Joint Selection of Wavenumber Regions for MidIR and Raman Spectra and Variables in PLS Regression using Genetic AlgorithmsThe Context

Lidwine Grosmaire, Pedro Maldonado-Alvarado, Christelle Reynès, Robert

Sabatier, Dominique Dufour, Thierry Tran and Jean-Louis Delarbre

Joint Selection of Wavenumber Regions for MidIR and

Raman Spectra and Variables in PLS Regression using

Genetic Algorithms

The Context

Empirical and small-scale

processing Good breadmaking ability

Interest Increased production and consumption

of cassava From the crop to

the starch

2012

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Varietal and process impacts on breadmaking ability

 Standardize and scale-up the process Physicochemical parameters

 Improve product quality

 Industrial development of new gluten-free bread products

Aim

The Aim

The Data

Physicochemical parameters Spectral data

Predicting the expansion ability

PLS1

Y

IR 2

X

Ra 3

X

1 52 1 52 1 4562 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Int ens it é (c oup s) 500 1 000 1 500 2 000 2 500 3 000 3 500 Shift Raman (cm-1) RVA Relevant parameters



selGAmPLS

3351 1 c 1

X

 IR 2

X

13 Breadmaking ability Amylose content

RVA parameters (12 variables)

Mid-infrared spectra (3351 variables) Raman spectra (4562 variables)

How to explain the breadmaking ability from our data using a statistical regression method while selecting variables of different types: individual and intervals?

Explanatory variables are organized in a multitable in which intervals and individual variables are selected in order to predict one variable of interest: the breadmaking capacity.

A Genetic Algorithm (GA) was developed in a context of discrimination, jointly with the PLS1 method : this method is called selGAmPLS.

The Results

Fig 2: Final GA populations

characteristics: selected variables

are indicated by black points

Table 1: Comparison with other methods results

(number of selected variables, number of retained

PLS components, R² and cross-validation R²).

The 10 final populations are quite close indicating a global convergence of the GA

Individual populations seem to have converged

Method # var # comp R² R²_CV

PLS 7926 7 0.7836 0.6605 PLS + VIP 4 3 0.7210 0.6650 selAGmPLS 311 12 0.9936 0.8273 Physicochemical parameters 4 RVA parameters Peak Viscosity Holding Strenght Breakdown Relative Breakdown Spectroscopic variables 4 spectral regions

Crystalline order of starch

Selected variables

Conclusion

 Genetic Algorithms provide a very adaptable and efficient solution when dealing with both several kinds of variables selections (individual vs

intervals) and multiway tables.

 The results obtained are very interesting for a predictive use. In terms of interpretation the method allowed to highlight the importance of

some physico-chemical variables and to select a small number of short intervals in spectroscopic data.

 The data selected are related to the water absorptivity and the crystalline state of starch and play a key role in breadmaking ability

7926 variables

The Problematic

Fig 1: Selection of relevant intervals and variables in multitables with a GA