Spectral Matching Algorithm Research Articles

Utility of RNA-seq and GPMDB protein observation frequency for improving the sensitivity of protein identification by tandem MS.

Tandem mass spectrometry (MS/MS) followed by database search is the method of choice for protein identification in proteomic studies. Database searching methods employ spectral matching algorithms and statistical models to identify and quantify proteins in a sample. In general, these methods do not utilize any information other than spectral data for protein identification. However, considering the wealth of external data available for many biological systems, analysis methods can incorporate such information to improve the sensitivity of protein identification. In this study, we present a method to utilize Global Proteome Machine Database identification frequencies and RNA-seq transcript abundances to adjust the confidence scores of protein identifications. The method described is particularly useful for samples with low-to-moderate proteome coverage (i.e., <2000–3000 proteins), where we observe up to an 8% improvement in the number of proteins identified at a 1% false discovery rate.

氧气吸收通道的高光谱传感器在轨光谱定标

The Oxygen absorption characteristic was analyzed and four hyperspectral sensor models were built with full width at half maximum of 15,10,5and 2.5nm.Four spectral matching styles and six spectral matching algorithms were proposed.The measured spectrum and referenced spectrum with spectral offset were simulated based on MODTRAN radiative transfer model.The spectral calibrations of hyperspectral sensor with different FWHMs were processed using four spectral matching styles and six spectral matching algorithms. Then the in-flight spectral calibration accuracy was analyzed quantitatively.The results show that the spectral calibration based on correlative coefficient of measured apparent radiance and referenced apparent radiance has the highest accuracy.

Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing.

基于多光源的光谱分布可调谐光源系统

A spectrum-tunable integrating sphere light source based on a Light Emitting diode (LED) and basis sources was developed to eliminate the effect of spectral non-matching between calibration source and an on-orbit detecting target on the calibration accuracy of space optical remote sensor in a laboratory. The developed source was used to produce typical ground object spectra and to calibrate space optical remote sensors, which significantly diminishes the absolute radiometric calibration error caused by spectral non-matching mentioned above. Firstly, the affecting mechanism of the spectral non-matching on the absolute radiometric calibration accuracy of space optical remote sensor was analyzed. Then, the design scheme of the spectrum-tunable light source was given according to technical requirements. The scheme was mainly divided into four key issues: the determination of light source and design of spectral matching algorithm, the design of light source drive circuit and control system, the matching of target spectrum and spectral adjustment, and the optical-mechanical design of spectrum-tunable light source. In the end, the performance of calibrated spectrum-tunable light source was tested. The results show that the spectral non-matching errors of equi-energy spectrum and 6000 K blackbody spectrum are 6.37% and 8.76% respectively, the irradiance non-uniformity is 0.53%, the irradiance instability of the spectrum-tunable light source within 30 min is 0.03%, and the radiance angular non-uniformity of the spectrum-tunable light source within horizontally 30 is 0.80%. Moreover, it offers the spectral radiance above 0.07 W/(m2srnm) and the integral radiance in 410-900 nm of 58.3 W/(m2sr). These data all meet the design requirements of spectrum-tunable light sources.

Robust point pattern matching based on spectral context

Finding correspondences between two related feature point sets is a basic task in computer vision and pattern recognition. In this paper, we present a novel method for point pattern matching via spectral graph analysis. In particular, we aim to render the spectral matching algorithm more robust for positional jitter and outlier. A local structural descriptor, namely the spectral context, is proposed to describe the attribute domain of point sets, which is fundamentally different from the previous methods. Furthermore, the approximate distance order is defined and employed as the metric for geometric consistency of neighboring points in this work. By combining these two novel ingredients, we formulate feature point set matching as an optimization problem with one-to-one constraints. The correspondences are then obtained by maximizing the given objective function via the technique of probabilistic relaxation. Comparative experiments conducted on both synthetic and real data demonstrate the effectiveness of the proposed method, especially in the presence of positional jitter and outliers.

Military target detection using spectrally modeled algorithms and independent component analysis

Most military targets of strategic importance are very small in size. Though some of them may get spatially resolved, most cannot be detected due to lack of adequate spectral resolution. Hyperspectral data, acquired over hundreds of narrow contiguous wavelength bands, are extremely suitable for most military target detection applications. Target detection, however, still remains complicated due to a host of other issues. These include, first, the heavy volume of hyperspectral data, which leads to computational complexities; second, most materials in nature exhibit spectral variability and remain unpredictable; and third, most target detection algorithms are based on spectral modeling and availability of a priori target spectra is an essential requirement, a condition difficult to meet in practice. Independent component analysis (ICA) is a new evolving technique that aims at finding components that are statistically independent or as independent as possible. It does not have any requirement of a priori availability of target spectra and is an attractive alternative. This paper, presents a study of military target detection using four spectral matching algorithms, namely, orthogonal subspace projection (OSP), constrained energy minimisation, spectral angle mapper and spectral correlation mapper, four anomaly detection algorithms, namely, OSP anomaly detector (OSPAD), Reed–Xiaoli anomaly detector (RXD), uniform target detector (UTD), a combination of RXD–UTD. The performances of these spectrally modeled algorithms are then also compared with ICA using receiver operating characteristic analysis. The superior performance of ICA indicates that it may be considered a viable alternative for military target detection.

Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data

In this paper, a spectral encoding and matching algorithm inspired by biological deoxyribonucleic acid (DNA) computing is proposed to perform the task of spectral signature classification for hyperspectral remote sensing data. As a novel branch of computational intelligence, DNA computing has the strong computing and matching capability to discriminate the tiny differences in DNA strands by DNA encoding and matching in the molecule layer. Similar to DNA discrimination, a hyperspectral remote sensing data matching approach is used to recognize the land cover material from a spectral library or image, according to the rich spectral information. However, it is difficult to apply DNA computing to hyperspectral remote sensing data processing because traditional DNA computing often relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. To utilize the advantages and avoid the problems of biological DNA computing, an artificial DNA computing approach is proposed for spectral encoding and matching for hyperspectral remote sensing data. A DNA computing-based spectral matching approach is used to first transform spectral signatures into DNA codewords by capturing the key spectral features with a spectral feature encoding operation. After DNA encoding, the typical DNA database for interesting classes is constructed and saved by DNA evolutionary operating mechanisms such as crossover, mutation, and structured mutation. During the course of spectral matching, each pixel of the hyperspectral image, or each signature measured in the field, is input to the constructed DNA database. By computing the distance between an unclassified spectrum and the typical DNA codewords from the database, the class property of each pixel is set as the minimum distance class. Experiments using different hyperspectral data sets were performed to evaluate the performance of the proposed artificial DNA computing-based spectral matching algorithm by comparing it with other traditional hyperspectral classifiers, including spectral matching classifiers (binary coding, spectral angle mapper and spectral derivative feature coding (SDFC) matching methods) and a novel statistical method of machine learning termed support vector machine (SVM). Experimental results demonstrate that the proposed algorithm is distinctly superior to the three traditional hyperspectral data classification algorithms. It presents excellent processing efficiency, compared to SVM, with high-dimensional data captured by the Hyperspectral Digital Imagery Collection Experiment sensor, and hence provides an effective option for spectral matching classification of hyperspectral remote sensing data.

Fast and scalable approximate spectral graph matching for correspondence problems

Establishing consistent correspondences between two sets of features is a fundamental problem in computer vision. This problem can be well formulated as graph matching in which nodes and edges represent feature points and pairwise relationships between feature points, respectively. Spectral matching [19] is the state-of-the-art eigenvector-based method for graph matching. The spectral matching algorithm has been used successfully for small data, but its heavy memory requirement limited the maximum data sizes and contexts it can be used. In this paper, we propose FaSM, a fast and scalable approximate spectral matching algorithm. The main ideas are twofold. First, we exploit the redundancy in the data generation process to approximate the affinity matrix with the linear combination of Kronecker products between bases and index matrices. The bases and index matrices are highly compressed representation of the approximated affinity matrix, requiring much smaller memory than in previous works which store the whole affinity matrix. Second, we compute the eigenvector of the approximated affinity matrix using the small bases and index matrices without explicitly materializing the approximated matrix. Experimental results show that our proposed method is up to 33× faster, requiring up to 645× smaller memory than the exact algorithm, with little or no loss of accuracy.

Identification of synthetic organic pigments: the role of a comprehensive digital Raman spectral library

Identification of pigments in modern and contemporary arts is indispensable to determine correct conservation strategies, to study degradation processes and to answer authenticity‐related questions. Since the early 20th century, the introduction of synthetic organic pigments (SOPs) has enormously increased the number of available pigments. Micro‐Raman spectroscopy has proven to be the first line technique for the identification of these pigments, which often only show small variations on the same basic chemical structures. To ensure a correct identification, however, an as complete as possible library of reference spectra is needed. Although reference spectra of SOPs have been published before, they have always been limited to a certain number of pigments. Some publications discuss only one or a few chemical classes, and others are limited to a certain number of pigments belonging to different classes; none, however, have attempted to create an extensive library of commercially available pigments. Moreover, most of the reference spectra published so far are only available as small imprints or as peak lists and, as such, hardly usable for spectral matching algorithms. Often, flow charts have been developed on the basis of the pigments studied, to help in identifying unknown SOPs. In this paper, a digital spectral library that comprehends almost 300 spectra of different SOPs is presented. The library was tested by means of non‐invasive analysis of four contemporary paintings from the collections of the Stedelijk Museum voor Actuele Kunst (Ghent, Belgium). Published flow charts have been evaluated in relation to digital search algorithms by using the extensive library of SOPs. To enable fellow research and conservation institutes to make use of these data, the nearly 300 reference spectra of synthetic organic pigments are available in digital format on http://modern.kikirpa.be. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by using nonsubsampled contourlet transform and scale-invariant feature transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency image. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix (or matching result) reflecting the matching degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Clinical Proteomics

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

Wind‐induced resuspension in a shallow lake from Medium Resolution Imaging Spectrometer (MERIS) full‐resolution reflectances

A lack of empirical evidence impedes assessment of the spatial and temporal extent of critical conditions for recurring high turbidity in large wind‐exposed shallow lakes. Here spatiotemporal variation in total suspended matter (TSM) concentration was captured by processing 30 Envisat Medium Resolution Imaging Spectrometer (MERIS) images of a shallow lake (Markermeer) with a spectral matching algorithm. The TSM maps showed elevated downwind concentrations for moderate winds (from 4 to 9 m s−1), which occur 68% of the time. Regressions confirmed the relationship between hourly averaged wind speed and TSM. To explore critical conditions for resuspension, wind speed, linear fetch, and water depth were combined in a spatial model based on simplified linear wave equations. Remotely sensed TSM patterns matched predicted areas of resuspension from these wave equations. On average, over 70% of cells were true positive or negative, with elevated TSM matching the predicted resuspending bottom area and background TSM matching no resuspension. Images acquired during moderate winds register local resuspension. This implies that under these conditions, a critical shear stress threshold for resuspension is passed, followed by upward mixing over the few meters of water column. Images acquired during low wind speeds (≤3 m s−1) either do not show a TSM pattern or display settling because it takes several hours of low wind before all particles are removed from the visible top layer. Because of the good spatial matching, the resuspension model can also be used for future verification of the retrieval capacity of the spectral matching algorithm.

An assessment of independent component analysis for detection of military targets from hyperspectral images

Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed–Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed–Xiaoli anomaly detector and Uniform Target Detector (RXD–UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.

Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data

Successful discrimination of a variety of natural and urban landscape components has been achieved with remote sensing data using multiple endmember spectral mixture analysis (MESMA). MESMA is a spectral matching algorithm that addresses spectral variability by allowing multiple reference spectra (i.e., endmembers) to represent each material class. However, materials that have a high-degree of spectral similarity between classes, such as similar plant-types or closely related plant species, and large variations in albedo present an ongoing challenge for accurate class discrimination with imaging spectrometry. Continuum removal (CR) analysis may improve class separability by emphasizing individual absorption features across a normalized spectrum. The spectral and structural characteristics common to most Eucalyptus trees make them notoriously difficult to discriminate in closed-canopy forests with imaging spectrometry. We evaluated whether CR applied to hyperspectral remote sensing data improved the performance of MESMA in classifying and mapping nine eucalypt tree species according to the two major Eucalyptus subgenera, Eucalyptus (common name “monocalypt”) and Symphyomyrtus (common name “symphyomyrtle”). Mixed-canopies comprised of monocalypts and symphyomyrtles are common in Australia, although their spatial distribution is not random. The ability to map these functional types on a landscape-scale could provide important information about ecosystem processes, landscape disturbance history and wildlife habitat. We created a spectral library of 229 pixels from 37 symphyomyrtle tree canopies and 406 pixels from 62 monocalypt tree canopies selected from HyMap imagery and verified with field data. Based on these reference data, we achieved overall classification accuracies at the subgenera-level of 75% (Kappa 0.48) for non-CR spectra and 83% (Kappa 0.63) for the CR spectra. We found that continuum-removal improved the classification performance of most endmember-models, although a larger portion of pixels remained unmodeled with the CR spectra (2%) compared to the non-CR spectra (0%). We utilized a new method for model optimization and created maps of monocalypt and symphyomyrtle distribution in our study area based on our best performing endmember-models. Our vegetation maps were largely consistent with our expectations of subgenera distribution based on our knowledge of the region.

基于核偏最小二乘方法的点模式匹配

Point pattern matching is an essential step in many image processing applications. This letter investigates the spectral approaches of point pattern matching, and presents a spectral feature matching algorithm based on kernel partial least squares (KPLS). Given the feature points of two images, we define position similarity matrices for the reference and sensed images, and extract the pattern vectors from the matrices using KPLS, which indicate the geometric distribution and the inner relationships of the feature points. Feature points matching are done using the bipartite graph matching method. Experiments conducted on both synthetic and real-world data demonstrate the robustness and invariance of the algorithm.

Improved Velocity and Displacement Time Histories in Frequency Domain Spectral-Matching Procedures

Existing spectral-matching techniques in the frequency domain distort the displacement time history of the ground motions. In the time domain spectral- matching procedures, scale functions (wavelets) are additive and, with an appropriate scale functional form, extra displacement will not be imposed to the record. However, matching in the frequency domain with a multiplicative scale function applied to the Fourier spectrum requires a special attention in the matching process to have control on the displacement time history. This study shows that thevelocity value at the end of the record is not affected by the Fourier amplitude spectrum scaling, but the displace- ment may linearly increase (or decrease) boundlessly. Two numerical solutions are proposed to solve the displacement drift problem in the frequency domain. Following the proposed frequency domain baseline correction procedure, one does not need to perform baseline correction in the time domain after completing the spectral matching. As a result, acceleration, velocity, and displacement time histories will remain fully compatible, and the boundary conditions of the velocity and displacement time histories will be preserved. The proposed procedure is not limited to spectral-matching methods and can be used with any general filtering process to retain the final dis- placement of the record. The possibility of applying a zero-padding technique to the spectral-matching filter is also discussed. It is shown that applying an appropriate window along with the zero-padding technique can lead to a reasonable displacement time history. The proposed procedures can be easily added to the existing or new frequency domain spectral-matching algorithms without significantly disrupting the spectral-matching process.

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.

Spectral-based automatic labeling and refining of human cortical sulcal curves using expert-provided examples

We present a spectral-based method for automatically labeling and refining major sulcal curves of a human cerebral cortex. Given a set of input (unlabeled) sulcal curves automatically extracted from a cortical surface and a collection of expert-provided examples (labeled sulcal curves), our objective is to identify the input major sulcal curves and assign their neuroanatomical labels, and then refines these curves based on the expert-provided example data, without employing any atlas-based registration scheme as preprocessing. In order to construct the example data, neuroanatomists manually labeled a set of 24 major sulcal curves (12 each for the left and right hemispheres) for each individual subject according to a precise protocol. We collected 30 sets of such curves from 30 subjects. Given the raw input sulcal curve set of a subject, we choose the most similar example curve to each input curve in the set to label and refine the latter according to the former. We adapt a spectral matching algorithm to choose the example curve by exploiting the sulcal curve features and their relationship. The high dimensionality of sulcal curve data in spectral matching is addressed by using their multi-resolution representations, which greatly reduces time and space complexities. Our method provides consistent labeling and refining results even under high variability of cortical sulci across the subjects. Through experiments we show that the results are comparable in accuracy to those done manually. Most output curves exhibited accuracy values higher than 80%, and the mean accuracy values of the curves in the left and the right hemispheres were 84.69% and 84.58%, respectively.

Validation of a SeaWiFS dust-correction methodology in the Mediterranean Sea: Identification of an algorithm-switching criterion

A spectral matching algorithm (SMA) that allows atmospheric correction in the presence of dust aerosols is applied to SeaWiFS imagery in the northwest Mediterranean Sea. The goal is to find criteria that could be used to identify SMA target pixels and to gain insights into the method's accuracy relative to the standard SeaWiFS processing scheme (STD). This work also represents the first validation of SMA using in situ data. The validation dataset includes water-leaving radiances collected from both a fixed buoy site and from a ship during the Advanced Optical Properties Experiment (AOPEX) cruise in August 2004. Supplementary information was provided by the ship LIDAR and coastal AERONET stations in Villefranche (France) and Blida (Algeria) that recorded aerosol conditions near the buoy and proximal to the dust sources, respectively. Backward aerosol transport trajectories were also available for the AERONET sites, allowing identification of potential dust sources, especially for aerosol layers observed by the LIDAR. Over the study period, four aerosol events affected the buoy vicinity, but SMA retrievals proved superior to standard processing results only when dust was dominant, rather than when dust was simply present. The conditions appropriate for an SMA application could be defined using AERONET parameters. They are a combination of high aerosol optical depth τ a and low Ångström exponent α (or τ a / α > 0.2). Similar results are obtained using the equivalent SeaWiFS parameters produced by the STD method although the threshold value is different. Since it is preferable to apply the criterion on a per-pixel basis prior to atmospheric correction to select SMA or STD processing, an analogous test using aerosol model-independent quantities derived from SeaWiFS data is proposed. Thus, SMA and STD processing can be applied to a single image, where appropriate.

Increased Confidence in Large-Scale Phosphoproteomics Data by Complementary Mass Spectrometric Techniques and Matching of Phosphopeptide Data Sets

Large-scale phosphoproteomics studies are of great interest due to their potential for the dissection of signaling pathways controlled by protein kinases. Recent advances in mass spectrometry (MS)-based phosphoproteomic techniques offer new opportunities to profile protein kinase activities in a comprehensive manner. However, this increasingly used approach still poses many analytical challenges. On one hand, high stringency criteria for phosphopeptide identification based on MS/MS data are needed in order to avoid false positives; however, on the other hand, these stringent criteria also result in the introduction of many false negatives. In the current report, we employ different mass spectrometric techniques for large-scale phosphoproteomics in order to reduce the presence of false negatives and enhance data confidence. A LTQ-Orbitrap LC-MS/MS platform identified approximately 3 times more phosphopeptides than Q-TOF LC-MS/MS instrumentation (4308 versus 1485 identifications, respectively). In both cases, collision induced dissociation (CID) was used to fragment peptides. Interestingly, the two platforms produced complementary data as many of the low scoring phosphopeptide ions identified by LTQ-Orbitrap MS/MS gave rise to high score identifications by Q-TOF MS/MS analysis, and vice versa. In fact, approximately 450 phosphopeptides identified by the Q-TOF instrument were not identified by the LTQ-Orbitrap. Further data comparison revealed the extent of the problem: in one experiment, the estimated number of false negatives (1066) was close to the number of identified phosphopeptides (1485). This work demonstrates that by using standard procedures for phosphopeptide identification the number of false negatives can be even greater than the number of false positives. We propose using historical phosphoproteomic data and spectral matching algorithms in order to efficiently minimize false negative rates.