FoGGAN: Generating Realistic Parkinson's Disease Freezing of Gait Data Using GANs.

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson's Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier's performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.

Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses.

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.

Heterogeneity of single cell inactivation: Assessment of the individual cell time to death and implications in population behavior.

A direct microscopic time-lapse method, using appropriate staining for cell viability in a confocal scanning laser microscope, was used for the direct assessment of Salmonella Agona individual cell inactivation in small two-dimensional colonies exposed to osmotic stress. Individual cell inactivation times were fitted to a variety of continuous distributions using @Risk software. The best fitted distribution (LogLogistic) was further used to predict the inactivation of Salmonella populations of various initial levels using Monte Carlo simulation. The simulation results showed that the variability in inactivation kinetics is negligible for concentrations down to 100 cells and the population behavior can be described with a deterministic model. As the concentration decreases below 100 cells, however, the variability increases significantly indicating that the traditional D-value used in deterministic first order kinetic models is not valid. At a second stage, single cell behavior was monitored in larger three dimensional colonies. The results showed that colony size can affect the inactivation pattern. The effect of colony size on microbial inactivation was confirmed with validation experiments which showed a higher inactivation rate for populations consisting of single cells or small colonies compared to those consisting of cells organized in larger colonies.

Evaluation of Fourier transform infrared spectroscopy and multispectral imaging as means of estimating the microbiological spoilage of farmed sea bream.

The objective of the present study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI), in tandem with multivariate data analysis, as means of estimating the microbiological quality of sea bream. Farmed whole ungutted fish were stored aerobically at 0, 4 and 8 °C. At regular time intervals, fish samples (i.e. cut portions) were analysed microbiologically, while FTIR and MSI measurements also were acquired at both the skin and flesh sides of the samples. Partial least squares regression (PLSR) models were calibrated to provide quantitative estimations of the microbiological status of fish based on spectral data, in a temperature-independent manner. The PLSR model based on the FTIR data of fish skin exhibited good performance when externally validated, with the coefficient of determination (R2) and the root mean square error (RMSE) being 0.727 and 0.717, respectively. Hence, FTIR spectroscopy appears to be promising for the rapid and non-invasive monitoring of the microbiological spoilage of whole sea bream. Contrarily, the MSI models' performance was unsatisfactory, delimitating their potential exploitation in whole fish quality assessment. Model optimization results concerning fish flesh indicated that MSI may be propitious in skinned fish products, with its definite competence warranting further investigation.

Spontaneous Neuronal Network Persistent Activity in the Neocortex: A(n) (Endo)phenotype of Brain (Patho)physiology.

Abnormal synaptic homeostasis in the cerebral cortex represents a risk factor for both psychiatric and neurodegenerative disorders, from autism and schizophrenia to Alzheimer's disease. Neurons via synapses form recurrent networks that are intrinsically active in the form of oscillating activity, visible at increasingly macroscopic neurophysiological levels: from single cell recordings to the local field potentials (LFPs) to the clinically relevant electroencephalography (EEG). Understanding in animal models the defects at the level of neural circuits is important in order to link molecular and cellular phenotypes with behavioral phenotypes of neurodevelopmental and/or neurodegenerative brain disorders. In this study we introduce the novel idea that recurring persistent network activity (Up states) in the neocortex at the reduced level of the brain slice may be used as an endophenotype of brain disorders that will help us understand not only how local microcircuits of the cortex may be affected in brain diseases, but also when, since an important issue for the design of successful treatment strategies concerns the time window available for intervention.

High-Affinity Nicotinic Receptors Modulate Spontaneous Cortical Up States In Vitro.

Nicotinic acetylcholine receptors (nAChRs) play an important role in the modulation of many cognitive functions but their role in integrated network activity remains unclear. This is at least partly because of the complexity of the cholinergic circuitry and the difficulty in comparing results from in vivo studies obtained under diverse experimental conditions and types of anesthetics. Hence the role of nAChRs in the synchronization of cortical activity during slow-wave sleep is still controversial, with some studies showing they are involved in ACh-dependent EEG desynchronization, and others suggesting that this effect is mediated exclusively by muscarinic receptors. Here we use an in vitro model of endogenous network activity, in the form of recurring self-maintained depolarized states (Up states), which allows us to examine the role of high-affinity nAChRs on network dynamics in a simpler form of the cortical microcircuit. We find that mice lacking nAChRs containing the ß2-subunit (ß2-nAChRs) have longer and more frequent Up states, and that this difference is eliminated when ß2-nAChRs in wild-type mice are blocked. We further show that endogenously released ACh can modulate Up/Down states through the activation of both ß2- and α7-containing nAChRs, but through distinct mechanisms: α7-nAChRs affect only the termination of spontaneous Up states, while ß2-nAChRs also regulate their generation. Finally we provide evidence that the effects of ß2-subunit-containing, but not α7-subunit-containing nAChRs, are mediated through GABAB receptors. To our knowledge this is the first study documenting direct nicotinic modulation of Up/Down state activity. SIGNIFICANCE STATEMENT: Through our experiments we were able to uncover a clear and previously disputed effect of nicotinic signaling in synchronized activity of neuronal networks of the cortex. We show that both high-affinity receptors (containing the ß2-subunit, ß2-nAChRs) and low-affinity receptors (containing the α7-subunit, α7-nAChRs) can regulate cortical network function exhibited in the form of Up/Down states. We further show that the effects of ß2-nAChRs, but not α7-nAChRs, are mediated through the activation of GABAB receptors. These results suggest a possible synthesis of seemingly contradictory results in the literature and could be valuable for informing computational models of cortical function and for guiding the search for therapeutic interventions.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud.

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

Deciphering the induction of Listeria monocytogenes into sublethal injury using fluorescence microscopy and RT-qPCR.

The adaptive response of bacterial cells to changing environmental conditions depends on the behavior of single cells within the population. Exposure of Listeria monocytogenes to sublethal acidic conditions in foods or in the gastrointestinal track of the host may induce injuries relevant to difficult physiological states within the dormancy continuum. In this study, exposure to acidic conditions (acetic-AA and hydrochloric acid-HCl adjusted to pH 3.0, 2.7, 2.5 at 20 °C for 5 h) was used to evaluate injury of L. monocytogenes, Scott A strain. To differentiate the resistant sub-population from the total, Tryptic Soy Agar with 0.6 % Yeast Extract (TSAYE) supplemented or not with 5 % NaCl were comparatively used. Sublethally injured cells were detected by comparing plate counts with fluorescence microscopy, using combinations of CFDA (viability) and Propidium-Iodide (death). Effect of acid stress on the relative transcription of clpP, mazE, mazF, relA, gadC, gadD, gadB, sigB, inlA and prfA upon transition of total population into different physiological stages was evaluated through RT-qPCR. AA treated cells showed measurable logarithmic reduction at pH 2.7 and 2.5, while there was a significant percentage of CFDA-/PI+ cells. Evaluation of the potentially culturable population on TSAYE, from the percentage of CFDA/PI-stained cells, revealed that unstained cells represented a non-culturable sub-population. Exposure to Ringer's solution pH 2.7, adjusted with AA, resulted in higher percentages of non-esterase active with membrane integrity cells (CFDA-/PI-) compared to the percentages of the enumerated culturable cells on TSAYE after 4 and 5 h. Under the same conditions, after 1 h of exposure macroscopic observation revealed size colony variations (SCVs) of the total population (CFU on TSAYE). L. monocytogenes retained its culturability after hydrochloric acid exposure, while cells remained metabolically active (CFDA+). However, a stochastic change in cell's shape, was detected after exposure to pH 3.0 and 2.5, adjusted with HCl, for 2 h at 20 °C. A pattern of gene up-regulation was observed during treatment with AA pH 2.7 and HCl pH 3.0 at the 3rd h of exposure. Deciphering L. monocytogenes sublethal injury sheds light into the physiological and molecular characteristics of this state and provides the food science community with quantitative data to improve risk assessment.

Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol.

(1) Objective: We explore the predictive power of a novel stream of patient data, combining wearable devices and patient reported outcomes (PROs), using an AI-first approach to classify the health status of Parkinson's disease (PD), multiple sclerosis (MS) and stroke patients (collectively named PMSS). (2) Background: Recent studies acknowledge the burden of neurological disorders on patients and on the healthcare systems managing them. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients. (3) Methods: We introduce the data collection journey within the ALAMEDA project, which continuously collects PRO data for a year through mobile applications and supplements them with data from minimally intrusive wearable devices (accelerometer bracelet, IMU sensor belt, ground force measuring insoles, and sleep mattress) worn for 1-2 weeks at each milestone. We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. (4) Results: A novel combination of wearable devices and smartphone applications required for the desired analysis of motor, sleep, emotional and quality-of-life outcomes is introduced. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups (in terms of standard medical test targets). Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.

Spectroscopic Data for the Rapid Assessment of Microbiological Quality of Chicken Burgers.

The rapid assessment of the microbiological quality of highly perishable food commodities is of great importance. Spectroscopic data coupled with machine learning methods have been investigated intensively in recent years, because of their rapid, non-destructive, eco-friendly qualities and their potential to be used on-, in- or at-line. In the present study, the microbiological quality of chicken burgers was evaluated using Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) in tandem with machine learning algorithms. Six independent batches were purchased from a food industry and stored at 0, 4, and 8 °C. At regular time intervals (specifically every 24 h), duplicate samples were subjected to microbiological analysis, FTIR measurements, and MSI sampling. The samples (n = 274) acquired during the data collection were classified into three microbiological quality groups: "satisfactory": 4−7 log CFU/g, "acceptable": 7−8 log CFU/g, and "unacceptable": >8 logCFU/g. Subsequently, classification models were trained and tested (external validation) with several machine learning approaches, namely partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and ordinal logistic regression (OLR). Accuracy scores were attained for the external validation, exhibiting FTIR data values in the range of 79.41−89.71%, and, for the MSI data, in the range of 74.63−85.07%. The performance of the models showed merit in terms of the microbiological quality assessment of chicken burgers.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables.

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.

Protein spot detection and quantification in 2-DE gel images using machine-learning methods.

Two-dimensional gel electrophoresis (2-DE) is the most established protein separation method used in expression proteomics. Despite the existence of sophisticated software tools, 2-DE gel image analysis still remains a serious bottleneck. The low accuracies of commercial software packages and the extensive manual calibration that they often require for acceptable results show that we are far from achieving the goal of a fully automated and reliable, high-throughput gel processing system. We present a novel spot detection and quantification methodology which draws heavily from unsupervised machine-learning methods. Using the proposed hierarchical machine learning-based segmentation methodology reduces both the number of faint spots missed (improves sensitivity) and the number of extraneous spots introduced (improves precision). The detection and quantification performance has been thoroughly evaluated and is shown to compare favorably (higher F-measure) to a commercially available software package (PDQuest). The whole image analysis pipeline that we have developed is fully automated and can be used for high-throughput proteomics analysis since it does not require any manual intervention for recalibration every time a new 2-DE gel image is to be analyzed. Furthermore, it can be easily parallelized for high performance and also applied without any modification to prealigned group average gels.

Data Science in the Food Industry.

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Listeria monocytogenes Sublethal Injury and Viable-but-Nonculturable State Induced by Acidic Conditions and Disinfectants.

The dormancy continuum hypothesis states that in response to stress, cells enter different stages of dormancy ranging from unstressed living cells to cell death, in order to ensure their long-term survival under adverse conditions. Exposure of Listeria monocytogenes cells to sublethal stressors related to food processing may induce sublethal injury and the viable-but-nonculturable (VBNC) state. In this study, exposure to acetic acid (AA), hydrochloric acid (HCl), and two disinfectants, peracetic acid (PAA) and sodium hypochlorite (SH), at 20°C and 4°C was used to evaluate the potential induction of L. monocytogenes strain Scott A into different stages of dormancy. To differentiate the noninjured subpopulation from the total population, tryptic soy agar with 0.6% yeast extract (TSAYE), supplemented or not with 5% NaCl, was used. Sublethally injured and VBNC cells were detected by comparing plate counts obtained with fluorescence microscopy and by using combinations of carboxyfluorescein and propidium iodide (viable/dead cells). Induction of sublethal injury was more intense after PAA treatment. Two subpopulations were detected, with phenotypes of untreated cells and small colony variants (SCVs). SCVs appeared as smaller colonies of various sizes and were first observed after 5 min of exposure to 5 ppm PAA at 20°C. Increasing the stress intensity from 5 to 40 ppm PAA led to earlier detection of SCVs. L. monocytogenes remained culturable after exposure to 20 and 30 ppm PAA for 3 h. At 40 ppm, after 3 h of exposure, the whole population was considered nonculturable, while cells remained metabolically active. These results corroborate the induction of the VBNC state. IMPORTANCE Sublethally injured and VBNC cells may evade detection, resulting in underestimation of a food product's microbial load. Under favorable conditions, cells may regain their growth capacity and acquire new resistant characteristics, posing a major threat for public health. Induction of the VBNC state is crucial for foodborne pathogens, such as L. monocytogenes, the detection of which relies almost exclusively on the use of culture recovery techniques. In the present study, we confirmed that sublethal injury is an initial stage of dormancy in L. monocytogenes that is followed by the VBNC state. Our results showed that PAA induced SCVs (a phenomenon potentially triggered by external factors) and the VBNC state in L. monocytogenes, indicating that tests of lethality based only on culturability may provide false-positive results regarding the effectiveness of an inactivation treatment.

Assessing the Biofilm Formation Capacity of the Wine Spoilage Yeast Brettanomyces bruxellensis through FTIR Spectroscopy.

Brettanomyces bruxellensis is a wine spoilage yeast known to colonize and persist in production cellars. However, knowledge on the biofilm formation capacity of B. bruxellensis remains limited. The present study investigated the biofilm formation of 11 B. bruxellensis strains on stainless steel coupons after 3 h of incubation in an aqueous solution. FTIR analysis was performed for both planktonic and attached cells, while comparison of the obtained spectra revealed chemical groups implicated in the biofilm formation process. The increased region corresponding to polysaccharides and lipids clearly discriminated the obtained spectra, while the absorption peaks at the specific wavenumbers possibly reveal the presence of ß-glucans, mannas and ergosterol. Unsupervised clustering and supervised classification were employed to identify the important wavenumbers of the whole spectra. The fact that all the metabolic fingerprints of the attached versus the planktonic cells were similar within the same cell phenotype class and different between the two phenotypes, implies a clear separation of the cell phenotype; supported by the results of the developed classification model. This study represents the first to succeed at applying a non-invasive technique to reveal the metabolic fingerprint implicated in the biofilm formation capacity of B. bruxellensis, underlying the homogenous mechanism within the yeast species.

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.

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 (R 2), 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 R 2 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.

Improving 2-DE gel image denoising using contourlets.

One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false-spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2-DE gel images denoising. We compare its effectiveness with state-of-the-art methods such as wavelets-based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average S/N performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.

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.