**2. Materials and method**

### **2.1 Sampling**

*Modern Spectroscopic Techniques and Applications*

and an embryo (or germ) [6].

additive list, is a polysaccharide of natural origin, extracted from the seed of *Cyamopsis tetragonoloba* L. (Fabaceae), a plant also called guar or guar bean and native. The world's production of guar is concentrated in India, Pakistan, and the United States with limited amounts grown in South Africa and Brazil. The annual guar plant grows to about 0.6–1 m in height and produces seed pods growing in clusters giving guar pods the common name cluster bean (6–9 guar beans per pod). Locust bean gum (LBG) or E410 is the crushed endosperm of locust bean tree seeds, a dioecious evergreen tree grown in a Mediterranean climate, botanically known as *Ceratonia siliqua* L., belonging to the subfamily *Caesalpinioideae* of the *Fabaceae* family. It is abundant in Spain, Italy, and Cyprus and is also found in other Mediterranean countries, in various regions of North Africa, South America, and Asia. It can reach 8–15 m in height and live up to 500 years. Its fruits are long thick and tough pods containing 10–15 oval-shaped locust bean seeds or kernels. The locust tree can produce annually 300–800 kg of locust bean seeds from which LBG will be produced, also referred as locust bean seed gum, locust bean flour, or *ceratonia*. The guar seed is smaller than the locust bean seed but they both have the same structure. They consist of four elements (**Figure 1**): the tegument (outer husk or seed coat), a translucent endosperm representing 40–50% of the weight of the locust bean seed and 35–45% of the weight of the guar seed, two cotyledons,

The number of tissues within the locust bean would even be 10 according to microscopic studies [7]. These seeds are a basic material for the manufacture of gum and contain hydrocolloids (polysaccharides), called galactomannans, which serve as a reserve for the embryo during germination. With the help of various thermal, mechanical, or chemical processes [2, 5], the seeds are dehusked without damaging the endosperm and the embryo (germ). After this peeling process, the endosperm is split from the cotyledons and then it is ground to produce gum. To eliminate protein content and impurities for certain industrial applications, the gum is purified by washing with solvent or dispersing in boiling water, followed by filtering, evaporation, and drying [2, 5, 6, 8]. Galactomannans are heterogeneous polysaccharides with a high molecular weight, composed by linear chain of β-(1-4)-D-mannopyranosyl units with a single α-D-galactopyranosyl (1-6) linked residue, a conformation similar to that of cellulose. The structure of these gums differs according to the distribution and the number of galactose residue along the mannose chain, randomly arranged in pairs and triplets in the case of the GG [5] leading to regions of low or high substitution, or in blockwise while LBG presents a random, blockwise, and ordered distribution of α-D-galactopyranosyl residues along the β-D-mannose backbone [9]. The degree of galactosyl substitution is responsible for water solubility differences of galactomannans; an increase in the substitution leads to higher solubility through steric effects, whereas galactose-poor regions are less soluble and can involve both inter- and intramolecular associations. Then, GG is dissolved in cold water, while heating is needed to solubilize LBG. The

**76**

**Figure 1.**

*Scheme of gum seed cross-section.*

Guar (n = 74) and locust bean (n = 25) commercial gums were obtained from different suppliers without information about their geographic origins, manufacturing processes, mannose/galactose ratios, and the mesh size of particles (**Table 1**).

These powders were freeze-dried before spectroscopic characterization to eliminate the available water interactions. Mathematical binary blends were also built with simplex approach in different GG percentages (varying between 0 and 100% in weight) from simplex method to take into account the variability of spectral signature of GG and LBG.

Pure sugars (D-mannose and D-galactose) were purchased from Sigma-Aldrich (99% of purity) to obtain its FTIR-ATR profile in the same conditions of the gum sample.

#### **2.2 Attenuated total reflectance (ATR) characterization**

The technique of attenuated total reflectance (ATR) is making easier the solid and liquid analysis by reducing the sample preparation time and increasing spectral


**Table 1.** *Gum suppliers.* *Modern Spectroscopic Techniques and Applications*

**Figure 2.** *A simple reflection ATR system.*

reproducibility by depositing the sample on the crystal of the attenuated total reflection accessory. By crossing the optical dense crystal (with a high refractive index), the infrared beam will undergo the phenomenon of total internal reflection creating an evanescent wave that extends beyond the surface of the crystal and penetrates a few microns into the sample (the least dense medium). If the sample absorbs light, a part of light energy is retained and the total reflection is attenuated (**Figure 2**).

The penetration depth Dp of the evanescent wave at any specific wavelength is a function of the angle ϕ of incidence of the internally reflected beam and of the ratio of the refractive index n1 of the crystal to the index n2 of the sample:

$$\mathbf{D}\_{\mathbf{p}} = \lambda / \left[2\pi \,\mathrm{n}\_{1} \left(\sin^{2}\phi - \left(\mathrm{n}\_{2}/\mathrm{n}\_{1}\right)^{2}\right)\right]^{\mathrm{M}} \tag{1}$$

The refractive index of a diamond crystal is n1 = 2.4 and for the organic compound n2 = 1.5 on the average. For an angle of incidence of 45°, the penetration depth is approximated by Dp = 0.2λ (i.e., between 0.5 and 5/μm approximately for the mid-infrared range). This ATR experiment supposes a very good optical contact between the crystal and the sample. To improve this contact, a press is used.

GG and LBG powders were directly deposited on the attenuated total reflectance (ATR) accessory (Specac's "Golden Gate") equipped with a diamond crystal prism (brazed in only one tungsten carbide part), four mirrors, and two ZnSe focusing lenses to reflect the optical path. The sample was pressed on the crystal area with the pressure arm. FTIR-ATR spectra were recorded with a Thermo Nicolet IS10 spectrometer equipped with a Mercury Cadmium Telluride (MCT) detector, an Ever-Glo source, and a KBr/Ge beam-splitter, at room temperature. Data acquisition was done in absorbance mode from 4000 to 650 cm<sup>−</sup><sup>1</sup> with a 4 cm<sup>−</sup><sup>1</sup> nominal resolution (OMNIC 8.1 software). For each spectrum, 100 scans were co-added. A background scan in air (in the same resolution and scanning conditions used for the samples) was carried out before the acquisition. The ATR crystal was carefully cleaned with ethanol to remove any residual trace of the previous sample. Three spectra were recorded for each sample.

## **2.3 Spectral corrections**

The spectral range of the absorption of the carbon dioxide was removed (between 2400 and 1900 cm<sup>−</sup><sup>1</sup> ) and then a baseline correction was used to adjust the spectral offset. A unit vector normalization was applied to compensate for additive and/or multiplicative effects (**Figure 3**).

**79**

located.

**Figure 3.**

**2.4 Chemometrics**

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

A selection of variable was used between 1450 and 650 cm<sup>−</sup><sup>1</sup>

*Guar and locust bean gum FTIR-ATR spectra: original data (a) and corrected data (b).*

calculated according to the following equations:

treatments to keep only the fingerprint of gums where the anomeric region was

Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) regression are two tools currently used in chemometrics, they are described in a previous study [18, 19]. The optimal number of principal components and latent variables was determined by a cross validation. A binary codification was used to build PLS-DA models which will predict continuous values (positive or negative) and arbitrary intervals of predicted values were chosen as zones delimited the predicted samples well and no-recognized. Also samples with a reference value of one (expected positive) were considered true positive if their predicted value was between 0.7 and 1.3, false negative if predicted under 0.5, and uncertain if predicted between 0.5 and 0.7 or over 1.3. On the contrary, samples with a reference value of zero (expected negative) were considered true negative if predicted between −0.3 and 0.3, false positive over 0.5, and uncertain between 0.3 and 0.5 or under −0.3. The quality of the model was evaluated by the total percentage of correct classification, as well as sensitivity and specificity,

%correct classification of variety = true positive <sup>+</sup> true negative \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ total number of predicted samples × <sup>100</sup> (2)

%sensitivity = true positive \_\_\_\_\_\_\_\_\_\_\_\_\_\_ expected positive × <sup>100</sup> (3)

for the chemometric

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

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

**Figure 3.** *Guar and locust bean gum FTIR-ATR spectra: original data (a) and corrected data (b).*

A selection of variable was used between 1450 and 650 cm<sup>−</sup><sup>1</sup> for the chemometric treatments to keep only the fingerprint of gums where the anomeric region was located.

## **2.4 Chemometrics**

*Modern Spectroscopic Techniques and Applications*

reproducibility by depositing the sample on the crystal of the attenuated total reflection accessory. By crossing the optical dense crystal (with a high refractive index), the infrared beam will undergo the phenomenon of total internal reflection creating an evanescent wave that extends beyond the surface of the crystal and penetrates a few microns into the sample (the least dense medium). If the sample absorbs light, a part of light energy is retained and the total reflection is attenuated

of the refractive index n1 of the crystal to the index n2 of the sample:

Dp = λ/

tion was done in absorbance mode from 4000 to 650 cm<sup>−</sup><sup>1</sup>

spectra were recorded for each sample.

additive and/or multiplicative effects (**Figure 3**).

**2.3 Spectral corrections**

(between 2400 and 1900 cm<sup>−</sup><sup>1</sup>

The penetration depth Dp of the evanescent wave at any specific wavelength is a function of the angle ϕ of incidence of the internally reflected beam and of the ratio

[2π n1(sin<sup>2</sup> ϕ − (n2/ nl)<sup>2</sup>

The refractive index of a diamond crystal is n1 = 2.4 and for the organic compound n2 = 1.5 on the average. For an angle of incidence of 45°, the penetration depth is approximated by Dp = 0.2λ (i.e., between 0.5 and 5/μm approximately for the mid-infrared range). This ATR experiment supposes a very good optical contact between the crystal and the sample. To improve this contact, a press is

GG and LBG powders were directly deposited on the attenuated total reflectance (ATR) accessory (Specac's "Golden Gate") equipped with a diamond crystal prism (brazed in only one tungsten carbide part), four mirrors, and two ZnSe focusing lenses to reflect the optical path. The sample was pressed on the crystal area with the pressure arm. FTIR-ATR spectra were recorded with a Thermo Nicolet IS10 spectrometer equipped with a Mercury Cadmium Telluride (MCT) detector, an Ever-Glo source, and a KBr/Ge beam-splitter, at room temperature. Data acquisi-

resolution (OMNIC 8.1 software). For each spectrum, 100 scans were co-added. A background scan in air (in the same resolution and scanning conditions used for the samples) was carried out before the acquisition. The ATR crystal was carefully cleaned with ethanol to remove any residual trace of the previous sample. Three

The spectral range of the absorption of the carbon dioxide was removed

the spectral offset. A unit vector normalization was applied to compensate for

)] ½

with a 4 cm<sup>−</sup><sup>1</sup>

) and then a baseline correction was used to adjust

(1)

nominal

**78**

(**Figure 2**).

**Figure 2.**

*A simple reflection ATR system.*

used.

Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) regression are two tools currently used in chemometrics, they are described in a previous study [18, 19]. The optimal number of principal components and latent variables was determined by a cross validation. A binary codification was used to build PLS-DA models which will predict continuous values (positive or negative) and arbitrary intervals of predicted values were chosen as zones delimited the predicted samples well and no-recognized. Also samples with a reference value of one (expected positive) were considered true positive if their predicted value was between 0.7 and 1.3, false negative if predicted under 0.5, and uncertain if predicted between 0.5 and 0.7 or over 1.3. On the contrary, samples with a reference value of zero (expected negative) were considered true negative if predicted between −0.3 and 0.3, false positive over 0.5, and uncertain between 0.3 and 0.5 or under −0.3. The quality of the model was evaluated by the total percentage of correct classification, as well as sensitivity and specificity, calculated according to the following equations:

$$\begin{aligned} \text{total percentage or correct classification, as well as ensuring and specification} \\ \text{calculated according to the following equations:} \\ \text{@ correct classification of variety} &= \frac{\text{true positive } \star \text{true negative}}{\text{total number of predicted samples}} \times 100 \text{ (2)} \end{aligned}$$

$$\text{return number or percentage number}$$

$$\text{\textbullet\textbullet\textbullet\textbullet\textbullet\textbullet\textbullet} = \frac{\text{true\\_positive}}{\text{expected\\_positive}} \times 100\tag{3}$$

$$\% \text{\\$Specificity} = \frac{\text{true negative}}{\text{expected negative}} \times 100\tag{4}$$

Linear discriminant analysis (LDA) is the simplest of all possible classification methods that provide a linear transformation of n-dimensional samples into an m-dimensional space (m < n). LDA allowed to develop a model based on predefined classes (GG proportion-LBG proportion) and corresponding FTIR-ATR data of pure gums and gum blends. Here calibration model was built with gum blends generated mathematically from Scheffé's simplex approach (described in the following section) in order to obtain a large number of combinations of gum proportions with different increments (between 0 and 100%) and determine the best fit parameters for classification of gums. These models were then used to classify pure gums or blends not considered in the calibration step. For models, the percentage of recognition (or correct classification) of gum proportions was obtained by calculating first the absolute error on the proportion of each gum as follows:

$$\text{Absolute error} = \left| \text{proportion}^{\text{predicted}} - \text{proportion}^{\text{reference}} \right|\_{\text{Run or local gain}} \quad \text{(5)}$$

A correct classification was considered when absolute error on gum proportion was inferior to the increment and a value of one was attributed to the sample. Otherwise, a zero value was affected to samples with badly predicted gum proportion. Then a percentage of correct classification was calculated.
