TSI 9510 Biotrak Real-Time Viable Particle Counter User Guide
- June 6, 2024
- tsi
Table of Contents
- Introduction—Improved Viable Particle Discrimination for Pharma
- Background—Evolving Particle Detection Algorithms
- Scope—Changes Associated with Updating the Algorithm
- Methods—Comparing Algorithms
- Results—Proof is in the Data
- Summary & Conclusions—TSI Delivers Trusted Detection
- Read User Manual Online (PDF format)
- Download This Manual (PDF format)
IMPROVED VIABLE
PARTICLE DISCRIMINATION
TSI BIOTRAK REAL-TIME VIABLE
PARTICLE COUNTER
MODEL 9510
APPLICATION NOTE CC-129
(9/16/2021) Rev A (US)
Introduction—Improved Viable Particle Discrimination for Pharma
This application note describes the performance of a new algorithm developed
for use by the BioTrak ® Real-Time Viable Particle Counter to determine the
viability of a particle-based on its measured optical properties. The new
algorithm will be compared relative to the previously validated high
sensitivity algorithm—specifically describing the impact of the new algorithm
on viable particle discrimination with respect to test aerosols of
microorganisms and interference (non-viable particulates).
The new algorithm is a conservative improvement to the previously validated
version. This means that any particle that was considered viable by the
original high-sensitivity algorithm is also considered viable by the new
algorithm.
The new algorithm was developed chiefly to improve the detection capability of molds. Molds were not readily aerosolized during the development of the original algorithm and, therefore, it was not specifically trained to detect those optical signatures. Improvements made in the detection of molds are clearly demonstrated in the results section.
Background—Evolving Particle Detection Algorithms
During the initial product development of the BioTrak Real-Time Viable
Particle Counter, two viable particle detection algorithms, labeled as high-
sensitivity and normal, were created. The normal sensitivity algorithm was
developed with the intent to balance false positive and false negative counts
to produce an accurate estimate in samples with relatively high counts. The
high-sensitivity algorithm was more conservative in that it prioritized the
decreasing likelihood of false-negative counts at the risk of slightly
increasing the likelihood of false-positive counts. Given the desire to
optimize the detection of as many viable particles as possible in Grade A
areas, the pharmaceutical industry most widely adopted the use of the high-
sensitivity setting. For this reason, the high-sensitivity algorithm was
utilized for validation testing conducted in 2013.
The algorithms were empirically derived from the data generated by the optical
sensors in the viability chamber of the instrument while sampling aerosolized
microorganisms and interferents. Aerosolization of microorganisms during
testing was achieved using an Ink Jet Aerosol Generator (IJAG). Some
microorganisms proved to be sensitive to this method and retained little to no
ability upon dissemination. Limited data was available for algorithm
development and some validation tests, most notably those related to detecting
mold spores.
To obtain a better understanding of the BioTrak Real-Time Viable Particle
Counter’s ability to detect viable particles composed of these microorganisms,
different aerosolization methods were explored. A method that closely emulates
the natural dispersal of mold spores proved to be effective. It provided an
opportunity to reliably obtain optical data. This data indicated that these
particles did have consistent optical signatures, however, they were largely
falling outside the algorithms. The high-sensitivity algorithm was expanded to
optimize the detection of these microorganisms without significantly affecting
the exclusion of interferent particles. The available historical data of other
microorganisms was also assessed with additional modifications being made to
the algorithm to further optimize detection.
The result of this work is an updated viability determination algorithm that
demonstrates improved detection of a broader array of microorganisms. Due to
the historical prevalence in the use of the high-sensitivity algorithm and the
improved accuracy of the new algorithm, the new algorithm will replace both
the high-sensitivity and normal algorithm options.
Scope—Changes Associated with Updating the Algorithm
This change to the BioTrak Real-Time Viable Particle Counter alters the viable
discrimination algorithm, impacting the viable particle counts only. It does
not impact the total particle counts in any way.
The new algorithm is a change to the firmware code only—requiring no changes
to the instrument hardware. It utilizes the same exact raw optical data as the
originally validated algorithm.
The new algorithm does not affect instrument communication with external
software platforms.
Methods—Comparing Algorithms
The data presented herein is only intended to assess the relative detection
capability of one algorithm versus another. The validation of the instrument’s
bio-detection capabilities compared to traditional methods—such as active air
sampling—is outside the scope of this document. This validation work has been
performed as per applicable guidance, USP <1223>, EP 5.1.6, and PDA TR33, and
is detailed elsewhere in separate Validation Plan and Report documents.
For each particle that enters the viable detection optics of the BioTrak Real-
Time Viable Particle Counter, three parameters are measured: 1) scattered
light intensity, 2) fluorescent light intensity at relatively low wavelengths,
and 3) fluorescent light intensity at relatively high wavelengths. During
normal operation, these parameters are assessed in real-time by an algorithm
that counts each particle as either a viable particle or a non-viable
particle.
During normal operation, only the result of the algorithm is reported, and not
the raw optical data. However, TSI has the ability to use a proprietary
instrument mode to obtain the raw measurements from the optical sensors for
each particle. Since it is this raw data that is entered into an algorithm,
once collected, it can be entered into other proposed algorithms to compare
the effectiveness (see Figure 1).
This provides a powerful tool for developing an improved algorithm and to
demonstrate its relative detection capabilities in comparison to the
originally validated high-sensitivity algorithm.
Figure 1. Test Process-flow Schematic. Left to right: 1) sample a test aerosol, 2) record optical parameters for each particle, 3) assess the optical data with different algorithms.
Results—Proof is in the Data
Microorganisms
Several bioaerosols were used to assess the new, improved algorithm. During
these tests, four replicate samples were taken and the optical data recorded.
The optical data was evaluated using the original high-sensitivity algorithm
and the newly improved algorithm. A comparison of the results obtained can be
seen in Figures 2–4. Since the new algorithm is an expansion of the original
high-sensitivity algorithm, a higher count was obtained for all replicates of
each microorganism when using the new
algorithm.
Figure 2. Comparison of the high-sensitivity algorithm to the new, improved
algorithm for the detection of bacterial endospores.
Figure 3. Comparison of
the high-sensitivity algorithm to the new, improved algorithm for the
detection of vegetative bacteria.
Figure 4. Comparison of the high-sensitivity algorithm to the new, improved
algorithm for the detection of fungi (yeast and mold spores).
Non-Viable Particles (i.e., interferents)
Several aerosols of possible interfering non-viable particles were generated
to assess the new, improved algorithm. Since most of the materials tested are
manufactured to produce low numbers of particles, vigorous manual
manipulations were performed in an effort to generate as many particles as
possible during testing. This was performed inside a HEPA filtered cabinet
with the instrument sampling from within the cabinet directly below the
manipulations. The optical data was evaluated using the original high-
sensitivity algorithm and the new, improved algorithm.
Figure 5. Comparison of the high-sensitivity algorithm to the new, improved algorithm for the detection of interferents.
Zero-Counting
No changes to the zero-count specification are associated with the new
algorithm. Since no changes are made to the optical sensors, an algorithm
change should not result in any spurious counts not related to a particle
(i.e. instrument noise). When tested with a filter attached to the inlet, no
spurious viable counts were detected.
Summary & Conclusions—TSI Delivers Trusted Detection
The new algorithm significantly improves the detection of microorganisms, most
markedly molds. Since the changes were conservative in nature, some non-viable
particles were detected as viable at slightly higher rates in comparison to
the high sensitivity algorithm. However, the overall interferent detection
rate with respect to the total number of particles generated was still less
than7% in all cases. This continues to represent a very small risk for false-
positive counts given that these materials generate particles at a much
smaller rate during routine use than produced during this testing.
It is clear that the new algorithm does indeed impact the discrimination and
counting of viable particles. Therefore, validation tests of the original
high-sensitivity algorithm would not implicitly transfer to the new algorithm.
To address this, TSI performed a primary validation of the BioTrak Real-Time
Viable Particle Counter running the new algorithm per industry guidance.
Users that have performed testing should also consider the impact of the new
algorithm on any conclusions drawn from that testing and determine what
additional testing or retesting may be applicable for their intended use.
TSI, TSI logo, and BioTrak are registered trademarks of TSI Incorporated in the United States and may be protected under other country’s trademark registrations.
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CC-129 Rev. A (9/16/2021) US
©2021 TSI Incorporated
Printed in U.S.A.
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