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

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

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging.

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

Microbial detection and identification methods: Bench top assays to omics approaches.

Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.

Efficient succinic acid production from high-sugar-content beverages by Actinobacillus succinogenes.

This study presents the production of succinic acid (SA) by Actinobacillus succinogenes using high-sugar-content beverages (HSCBs) as feedstock. The aim of this study was the valorization of a by-product stream from the beverage industry for the production of an important building block chemical, such as SA. Three types of commercial beverages were investigated: fruit juices (pineapple and ace), syrups (almond), and soft drinks (cola and lemon). They contained mainly glucose, fructose, and sucrose at high concentration-between 50 and 1,000 g/L. The batch fermentation tests highlighted that A. succinogenes was able to grow on HSCBs supplemented with yeast extract, but also on the unsupplemented fruit juices. Indeed, the bacteria did not grow on the unsupplemented syrup and soft drinks because of the lack of indispensable nutrients. About 30-40 g/L of SA were obtained, depending on the type of HSCB, with yield ranging between 0.75 and 1.00 gSA /gS . The prehydrolysis step improved the fermentation performance: SA production was improved by 6-24%, depending on the HSCB, and sugar conversion was improved of about 30-50%.

Bioreactors for succinic acid production processes.

Succinic acid (SA) has been recognized as one of the most important bio-based building block chemicals due to its numerous potential applications. Fermentation SA production from renewable carbohydrate feedstocks can have the economic and sustainability potential to replace petroleum-based production in the future, not only for existing markets, but also for new larger volume markets. Design and operation of bio-reactors play a key role. During the last 20 years, many different fermentation strategies for SA production have been described in literature, including utilization of immobilized biocatalysts, integrated fermentation and separation systems and batch, fed-batch, and continuous operation modes. This review is an overview of different fermentation process design developed over the past decade and provides a perspective on remaining challenges for an economically feasible succinate production processes. The analysis stresses the idea of improving the efficiency of the fermentation stage by improving bioreactor design and by increasing bioreactor performance.

Continuous Succinic Acid Fermentation by Actinobacillus Succinogenes: Assessment of Growth and Succinic Acid Production Kinetics.

Succinic acid is one of the most interesting platform chemicals that can be produced in a biorefinery approach. The paper reports the characterization of the growth kinetics of Actinobacillus succinogenes DSM 22257 using glucose as carbon source. Tests were carried out in a continuous bioreactor operated under controlled pH. Under steady-state conditions, the conversion process was characterized in terms of concentration of glucose, cells, acids, and pH. The effects of acid-succinic, acetic, and formic-concentration in the medium on fermentation performance were investigated. The fermentation was interpreted according to several models characterized by substrate and product inhibition. The selected kinetic model of biomass growth and of metabolite production described the microorganism growth rate under a broad interval of operating conditions. Under the investigated operating conditions, results pointed out that: no substrate inhibition was observed; acetic acid did not inhibit the cell growth and succinic acid production.

Continuous succinic acid fermentation by Actinobacillus succinogenes in a packed-bed biofilm reactor.

BACKGROUND: Succinic acid is one of the most interesting platform chemicals that can be produced in a biorefinery approach. In this study, continuous succinic acid production by Actinobacillus succinogenes fermentation in a packed-bed biofilm reactor (PBBR) was investigated. RESULTS: The effects of the operating conditions tested, dilution rate (D), and medium composition (mixture of glucose, xylose, and arabinose-that simulate the composition of a lignocellulosic hydrolysate)-on the PBBR performances were investigated. The maximum succinic acid productivity of 35.0 g L-1 h-1 and the maximum SA concentration were achieved at a D = 1.9 h-1. The effect of HMF and furfural on succinic acid production was also investigated. HMF resulted to reduce succinic acid production by 22.6%, while furfural caused a reduction of 16% in SA production at the same dilution rate. CONCLUSION: Succinic acid production by A. succinogenes fermentation in a packed-bed reactor (PBBR) was successfully carried out for more than 5 months. The optimal results were obtained at the dilution rate 0.5 h-1: 43.0 g L-1 of succinic acid were produced, glucose conversion was 88%; and the volumetric productivity was 22 g L-1 h-1.

Biosuccinic Acid from Lignocellulosic-Based Hexoses and Pentoses by Actinobacillus succinogenes: Characterization of the Conversion Process.

Succinic acid (SA) is a well-established chemical building block. Actinobacillus succinogenes fermentation is by far the most investigated route due to very promising high SA yield and titer on several sugars. This study contributes to include the SA production within the concept of biorefinery of lignocellulose biomass. The study was focused on the SA production by A. succinogenes DSM 22257 using sugars representative from lignocellulose hydrolysis-glucose, mannose, arabinose, and xylose-as carbon source. Single sugar batch fermentation tests and mixture sugar fermentation tests were carried out. All the sugars investigated were converted in succinic acid by A. succinogenes. The best fermentation performances were measured in tests with glucose as carbon source. The bacterial growth kinetics was characterized by glucose inhibition. No inhibition phenomena were observed with the other sugar investigated. The sugar mixture fermentation tests highlighted the synergic effects of the co-presence of the four sugars. Under the operating conditions tested, the final concentration of succinic acid in the sugar mixture test was larger (27 g/L) than that expected (25.5 g/L) by combining the fermentation of the single sugar. Moreover, the concentration of acetic and formic acid was lower, consequently obtaining an increment in the succinic acid specificity.