Microbial evaluation of raw and processed food products by Visible/Infrared, Raman and Fluorescence spectroscopy

Production of cost-effective rhamnolipid from Halopseudomonas sabulinigri OZK5 using waste frying oil

The major barrier to the wide-range application of biosurfactants is their high cost of production and low yield. In this study, waste frying oil (WFO) was used as the sole carbon source to produce cost-effective and eco-friendly rhamnolipids by Halopseudomonas sabulinigri OZK5 isolated from crude oil-contaminated soil samples. The optimal culture conditions for rhamnolipid production were determined as 30 ml/l waste frying oil, 37 °C temperature, pH 8, and 72 h incubation time. Under the optimized conditions 2.97 g/l rhamnolipid production was achieved. With a critical micelle concentration of 50 mg/l, the rhamnolipids could reduce the surface tension of water to 37.5 mN/m and demonstrate strong emulsifying activity (E24 = 67.3%). As a result of FTIR analyses, major peaks were obtained at 2924, 2854, 1720, 1570, 1396, 1051, and 981 cm-1. In conclusion, rhamnolipid production by non-pathogenic Halopseudomonas sabulinigri OZK5 using a low-cost fermentation medium has been shown to be biotechnologically promising.

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

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

Regulating bacterial biofilms in food and biomedicine: unraveling mechanisms and Innovating strategies

Bacterial biofilm has brought a lot of intractable problems in food and biomedicine areas. Conventional biofilm control mainly focuses on inactivation and removal of biofilm. However, with robust construction and enhanced resistance, the established biofilm is extremely difficult to eradicate. According to the mechanism of biofilm development, biofilm formation can be modulated by intervening in the key factors and regulatory systems. Therefore, regulation of biofilm formation has been proposed as an alternative way for effective biofilm control. This review aims to provide insights into the regulation of biofilm formation in food and biomedicine. The underlying mechanisms for early-stage biofilm establishment are summarized based on the key factors and correlated regulatory networks. Recent developments and applications of novel regulatory strategies such as anti/pro-biofilm agents, nanomaterials, functionalized surface materials and physical strategies are also discussed. The current review indicates that these innovative methods have contributed to effective biofilm control in a smart, safe and eco-friendly way. However, standard methodology for regulating biofilm formation in practical use is still missing. As biofilm formation in real-world systems could be far more complicated, further studies and interdisciplinary collaboration are still needed for simulation and experiments in the industry and other open systems.

What is cooking in your kitchen: seeing "invisible" with time-resolved coherent anti-Stokes Raman spectroscopy

Cooking oil is a critical component of human food and its main component, lipid, is influential to health, but assessing its authenticity and quality can be challenging due to its complex chemical composition. In this study, we introduce a novel application of time-resolved coherent anti-Stokes Raman scattering (T-CARS) spectroscopy for detecting adulteration and understanding the mechanisms of lipid oxidation in various cooking oils. Our research surpasses the limitations of conventional spontaneous Raman spectroscopy, demonstrating that intra-molecular interactions from unsaturated bonds in triglycerides significantly influence vibrational dephasing time. We observed that these dephasing times, although diverse initially, converge to a similar value after heating cycles. Notably, a longer vibrational dephasing of the CH2 symmetric stretching mode was found to correlate with a higher lipid oxidation rate. These findings underscore the potential of T-CARS in identifying and characterizing subtle molecular interactions, offering a transformative approach to understanding molecular dynamics. This research paves the way for broader applications of T-CARS across fields such as chemistry, biomedicine, and material science, marking a significant advancement in the development of innovative analytical techniques.

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems

Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

Fluorescence-based characterisation of selected edible insect species: Excitation emission matrix (EEM) and parallel factor (PARAFAC) analysis

Fluorescence spectroscopy coupled with chemometric tools is a powerful analytical method, largely used for rapid food quality and safety evaluations. However, its potential has not yet been explored in the novel food sector. In the present study, excitation emission matrices (EEMs) of 15 insect powders produced by milling insects belonging to 5 Orthoptera species (Acheta domesticus, Gryllus assimilis, Gryllus bimaculatus, Locusta migratoria, Schistocerca gregaria) from 3 different origins were investigated. Parallel factor (PARAFAC) analysis performed on the overall averaged dataset was validated for five components, highlighting the presence of five different fluorescence peaks. The presence of these peaks was confirmed on each species, suggesting that fluorescence compounds of edible insects are the same in several species. PARAFAC analysis performed on the overall averaged dataset after alternatively adding the EEM recorded from one standard compound allowed to speculate that edible insects fluorescence raises from mixtures of: tryptophan + tyrosine (PARAFAC component-1), tryptophan + tyrosine + tocopherol (PARAFAC component-2), collagen + pyridoxine + pterins (PARAFAC component-3). This study suggests that fluorescence spectroscopy may represent a powerful method for investigating composition and quality of insect-based foods.

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Fluorescence landscape of edible insects comprises of 5 different peaks.

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Similar fluorescence compounds are present among several Orthoptera species.

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Fluorescence peaks of edible insects result from several chemical molecules.

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Fluorescence intensity of edible insects depends on their species and origin.

Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 10³ colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R²), and the bias (B_f) and accuracy (A_f) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited A_f and B_f between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R² and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver.

Use of Spectroscopic Techniques to Monitor Changes in Food Quality during Application of Natural Preservatives: A Review

Consumer demand for food of high quality has driven research for alternative methods of food preservation on the one hand, and the development of new and rapid quality assessment techniques on the other hand. Recently, there has been a growing need and interest in healthier food products, which has led to an increased interest in natural preservatives, such as essential oils, plant extracts, and edible films and coatings. Several studies have shown the potential of using biopreservation, natural antimicrobials, and antioxidant agents in place of other processing and preservation techniques (e.g., thermal and non-thermal treatments, freezing, or synthetic chemicals). Changes in food quality induced by the application of natural preservatives have been commonly evaluated using a range of traditional methods, including microbiology, sensory, and physicochemical measurements. Several spectroscopic techniques have been proposed as promising alternatives to the traditional time-consuming and destructive methods. This review will provide an overview of recent studies and highlight the potential of spectroscopic techniques to evaluate quality changes in food products following the application of natural preservatives.

A machine learning workflow for raw food spectroscopic classification in a future industry

Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure quality and affordability, reducing at the same time the costs of keeping the food fresh and increase productivity. A system, as the one presented herein, for raw food categorization is needed in future food industries to automate food classification according to type, the process of algorithm approaches that will be applied to every different food origin and also for serving disabled people. The purpose of this work was to develop a machine learning workflow based on supervised PLS regression and SVM classification, towards automated raw food categorization from FTIR. The system exhibited high efficiency in multi-class classification of 7 different types of raw food. The selected food samples, were diverse in terms of storage conditions (temperature, storage time and packaging), while the variability within each food was also taken into account by several different batches; leading in a classifier able to embed this variation towards increased robustness and efficiency, ready for real life applications targeting to the digital transformation of the food industry.

Microbial quality assessment of minimally processed pineapple using GCMS and FTIR in tandem with chemometrics

Microbial quality is the critical parameter determining the safety of refrigerated perishables. Traditional methods used for assessing microbial quality are time consuming and labour intensive. Thus rapid, non-destructive methods that can accurately predict microbial status is warranted. Models using partial least square regression (PLS-R) from chemical finger prints of minimally processed pineapple during storage obtained by Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry (HS-SPME-GCMS), Fourier Transform Infrared (FTIR) spectroscopy and their data fusion are developed. Models built using FTIR data demonstrated good prediction for unknown samples kept under non-isothermal conditions. FTIR based models could predict 87 and 80% samples within ±1 log CFU/g for TVC and Y&M, respectively. Analysis of PLS-R results suggested the production of alcohols and esters with utilization of sugars due to microbial spoilage.

Estimation of Minced Pork Microbiological Spoilage through Fourier Transform Infrared and Visible Spectroscopy and Multispectral Vision Technology

Spectroscopic and imaging methods coupled with multivariate data analysis have been increasingly studied for the assessment of food quality. The objective of this work was the estimation of microbiological quality of minced pork using non-invasive spectroscopy-based sensors. For this purpose, minced pork patties were stored aerobically at different isothermal (4, 8, and 12 °C) and dynamic temperature conditions, and at regular time intervals duplicate samples were subjected to (i) microbiological analyses, (ii) Fourier transform infrared (FTIR) and visible (VIS) spectroscopy measurements, and (iii) multispectral image (MSI) acquisition. Partial-least squares regression models were trained and externally validated using the microbiological/spectral data collected at the isothermal and dynamic temperature storage conditions, respectively. The root mean squared error (RMSE, log CFU/g) for the prediction of the test (external validation) dataset for the FTIR, MSI, and VIS models was 0.915, 1.173, and 1.034, respectively, while the corresponding values of the coefficient of determination (R²) were 0.834, 0.727, and 0.788. Overall, all three tested sensors exhibited a considerable potential for the prediction of the microbiological quality of minced pork.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.

Ultrasensitive analysis of kanamycin residue in milk by SERS-based aptasensor

An ultrasensitive method for the kanamycin (KANA) detection in milk sample using surface-enhanced Raman spectroscopy-based aptasensor was employed in the current study. Double strand DNA binding bimetallic gold@silver nanoparticles were developed as a sensing platform. Probe DNAs were first embedded on the surface of gold nanoparticles by the end-modified thiol, and after silver shell encapsulating, KANA aptamer DNAs with the Raman reporter Cy3 were then hybridized with probe DNAs by complementary base pairing. Results showed that with increase in the KANA concentration, the Raman intensity of Cy3 decreased. Besides achieving selectivity, an ultralow detection limit of 0.90 pg/mL, a broad linear relationship ranging from 10 μg/mL to 100 ng/mL in aqueous reagent and satisfactory recoveries of 90.4-112% in liquid whole milk were obtained. The result of actual sample proved that this aptasensor was promising in trace determination of KANA residue.

Determination of Cell Abundances and Paralytic Shellfish Toxins in Cultures of the Dinoflagellate Gymnodinium catenatum by Fourier Transform Near Infrared Spectroscopy

Harmful algal blooms are responsible worldwide for the contamination of fishery resources, with potential impacts on seafood safety and public health. Most coastal countries rely on an intense monitoring program for the surveillance of toxic algae occurrence and shellfish contamination. The present study investigates the use of near infrared (NIR) spectroscopy for the rapid in situ determination of cell concentrations of toxic algae in seawater. The paralytic shellfish poisoning (PSP) toxin-producing dinoflagellate Gymnodinium catenatum was selected for this study. The spectral modeling by partial least squares (PLS) regression based on the recorded NIR spectra enabled the building of highly accurate (R2 = 0.92) models for cell abundance. The models also provided a good correlation between toxins measured by the conventional methods (high-performance liquid chromatography with fluorescence detection (HPLC-FLD)) and the levels predicted by the PLS/NIR models. This study represents the first necessary step in investigating the potential of application of NIR spectroscopy for algae bloom detection and alerting.

Fabrication of gold nanorods for SERS detection of thiabendazole in apple

Thiabendazole (TBZ) is a kind of pesticide that is widely used in agriculture, and its residue may pose a threat to human health. In order to measure TBZ residues in food samples, a surface-enhanced Raman spectroscopy (SERS) method combined with a homogeneous and reusable gold nanorods (GNR) array substrate was proposed. GNR with a high uniformity was synthesized and then applied to the self-assembly of a GNR vertically aligned array. The relative standard deviation (RSD) of the array for SERS could reach 15.4%, and the array could be reused for more than seven times through the treatment of plasma etching. A logarithmic correlation between TBZ concentration and Raman intensity was obtained, with the best determination coefficient (R²) and the corresponding limit of detection (LOD) of 0.991 and 0.037 mg/L in methanol solution, and 0.980 and 0.06 ppm in apple samples, respectively. The recoveries of TBZ in apple samples ranged from 76% to 107%. This study provided a rapid and sensitive approach for detecting TBZ in apples based on SERS coupled with GNR array substrate, showing great potential for analyzing other trace contaminants in food matrices.

Use of Fourier transform infrared spectroscopy for monitoring the shelf life of ham slices packed with probiotic supplemented edible films after treatment with high pressure processing

The aim of the present study was to investigate the potential use of Fourier transform infrared (FTIR) spectroscopy to quantify biochemical changes occurring in ham slices packed with probiotic supplemented edible films and treated with High Pressure Processing (HPP), in monitoring spoilage. Details regarding the data collection and experimental procedure were presented by Pavli et al. (2017). A series of Partial Least Squares (PLS) models were developed to correlate spectral data from FTIR analysis with ham spoilage during storage under vacuum at different temperatures (4, 8 and 12°C). FTIR spectra were collected from the surface of the ham samples in parallel with microbiological analysis of total viable counts (TVC) and lactic acid bacteria (LAB). Qualitative interpretation of spectral data was based on a sensory evaluation, using a hedonic scale, classifying the samples in three quality classes, fresh, semi-fresh and spoiled. The scope of the modeling approach was to discriminate the ham slices in their respective quality class and additionally to predict the microbial population directly from spectral data. The results obtained demonstrated that the processing of the samples affected the performance of classification in the sensory classes, with better results observed in the case of for ham slices packed with probiotic supplemented (PS) edible films and of control samples without HPP. The performance of PLS regression models on providing quantitative estimations of microbial counts were based on specific figures of merit (bias factor, accuracy factor, root mean square error, percentage of prediction error). Bias and accuracy factors were close to unity for both microbial groups tested for samples without HPP, whereas for HPP treated samples the values of these indices ranged from 0.963 to 1.332, depending on the case and indice. The results of this study demonstrated for the first time that although FTIR can be used reliably for the rapid assessment of sliced ham, additional processes such as HPP can affect its performance.