Rapid classification of heavy metal-exposed freshwater bacteria by infrared spectroscopy coupled with chemometrics using supervised method

Antimicrobial susceptibility testing using infrared attenuated total reflection (IR-ATR) spectroscopy to monitor metabolic activity

Fast and accurate detection of antimicrobial resistance in pathogens remains a challenge, and with the increase in antimicrobial resistance due to mis- and overuse of antibiotics, it has become an urgent public health problem. We demonstrate how infrared attenuated total reflection (IR-ATR) can be used as a simple method for assessment of bacterial susceptibility to antibiotics. This is achieved by monitoring the metabolic activities of bacterial cells via nutrient consumption and using this as an indicator of bacterial viability. Principal component analysis of the obtained spectra provides a tool for fast and simple discrimination of antimicrobial resistance in the acquired data. We demonstrate this concept using four bacterial strains and four different antibiotics, showing that the change in glucose concentration in the growth medium after 2 h, as monitored by IR-ATR, can be used as a spectroscopic diagnostic technique, to reduce detection time and to improve quality in the assessment of antimicrobial resistance in pathogens.

A Study of the Interactions of Heavy Metals in Dairy Matrices Using Fourier Transform Infrared Spectroscopy, Chemometric, and In Silico Analysis

Heavy metals are among the toxic substances longest recognized by man. Today, due to the myriad sources of exposure, such as contaminated water, food, or air, they have become a major public health problem. This work presents the effects manifested in the infrared spectrum behavior caused by the presence of Cd²⁺, Cr⁶⁺, and Pb²⁺ at different concentrations in three different matrices: water, casein, and milk; observing that the spectral modifications in the regions of different vibrational modes of nucleophilic groups such as -OH, COO- and NH₂ depending on the nature of the metal and its concentration. These findings were correlated in-silico using optimized models in Gabedit software and structural optimization was performed with MOPAC 2016 showing stable structures between the metals and Gln, Hys, Glu, and Phe of casein. By applying chemometrics (Principal Component Analysis), it was possible to observe a good correlation between the experimental data and to discriminate between the type of metal, the matrix that contains it, and the concentration could be represented through linear models that showed adjustments with a value of r² ≥ 0.95.

Evaluation of copper-induced biomolecular changes in different porin mutants of Escherichia coli W3110 by infrared spectroscopy

Copper (Cu), one of the heavy metals, plays a vital role in many complex biochemical reactions as a trace element. However, it often becomes toxic when its concentration in the cell exceeds a certain level. Homeostasis of metals in the cell is primarily related to regulating metal transport into and out of the cell. Therefore, it is thought that porin proteins, which have a role in membrane permeability, may also play a role in developing Cu resistance. This study identified the differences between the molecular profiles of wild-type Escherichia coli W3110 and its seven different porin mutants exposed to Cu ions using attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results showed that the absence of porin genes elicits global changes in the structure and composition of membrane lipids and proteins, in both the absence and presence of Cu. The lack of porin genes significantly elevated the amounts of fatty acids and phospholipids. When the alterations in protein secondary structures were compared, the quantity of amide I proteins was diminished by the presence of Cu. However, the amount of amide II proteins increased in porin mutant groups independent of Cu presence or absence. The DNAs are transformed from B- and Z-form to A-form due to porin mutations and the presence of Cu ions. The lack of porin genes increased polysaccharide content independent of Cu presence. This study can help characterize Cu detoxification efficiency and guide for obtaining active living cells to be used in bioremediation.

Physicochemical and biological ageing processes of (micro)plastics in the environment: a multi-tiered study on polyethylene

Pollution by plastic and microplastic impacts the environment globally. Knowledge on the ageing mechanisms of plastics in natural settings is needed to understand their environmental fate and their reactivity in the ecosystems. Accordingly, the study of ageing processes is gaining focus in the context of the environmental sciences. However, laboratory-based experimental research has typically assessed individual ageing processes, limiting environmental applicability. In this study, we propose a multi-tiered approach to study the environmental ageing of polyethylene plastic fragments focusing on the combined assessment of physical and biological processes in sequence. The ageing protocol included ultraviolet irradiation in air and in a range of water solutions, followed by a biofouling test. Changes in surface characteristics were assessed by Fourier transform infrared spectroscopy, scanning electron microscopy, and water contact angle. UV radiation both in air and water caused a significant increase in the density of oxidized groups (i.e., hydroxyl and carbonyl) on the plastic surface, whereby water solution chemistry influenced the process both by modulating surface oxidation and morphology. Biofouling, too, was a strong determinant of surface alterations, regardless of the prior irradiation treatments. All biofouled samples present (i) specific infrared bands of new surface functional groups (e.g., amides and polysaccharides), (ii) a further increase in hydroxyl and carbonyl groups, (iii) the diffuse presence of algal biofilm on the plastic surface, and (iv) a significant decrease in surface hydrophobicity. This suggests that biological-driven alterations are not affected by the level of physicochemical ageing and may represent, in real settings, the main driver of alteration of both weathered and pristine plastics. This work highlights the potentially pivotal role of biofouling as the main process of plastic ageing, providing useful technical insights for future experimental works. These results also confirm that a multi-tiered laboratory approach permits a realistic simulation of plastic environmental ageing in controlled conditions.

Phenotypic changes in microalgae at acidic pH mediate their tolerance to higher concentrations of transition metals

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Acid-tolerant microalgae were grown at pH 3.5 and 6.7 in presence of heavy metals (HMs).

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HMs-induced phenotypic changes in microalgae were evaluated by ATR-FTIR spectroscopy.

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Higher HMs bioavailability affected microalgae more at pH 6.7 than pH 3.5.

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Acclimation of microalgal strains to acidic pH significantly alleviates HMs toxicity.

Acclimatory phenotypic response is a common phenomenon in microalgae, particularly during heavy metal stress. It is not clear so far whether acclimating to one abiotic stressor can alleviate the stress imposed by another abiotic factor. The intent of the present study was to demonstrate the implication of acidic pH in effecting phenotypic changes that facilitate microalgal tolerance to biologically excess concentrations of heavy metals. Two microalgal strains, Desmodesmus sp. MAS1 and Heterochlorella sp. MAS3, were exposed to biologically excess concentrations of Cu (0.50 and 1.0 mg L^‒1), Fe (5 and 10 mg L^‒1), Mn (5 and 10 mg L^‒1) and Zn (2, 5 and 10 mg L^‒1) supplemented to the culture medium at pH 3.5 and 6.7. Chlorophyll autofluorescence and biochemical fingerprinting using FTIR-spectroscopy were used to assess the microalgal strains for phenotypic changes that mediate tolerance to metals. Both the strains responded to acidic pH by effecting differential changes in biochemicals such as carbohydrates, proteins, and lipids. Both the microalgal strains, when acclimated to low pH of 3.5, exhibited an increase in protein (< 2-fold) and lipid (> 1.5-fold). Strain MAS1 grown at pH 3.5 showed a reduction (1.5-fold) in carbohydrates while strain MAS3 exhibited a 17-fold increase in carbohydrates as compared to their growth at pH 6.7. However, lower levels of biologically excess concentrations of the selected transition metals at pH 6.7 unveiled positive or no effect on physiology and biochemistry in microalgal strains, whereas growth with higher metal concentrations at this pH resulted in decreased chlorophyll content. Although the bioavailability of free-metal ions is higher at pH 3.5, as revealed by Visual MINTEQ model, no adverse effect was observed on chlorophyll content in cells grown at pH 3.5 than at pH 6.7. Furthermore, increasing concentrations of Fe, Mn and Zn significantly upregulated the carbohydrate metabolism, but not protein and lipid synthesis, in both strains at pH 3.5 as compared to their growth at pH 6.7. Overall, the impact of pH 3.5 on growth response suggested that acclimation of microalgal strains to acidic pH alleviates metal toxicity by triggering physiological and biochemical changes in microalgae for their survival.

Liquid chromatographic methods coupled to chemometrics: a short review to present the key workflow for the investigation of wine phenolic composition as it is affected by environmental factors

The guarantee of wine authenticity arises great concern because of its nutritional and economic importance. Phenolic fingerprints have been used as a source of chemical information for various authentication issues, including botanical and geographical origin, as well as vintage age. The local environment affects wine production and especially its phenolic metabolites. Integrated analytical methodologies combined with chemometrics can be applied in wine fingerprinting studies for the determination and establishment of phenolic markers that contain comprehensive and standardized information about the wine profile and how it can be affected by various environmental factors. This review summarizes all the recent trends in the generation of chemometric models that have been developed for treating chromatographic data and have been used for the investigation of critical wine authenticity issues, revealing phenolic markers responsible for the botanical, geographical, and vintage age classification of wines. Overall, the current review suggests that chromatographic methodologies are promising and powerful techniques that can be used for the accurate determination of phenolic compounds in difficult matrices like wine, highlighting the advantages of the applications of supervised chemometric tools over unsupervised for the construction of prediction models that have been successfully used for the classification based on their territorial and botanical origin.

Research progress on mutton origin tracing and authenticity

With the globalization of the food market and the convenience of food transportation between countries, consumers are increasingly worried about the source and safety of the food they eat. Traceability has been identified as an important tool for ensuring food safety and quality. This review mainly introduces the principles of five food traceability technologies, summarizes the progress in mutton application, comprehensively compares and analyzes the five traceability technologies, and discusses their application prospects, advantages and disadvantages. It is aimed at promoting research and application of traceability technology in mutton safety, promoting establishment and improvement of food traceability system.

Investigation and Rapid Discrimination of Food-Related Bacteria under Stress Treatments Using IR Microspectroscopy

Because the robust and rapid determination of spoilage microorganisms is becoming increasingly important in industry, the use of IR microspectroscopy, and the establishment of robust and versatile chemometric models for data processing and classification, is gaining importance. To further improve the chemometric models, bacterial stress responses were induced, to study the effect on the IR spectra and to improve the chemometric model. Thus, in this work, nine important food-relevant microorganisms were subjected to eight stress conditions, besides the regular culturing as a reference. Spectral changes compared to normal growth conditions without stressors were found in the spectral regions of 900-1500 cm^-1 and 1500-1700 cm^-1. These differences might stem from changes in the protein secondary structure, exopolymer production, and concentration of nucleic acids, lipids, and polysaccharides. As a result, a model for the discrimination of the studied microorganisms at the genus, species and strain level was established, with an accuracy of 96.6%. This was achieved despite the inclusion of various stress conditions and times after incubation of the bacteria. In addition, a model was developed for each individual microorganism, to separate each stress condition or regular treatment with 100% accuracy.

Gold nanorod arrays enable highly sensitive bacterial detection via surface-enhanced infrared absorption (SEIRA) spectroscopy

Infrared (IR) spectroscopy is a unique and powerful method in the identification, characterization, and classification of chemical and biological molecules. However, the low absorbance of biological molecules has arisen as a major bottleneck and inhibits the application of IR in practical applications. To overcome this limitation, in the last four decades, surface-enhanced IR absorption (SEIRA) spectroscopy has been proposed and has become the focus of interest in various applications. In this study, for the first time, we proposed the employment of 3D anisotropic gold nanorod arrays (GNAs) as a highly active SEIRA platform in bacterial detection. For this, GNA platforms were fabricated through an oblique angle deposition (OAD) approach by using a physical vapor deposition (PVD) system. OAD of gold at proper deposition angle (10°) created closely-packed and columnar gold nanorod structures onto the glass slides in a well-controlled manner. GNA platform was tested as a SEIRA system in three different species of bacteria (Escherichia coli, Staphylococcus aureus, and Bacillus subtilis) by collecting IR spectra of each bacteria from different parts of GNA. The employment of GNA provided robust IR spectra with high reproducibility and signal-to-noise ratio. For the comparison, IR spectra of each bacteria were collected from aluminum foil and a smooth gold surface (SGS). No or very low IR spectra were observed in comparison to the GNA platform for these substrates. Unsupervised (PCA, HCA) and supervised (SIMCA, LDA, and SVM classification) machine learning analysis of bacteria spectra obtained from GNA substrate indicated that all bacteria samples can be detected and identified without using a label-containing biosensor, in a fast and simple manner.

Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy

Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.

Biospectroscopy and chemometrics as an analytical tool for comparing the antibacterial mechanism of silver nanoparticles with popular antibiotics against Escherichia coli

Despite the fact that silver nanoparticles (AgNPs) have been widely studied in medical and correlated fields, details on their mechanisms are yet to be fully understood. Herein we present the first study on the combination of infrared spectroscopy and chemometrics as an analytical tool to investigate the mechanism of action of AgNPs against Escherichia coli by comparison with popular and commercially available antibiotics. The rationale behind this study is that the selected antibiotics act on bacteria in specific and distinct manners (DNA, cell membrane, mitochondria, etc.). Hence, via multivariate analysis we were able to compare the spectra of bacteria treated with the antibiotics and AgNPs to determine the main target of the latter. Spectral comparison, exploratory analysis, clustering and classification based on infrared spectra were carried out for E. coli samples in the absence and presence (treated) of four widely known antibiotics (ampicillin, ciprofloxacin, gentamicin and sulfadiazine) as well as RA-AgNPs and ERA-AgNPs. Chemometrics models indicated an interesting similarity between infrared spectra from E. coli treated with sulfadiazine and AgNPs, in which vibrational modes associated to phosphate groups were found to be the most representative. This result suggests that both AgNPs and sulfadiazine affects DNA structural features and availability, but not necessarily through the same mechanism. This biospectroscopy-based approach opens an interesting possibility for the understanding over the mechanism of antibacterial activity of AgNPs.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

A novel approach for the discrimination of culture medium from Vascular Endothelial Growth Factor (VEGF) overexpressing colorectal cancer cells

Objectives

The expression level of Vascular Endothelial Growth Factor (VEGF) is assumed as a prognostic marker for several tumor types, including colorectal cancer. Therefore, the determination of pre- and post-therapy levels of VEGF appears to have great value in the assessment of tumor prognosis. Enzyme-Linked Immunosorbent Assay (ELISA) is commonly used for the determination of serum or plasma VEGF levels, but the method is costly and time-consuming. In this study, we aimed to describe a rapid and cost-effective analysis method to discriminate VEGF overexpressing colorectal cancer-derived conditioned medium (CM).

Methods

Attenuated Total Reflection (ATR)-Fourier Transform Infrared (FTIR) spectroscopy, combined with Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was used to differentiate VEGF overexpressing colorectal cancer cell line CM from CM obtained from the corresponding control cells which express and secrete relatively lower amount of VEGF.

Results

Samples belong to VEGF overexpressing colorectal cancer cells were clearly distinguished from the control group with very high PC scores as PC1 + PC2 = 96%. Besides, a 100% accurate distinction between these two groups was achieved by the LDA analysis.

Conclusions

ATR-FTIR spectroscopy combined with pattern recognition techniques was able to discriminate CM of VEGF overexpressing colorectal cancer cells with high efficiency and accuracy.

Opportunities for fraudsters: When would profitable milk adulterations go unnoticed by common, standardized FTIR measurements?

Milk is regarded as one of the top food products susceptible to adulteration where its valuable components are specifically identified as high-risk indicators for milk fraud. The current study explores the impact of common milk adulterants on the apparent compositional parameters of milk from the Dutch market as measured by standardized Fourier transform infrared (FTIR) spectroscopy. More precisely, it examines the detectability of these adulterants at various concentration levels using the compositional parameters individually, in a univariate manner, and together in a multivariate approach. In this study we used measured boundaries but also more practical variance-adjusted boundaries to set thresholds for detection of adulteration. The potential economic impact of these adulterations under a milk payment scheme is also evaluated. Twenty-four substances were used to produce various categories of milk adulterations, each at four concentration levels. These substances comprised five protein-rich adulterants, five nitrogen-based adulterants, seven carbohydrate-based adulterants, six preservatives and water, resulting in a set of 360 samples to be analysed. The results showed that the addition of protein-rich adulterants, as well as dicyandiamide and melamine, increased the apparent protein content, while the addition of carbohydrate-based adulterants, whey protein isolate, and skimmed milk powder, increased the apparent lactose content. When considering the compositional parameters univariately, especially protein- and nitrogen-based adulterants did not raise a flag of unusual apparent concentrations at lower concentration levels. Addition of preservatives also went unnoticed. The multivariate approach did not improve the level of detection. Regarding the potential profit of milk adulteration, whey protein and corn starch seem particularly interesting. Combining the artificial inflation of valuable components, the resulting potential profit, and the gaps in detection, it appears that the whey protein isolates deserve particular attention when thinking like a criminal.

Prevalence of Milk Fraud in the Chinese Market and its Relationship with Fraud Vulnerabilities in the Chain

This study aimed to assess the prevalence of ultra-high-temperature (UHT) processed milk samples suspected of being adulterated on the Chinese market and, subsequently, relate their geographical origin to the earlier determined fraud vulnerability. A total of 52 UHT milk samples purchased from the Chinese market were measured to detect possible anomalies. The milk compositional features were determined by standardized Fourier transform-infrared spectroscopy, and the detection limits for common milk adulterations were investigated. The results showed that twelve of the analysed milk samples (23%) were suspected of having quality or fraud-related issues, while one sample of these was highly suspected of being adulterated (diluted with water). Proportionally, more suspected samples were determined among milks produced in the Central-Northern and Eastern areas of China than in those from the North-Western and North-Eastern areas, while those from the South were in between. Combining the earlier collected results on fraud vulnerability in the Chinese milk chains, it appears that increased fraud prevalence relates to poorer business relationships and lack of adequate managerial controls. Since very few opportunities and motivations differ consistently across high and low-prevalence areas, primarily the improvement of control measures can help to mitigate food fraud in the Chinese milk supply chains.

Combining Chemometrics and Sensors: Toward New Applications in Monitoring and Environmental Analysis

For many years, an extensive array of chemometric methods have provided a platform upon which a quantitative description of environmental conditions can be obtained. Applying chemometric methods to environmental data allows us to identify and describe the interrelations between certain environmental drivers. They also provide an insight into the interrelationships between these drivers and afford us a greater understanding of the potential impact that these drivers can place upon the environment. However, an effective marriage of these two systems has not been performed. Therefore, it is the aim of this review to highlight the advantages of using chemometrics and sensors to identify hidden trends in environmental parameters, which allow the state of the environment to be effectively monitored. Despite the combination of chemometrics and sensors, to capture new developments and applications in the field of environmental sciences, these methods have not been extensively used. Importantly, although different parameters and monitoring procedures are required for different environments (e.g., air, water, soil), they are not distinct, separate entities. Contemporary developments in the use of chemometrics afford us the ability to predict changes in different aspects of the environment using instrumental methods. This review also provides an insight into the prevailing trends and the future of environmental sensing, highlighting that chemometrics can be used to enhance our ability to monitor the environment. This enhanced ability to monitor environmental conditions and to predict trends would be beneficial to government and research agencies in their ability to develop environmental policies and analysis procedures.

Infrared spectroscopy with multivariate analysis to interrogate the interaction of whole cells and secreted soluble exopolimeric substances of Pseudomonas veronii 2E with Cd(II), Cu(II) and Zn(II)

Extracellular polymeric substances (EPS) are bacterial products associated to cell wall or secreted to the liquid media that form the framework of microbial mats. These EPS contain functional groups as carboxyl, amino, hydroxyl, phosphate and sulfhydryl, able to interact with cations. Thus, EPS may be considered natural detoxifying compounds of metal polluted waters and wastewaters. In this work Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with multivariate analysis (Principal Component Analysis-PCA-) were used to study the interaction of Cd(II), Cu(II) and Zn(II) and Pseudomonas veronii 2E cells, including bound EPS and cell wall, and its different soluble EPS fractions, previously characterized as Cd(II) ligands of moderate strength. Amino groups present in exopolysaccharide fraction were responsible for Zn(II) and Cu(II) complexation, while carboxylates chelated Cd(II). In lipopolysaccharide fraction, phosphoryl and carboxyl sites were involved in Cd(II) and Cu(II) binding, while Zn(II) interacted with amino groups. Similar results were obtained from cells. These studies confirmed that FTIR-PCA is a rapid analytical tool to provide valuable information regarding the functional groups in biomolecules related to metal interaction. Moreover, a discrimination and identification of functional groups present in both EPS and cells that interacted with Cd(II), Zn(II) and Cu(II) was demonstrated.

Determination of the dose-dependent toxic effects of mad honey on mouse liver using ATR-FTIR spectroscopy

Mad honey (MH) is obtained from Rhododendron plants, which are extensively grown in some regions of the world such as Europe, North America, Tropical Asia and Turkey. Although it has been known that MH induces adverse effects in the body due to grayanotoxin (GTX) in it, it is widely used for some medical purposes by the public. In this study, the effects of MH (25, 50 and 75 mg/kg) and GTX-III (0.01 mg/kg), which is the pure form of the most toxic type of the GTXs in MH, were investigated on the mouse liver at molecular level via Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The results showed that 25 and 50 mg/kg of MH didn't cause any significant alterations in the liver tissue except a decrease in the glycogen amount. However, significant differences were observed between 75 mg/kg MH and GTX-III treated groups and control group. For example, the amounts of saturated lipids, nucleic acids and proteins increased in the 75 mg/kg MH and GTX-III treated groups. A decrease in the ratios of unsaturated/saturated lipid, CH₂/lipid and carbonyl/lipid and an increase in the ratio of CH₃/lipid were observed after the administration of 75 mg/kg MH and GTX-III, all of which may be a consequence of lipid peroxidation. Moreover, 75 mg/kg MH and GTX-III caused a decrease in the membrane order, an increase in the membrane fluidity and some important changes on the secondary structure of proteins indicating protein denaturation. In addition, Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) confirmed these findings. These results revealed that MH induces significant dose-dependent toxic effects in the structure and function of the liver tissue. This study also showed that ATR-FTIR spectroscopy provides a rapid and sensitive monitoring of the changes induced by a toxic compound on biological tissues at molecular level.

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images

Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hyperspectral microscope imaging (HMI) method, which provides spectral "fingerprints" information of bacterial cells, was employed to classify serogroups at the cellular level. In spectral analysis, principal component analysis (PCA) method and stacked auto-encoder (SAE) method were conducted to extract principal spectral features for classification task. Based on these features, multiple classifiers including linear discriminant analysis (LDA), support vector machine (SVM) and soft-max regression (SR) methods were evaluated. Different sizes of datasets were also tested in search for the suitable classification models. Among the results, SAE-based classification models performed better than PCA-based models, achieving classification accuracy of SAE-LDA (93.5%), SAE-SVM (94.9%) and SAE-SR (94.6%), respectively. In contrast, classification results of PCA-based methods such as PCA-LDA, PCA-SVM and PCA-SR were only 75.5%, 85.7% and 77.1%, respectively. The results also suggested the increasing number of training samples have positive effects on classification models. Taking advantage of increasing dataset, the SAE-SR classification model finally performed better than others with average accuracy of 94.9% in classifying STEC serogroups. Specifically, O103 serogroup was classified with the highest accuracy of 97.4%, followed by O111 (96.5%), O26 (95.3%), O121 (95%), O145 (92.9%) and O45 (92.4%), respectively. Thus, the HMI technology coupled with SAE-SR classification model has the potential for "Big-Six" identification.

FRET-based fluorescent nanoprobe platform for sorting of active microorganisms by functional properties

Fluorescence-activated cell sorting (FACS) has rarely been applied to screening of microorganisms because of poor detection resolution, which is compromised by poor stability, toxicity, or interference from background fluorescence of the fluorescence sensors used. Here, a fluorescence-based rapid high-throughput cell sorting method was first developed using a fluorescence resonance energy transfer (FRET) fluorescent nanoprobe NP-RA, which was constructed by coating a silica nanoparticle with Rhodamine B and methyl-red (an azo dye). Rhodamine B (inner layer) is the FRET donor and methyl-red (outer layer) is the acceptor. This ready-to-use NP-RA is non-fluorescent, but fluoresces once the outer layer is degraded by microorganisms. In our experiment, NP-RA was ultrasensitive to model strain Shewanella decolorationis S12, showing a broad detection range from 8.0 cfu/mL to 8.7 × 10⁸ cfu/mL under confocal laser scanning microscopy, and from 1.1 × 10⁷ to 9.36 × 10⁸ cfu/mL under a fluorometer. In addition, NP-RA bioimaging can clearly identify other azo-respiring cells in the microbial community, including Bosea thiooxidans DSM 9653 and Lysinibacillus pakistanensis NCCP-54. Furthermore, the fluorescent probe NP-RA is compatible with downstream FACS so that azo-respiring cells can be rapidly sorted out directly from an artificial microbial community. To our knowledge, no fluorescent nanoprobe has yet been designed for tracking and sorting azo-respiration functional microorganisms.

Glycogen synthase kinase-3 inhibition in glioblastoma multiforme cells induces apoptosis, cell cycle arrest and changing biomolecular structure

Glioblastoma multiforme (GBM) is the most malignant and aggressive primary human brain tumors. The regulatory pathways of apoptosis are altered in GBMs, leading to a survival advantage of the tumor cells. Thus, identification of target molecules, which are effective in triggering of the cell death mechanisms in GBM, is an essential strategy for therapeutic purposes. Glycogen synthase kinase-3 (GSK-3) plays an important role in apoptosis, proliferation and cell cycle. This study focused on the effect of GSK-3 inhibitor IX in the GBM cells. Apoptosis induction was determined by Annexin-V assay, multicaspase activity and immunofluorescence analyses. Concentration-dependent effects of GSK-3 inhibitor IX on the cell cycle were also evaluated. Moreover, the effect of GSK inhibitor on the cellular biomolecules was assessed by using ATR-FTIR spectroscopy. Our assay results indicated that GSK-3 inhibitor IX induces apoptosis, resulting in a significant increase in the expression of caspase-3 and caspase-8 proteins. Cell cycle analyses revealed that GSK-3 inhibitor IX leads to dose-dependent G2/M-phase cell cycle arrest. Based on the FTIR data, treatment of GBM cells causes dysregulation in the carbohydrate metabolism and induces apoptotic cell death which was characterized by the spectral alterations in nucleic acids, an increment in the lipid amount with disordering state and compositional changes in the cellular proteins. These findings suggest that GSK-3 inhibitor IX exhibits anti-cancer effects by inducing apoptosis and changing biomolecular structure of membrane lipids, carbohydrates, nucleic acids and proteins, and thus, may be further evaluated as a potential effective candidate agent for the GBM combination therapies.

Rapid classification of virgin and recycled EPS containers by Fourier transform infrared spectroscopy and chemometrics

A rapid and sensitive method for classification of virgin and recycled expanded polystyrene (EPS) food containers was developed using Fourier transform infrared spectroscopy (FTIR) and chemometrics. This method includes preparing a transparent film by dissolution, examining by FTIR and developing classification models. The degradation of EPS containers occurring during the recycling process was reflected by the carbonyl region of the infrared spectrum which was used as variables for multivariate data analysis. PCA was used to reduce the data dimension and view the sample similarities. Soft independent modelling of class analogy (SIMCA), partial least squares-discrimination analysis (PLS-DA) and linear discrimination analysis (LDA) were applied to construct three classification models. The best discrimination results were obtained by an LDA model, with all samples correctly classified. PLS-DA and SIMCA could not classify the recycled EPS samples with low levels of adulteration. When applying this method to commercially available EPS containers, about 45% of samples were shown to contain recycled polystyrene resins. It is concluded that the carbonyl region of the infrared spectra coupled with chemometrics could be a powerful tool for the classification of virgin and recycled EPS food containers.

Low dose dimethyl sulfoxide driven gross molecular changes have the potential to interfere with various cellular processes

Dimethyl sulfoxide (DMSO) is a small molecule with polar, aprotic and amphiphilic properties. It serves as a solvent for many polar and nonpolar molecules and continues to be one of the most used solvents (vehicle) in medical applications and scientific research. To better understand the cellular effects of DMSO within the concentration range commonly used as a vehicle (0.1-1.5%, v/v) for cellular treatments, we applied Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FT-IR) spectroscopy to DMSO treated and untreated epithelial colon cancer cells. Both unsupervised (Principal Component Analysis-PCA) and supervised (Linear Discriminant Analysis-LDA) pattern recognition/modelling algorithms applied to the IR data revealed total segregation and prominent differences between DMSO treated and untreated cells at whole, lipid and nucleic acid regions. Several of these data were supported by other independent techniques. Further IR data analyses of macromolecular profile indicated comprehensive alterations especially in proteins and nucleic acids. Protein secondary structure analysis showed predominance of β-sheet over α-helix in DMSO treated cells. We also observed for the first time, a reduction in nucleic acid level upon DMSO treatment accompanied by the formation of Z-DNA. Molecular docking and binding free energy studies indicated a stabilization of Z-DNA in the presence of DMSO. This alternate DNA form may be related with the specific actions of DMSO on gene expression, differentiation, and epigenetic alterations. Using analytical tools combined with molecular and cellular biology techniques, our data indicate that even at very low concentrations, DMSO induces a number of changes in all macromolecules, which may affect experimental outcomes where DMSO is used as a solvent.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.