1
Cordella C.B.Y. a , Boussard A c , Rakotozafy L c , Nicolas J c , Potus J c ., Garcia R. c , Rutledge, N. D. b a UMR 1145, INRA, 16 rue Claude Bernard 75231 PARIS cedex 05 b UMR 1145, AgroParisTech, 16 rue Claude Bernard 75231 PARIS cedex 05 c Cnam, UMR 1145, 292 rue Saint-Martin 75141 PARIS cedex 03 This work is designed to correlate the changes in the overall fluorescence of samples to their chemical and physical structure involving the gluten proteins, cinnamic derivatives in arabinoxylans, the products of lipid oxidation and carotenoid pigments. We want to model the chemical or / and biochemical kinetics involved in the formation of wheat dough by using 3D-Front Face Fluorescence Spectroscopy (3D-FFS) with an aim to automate the monitoring of the structuring of the dough during kneading. Flour contains several fluorophores (tryptophan and tyrosine in the protein fraction, ferulic acid in arabinoxylans, carotenoid pigments ...) that may be involved in the structuring reactions of dough during kneading. Doughs are kneaded alone or in the presence of effectors chosen for their aptitude to modify the structure of the dough. Flour dough baking qualities can be appreciated by biochemical determinations that are time-consuming : they imply many manipulations work and cannot be automated easily. Context and aims of the work Samples preparation 8 Formulations with 250g wheat flour Kneading Time (min) + 4 g glucose (Glc) + 30 mg Ferulic acid (FA) + 1.22 μkat Glucose oxidase (GOX) 2 4 6 8 Formulation Description Q1 Control (No effectors) Q2 Glc Q3 Glc + GOX Q4 Glc + FA Q5 Glc + GOX + FA Q6 GOX Q7 FA Q8 GOX + FA Kneading (with Chopin consistograph)(see Table) Freezing (- 20°C) Lyophilization Grinding Sieving Biochemical assays oxidized lipids, proteins and cinnamic derivatives 23 biochemical variables resulting in 23 statistical variables for each sample SAFAS 2000 spectrofluorimeter, (SAFAS S.A., France) Excitation wavelengths from 250 to 400 nm Emission wavelengths from 260 to 550 nm 3 excitation-emission matrices (EEM) recorded for each sample. Spectroscopic Analyzes Materials & Methods Contact : [email protected] Websites: http://www.chimiometrie.fr http://biochimie.cnam.fr Data treatment Methodology Exploratory PCA ICA Modeling Interval-PLS Software: * R² and RMSECV are calculated using i-PLS toolbox. F5s320: the fifth soluble protein fraction detected by HPLC-SE at 320 nm PLS models were calculated for all formulations of dough for 23 biochemical variables. We compared the fit of models to data when the models were calculated with full 3D spectra or from a selection of spectral window of variables. For example, for formulations Q1, Q6 and Q8, we find that models obtained with a window selection of spectral variables are always better than those obtained from the ICs. Moreover, the performance deteriorates especially for the formulation Q1. This can be explained by the presence of GOX which expands the measurement range and induces advanced kinetics. The result is a poor PLS model for biochemical variable considered. Table above presents best results for the biochemical variables “solFA” and “F5s320” (variables linked to esterified FA present in arabinoxylans) in dough Q6, respectively for PLS model obtained from full 3D fluorescence spectra and for PLS model obtained from IC1. Control With FA Control With GOX Control With GOX only With Glucose + GOX Legend GOX effect Glucose effect Ferulic Acid effect Legend Ferulic Acid effect Kinetic effect per formulation Q1 Q8 Q1 Q6 Unfolded spectra Native spectrum Selected spectral zone used in the regression model Results PCA and ICA can be complementary applied with success to improve chemical interpretation of fluorescence data. Whereas PCA is able to show FA effect in the same way that ICA does (Fig. 1a and Fig. 2b), ICA provides a more convenient way to outsight the kinetic effect by providing more pure chemical components (Fig. 2a and 2c). As depicted in figure 1a, PCA indicates that samples without FA (Q1, Q2, Q3, Q6) are characterized by negative loadings in the spectral zone (Excitation: 260-310 nm; Emission: 300-380 nm) corresponding to the protein-like fluorophore components. Additionally, samples with FA (Q4, Q5, Q7, Q8) are characterized by the fluorescence area corresponding to ferulic acid and related components (Ex.°: 340-390 nm; Em.°: 400-470 nm). ICA provides a more specific and efficient way to detect FA effect. As depicted in figure 2b, ICA is a single-signal component that focuses on the protein-like area. The corresponding scores (ICA proportions) show a great distinction between samples with and without FA (Controls). Therefore, a possible conclusion is the presence of FA in samples has a significant impact on the fluorescence of the protein-like compounds. That is what is materialized by IC4. It makes sense from practical point of view because spectra of samples containing only endogenous esterified FA (Q1) have lower fluorescence intensities than those containing added free FA (Q7 and Q8). This observation is verified in all formulations containing FA (Q4, Q5, Q8). Kinetic effect is well depicted both by Fig. 1b (PCA), 2a and 2c (ICA) but the extracted PC contains 2 different signals whereas ICA provides a single-signal component allowing a simpler chemical interpretation of the kinetic effect. Particularly, figures 2a and 2c show respectively the effect of GOX and Glucose on the kinetics of dough structuring. When GOX is present without any other added component (fig. 2a) the fluorescence modifications are increased and accelerated. When glucose is added with GOX (fig. 2c), the kinetic effect is still present but modified. This could be due to a double effect, one “chemical” by enhancing the GOX activity and one “physical” by modifying the distribution of water among gluten proteins, starch and arabinoxylans. The fluorenscence of dough was followed for eight different formulations during kneading. These measurements can easily differentiate the 8 types of dough and some of them clearly show several chemical and/or physical effects such as ferulic acid, GOX, glucose and "kneading time” effects. 3D-front face fluorescence, coupled with chemometric treatments of 3D spectra appears to be a promising tool to monitor the structure of dough and to evaluate the action of effectors. One possible application is the development of a fast on-line control tool for monitoring bread dough preparation. Figure 1a Figure 1b Figure 2a Figure 2c Figure 2b MATLAB environnent, version R2007b (Mathworks, Natick, MA, USA), the SAISIR Package v1.0 [1] and iPLS toolbox [2]. Chemometrics Data set Data cube [59 × 31 × 102] All data Data cube [59 × 31 × 12] Q1 – Q8 Data cube [59 × 31 × 24] Q1/Q2 Glucose (Glc) Data cube [59 × 31 × 24] Q1/Q6 GOX Couple Effect of Data cube [59 × 31 × 24] Q1/Q7 FA Data cube [59 × 31 × 24] Q2/Q4 FA + Glc Data cube [59 × 31 × 24] Q3/Q5 FA + Glc + GOX Data cube [59 × 31 × 24] Q6/Q8 FA + GOX Data cube [59 × 31 × 24] Q7/Q8 GOX + FA Datasets flyer Datasets flyer Data cube [59 × 31 × 102] All data Data cube [59 × 31 × 12] Q1 – Q8 Data cube [59 × 31 × 24] Q1/Q2 Glucose (Glc) Data cube [59 × 31 × 24] Q1/Q6 GOX Couple Effect of Data cube [59 × 31 × 24] Q1/Q7 FA Data cube [59 × 31 × 24] Q2/Q4 FA + Glc Data cube [59 × 31 × 24] Q3/Q5 FA + Glc + GOX Data cube [59 × 31 × 24] Q6/Q8 FA + GOX Data cube [59 × 31 × 24] Q7/Q8 GOX + FA Data cube [59 × 31 × 102] All data Data cube [59 × 31 × 102] All data Data cube [59 × 31 × 12] Q1 – Q8 Data cube [59 × 31 × 12] Q1 – Q8 Data cube [59 × 31 × 24] Q1/Q2 Glucose (Glc) Data cube [59 × 31 × 24] Q1/Q2 Glucose (Glc) Data cube [59 × 31 × 24] Q1/Q6 GOX Data cube [59 × 31 × 24] Q1/Q6 GOX Couple Effect of Data cube [59 × 31 × 24] Q1/Q7 FA Data cube [59 × 31 × 24] Q1/Q7 FA Data cube [59 × 31 × 24] Q2/Q4 FA + Glc Data cube [59 × 31 × 24] Q2/Q4 FA + Glc Data cube [59 × 31 × 24] Q3/Q5 FA + Glc + GOX Data cube [59 × 31 × 24] Q3/Q5 FA + Glc + GOX Data cube [59 × 31 × 24] Q6/Q8 FA + GOX Data cube [59 × 31 × 24] Q6/Q8 FA + GOX Data cube [59 × 31 × 24] Q7/Q8 GOX + FA Data cube [59 × 31 × 24] Q7/Q8 GOX + FA Datasets flyer Datasets flyer Conclusion F5s320 With window selection [1] D. Bertrand and B. Y. Cordella in SAISIR package. Free toolbox for chemometrics in the Matlab, Octave or Scilab environments. Available at http://www.chimiometrie.fr/saisir_webpage.html ., INRA, Paris-Nantes, 2011. [2] L. Nørgaard, J. Saudland, J. Wagner, J. P. Nielsen, L. Munck and S. B. Engelsen, Applied Spectroscopy 2000, 54, 413-419.

Materials & Methods - AgroParisTech€¦ · Cordella C.B.Y.a, Boussard Ac , Rakotozafy Lc, Nicolas Jc, Potus Jc., Garcia R.c, Rutledge, N. D.b aUMR 1145, INRA, 16 rue Claude Bernard

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Page 1: Materials & Methods - AgroParisTech€¦ · Cordella C.B.Y.a, Boussard Ac , Rakotozafy Lc, Nicolas Jc, Potus Jc., Garcia R.c, Rutledge, N. D.b aUMR 1145, INRA, 16 rue Claude Bernard

Cordella C.B.Y.a, Boussard Ac , Rakotozafy Lc, Nicolas Jc, Potus Jc., Garcia R.c, Rutledge, N. D.b

aUMR 1145, INRA, 16 rue Claude Bernard 75231 PARIS cedex 05 bUMR 1145, AgroParisTech, 16 rue Claude Bernard 75231 PARIS cedex 05

c Cnam, UMR 1145, 292 rue Saint-Martin 75141 PARIS cedex 03

• This work is designed to correlate the changes in the overall fluorescence of samples to their chemical and physical structure involving the gluten proteins, cinnamic derivatives in arabinoxylans, the products of lipid oxidation and carotenoid pigments. • We want to model the chemical or / and biochemical kinetics involved in the formation of wheat dough by using 3D-Front Face Fluorescence Spectroscopy (3D-FFS) with an aim to automate the monitoring of the structuring of the dough during kneading.

Flour contains several fluorophores (tryptophan and tyrosine in the protein fraction, ferulic acid in arabinoxylans, carotenoid pigments ...) that may be involved in the structuring reactions of dough during kneading. Doughs are kneaded alone or in the presence of effectors chosen for their aptitude to modify the structure of the dough. Flour dough baking qualities can be appreciated by biochemical determinations that are time-consuming : they imply many manipulations work and cannot be automated easily.

Context and aims of the work

Samples preparation 8 Formulations with

250g wheat flour Kneading Time (min)

+ 4 g glucose (Glc) + 30 mg Ferulic acid (FA) + 1.22 µkat Glucose oxidase (GOX)

2 4 6 8

Formulation Description Q1 Control (No effectors) Q2 Glc Q3 Glc + GOX Q4 Glc + FA Q5 Glc + GOX + FA Q6 GOX Q7 FA Q8 GOX + FA

Kneading (with Chopin consistograph)(see Table)

Freezing (- 20°C)

Lyophilization

Grinding

Sieving

Biochemical assays oxidized lipids, proteins and cinnamic derivatives 23 biochemical variables resulting in 23 statistical variables for each sample

SAFAS 2000 spectrofluorimeter, (SAFAS S.A., France) Excitation wavelengths from 250 to 400 nm Emission wavelengths from 260 to 550 nm 3 excitation-emission matrices (EEM) recorded for each sample.

Spectroscopic Analyzes

Materials & Methods

Cont

act :

Chr

isto

phe.

Cord

ella

@pa

ris.

inra

.fr

Web

site

s:

http

://w

ww

.chi

mio

met

rie.fr

ht

tp://

bioc

him

ie.c

nam

.fr

Data treatment Methodology

Exploratory

PCA ICA

Modeling

Interval-PLS

Software:

* R² and RMSECV are calculated using i-PLS toolbox. F5s320: the fifth soluble protein fraction detected by HPLC-SE at 320 nm

PLS models were calculated for all formulations of dough for 23 biochemical variables. We compared the fit of models to data when the models were calculated with full 3D spectra or from a selection of spectral window of variables. For example, for formulations Q1, Q6 and Q8, we find that models obtained with a window selection of spectral variables are always better than those obtained from the ICs. Moreover, the performance deteriorates especially for the formulation Q1. This can be explained by the presence of GOX which expands the measurement range and induces advanced kinetics. The result is a poor PLS model for biochemical variable considered. Table above presents best results for the biochemical variables “solFA” and “F5s320” (variables linked to esterified FA present in arabinoxylans) in dough Q6, respectively for PLS model obtained from full 3D fluorescence spectra and for PLS model obtained from IC1.

Control

With FA

Control With GOX

Control With GOX only

With Glucose

+ GOX Legend

GOX effect

Glucose effect

Ferulic Acid effect

Legend Ferulic Acid effect

Kinetic effect per formulation

Q1 Q8

Q1

Q6

Unfolded spectra Native spectrum

Selected spectral zone used in the regression

model

Results

PCA and ICA can be complementary applied with success to improve chemical interpretation of fluorescence data. Whereas PCA is able to show FA effect in the same way that ICA does (Fig. 1a and Fig. 2b), ICA provides a more convenient way to outsight the kinetic effect by providing more pure chemical components (Fig. 2a and 2c).

As depicted in figure 1a, PCA indicates that samples without FA (Q1, Q2, Q3, Q6) are characterized by negative loadings in the spectral zone (Excitation: 260-310 nm; Emission: 300-380 nm) corresponding to the protein-like fluorophore components. Additionally, samples with FA (Q4, Q5, Q7, Q8) are characterized by the fluorescence area corresponding to ferulic acid and related components (Ex.°: 340-390 nm; Em.°: 400-470 nm). ICA provides a more specific and efficient way to detect FA effect. As depicted in figure 2b, ICA is a single-signal component that focuses on the protein-like area. The corresponding scores (ICA proportions) show a great distinction between samples with and without FA (Controls). Therefore, a possible conclusion is the presence of FA in samples has a significant impact on the fluorescence of the protein-like compounds. That is what is materialized by IC4. It makes sense from practical point of view because spectra of samples containing only endogenous esterified FA (Q1) have lower fluorescence intensities than those containing added free FA (Q7 and Q8). This observation is verified in all formulations containing FA (Q4, Q5, Q8).

Kinetic effect is well depicted both by Fig. 1b (PCA), 2a and 2c (ICA) but the extracted PC contains 2 different signals whereas ICA provides a single-signal component allowing a simpler chemical interpretation of the kinetic effect. Particularly, figures 2a and 2c show respectively the effect of GOX and Glucose on the kinetics of dough structuring. When GOX is present without any other added component (fig. 2a) the fluorescence modifications are increased and accelerated. When glucose is added with GOX (fig. 2c), the kinetic effect is still present but modified. This could be due to a double effect, one “chemical” by enhancing the GOX activity and one “physical” by modifying the distribution of water among gluten proteins, starch and arabinoxylans.

The fluorenscence of dough was followed for eight different formulations during kneading. These measurements can easily differentiate the 8 types of dough and some of them clearly show several chemical and/or physical effects such as ferulic acid, GOX, glucose and "kneading time” effects. 3D-front face fluorescence, coupled with chemometric treatments of 3D spectra appears to be a promising tool to monitor the structure of dough and to evaluate the action of effectors. One possible application is the development of a fast on-line control tool for monitoring bread dough preparation.

Figure 1a

Figure 1b

Figure 2a

Figure 2c

Figure 2b

MATLAB environnent, version R2007b (Mathworks, Natick, MA, USA), the SAISIR Package v1.0 [1] and iPLS toolbox [2].

Chemometrics

Data set

Data cube [59 × 31 × 102]

All data

Data cube [59 × 31 × 12]

Q1 – Q8

Data cube [59 × 31 × 24]

Q1/Q2 Glucose (Glc)

Data cube [59 × 31 × 24]

Q1/Q6 GOX

Couple Effect of

Data cube [59 × 31 × 24]

Q1/Q7 FA

Data cube [59 × 31 × 24]

Q2/Q4 FA + Glc

Data cube [59 × 31 × 24]

Q3/Q5 FA + Glc+ GOX

Data cube [59 × 31 × 24]

Q6/Q8 FA + GOX

Data cube [59 × 31 × 24]

Q7/Q8 GOX + FA

Datasets flyerDatasets flyerData cube [59 × 31 × 102]

All data

Data cube [59 × 31 × 12]

Q1 – Q8

Data cube [59 × 31 × 24]

Q1/Q2 Glucose (Glc)

Data cube [59 × 31 × 24]

Q1/Q6 GOX

Couple Effect of

Data cube [59 × 31 × 24]

Q1/Q7 FA

Data cube [59 × 31 × 24]

Q2/Q4 FA + Glc

Data cube [59 × 31 × 24]

Q3/Q5 FA + Glc+ GOX

Data cube [59 × 31 × 24]

Q6/Q8 FA + GOX

Data cube [59 × 31 × 24]

Q7/Q8 GOX + FA

Data cube [59 × 31 × 102]

All dataData cube [59 × 31 × 102]

All data

Data cube [59 × 31 × 12]

Q1 – Q8Data cube [59 × 31 × 12]

Q1 – Q8

Data cube [59 × 31 × 24]

Q1/Q2 Glucose (Glc) Data cube [59 × 31 × 24]

Q1/Q2 Glucose (Glc)

Data cube [59 × 31 × 24]

Q1/Q6 GOX Data cube [59 × 31 × 24]

Q1/Q6 GOX

Couple Effect of

Data cube [59 × 31 × 24]

Q1/Q7 FAData cube [59 × 31 × 24]

Q1/Q7 FA

Data cube [59 × 31 × 24]

Q2/Q4 FA + GlcData cube [59 × 31 × 24]

Q2/Q4 FA + Glc

Data cube [59 × 31 × 24]

Q3/Q5 FA + Glc+ GOX

Data cube [59 × 31 × 24]

Q3/Q5 FA + Glc+ GOX

Data cube [59 × 31 × 24]

Q6/Q8 FA + GOXData cube [59 × 31 × 24]

Q6/Q8 FA + GOX

Data cube [59 × 31 × 24]

Q7/Q8 GOX + FAData cube [59 × 31 × 24]

Q7/Q8 GOX + FA

Datasets flyerDatasets flyer

Conclusion

F5s320 With

window selection

[1] D

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