• development, validation and comparison of three detection

    Development, validation and comparison of three detection

    3.3. Evaluation and comparison of three analysis methods. For the performance of the GC-MS, the instrument detection limit (IDL) was calculated on the basis of threefold of signal-to-noise ratio (S/N), IDLs of 9 volatile VMSs were detected by GC-MS ranged from 0.19 to 1.48 ng/mL.

  • development, validation and comparison of three detection

    Development, validation and comparison of three detection

    Development, validation and comparison of three detection methods for 9 volatile methylsiloxanes in food-contact silicone rubber products Author links open overlay panel Di Feng Xirong Zhang Wenjuan Wang Zhenzhen Li Xueli Cao

  • development, validation and comparison of three methods

    Development, validation and comparison of three methods

    The detection (LOD) and quantification (LOQ) limits of TNT and its degradation products determined with GC-MS/MS range from 0.3 ng mL −1 to 173.4 ng mL −1. 4-Nitrotoluene is characterized by the lowest LOQ and LOD, respectively, of 0.9 ng mL −1 and 0.3 ng mL −1. The analyte recoveries of all compounds ranged from 64.6% to 91.8%, with

  • development, validation and comparison of three detection

    Development, validation and comparison of three detection

    Development, validation and comparison of three detection methods for 9 volatile methylsiloxanes in food-contact silicone rubber products October 2018 Polymer Testing 73

  • development and validation of deep learning–based automatic

    Development and Validation of Deep Learning–based Automatic

    Regarding nodule detection performance comparison (DLAD vs test 1), DLAD exhibited a JAFROC FOM of 0.885, higher than all physicians and significantly higher than those of 15 of 18 physicians (P < .05; Table 3).

  • development and validation of deep learning-based automatic

    Development and Validation of Deep Learning-based Automatic

    Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs. Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, Goo JM, Aum J, Yim JJ, Park CM; Deep Learning-Based Automatic Detection Algorithm Development and Evaluation Group.

  • development and validation of a deep learning–based automatic

    Development and Validation of a Deep Learning–based Automatic

    Eui Jin Hwang, Sunggyun Park, Kwang-Nam Jin, Jung Im Kim, So Young Choi, Jong Hyuk Lee, Jin Mo Goo, Jaehong Aum, Jae-Joon Yim, Chang Min Park, Deep Learning-Based Automatic Detection Algorithm Development and Evaluation Group, Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs, Clinical Infectious Diseases

  • development and validation of a variant detection workflow

    Development and validation of a variant detection workflow

    Development and validation process The in-house -designed primer strategy presented a reduction of about 5× in the capture costs, when compared to the commercial available one. It was possible to cover the entire target region; however, there was a great coverage variability; while some amplicons presented 20× on average, others showed 1500

  • development and validation of a one-step real-time pcr assay

    Development and Validation of a One-Step Real-Time PCR Assay

    In this study, we present data on the development and validation of a real-time hydrolysis probe-based RT-PCR assay for the simultaneous detection of AI viruses belonging to subtypes H5, H7, and H9. Our results prove that the assay is highly specific and sensitive.

  • comparison of three selective media and validation of the

    Comparison of Three Selective Media and Validation of the

    Recipient(s) will receive an email with a link to 'Comparison of Three Selective Media and Validation of the VIDAS Campylobacter Assay for the Detection of Campylobacter jejuni in Ground Beef and Fresh-Cut Vegetables' and will not need an account to access the content.

  • development and validation of quantitative real-time pcr

    Development and Validation of Quantitative Real-Time PCR

    The validation results demonstrated that the method has appropriate specificity, sensitivity, accuracy, and precision according to ICH guidelines. The limit of detection and quantitation reached 3 fg/ul and 0.3 pg/reaction respectively, which satisfies the requirement of limit of residual DNA detection in biologics.

  • development and validation of an innovative method for the

    Development and Validation of an Innovative Method for the

    2.3.6. Detection and Quantitation Limits The limits of detection and quantification were based and calculated according to the standard deviation and interception curve slope. Three different curves were performed to obtain the data necessary to calculate. The values were calculated using the equations 1 and 2. Equation 1. LD = 3.3 x (SD/a