decoding the acronyms and finding insights · 2010-11-22 · microsoft powerpoint - training series...
TRANSCRIPT
![Page 1: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/1.jpg)
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Interpreting Your PSA ResultsDecoding the Acronyms and Finding Insights
Ian [email protected]/us/particle
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The Basics
Define Parameters
Choose Parameters
Interpret Results
Outline
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The Workflow
+ +
=
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The Problem
=
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The BasicsParticle Particle Distribution
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The BasicsParticle Size Particle Size Distribution
4 µm
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The Basics
Which is the most meaningful size?
differentsize definitions
differentresults
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The Basics
What sizes can be measured?
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The Basics
Laser DiffractionEquivalent Spherical Diameter
Dynamic Light ScatteringHydrodynamic Radius
Image AnalysisLengths, Widths, Equivalent Spherical
Acoustic SpectroscopyEquivalent Spherical Diameter
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The BasicsLaser DiffractionAssumes hard, spherical shape model
q% = amount of each sizeby volume
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The BasicsDynamic Light ScatteringAssumes hard, spherical shape model
Frequency % = amount of each sizeby volume
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The BasicsImage AnalysisMeasures particle projection no shape assumption
UndersizePassingQ3
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The BasicsAcoustic SpectroscopyAssumes hard, spherical shape model
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Quiz!
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Understand the dataA little goes a long way!
Know something about the particles in your sample
Particles have multiple dimensions, know which dimension your analyzer measures!
Conclusions
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Defining Parameters
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Terms, Terms, and more Terms
Particle SizeMonomodal: One PeakBimodal: Two PeaksMultimodal: Multiple Peaks
Monodisperse: All particles have same sizePolydisperse: Particles have many sizes
Volume diameter: Diameter of a sphere having the same volume as the particle
Surface diameter: Diameter of a sphere having the same surface as the particle
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Terms, Terms, and more Terms
Particle SizeFrequency% / q% / Amount of each size by volumep3 / Retained / Sph Vol%
Volume-based diameter Calculated from vol. distribution emphasizes coarse particles (larger volume)
Number-based diameter Calculated from number dist. (individual particles) emphasizes fine particles
Cumulative% on diameter % of distribution finer/coarser than specified size
Diameter on cumulative% Size at which a specified % of distribution is finer/coarser
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Terms, Terms, and more Terms
Particle ShapeAcicular: Needle-shaped, rigidAngular: Edgy, hard anglesFibrous: Thread-like, non-rigidGranular/Blocky: Irregular-shaped, low aspect-ratioSpherical: Regular-shaped, unity aspect ratio
Aspect ratio: Breadth / length OR Length / breadthSphericity: How spherical is the particle?Roundness: How round is the particle?
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Quiz!
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Size Terminology
The most common designation is micrometers ormicrons. When very small, in colloid region, measured in nanometers, with electron microscopes or by dynamic light scattering.
10-10 10-810-9 10-610-7 10-410-5 10-210-3 10-1 10-0
meternanometer
Angstrom(Å)
micrometer millimeterMicron or µm mnm mm
0.1µm 1.0µm 10µm 100µm
100 nm
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Visible Particles: Lint, Dust, width of the ridges of fingertips.
Human Hair
Proteins, Viruses, and Macro-Molecules
Emulsions and ColloidsSuspensions and fine powdersSuspensions and fine powdersDust and free flowing powders
Relative Size
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Quiz!
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Central Values
Mean Medianand Mode
Size
Freq
uenc
y
MeanWeighted AverageCenter of Gravity
Median50% Point
ModePeak of the distributionMost common value
![Page 25: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/25.jpg)
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Three spheres of diameters 1,2,3 units
What is the average size of these spheres?Average size = (1+2+3) ÷ 3 =2.00
This is called the D[1,0] - the number mean
12 3
What does “Mean” mean?
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None of the answersare wrong they have just been calculated using different techniques
X Dnl = =+ +
=[ , ] .1 01 2 3
32 00
X Dns = =+ +
=[ , ] .2 01 4 9
3216
X Dnv = =+ +
=[ , ] .3 01 8 27
32 293
X Dsv = =+ ++ +
=[ , ] .3 21 8 271 2 3
2 57
X Dvm = =+ ++ +
=[ , ] .4 31 16 811 8 27
2 72
Many possible Mean values
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D[4,3] which is often referred to as the Volume Mean Diameter [ VMD ]
D [4,D [4,3]3] ==∑∑
i i
i i
D nD n
4
3
Monitoring the D[4,3] value in your specification will emphasize the detection of large particles
Volume-based Mean diameter
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Mode
Median
Mean
D[4,3]
Fre q
u enc
y
Size
Remember: D[4,3] is sensitive to large particles
Central Values revisited
MeanWeighted AverageCenter of Gravity
Median50% Point
ModePeak of the distributionMost common value
![Page 29: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/29.jpg)
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D(v,0.9)D(v,0.1)
Size µmD(4,3) sensitive to large particles
D(3,2)
D(v,0.5)median
D(v,1.0)Never use the D100!
sensitive to small particles
10% of the particles liebelow this diameter
90% of the particles liebelow this diameter
half are larger than this diameterhalf are smaller than this diameter
Most Common Statistics
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Normal (Gaussian) Distribution Curveμ = distribution meanσ = standard deviationExp = base of natural logarithms
1σ 2σY = Exp [ - (x - μ)2
2σ2 ]
Standard Deviation
Mean
+1 STD DEV-1 STD DEV
+2 STD DEV-2 STD DEV
68.27%
95.45%
![Page 31: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/31.jpg)
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Polydispersity Index (PI, PDI)SpanGeometric Std. Dev.VarianceEtc…
Distribution Width
![Page 32: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/32.jpg)
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Quiz!
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Be familiar with the nomenclature
Many parameters can describe distributionD50, D10, D90 commonly used
Which Mean do you mean?
Conclusions
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Choosing Parameters
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Choosing good statistics
Statistics describing the distribution must…Tell us about our processMust be relevantMust be controlled wellBe reproducible!
Poor precision is the result of either a poor method or poor statistical choices
![Page 36: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/36.jpg)
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Reproducibility!Prepare, measure, empty, repeat
What would be good reproducibility?Look at the accepted standards
ISO 13320COV < 3% at Median (D50) COV = 100 * (StDev / Mean)COV < 5% at D10 and D90
USP <429>COV < 10% at Median (D50)COV < 15% at D10 and D90
Note: All limits double when D50 < 10 µmNote: Must acquire at least 3 measurements from unique samplings
The Basis for Reliable Data
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Unique, automatic feature in LA-950 software
Calculation Automation
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At a distance of a few standard deviations, non-instrumental errors can dominate
Distribution Extremes
![Page 39: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/39.jpg)
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95% of the distribution is within 2 standard deviations from the Mean99.7% of the distribution is within 3 standard deviations from the mean
2 and 3 Standard Deviations
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If we want the same level of reproducibility at the D99 value as the D50, we need to analyze similar amounts of material in the D99 histogram band
Reproducibility at the Extremes
![Page 41: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/41.jpg)
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Instead of specifying the D95, D99, D99.99, D100, DMax
Specify the % of material greater than a certain size
Better Method to Monitor Extremes
![Page 42: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/42.jpg)
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Quiz!
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Parameters should reflect product performanceBut don’t make your life more difficult than it needs to be!
Look to appropriate standardsISO 13320, USP <429> can provide guidance
Avoid monitoring the extremes whenever possibleBetter to use D(4,3) when you want spec. to be sensitive to presence of large particles
Conclusions
![Page 44: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/44.jpg)
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Advanced Result Interpretation
![Page 45: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/45.jpg)
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GeneralMultiple ModesMultiple peaks can be better described individually
D50 D90D10
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Laser DiffractionMultiple ScatteringWatch for finer “particles” appearing with increasing concentration
![Page 47: Decoding the Acronyms and Finding Insights · 2010-11-22 · Microsoft PowerPoint - Training Series - Interpreting PSA Results.ppt Author: itreviranus Created Date: 10/14/2010 10:28:12](https://reader033.vdocuments.site/reader033/viewer/2022050608/5faef8af66ef6c32a44ad116/html5/thumbnails/47.jpg)
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Multiple Scattering Example Zoom
0.00E+00
1.00E-01
2.00E-01
3.00E-01
4.00E-01
5.00E-01
6.00E-01
65 70 75
Detector Number (higher detector numbers for smaller particles)
Ligh
t Int
ensi
ty
1 Bottle2 Bottles3 Bottles4 Bottles5 Bottles6 Bottles7 Bottles8 Bottles9 Bottles10 Bottles
Multiple Scattering Example
0.00E+00
5.00E-01
1.00E+00
1.50E+00
2.00E+00
2.50E+00
45 50 55 60 65 70 75
Detector Number (higher detector numbers for smaller particles)
Ligh
t Int
ensi
ty
1 Bottle2 Bottles3 Bottles4 Bottles5 Bottles6 Bottles7 Bottles8 Bottles9 Bottles10 Bottles
Multiple Scattering
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1 bar
2 bar
3 bar
Laser DiffractionDispersing AgglomeratesWatch for no change in coarsest particles with changing energy
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High = 3 barMid = 2 barLow = 1 bar
Laser DiffractionBreaking ParticlesWatch for finer particles being created with increasing energy
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Dynamic Light ScatteringRestricted DiffusionUse bulk viscosity for concentrated sampleApparent size shifts larger with increasing concentrationPolydispersity (distribution width) remains a constant
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Dynamic Light Scattering
Dilute
Concentrated
Size
Inte
nsity
Multiple ScatteringIncident light scatters off of multiple particlesParticles appear smaller, distribution shifts finerDistribution broadens in comparison to dilute analysis
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Dynamic Light Scattering
Aggregate
Size
Inte
nsity
Aggregation EquilibriumCannot remove aggregates when created by equilibriumFiltration will only remove already-formed aggregatesImportant test for understanding formulation stability
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Electrostatic Effects
The electrostatic interaction of particlesOverlapping double layersgive larger size valuesSalt will suppress the double layer
Particles
Double Layer
Na+
Cl-Cl-
Na+
Na+
Cl-
Suppressed Double Layer
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Image AnalysisOverlapping ParticlesCheck images of coarsest particles to verify
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Image AnalysisNoise Detection as ParticlesCheck images of finest particles to verify
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Image AnalysisInsufficient Particle CountVary number of particles detected/analyzed
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Look at the distribution graphSee the forest AND the trees
Precision != AccuracyVary measurement and calculation parameters
Conclusions
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