Fourier Transform Infrared Spectroscopy and Chemometrics for the Rapid Screening of Economically Motivated Adulteration of Honey Spiked With Corn or Rice Syrup.

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

Determination of Moisture in Olive Oil: Rapid FT-NIR Spectroscopic Procedure Based on the Karl-Fischer Reference Method.

According to CODEX, moisture and volatile matter are olive oil quality parameters and the development of a rapid screening method for the determination of moisture is of interest. We recently demonstrated for the first time that the weak near-infrared (NIR) band near 5260 cm-1 is primarily attributed to a water O-H combination band. To determine the intensity of this band, we measured the peak-to-peak (p-p) height of its first derivative and generated exponential calibration curves for p-p height versus gravimetrically determined concentrations of spiked water in olive oils that had been purged of their initial moisture contents. To further optimize this univariate calibration method, calibration curves were generated in the present study based on plotting the moisture band first derivative p-p heights for neat olive oils (that were neither purged nor spiked) versus the moisture concentrations obtained by the Karl-Fischer (KF) primary reference method. To enhance the speed of FT-NIR data collection, measurements were carried in the transmission mode using disposable glass tubes. We also developed and compared a multivariate partial least squares approach to the univariate one. All the spectra were collected in two separate laboratories using two FT-NIR spectrometers of the same brand and model and no significant difference (p > 0.05) was found between the two laboratory determinations and the KF reference values at a 95% confidence interval. High accuracies were found with the two FT-NIR instruments used, as indicated by the low root mean squared error (RMSE, %) for predicted values obtained with the univariate procedure (RMSE = 0.008% and 0.010%) and the multivariate one, which yielded an even lower value (RMSE= 0.007% for both instruments). These results suggest that, once validated, the FT-NIR approach could potentially be a rapid substitute for the KF method.

Comparison of the Performance of Partial Least Squares and Support Vector Regressions for Predicting Fatty Acids and Fatty Acid Classes in Marine Oil Dietary Supplements by Using Vibrational Spectroscopic Data.

ABSTRACT: Simple, fast, and accurate analytical techniques for verifying the accuracy of label declarations for marine oil dietary supplements containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are required because of the increased consumption of these products. We recently developed broad-based partial least squares regression (PLS-R) models to quantify six fatty acids (FAs) and FA classes by using the spectroscopic data from a portable Fourier transform infrared (FTIR) device and a benchtop Fourier transform near infrared (FT-NIR) spectrometer. We developed an improved quantification method for these FAs and FA classes by incorporating a nonlinear calibration approach based on the machine learning technique support vector machines. For the two spectroscopic methods, high accuracy in prediction was indicated by low root mean square error of prediction and by correlation coefficients (R2) close to 1, indicating excellent model performance. The percent accuracy of the support vector regression (SV-R) model predicted values for EPA and DHA in the reference material was 90 to 110%. In comparison to PLS-R, SV-R accuracy for prediction of FA and FA class concentrations was up to 2.4 times higher for both ATR-FTIR and FT-NIR spectroscopic data. The SV-R models also provided closer agreement with the certified and reference values for the prediction of EPA and DHA in the reference standard. Based on our findings, the SV-R methods had superior accuracy and predictive quality for predicting the FA concentrations in marine oil dietary supplements. The combination of SV-R with ATR-FTIR and/or FT-NIR spectroscopic data can potentially be applied for the rapid screening of marine oil products to verify the accuracy of label declarations.

A Rapid, Univariate FT-NIR Procedure to Determine Moisture Concentration in Olive Oil.

We recently observed that the weak near-infrared (NIR) band near 5260 cm-1 was relatively more intense for extra virgin olive oil (EVOO) than for refined olive oil (ROO). We also observed that its intensity was diminished upon heating and erroneously presumed that it may be attributed to volatile carbonyl components in EVOO. In the present study we demonstrate for the first time that this band is primarily attributed to a water O-H combination band. To accurately determine the intensity of this weak band, observed on a shifted and sloping baseline, we measured the peak-to-peak (p-p) height of its first derivative. An exponential calibration curve for p-p height versus gravimetrically-determined concentration of spiked water was satisfactorily generated. The calibration curve was first evaluated by using independent sets of gravimetrically prepared test samples. Subsequently, it was used to determine the moisture content, a quality parameter, for a limited set of authenticated reference olive oils whose quality and purity were confirmed by official methods. These concentrations, 0.098-0.12% H2O (w/w) for EVOO, 0.022-0.030% H2O (w/w) for ROO, and 0.028-0.054% H2O (w/w) for pomace olive oil (POO), were consistent with those reported in the literature. For 88 commercial products investigated, the moisture levels fell in the range from 0.026% to 0.13% (w/w). The correlation between moisture content and other olive oil quality parameters has been reported in the literature and has yet to be further investigated.

Portable Mid-Infrared Device and Chemometrics for the Prediction of Low (0.5%) Total Trans Fat Content in Fast Foods.

The ruling that partially hydrogenated oils (PHO) are no longer "generally recognized as safe (GRAS)," has accelerated the replacement of PHO ingredients with fat alternatives having increasingly lower or no trans fat content. In the present study, we developed a Fourier-transform infrared (FTIR) spectroscopic procedure in conjunction with multivariate partial least squares regression (PLSR) and found it suitable for the accurate prediction of low (0.5%) total trans fat content, as percentage of total fat, measured as fatty acid methyl esters (FAME), in the lipids extracted from 24 representative fast foods. This multivariate data analysis approach is relevant because the precision of the current univariate FTIR official method (AOCS Cd 14-09) is reportedly poor below 2% of total fat, while PLSR has allowed us to accurately predict the concentration of low trans fat in fast foods. The performance of a portable FTIR device was also evaluated and compared to that of a benchtop FTIR spectrometer. For both infrared data sets, PLSR-predicted concentrations of total trans FAME, ranging from approximately 0.47% to 11.40% of total FAME, were in good agreement with those determined by a primary reference gas chromatography (GC) method (R2>0.99); high prediction accuracy was also evidenced by low root mean square error of cross-validation (RMSECV) values. The lowest RMSECV error of 0.12% was obtained with the portable device. The lowest total trans FAME concentration, determined by GC to be 0.42%, was accurately predicted by the portable FTIR/PLSR procedure as 0.47% of total FAME.

Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression.

The United States Food and Drug Administration (FDA) ruled that partially hydrogenated oils (PHO), the major dietary source of industrially produced trans fat (TF), were no longer "generally recognized as safe (GRAS)" for any use in human food. Consequently, the objective of this study was to develop a rapid screening procedure using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with partial least squares regression (PLSR) for the quantitative and accurate prediction of low concentrations of trans fatty acid (TFAs) (<1% of total fatty acids (FAs)). Broad-based calibration models were developed for a combined set of samples consisting of edible oils and fast food lipid extracts. Predicted concentrations of TFAs in the two matrices showed good correlation with the primary reference data generated by gas chromatography (GC) (R2 > 0.99) and high accuracy as evidenced by low root-mean-square error of cross-validation (RMSECV) values. The lowest TFA concentration, determined by GC to be 0.13% of total FAs, was accurately predicted by ATR-FTIR/PLSR as 0.18% of total FAs. This simple, rapid ATR-FTIR/PLSR methodology has the potential for use as a screening alternative to conventional gas chromatographic methods for predicting the TFA content of edible oils and food lipid extracts for regulatory purposes and quality control of raw material and processed food. PRACTICAL APPLICATIONS: FDA ruled that partially hydrogenated oils were no longer "generally recognized as safe (GRAS)" for any use in human food. Consequently, we have proposed a rapid screening procedure, based on infrared spectroscopy and chemometrics, to rapidly and accurately predict low concentrations of trans fatty acids (<1% of total fatty acids) in edible oils and food lipid extracts.

Effects of Wet-Blending on Detection of Melamine in Spray-Dried Lactose.

During the development of rapid screening methods to detect economic adulteration, spray-dried milk powders prepared by dissolving melamine in liquid milk exhibited an unexpected loss of characteristic melamine features in the near-infrared (NIR) and Raman spectra. To further characterize this "wet-blending" phenomenon, spray-dried melamine and lactose samples were produced as a simplified model and investigated by NIR spectroscopy, Raman spectroscopy, proton nuclear magnetic resonance (1H NMR), and direct analysis in real time Fourier transform mass spectrometry (DART-FTMS). In contrast to dry-blended samples, characteristic melamine bands in NIR and Raman spectra disappeared or shifted in wet-blended lactose-melamine samples. Subtle shifts in melamine 1H NMR spectra between wet- and dry-blended samples indicated differences in melamine hydrogen-bonding status. Qualitative DART-FTMS analysis of powders detected a greater relative abundance of lactose-melamine condensation product ions in the wet-blended samples, which supported a hypothesis that wet-blending facilitates early Maillard reactions in spray-dried samples. Collectively, these data indicated that the formation of weak, H bonded complexes and labile, early Maillard reaction products between lactose and melamine contribute to spectral differences observed between wet- and dry-blended milk powder samples. These results have implications for future evaluations of adulterated powders and emphasize the important role of sample preparation methods on adulterant detection.

First Application of Newly Developed FT-NIR Spectroscopic Methodology to Predict Authenticity of Extra Virgin Olive Oil Retail Products in the USA.

Economically motivated adulteration (EMA) of extra virgin olive oils (EVOO) has been a worldwide problem and a concern for government regulators for a long time. The US Food and Drug Administration (FDA) is mandated to protect the US public against intentional adulteration of foods and has jurisdiction over deceptive label declarations. To detect EMA of olive oil and address food safety vulnerabilities, we used a previously developed rapid screening methodology to authenticate EVOO. For the first time, a recently developed FT-NIR spectroscopic methodology in conjunction with partial least squares analysis was applied to commercial products labeled EVOO purchased in College Park, MD, USA to rapidly predict whether they are authentic, potentially mixed with refined olive oil (RO) or other vegetable oil(s), or are of lower quality. Of the 88 commercial products labeled EVOO that were assessed according to published specified ranges, 33 (37.5%) satisfied the three published FT-NIR requirements identified for authentic EVOO products which included the purity test. This test was based on limits established for the contents of three potential adulterants, oils high in linoleic acid (OH-LNA), oils high in oleic acid (OH-OLA), palm olein (PO), and/or RO. The remaining 55 samples (62.5%) did not meet one or more of the criteria established for authentic EVOO. The breakdown of the 55 products was EVOO potentially mixed with OH-LNA (25.5%), OH-OLA (10.9%), PO (5.4%), RO (25.5%), or a combination of any of these four (32.7%). If assessments had been based strictly on whether the fatty acid composition was within the established ranges set by the International Olive Council (IOC), less than 10% would have been identified as non-EVOO. These findings are significant not only because they were consistent with previously published data based on the results of two sensory panels that were accredited by IOC but more importantly each measurement/analysis was accomplished in less than 5 min.

Non-targeted detection of milk powder adulteration using Raman spectroscopy and chemometrics: melamine case study.

Raman spectroscopy in combination with chemometrics was explored as a rapid, non-targeted screening method for the detection of milk powder (MP) adulteration using melamine as an example contaminant. Raman spectroscopy and an unsupervised pattern-recognition method, principal component analysis (PCA), allowed for the differentiation of authentic MPs from adulterated ones at concentrations > 1.0% for dry-blended (DB) samples and > 0.30% for wet-blended (WB) ones. Soft independent modelling of class analogy (SIMCA), a supervised pattern-recognition method, was also used to classify test samples as adulterated or authentic. Combined statistics at a 97% confidence level from the SIMCA models correctly classified adulteration of MP with melamine at concentrations ≥ 0.5% for DB samples and ≥ 0.30% for WB ones, while no false-positives from authentic MPs were found when the spectra in the 600-700 cm-1 range were pre-processed using standard normal variate (SNV) followed by a gap-segment derivatisation. The combined technique of Raman spectroscopy and chemometrics proved to be a useful tool for the rapid and cost-efficient non-targeted detection of adulteration in MP at per cent spiking levels.

Rapid Prediction of Fatty Acid Content in Marine Oil Omega-3 Dietary Supplements Using a Portable Fourier Transform Infrared (FTIR) Device and Partial Least-Squares Regression (PLSR) Analysis.

Using a portable field device, a Fourier transform infrared spectroscopy (FTIR) and partial least-squares regression (PLSR) method was developed for the rapid (<5 min) prediction of major and minor fatty acid (FA) concentrations in marine oil omega-3 dietary supplements. Calibration models were developed with 174 gravimetrically prepared samples. These models were tested using an independent validation set of dietary supplements. FAs analyzed included eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA); the sums of saturated, branched-chain, and monounsaturated FAs; and n-6, n-4, n-3, n-1, and trans polyunsaturated FA. The spectral ranges 650-1500 or 650-1500 and 2800-3050 cm-1 provided reliable predictions for FA components in 34 neat oil products: standard error of prediction, 0.73-1.58%; residual predictive deviation, 6.41-12.6. This simple, nondestructive quantitative method is a rapid screening tool and a time and cost-saving alternative to gas chromatography for verifying label declarations and in quality control.

Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils.

It was previously demonstrated that Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5 % of total fatty acids. An FT-NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT-NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.

Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO.

Characterising variances of milk powder and instrumentation for the development of a non-targeted, Raman spectroscopy and chemometrics detection method for the evaluation of authenticity.

There is a need to develop rapid tools to screen milk products for economically motivated adulteration. An understanding of the physiochemical variability within skim milk powder (SMP) and non-fat dry milk (NFDM) is the key to establishing the natural differences of these commodities prior to the development of non-targeted detection methods. This study explored the sources of variance in 71 commercial SMP and NFDM samples using Raman spectroscopy and principal component analysis (PCA) and characterised the largest number of commercial milk powders acquired from a broad number of international manufacturers. Spectral pre-processing using a gap-segment derivative transformation (gap size = 5, segment width = 9, fourth derivative) in combination with sample normalisation was necessary to reduce the fluorescence background of the milk powder samples. PC scores plots revealed no clear trends for various parameters, including day of analysis, powder type, supplier and processing temperatures, while the largest variance was due to irreproducibility in sample positioning. Significant chemical sources of variances were explained by using the spectral features in the PC loadings plots where four samples from the same manufacturer were determined to likely contain an additional component or lactose anomers, and one additional sample was identified as an outlier and likely containing an adulterant or differing quality components. The variance study discussed herein with this large, diverse set of milk powders holds promise for future use as a non-targeted screening method that could be applied to commercial milk powders.

Novel, Rapid Identification, and Quantification of Adulterants in Extra Virgin Olive Oil Using Near-Infrared Spectroscopy and Chemometrics.

A new, rapid Fourier transform near infrared (FT-NIR) spectroscopic procedure is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT-NIR index based mainly on the relative intensities of two unique carbonyl overtone absorptions in the FT-NIR spectra of EVOO and other mixtures attributed to volatile (5280 cm(-1)) and non-volatile (5180 cm(-1)) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models. Some adulterated mixtures could be identified provided the FA profile was sufficiently different from those of EVOO. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking EVOO with different concentrations of each adulterant. Based on FT-NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants, each with a characteristic FA composition. Using these different PLS1 calibration models for prediction, plots of predicted vs. gravimetric concentrations of an adulterant in EVOO yielded linear regression functions with four unique sets of slopes, one for each group of adulterants. Four corresponding slope rules were defined that allowed for the determination of the nature and concentration of an adulterant in EVOO products by applying these four calibration models. The standard addition technique was used for confirmation.

Microarray chip development using infrared imaging for the identification of catfish species.

Several families of catfish species are extensively aquacultured around the world; however, only those from the family Ictaluridae can be labeled as catfish in the United States. Non-Ictalurid catfish species that are marketed as "catfish" in the USA are considered misbranded. Misbranding in general has led to an increased interest in developing deoxyribonucleic acid (DNA)-based methods such as DNA barcoding, polymerase chain reaction restriction fragment length polymorphism, and DNA microarrays with fluorescence detection for the identification of fish species. In this proof-of-concept study, DNA microarrays coupled with a newly developed mid-infrared imaging detection method were applied to the identification of seven species of catfish for the first time. Species-specific DNA probes targeting three regions per species of the cytochrome c oxidase 1 (barcoding) gene were developed and printed as microarrays on glass slides. Deoxyribonucleic acid targets labeled with biotin were hybridized to their complementary probes using a strategy that allowed the selective formation of a silver layer on hybridized spots needed for detection. Using this three-probe format, the seven species were all identified correctly, even when a limited number of false positive spots were observed. Raman spectroscopy was employed to further characterize the arrays.

Rapid turbidimetric detection of milk powder adulteration with plant proteins.

Development of assays to screen milk for economically motivated adulteration with foreign proteins has been stalled since 2008 due to strong international reactions to the melamine poisoning incident in China and the surveillance emphasis placed on low molecular weight nitrogen-rich adulterants. New screening assays are still needed to detect high molecular weight foreign protein adulterants and characterize this understudied potential risk. A rapid turbidimetric method was developed to screen milk powder for adulteration with insoluble plant proteins. Milk powder samples spiked with 0.03-3% by weight of soy, pea, rice, and wheat protein isolates were extracted in 96-well plates, and resuspended pellet solution absorbance was measured. Limits of detection ranged from 100 to 200 µg, or 0.1-0.2% of the sample weight, and adulterant pellets were visually apparent even at ∼0.1%. Extraction recoveries ranged from 25 to 100%. Assay sensitivity and simplicity indicate that it would be ideally suitable to rapidly screen milk samples in resource poor environments where adulteration with plant protein is suspected.

Determination of trans fat in edible oils: current official methods and overview of recent developments.

The adverse effects of dietary trans fat on biomarkers of chronic disease are well documented. Regulatory authorities in many countries have enacted legislation aimed at reducing trans fat content of their food supplies, either by requiring trans fat labeling on pre-packaged foods or by limiting the amount of trans fat in oils used for food production. Increased use by the food industry of oils with a low trans fat content necessitates reevaluation of official methods used by the food industry and regulatory agencies for the determination of total trans fat. Attenuated total reflection-Fourier-transform infrared (ATR-FTIR) spectroscopy and gas chromatography with flame ionization detection (GC-FID) are two techniques used in official methods approved by method-endorsing organizations, for example AOAC International and the American Oil Chemists' Society. Here, we review current official ATR-FTIR and GC-FID methods for determination of trans fat, with a focus on factors affecting quantification of low levels of trans fat. We include new data on method performance that have only recently become available, and provide an overview of notable recent developments in lipid analysis (e.g. IR spectroscopy procedures, ionic-liquid GC columns, and multidimensional chromatographic techniques) that have the potential to substantially improve the accuracy, sensitivity, and/or speed of trans fat determination.

Enhanced mid-infrared chemical imaging (IRCI) detection of DNA microarrays.

We report on the optimization of a recently proposed mid-infrared chemical imaging (IRCI) detection method for the analysis of DNA microarrays. The improved protocol allowed for a ten-fold reduction in the time needed to generate a mosaic image of an entire microarray and the production of IR images with high contrast that would facilitate data analysis and interpretation. Advantages of using this protocol were evaluated by applying it to the analysis of four virulence genes in the genomes of 19 strains of the food bacterial pathogen Yersinia enterocolitica.

Evaluation of low trans fat edible oils by attenuated total reflection-Fourier transform infrared spectroscopy and gas chromatography: a comparison of analytical approaches.

Current interest by the food industry in exploring reformulation options that lower the content of trans fat in edible fats and oils requires methods to accurately measure low levels of trans fat. In the present study, the quantitation of trans fat in 25 edible fat and oil samples was evaluated using two current analytical approaches, attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), and gas chromatography with flame ionization detection (GC-FID) according to Official Methods of the American Oil Chemists' Society. Significant differences between the ATR-FTIR and reference GC-FID quantitations were found for samples with a trans fat content <2% of total fat. These discrepancies could be explained, in part, by the presence of certain oil constituents (e.g., vitamins, carotenoids, high levels of saturated fat) that produced absorbance bands at or near 966 cm(-1) in the ATR-FTIR spectra, a region that was previously identified as being characteristic of isolated trans double bonds. Results demonstrate that the natural content of such oil constituents could result in significant overestimations of trans fat when ATR-FTIR is used to analyze edible fats and oils with a trans fat content <2% of total fat.

Bacterial identification and subtyping using DNA microarray and DNA sequencing.

The era of fast and accurate discovery of biological sequence motifs in prokaryotic and eukaryotic cells is here. The co-evolution of direct genome sequencing and DNA microarray strategies not only will identify, isotype, and serotype pathogenic bacteria, but also it will aid in the discovery of new gene functions by detecting gene expressions in different diseases and environmental conditions. Microarray bacterial identification has made great advances in working with pure and mixed bacterial samples. The technological advances have moved beyond bacterial gene expression to include bacterial identification and isotyping. Application of new tools such as mid-infrared chemical imaging improves detection of hybridization in DNA microarrays. The research in this field is promising and future work will reveal the potential of infrared technology in bacterial identification. On the other hand, DNA sequencing by using 454 pyrosequencing is so cost effective that the promise of $1,000 per bacterial genome sequence is becoming a reality. Pyrosequencing technology is a simple to use technique that can produce accurate and quantitative analysis of DNA sequences with a great speed. The deposition of massive amounts of bacterial genomic information in databanks is creating fingerprint phylogenetic analysis that will ultimately replace several technologies such as Pulsed Field Gel Electrophoresis. In this chapter, we will review (1) the use of DNA microarray using fluorescence and infrared imaging detection for identification of pathogenic bacteria, and (2) use of pyrosequencing in DNA cluster analysis to fingerprint bacterial phylogenetic trees.