**3.3 Prediction of species origin by PLS-1-DA regression**

The classification into varietal origin (GG or LBG) was performed using PLS-1-DA analysis. The calibration dataset was composed of 37 GG and 13 LBG (n = 50 samples × 3 spectra = 150). Different samples of calibration set, 12 LBG and 37 GG (n = 49 samples × 3 spectra = 147), have constituted the validation set.

*Modern Spectroscopic Techniques and Applications*

3120

1358, 1299, 1248

1064, 1043, 995, 974

706

**Wavelength (cm<sup>−</sup><sup>1</sup>**

**D-Galactose D-Mannose**

3000–2800 2937 2916 C–H stretching vibration of CH2 group

1452, 1419, 1367, 1271, 1203

1109, 1065, 1036, 1012, 956, 958, 912

**Spectral region**

3500–3000 3365, 3191,

1500–1200 1495, 1421,

1200–950 1151, 1103,

950–700 835, 793, 764,

*Spectral interpretations of pure sugars.*

the most intense spectral bands in GG samples at 1066, 1012, 964, 863, 819, and

*Representation of GG and LBG samples in (PC1 and PC2) plan (normalized spectral data).*

galactose units (1-6) linked to β-D-mannopyranosyl backbone in GG.

β-D-(1-4) and α-D-(1-6) linkages. These last ones are specific to the anomeric region where C–O stretching bands are more representative because of the largest number of

While GG was richer in galactosyl residue, no specific bands of D-galactose were found in positive part of PC1 loading representing the GG samples. The comparison with the spectral bands of pure D-galactose or D-mannose is not a good way because of their crystalline structure (free form) that is not the case in their polymer form. Another explanation could be a possible interference with the presence of water that modifies the band's resolution in the anomeric region as it is observable in **Figure 9** presenting sugar profiles under crystalline and hydrated forms.

are attributed to C–O–H and C–O vibrations,

is due to ring stretching and ring deformation of

**) Chemical group assignment**

3425 O–H stretching vibration, hydrogen bonded

845, 829, 802 Anomeric C–H deformation (α or β), equatorial

CH2 symmetric deformation, C–OH deformation

C–C skeletal, C–O–C, CO–O–C, C–O stretching vibration, C–O bending of C–OH group, OCH bending

C–H deformation (nonglycosidic) and asymmetric and symmetric ring vibration

. Bands at 1012 and 964 cm<sup>−</sup><sup>1</sup>

respectively. The band at 771 cm<sup>−</sup><sup>1</sup>

**84**

771 cm<sup>−</sup><sup>1</sup>

**Figure 7.**

**Table 2.**


#### **Table 3.**

*PLS-1-DA parameters for LBG.*

#### **Figure 10.**

*Predicted versus reference value of gum classification (validation step).*

The calibration model had very good quality parameters, as shown in **Table 3**, and very good validation results for LBG were obtained using normalized spectra with a selection of wavelength region from 1450 to 700 cm<sup>−</sup><sup>1</sup> .

For GG validation, the different statistic parameters were also closed to 100%. The following graph (**Figure 10**) shows that predicted values from validation data are closed to zero for GG and 1 for LBG.

Calibration model has satisfying quality parameters, as shown in **Figure 10**, with RMSEP ranging from 0.11 for LBG to 0.94 for Q2 (R-square), and 100% good classification is obtained. The predicted species origins are never given by zero or one results because the different rates of carbohydrates in the samples vary according to the origins. As a matter of fact, there is a natural variation of the carbohydrate rates that can notably be a function of geographic origins and harvest dates.

Contrary to the results of Prado et al. [12] who published that diffuse reflectance (DRIFT) method was better suited for differentiation of gum type, ATR technique showed here a very good classification. It was noted that these authors have realized spectra with a ZnSe multiple bounce ATR on gum aqueous solutions, heated for their preparation. Nevertheless, the work of Wang et al. [14] showed that the computer-simulated molecular space filling structure of GG and LBG was different in no solvent and aqueous environments. In the aqueous environment, GG form presented a more complicated structure than LBG form because of the increase in galactose units on the mannose backbone [14]. In conclusion, spectral data in solid or liquid environment were difficult to compare as done by Prado et al. [12] because the intermolecular interactions in the structure of gums were not the same.

**87**

**Figure 11.**

*Discrimination by Infrared Spectroscopy: Application to Micronized Locust Bean and Guar Gums*

As PLS-DA allowed easily the prediction of the botanical origin of galactomannans, LDA was adapted to the prediction of the proportions (or weights, wi) of pure compounds in blends with the advantage to be governed by a constant sum of wj (equal to 100%). But the fact to provide ordinal predictions (or class) leads to a strict response of model, which considers a wrong classification even if the predictive weights are slightly different from reference data. A selection

variables in this spectral zone were performed to make simplex iterative operations possible, and because in LDA classification method, the number of objects (or pairs of weights) should be larger than the number of variables (wavenumbers). In this way, 325 variables, 11 pairs of weights between 0 and 1 (with a constant increment of 0.1), and a value of *k* (number of iterations in Scheffé's simplex) equal to 400 to be superior at the variable number (the constraint of LDA calibration step) were used to generate 4400 artificial bends (11 × 400)

An example of the repartition of simulated blends generated with this method is given in **Figure 11** where a step of 10% was chosen to clarify the graphic representation. The graph has been obtained after realizing a PCA on

the high variability of chemical composition of pure gums is also observed.

the increment chosen in validation was lower than the calibration step.

*Location of the binary blends in the simplex space defined by GG and LBG proportions (step of 10%).*

Dispersed experimental samples were placed at the extrema of PC1 axis: LBG at the left in the negative part of PC1 and GG at the right in the positive part, respectively. It well appears that simulated blends well take into account the intrinsic variability of pure components, but in certain regions, an inevitable overlapping originating from

Five different LDA calibration models were built with five values of weight step (0.100, 0.050, 0.04, 0.02, and 0.007). The robustness of each calibration model has been tested with four validation sets obtained with steps of 0.10, 0.083, 0.067, and 0.002, without constraint about the number of blends. The results are resumed in

All weights of GG and LBG in pure state and mixtures containing 2–10% of GG were well predicted in the validation step. A percentage of 61% was obtained when

and an average reduction (by two) of

*DOI: http://dx.doi.org/10.5772/intechopen.87568*

**3.4 Quantitative analysis of gum mixtures**

of variables between 1900 and 650 cm<sup>−</sup><sup>1</sup>

from simplex design.

blends' binary data.

**Table 4**.

*Discrimination by Infrared Spectroscopy: Application to Micronized Locust Bean and Guar Gums DOI: http://dx.doi.org/10.5772/intechopen.87568*
