with different fluorescent whitening agent content ranging from 0.02mg/ml to 0.5mg/ml. The minimum distance method is carried out to match the testing sample with the calibration template, which realizes the quantitative analysis. To verify the effectiveness of the presented method, the prediction experiment is carried out. Results show that the absolute errors are within 0.005mg/ml and the relative errors are within 5%, which means this method can measure the fluorescent whitening agent in soybean milk. This research presents a new approach for detecting the illegal fluorescence additive in food production.
SEIRA (surface- enhanced infrared absorption) is based on the effect of enhancement when the sample is absorbed onto islands produced by the deposition or electric deposition of a noble metal [1]. Since the enhancement factors (about 10-1000 times) of SEIRA is not competitive when compared with those of SERS, few attention has been paid on its possible applications [2-5]. However, the cross sections for absorption in the infrared are order of magnitude higher than the corresponding Raman cross sections. Thus, even if SEIRA enhancement is modest, it can have effect in practical applications [6].
In the present study, an advanced and alternative SEIRA based analytical protocol for the analysis of small quantities of colorants, have been proposed.
In more details, Acid Orange 7 - a synthetic colorant used for dyeing - has been selected. Moreover, gold nanoparticles obtained by laser ablation in solution (LASiS), which allows to synthesize stable colloidal solution without interfering molecules (such as stabilizing agents), has been used for the development of the method.
Previous research has demonstrated the potential use of near infrared (NIR) hyperspectral imaging for non-destructive monitoring of mushroom quality. The mushroom industry demands economical and high-throughput imaging systems that can reliably classify groups of mushrooms according to quality parameters. Multispectral imaging systems based on the acquisition of just a few (2-10) wavelengths fulfil these criteria. This research concerns the development of a low-cost robust multispectral system for mushroom quality control which can identify slightly damaged mushroom tissue using NIR spectral images. A three step approach was employed: (1) the most suitable pre-treatment was selected; (2) wavelengths with the most stable normalised regression coefficients were identified using ensemble Monte Carlo variable selection (EMCVS); and (3) partial least square discriminant analysis (PLS-DA)) models were built using the selected regions (49 nm bandwidth) to simulate a multispectral system. Minimum scaled reflectance spectra produced better results than maximum scaled, mean scaled, median scaled or raw spectra. Five key spectral regions were identified, centred around 971 nm, 1090 nm, 1188 nm, 1384 nm and 1454 nm. A PLS-DA model built using three spectral regions (1090 nm, 1188 nm, 1384 nm) and scaled by the 1454 nm band (minimum reflectance) correctly classified 100% of the physically damaged mushrooms.
This study was aimed at developing a nondestructive method for measuring the firmness, skin and flesh color, and dry matter content of pickling cucumbers by means of visible and near-infrared (Vis/NIR) spectroscopy. ‘Journey’ and ‘Vlaspik’ pickling cucumbers were hand harvested and then stored at 10°C and 95% relative humidity for various periods up to 18 days. Spectroscopic measurements were made from each intact cucumber in interactance mode with a low-cost CCD-based Vis/NIR spectrometer over 550–1100nm and an InGaAs-based NIR spectrometer over 800–1650nm. Standard methods were used to measure skin and flesh color, firmness, and dry matter content of the pickling cucumbers. Calibration models were developed using the partial least squares method for predicting firmness, skin and flesh chroma and hue, and dry matter content.NIR measurements correlated well with Magness–Taylor slope or area, with values for the coefficient of determination (R2) of 0.70–0.73 for calibration and 0.67–0.70 for validation, better than those obtained with the Vis/NIR spectrometer. Vis/NIR measurements had good correlations with skin chroma (R2=0.89 and 0.83 for calibration and validation, respectively) and hue (R2=0.76 for calibration and validation). Promising results were obtained in predicting dry matter content of the cucumbers with R2=0.65 in validation for ‘Journey’ cucumbers. Visible and NIR spectroscopy is potentially useful for sorting and grading pickling cucumbers.
Food industry is increasingly concerned in developing and applying rapid and nondestructive methods to offer safer and high quality foods to consumers. During the last years, Fourier transform near-infrared (FT-NIR) has been widely used to determine food quality based on spectrum. Likewise, FT-NIR has been proposed as an innovative and promising nondestructive rapid method capable to detect and identify microorganisms in foods; however, little progress has been made to date in this field. This study is a new attempt to apply FT-NIR technology to identify and quantify bacteria species in water-based systems in order to simulate water-based food matrices. For that, three different lactic acid bacteria-Lactobacillus plantarum, Leuconostoc mesenteroides, and Lactobacillus sakei-associated with spoilage in ready-to-eat meat, were analyzed by reflectance-transmitance FT-NIR in the spectral range 1,100-2,500 nm. Principal component analysis (PCA), and partial least squares (PLS) were applied to obtain prediction models. PCA and PLS showed a clear discrimination between the tested bacteria species whereas PLS method could succesfully quantify the concentration levels (3-9 log cfu/mL) and also distinguish between spoilage (7-9 log cfu/mL) and nonspoilage concentration levels (3-6 log cfu/mL). Results suggest that FT-NIR could be used efficiently to detect and quantify microorgasnisms in water-based food matrices. However, this study is an initial approach and therefore, it will be necessary to further research in order to really carry out its application to more complex food matrices and other microorganisms (i.e., food-borne pathogens).
Fourier Transform Infrared (FT-IR) spectroscopy was used to analyse 56 strains from four closely related species of Lactobacillus, L. sakei, L. plantarum, L. curvatus and L. paracasei. Hierarchical Cluster Analysis (HCA) was used to study the clusters in the data, but in the dendrogram, the spectra were not differentiated into four separate clusters corresponding to species. When the data were analysed with Partial Least Squares Regression (PLSR), the strains were differentiated into four clusters according to species. It was also possible to recognise strains that were incorrectly identified by conventional methods prior to the FT-IR analysis. PLSR was used to identify strains from three of the species, and the results were compared to two other multivariate methods, Soft Independent Modelling of Class Analogy (SIMCA) and K-Nearest Neighbour (KNN). The three methods gave equally good identification results. The results show that FT-IR spectroscopy in combination with PLSR, or other multivariate methods, is well suited for identification of Lactobacillus at the species level, even in quite large data sets.
Based on the integration of computer vision and spectrscopy techniques, hyperspectral imaging is a novel technology for obtaining both spatial and spectral information on a product. Used for nearly 20 years in the aerospace and military industries, more recently hyperspectral imaging has emerged and matured into one of the most powerful and rapidly growing methods of non-destructive food quality analysis and control. Hyperspectral Imaging for Food Quality Analysis and Control provides the core information about how this proven science can be practically applied for food quality assessment, including information on the equipment available and selection of the most appropriate of those instruments. Additionally, real-world food-industry-based examples are included, giving the reader important insights into the actual application of the science in evaluating food products. *Presentation of principles and instruments provides core understanding of how this science performs, as well as guideline on selecting the most appropriate equipment for implementation *Includes real-world, practical application to demonstrate the viability and challenges of working with this technology *Provides necessary information for making correct determination on use of hyperspectral imaging.
A cooked packaged ham alteration has been described. It is characterized by muscular tissue digestion (liquefaction) due to thermotolerant Enterococcus faecalis. Time and temperature, which are useful for destroying the microorganisms, have been indicated.
This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral
images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory
analysis, hyperspectral image resolution, classification and final digital image processing will be exposed,
and some guidelines given and discussed.
Due to the broad character of current applications and the vast number of multivariate methods
available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner,
how to develop a classification methodology to differentiate between several types of plastics by using
Near infrared hyperspectral imaging and Partial Least Squares e Discriminant Analysis. Thus, the reader
is guided through every single step and oriented in order to adapt those strategies to the user's case.
The objectives of this study were to investigate the feasibility of hyperspectral scattering imaging to predict the bacterial contamination in meat nondestructively, and propose an optimal approach for detecting low levels of total viable count (TVC) contamination in beef. Fresh beef samples were obtained from a commercial slaughtering plant, and stored at 4 °C for 0–12 days. The visible/near-infrared (VIS/NIR) hyperspectral images in the backscattering mode were acquired from 3–5 beef samples on each day of the experiment, in parallel with microbiological analysis to enumerate the TVC population. Lorentzian function was used to resolve the light scattering information within the hyperspectral image and consequently Lorentzian parameters, which represented different hyperspectral scattering characteristics were extracted. In this study, not only the individual Lorentzian parameters but also the parameter combinations were used to establish the multivariate statistical models for predicting beef TVC, based on the modeling methods of principal component regression (PCR), partial least squares regression (PLSR), and back propagation neural network (BPNN), respectively. The models established using individual Lorentzian parameters did not perform well in predicting low levels of TVC contamination in beef, and the best prediction result could only achieved with the correlation coefficient of prediction set (RP) and root mean squared error of prediction set (RMSEP) of 0.81 and 1.27 log CFU/g, respectively. Based on the parameter combinations, the best modeling results were achieved with RP and RMSEP of 0.86 and 0.93 log CFU/g, 0.87 and 0.79 log CFU/g, 0.90 and 0.88 log CFU/g by PCR, PLSR, and BPNN methods, respectively, which confirmed the superiority of the parameter combination method. The results of this study demonstrated for the first time that hyperspectral scattering imaging combined with Lorentzian function and the proposed parameter combination method could be used to detect low levels of bacterial contamination in beef nondestructively.
In attempting to analyze, on digital computers, data from basically continuous physical experiments, numerical methods of performing familiar operations must be developed. The operations of differentiation and filtering are especially important both as an end in themselves, and as a prelude to further treatment of the data. Numerical counterparts of analog devices that perform these operations, such as RC filters, are often considered. However, the method of least squares may be used without additional computational complexity and with considerable improvement in the information obtained. The least squares calculations may be carried out in the computer by convolution of the data points with properly chosen sets of integers. These sets of integers and their normalizing factors are described and their use is illustrated in spectroscopic applications. The computer programs required are relatively simple. Two examples are presented as subroutines in the FORTRAN language.
The effectiveness of Hyperspectral imaging (HSI) in the near infrared (NIR) range (1000–1700 nm) was evaluated to discriminate PET (polyethylene terephthalate) from PLA (poly(lactic acid)), two polymers commonly utilized as packaging for foodstuff, in order to improve their further recycling process. An internal calibration based on five reference materials was initially used to eliminate the variability existing among images, then Partial Least Squares-Discriminant Analysis (PLS-DA) was used to distinguish and classify the three classes, i.e., background, PET and PLA. Considering the high amount of data conveyed by the training image, the PLS-DA models were also calculated using as training set a reduced version of the original matrix, with the twofold aim to reduce the computational time and to deal with an equal number of spectra for each class, independently from the initial selected areas. A variable selection procedure by means of iPLS-DA was also applied on both the whole and the reduced matrix. The results obtained on the reduced matrix using only six variables provided a prediction efficiency higher than 98%. Moreover, the possibility to recognize PET and PLA polymers by HSI in the NIR range was further confirmed by using Multivariate Curve Resolution (MCR) as an alternative approach, which also allowed to evaluate the effect of thickness of the transparent plastic samples.
The majority of Escherichia coli O157:H7 outbreaks are associated with ground beef. To detect this pathogen, separation techniques were tested with E. coli O157:H7 in ground beef followed by FT-IR analyses. Ground beef samples were inoculated with various levels of live and heat treated E. coli O157:H7 cells and the bacteria were extracted by filtration or immunomagnetic separation (IMS). Spectra were collected and detection limits were established by discriminant analysis of the 1800-800 cm-1 region and comparison to standard plate counts. The detection limit for the Filtration-FT-IR and IMS-FT-IR assays was 105 CFU/g. Partial least squares model established significant linear relationships between plate counts and spectra [R ≥ 0.99]. Discriminant analysis and canonical variate analysis of the spectra differentiated live and heat treated cells of E. coli O157:H7. Validation experiments using ground beef inoculated with fewer cells (101- 102 CFU/g) reached the detection limit within a six hour incubation. A portable IR sensor was also used to detect E. coli O157:H7 in ground beef, and the detection limit was 107 CFU/g. The total time to detection for Filtration-FT-IR and IMS-FT-IR were one hour and 3.75 h, respectively which is faster than conventional plate count methods (48h) and conventional IMS methods (48h). The FT-IR methods developed are potentially rapid and simple protocols that could be further developed for the detection of different species of pathogenic bacteria in complex food systems.
Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size
accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual
NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of
powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.
J. SAMELIS, E. TSAKALIDOU, J. METAXOPOULOS AND G. KALANTZOPOULOS. 1995. To confirm the phenotypic characterization of 169 predominant sausage isolates of the Lactobacillus sake/curvatus group, SDS-polyacrylamide gel electrophoresis (SDS-PAGE) of whole-cell proteins of 29 representative strains was undertaken. The results of the electrophoretic analysis indicated that each species yielded a specific protein profile, identical to the respective type strain. It was shown that all melibiose-positive isolates belonged to Lact. sake, whether or not they were arginine- and maltose-positive, whereas all melibiose- and arginine-negative isolates were assigned to Lact. curvatus. A group which phenotypically appeared to lie between Lact. sake and Lact. curvatus, on the basis of arginine (+), melibiose (-) and maltose (-) reactions, was shown to be more closely related to Lact. sake. The SDS-PAGE method was valuable in distinguishing between species, but did not allow further differentiation of Lact. curvatus or Lact. sake strains displaying high heterogeneity in secondary physiological and biochemical properties.
Two infrared-based methodologies were developed for metribuzin determination in pesticide formulations after extraction with acetonitrile. Fourier transform mid infrared (MIR) procedure was based on peak area measurements between 1692 and 1670cm−1 corrected with a baseline fixed at 1877cm−1. Fourier transform near infrared (NIR) determination was made by measuring the peak area between 6498 and 6332cm−1 corrected using a two points baseline defined between 6570 and 6212cm−1. Repeatability, as relative standard deviation, of 5 independent measurements at mgg−1 concentration level was 0.3% and 0.03% for MIR and NIR, respectively, and limit of detection values of 9 and 17mgkg−1 in the solid sample were obtained for MIR and NIR, respectively. NIR determination provides a measurement sample frequency of 120h−1, higher than that found by MIR and reference chromatography methods (60 and 10h−1, respectively). On the other hand, the NIR method reduces the solvent consumption and waste generation, to only 10.5ml acetonitrile per sample as compared with 16.5ml required for MIR and 66ml used in the chromatography reference procedure. However the mean accuracy relative error obtained for the analysis of commercial samples was 0.7 and 2.9% (w/w) for MIR and NIR procedures, respectively as compared with data found by the reference procedure, being thus more accurate the MIR than the NIR methodology. So, vibrational procedures, employing a non-chlorinated solvent, such as acetonitrile, for the extraction of the active principle, can be considered serious alternatives to sensitive but expensive reagents and time consuming chromatography methods usually recommended for quality control of agrochemicals.
This paper describes an approach for the colour-based classification of RGB images, taken with a common digital CCD camera on inhomogeneous food matrices. The aim was that of elaborating a feature selection/classification method independent of the specific food matrix that is analysed, in the sense that the variables that are the most relevant ones for the classification of the analysed samples are selected in a blind way, with no a priori assumptions on the basis of the nature of the considered food matrix. A one-dimensional signal describing the colour content of each acquired digital image, which we have called colourgram, is created as the contiguous sequence of the frequency distribution curves of the three red, green and blue colours values, of related parameters (also including hue, saturation and intensity) and of the scores values deriving from the PCA analysis of the unfolded 3D image array, together with the corresponding loadings values and eigenvalues. Once a sufficient number of digital images has been acquired, the corresponding colourgrams are then analysed by means of a feature selection/classification algorithm based on the wavelet transform, wavelet packet transform for efficient pattern recognition (WPTER). This approach was tested on a series of samples of “pesto”, a typical Italian vegetable pasta sauce, which presents high colour variability, mainly due to technological variables (raw materials, processes) and to the degradation of chlorophylls during storage. Good classification results (100% of correctly classified objects with very parsimonious models) have been obtained, also in comparison with the visual evaluation results of a panel test.
Hyperspectral imaging (HSI) combines spectroscopy and imaging resulting in three dimensional multivariate data structures (‘hypercubes’). Each pixel in a hypercube contains a spectrum representing its light absorbing and scattering properties. This spectrum can be used to estimate chemical composition and/or physical properties of the spatial region represented by that pixel. One of the advantages of HSI is the large volume of data available in each hypercube with which to create calibration and training sets. This is also known as the curse of dimensionality, due to the resultant high computational load of high dimensional data. It is desirable to decrease the computational burden implied in hyperspectral imaging; this is especially relevant in the development of real time applications. This paper gives an overview of some pertinent issues for the handling of HSI data. Computational considerations involved in acquiring and managing HSI data are discussed and an overview of the multivariate analysis methods available for reducing the considerable data load encountered in HSI data is presented.
Hyperspectral imaging instruments produce large amounts of raw data. These raw data in A/D converter counts have a number of errors that can be corrected by calibration. The use of multiple Spectralon calibration standards is shown to correct for both spectral and spatial variations. Optimal results are achieved using a two-step calibration and correction process. A series of full field of view or external calibration standards is used to transform raw data counts to reflectance values. A grayscale series of internal standards embedded within each hyperspectral image is used to compensate for instrument instability. Second-order regression models based on these multiple standards provide maximum accuracy. The external standards allow for standardization within a hyperspectral image. The internal standards permit instrument standardization or calibration transfer between hyperspectral images. Copyright © 2006 John Wiley & Sons, Ltd.
Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken
breast muscle packed under aerobic conditions and stored at 4° for 14days were collected and investigated for their potential
use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal
variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant
analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between
days 0 and 4 samples (total viable count (TVC) days 0 and 4 = 5.23 and 6.75log10 cfug−1) which had bacterial loads smaller than the accepted levels (8log10 cfug−1) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14 = 9.61 and 10.37log10 cfug−1). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in
influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor
in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become
useful, rapid, non-destructive tools for spoilage detection.
KeywordsNIR–FT-IR–OPA–PCA–PLS2-DA–Chicken breast muscle–Spoilage
Correspondence analysis was used to classify the pattern-like FT-IR spectra of intact bacteria. The analysis was performed on a data set of approximately 80 normalized spectral derivatives of a selection of pathogenic bacteria. The correspondence analysis proved that the various different bacterial species were clustering in distinct regions of the correspondence maps suggesting that there do exist correlations between spectral data and biochemical/microbiological classification.
This study examines the diversity of spore-forming bacteria isolated from raw materials/bread using molecular methods along with a rapid and innovative technology, the FT-NIR spectroscopy. Microbiological analysis showed that 23% of semolina and 42% of other raw materials (including grain, brewer yeast, improvers) contained more than 100 spores/g and more than 50% of each kind of sample was contaminated at a level ranging from 1 to 100 spores/g. A high bacterial diversity characterized raw materials. In total 176 isolates were collected and characterized: 13 bacterial species belonging to Bacillus (10) and Paenibacillus (3) genera were identified by sequencing of 16S rRNA, gyrA or gyrB genes. The two closely related species Bacillus amyloliquefaciens (strain N45.1) and Bacillus subtilis (strain S63) were also analyzed by the spectroscopic technique FT-NIR. This analysis gave clear discrimination between the strains in the score plot obtained by the PCA and allowed to identify the spectral region 5600-4000 cm(-1) as the information-rich region for discrimination. B. amyloliquefaciens, possibly misidentified as B. subtilis in previous studies, was recognized as the most frequent species, found also in ropy bread. Moreover, the screening test for rope production indicated that mainly B. amyloliquefaciens, together with B. subtilis and Bacillus pumilus, could cause spoilage in bread, even if the last two species were represented by a low number of isolates. The Bacillus cereus group and Bacillus megaterium showed a lower percentage (30-70%) of isolates potentially able to cause the rope, but considering the high number of B. cereus group isolates detected in this study, this bacterial group should also be considered important in rope spoilage. In conclusion, results demonstrate that raw materials used to produce bread represent a rich source of spore-forming bacteria, therefore their microbiological quality should be monitored before use. Moreover, this study highlights for the first time the importance of the species B. amyloliquefaciens in rope spoilage and indicates that other species may also cause this alteration although strains of the same species may behave differently.
Hyperspectral imaging (HSI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool for non-destructive food analysis. This paper provides an introduction to hyperspectral imaging: HSI equipment, image acquisition and processing are described; current limitations and likely future applications are discussed. In addition, recent advances in the application of HSI to food safety and quality assessment are reviewed, such as contaminant detection, defect identification, constituent analysis and quality evaluation.
Near infrared reflectance spectroscopy (NIRS) is a non-destructive and rapid technique applied increasingly for food quality evaluation in recent years. It provides us more information to research the quality of food products. This review intends to give an overview of the type of information that can be obtained based on some developed theory and food research of NIRS. It includes the principle of NIRS technique, the specific techniques with chemometrics for data pre-processing methods, qualitative and quantitative analysis and model transfer, and the wide applications of NIRS in food science. In addition, the promise of NIRS technique for food quality evaluation is demonstrated, and some problems which need to be solved or investigated further are also discussed.
16S rDNA DGGE fingerprinting and phylogenetic analysis were used to reveal the dynamics and identification of the predominant spoilage bacterial in sliced vacuum-packed cooked ham during storage at 4 °C. Total bacteria DNA was directly extracted from the ham. Simultaneously, culture methods were performed. The Nest PCR and touchdown protocol were applied to amplify the V3 region of the 16S rDNA. By analysis of the community dynamic directly obtained from the DGGE profiles, the predominant spoilage bacteria were found to be Lactobacillus sakei, Lactobacillus curvatus and minor components were members of the genus Leuconostoc (Leuconostoc mesenteroides and uncultured Leuconostoc).
The populations of lactic acid bacteria (LAB) in different types of Iberian dry-fermented sausages from central-west Spain were identified. A simple and rapid electrophoretic method of whole-cell protein profiles was evaluated, correlating it with 16S rRNA gene sequence analysis and biochemical identification by API 50 CHL. A total of 96 isolates were identified by SDS-PAGE showing stable profiles corresponding to 30-45 polypeptides in the range 95-8kDa that were clearly different for the different species and were grouped with those of the 9 reference strains used in this study. The SDS-PAGE method showed that the predominant species were Pediococcus acidilactici (48%) followed by Lactobacillus plantarum (23%) and Lactobacillus brevis (18%). The identifications obtained by this approach were confirmed by sequencing the V2-V3 region of the 16S rRNA gene and by a BLAST search of the GenBank database. However, biochemical identifications by API 50 CHL showed different errors at the genus and species level. In sum, the SDS-PAGE analysis showed itself to be a rapid and accurate differentiation method for the most commonly encountered LAB isolates in dry-fermented sausages.
In the present paper, the possibility to use the information contained in RGB digital images to gain a fast and inexpensive quantification of colour-related properties of food is explored. To this aim, we present an approach which consists, as first step, in condensing the colour related information contained in RGB digital images of the analysed samples in one-dimensional signals, named colourgrams. These signals are then used as descriptor variables in multivariate calibration models. The feasibility of this approach has been tested using as a benchmark a series of samples of pesto sauce, whose RGB images have been used to predict both visual attributes defined by a panel test and the content of various pigments (chlorophylls a and b, pheophytins a and b, β-carotene and lutein). The possibility to predict correctly the values of some of the studied parameters suggests the feasibility of this approach for fast monitoring of the main aspect-related properties of a food matrix. The values of the squared correlation coefficient computed in prediction on a test set (R(Pred)(2)) for green and yellow hues were greater than 0.75, while R(Pred)(2) values greater than 0.85 were obtained for the prediction of total chlorophylls content and of chlorophylls/pheophytins ratio. The great flexibility of this blind analysis method for the quantitative evaluation of colour related features of matrices with an inhomogeneous aspect suggests that it is possible to implement automated, objective, and transferable systems for fast monitoring of raw materials, different stages of the manufacture and end products, not necessarily for the food industry only.
Vibrational spectroscopy techniques have shown capacity to provide non-destructive, rapid, relevant information on microbial systematics, useful for classification and identification. Infrared spectroscopy enables the biochemical signatures from microbiological structures to be extracted and analyzed, in conjunction with advanced chemometrics. In addition, a number of recent studies have shown that Fourier Transform Infrared (FT-IR) spectroscopy can help understand the molecular basis of events such as the adaptive tolerance responses expressed by bacteria when exposed to stress conditions in the environment (e.g. those that cells confront in food and during food processing). The current review gives an overview of the published experimental techniques, data-processing algorithms and approaches used in FT-IR spectroscopy to assess the mechanisms of bacterial inactivation by food processing technologies and antimicrobial compounds, to monitor the spore and membrane properties of foodborne pathogens in changing environments, to detect stress-injured microorganisms in food-related environments, to assess dynamic changes in bacterial populations, and to study bacterial tolerance responses.
FT-IR spectroscopy methods for detection, differentiation, and quantification of E. coli O157:H7 strains separated from ground beef were developed. Filtration and immunomagnetic separation (IMS) were used to extract live and dead E. coli O157:H7 cells from contaminated ground beef prior to spectral acquisition. Spectra were analyzed using chemometric techniques in OPUS, TQ Analyst, and WinDAS software programs. Standard plate counts were used for development and validation of spectral analyses. The detection limit based on a selectivity value using the OPUS ident test was 10(5) CFU/g for both Filtration-FT-IR and IMS-FT-IR methods. Experiments using ground beef inoculated with fewer cells (10(1) to 10(2) CFU/g) reached the detection limit at 6 h incubation. Partial least squares (PLS) models with cross validation were used to establish relationships between plate counts and FT-IR spectra. Better PLS predictions were obtained for quantifying live E. coli O157:H7 strains (R(2)> or = 0.9955, RMSEE < or = 0.17, RPD > or = 14) and different ratios of live and dead E. coli O157:H7 cells (R(2)= 0.9945, RMSEE = 2.75, RPD = 13.43) from ground beef using Filtration-FT-IR than IMS-FT-IR methods. Discriminant analysis and canonical variate analysis (CVA) of the spectra differentiated various strains of E. coli O157:H7 from an apathogenic control strain. CVA also separated spectra of 100% dead cells separated from ground beef from spectra of 0.5% live cells in the presence of 99.5% dead cells of E. coli O157:H7. These combined separation and FT-IR methods could be useful for rapid detection and differentiation of pathogens in complex foods.
Hyperspectral imaging techniques have widely demonstrated their usefulness in different areas of interest in pharmaceutical research during the last decade. In particular, middle infrared, near infrared, and Raman methods have gained special relevance. This rapid increase has been promoted by the capability of hyperspectral techniques to provide robust and reliable chemical and spatial information on the distribution of components in pharmaceutical solid dosage forms. Furthermore, the valuable combination of hyperspectral imaging devices with adequate data processing techniques offers the perfect landscape for developing new methods for scanning and analyzing surfaces. Nevertheless, the instrumentation and subsequent data analysis are not exempt from issues that must be thoughtfully considered. This paper describes and discusses the main advantages and drawbacks of the measurements and data analysis of hyperspectral imaging techniques in the development of solid dosage forms.
This paper details the post-mortem changes that take place in the muscular tissue of poultry and the consequences of these on the resulting meat quality at the point of consumption. The history of the development if the modern meat type chicken, the form and function of its muscles, the factors that determine muscle growth and their effects on meat quality are all described. Past studies tend to have been concentrated on the processes occuring in mammalian tissue and those mainly on beef, with little attention being directed at the changes taking place in poultry muscle. In this context the view that modern broilers grow “at the edge of what is metabolically possible” is important. This hypothesis owes its origin to the fact that muscle, and thus protein, accretion is accomplished through a dynamic equilibrium between synthesis and degradation. Evidence is provided to show that the muscle cell reaches a certain maximum synthesis capacity, to grow beyond which requires it to decrease its rate of degradation. This property is possibly of considerable influence in meat ageing and forms the basis for the proposition that the breast muscle of poultry is especially suited to study the effects of post-mortem proteolytic degradation on meat ageing and product quality.
Infrared signals of microorganisms are highly specific fingerprint-like patterns that can be used for probing the identity of microorganisms. The simplicity and versatility of Fourier-transform infrared spectroscopy (FT-IR) makes it a versatile technique for rapid differentiation, classification, identification and large-scale screening at the subspecies level.
The clinical diagnosis of sepsis remains difficult, particularly in the young child, and would be improved by a rapid and reliable method for identification of bacteria in blood and other body fluids. Polymerase chain reaction (PCR) amplification of highly conserved DNA sequences found in all bacteria would permit fast and sensitive determination of the presence of bacteria in clinical specimens.
A primer pair for highly conserved regions of bacterial DNA encoding 16S ribosomal RNA (rDNA) was utilized for PCR amplification. PCR products were analyzed by gel electrophoresis, and, after modification of the primers, by an automated 96-well plate reader.
rDNA was amplified from 12 different species of bacteria, including Gram-negative and -positive organisms. No signal was observed when total human DNA was used as template. Colorimetric analysis of amplified sequences using a 96-well format was also successful.
We conclude that a single primer pair designed to anneal to a highly conserved region of bacterial DNA can amplify DNA specimens from a variety of different bacteria, while not amplifying human DNA. Such a molecular genetics approach can be fully automated with existing robotic technology. Because of speed, sensitivity, and cost, molecular triage of patients with signs and symptoms of possible bacterial infection will decrease morbidity and mortality among those with unrecognized bacteremia who are managed as outpatients, and will dramatically reduce hospital expenses from individuals who are admitted and are not bacteremic.
To evaluate the feasibility of visible and short-wavelength near-infrared (SW-NIR) diffuse reflectance spectroscopy (600-1100 nm) to quantify the microbial loads in chicken meat and to develop a rapid methodology for monitoring the onset of spoilage.
Twenty-four prepackaged fresh chicken breast muscle samples were prepared and stored at 21 degrees C for 24 h. Visible and SW-NIR was used to detect and quantify the microbial loads in chicken breast muscle at time intervals of 0, 2, 4, 6, 8, 10, 12 and 24 h. Spectra were collected in the diffuse reflectance mode (600-1100 nm). Total aerobic plate count (APC) of each sample was determined by the spread plate method at 32 degrees C for 48 h. Principal component analysis (PCA) and partial least squares (PLS) based prediction models were developed. PCA analysis showed clear segregation of samples held 8 h or longer compared with 0-h control. An optimum PLS model required eight latent variables for chicken muscle (R = 0.91, SEP = 0.48 log CFU g(-1)).
Visible and SW-NIR combined with PCA is capable of perceiving the change of the microbial loads in chicken muscle once the APC increases slightly above 1 log cycle. Accurate quantification of the bacterial loads in chicken muscle can be calculated from the PLS-based prediction method. SIGNIFICANCE AND THE IMPACT OF THE STUDY: Visible and SW-NIR spectroscopy is a technique with a considerable potential for monitoring food safety and food spoilage. Visible and SW-NIR can acquire a metabolic snapshot and quantify the microbial loads of food samples rapidly, accurately, and noninvasively. This method would allow for more expeditious applications of quality control in food industries.