Probability Of False Negatives Research Articles

Validation of a Method for Surveillance of Nanoparticles in Mussels Using Single-Particle Inductively Coupled Plasma-Mass Spectrometry.

Determining the concentration of nanoparticles (NPs) in marine organisms is important for evaluating their environmental impact and to assess potential food safety risks to human health. The current work aimed at developing an in-house method based on single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS) suitable for surveillance of NPs in mussels. A new low-cost and simple protease mixture was utilized for sample digestion, and novel open-source data processing was used, establishing detection limits on a statistical basis using false-positive and false-negative probabilities. The method was validated for 30 and 60 nm gold NPs spiked to mussels as a proxy for seafood. Recoveries were 76-77% for particle mass concentration and 94-101% for particle number concentration. Intermediate precision was 8-9% for particle mass concentration and 7-8% for particle number concentration. The detection limit for size was 18 nm, for concentration 1.7 ng/g, and 4.2 × 105 particles/g mussel tissue. The performance characteristics of the method were satisfactory compared with numeric Codex criteria. Further, the method was applied to titanium-, chromium- and copper-based particles in mussels. The method demonstrates a new practical and cost-effective sample treatment, and streamlined, transparent, and reproducible data treatment for the routine surveillance of NPs in mussels.

Construction and validation of a nomogram to predict left ventricular hypertrophy in low-risk patients with hypertension.

Electrocardiography (ECG) is an accessible diagnostic tool for screening patients with hypertensive left ventricular hypertrophy (LVH). However, its diagnostic sensitivity is low, with a high probability of false-negatives. Thus, this study aimed to establish a clinically useful nomogram to supplement the assessment of LVH in patients with hypertension and without ECG-LVH based on Cornell product criteria (low-risk hypertensive population). A cross-sectional dataset was used for model construction and divided into development (n=2906) and verification (n=1447) datasets. A multivariable logistic regression risk model and nomogram were developed after screening for risk factors. Of the 4353 low-risk hypertensive patients, 673 (15.4%) had LVH diagnosed by echocardiography (Echo-LVH). Eleven risk factors were identified: hypertension awareness, duration of hypertension, age, sex, high waist-hip ratio, education level, tea consumption, hypochloremia, and other ECG-LVH diagnostic criteria (including Sokolow-Lyon, Sokolow-Lyon products, and Peguero-Lo Presti). For the development and validation datasets, the areas under the curve were 0.724 (sensitivity=0.606) and 0.700 (sensitivity=0.663), respectively. After including blood pressure, the areas under the curve were 0.735 (sensitivity=0.734) and 0.716 (sensitivity=0.718), respectively. This novel nomogram had a good predictive ability and may be used to assess the Echo-LVH risk in patients with hypertension and without ECG-LVH based on Cornell product criteria.

A Historical Survey of Corrective and Preventive Maintenance Models with Imperfect Inspections: Cases of Constant and Non-Constant Probabilities of Decision Making

Maintenance strategies play a crucial role in ensuring the reliability and performance of complex systems. Imperfect inspections, characterized by the probabilities of false positives and false negatives, significantly impact the effectiveness of maintenance decisions. This survey explores maintenance models under imperfect inspections, characterized by constant and non-constant probabilities of false positives and false negatives. This study investigates various maintenance approaches, such as preventive and corrective maintenance, and evaluates their performance, considering the uncertainties introduced by imperfect inspections. By analyzing the existing literature and research findings, this survey provides valuable insights into the challenges and opportunities associated with maintenance decision making in the presence of inspection imperfections. The comparison between maintenance models with constant and non-constant probabilities of false positives and false negatives sheds light on the dynamic nature of these models, enabling a deeper understanding of their real-world applicability and effectiveness. This comprehensive overview is a valuable resource for researchers, practitioners, and decision makers involved in maintenance planning and optimization in diverse industrial sectors.

SERS-Temperature Dual-Mode T-type Lateral Flow Strip for Accurate Detection of Free and Total Prostate-Specific Antigens in Blood.

Accurate point-of-care (POC) analysis of cancer markers is the essence in the comprehensive early screening and treatment of cancer. Dual-mode synchronous detection is one of the effective approaches to reduce the probability of false negatives or false positives. As a result, this can greatly improve the accuracy of diagnosis. In this work, a surface-enhanced Raman scattering (SERS)-temperature dual-mode T-type lateral flow strip was fabricated to direct and simultaneous POC detection of total and free prostate-specific antigens (t-PSA and f-PSA) in blood. With the advantage of high stability of T-type lateral flow strip and simultaneous acquirement of assay results for t-PSA and f:t PSA ratio, the proposed method has high accuracy in the diagnosis of prostate cancer, especially in the diagnostic gray zone between 4.0 and 10.0 ng/mL. The SERS-temperature dual-signal has a good linear correlation with either f-PSA or t-PSA. To evaluate the clinical diagnostic performance of the proposed method, spiked human serum samples and the whole blood sample were analyzed. The assay results showed good recovery, and compared with traditional electrochemiluminescence immunoassay (ECLIA) method (t-PSA: 43.151; f/t ratio: 0.08), the results obtained by the proposed method were similar (t-PSA: 40.15 (SERS), 36.21 (temperature); f/t ratio: 0.08 (SERS), 0.08 (temperature), but the detection time (15 min) and cost ($0.05) had been greatly reduced. Therefore, the proposed SERS-temperature synchronous dual-mode T-type lateral flow strip has a strong application potential in the field of accurate large-scale diagnostics of prostate cancer on-site by simultaneous POC detection of t-PSA and f-PSA in blood.

Host factors associated with false negative results in an interferon‐γ release assay in adults with active tuberculosis

ObjectiveTo identify host factors associated with false-negative results of interferon-γ release tests in adults with active tuberculosis. MethodsThe clinical data of 943 patients with active tuberculosis diagnosed by acid-fast smear staining, Mycobacterium tuberculosis culture, Mycobacterium tuberculosis PCR and pathological examination at West China Hospital of Sichuan University were retrospectively analysed. According to the results of the interferon γ release test (IGRA), the patients were divided into the IGRA− group and IGRA+ group. Logistic regression was used to analyze the sociodemographic data and clinical characteristics of participants in the IGRA− group and IGRA+ group. ResultsAmong 943 patients with active tuberculosis, 174 (18.5 %) were IGRA negative (false negative), and 769 (81.5 %) were IGRA positive. Multivariate logistic regression analysis identified the following characteristics independently associated with IGRA negativity: age (OR: 1.02; 95 % CI: 1.01 1.03; p = 0.006), anti-tuberculosis treatment >1 month (OR: 1.68; 95 % CI: 1.12 2.52; p = 0.013), HIV infection (OR: 9.48; 95 % CI: 3.23 27.85; p = 0.000), combined with connective tissue diseases (OR: 2.78; 95 % CI: 1.30 5.94; p = 0.008) and low hemoglobin (OR: 0.99; 95 % CI: 0.98 1.00; p = 0.044) was associated with an increased false-negative probability of IGRA. ConclusionAge, anti-tuberculosis therapy >1 month, coinfection with HIV, coassociated connective tissue disease and decreased hemoglobin were identified as risk factors for false-negative results of IGRA. Our results suggest a careful interpretation of IGRA in adults with these characteristics.

Non Fungible Gratings as Physical Unclonable Functions

Physical Unclonable Functions (PUFs) are physical devices exploiting intrinsic randomness through the fabrication process that can be used for authentication and secure key generation. In this article, we describe a novel weak PUF architecture based on the inscription of random gratings in an optical fiber, which can be easily interrogated by a commercial instrument due to the enhanced backscatter. With PUF lengths of only 40 mm, we show that the false positive and false negative probabilities can reach below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-27</sup> . We also describe a temperature and strain compensation algorithm to mitigate environmental fluctuations. Furthermore, we describe an alternative technique which increases the number of bits used for digitizing the PUF's signature, thus significantly decreasing the false identification probabilities by almost two orders of magnitude.

Novel Clinical Tool to Estimate Risk of False-Negative KRAS Mutations in Circulating Tumor DNA Testing.

In metastatic colorectal cancer, the detection of RAS mutations by circulating tumor DNA (ctDNA) has emerged as a valid and noninvasive alternative approach to determining RAS status. However, some RAS mutations may be missed, that is, false negatives can occur, possibly compromising important treatment decisions. We propose a statistical model to assess the probability of false negatives when performing ctDNA testing for RAS. Cohorts of 172 subjects with tissue and multipanel ctDNA testing from MD Anderson Cancer Center and 146 subjects from Massachusetts General Hospital were collected. We developed a Bayesian model that uses observed frequencies of reference mutations (the maximum of APC and TP53) to provide information about the probability of KRAS false negatives. The model was alternatively trained on one cohort and tested on the other. All data were collected on Guardant assays. The model suggests that negative KRAS findings are believable when the maximum of APC and TP53 frequencies is at least 8% (corresponding posterior probability of false negative <5%). Validation studies demonstrated the ability of our tool to discriminate between false-negative and true-negative subjects. Simulations further confirmed the utility of the proposed approach. We suggest clinicians use the tool to more precisely quantify KRAS false-negative ctDNA results when at least one of the reference mutations (APC, TP53) is observed; usage may be especially important for subjects with a maximum reference frequency of <8%. Extension of the methodology to predict false negatives of other genes is possible. Additional reference genes can also be considered. Use of personal training data sets is supported. An open-source R Shiny application is available for public use.

A Q-Learning-Based Two-Layer Cooperative Intrusion Detection for Internet of Drones System

The integration of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) has opened up new possibilities in various industries. However, with the increasing number of Internet of Drones (IoD) networks, the risk of network attacks is also rising, making it increasingly difficult to identify malicious attacks on IoD systems. To improve the accuracy of intrusion detection for IoD and reduce the probability of false positives and false negatives, this paper proposes a Q-learning-based two-layer cooperative intrusion detection algorithm (Q-TCID). Specifically, Q-TCID employs an intelligent dynamic voting algorithm that optimizes multi-node collaborative intrusion detection strategies at the host level, effectively reducing the probability of false positives and false negatives in intrusion detection. Additionally, to further reduce energy consumption, an intelligent auditing algorithm is proposed to carry out system-level auditing of the host-level detections. Both algorithms employ Q-learning optimization strategies and interact with the external environment in their respective Markov decision processes, leading to close-to-optimal intrusion detection strategies. Simulation results demonstrate that the proposed Q-TCID algorithm optimizes the defense strategies of the IoD system, effectively prolongs the mean time to failure (MTTF) of the system, and significantly reduces the energy consumption of intrusion detection.

False-negative probability in the SEM/EDS automated discovery of iGSR particles: A Bayesian approach.

The automated search software integrated with a scanning electron microscope (SEM/EDS) has been the standard tool for detecting inorganic gunshot residues (iGSR) for several decades. The detection of these particles depends on various factors such as collection, preservation, contamination with organic matter, and the method for sample analysis. This article focuses on the influence of equipment resolution setup on the backscattered electron images of the sample. The pixel size of these images plays a crucial role in determining the detectability of iGSR particles, especially those with sizes close to the pixel size. In this study, we calculated the probability of missing all characteristic iGSR particles in a sample using an SEM/EDS automated search and how it depends on the image pixel resolution setup. We developed and validated an iGSR particle detection model that links particle size with equipment registers and applied it to 320 samples analyzed by a forensic science laboratory. Our results show that the probability of missing all characteristic iGSR particles due to their size is below 5% for pixel sizes below 0.32 μm2 . These findings indicate that pixel sizes as large as twice the one commonly used in laboratory casework, that is, 0.16 μm2 , are effective for initial sample scanning, yielding good detection rates of characteristic particles that could exponentially reduce laboratory workload.

P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network.

Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.

A three-sequence dynamic contrast enhanced abbreviated MRI protocol to evaluate response to breast cancer neoadjuvant chemotherapy

PurposeTo develop an ABP-MRI to evaluate response to NAC for invasive breast carcinoma. Study typeA single-center, cross-sectional study. SubjectsA consecutive series of 210 women with invasive breast carcinoma who underwent breast MRI after NAC between 2016 and 2020. Field strength/sequence1.5 T / Dynamic contrast-enhanced. AssessmentMRI scans were independently reevaluated, with access to dynamic contrast-enhanced without contrast and to the first, second, and third post-contrast time (ABP-MRI 1–3). Statistical testsThe diagnostic performance of the ABP-MRIs and the Full protocol (FP-MRI) were analyzed. The Wilcoxon non-parametric test (p-value <0.050) was used to compare the capability in measuring the most extensive residual lesion. ResultsThe median age was 47 (24–80) years. ABP-MRI 1 showed higher specificity (84.6%; 77/91) but a higher probability of false-negatives (16.8%) and lower sensitivity (83.2%; 99/119) than ABP-MRI 2,3 and the FP-MRI, which were identical in specificity (81.3%; 74/91), probability of false-negatives (8.4%), and sensitivity (91.6%; 109/119). ABP-MRI 2 showed a mean underestimation of only 0.03 cm in the measurement of the longest axis of the residual lesion (p = 0.008) with an average reduction in the acquisition time of 75%, compared with the FP-MRI. ConclusionABP-MRI 2 showed diagnostic performance equivalent to the FP-MRI with a 75% reduction in the acquisition time.

AN ALGORITHM FOR DETECTING PRECIPITATION IN COMPUTER PROCESSING OF VIDEO IMAGES

The importance of detecting and reducing the visibility of precipitation in video images obtained by fixed cameras is shown. A statistical analysis of the geometric (area, shape factor, and orientation deviation from the frame average), and color–brightness (intensity and color saturation) characteristics of rain and snow particles is performed in order to substantiate decision rules for detecting pixels corresponding to precipitation particles. This analysis consists in obtaining distributions of the particle parameters and approximating them by known distribution laws using the family of Pearson’s curves, the Kolmogorov criterion, and the Nelder–Mead simplex algorithm. An algorithm for detecting raindrops and snowflakes in video sequences is developed, which is supposed to be used as part of an algorithm for reducing the visibility of precipitation. The proposed approach is presented in the form of a multistage classification of frame pixels into zones with moving objects and regions of a stationary background distorted and undistorted by precipitation particles in accumulated frames. Depending on the region to which the processed pixel belongs, the final decision to assign it to the class of precipitation is made using the proposed decision rules or the developed thresholding procedure with automatic determination of local threshold values. The proposed algorithm is experimentally investigated and, using a two-criteria approach, the optimal values for the number of accumulated frames for the correct operation of the algorithm are determined—100 frames for video images with rain and 140 frames for video with snow. The gain of the developed approach in comparison with the known estimates of the probabilities of false positives and false negatives is up to 1.7% and 9.1%, respectively.

Optimal Decision Rules for the Discursive Dilemma

We consider the classical problem in truth-tracking judgment aggregation of a conjunctive agenda with two premisses and one conclusion. We study this problem from the point of view of finding the best decision rule according to a quantitative criterion, under very mild restrictions on the set of admissible rules. The members of the deciding committee are assumed to have a certain probability to assess correctly the truth or falsity of the premisses, and the best rule is the one that minimises a combination of the probabilities of false positives and false negatives on the conclusion.

SARS-CoV-2 Detection Using Colorimetric Plasmonic Sensors: A Proof-of-Concept Computational Study.

Traditional molecular techniques for SARS-CoV-2 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of SARS-CoV-2 detection using plasmonic gold nanoparticles. The resonance wavelength of a SARS-CoV-2 virus was recently estimated to be in the near-infrared region. By engineering gold nanospheres to specifically bind with the outer surface of the SARS-CoV-2 virus, the resonance frequency can be shifted to the visible range (380 nm-700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a specific coverage percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. Our observation also suggests that this technique is unaffected by the number of protein spikes present on the virus outer surface, hence can pave a potential path for a label-free COVID-19 diagnostic tool independent of the number of protein spikes.

Evaluation of MRI Signal Changes of the Distal Biceps Tendon in Asymptomatic Patients

Magnetic resonance imaging (MRI) is used widely for complete ruptures of the distal biceps tendon. The validity of this investigation for bicipital bursitis and tendinosis is unknown. The purpose of present study was to assess the prevalence of incidental (asymptomatic) signal changes in the distal biceps tendon in patients who underwent MRI including the elbow. Our null hypothesis was that signal changes of the distal biceps tendon do not occur in asymptomatic patients. This would empower MRI as a diagnostic tool for bicipital bursitis and tendinosis as well as complete and partial distal biceps tendon ruptures. We evaluated 1,191 elbow MRI scans including the distal biceps tendon insertion. The prevalence of incidental findings was calculated and sensitivity, specificity, positive predictive value, negative predictive value, false positive probability, and false negative probability were calculated. Signal changes of the distal biceps tendon or bursitis were identified in 8 of 1,191 asymptomatic patients (prevalence 0.6%). The sensitivity of MRI for distal biceps pathology was 97% (95% confidence interval [CI], 93%-99%), specificity 99% (95% CI, 98%-99%), positive predictive value 94% (95% CI, 89%-97%), negative predictive value 99% (95% CI, 99%-99%), false positive probability 6% (95% CI, 3%-10%), and false negative probability 0.3% (95% CI, 0.1%-0.9%). There was no correlation between explanatory variables, including age, sex, race, occupation, and inflammatory disease and incidental distal biceps tendon signal changes. The prevalence of distal biceps tendon signal changes on MRI in asymptomatic patients is very low. The negative predictive value of 99% shows that patients without signal changes on MRI may be assumed to have no distal biceps tendon pathology. MRI investigation of distal biceps tendon is a valuable tool in the diagnosis of tendinosis and bicipital bursitis.

Basis risk in the pasture, rangeland, and forage insurance program: Evidence from California

AbstractAgricultural producers who are reliant upon rangelands and pasturelands are some of the most vulnerable to weather‐related risk given their dependence on climate‐sensitive resources. Index‐based insurance products, like the Pasture, Rangeland, and Forage Insurance Program, are considered to be an adaptation strategy for mitigating climatic risks. Given that indices are imperfect predictors of losses, residual (basis) risk persists. This study quantifies basis risk and assesses insurance contract quality for nearly 63 million acres of rangelands in California. Basis risk can be summarized by considering false negative probabilities—the probability that an insured producer suffers a loss without receiving an indemnity payment. On California rangelands the false negative probabilities associated with one (both) of insured time intervals failing to indemnify a loss are 31% to 46% (14%–25%). When indemnities were paid, in 36% of the cases the payments are not sufficient to compensate for forage‐related losses; the average shortfall in indemnity‐related compensation ranged from $1.74/acre to $2.73/acre depending upon the location and underlying value of forage.

Métodos diagnósticos para lesões de cárie com sulcos pigmentados em superfície oclusal

ABSTRACT Objective: The aim of this study was to compare methods for in vitro diagnosis of pigmented pits and fissures on the occlusal surface. Methods: Forty-one human molars and premolars extracted with pigmentation in pits and fissures were used. The teeth were evaluated with artificial illumination for classification according to ICDAS, followed by fluorescent laser evaluation. For the radiographic evaluation, the teeth were positioned to obtain interproximal images, adopting the criterion of Rodrigues et al. 2008 for diagnosis of lesions. For histological evaluation, the teeth were sectioned in the mesiodistal direction in the occlusal sulcus region and evaluated in a 20-fold stereoscopic magnifying glass, using the criterion of Ekstrand et al. (1997) for evaluation. The methods were compared with the histological evaluation, obtaining values of accuracy, sensitivity and specificity regarding the response variables “presence / absence” of enamel caries. Results: When comparing visual inspection with histological analysis, 65.85% accuracy, 40% sensitivity and 90.48% specificity were observed with false positive and negative probability of 20% and 38.71%. When comparing the fluorescent laser with the histological analysis, the accuracy was 58.54%, sensitivity 70% and specificity 47.62%. Comparing the radiographic analysis with the histological analysis, it was obtained 53.66% accuracy, sensitivity of 42.11% and specificity of 73.68%. Conclusion: The ROC curve and the Yuden index showed that visual inspection presented the best combination of sensitivity and specificity than fluorescent laser and radiographic analysis for diagnosis, which represented the best method for detecting inactive carious lesions in enamel.

D-ARP: An Efficient Scheme to Detect and Prevent ARP Spoofing

Nowadays, cyber-attack is a severe criminal violation, and it is one of the most active fields of research. Man-in-the-middle attack (MITM) is a type of cyber-attack in which an unauthorized third party secretly accesses the communication between two hosts in the same network to read=modify the transferred data between them. ARP spoofing-based MITM attack exploits ARP protocol weakness where the attacker associates its MAC address with the IP address of an intended legitimate host. Although there are many defense approaches for ARP spoofing based-MITM attacks, these methods are uncompleted or have a performance overhead since they modify the original ARP protocol. Also, some of these approaches depend on the centralized server which leads to a single point of failure. This paper presents a detection scheme for ARP spoofing-based MITM attack called D-ARP which is compatible with the original ARP protocol. The main idea of D-ARP is to send an ARP packet signed with a key in parallel with the original ARP packets to make a correlation between requests and replies. Each host records the signed ARP packets whether it is a request or a reply in a log file. Based on this correlation, D-ARP matches the injected key to detect ARP spoofing if there is a duplicate or conflict in the MAC address. For more reliability, D-ARP uses the DHCP server and the Nmap feature to detect the MAC addresses of MITM attackers. Moreover, this scheme also offers a module for Admin to create a trusted list of hosts. The experimental results show that D-ARP is very effective to detect and prevent ARP spoofing with zero false positives and zero false negative probabilities without any modifications in the original ARP protocol.

Evaluation of organic and inorganic gunshot residues in various populations using LC-MS/MS

This work investigated the prevalence of organic and inorganic gunshot residue within two main subpopulations, 1) non-shooters, including groups with low- and high-risk of potentially containing GSR-like residues, and 2) individuals involved in a firing event (shooters, bystanders, and shooters performing post-shooting activities). The study analyzed over 400 samples via a liquid chromatography-mass spectrometry (LC-MS/MS) methodology with complexing agents. Exploratory statistical tools and machine learning algorithms (neural networks, NN) were used to evaluate the resulting mass spectral and quantitative data. This study observed lower occurrences of OGSR compounds in the non-shooter populations compared to IGSR analytes. The presence of GSR on authentic shooters versus other potential sources of false positives, such as bystanders and professions including police officers, agricultural workers, and mechanics, were further assessed by utilizing machine learning algorithms trained with the observed OGSR/IGSR traces. The probability of false negatives was also estimated with groups who performed regular activities after firing. Additionally, the low-risk background set allowed documentation of GSR occurrence in the general population. The probabilistic outputs of the neural network models were utilized to calculate likelihood ratios (LR) to measure the weight of the evidence. Using both the IGSR and OGSR profiles, the NN model’s accuracy ranged from 90 to 99%, depending on the subpopulation complexity. The log-LR histograms and Tippet plots show the method can discriminate between each sub-population and low rates of misleading evidence, suggesting that the proposed approach can be effectively used for a probabilistic interpretation of GSR evidence.

Factors affecting DNA barcoding classification accuracy for piscine prey: An experimental assessment with invasive lionfish

The use of DNA barcoding in fish diet studies is becoming more widespread, but the effect of prey digestion on barcoding accuracy has been poorly studied. We conducted a series of controlled feeding experiments, with red lionfish (Pterois volitans) as predators and Gulf killifish (Fundulus grandis) as prey, designed to test the effect of digestion time (12–42 h), prey item (sample) preservation (inside versus outside stomach), and sample preparation (washed versus unwashed) on DNA barcoding classification accuracy. Competitive interactions occurred between lionfish and killifish DNA during PCR amplification when universal COI primers were utilized; thus, primers were designed that matched the same priming site in both killifish and lionfish DNA. The proportion of amplified killifish DNA relative to lionfish DNA per sample was estimated using multiplex quantitative PCR (qPCR) and shown to decrease with longer digestion times, especially when samples were retained within lionfish stomachs for preservation. The effects of digestion and preservation method on amplifiable DNA resulted in significant effects on barcoding accuracy, resulting in erroneous identification of the predator (i.e., self-DNA) for nearly 25% of samples. Removing prey from predator stomachs prior to preservation significantly increased the proportion of amplified killifish DNA from samples, and enhanced barcoding accuracy. Overall, study results have important implications for the probability of false negatives for prey items, as well as false positives for self-DNA, when utilizing barcoding to characterize piscine diet. Results also highlight the need to conduct more method validation research given the increasing prevalence of DNA barcoding as a means to identify specific prey items in fish diet studies.