INTRODUCTION: A study was designed to assess variability between different fluorescence spectroscopy devices. Measurements were made with all combinations of three devices, four probes, and three sets of standards trays. Additionally, we made three measurements on the same day over 2 days for the same combination of device, probe, and standards tray to assess reproducibility over a day and across days. MATERIALS AND METHODS: The devices consisted of light sources, fiber-optics, and cameras. We measured thirteen standards and present the data from the frosted cuvette, water, and rhodamine standards. A preliminary analysis was performed with the data that were wavelength calibrated and background subtracted; however, the system has not been corrected for systematic intensity variations caused by the devices. Two analyses were performed on the rhodamine, water, and frosted cuvette standards data. The first one is based on first clustering the measurements and then looking for association between the 5 factors (device, probe, standards tray, day, measurement number) using chi-squared tests on the cross-tabulation of cluster and factor level. This showed that only device and probe were significant. We then did an analysis of variance to assess the percent variance explained by each factor that was significant from the chi-squared analysis. RESULTS: The data were remarkably similar across the different combinations of factors. The analysis based on the clusters showed that sometimes devices alone, probes alone, but most often combinations of device and probe caused significant differences in measurements. The analysis showed that time of day, location of device, and standards trays do not vary significantly; whereas the devices and probes account for differences in measurement. We expected this type of significance using unprocessed data since the processing corrects for differences in devices. However, this analysis on raw data is useful to explore what combination of device and probe measurements should be targeted for further investigation. This experiment affirms that online quality control is necessary to obtain the best excitation-emission matrices from optical spectroscopy devices. CONCLUSION: The fact that the device and probe are the primary sources of variability indicates that proper correction for the transfer function of the individual devices should make the measurements essentially equivalent.
INTRODUCTION: A study was designed to assess variability between different fluorescence spectroscopy devices. Measurements were made with all combinations of three devices, four probes, and three sets of standards trays. Additionally, we made three measurements on the same day over 2 days for the same combination of device, probe, and standards tray to assess reproducibility over a day and across days. MATERIALS AND METHODS: The devices consisted of light sources, fiber-optics, and cameras. We measured thirteen standards and present the data from the frosted cuvette, water, and rhodamine standards. A preliminary analysis was performed with the data that were wavelength calibrated and background subtracted; however, the system has not been corrected for systematic intensity variations caused by the devices. Two analyses were performed on the rhodamine, water, and frosted cuvette standards data. The first one is based on first clustering the measurements and then looking for association between the 5 factors (device, probe, standards tray, day, measurement number) using chi-squared tests on the cross-tabulation of cluster and factor level. This showed that only device and probe were significant. We then did an analysis of variance to assess the percent variance explained by each factor that was significant from the chi-squared analysis. RESULTS: The data were remarkably similar across the different combinations of factors. The analysis based on the clusters showed that sometimes devices alone, probes alone, but most often combinations of device and probe caused significant differences in measurements. The analysis showed that time of day, location of device, and standards trays do not vary significantly; whereas the devices and probes account for differences in measurement. We expected this type of significance using unprocessed data since the processing corrects for differences in devices. However, this analysis on raw data is useful to explore what combination of device and probe measurements should be targeted for further investigation. This experiment affirms that online quality control is necessary to obtain the best excitation-emission matrices from optical spectroscopy devices. CONCLUSION: The fact that the device and probe are the primary sources of variability indicates that proper correction for the transfer function of the individual devices should make the measurements essentially equivalent.
Authors: Kevin R Coombes; W Edward Highsmith; Tammy A Krogmann; Keith A Baggerly; David N Stivers; Lynne V Abruzzo Journal: J Comput Biol Date: 2002 Impact factor: 1.479
Authors: N Ramanujam; M F Mitchell; A Mahadevan-Jansen; S L Thomsen; G Staerkel; A Malpica; T Wright; N Atkinson; R Richards-Kortum Journal: Photochem Photobiol Date: 1996-10 Impact factor: 3.421
Authors: T J Römer; M Fitzmaurice; R M Cothren; R Richards-Kortum; R Petras; M V Sivak; J R Kramer Journal: Am J Gastroenterol Date: 1995-01 Impact factor: 10.864
Authors: L Ramdas; K R Coombes; K Baggerly; L Abruzzo; W E Highsmith; T Krogmann; S R Hamilton; W Zhang Journal: Genome Biol Date: 2001-10-18 Impact factor: 13.583
Authors: Timon P H Buys; Scott B Cantor; Martial Guillaud; Karen Adler-Storthz; Dennis D Cox; Clement Okolo; Oyedunni Arulogon; Oladimeji Oladepo; Karen Basen-Engquist; Eileen Shinn; José-Miguel Yamal; J Robert Beck; Michael E Scheurer; Dirk van Niekerk; Anais Malpica; Jasenka Matisic; Gregg Staerkel; Edward Neely Atkinson; Luc Bidaut; Pierre Lane; J Lou Benedet; Dianne Miller; Tom Ehlen; Roderick Price; Isaac F Adewole; Calum MacAulay; Michele Follen Journal: Gend Med Date: 2011-09-22
Authors: Scott B Cantor; Jose-Miguel Yamal; Martial Guillaud; Dennis D Cox; E Neely Atkinson; John L Benedet; Dianne Miller; Thomas Ehlen; Jasenka Matisic; Dirk van Niekerk; Monique Bertrand; Andrea Milbourne; Helen Rhodes; Anais Malpica; Gregg Staerkel; Shahla Nader-Eftekhari; Karen Adler-Storthz; Michael E Scheurer; Karen Basen-Engquist; Eileen Shinn; Loyd A West; Anne-Therese Vlastos; Xia Tao; J Robert Beck; Calum Macaulay; Michele Follen Journal: Int J Cancer Date: 2010-11-09 Impact factor: 7.396
Authors: Elizabeth M Kanter; Elizabeth Vargis; Shovan Majumder; Matthew D Keller; Emily Woeste; Gautam G Rao; Anita Mahadevan-Jansen Journal: J Biophotonics Date: 2009-02 Impact factor: 3.207