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GiuseppeDM
Level I

Multivariate Analysis of Infrared Spectra Using Wavelet Model in FDE (2023-EU-30MP-1261)

Giuseppe De Martino, Scientist, Topsoe
Christopher Gotwalt, Multivariate analysis of Infrared Spectra using wavelet model in FDE, JMP

 

In catalyst development, tests to measure performances can be extremely time-consuming and expensive. In this study, we have explored the possibility of using faster and less expensive characterization data to validate mathematical models linking production parameters and performances. Specifically, we first modeled the production parameters to the performance data available in our dataset, using generalized regression, and from this model, we predicted a set of optimal production parameters. We then analyzed the Infrared Spectroscopy (IR) data using the Wavelet model in Functional Data Explorer plugging the production parameters as additional parameters. Thanks to this addition we were finally able to generate a synthetic spectrum for an optimal catalyst. The generated spectra combined with the predicted production parameters can be used by the scientist to more quickly understand the underlying mechanisms driving performances. Finally, a new catalyst material developed using the predicted parameters can be analyzed using IR and the synthetic spectra can be used to validate the model.

 

 

Hello,  I'm  Chris   Gotwalt with  JMP,  and  my  co- presenter,  Giuseppe  De  Martino  from  Topsoe ,  and  I  are  giving  a  presentation  that  tells  the  story  of  a  data  analysis  project  that  showcases  the  new  wavelet  analysis  in  the  Functional  Data  Explorer,  one  of  the  most  exciting  new  capabilities  in  JMP  17.

The  case  study  begins  with  a  product  formulation  problem  where  Tops oe  wanted  to  design  a  catalyst  that  optimizes  two  responses,  but  the  responses  are  in  conflict  with  one  another  in  that  improving  one  response  often  comes  at  the  expense  of  the  other.

A  candidate  for  the  optimal  tradeoff  was  found  with  models  fit  by  the  generalized  regression  platform,  and  the  optimal  factor  settings  were  found  using  the  desirability  functions  in  the  profiler.  This  was  a  fairly  standard  DoE  analysis.  But  in  addition  to  the  measured  responses,  NIR  spectra  were  taken  from  some,  but  not  all  of  the  sample  batches.  This  is  information  that  can  be  used  to  give  a  clue  to  the  R&D  team  about  what  the  chemical  structure  of  the  ideal  formulation  should  look  like.

In  addition  to  the  GenReg  model  of  the  responses,  we  also  used  wavelet  models  as  the  basis  function  of  a  functional  DoE  analysis  of  the  spectra  using  the  DoE  factors  as  inputs.  We  were  then  able  to  get  a  synthetic  spectra  of  the  optimal  formulation  by  plugging  in  the  optimal  factor  settings  found  in  the  initial  analysis  of  the  two  critical  responses.

Before  going  into  the  presentation,  I  want  to  point  out  that  at  the  beginning  of  the  project,  Giuseppe  was  very  new  to  JMP  and  didn't  have  a  background  in  this  type  of  statistical  analysis.  Giuseppe  learned  all  he  needed  to  do  the  analysis  on  his  own  after  a  couple  of  web  meetings  with  me.

This  obviously  shows  he's  a  clever  guy,  but  also  that  JMP  makes  learning  how  to  do  some  very  sophisticated  data  analysis  projects  quick  and  easy.  Now  I'm  going  to  hand  the  show  over  to  Giuseppe.

Thank  you,  Chris.  Here  is  some  background  about  our  project.  It's  a  catalyst  development  project.  Therefore,  we  are  developing  many  different  recipes.  Each  recipe  has  a  unique  set  of  production  parameters,  and  once  the  sample  is  prepared,  we  characterize  it  in  our  analysis  lab  in   Topsoe.  And  finally,  we  do  a  performance  test.

During  the  performance  test,  we  look  for  two  values  that  here  we  call  Response  1  and  Response  2.  In  this  specific  case,  we  are  trying  to  minimize  response   1 while  maximizing  response  2.  That  will  lead  us  to  this  ideal  space  in  the  top  left  corner  of  the  graph.  But  as  you  can  see,  the  55  samples  that  we've  tested  get  stuck  in  the  middle  of  the  graph.  That  is  because   Response 1  and   Response 2  are  inter- correlated,  meaning  that  improving  one  comes  with  the  expenses  of  the  other.

Therefore,  we  move  to  JMP,  and  we  try  to  look  at  our  response  data  and  our  characterization  data,  try  to  see  if  we  can  move  away  from  this  line  in  the  middle  of  the  graph.  We  identified  two  targets  areas  that  we  want  to  reach,  and  together  with  Chris,  we  thought  about   using  JMP  to  create  a  model  that  would  connect  the  production  parameters  to  the  response  values. T hen  we  further  looked  into  our  infrared  spectroscopy  data  to  try  to  validate  our  model  and  to  get  some  extra  information  about  the  target  samples.

Here  is  an  overview  of  the  data  set.  We  have  produced  112  samples.  Each  sample  has  a  unique  set  of  production  parameters.  We  have  analyzed  all  the  samples  using  infrared  spectroscopy.  We  have  one  spectrum  for  each  sample.

Then  we  have  used  many  other  characterization  techniques  that  we  have  in- house  that  accounts  for  21  more  columns  of  data.  Finally,  we  have  tested  half  of  the  samples  and  that  accounts  for  the  last  two  columns  that  we  called  response.

At  the  beginning  of  our  project,  we  actually  wanted  to  include  the  infrared  spectroscopic  data  in  our  larger  data  set,  and  that's  why  we  wanted  to  use  JMP,  because  now  we  have  this  new  wavelet  model  possibility.  And  that  would  enable  us  to  include  the  principal  components  coming  from  the  wavelet  model  in  our  data  set.  And  we  could  use  that  to  create  models  in  JMP.  But  before  we  start  our  analysis,  we  need  to  have  a  look  at  the  Pro  data  to  find  outliers.  We  do  that  by  clicking  Analyze  and  the  Multivariate  Methods,  Multivariate.

H ere  we  can  select  our  production  parameters  and  characterization  data  as  Y  columns,  and  we  get  this  scatterplot  matrix.  This  is  an  example  of  just  looking  at  the  production  parameters  at  what  we  would  identify  as  an  outlier.  We  can  see  that  there  is  a  set  of  points  in  production  parameters  too  that  is  far  away  for  all  the  other  points.  Furthermore,  we  have  background  knowledge  about  these  samples  that  we  know  wouldn't  be  optimal  for  our  catalyst  development,  so  we  decided  to  right  click  and  say  "Row,  hide  and  exclude."

We  did  this  also  with  other  points  looking  at  the  scatterplot  metrics  of  all  the  characterization  data.  Now  that  we  have  cleaned  up  the  data,  we  can  fit  a  model.  We  click  Analyze  and  Fit  Model,  and  we  select  the  production  parameters  as  variables  in  our  model.  We  click  Macros  and  response  surface  to  create  a  second  polynomial  combination  of  these  variables.   Then  we  select  our  responses  as  Y  values.

Then  we  decided  to  use  generalized  regression.  H ere  Chris  can  add  some  more  info  about  why  we  decided  to  use  this  specific  type  of  model.

We  used  a  quadratic  response  surface  model  because  the  design  had  three  or  more  levels  for  each  factor,  so  I  knew  that  we  would  be  able  to  fit  curvature  terms  if  necessary  and  also  be  able  to  fit  quite  a  variety  of  different  interaction  terms.  We  use  the  generalized  regression  platform  because  it  does  model  selection  with  non  Gaussian  distributions  like  the  log  normal.  In  my  opinion,  there  aren't  many  reasons  not  to  use  the  generalized  regression  platform  if  you  have  JMP  Pro  because  it  is  so  easy  to  use  while  in  many  ways  being  so  much  more  powerful  for  DoE  analysis  than  the  other  options  in  JMP  and  JMP  Pro.

After  that,  we  can  select  our  distribution.  We  know  that  the  responses  are  going  to  be  strictly  positive,  so  we  select  the  log  normal  distribution  and  then  we  click  Run,  and  we  say  no.

In  this  slide,  we  can  see  that  we  have  now  created  a  model,  but  we  have  also  the  possibility  of  creating  other  type  of  models  using  different  estimation  methods.  We  decided  to  use  best  subset.  Here,  Chris  can  add  some  more  words  about  it.

Well,  so  here  we  use  best  subset  selection  because  the  full  model  isn't  terribly  large,  so  why  not  try  every  possible  subset  of  that  full  model  and  find  the  one  that  provides  the  absolute  best  tradeoff  between  accuracy  and  model  simplicity?

On  the  other  hand,  had  there  been  eight  or  more  factors,  I  would  have  used  a  faster  algorithm  like  forward  selection  or  pruned  forward  selection  because  with  larger  base  models,  it  would  take  a  very  long  time  to  fit  every  possible  submodel  to  the  data.  We're  going   to  be  using  the  AICc  model  selection  criteria  to  compare  GenReg  models.

The  AICc  allows  you  to  compare  models  with  different  effects  in  them  as  well  as  different  response  distributions.  With  the  AICc,  smaller  values  are  better  and  the  rule  of  thumb  I  use  is  that  if  a  model  has  an  AICc  value  that  is  within  4  of  the  smallest  AICc  value  seen,  then  those  two  models  are  practically  identical  in  quality  of  fit.

If  the  two  models  have  AICc  values  within  10  of  each  other,  then  they  are  statistically  similar  to  one  another.  The  main  point  here  being  that  if  we  have  two  models  and  their  AICc's  differ  by  more  than  10,  then  the  data  are  pretty  strongly  suggesting  that  the  one  with  the  smaller  AICc  is  the  better  model  to  be  working  with.

As  with  any  individual  statistic,  you  should  view  the  A ICc  as  a  suggestion.  If  your  subject  matter  experience  strongly  suggests  one  model  over  the  other,  you  may  want  to  trust  your  instincts  and  ignore  the  recommendation  of  the  A ICc.

Once  we  have  created  this  new  model,  we  can  see  that  the  non- zero  parameters  have  now  dropped  from  16  to  nine.  If  we  want  to  compare,  if  the  model  has  improved,  we  can  look  at  the  A ICc  values.  We  can  see  that  there  is  an  improvement  of  more  than  10,  which  is  an  important  difference.  Therefore,  we  decided  to  go  with  the  best  subset.

We  did  the  same  for   Response 2,  and  then  we  moved  on.  Now  we  can  click  on  the  red  arrow  and  say  profiler.  In  the  profiler,  we  can  play  around  with  the  production  parameters  and  see  how  the  model  is  expecting   Response 1  and   Response 2  to  change.  This  is  already  a  great  tool  for  the  scientist  to  understand  how  the  model  is  expecting  the  responses  to  vary,  but  we  can  do  more.  We  can  click  on  optimization  and   desirability  and  desirability  functions.

Since  from  slide  one,  we  know  that  we  have  two  targets  that  we  want  to  reach,  we  can  change  the  desirability  function  to  match  those  targets.  So  we  double  click  on  the  Desirability  function and  we  say  match  target  and  select  the  target  area  that  we  want  to  reach.  Finally,  we  can  click  again  on  optimization  and  desirability  and  say  maximize  desirability.

Here,  the  profiler  will  try  to  reach  the  optimal  points  for  the  production  parameters.  To  summarize,  we  can  say  that  now  we  have  the  first  model,  we  go  from  production  parameters  to  responses  and  we  have  set  two  targets  that  we  want  to  reach.  This  way  we  can  get  ideal  production  parameters  that  we  can  communicate  to  the  development  team  and  they  can  use  to  move  on  in  their  research.

In  the  second  part  of  the  presentation,  I'm  going  to  talk  about  how  we  use  the  IR  Spectra.  Here  we  have  a  file  for  each  spectrum  and  therefore  we  need  to  click  on  file  and  import  multiple  files.  Then  we  need  to  specify  that  we  want  the  file  name  to  be  columned  in  the  data  set  and  then  we  can  use  this  sample  name,  which  is  the  name  of  the  file  as  an  ID  to  connect  it  to  the  other  table  where  we  have  all  the  data.  We  click  on  the  column  and  say  link  reference.

Now  that  the  two  tables  are  connected,  we  can  click  Analyze  and  specialize  modeling  and functional  data  explorer.  In  the  Functional  Data  Explorer,  we  want  to  use  the  intensity  value  as  the  Y  output.  We  can  use  the  sample  name,  matrix  name  as  our  ID  function.

Then  this  is  very  important.  We  use  the  production  parameters  as  supplementary  data.  And  the  weight  number  is,  of  course,  the  X- axis.  And  we  say,  "Okay,  here  we  can  see  that  the  data  is  already  clean."  We've  imported  all  the  Spectra  and  the  data  looks  clean  because  I've  done  the  preprocessing  outside  of  JMP.  I  used  Python  because  I'm  more  familiar  with  that  and  there  is  a  very  nice  module  that  is  able  to  remove  the  background  and  reduce  the  range  that  we  want  to  look  at.

JMP  was  good  to  work  with  as  an  extra  tool  after  this  reprocessing.  Then  we  decide  to  click  on  models  and  wavelets.  We  move  from  discrete  data  to  continuous  data.  Now  we  can  also  look  at  the  diagnostic  plots.  This  is  for  you,  Chris,  to  take talk  about.

It's  a  good  idea  to  look  at  actual  by  predicted  plots  as  you  proceed  through  a  functional  data  analysis.  These  have  the  actual  observed  values  in  the  data  on  the  Y  axis  and  the  predicted  values  on  the  X  axis.  We  want  the  predicted  values  to  be  as  close  to  the  actual  values  as  possible.  A  plot  like  this  one  that  is  tight  along  the  45  degree  line  indicates  that  we  have  a  good  model.

Now,  some  of  you  may  be  concerned  about  overfit  since  the  predictions  fit  the  data  so  well.  In  my  experience,  I  haven't  found  that  to  be  a  problem  in  the  basis  function  fitting  and  functional  principal  component  steps  of  functional  DoE  analysis.  I'd  also  like  to  point  out  that  in  JMP  17,  we've  added  a  lot  of  new  features  for  spectral  preprocessing  like  standard  normal  variant,  multiplicative  scatter  correction,  and  Savitzky-Golay filters .  Those  of  you  that  don't  know  Python  have  access  to  these  capabilities  in  JMP  Pro  17.

After  that,  we  can  also  have  a  look  at  the  Functional  PCA  analysis.  Here,  we'll  spend  some  more  words  on  it.

After  the  wavelet  model  is  fit,  JMP  Pro  automatically  does  a  functional  principal  components  analysis  of  the  wavelet  model.  This  decomposes  the  spectra  into  an  overall  mean  spectra and  a  linear  combination  of  shape  components  and  coefficients  that  are  unique  to  each  sample  spectra.  When  we  do  the  functional  DoE  analysis,  GenReg  automatically  fits  models  to  these  coefficients  behind  the  scenes  and  combines  the  resulting  model  with  the  mean  function  and  the  shape  components  to  predict  the  shape  of  the  spectra  at  new  values  of  the  input  parameters.

If  we  look  at  the  principal  component  analysis,  we  can  see  that  the  wavelet  model  has  created  a  mean  function  of  all  the  spectra  that  we've  set  as  input,  and  then  it  has  created  different  shape  functions.  We  decided  to  stop  at  six.   What  this  shape  function  described  is  the  variation  of  the  data  that  we  are  analyzing.

As  you  can  see  from  the  left,  the  first  shape  function  is  accounting  for  72 %  of  the  variation,  while  the  second  is  accounting  for  22 %.   Together,  the  account  called  six  account  for  99.5 %  of  the  variation.   As  an  example,  we  can  look  at  principal  component  2,  and  we  can  see  that  around  3,737,  there  is  a  reduction.   There  is  a  minus.  That  means  that  increasing  the  principal  component   2 will  decrease  the  peak  at  3,737.

This  is  just  an  example  to  say  that  already  from  these  six  shape  functions,  we  can  get  a  lot  of  information.  If  we  have  subject  matter  knowledge,  about  the  infrared  spectroscopy  and  this  catalyst  system.  Already  here,  we  spent  quite  a  lot  of  time  looking  at  the  principal  components,  but  this  is  not  what  we  are  going  to  focus  on  in  the  next  slides.

What  we  want  to  look  at  instead  is  the  functional  DoE  analysis.  Here  we  have  as  well  a  profiler,  but  we  can  now  plug  the  production  parameters  that  we  got  from  the  first  model  that  we  developed.  Therefore,  knowing  the  target  production  parameters  that  we  want  to  use,  we  can  generate  a  fake  spectrum  or  a synthetic  spectrum,  we  can  call  it.  This  is  a  spectrum  of  a  sample  that  was  never  produced.  It  could  be  wrong,  but  it  can  give  some  ideas  to  the  scientists  about  what  you  would  expect  to  get  from  these  new  production  parameters.

To  sum  up,  we  can  say  that  now  we  move  from  production  parameters  to  infrared  spectrum.  We  have  a  second  model  that  uses  the  wavelet  model  to  generate  synthetic  spectrum.  I  imagine  this  to  be  like  when  you're  baking  a  cake,  now  you  have  the  recipe  but  you  got  also  a  snapshot  picture  of  the  final  cake.  It  doesn't  explain  you  how  to  do  it  but  it  adds  information  about  what  you  want  to  achieve.

Finally,  we  can  move  from  model  to  test.  That  means  that  we  can  give  the  R&D  team  a  new  recipe  and  also  the  synthetic  spectrum  of  that recipe.   Together  with  this  information,  they  can  try  to  develop  the  new  catalyst  and  see  if  the  model  is  validated  or  is  wrong.

Another  thing  the  group  can  do  is  look  back  at  the  previous  samples.  Now  we  have  half  of  the  samples  that  were  not  tested.  Those  are  the  black  dots  in  the  slide.   We  can  look  for  outliers.  Is  there  a  sample  that  could  perform  really  good  that  we  haven't  looked  at?  We  actually  have  one.  So  that's  another  test  we  can  do.

Looking  at  future  work,  I  added  this  slide  that  was  at  the  beginning  just  to  say  that  we  focused  on  the  production  parameters  and  the  IR  spectra,  but  we  haven't  really  looked  at  these  21  more  columns  of  characterization  data.

In  the  future,  we  could  spend  some  time  trying  to  identify  the  most  predictive  parameters  in  these  21  columns  and  create  maybe  a  new  model  from  this  characterization  data  to  the  response  and  use  this  model  as  a  screening  model  to  avoid  testing  samples  that  would  not  perform  as  good.  That's  the  end  of  the  presentation.

Comments
bmallmann

Hello, could I use this analysis to compare 2 methods? meaning I have the same sample but I do different procedures with it and I would do like to compare the methodologies and see if they have the same result. 

Thanks

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