Multivariate data analysis: from chemometrics modeling to ... · Discover how data-driven tools can transform your business from R&D to production Dr. Sylvie ROUSSEL, Ph.D. - Ondalys

2019 Analytics Solution Conference – MN, June 18-20 Making sense of your data

Multivariate data analysis: from chemometrics modeling to process implementation

S Roussel, J Lallemand, S Preys

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Discover how data-driven tools can transform your

business from R&D to production

Dr. Sylvie ROUSSEL, Ph.D. - Ondalys CEO

[email protected] – Tel : +33 (0)4 67 67 97 87


Making sense of your data…

based on Machine Learning

for industrial measurements

Donnez du sens à vos données

Sylvie ROUSSEL, Ph.D.

Ondalys CEO

[email protected]

mailto:[email protected]

Pharmaceutical

BiotechnologicalChemicals

Petrochemicals

Agriculture

Food Industry

Cosmetics

Environment

Public researchTechnical centers

2019 Analytics Solution Conference – MN, June 18-20 Making sense of your dataANAYSE DE DONNEES MULTIVARIEES

Massive data available with Industry 4.0 ➔ Data Analytics for Big Data or Smart Data?

Analytical Chemistry

OMICS

HPLC – GC – MS…

RGB & Hyperspectral

Imaging

NIR

FTIR

Raman

Spectroscopy

UV – Visible – NIR – FTIR

Raman – Fluo – LIBS…

Time series

Process parameters

Industrial IoT - Inline analyzers

Time pH

Temp

(°C) …

ID01 4.8 25 …

ID02 5.2 27 …

ID03 5.0 30 …

… … … …


Data Analytics Methodology

Audit

Consulting

Proof of Concept

Model development

Software Implementation

Model maintenance

Training

Coaching

Audit

✓ Acquisition optimization

✓ DoE / QbD

✓ ML testing

✓ Data fusion

✓ MSPC/BSPC

✓ Scale-up

✓ Transfer

✓ Update

✓ Knowledge Transfer

✓ Open-courses

✓ In-house courses

Audit

✓ Robustness

✓ Validation

2 application cases

A. Maturity Control of plums based on portable NIR spectroscopy

B. Batch process monitoring using BSPC


Application case

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Sugar and acidity content prediction

in plums based on NIR spectroscopy

Agen Prunes

Plums


Objective: predict sugar and acidity contents of plums directly in the field to optimize the harvest time

Proof of Concept

Model development

Model Implementation & maintenance

Model Transfer

1. Proof Of Concept:

✓ PLS model development in the lab with

✓ One year

✓ Orchard impact ?

Agen Prunes

Plums

2. Robust PLS model

✓ Lab measurements

✓ Several years

✓ Several orchards

4. Model Transfer

✓ From one lab

instrument to another

of the same brand

✓ From lab instrument

to field instrument

3. Model Maintenance & Update

✓ 4 years of data

✓ More than 5 600 fruits

✓ 15 different orchards

Indico Pro

software

Study performed for Technical Center for Prunes (BIP)

Proof of Concept


1. Proof of concept

Data acquisition

⚫ NIR instrument : LabSpec 4 (ASD Inc.)

• Range 350-2500 nm - Step 1 nm

• Reflectance spectra on plums in the laboratory

• 1 Year: 2013

⚫ Ref: sugar and acidity measurement with wet chemistry

Data import and preparation

⚫ Average of several measurement points

➔ Obtain the best representativity of the fruit

Model development on one year

⚫ Exploratory analysis (PCA)

• Detection of outliers

• Impact of orchard / date on the signal

⚫ Model development (PLS) for sugar and acidity prediction

• One orchard

• Several orchards

Task/ Transform/ Reduce (Average)


1. Proof of concept

⚫ Exploratory analysis

• No orchard difference ➔ no need for a model by orchard = one global model

• Maturity effect (plum color)

No orchard differenceMaturity effect (plum color)

Orchard :


1. Proof of concept

⚫ First PLS prediction model for sugar and acidity

• One global model for one year on all orchards

Sugar content (°Brix) in cross-validation

R² = 0.88

RMSECV = 1.59



Proof of Concept

Model development


Model Transfer



✓ One year


Agen Prunes

Plums

2. Robust PLS model


✓ Several years


4. Model Transfer

✓ From one lab


of the same brand


to field instrument


✓ 4 years of data



Indico Pro

software



2. Model development and implementation

Test of the 1st PLS model on the next year

⚫ Not robust model ➔ Model update required

Test on 2014Model on 2013

R² = 0.76

RMSEP = 2.49

R² = 0.88

RMSECV = 1.59

➔ For agronomic data : Need to include several years to have a robust model

Example with sugar content (°Brix)


2. Robust Model

⚫ Model development on several years

2. Model development and implementation

Test on 2016Model on 2013+2014+2015

R² = 0.91

RMSEP = 1.32 ☺

R² = 0.84

RMSECV = 1.89

Example with sugar content (°Brix)



Proof of Concept

Model development


Model Transfer



✓ One year


Agen Prunes

Plums

2. Robust PLS model


✓ Several years


4. Model Transfer

✓ From one lab


of the same brand


to field instrument


✓ 4 years of data



Indico Pro

software



3. Model implementation, Maintenance and update

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• Final models = 4 years 2013-2016, developed on The Unscrambler X®

• Exported to IndicoPro® (LabSpec 4 software)

➔ Implemented for real time prediction

➔ compatibility

Sugar content (obrix) Acidity (Meq)

R² = 0.87

RMSECV = 1.67

R² = 0.74

RMSECV = 1.56



Proof of Concept

Model development


Model Transfer



✓ One year


Agen Prunes

Plums

2. Robust PLS model


✓ Several years


4. Model Transfer

✓ From one lab


of the same brand


to field instrument


✓ 4 years of data



Indico Pro

software



4. Model transfer

• NIR instruments

• Lab instrument: LabSpec 4 (ASD Inc.)

• Range 350-2500 nm

• Step 1 nm

• Reflectance spectra

• Years: 2013-2016

• Field instrument: MicroNIRTM OnSite (VIAVI Solutions Inc.)

• Range 800-1676 nm

• Step ~6 nm

• Absorbance spectra

• Year : 2016

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Transfer needed !


4. Model transfer

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4. Model transfer

• Transfer from lab (LabSpec 4) to field (MicroNIRTM OnSite)

• Lab instrument :

conversion from reflectance to absorbance


4. Model transfer

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• Lab instrument :

reduction of the spectral range and interpolation to cover MicroNIR range


4. Model transfer

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4. Model transfer


• Comparison of spectra before transfer

LabSpec 4transformed in absorbance

and interpolated across MicroNIR range

MicroNIR


4. Model transfer

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4. Model transfer


• Transfer trials• Piecewise Direct Standardization (PDS)

• Mean difference spectrum


4. Model transfer

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4. Model transfer


• Comparison of spectra after transfer

LabSpec4 spectraAfter transfer

MicroNIR spectra


4. Model transfer

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• MicroNIRTM OnSite models

• Concatenation of the LabSpec transferred historical database [2013-2016] with MicroNIRTM spectra measured in 2016

• Redevelopment of models with The Unscrambler X®

• Possible export to MicroNIRTM OnSite Software

➔Results on an independent

test set (2017) are quite

promising

➔Need to add progressively

new “real” spectra to

improve models, especially

for acidity

➔ Compatibility

Sugar content (Brix)

Slope Offset RMSEPR-

Square

LabSpec 4 0.87 2.75 1.7 0.87

LabSpec 4 transf+ MicroNIR

0.83 3.59 1.9 0.83

Acidity (Meq)

Slope Offset RMSEPR-

Square

LabSpec 4 0.74 24.5 15.6 0.74

LabSpec 4 transf+ MicroNIR

0.51 46.5 22.8 0.48


Industrial Application case withThe Unscrambler® X - Process Pulse II

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2019 Analytics Solution Conference – MN, June 18-20

CONSORTIUM OF INDUSTRIALS INDUSTRIAL ANALYSIS PLATFORM

SENSORS & CHEMOMETRICS

FOR PROCESS MONITORING

USING PROCESS PULSE II

DATA ANALYTICS & PROCESS MONITORING SOFTWARE PROVIDER

DATA ANALYTICS & MACHINE LEARNING

CONSULTING AND TRAINING


AXEL’ONE ANALYSIS CONSORTIUM OF INDUSTRIALS

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R&D & IMPLEMENTATION TOPICS

The platform works on 3 themes :

1. Separation techniques

2. Spectral techniques and chemometrics

3. Physical and chemical sensors


Main objective: process monitoring using at-line/online/inline analyzers

1. Proof of Concept

2. Model development

3. Software Implementation

4. Model maintenance

✓ Data fusion (several analyzers)

✓ MSPC

✓ BSPC

✓ Scale-up

✓ Transfer

✓ Update

✓ Robustness

✓ Validation

✓ Continuous process:

Multivariate Statistical Process Control (MSPC)

✓ Batch process:

Batch Statistical process control (BSPC)

1. Proof of Concept


EXEMPLE: BATCH STATISTICAL PROCESS CONTROL (BSPC)

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Process

Proof of concept & Model development

Process parameters: temperature, pressure,

speed, etc.

Univariate sensors: pH, viscosity, etc…

Inline spectroscopy:

Raman, NIR, UV-VIS, etc…

BSPC model: Golden batch (PCA)

Concatenated datawith Maturity index

Under-control trajectory

New batch monitoring ➔ real-time drift

detection


Model implementation

EXEMPLE: BATCH STATISTICAL PROCESS CONTROL (BSPC)


THANK YOU

FOR YOUR ATTENTION!

Sylvie ROUSSEL, Ph.D.

[email protected] – Tel: +33(0)4 67 67 97 87

Minneapolis

Documents

Multivariate data analysis: from chemometrics modeling to ... · Discover how data-driven tools can transform your business from R&D to production Dr. Sylvie ROUSSEL, Ph.D. - Ondalys