Routine Classification for Food Authenticity

Karen E. Yannell, PhD

Agilent Technologies

Santa Clara, CA

Using the 6546 LC/Q-TOF, Profinder 10.0, Mass Profiler Professional 15.0, and Classifier 1.0

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Two different roles: • Method Developer (Scientist)

• Analyst (Technician) [New]

Routine Analysis

• MH Acq and Classifier software ONLY

Method Developer Improvements• Faster Profinder with less RAM

• Method automation in MPP [New]

LC/Q-TOF (.d), GC/SQ and GC/Q-TOF (.cef)

Classifier comes with a MPP 15.0 license

Not unique to food

• Terms are generic in the software

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Classifier 1.0 Allows for Routine Classification Analysis

June 14, 20193

Overview of Acquisition and Analysis for Mango Data

Application: Using 6546 and Classifier to Determine Mango Puree Purity

• RP chromatography

• Tune: m/z 750

• MS only acquisition in

positive mode (m/z 50-1000)

• QuEChERS extraction

• Liquid extraction is a

good place to start for

discovery

• Simple workflow for

technician without Profinder

or MPP

• 100% accuracy for QC

samples

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Critical for good results

Sample Design

Authentic samples are needed• Mangos are proof of concept

Six individual samples

Positive QCs • Mimics pure samples

• Pooled from knowns

Negative QCs• Mimics adulterated samples• Ratios of different Positive QCs

Worklist: Randomized and Blocked• Model samples go first

• Unknowns after• All bracketed with QCs

Internal Standards

Optional but a good idea to

check data quality• At least in method validation

step

Anything exogenous to the

sample• Drugs and deuterated

pesticides were used here

Ideal: added at multiple points:

Use MH Quant to for quick DA• RT, area, accuracy

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Important for quality control and troubleshooting

Internal Standard Results

• Five days of data collection without daily calibration or maintenance.

• Internal standard was stable in the method development & validation.

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Profinder is faster and MPP has method automation to streamline analysis

Feature Finding in Profinder and Statistics and Model Building in MPP

New Analysis and Models

MPP 15.0 VIP Scores & New Models

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• VIP scores added

• Customer requested class

prediction algorithms

(SIMCA)

• All ‘Workflow’ tasks can be created in an automated method.

• Parameters are customizable.

• Statistical workflows can be saved, shared, and re-run for fast reanalysis.

• Results in fewer clicks and less error for analysis.

MPP 15.0: Method Automation

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eFam is Great for Familiarizing New Users with the Software and Statistics

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Software for Routine Classification Analysis

MassHunter Classifier 1.0

Classifier Inputs

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Given by a method

development scientist

MassHunter Classifier 1.0 Results

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Sample Table lists

processed and queued

samples, list predicted

group, and confidence

value

2D and 3D PCA plots

show selected sample

orientation to the

model groups

Compound Table

lists the features in

the model

Accurate Results Based on Confidence Scores

• Set a confidence score threshold to call a sample pure or adulterated

• This should be based on a method validation and sensitivity and specificity needs (false positive

and false negative) of the assay

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Classifier 1.0 Allows for Routine Classification Analysis

Two different roles:

• Method Developer (Scientist)

• Analyst (Technician) [New]

Routine Analysis

• MH Acq and Classifier software ONLY

Method Developer Improvements

• Faster Profinder with less RAM

• Method automation in MPP [New]

Clear results describing adulteration from confidence scores

Thank you!

Questions?

June 14, 2019 Title Confidentiality label Regulatory statement (if applicable)16