An Application of Serially Balanced Designs for the Study of Taste Samples with the aASTREE Electron

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An Application of Serially Balanced Designs for
the Study of Taste Samples with the a-ASTREE
Electronic Tongue
Stan Altan, Marc Francois, Sabine Inghelbrecht,
Areti Manola, Yan Shen Raritan, NJ, USA
Beerse, Belgium
Correspondence Email Yshen10_at_prdus.jnj.com
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Outline

• Introduction
• E-Tongue Experiment
• DOE- Serially Balanced Design
• Statistical Analysis
• Results and Discussion
• References

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Introduction

• Taste Perception is a biological process, where
  taste buds are stimulated to send messages
  through nerves to the brain.
• Five basic tastes sweet, sour, bitter, salty and
  unami (glutamate).
• The pharmaceutical industry is most concerned
  with bitterness, since a drugs bitter taste may
  prevent patients from completing a therapeutic
  course of treatment.

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Introduction

• Taste Testing Tools
• Human sensory testing.
• The drawbacks are
• Subjectivity of panel members.
• Time consuming, expensive.
• Potential toxicity of drugs.
• ?-Astree Electronic-Tongue (E-Tongue)
• Developed by French company AMOS during the past
  six years, started to emerge within the past
  three years in the pharmaceutical industry.
• Multi-sensor system for liquids.
• Relies on chemical sensors and pattern
  recognition.

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Introduction

• E-Tongue Mechanics
• An array of 7 electrochemical sensors and 1
  reference electrode.
• A liquid auto-sampler with a 16-position
  carrousel.
• An electronic unit for auto-sampler control.
• Software.

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Introduction

• How E-Tongue works
• Each sensor has a different organic coating,
  which reacts with the test compound and generates
  different potentials relative to the reference
  electrode (Ag/AgCl).
• The potentiometric difference between sensors
  produces the measurement output.
• Quantification is based on the combined response
  across sensors thought to be related to the test
  compounds chemical properties.

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Introduction

• Statistical Design Considerations
• Samples are presented in a serial order.
• Possibility of residual carryover effects of one
  sample to subsequent samples, even with
  intervening wash samples since the wash vessels
  can become increasingly more contaminated by
  repeated washings.
• Ignoring carryover effects could bias the results.

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E-Tongue Experiment

• Description of the Experiment
• Four Test Samples and 4 Standard Samples.
• Eight Washes (designated W1, W2,,W8).
• Each Sample must be followed by a Wash.
• Sensors designated AB, BB, ZZ, JE, DA, BA, CA.
• Objectives
• Estimate residual and main effects.
• Relate Test Samples to Standard Samples.

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E-Tongue Experiment

• 4 Aqueous Test Samples
• C0 Water alone,
• C1 Sodium Topiramate 0.5 mg/ml (low bitterness),
• C2 Sodium Topiramate 5 mg/ml (middle
  bitterness),
• C3 Sodium Topiramate 10 mg/ml (high bitterness),
  
• 4 Aqueous Standard Samples
• S1 0.01 mM HCl (known acidic taste),
• S2 0.01 mM NaCl (known salty taste),
• S3 0.01 mM Sodium-L-Glutamate (known unami
  taste),
• S4 1 mM Quinine Hydrochloride (known bitter
  taste).

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DOE-Serially Balanced Design

• Two types of serially balanced sequences,
  referred to as Type 1 and 2. Type 1 includes
  residual effect of the same treatment on itself,
  Type 2 does not.
• Can be derived from a Latin Square (Williams
  Design).
• Balanced for residual effect of minimal length.

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DOE-Serially Balanced Design

• Construction
• Start with a Latin Square with v2m treatments.
• Use the ith and (mi)th column (i0,1,,m-1)
  omitting the 0th row, start from (2i1)th row,
  forming a chain as indicated in the following
  diagram.
• Augment 0 to the beginning of the chain which
  gives a Type 2 sequence.
• Repeat each treatment once to obtain Type 1
  sequence.

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DOE-Serially Balanced Design

• Construction (Continued)
• If v is odd, v-12m is even and we construct a
  balanced sequence in symbols 0, 1,,v-2 as before
  and insert the symbol v-1 between 0 and 1, 1 and
  2, 2 and 3, v-2 and 0. Then augment this added
  sequence on the right by the symbols 1, 2,, v-2,
  0.

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DOE-Serially Balanced Design

• Example for v8
• Start with a Williams Latin Square with v8
  treatments

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DOE-Serially Balanced Design

• Example for v8
• Augment 0 to the beginning of the chain to obtain
  a Type 2 sequence
• 0 17263545362710 37465647302120
• 57675041323140 70615243425160
• Repeat each treatment once to obtain the Type 1
  sequence
• 0 1726354553627110 3746564733002120
• 5767750413223140 7066152434425160

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DOE for E-Tongue Experiment

• Use a Type 1 Serially Balanced Sequence to define
  ordering of Samples.
• Washes inserted into sequence of Samples
  according to the row-column designation of an 8x8
  Latin square.
• Final design consists of a chain of Samples and
  Washes of length 1281.
• Each Sample and Wash evaluated 8 times with C0
  evaluated 9 times to balance residual effects.

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Statistical Analysis

• Statistical Model Eestimates (by Sensor)
• Direct effects of samples and washes.
• 1st and 2nd order residual effects of samples.
• 1nd order residual effects of washes.
• Principal Components Analysis (PCA)
• Least squares means (lsm) from the model for each
  Sample and Wash calculated by sensor, forming a
  16?7 matrix.
• The matrix of lsms used to calculate a 7?7
  covariance matrix corresponding to the sensors in
  preparation for a principal component analysis.

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Statistical Analysis

• Statistical model used to estimate direct and
  residual effects

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Statistical Analysis

• Statistical Model (Continued)

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Figure 1 Pairwise Scatter Plot of 7 Sensors
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Figure 2 Serial Measurements of Samples by Sensor
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Results and Discussion

• Small or no residual effects were attributable to
  Washes on Samples,
• Residual effects of Samples on Washes were
  frequently found to be statistically significant,
  
• Second order residual effects were found for
  Sensors BB and BA, only with respect to Sample S1
  (0.01mM HCL),
• Direct Treatment effects compared to C0 for
  Samples indicated frequent differences for
  sensors CA, ZZ and BB, some difference for JE,
  and none for BA, DA and AB.

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RESULTS Least Squares Means by Sample and
Sensor are given in the following table along
with results of significance testing of Samples
(compared to C0) and Washes (compared to each
other).
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Results and Discussion

• PCA The first component explained 87 of the
  total variance and the second 12.

Figure3 Samples and Washes in relation to the
first two principal components
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Results and Discussion

• Washes were clustered together around the origin
  as expected.
• Control (C0) was very close to the washes since
  they were basically the same. All other samples
  were displaced from the washes.
• Standard Samples were distinguishable with
  respect to relative location but S4 (standard
  bitter taste) was in the opposite quadrant to C1,
  C2, and C3 (Topiramate).
• Bitterness as a sensory quality arising from
  different chemical compounds can fall in
  different locations on the 2-dimensional scale.
• PCA could not distinguish between C1 (low
  concentration) and C2 (middle concentration) but
  C1 and C2 were separated from C3 (high
  concentration).

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References

• S. Altan, A. Manola, R. Pandey, J. Troisis, D.
  Ragahavarao, A statistical design consideration
  in robotic systems, Drug Information Journal
  (2004), 3, 283-287.
• S. Altan, D. Ragahavarao (2005) Serially Balanced
  Designs for Two Sets of Treatments. Journal of
  Biopharmaceutical Statistics 15(2) 279-282
• T. Uchida, A. Tanigake, Y. Miyanaga, K.
  Matsuyama, M. Kunitomo, Y. Kobayashi, H.
  Ikezaki, A. Taniguchi, Evaluation of the
  bitterness of antibiotics using a taste sensor,
  Journal of Pharmacy and Pharmacology (2003), 55,
  1479-1485.
• L. Zhu, R.A. Seburg, K. Thompson, E. Tsai, S.
  Isz, Feasibility Study of an Electronic-Tongue
  for Potential Pharmaceutical Applications
• L. Zhu, R.A. Seburg, E. Tsai, S. Puech, J.
  Mifsud, (2003) Flavor Analysis in a
  Pharmaceutical Oral Solution Formulation Using an
  Electronic-nose. Journal of Pharmaceutical and
  Biomedical Analysis 34 453-461