Detection and identification of bacteria in a juice matrix with Fourier transform-near infrared spectroscopy and multivariiate analysis

Bacterial discrimination by Fourier transform infrared spectroscopy, MALDI-mass spectrometry and whole-genome sequencing

Aim: Proof-of-concept study, highlighting the clinical diagnostic ability of FT-IR compared with MALDI-TOF MS, combined with WGS. Materials & methods: 104 pathogenic isolates of Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus pyogenes and Staphylococcus aureus were analyzed. Results: Overall prediction accuracy was 99.6% in FT-IR and 95.8% in MALDI-TOF-MS. Analysis of N. meningitidis serogroups was superior in FT-IR compared with MALDI-TOF-MS. Phylogenetic relationship of S. pyogenes was similar by FT-IR and WGS, but not S. aureus or S. pneumoniae. Clinical severity was associated with the zinc ABC transporter and DNA repair genes in S. pneumoniae and cell wall proteins (biofilm formation, antibiotic and complement permeability) in S. aureus via WGS. Conclusion: FT-IR warrants further clinical evaluation as a promising diagnostic tool.

Optimization and Validation of an FTIR-based, All-in-one System for Viable MDR Bacteria Detection in Combat-related Wound Infection

INTRODUCTION

The U.S. Military members experiencing combat-related injuries have a higher chance of developing infections by multidrug-resistant (MDR) bacteria at admission to military hospitals. MDR wound infections result in higher amputation rates and greater risks for subsequent or chronic infections that require readmission or extended stay in the hospital. Currently, there is no FDA-clear, deployable early diagnostic system for suitable field use.We are reporting our efforts to improve a previously developed Rapid Label-free Pathogen Identification (RAPID) system to detect viable MDR bacteria in wound infections and perform antibiotic susceptibility testing (AST). Specifically, we added multiplex and automation capability and significantly simplified the sample preparation process. A functional prototype of the improved system was built, and its performance was validated using a variety of lab-prepared spiked samples and real-world samples.

MATERIALS AND METHODS

To access the baseline performance of the improved RAPID system in detecting bacteria presence, we selected 17 isolates, most of them from blood or wound infections, and prepared mono-strain spiked samples at 104 to 106 cfu/mL concentration. These samples were processed and analyzed by the RAPID system. To demonstrate the AST capability of the system, we selected 6 strains against 6 different antibiotics and compared the results from the system with the ones from the gold standard method.To validate the system's performance with real-world samples, we first investigated its performance on 3 swab samples from epicutaneous methicillin-resistant Staphylococcus aureus-exposed mouse model. The AST results from our system were compared with the ones from the gold standard method. All animal experiments were approved by the Johns Hopkins University Animal Care and Use Committee (Protocol No. MO21M378). Then, we obtained swab samples from 7 atopic dermatitis (AD) patients and compared our AST results with the ones from the gold standard method. The human subject protocol was approved by the Johns Hopkins Medicines Institutional Review Boards (Study No. CR00043438/IRB00307926) and by USAMRDC (Proposal Log Number/Study Number 20000251).

RESULTS

High-quality data were obtained from the spiked samples of all 17 strains. A quantitative analysis model built using these data achieved 94% accuracy in predicting the species ID in 8 unknown samples. The AST results on the spiked samples had shown 100% matching with the gold standard method. Our system successfully detects the presence/absence of viable bacteria in all 3 mouse and 7 AD patient swab samples. Our system shows 100% and 85.7% (6 out of 7) accuracy when compared to the oxacillin susceptibility testing results for the mouse and the AD patient swabs, respectively.

CONCLUSIONS

Our system has achieved excellent performance in detecting viable bacteria presence and in performing AST in a multiplex, automated, and easy-to-operate manner, on both lab-prepared and real samples. Our results have shown a path forward to a rapid (sample-to-answer time ≤3 hours), accurate, sensitive, species-specific, and portable system to detect the presence of MDR combat-related wound infections in the field environment. Our future efforts involve ruggedizing the RAPID system and evaluating performance under relevant environmental conditions.

Rapid, label-free pathogen identification system for multidrug-resistant bacterial wound infection detection on military members in the battlefield

US military service members experiencing combat-related wounds have higher risk of infection by multidrug-resistant bacteria. The gold standard culture-based antimicrobial susceptibility testing (AST) is not feasible in the battlefield environment. Thus, a rapid deployable system for bacteria identification and AST directly from wound sample is urgently needed. We report the potential of a Rapid, Label-free Pathogen Identification (RAPID) diagnostic system based on ATR-FTIR method to detect and distinguish multi-drug resistant strains for six different species in the ESKAPEE group. Our RAPID system combines sample processing on-broad to isolate and enrich bacteria cells from wound sample, ATR-FTIR measurement to detect antimicrobial-induced bacterial cell spectral changes, and machine learning model for automated, objective, and quantitative spectral analysis and unknown sample classification. Based on experimental results, our RAPID system is a promising technology for label-free, sensitive (104 cfu/mL from mixture), species-specific (> 95% accuracy), rapid (< 10 min for identification, ~ 4 hours for AST) bacteria detection directly from wound samples.

Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM

Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. This study developed a near-infrared (NIR) spectral characteristic extraction method based on a three-dimensional analysis space and establishes a high-accuracy qualitative identification model. First, the Norris derivative filtering algorithm was used in the pre-processing of the NIR spectrum to obtain a smooth main absorption peak. Then, the third-order tensor robust principal component analysis (TRPCA) algorithm was used for characteristic extraction, which effectively reduced the dimensionality of the raw NIR spectral data. Finally, on this basis, a qualitative identification model based on support vector machines (SVM) was constructed, and the classification accuracy reached 98.94%. Therefore, it is possible to develop a non-destructive, rapid qualitative detection system based on NIR spectroscopy to mine the subtle differences between classes and to use low-dimensional characteristic wavebands to detect the quality of complex multi-component mixtures. This method can be a key component of automatic quality control in the production of multi-component products.

Effect of Sugar Beet Harvest Date on Its Technological Quality Parameters by Exploratory Analysis

Choice of the harvest date is one of the foundations of vintage quality and good-yield sugar in sugar beet. However, it is difficult to define the harvest date and more precisely the date of maturity of beet roots, in an exact and absolute way. Indeed, maturity is divided into several stages and degrees depending on environmental and climatic conditions such as temperature, precipitation, geographic area, and others. The present study evoked the effect of three harvest dates (at esteemed maturity, 7 days after maturity, and 15 days after maturity) on the technological quality parameters, namely, sucrose, nitrogen, potassium, and sodium, using the most popular chemometric method, principal component analysis (PCA). To do this, samples from the Tadla irrigated perimeter were used. The results of exploratory analyses by the application of PCA clearly showed the influence of harvest date, in an important way, on the three quality parameters, composition of sucrose, potassium, and sodium. But, for nitrogen composition, there were negligible variations between samples.

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.

Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy

Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production and better understanding of lactic acid bacteria (LAB) fermentation. UHT (ultra-high temperature) sterilized milk was inoculated with Bulgarian yogurt and placed into a quartz cuvette (1 mm pathlength) and test-tubes. Yogurt absorbance spectra (830-2500 nm) were acquired every 15 min, and pH, in the respective test-tubes, was measured every 30 min, during 8 h of fermentation. Spectral data showed substantial baseline and slope changes with acidification. These variations corresponded to respective features of the microbiological growth curve showing water structural changes, protein denaturation, and coagulation of milk. Moving Window Principal Component Analysis (MWPCA) was applied in the spectral range of 954-1880 nm to detect absorbance bands where most variations in the loading curves were caused by LAB fermentation. Characteristic wavelength regions related to the observed physical and multiple chemical changes were identified. The results proved that NIRS is a valuable tool for real-time monitoring and better understanding of the yogurt fermentation process.

Discrimination of heavy metal acclimated environmental strains by chemometric analysis of FTIR spectra

Heavy metal acclimated bacteria are profoundly the preferred choice for bioremediation studies. Bacteria get acclimated to toxic concentrations of heavy metals by induction of specific enzymes and genetic selection favoring new metabolic abilities leading to activation of one or several of resistance mechanisms creating bacterial populations with differences in resistance profile and/or level. Therefore, to use in bioremediation processes, it is important to discriminate acclimated bacterial populations and choose a more resistant strain. In this study, we discriminated heavy metal acclimated bacteria by using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy and multivariate analysis methods namely Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Two acclimation methods, acute and gradual, were used which cause differences in molecular changes resulting in bacterial populations with different molecular and resistance profiles. Brevundimonas sp., Gordonia sp., and Microbacterium oxydans were exposed to the toxic concentrations of Cd (30 μg/ml) or Pb (90 μg/ml) by using broth medium as a growth media. Our results revealed that PCA and HCA clearly discriminated the acute-acclimated, gradual-acclimated, and control bacteria from each other in protein, carbohydrate, and whole spectral regions. Furthermore, we classified acclimated (acute and gradual) and control bacteria more accurately by using SIMCA with 99.9% confidence. This study demonstrated that heavy metal acclimated and control group bacteria can be discriminated by using chemometric analysis of FTIR spectra in a powerful, cost-effective, and handy way. In addition to the determination of the most appropriate acclimation procedure, this approach can be used in the detection of the most resistant bacterial strains to be used in bioremediation studies.

Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

Rapid, accurate, and comparative differentiation of clinically and industrially relevant microorganisms via multiple vibrational spectroscopic fingerprinting

Despite the fact that various microorganisms (e.g., bacteria, fungi, viruses, etc.) have been linked with infectious diseases, their crucial role towards sustaining life on Earth is undeniable. The huge biodiversity, combined with the wide range of biochemical capabilities of these organisms, have always been the driving force behind their large number of current, and, as of yet, undiscovered future applications. The presence of such diversity could be said to expedite the need for the development of rapid, accurate and sensitive techniques which allow for the detection, differentiation, identification and classification of such organisms. In this study, we employed Fourier transform infrared (FT-IR), Raman, and surface enhanced Raman scattering (SERS) spectroscopies, as molecular whole-organism fingerprinting techniques, combined with multivariate statistical analysis approaches for the classification of a range of industrial, environmental or clinically relevant bacteria (P. aeruginosa, P. putida, E. coli, E. faecium, S. lividans, B. subtilis, B. cereus) and yeast (S. cerevisiae). Principal components-discriminant function analysis (PC-DFA) scores plots of the spectral data collected from all three techniques allowed for the clear differentiation of all the samples down to sub-species level. The partial least squares-discriminant analysis (PLS-DA) models generated using the SERS spectral data displayed lower accuracy (74.9%) when compared to those obtained from conventional Raman (97.8%) and FT-IR (96.2%) analyses. In addition, whilst background fluorescence was detected in Raman spectra for S. cerevisiae, this fluorescence was quenched when applying SERS to the same species, and conversely SERS appeared to introduce strong fluorescence when analysing P. putida. It is also worth noting that FT-IR analysis provided spectral data of high quality and reproducibility for the whole sample set, suggesting its applicability to a wider range of samples, and perhaps the most suitable for the analysis of mixed cultures in future studies. Furthermore, our results suggest that while each of these spectroscopic approaches may favour different organisms (sample types), when combined, they would provide complementary and more in-depth knowledge (structural and/or metabolic state) of biological systems. To the best of our knowledge, this is the first time that such a comparative and combined spectroscopic study (using FT-IR, Raman and SERS) has been carried out on microbial samples.

Development of Methods for Determination of Aflatoxins

Aflatoxins can cause damage to the health of humans and animals. Several institutions around the world have established regulations to limit the levels of aflatoxins in food, and numerous analytical methods have been extensively developed for aflatoxin determination. This review covers the currently used analytical methods for the determination of aflatoxins in different food matrices, which includes sampling and sample preparation, sample pretreatment methods including extraction methods and purification methods of aflatoxin extracts, separation and determination methods. Validation for analysis of aflatoxins and safety considerations and precautions when doing the experiments are also discussed.

Identification of different species of Bacillus isolated from Nisargruna Biogas Plant by FTIR, UV-Vis and NIR spectroscopy

Definitive identification of microorganisms, including pathogenic and non-pathogenic bacteria, is extremely important for a wide variety of applications including food safety, environmental studies, bio-terrorism threats, microbial forensics, criminal investigations and above all disease diagnosis. Although extremely powerful techniques such as those based on PCR and microarrays exist, they require sophisticated laboratory facilities along with elaborate sample preparation by trained researchers. Among different spectroscopic techniques, FTIR was used in the 1980s and 90s for bacterial identification. In the present study five species of Bacillus were isolated from the aerobic predigester chamber of Nisargruna Biogas Plant (NBP) and were identified to the species level by biochemical and molecular biological (16S ribosomal DNA sequence) methods. Those organisms were further checked by solid state spectroscopic absorbance measurements using a wide range of electromagnetic radiation (wavelength 200 nm to 25,000 nm) encompassing UV, visible, near Infrared and Infrared regions. UV-Vis and NIR spectroscopy was performed on dried bacterial cell suspension on silicon wafer in specular mode while FTIR was performed on KBr pellets containing the bacterial cells. Consistent and reproducible species specific spectra were obtained and sensitivity up to a level of 1000 cells was observed in FTIR with a DTGS detector. This clearly shows the potential of solid state spectroscopic techniques for simple, easy to implement, reliable and sensitive detection of bacteria from environmental samples.

Near-infrared spectroscopy for the detection and quantification of bacterial contaminations in pharmaceutical products

Accurate detection and quantification of microbiological contaminations remains an issue mainly due the lack of rapid and precise analytical techniques. Standard methods are expensive and time-consuming being associated to high economic losses and public health threats. In the context of pharmaceutical industry, the development of fast analytical techniques able to overcome these limitations is crucial and spectroscopic techniques might constitute a reliable alternative. In this work we proved the ability of Fourier transform near infrared spectroscopy (FT-NIRS) to detect and quantify bacteria (Bacillus subtilis, Escherichia coli, Pseudomonas fluorescens, Salmonella enterica, Staphylococcus epidermidis) from 10 to 10(8) CFUs/mL in sterile saline solutions (NaCl 0.9%). Partial least squares discriminant analysis (PLSDA) models showed that FT-NIRS was able to discriminate between sterile and contaminated solutions for all bacteria as well as to identify the contaminant bacteria. Partial least squares (PLS) models allowed bacterial quantification with limits of detection ranging from 5.1 to 9 CFU/mL for E. coli and B. subtilis, respectively. This methodology was successfully validated in three pharmaceutical preparations (contact lens solution, cough syrup and topic anti-inflammatory solution) proving that this technique possess a high potential to be routinely used for the detection and quantification of bacterial contaminations.

Homogeneous Nature of Malaysian Marine Fish Epinephelus fuscoguttatus (Perciformes; Serranidae): Evidence Based on Molecular Markers, Morphology and Fourier Transform Infrared Analysis

Taxonomic confusion exists within the genus Epinephelus due to the lack of morphological specializations and the overwhelming number of species reported in several studies. The homogenous nature of the morphology has created confusion in the Malaysian Marine fish species Epinephelus fuscoguttatus and Epinephelus hexagonatus. In this study, the partial DNA sequence of the 16S gene and mitochondrial nucleotide sequences of two gene regions, Cytochrome Oxidase Subunit I and III were used to investigate the phylogenetic relationship between them. In the phylogenetic trees, E. fuscoguttatus was monophyletic with E. hexagonatus species and morphology examination shows that no significant differences were found in the morphometric features between these two taxa. This suggests that E. fuscoguttatus is not distinguishable from E. hexagonatus species, and that E. fuscoguttatus have been identified to be E. hexagonatus species is likely attributed to differences in environment and ability to camouflage themselves under certain conditions. Interestingly, this finding was also supported by Principal Component Analysis on Attenuated Total Reflectance-Fourier-transform Infrared (ATR-FTIR) data analysis. Molecular, morphological and meristic characteristics were combined with ATR-FTIR analysis used in this study offer new perspectives in fish species identification. To our knowledge, this is the first report of an extensive genetic population study of E. fuscoguttatus in Malaysia and this understanding will play an important role in informing genetic stock-specific strategies for the management and conservation of this highly valued fish.

A rapid identification of four medicinal chrysanthemum varieties with near infrared spectroscopy

Background:

For genuine medicinal material in Chinese herbs; the efficient, rapid, and precise identification is the focus and difficulty in the filed studying Chinese herbal medicines. Chrysanthemum morifolium as herbs has a long planting history in China, culturing high quality ones and different varieties. Different chrysanthemum varieties differ in quality, chemical composition, functions, and application. Therefore, chrysanthemum varieties in the market demands precise identification to provide reference for reasonable and correct application as genuine medicinal material.

Materials and Methods:

A total of 244 batches of chrysanthemum samples were randomly divided into calibration set (160 batches) and prediction set (84 batches). The near infrared diffuses reflectance spectra of chrysanthemum varieties were preprocessed by first order derivative (D1) and autoscaling and was built model with partial least squares (PLS).

Results:

In this study of four chrysanthemum varieties identification, the accuracy rates in calibration sets of Boju, Chuju, Hangju, and Gongju are respectively 100, 100, 98.65, and 96.67%; while the accuracy rates in prediction sets are 100% except for 99.1% of Hangju.

Conclusion:

The research results demonstrate that the qualitative analysis can be conducted by machine learning combined with near infrared spectroscopy (NIR), which provides a new method for rapid and noninvasive identification of chrysanthemum varieties.

FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn

Protein and total fat are two ingredients to measure the quality of corn. The aim of this study is to evaluate the quality of corn by the dual-component join determination through Fourier transform near infrared (FT-NIR) spectroscopic analysis. The calibration models were established by the systematic study performed respectively in the four regions of the whole range, the second overtone, the first overtone, and the combination. Whittaker smoother was introduced as an attractive alternative data preprocessing method. With the optimized parameters, Whittaker smoother indicates its priority for improving modeling results in any of the four regions. The predictive abilities were compared between the joint analysis of protein and total fat and the separate analysis of each single component by partial least squares (PLS) modeling. The uncertainty in parameter was further estimated for the linear models. It is suggested that the joint analysis of dual-component always leads to better predictive results, and also provided good evaluation results for the independent validation samples. For the joint analysis, the optimal region for protein was the combination (5400-4000 cm(-1)), and the optimal region for total fat was the first overtone (7200-5400 cm(-1)). The optimal PLS models also provided appreciate predictive performance for both protein and total fat. And the parameter uncertainty determination provided an acceptable estimate of the measured uncertainty for the FT-NIR analysis of corn. In general, the joint analysis of dual-component is a better strategy for FT-NIR analysis of corn, and it is hoped to be tested for other objects.

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: a comparative study between different modeling methods

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm(-1). Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity.

Diversity of spore-forming bacteria and identification of Bacillus amyloliquefaciens as a species frequently associated with the ropy spoilage of bread

This study examines the diversity of spore-forming bacteria isolated from raw materials/bread using molecular methods along with a rapid and innovative technology, the FT-NIR spectroscopy. Microbiological analysis showed that 23% of semolina and 42% of other raw materials (including grain, brewer yeast, improvers) contained more than 100 spores/g and more than 50% of each kind of sample was contaminated at a level ranging from 1 to 100 spores/g. A high bacterial diversity characterized raw materials. In total 176 isolates were collected and characterized: 13 bacterial species belonging to Bacillus (10) and Paenibacillus (3) genera were identified by sequencing of 16S rRNA, gyrA or gyrB genes. The two closely related species Bacillus amyloliquefaciens (strain N45.1) and Bacillus subtilis (strain S63) were also analyzed by the spectroscopic technique FT-NIR. This analysis gave clear discrimination between the strains in the score plot obtained by the PCA and allowed to identify the spectral region 5600-4000 cm(-1) as the information-rich region for discrimination. B. amyloliquefaciens, possibly misidentified as B. subtilis in previous studies, was recognized as the most frequent species, found also in ropy bread. Moreover, the screening test for rope production indicated that mainly B. amyloliquefaciens, together with B. subtilis and Bacillus pumilus, could cause spoilage in bread, even if the last two species were represented by a low number of isolates. The Bacillus cereus group and Bacillus megaterium showed a lower percentage (30-70%) of isolates potentially able to cause the rope, but considering the high number of B. cereus group isolates detected in this study, this bacterial group should also be considered important in rope spoilage. In conclusion, results demonstrate that raw materials used to produce bread represent a rich source of spore-forming bacteria, therefore their microbiological quality should be monitored before use. Moreover, this study highlights for the first time the importance of the species B. amyloliquefaciens in rope spoilage and indicates that other species may also cause this alteration although strains of the same species may behave differently.

Using Fourier transform infrared (FT-IR) absorbance spectroscopy and multivariate analysis to study the effect of chlorine-induced bacterial injury in water

The effect of chlorine-induced bacterial injury on spectral features using Fourier transform infrared (FT-IR) absorbance spectroscopy was studied using a mixed bacterial culture of (1:1) ca. 500 CFU/mL each Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 15442 in 0.9% saline. Bacterial cells were treated with 0, 0.3, or 1.0 ppm of initial free chlorine (21 degrees C, 1 h of contact time). Chlorine-injured and dead bacterial cells retained the ATR spectral properties of uninjured or live cells in the region of C-O-C stretching vibrations of polysaccharides, indicative of the cell wall peptidoglycan layer and lipopolysaccharide outer leaflet. This confirms the observations of others that extensive bacterial membrane damage is not a key factor in the inactivation of bacteria by chlorine. The bactericidal effect of chlorine caused changes in the spectral features of bacterial ester functional groups of lipids, structural proteins, and nucleic acids, with apparent denaturation reflected between 1800 and 1300 cm (-1) for injured bacterial cells. Three-dimensional principal component analysis (PCA) showed distinct segregation and clustering of chlorine-treated and untreated cells. Cells exposed to chlorine at 0.3 or 1.0 ppm could be distinguished from the untreated control 73 and 80% of the time, respectively, using soft independent modeling of class analogy (SIMCA) analysis. This study suggests that FT-IR spectroscopy may be applicable for detecting the presence of injured and viable but not culturable (VBNC) waterborne pathogens that are underestimated or not discernible using conventional microbial techniques.

Detection and identification of bacteria in an isolated system with near-infrared spectroscopy and multivariate analysis

Near-infrared (NIR) transflectance spectra of Listeria innocua FH, Lactococcus lactis, Pseudomonas fluorescens, Pseudomonas mendocina, and Pseudomonas putida suspensions were collected and investigated for their potential use in the identification and classification of bacteria. Unmodified spectral data were transformed (first and second derivative) using the Savitzsky-Golay algorithm. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA), and soft independent modeling of class analogy (SIMCA) were used in the analysis. Using either full cross-validation or separate calibration and prediction data sets, PLS2 regression classified the five bacterial suspensions with 100% accuracy at species level. At Pseudomonas genus level, PLS2 regression classified the three Pseudomonas species with 100% accuracy. In the case of SIMCA, prediction of an unknown sample set produced correct classification rates of 100% except for L. innocua FH (77%). At genus level, SIMCA produced correct classification rates of 96.7, 100, and 100% for P. fluorescens, P. mendocina, and P. putida, respectively. This successful investigation suggests that NIR spectroscopy can become a useful, rapid, and noninvasive tool for bacterial identification.

Monitoring quality loss of pasteurized skim milk using visible and short wavelength near-infrared spectroscopy and multivariate analysis

Visible and short wavelength near-infrared diffuse reflectance spectroscopy (600 to 1,100 nm) was evaluated as a technique for detecting and monitoring spoilage of pasteurized skim milk at 3 storage temperatures (6, 21, and 37 degrees C) over 3 to 30 h (control, t = 0 h; n = 3). Spectra, total aerobic plate count, and pH were obtained, with a total of 60 spectra acquired per sample. Multivariate statistical procedures, including principal component analysis, soft independent modeling of class analogy, and partial least squares calibration models were developed for predicting the degree of milk spoilage. Principal component analysis showed apparent clustering and segregation of milk samples that were stored at different time intervals. Milk samples that were stored for 30 h or less at different temperatures were noticeably separated from control and distinctly clustered. Soft independent modeling of class analogy analysis could correctly classify 88 to 93% of spectra of incubated samples from control at 30 h. A partial least squares model with 5 latent variables correlating spectral features with bacterial counts and pH yielded a correlation coefficient (R = 0.99 and 0.99) and a standard error of prediction (0.34 log(10) cfu/mL and 0.031 pH unit), respectively. It may be feasible to use short wavelength near-infrared spectroscopy to detect and monitor milk spoilage rapidly and noninvasively by correlating changes in spectral features with the level of bacterial proliferation and milk spoilage.

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories. This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications.

Fourier transform infrared spectroscopy, detection and identification of Escherichia coli O157:H7 and Alicyclobacillus strains in apple juice

Fourier Transform Infrared (FT-IR) spectroscopy (4000-400 cm(-1)) combined with multivariate statistical methods were used to identify and detect Escherichia coli O157:H7 from Alicyclobacillus spp. recovered from apple juice. Four treatments and a control in triplicate experiments (N=3) were studied; the first three treatments of pasteurized apple juice were inoculated with E. coli O157:H7 ATCC 35150, Alicyclobacillus acidoterrestris 1016 and Alicyclobacillus spp. C-Fugi-6 respectively. The fourth treatment was a 1:1 (v:v) mixed culture of both A. acidoterrestris 1016 and Alicyclobacillus spp. C-Fugi-6. The control was uninoculated pasteurized apple juice. The second derivative transformation and loadings plot over the range of 1800-900 cm(-1) highlighted the most distinctive variations among bacterial spectra. Loadings 1 and 2 were distinctively representative of the bacterial spectral data and accounted for 73% of the total variability. Treatments were noticeably segregated with distinct clustering by principal component analysis (PCA). Using soft independent modeling of class analogy (SIMCA) analysis, 88.3% of (E. coli O157:H7 ATCC 35150) spectra, 75.0% of (A. acidoterrestris 1016) spectra, 88.3% of (Alicyclobacillus spp. C-Fuji-6) spectra, and 80.0% of the mixed culture of both Alicyclobacillus strains spectra were correctly classified. Using the spectral features of bacterial cellular constituents such as nucleic acids, proteins, phospholipids, peptidoglycan, and lipopolysaccharides from examined bacterial cells, pure and mixed cultures of Alicyclobacillus spp. cells, and the pathogenic E. coli cells could be detected in apple juice.

Detection of Escherichia coli O157:H7 and Salmonella typhimurium using filtration followed by Fourier-transform infrared spectroscopy

Fourier-transform infrared spectroscopy has been successfully used as a nondestructive method for identifying, distinguishing, and classifying pathogens. In this study, a less time-consuming Fourier-transform infrared procedure was developed to identify Escherichia coli O157:H7 and Salmonella Typhimurium. Samples containing 10(9) CFU/ml were prepared in tryptic soy broth and then serially diluted (up to eight times) to obtain bacterial solutions of 10(9) to 10 CFU/ml. These dilutions were incubated at 37 degrees C for 6 h, samples were filtered through a Metricel filter hourly (for 0 to 6 h), and spectra were obtained using a ZnSe contact attenuated total reflectance accessory on a Continu mum infrared microscope. Midinfrared spectra (4,000 to 700 cm(-1)) of Salmonella Typhimurium and E. coli O157:H7 were generated, and peak areas in the region of 1,589 to 1,493 cm(-1) were used to detect the pathogens. Initially, detection limits were between 10(6) and 10(7) CFU/ml without preenrichment, and samples starting with 500 CFU/ml were detectable following incubation for 6 h, when counts reached at least 10(6) CFU/ ml. Compared with results of previously published studies in which Fourier-transform infrared spectroscopy was used to identify select pathogens, this method is more rapid and less expensive for practical large-scale sample analysis.

Differentiation of selected Salmonella enterica serovars by Fourier transform mid-infrared spectroscopy

Salmonella enterica serovars include pathogens responsible for high numbers of foodborne salmonellosis. Fourier transform infrared (FT-IR) spectroscopy can be used to rapidly and accurately identify microorganisms based on unique spectra of bacterial cell components. The objectives of this study were to discriminate closely related Salmonella enterica serovars by using FT-IR spectroscopy and multivariate analysis and to compare the performance of three techniques for differentiating among Salmonella serovars. Selected serovars of S. enterica were streaked onto plate count agar and incubated (37 degrees C, 24 h). Isolated colonies were suspended in phosphate buffer or 50% ethanol (10 microL). Suspensions were placed on (1) ZnSe crystals for transmission, (2) disposable polyethylene membranes (DPM) for transmission, and (3) diamond crystal plate for attenuated total reflectance (ATR) analyses; all samples were dried under vacuum. Classification models, soft independent modeling of class analogy (SIMCA), from derivatized infrared spectra (1300-900 cm(-1)), discriminated among Salmonella serovars presumably attributed to cell's lipopolysaccharides (1000-980 cm(-1)). Samples on DPM required high cell density for reliable spectra. High-quality spectra were obtained when a single colony was suspended in ethanol or buffer and mounted on ZnSe crystals for transmission or diamond plate for ATR analysis. Prediction of unknowns, representative of serovars used to construct classification models, showed that all techniques were suitable for the rapid and accurate differentiation of Salmonella serovars.