*Article* **Geographic Variability of Biologically Active Compounds, Antioxidant Activity and Physico-Chemical Properties in Wild Bilberries (***Vaccinium myrtillus* **L.)**

**Dalia Urbonaviciene 1,\*, Ramune Bobinaite 1 , Pranas Viskelis 1 , Ceslovas Bobinas 1 , Aistis Petruskevicius 1 , Linards Klavins 2 and Jonas Viskelis 1**

**Abstract:** The aim of this study was to characterize the variation in biologically active compounds, antioxidant activity and physico-chemical properties in naturally grown bilberries gathered from different sites in Northern Europe. The variability in the biologically active compounds, antioxidant capacity and physico-chemical properties, as well as the development of tools for the authenticity and quality control of wild bilberries (*V. myrtillus* L.) in different geographical locations was evaluated. The berries of bilberries were handpicked during the summers of 2019 and 2020 during the time periods when they are typically harvested for commercial purposes in Northern Europe (Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU)). Berries from locations in NOR were distinguished by their higher mean TPC (791 mg/100 g FW, average), whereas the mean TPC of samples from the most southern country, LTU, was the lowest (587 mg/100 g FW). The TPC of bilberries ranged from 452 to 902 mg/100 g FW. The TAC values of investigated bilberry samples varied from 233 to 476 mg/100 g FW. A high positive correlation was found between TPC and antioxidant activity of the bilberry samples (R = 0.88 and 0.91 (FRAP and ABTS assays, respectively)), whereas the correlation between TAC and antioxidant activity was lower (R = 0.65 and 0.60). There were variations in the TPC and TAC values of investigated berries, suggesting that genotype also affects the TPC and TAC in berries. In 2020, the pH values and TSS contents of berries were significantly lower than in 2019. To the best of our knowledge, this is the first comprehensive reported evaluation of the biologically active compounds in wild bilberries from different Northern European countries using one laboratory-validated method.

**Keywords:** bilberry; natural habitats; polyphenol; anthocyanins; antioxidant activity; different geographical locations

## **1. Introduction**

Authenticity and traceability are very important criterions of product quality. In order to assure quality, the chemometric approach can be efficiently used for berries and their products. Phenolic compounds are the major group of phytochemicals found in berries. They possess one or more aromatic rings with hydroxyl groups and their structures may range from that of a simple phenolic molecule to that of a complex high-molecular mass polymer [1]. The content of phenolic compounds in berries is determined by many factors, such as genotype, growing conditions, ripeness, storage time and other conditions [2–5]. Researchers have shown that the fruits of berry plants that grow in a cold climate with a short vegetation season and without fertilizers are marked by higher contents of phenolic compounds than the same varieties that grow in a milder climate [6,7]. Phenolic compounds

**Citation:** Urbonaviciene, D.; Bobinaite, R.; Viskelis, P.; Bobinas, C.; Petruskevicius, A.; Klavins, L.; Viskelis, J. Geographic Variability of Biologically Active Compounds, Antioxidant Activity and Physico-Chemical Properties in Wild Bilberries (*Vaccinium myrtillus* L.). *Antioxidants* **2022**, *11*, 588. https:// doi.org/10.3390/antiox11030588

Academic Editors: Agustín G. Asuero, Noelia Tena and Volker Böhm

Received: 23 January 2022 Accepted: 16 March 2022 Published: 19 March 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

can be used for the assessment of authenticity since genotype has a profound impact on the concentration and qualitative composition of these compounds in berries [2,8,9].

Anthocyanin pigments represent a very important group of phenolic substances because they determine the color of both berry fruits and their products [10]. Anthocyanins are glycosides of anthocyanidins of which the most important in the plants are: cyanidin, delphinidin, pelargonidin, peonidin, malvidin and petunidin. The distribution of these anthocyanidins in fruits and vegetables is 50%, 12%, 12%, 12%, 7% and 7%, respectively [11]. Glucose and rhamnose are the more common sugar moieties attached to aglycone (anthocyanidin), but galactose, arabinose, xylose, rutinose, sambubiose and other sugars are also frequently found [12]. Anthocyanins are usually present in colored flavylium cation form but may also be in uncolored form depending on the pH. This characteristic is an important issue when analyzing anthocyanins [13].

The berry genotype predetermines its typical profile of anthocyanins [10,14], thus the anthocyanin profile can be used to identify berry variety [10] for product quality control, label claim verification and raw material source identification [15].

The official method of anthocyanin pigment analysis is the spectrophotometric pH differential method. It is a rapid and simple quantitative method. The pH differential method is not useful for qualitative analysis of anthocyanins [16], but on the other hand, the spectrophotometric method for the quantification of total anthocyanins is widely used for the standardization of natural raw materials [17]. Other validated methods for identification and quantification of anthocyanins include high-pressure liquid chromatography (HPLC) [18,19]. In general, liquid chromatography remains the most common technique used for identification and characterization of anthocyanins in fruit extracts, whereas its combination with electrospray ionization mass spectrometry (ESI-MS) [15] or quadrupole/time-of-flight mass spectrometry (QTOF-MS) gives better quantitation of anthocyanins in more complex samples [19,20], because retention times, UV–vis spectra, mass/charge ratios as well as the type of anthocyanidin and the nature of sugar substitution can be easily recorded and used together to solve anthocyanin structures [21].

Different laboratories use different analysis parameters and conditions in order to optimize separation, thus it is difficult to point to one single (standard) HPLC procedure for determination of anthocyanins [19], but the columns chosen for the determination of anthocyanins are almost exclusively reverse phase columns composed of a C18 stationary phase. The solvent system (mobile phase) typically consists of an aqueous phase and an organic phase (mainly methanol or acetonitrile) with the acidic modifier (mainly formic or acetic acid) [19,22]. Acidic modifiers ensure that anthocyanins predominantly exist in their colored (flavylium cationic) form, having a maximum absorbance in the visible region around 520 nm [23].

The acid hydrolysis HPLC method simplifies the anthocyanin profile of berry samples by converting anthocyanins to anthocyanidin aglycones that can be separated and quantified using external standards. However, in this case part of the information concerning the precise composition and quantity of individual anthocyanins present is lost [24,25]. Furthermore, acid hydrolysis as part of the sample preparation tends to overestimate the anthocyanin content, since proanthocyanidins may be de-polymerized and converted into anthocyanidins [26].

Bilberry (*Vaccinium myrtillus* L.) is one of the richest sources of anthocyanin pigments among berry fruits [27,28]. These colored compounds accumulate not only in the skin of bilberries but also throughout the fruit flesh [25]. Bilberries are characterized by 15 anthocyanins. These include delphinidin, cyanidin, petunidin, peonidin and malvidin, glycosidically linked to glucose, arabinose or galactose [24,27,29]. As mentioned previously, the HPLC profiles of anthocyanins and anthocyanidins present in berries can be used as fingerprints for the evaluation of the authenticity of raw materials [30] as well as finished food products [31].

The aim of this study was to characterize the variation in biologically active compounds, antioxidant activity and physico-chemical properties in naturally grown bilberries

gathered from different sites in Northern Europe. The variability of the biologically active compounds (polyphenol (TPC) and total anthocyanin (TAC) content), antioxidant capacity (FRAP; ABTS RSA) and physico-chemical properties (total soluble solids (TSS), pH), as well as the development of tools for the authenticity and quality control of wild bilberries (*V. myrtillus* L.) in different geographical locations was evaluated.

#### **2. Materials and Methods**

#### *2.1. Extraction*

For determination of anthocyanins, total phenols and antioxidant activity in 50 g of defrosted wild blueberries (*V. myrtillus* L.) were homogenized using Polytron (PT 1200E) (Kinematica, Luzern, Switzerland) for 5 min, then 5.000 g of the homogenized sample was extracted with 40 mL of acidified (0.5% HCl) aqueous ethanol solution (70% *v*/*v*). After a 24 h extraction at 25 ◦C with constant shaking at 160 rpm (Sklo Union LT, Teplice, Czech Republic), samples were filtered with a filter paper (Watman no.1) in a Buchner funnel and stored at 4 ± 1 ◦C until analyzed.

#### *2.2. Analysis*

2.2.1. Determination of Total Phenolic Content (TPC)

The total phenolic contents of berry extracts were determined using the Folin–Ciocalteu method as previously described by Bobinaite et al. [32]. Briefly, the test tubes were filled with 1.0 mL of appropriately diluted extract and mixed with 5.0 mL of Folin–Ciocalteu's phenol reagent diluted in distilled water (1/10, *v*/*v*) and 4.0 mL of Na2CO3 (7.5%). The absorbance of the test solution was read at 765 nm after 60 min of incubation in the darkness using a Genesys-10 UV/Vis spectrophotometer (Thermo Spectronic, Rochester, NY, USA). Gallic acid was used as the standard for the calibration curve and results are expressed in mg of gallic acid equivalents in 100 g of berries (FW) (Table 1).

**Table 1.** Summary of analysis methods.

#### 2.2.2. HPLC Analysis of Total Anthocyanin Content (TAC)

Anthocyanins were analysed using a Waters 2695 series HPLC system, equipped with a Waters 2998 photo diode array detector (DAD) (Waters Corporation, Milford, MA, USA). Analytical separation was carried out using a LiChroCART Purospher® STAR RP-18 endcapped column (250 × 4.6 mm, 5 µm particle size) with a guard column, Purospher STAR RP 18e 4.0 × 4.0 mm 5 µm (Merck KgaA, Darmstadt, Germany), using a slightly modified procedure of Lätti et al. [27]. The temperature of the column oven was set at 25 ◦C. The mobile phase consisted of aqueous 10% formic acid (eluent A) and ACN–MeOH (85:15, *v*/*v*) (eluent B). The gradient program was as follows: 0–2 min, 4–6% eluent B; 2–4 min, 6–8% eluent B; 4–12 min, 8–9% eluent B; 12–46 min, 9–11% eluent B; 46–48 min, 11–24% eluent B; 48–52 min, 24–34% eluent B; 52–59 min, 34–80% eluent B; 59–61 min, 80–20% eluent B; 61–65 min, 4% eluent B. The injection volume was 10 µL.

Anthocyanins were detected at the wavelength of 520 nm. DAD data were recorded from 200 to 600 nm. Anthocyanins in bilberry extracts were identified according to the HPLC retention times (RT) and UV absorbance maximum, in comparison with commercial standards or with literature data [27]. The chromatogram is added to supplementary materials (Figure S1).

Commercial standard (cyanidin-3-glucoside) was dissolved in solvent B (10%) and solvent A (90%) to generate a seven-point external standard calibration curve (with a concentration range from 1 to 100 mg/L) whose linearity was acceptable (R2 = 0.999).

The total content of anthocyanins in the extracts was determined by the sum of the amounts of the individually quantified compounds as equivalents of cyanidin-3-glucoside (C3G) per 100 g of FW of berry (Table 1).

#### *2.3. Determination of Ferric-Reducing Antioxidant Power (FRAP)*

A FRAP assay was performed according to the method of Benzie and Strain [33], with slight modifications [4].

For the FRAP assay, 0.3 M of sodium acetate buffer (pH 3.6) was prepared by dissolving 3.1 g of sodium acetate and 16 mL of acetic acid in 1000 mL of distilled water; a 10 mM TPTZ solution was prepared by dissolving 0.031 g of TPTZ in 10 mL of 40 mM HCl; and a 20 mM ferric solution was prepared by dissolving 0.054 g of FeCl3·6H2O in 10 mL of distilled water. Working FRAP reagent was prepared by freshly mixing acetate buffer, TPTZ and ferric solutions at a ratio of 10:1:1.

For the analysis, 2 mL of freshly prepared FRAP working solution and 20 µL of diluted extract were mixed and incubated for 30 min at ambient temperature. The change in absorbance due to the reduction of ferric-tripyridyltriazine (Fe III-TPTZ) complex by the antioxidants present in the samples was monitored at 593 nm using a Genesys-10 UV/Vis (Thermo Spectronic, Rochester, NY, USA) spectrophotometer. The absorptions of blank samples (by applying the same analysis conditions) were tested each time before and after analysis.

Trolox was used as the standard and the antioxidant activity is expressed as µmol of trolox equivalents (µmol TE) per g of berries (FW) (Table 1).

#### *2.4. Determination of ABTS Radical Scavenging Activity (ABTS RSA)*

The RSA of extracts was also measured by ABTS•+ radical cation assay [34]. ABTS solution (2 mM) was prepared by dissolving 2,20 -azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt in 50 mL of phosphate-buffered saline (PBS) obtained by dissolving 8.18 g NaCl, 0.27 g KH2PO4, 1.42 g Na2HPO4 and 0.15 g KCl in 1 L of pure water. The pH of the prepared solution was adjusted to 7.4 using NaOH. Then K2S2O8 solution (70 mM) was prepared in pure water.

The working solution (ABTS•+ radical cation) was produced by reacting 50 mL of ABTS solution with 200 µL of K2S2O8 solution and allowing the mixture to stand in the dark at room temperature for 15–16 h before use.

For the assessment of antiradical activity of the extracts, 2 mL of ABTS•+ solution was mixed with 20 µL extract in a 1 cm path length cuvette. The reaction mixture was kept at room temperature in the dark for 30 min and the absorbance was read at 734 nm.

Trolox was used as the standard and ABTS RSA is expressed as µmol of trolox equivalents (µmol TE) per g of berries (FW) (Table 1).

#### *2.5. Determination of Dry Matter (DM), Total Soluble Solids (TSS) and pH*

Dry matter content was determined after forced air convention drying at 105 ◦C to a constant weight (Table 1).

The total soluble solids were determined using a digital refractometer (ATAGO PR-32, Atago Co., Ltd., Tokyo, Japan) (Table 1).

The pH was measured using an inoLab Level 1 pH meter with a SenTix 81 (WTW) electrode (Table 1).

#### *2.6. Statistical Analysis*

All the experiments were carried out in triplicate. The mean values and standard deviations of the experimental data were calculated using SPSS 20 software (SPSS Inc., Chicago, IL, USA). One-way analysis of variance (ANOVA) along with the post hoc Tukey's HSD

test were employed for statistical analysis. Differences were considered to be significant at *p* < 0.05.

#### **3. Results**

#### *3.1. Sample Locations*

The ripe berries of bilberry (*V. myrtillus* L.) were handpicked during the summers of 2019 and 2020 during the time periods when they are typically harvested for commercial purposes in Norway, Finland, Latvia and Lithuania in three different locations (Table 2).

**Table 2.** Locations of bilberry sample collection in Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU).

The berry samples were cooled immediately to below 10 ◦C then frozen and stored at −55 ◦C until use.

#### *3.2. The pH and Total Soluble Solids (TSS) Content*

The pH measures the acidity, and the total soluble solids (TSS) shows a high positive correlation with sugar content of fruits and berries [35]. The organoleptic quality and storage life of berries is related to their TSS content and acidity [36]. The pH values of investigated bilberries varied from 2.94 to 3.47 (Figure 1). In 2019, lower mean pH values were found in berries from LVA and NOR (3.30 and 3.32, respectively), whereas in 2020, the lowest mean pH was measured in bilberries from LTU (2.95). In 2020, the pH of bilberries from all countries was significantly lower than in 2019 (Figure 1).

Previously, Giovanelli and Buratti (2009) [37] reported that the pH of Italian bilberries ranged from 3.13 to 3.22. Turkben et al. (2008) [38] reported pH values between 2.77 and 2.95 among wild bilberries from western Turkey. These results are in accordance with the pH values estimated in our study.

The content of TSS in bilberries varied from 9.4 to 15.8 Brix◦ (Figure 2). In 2019, berries from NOR and FIN had higher mean TSS content (12.6 and 13.0 Brix◦ , respectively) than berries from LVA and LTU, whereas in 2020, the mean TSS content of berries from all countries was similar (Figure 2). In 2020, TSS values of the bilberry samples, with the exception of the ones collected in LTU location B10, were significantly lower than the respective values determined in 2019.

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from Turkey ranged from 9.0 to 11.0%. The TSS content in wild bilberries from Italy varied

*Antioxidants* **2022**, *11*, x FOR PEER REVIEW 6 of 14

**Figure 1.** pH values of bilberries. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1– B12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry sample collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU). **Figure 1.** pH values of bilberries. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–B12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry sample collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU). from 10.8 to 11.1% [37], whereas the TSS content in bilberries from Romania—from 9.2 to 13.7% [39]. TSS content in Polish bilberries was 13.0% [40]. Different growth and environment conditions such as temperature, day length and light intensity possibly influenced the TSS of the berries [15].

**Figure 2.** TSS values of bilberries. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–B12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry sample collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU).

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Lithuania (LTU).

*3.3. Total Phenolic Content (TPC)*

TSS values of investigated bilberries were in accordance with previously reported findings. Turkben et al. (2008) [38] reported that the TSS content in *V. myrtillus* berries from Turkey ranged from 9.0 to 11.0%. The TSS content in wild bilberries from Italy varied from 10.8 to 11.1% [37], whereas the TSS content in bilberries from Romania from 9.2 to 13.7% [39]. TSS content in Polish bilberries was 13.0% [40]. Different growth and environment conditions such as temperature, day length and light intensity possibly influenced the TSS of the berries [15]. The total phenolic content (TPC) of the tested bilberry samples ranged from 452 (LTU B12 in 2020) to 902 mg/100 g FW (NOR B2 in 2019) (Figure 3). The highest average TPC value was found in the berries collected in Norway (791 mg/100 g FW in 2019 and 660 mg/100 g FW in 2020). For both years (in 2019 and 2020), the lowest mean TPC value was found in berry samples from Lithuania (587 mg/100 g FW in 2019 and 546 mg/100 g in Total phenolic contents of investigated samples were in accordance with previously

**Figure 2.** TSS values of bilberries. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1– B12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry sample collection include Norway (NOR), Finland (FIN), Latvia (LVA) and

#### *3.3. Total Phenolic Content (TPC)* reported findings. For instance, the TPC value of bilberries collected from a natural

*Antioxidants* **2022**, *11*, x FOR PEER REVIEW 7 of 14

The total phenolic content (TPC) of the tested bilberry samples ranged from 452 (LTU B12 in 2020) to 902 mg/100 g FW (NOR B2 in 2019) (Figure 3). The highest average TPC value was found in the berries collected in Norway (791 mg/100 g FW in 2019 and 660 mg/100 g FW in 2020). For both years (in 2019 and 2020), the lowest mean TPC value was found in berry samples from Lithuania (587 mg/100 g FW in 2019 and 546 mg/100 g in 2020). population in Macedonia was 706 mg/100 g FW [41] and in bilberries from Serbia—890 mg/100 g FW [42]. However, Milivojević et al. [43] determined a somewhat lower TPC of bilberries collected in Serbia (387 mg/100 g FW). The TPC of bilberries from the forest of Poland was reported to be 640 mg/100 g FW [39]. The TPC values of bilberries from natural populations in Norway were reported to range between 512 and 674 mg/100 g FW [44,45].

**Figure 3.** Total phenolic content (TPC) of bilberries mg/100 g FW. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–B12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry sample collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU). **Figure 3.** Total phenolic content (TPC) of bilberries mg/100 g FW. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–B12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry sample collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU).

It has previously been shown that both the growing conditions and the genetic origin of the wild bilberries affect the contents of phenolic compounds [46,47]. The latitude‐ related factor was reported as having a high influence on the quality and quantity of phenolic compounds in bilberries, suggesting that higher phenolic contents may be supported by northern latitudes, altitude and sunny weather [48]. In previous studies, Total phenolic contents of investigated samples were in accordance with previously reported findings. For instance, the TPC value of bilberries collected from a natural population in Macedonia was 706 mg/100 g FW [41] and in bilberries from Serbia— 890 mg/100 g FW [42]. However, Milivojevi´c et al. [43] determined a somewhat lower TPC of bilberries collected in Serbia (387 mg/100 g FW). The TPC of bilberries from the forest of Poland was reported to be 640 mg/100 g FW [39]. The TPC values of bilberries from natural populations in Norway were reported to range between 512 and 674 mg/100 g FW [44,45].

higher contents of phenolic compounds and anthocyanins were detected in bilberry clones originating from higher latitudes [27,46,49]. Interestingly, our data also show that the mean TPC of berries from Norway (the most northern country covered in the study) was the highest, whereas the mean TPC of samples from the most southern country It has previously been shown that both the growing conditions and the genetic origin of the wild bilberries affect the contents of phenolic compounds [46,47]. The latitude-related factor was reported as having a high influence on the quality and quantity of phenolic compounds in bilberries, suggesting that higher phenolic contents may be supported by

(Lithuania) was the lowest (Figure 3). Furthermore, bilberry samples from Norway

northern latitudes, altitude and sunny weather [48]. In previous studies, higher contents of phenolic compounds and anthocyanins were detected in bilberry clones originating from higher latitudes [27,46,49]. Interestingly, our data also show that the mean TPC of berries from Norway (the most northern country covered in the study) was the highest, whereas the mean TPC of samples from the most southern country (Lithuania) was the lowest (Figure 3). Furthermore, bilberry samples from Norway collected in the northernmost location (B2) had the highest TPC value, whereas the TPC values of the samples collected in the southernmost location (B1) in 2019 and 2020 were 23 and 17% lower, respectively. Similarly, among samples collected in Finland, the highest TPC value (700 mg/100 g FW in 2019 and 572 mg/100 g FW in 2020) was determined in berries from the northernmost location (B6) (Figure 3). In 2019, the same trend could also be observed for the bilberry samples from Lithuania, where TPC values also slightly increased with higher latitudes. On the other hand, for Lithuanian samples, this trend was not observed in 2020. The samples collected in Latvia had very similar TPC values, most likely due to the proximity of the sample collection sites. Our results also indicate that there were significant yearly variations in the TPC values of berries (Figure 3), suggesting that although genotype affects the TPC in bilberries, its final content also depends on weather conditions.

#### *3.4. Total Anthocyanin Content (TAC)*

Bilberry is one of the richest sources of anthocyanins, which have multiple biological activities [11]. The mean TAC values of investigated bilberry samples were 401.9 and 327.5 mg/100 g FW in 2019 and 2020, respectively. It is worth noting that within the same year, the mean TAC values of the berry samples from different countries did not differ significantly, with one exception—Lithuanian bilberries in 2019, which had a significantly lower mean TAC (Figure 4). The highest TAC values were from two berry samples collected in 2019 in the northernmost locations (B2 and B6) in Norway and Finland (475.4 and 454.6 mg/100 g FW, respectively). *Antioxidants* **2022**, *11*, x FOR PEER REVIEW 9 of 14

**Figure 4.** Total anthocyanin content (TAC) of bilberries mg/100 g FW. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry samples collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU). **Figure 4.** Total anthocyanin content (TAC) of bilberries mg/100 g FW. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry samples collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU).

The antioxidant activity of berry samples was evaluated using FRAP and ABTS assays. In the ABTS assay, antioxidants suppress the generation of a blue‐green ABTS radical cation by electron donation radical scavenging, whereas in the FRAP assay, there are no free radicals involved, but the reduction of ferric‐to‐ferrous iron is monitored.

In 2019, the FRAP of bilberry samples ranged from 36.0 (LTU B10) to 57.7 μmol TE/g FW (NOR B2) and in 2020, from 35.1 (LTU B12) to 49.1 μmol TE/g FW (NOR B2) (Figure 5). For both years of investigation, the highest mean FRAP values were from berry samples collected in Norway (50.6 μmol TE/g and 46.6 μmol TE/g FW in 2019 and 2020, respectively), followed by samples collected in Latvia (45.0 μmol TE/g and 46.3 μmol TE/g FW in 2019 and 2020, respectively). Bilberry samples from Lithuania had the lowest mean FRAP values (41.2 μmol TE/g and 40.2 μmol TE/g FW in 2019 and 2020, respectively). The FRAP results obtained in our study are close to those previously reported (53 and 57 μmol

*3.5. Antioxidant Activity (AA)*

TE/g FW) in *V. myrtillus* fruits [50].

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TAC values of investigated bilberry samples were in accordance with previously reported findings. For instance, Skrede and colleagues [44] reported that the concentration of anthocyanins in bilberry samples ranged from 429 to 627 mg/100 g FW [44]. The TAC of bilberries from Macedonia was 507 mg/100 g FW [41]. Rohloff et al. [45] reported somewhat lower amounts of total anthocyanins in Norwegian bilberries (from 330 to 449 mg/100 g FW) [45], whereas the TAC of Finnish bilberries from 20 different populations varied from 350 to 525 mg/100 g FW [27].

It has been shown that genotype and environment interaction affect accumulation of anthocyanins in bilberries [7,45,47]. The increasing trend in anthocyanin content has been repeatedly observed in bilberries toward northern latitudes of Europe [27,49]. However, when effects of different environmental factors on berry chemical composition were studied in eight forest fields of bilberry in the north, the middle and the south of Norway, previous findings concerning latitudinal effects on anthocyanin concentration were not confirmed [45]. The authors concluded that environmental impacts probably confounded the genetic (population) effects [45].

With regard to increased TAC towards northern latitudes, the trend was not clear in the present study. In 2019, the highest TAC values were from two samples collected in the northernmost locations (B2 and B6), however, in 2020, the berries from locations B2 and B6 had high, but not the highest TAC values (Figure 4).

In 2020, TAC of the bilberry samples, with the exception of the ones collected in LTU location B10, were 3 to 39% lower than the respective values determined in 2019, which suggest significant influence of environmental factors on the accumulation of anthocyanins.

#### *3.5. Antioxidant Activity (AA)*

The antioxidant activity of berry samples was evaluated using FRAP and ABTS assays. In the ABTS assay, antioxidants suppress the generation of a blue-green ABTS radical cation by electron donation radical scavenging, whereas in the FRAP assay, there are no free radicals involved, but the reduction of ferric-to-ferrous iron is monitored.

In 2019, the FRAP of bilberry samples ranged from 36.0 (LTU B10) to 57.7 µmol TE/g FW (NOR B2) and in 2020, from 35.1 (LTU B12) to 49.1 µmol TE/g FW (NOR B2) (Figure 5). For both years of investigation, the highest mean FRAP values were from berry samples collected in Norway (50.6 µmol TE/g and 46.6 µmol TE/g FW in 2019 and 2020, respectively), followed by samples collected in Latvia (45.0 µmol TE/g and 46.3 µmol TE/g FW in 2019 and 2020, respectively). Bilberry samples from Lithuania had the lowest mean FRAP values (41.2 µmol TE/g and 40.2 µmol TE/g FW in 2019 and 2020, respectively). The FRAP results obtained in our study are close to those previously reported (53 and 57 µmol TE/g FW) in *V. myrtillus* fruits [50].

Berries showed higher antioxidant activity in the ABTS reaction system (Figure 6). The ABTS RSA of bilberries ranged from 60.9 (LTU B12 in 2020) to 106.0 µmol TE/g FW (NOR B2 in 2020). In 2019, the highest mean ABTS RSA was from berry samples collected in Norway (95.1 µmol TE/g FW), followed by samples collected in Finland (81.3 µmol TE/g FW), whereas in 2020, the highest mean ABTS RSA was from berry samples from Latvia (89.9 µmol TE/g FW) followed by samples from Norway (83.3 µmol TE/g FW) (Figure 6).

It was observed that bilberries from different geographical locations vary significantly in quantity of antioxidant activity. It is stated that plants are exposed to natural climatic stress due to environmental differences. This study's results show that in the case of wild bilberries, the natural variation in biologically active compounds and antioxidant activity has shown an authenticity of the berries from northern countries in comparison to more southern natural habits.

(LTU).

the FRAP and ABTS assays, respectively).

antioxidant activity of bilberries.

natural climatic stress due to environmental differences. This study's results show that in

**Figure 4.** Total anthocyanin content (TAC) of bilberries mg/100 g FW. Values are presented as means ± standard deviations. Different letters within the same column indicate significant differences between the collection locations (B1–12) (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry samples collection include Norway (NOR),

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12

NOR FIN LVA LTU

The antioxidant activity of berry samples was evaluated using FRAP and ABTS

In 2019, the FRAP of bilberry samples ranged from 36.0 (LTU B10) to 57.7 μmol TE/g

assays. In the ABTS assay, antioxidants suppress the generation of a blue‐green ABTS radical cation by electron donation radical scavenging, whereas in the FRAP assay, there

FW (NOR B2) and in 2020, from 35.1 (LTU B12) to 49.1 μmol TE/g FW (NOR B2) (Figure 5). For both years of investigation, the highest mean FRAP values were from berry samples collected in Norway (50.6 μmol TE/g and 46.6 μmol TE/g FW in 2019 and 2020, respectively), followed by samples collected in Latvia (45.0 μmol TE/g and 46.3 μmol TE/g FW in 2019 and 2020, respectively). Bilberry samples from Lithuania had the lowest mean FRAP values (41.2 μmol TE/g and 40.2 μmol TE/g FW in 2019 and 2020, respectively). The

are no free radicals involved, but the reduction of ferric‐to‐ferrous iron is monitored.

Finland (FIN), Latvia (LVA) and Lithuania (LTU).

*3.5. Antioxidant Activity (AA)*

cd\*

0

100

200

**TAC,**

**mg/100g FW**

300

400

500

600

bc

a\*

b\*

a ab c

c\* c\*

ab\*

b\* b\* b\*

a a a ab a a a

d

c c\*

2019 2020

d

TE/g FW) in *V. myrtillus* fruits [50].

**Figure 5.** Ferric-reducing antioxidant power (FRAP) of bilberries (µmol TE/g FW). Different letters above the same color bars indicate significant differences between the mean values (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry samples collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU). the case of wild bilberries, the natural variation in biologically active compounds and antioxidant activity has shown an authenticity of the berries from northern countries in comparison to more southern natural habits.

**Figure 6.** ABTS radical scavenging activity (RSA) of bilberries (μmol TE/g FW). Different letters above the same color bars indicate significant differences between the mean values (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry samples collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania **Figure 6.** ABTS radical scavenging activity (RSA) of bilberries (µmol TE/g FW). Different letters above the same color bars indicate significant differences between the mean values (*p* < 0.05). Significant differences between 2019 and 2020 are indicated by asterisks (\*) (*p* < 0.05). Locations of bilberry samples collection include Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU).

determined by the FRAP and ABTS assays, respectively), whereas the correlation between TAC and antioxidant activity was somewhat lower (R = 0.65 and 0.60 as determined by

*corymbosum*) and wild bilberries (*Vaccinium myrtillus*) and reported that the antioxidant capacity of berries strongly correlated with the content of total anthocyanins and total phenolics. However, contrary to our findings, a higher correlation coefficient was found between the antioxidant capacity and total anthocyanin content (R = 0.93) than between the antioxidant capacity and total phenolic content (R = 0.89) of berries [37]. Uleberg et al. [46] also reported quite strong correlations between anthocyanins, total phenolics and

In agreement with previous findings [37,51] a high positive correlation was found

Previously, Giovanelli and Buratti [37] investigated cultivated blueberries (*Vaccinium*

In agreement with previous findings [37,51] a high positive correlation was found between TPC and antioxidant activity of the bilberry samples (R = 0.88 and 0.91 as determined by the FRAP and ABTS assays, respectively), whereas the correlation between TAC and antioxidant activity was somewhat lower (R = 0.65 and 0.60 as determined by the FRAP and ABTS assays, respectively).

Previously, Giovanelli and Buratti [37] investigated cultivated blueberries (*Vaccinium corymbosum*) and wild bilberries (*Vaccinium myrtillus*) and reported that the antioxidant capacity of berries strongly correlated with the content of total anthocyanins and total phenolics. However, contrary to our findings, a higher correlation coefficient was found between the antioxidant capacity and total anthocyanin content (R = 0.93) than between the antioxidant capacity and total phenolic content (R = 0.89) of berries [37]. Uleberg et al. [46] also reported quite strong correlations between anthocyanins, total phenolics and antioxidant activity of bilberries.

#### **4. Conclusions**

With the increasing globalization of the food supply chain, food quality and safety have become of increased concern for consumers, food producers and governments. Although there is no reconciled definition of food fraud, it is generally accepted that food fraud is committed intentionally for financial gain through consumers. Nowadays, the most common food categories susceptible to any type of food fraud are the so-called premium food products, including non-timber forest products such as bilberries. The biochemical composition and nutritional value of berries are particularly relevant in the production of various pharmaceutical products and food supplements. In addition, there are natural or unavoidable flaws which may be caused by climatic or annual conditions. Nevertheless, berry authenticity research is important to ensure that the berries offered for sale are of the nature, substance and quality expected by the purchaser. Bilberries have different amounts of bioactive substances (anthocyanins, phenolics, etc.), depending on the place of growth and climatic conditions, so buyers are interested in knowing where those berries came from, as berries from certain regions or countries will also fetch a higher price due to their unique qualities. Spectrophotometric methods are fast and easy and therefore highly suitable to be used in the comparison of berry quality, especially for commercial purposes.

This study provided information concerning polyphenol (TPC) and total anthocyanin (TAC) content, antioxidant capacity (FRAP; ABTS RSA) and physico-chemical properties (total soluble solids (TSS), pH) of wild bilberry (*V. myrtillus* L.) populations from the Northern Europe (Norway (NOR), Finland (FIN), Latvia (LVA) and Lithuania (LTU)), including three locations in each country. Variations in biologically active compounds, antioxidant activity and physico-chemical properties in wild bilberries (*Vaccinium myrtillus* L.) have not been previously systematically investigated. Some studies have been conducted in order to evaluate the extent of the variation in the anthocyanins of bilberries or blueberries in one country, but there is little information in the results indicating geomorphological influence on the biochemical and physico-chemical compositions of bilberries depending on their place of origin. These may be used as discriminating criteria for distinguishing between bilberries from different geographical origins.

In conclusion, our study results show that the pH values of investigated bilberries varied from 2.94 to 3.47. These results are in accordance with the pH values estimated from literature data (2.77–3.22) [36,37]. The TSS content of bilberries varied within a similar range—from 9.4 to 15.8 Brix◦ . In 2020, the pH values and TSS content of berries were significantly lower than in 2019, showing that different growth and environment conditions such as temperature, day length and light intensity possibly influence the TSS of the berries.

The TPC of bilberries ranged from 452 to 902 mg/100 g FW. The mean TPC of bilberries from Norway (the most northern country covered in the study) was the highest, whereas the mean TPC of samples from the most southern country (Lithuania) was the lowest. The TAC values of investigated bilberry samples varied from 232.7 to 475.5 mg/100 g FW and were somewhat lower in 2020 than in 2019, which could be due to several factors, including genetic variation and environmental conditions.

The ABTS RSA of bilberries ranged from 60.9 (LTU B12 in 2020) to 106.0 µmol TE/g FW (NOR B2 in 2020). In 2019, the highest mean ABTS RSA had berry samples collected in Norway (95.1 µmol TE/g FW), followed by samples collected in Finland (81.3 µmol TE/g FW), whereas in 2020, the highest mean ABTS RSA was from berry samples from Latvia (89.9 µmol TE/g FW) followed by samples from Norway (83.3 µmol TE/g FW). A high positive correlation was found between TPC and antioxidant activity of the bilberry samples (R = 0.88 and 0.91 as determined by the FRAP and ABTS assays, respectively), whereas the correlation between TAC and antioxidant activity was lower (R = 0.65 and 0.60 as determined by the FRAP and ABTS assays, respectively).

In recent years, a growing demand for healthy food has been noted in the market. Consumers are primarily interested in food which is appealing, helps in preventing various diseases and contains high levels of promoted bioactive compounds. Our results could therefore be very useful for nutritional studies on anthocyanin- and phenolic compoundrich food and berry authenticity research. Results indicated a geomorphological influence on the biochemical and physico-chemical compositions of bilberries depending on their place of origin. These may be used as discriminating criteria for distinguishing between bilberries from different geographical origins.

**Supplementary Materials:** The following supporting information can be downloaded online: https: //www.mdpi.com/article/10.3390/antiox11030588/s1, Figure S1: HPLC-PDA chromatogram (λ = 520 nm) of the wild bilberry B2 (*Vaccinium myrtillus* L.) samples extracts of 2019 and 2020.

**Author Contributions:** Conceptualization, D.U., R.B., P.V., C.B., L.K. and J.V.; writing—original draft preparation, D.U., R.B., P.V., C.B., A.P. and P.V.; writing—review and editing, D.U., R.B., P.V., L.K. and J.V.; visualization, D.U. and J.V.; supervision, D.U., R.B. and J.V.; project administration, D.U., R.B., P.V. and J.V.; funding acquisition, D.U., R.B., P.V., C.B. and L.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was financed by the Lithuanian Research Centre for Agriculture and Forestry and attributed to the long-term research program, "Horticulture: agrobiological foundations and technologies".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article and Supplementary Materials.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Acknowledgements**

We thank Professor Belboukhari Nacer from Tahri Mohammed University of Béchar, for the training and permission to use the laboratory instruments. His experience, his knowledge and his appetite for the field contributed greatly to the success of our research.

We appreciate the help of the staff of the Bioactive Molecules and Chiral Separation Laboratory at Béchar University.


**1. Introduction**

The global burden of aortic valvular disease continues to rise due to an increasingly aged population [1]. The traditional treatment of aortic valve disease involved surgical aortic valve replacement (SAVR). However, with the arrival of transcatheter aortic valve replacement (TAVR), the therapeutic landscape has dramatically changed. SAVR is often precluded in patients at a very high risk for surgery, for example, frailty, extreme obesity, porcelain aorta, severe pulmonary hypertension, severe right ventricular dysfunction, severe liver disease, severe lung disease, poorly controlled diabetes and impaired renal function [2]. TAVR's indication has now been expanded to intermediate and low-risk patients [3]. This is based on a series of clinical trials comparing TAVR with SAVR [4–9]. Thus, TAVR is now approved for all patient risk profiles, representing a therapeutic option for all patients regardless of age [3].

However, in young, low-risk patients with severe aortic stenosis, current guidelines recommend shared decision making, centred around patient preferences and beliefs [10, 11].

Of note, recent years, have seen an ever-increasing number of bioprostheses being implanted [12, 13]. More than 85% of implanted SAVRs are bioprosthetic [14]. This will inevitably lead to an enlarging population and potential future pandemic of patients requiring reoperation/reintervention for structural valve deterioration. Valve-in-valve transcatheter aortic valve replacement (ViV-TAVR) is a safe and effective alternative to redo SAVR and is currently approved for higher-risk patients deemed inoperable.

In this chapter, we examine the literature in detail and study the major reported technical issues with ViV-TAVR, the evidence supporting its use and the critical issue of what is the current optimum lifetime treatment strategy for aortic valve disease, particularly in younger patients. The advent of wider TAVR implantation in increasingly younger patients, having a longer life expectancy than the expected longevity of the bioprosthesis, has mandated a focused discussion of this issue. This is because the primary aortic valve intervention significantly influences subsequent valve therapies and what is best strategy, if indeed there is a single best strategy, is not yet established.

#### **1.1 Structural valve degeneration**

Bioprosthetic valve dysfunction is simply categorised as either (A) non-structural valve deterioration: valve thrombosis or endocarditis, paravalvular regurgitation, patient-prosthesis mismatch, *or* (B) structural valve deterioration: irreversible permanent degenerative intrinsic valve alterations [15, 16].

Of note, there is a wide variation in structural valve deterioration definition in the literature, leading to similar variations in reported valve failure incidences. The majority of SAVR studies define valve failure based on the need for reintervention. This likely underestimates the true incidence of structural valve deterioration, which is heavily dependent on manufacturer and prosthesis type.

2021 Valve Academic Research Consortium 3 (VARC-3) guidelines use 3 stages to define bioprosthetic valve failure: (1) any bioprosthetic valve dysfunction with clinically expressed criteria dysfunction, (2) valve intervention and (3) valve-related death [16, 17].

The optimum treatment of structural valve deterioration is yet to be defined and is likely to be bespoke and personalised according to anatomical, original valve- and patient-risk-related criteria. Approaches broadly compete between (A) traditional or (B) minimally invasive redo-SAVR and (C) ViV-TAVR valve.

#### **1.2 Valve in valve TAVR versus redo SAVR: the evidence**

There are no randomised controlled trials studying the best treatment of structural valve deterioration. There is also an obvious scarcity of long-term data on ViV-TAVR. Most studies are less than 5 years' duration, and there are no head-to-head comparison studies with redo-SAVR.

At present, ViV-TAVR is the treatment of choice for patients with structural valve deterioration considered high risk for redo-SAVR. However, redo-SAVR remains the first choice among patients at low-intermediate surgical risk unless unfavourable anatomies are present, for example, calcified aortic root or hostile chest.

*Valve-in-Valve Transcatheter Aortic Valve Replacement: Challenges for Now and the Future DOI: http://dx.doi.org/10.5772/intechopen.112764*

Several meta-analyses demonstrate lower incidence of post-operative complications and 30-day mortality and similar 1-year and mid-term mortality rates for ViV-TAVR versus redo SAVR [18–20].

Pompeu et al. analysed 12 studies with 16,207 patients, comparing ViV-TAVR with redo-SAVR, published between 2015 and 2020. In their pooled analysis, ViV-TAVR was associated with significantly lower rates of 30-day mortality, major bleeding and shorter hospital stay. However, patients receiving ViV-TAVR were 4 times more likely to have severe patient prosthesis mismatch [18]. No difference in mortality was seen at 1 year. Thandra et al. analysed 9 observational studies with 2891 patients and a mean follow-up of 26 months. They too demonstrated significantly lower 30-day mortality, bleeding and length of stay but higher post-operative gradients with ViV-TAVR compared with redo-SAVR [19]. Saleem et al. analysed 11 studies including 8326 patients and showed similar findings. At 30-days, the risk of all-cause mortality, cardiovascular mortality and major bleeding were significantly lower with ViV-TAVR. At up to a 5-year follow-up, no significant difference in all-cause mortality, cardiovascular mortality and stroke was seen. However, again, ViV-TAVR showed a higher risk of patient prosthesis mismatch and greater transvalvular pressure gradients [20]. Hirji et al. looked at more than 3000 US patients, comparing ViV-TAVR versus redo-SAVR using the National Readmissions Database. Using propensity score matching, VIV-TAVR showed superiority over redo-SAVR in terms of 30-day mortality, 30-day morbidity, bleeding and hospital length of stay [21].

In the absence of good randomised control trials, later published meta-analyses draw similar conclusions [22–24]. Raschpichler et al. analysed 15 studies and 8881 patients; 50.2% underwent ViV TAVR and 49.8% redo-SAVR. Short-term mortality was 2.8% with ViV-TAVR compared with 5.0% with redo-SAVR, and again, mid-term mortality did not significantly differ (maximum follow-up 5 years). Again, significant, prosthetic valve regurgitation was 4 times more likely with ViV-TAVR, and severe patient prosthesis mismatch was 3 times more likely [22].

Formica analysed 12 studies with 3457 patients. The redo-SAVR group included 1783 patients and ViV-TAVR 1764. Redo-SAVR showed a higher incidence of all-cause mortality within 30 days with no difference observed between 30 days and 1 year and at a 5-year follow-up [23].

Bruno et al. analysed 11 studies with 8570 patients, 4224 undergoing ViV-TAVR and 4346 redo-SAVR. The studies focussed on intermediate-high-risk patients. 30-day all-cause and cardiovascular mortality were significantly lower with ViV-TAVR. At a mean follow-up of 717 days, there was no mortality difference between techniques. Major bleeding and new-onset atrial fibrillation were significantly lower with ViV-TAVR [24].

#### *1.2.1 Limitations*

These meta-analyses include non-randomised retrospective studies and are vulnerable to the inherent weaknesses of observational data. Therefore, results are to be interpreted with caution. In addition, clinically relevant and important valveassociated factors such as size, design and the precise manner of deterioration were rarely analysed and are of vital importance.

Other limitations include limited follow-up (<1 year in many studies), small sample sizes, a lack of randomisation and the inclusion of many retrospective observational studies. The lack of clear reported selection criteria in many included studies as well as a wide variation of inclusion criteria among studies are other limitations.

This gives rise to the obvious negatives of selection and allocation bias. As mentioned earlier, lack of data relating to degenerated prosthesis type; implanted bioprosthesis type, for example, stented, stentless and rapid deployment; the type of implanted TAVR (self-expanded versus balloon-expandable) and TAVR approach route renders meaningful scientific hard conclusions difficult to make. Randomised control trials with longer follow-ups and large multi-centre registries are essential to better analyse and define the differences in survival between these two procedures.

The overall broad conclusion of these large meta-analyses is that ViV-TAVR demonstrates better short-term mortality compared with redo-SAVR, but mid-term mortality is similar. Higher rates of severe patient prosthesis mismatch, high transvalvular gradients and post-procedural aortic regurgitation are associated with ViV-TAVR. Given the likely selection/allocation bias in the included studies and limitations mentioned earlier, authors universally advocate an adequately powered multi-centre randomised control trial with sufficiently long follow-up.

In a recent retrospective, propensity score-matched, multi-centre UK study, 911 patients were studied between 2005 and 2021. 125 pairs for analysis were created with a mean age of 75 years. In-hospital mortality was 7.2% for redo-AVR versus 0% for ViV-TAVR (p = 0.002). Intensive care unit and hospital length of stay and postoperative complications were significantly reduced with ViV- TAVR, but rates of moderate aortic regurgitation at discharge and elevated post-procedural gradients were increased [25]. Median follow-up was 4.2 years for redo-AVR and 3.1 years for ViV-TAVR, and no difference in mid-term survival was found in discharged patients. **Table 1** summarising the publications comparing ViV-TAVR with redo-SAVR.

#### **1.3 Bioprosthetic valve failure**

#### *1.3.1 Pre-disposing factors*

Minimising the chances of bioprosthetic valve failure is critical, and modifiable factors should be addressed to the maximum if possible, to avoid/retard structural valve degeneration. Patient characteristics, comorbidities, the type and size of implanted valve contribute to valve failure. Ochi et al. identified multiple risk factors for structural valve degeneration. Presence of patient prosthesis mismatch, subcoronary implantation technique, absence of anti-calcification preparation, concomitant coronary artery bypass graft surgery, small valve sizes, high post-implantation gradients and renal disease were all implicated.

Meta-analysis identified younger age, increased body surface area, smoking and patient prosthesis mismatch as significant drivers of structural valve degeneration [26].

#### *1.3.2 Patient-prosthesis mismatch*

Discussion relating to patient prosthesis mismatch is complex and extensive and is not the focus of this chapter. However, review of the literature suggests that patient prosthesis mismatch is likely a critical factor contributing to structural valve degeneration [27]. Patient prosthesis mismatch can be and must be mitigated at the time of initial SAVR by implanting an appropriately sized valve, selecting the optimum valve design profile and/or surgical intervention to facilitate the implantation of an appropriately sized valve. Patients at high risk of significant patient prosthesis mismatch ideally should be identified pre-operatively, with the application of a targeted

*Valve-in-Valve Transcatheter Aortic Valve Replacement: Challenges for Now and the Future DOI: http://dx.doi.org/10.5772/intechopen.112764*

*Valve-in-Valve Transcatheter Aortic Valve Replacement: Challenges for Now and the Future DOI: http://dx.doi.org/10.5772/intechopen.112764*

preventative strategy to reduce the occurrence and severity of patient prosthesis mismatch. This is particularly important in younger patients and in those with depressed left ventricular function. Patient prosthesis mismatch is defined as occurring when the effective orifice area of the implanted prosthetic valve is inadequate for the patient's body surface area and activity. Patient prosthesis mismatch is defined by indexed effective orifice area/body surface area and is graded in severity as follows: none (>0.85 cm2 /m2 ), moderate (0.85–0.65 cm2 /m2 ), and severe (≤0.65 cm2 /m2 ). The incidence of moderate to severe patient prosthesis mismatch following SAVR has been reported as high as 65% [28], and patient prosthesis mismatch post-SAVR is more common than no patient prosthesis mismatch [29, 30]. Increasing patient prosthesis mismatch grade is associated with a stepwise increase in long-term all-cause mortality [30]. The seriousness and clinical relevance of moderate patient prosthesis mismatch is unclear, controversial and still debated. Some studies propose that only severe patient prosthesis mismatch translates into clinically relevant harmful effects, with others proposing that even moderate severity is clinically damaging [28–31]. Severe patient prosthesis mismatch following SAVR has been shown to be associated with an increased risk of redo-SAVR by some [28] and not others [29, 30], but significantly raised readmission rates and decreased survival are clearly demonstrated [28–31].

#### *1.3.3 Valve selection and surgical aortic root enlargement*

Selection of bioprosthesis and accurate sizing is critical in the initial treatment of aortic valve disease. The largest valve that can be safely implanted is the general principle to be followed, and internal orifice diameter is of the primary importance. This should be identified and appreciated and differs between valve models and manufacturers for the same labelled valve size. The minimal prosthetic valve effective orifice area required to avoid patient prosthesis mismatch should be calculated and then a prosthetic valve model and size that fits into the patient's aortic annulus/root selected, which meets the minimum effective orifice area calculated.

A small aortic annulus may necessitate aortic root enlargement or root replacement during SAVR. During TAVR, the initial valve that provides the largest effective orifice area and the best haemodynamics is chosen. One advantage of TAVR planning is the detailed CT aortography and annulus assessment performed pre-intervention, thus facilitating optimum prosthesis selection. Aortic root intervention during SAVR should be guided by effective orifice area index and considered when falling below ≤0.85 cm2 /m2 , particularly in young patients. However, aortic root enlargement is performed in 10% or less of patients receiving SAVR [32].

Several surgical techniques exist to augment aortic root diameter. Detailed discussion of them is not the focus of this chapter, but more awareness of and emphasis on the principle of their use at primary aortic valve intervention. Nicks and Manouguian procedures enlarge the aortic annulus using a posterior extension of the aortotomy. The Nicks extends through the non-coronary sinus and the Manouguian through the left/non-coronary commissure with extension onto the anterior mitral leaflet [33, 34]. Closure is usually then enabled with the use of an aortic patch technique. A Konno procedure is very rarely performed in adults and involves anterior annular augmentation extending onto the right ventricle [35]. Other less common enlargement techniques are also available but are rarely used in everyday practice. Aortic root replacement during SAVR reduces rates of patient prosthesis mismatch and is safe with no added risk, but whether it improves long-term outcomes remains unproven [36, 37].

*Valve-in-Valve Transcatheter Aortic Valve Replacement: Challenges for Now and the Future DOI: http://dx.doi.org/10.5772/intechopen.112764*

Of note, TAVR has been associated with reduced risk of patient prosthesis mismatch compared to SAVR, especially in patients with small aortic annuli, particularly in patients receiving a valve size ≤23 mm [38, 39]. SAVR with sutureless prosthesis has also shown excellent haemodynamics and similar rates of patient prosthesis mismatch to TAVR [40]. These findings and their exact future clinical relevance require further exploration and clarification. They re-highlight that valve genre/species selection as well as size, too, need careful consideration by all members of the structural heart team including the surgeon. This represents yet another critical factor when planning primary aortic valve intervention, particularly in the young and those with small aortic roots.

#### **1.4 Technical issues associated with valve-in-valve TAVR**

#### *1.4.1 Elevated post-implantation gradients*

Valve-in-Valve International Data (VIVID) Registry shows elevated postprocedural gradients and severe patient prosthesis mismatch to occur in 26.8% [41]. It is more common with balloon expandable devices compared to self-expanding devices and in surgical valves ≤21 mm. These figures apply to when the bioprosthetic valve ring fracture technique is not utilised [41]. It is suggested that only severe patient prosthesis mismatch post-ViV-TAVR may affect mortality [42]. However, it is wise to aim for as low post-procedural gradients as possible, to enhance valve durability and patient performance, particularly in patients having extended life expectancy.

Patient prosthesis mismatch is not infrequent following SAVR in patients with small anatomies and is highly relevant during the planning of reintervention for structural valve deterioration. Surgical 19 mm bioprostheses are of particular concern and display high physiological mean gradients (10–25 mmHg) [43].

ViV-TAVR is associated with haemodynamic deterioration with gradient increase ≥10 mmHg between discharge and 30-day follow-up in the STS/ACC TVT registry [44]. Understandably, patients at the greatest risk for severe patient prosthesis mismatch following ViV-TAVR were those arriving with structural valve deterioration following previous SAVR complicated by severe patient prosthesis mismatch [41, 45]. Severe patient prosthesis mismatch prior to ViV-TAVR displays higher 30 day and 1-year mortality [46]. Such clear findings again re-highlight the absolute importance of appropriate, far-sighted primary aortic valve intervention. The critical importance and complexity of post-ViV-TAVR patient prosthesis mismatch is reflected by the creation of a patient prosthesis mismatch predictive calculator by the VIVID registry [47].

Patient prosthesis mismatch following ViV-TAVR is complex and multi-factorial, and numerous contributing factors have been proposed: (A) pre-procedural—baseline patient prosthesis mismatch, stented bioprosthesis, small bioprosthesis and stenotic failure; (B) procedural—intra-annular transcatheter heart valve, deep implantation and non-fractureable valve and (C) post-procedural—structural valve deterioration, leaflets thrombosis and transcatheter heart valve-associated prosthesis-patient mismatch.

#### *1.4.2 Positioning of valve during valve-in-valve TAVR*

The choice of a supra-annular valve and a high position of implant have shown success in reducing the risk of high post-procedure gradients [48]. Better leaflet

function and haemodynamic results may be achieved using transcatheter heart valve with supra-annular valve position. Experimental in-vitro study has shown that in failed surgical 19 mm stented bioprostheses, a supra-annular implantation of a transcatheter heart valve lowers post-procedural gradients and augments effective orifice area [48]. A clinical study has shown high implantation depth inside failed bioprostheses to be a strong independent predictor of lower post-procedural gradients in both self-expanding and balloon-expandable transcatheter valves [49]. The situation is complex with variations that need to be appreciated between prosthesis types. Self-expanding valves display lower post-ViV-TAVR gradients than balloonexpandable valves especially in pre-existing severe patient prosthesis mismatch [50].

In TAVR, deep implantation strongly predicts patient prosthesis mismatch, with recommended cut-offs for high positioning for CoreValve/Evolut and SAPIEN 3 being 5 mm and 20%, respectively [51, 52]. Conversely, the optimal height for deployment for ViV-TAVR prostheses remains undefined. Elevated risk of aortic regurgitation and valve embolization are concerns surrounding higher valve implantation depth, concerns that affect different prostheses to varying degrees [53, 54].

#### *1.4.3 Bioprosthetic valve fracture*

Bioprosthetic valve fracture is proposed as another technique to ameliorate or prevent high post-procedural gradients [55]. The aim is to increase the true internal orifice diameter of the transcatheter heart valve to facilitate either a (A) larger transcatheter heart valve or (B) better expanded transcatheter heart valve to be implanted, increase effective orifice area and enhance haemodynamic function.

Importantly, not all stented valves allow fracture. For example, experimental testing reveals Abbott Trifecta and Medtronic Hancock II valves cannot be fractured [56, 57]. It follows that sutureless and stentless valves are also not suitable for fracture but can be remodelled using an over-expansion technique [58].

Bioprosthetic valve fracture is performed using high-pressure, non-compliant balloons, such as the Atlas Gold (BARD Peripheral Vascular, Tempe, Arizona, USA) and TRUE balloon (BARD Peripheral Vascular). A 60 mL syringe plus an indeflator assembly connected with a high-pressure three-way stopcock is used; under rapid ventricular pacing, the syringe is quickly emptied to inflate the balloon, then switched to cranking the indeflator to achieve high-pressure inflation [59].

Bioprosthetic valve fracture can be performed prior to, or after ViV-TAVR, but the majority is performed after. The timing of bioprosthetic valve fracture, before or after ViV-TAVR, represents an important question [60, 61]. A larger-sized prosthesis can be used with bioprosthetic valve fracture before transcatheter heart valve implant, whereas further expansion of the transcatheter heart valve itself can be performed if bioprosthetic valve fracture is performed afterwards. Prior bioprosthetic valve fracture allows the implantation of a self-expanding valve reducing sizing mismatch and allows confirmation of successful fracture prior to implantation [58–61]. However, it can induce haemodynamic instability from severe acute aortic regurgitation, necessitating post-dilation in order to improve haemodynamics. Correct sizing of the balloon, a balloon slightly smaller than the constrained segment of the self-expanding transcatheter heart valve, and positioning the balloon shoulder lower, more ventricular than the leaflet anchor position, can largely avoid this state of affairs [56].

Bioprosthetic valve fracture after ViV-TAVR is likely to allow greater transcatheter heart valve expansion and reduces the risk of haemodynamic instability from acute severe aortic regurgitation. However, possible bioprosthetic valve fracture leaflet

### *Valve-in-Valve Transcatheter Aortic Valve Replacement: Challenges for Now and the Future DOI: http://dx.doi.org/10.5772/intechopen.112764*

injury and unknown long-term effects on transcatheter heart valve durability are concerns. Other potential complications associated with bioprosthetic valve fracture include: transcatheter heart valve migration, annular rupture, debris embolization, coronary artery obstruction, leaflet tearing and accelerated degeneration with decreased transcatheter heart valve longevity [55, 56].

The minimum inflation pressures necessary for valve ring fracture differ according to the original surgical heart valve type. For surgical heart valve with metal ribbon ring (i.e. Magna and Magna Ease), the fracture threshold (18–24 atm) is greater than the surgical heart valve with a polymer ring (i.e. Biocor Epic, Mosaic, Mitroflow; 8–12 atm). In experimental settings, and most clinical cases, balloons sized 1 mm larger than the labelled valve size were used, although in clinical settings, smaller balloons have been used successfully. Balloons larger than the surgical heart valve internal orifice diameter are also able to fracture the valve, especially if a transcatheter heart valve is already implanted [62]. Recently, ex-vivo bench testing has shown that bioprosthetic valve fracture performed after transcatheter heart valve implantation improves residual gradients [63], but potential early and accelerated degeneration effects on the transcatheter heart valve remain unknown. Bioprosthetic valve fracture is a valid technique to be considered in avoiding and/or ameliorating high post-procedural gradients after a ViV-TAVR, but significant attention needs to be placed on balloon sizing and positioning to achieve optimal results. Improved expansion of the transcatheter heart valve leads to increased circularity of the transcatheter heart valve and therefore increased internal orifice diameter. An important mechanism thought to improve valve haemodynamic performance during higher implant, bioprosthetic valve fracture and post-implant dilatation during ViV-TAVR is the reduction of pinwheeling (**Figure 1**). Improved expansion of the transcatheter heart valve leads to increased circularity of the transcatheter heart valve and therefore increased internal orifice diameter. **Table 2** summarising the bench

#### **Table 2.**

*Summary of bench testing of high pressure balloon inflation to fracture the valve frame of commercial US surgical tissue valves (ATM 1/4 atmospheres; TRU 1/4 Tru dilation) [56].*

testing of high-pressure balloon inflation to cause bioprosthetic valve fracture of several commercially available valves [56].

#### *1.4.4 Coronary occlusion*

TAVR is associated with a coronary obstruction incidence of 1% [64], and during ViV-TAVR, the incidence rises to 4% [65]. This complication is very serious, associated with a more than 15 times increase in 30-day mortality (48% vs. 3%) [66]. The primary responsible mechanism is thought due to the displacement of native valve leaflets towards the coronary ostia. The obstruction may be partial or complete, and obstruction of the left coronary artery is more common (72%) than obstruction of

#### *Valve-in-Valve Transcatheter Aortic Valve Replacement: Challenges for Now and the Future DOI: http://dx.doi.org/10.5772/intechopen.112764*

both ostia (20%) or the right coronary artery alone (8%). In a third of cases, coronary obstruction has delayed onset, occurring in mainly self-expanding devices due to their continued expansion after deployment. Delayed coronary occlusion is defined as obstruction that occurs after the patient leaves the operating room. It occurs in almost two-thirds of patients within 7 days but in a third of patients beyond 60 days. Proposed mechanisms include continuous transcatheter heart valve expansion, aortic root haematoma and coronary dissection and endothelization of native or surgical bioprosthetic leaflets or thrombus embolization with delayed obstruction [67].

#### *1.4.5 Risk factors for coronary occlusion and difficult coronary re-access*

Several anatomical and valve-related risk factors have been identified for this dreaded complication. These include a low coronary ostium height and small sinus of Valsalva size. In addition, the original valve type is important, with ViV-TAVR in stented bioprostheses with leaflets mounted externally and stentless surgical bioprostheses associated with a greater incidence of coronary occlusion, compared with valves with internally mounted leaflets [68].

Other risk factors include those with small anatomies, especially narrow sinuses of Valsalva and narrow sinotubular junctions, who are likely to have received a small surgical valve.

The virtual transcatheter valve-to-coronary ostium distance predicts coronary occlusion, with a shorter distance increasing the risk. An optimal cut-off level of 4 mm has been proposed [69].

Using the VIVID registry, an anatomical classification of the aortic root and valve leaflet was designed to assess the risk of coronary obstruction [70]. Three types of patients were identified: Type I with aortic valve leaflets below the coronary ostium, Type II with leaflets above the ostium in the presence of wide (IIa) or effaced sinuses (IIb) and Type III leaflets above or very close to the sinotubular junction with wide sinotubular junction/sinuses (IIIa), with effaced sinuses (IIIb) and with narrow sinotubular junction (IIIc). According to this algorithm, some procedural strategy should be considered in case of a virtual transcatheter valve-to-coronary ostium distance <4 mm as in Types IIb, IIIb and IIIc [71].

After ViV-TAVR the leaflets of the original surgical prosthesis tilt up, creating a virtual cylinder. The height of this virtual cylinder is labelled and referred to as the neoskirt [72–74]. This forms a "barrier" to future coronary access and must be appreciated carefully during ViV-TAVR planning. The size of the sinotubular junction, the location of coronary ostia in relation to the neoskirt, the type of previous surgical prosthesis as well as the present THV all influence coronary re-access, adding to the complexity of ViV-TAVR planning [58].

#### *1.4.6 Interventions for the prevention of coronary occlusion during ViV-TAVR*

#### *<IP_ADDRESS> Coronary stenting*

In ViV-TAVR procedures with a high risk of coronary occlusion, coronary artery stenting is valuable. It is imperative that the guide wire used to access the coronary ostia does not interfere with transcatheter heart valve implantation. Low threshold for stent deployment has been recommended in high-risk candidates even in the presence of immediate adequate coronary flow, due to the not infrequent incidence of delayed coronary occlusion [75]. Numerous sophisticated coronary stenting techniques have

now evolved and are beyond the scope of this chapter [76–79]. Unfortunately, even these techniques may be associated with several complications such as the inability to withdraw the stent, mechanical stent deformation caused by bioprosthesis and inability to re-access the coronary arteries in the future. In addition, no data regarding the long-term patency of these stents are available [80].

Tarantini et al. have proposed an algorithm based on the anatomy of the aortic root and its relations with different transcatheter heart valves to predict the risk of acute coronary occlusion and feasibility of future coronary access after ViV-TAVR [72]. Using CT and coronary angiography analysis, they identified a risk plane below which the passage of a coronary catheter will be impossible after the second transcatheter heart valve and identified various situations based on a patient's anatomy and the first valve implant type, which could guide safe implantation.

#### *<IP_ADDRESS> Basilica procedure*

Another technique developed to prevent coronary obstruction is the Bioprosthetic Aortic Scallop Intentional Laceration to prevent Iatrogenic Coronary Artery obstruction (BASILICA) procedure [81, 82]. Valve leaflets are lacerated via an electrified guidewire, thereby facilitating blood flow to the coronary artery. Excellent success rates and low mortality in high-risk patients for coronary obstruction is demonstrated during TAVR [81, 82], but results for ViV-TAVR are awaited.


**2. Characterization of soil salinity**

Mougenot et al. [12] argues that visible reflectance of leaves from plants growing on nonsalt affected leaves before plant maturation is higher than after maturity. In addition, visible reflectance of leaves from plants growing on salt affected soil is lower than the visible reflectance of plants growing on non-salt affected soils. Near-infrared reflectance increases without water stress due to a succulent (cell thickening) effect and increases in other cases. Spatial information on soil moisture can be accessed through bands in the near- and middle-infrared spectral bands; this is confirmed by [13]. The study showed that near- to middle-infrared indices are indicators for chlorosis in stressed crops normalized difference for TM bands 4 and 5. This new ratio is however dull to color variations and provides an indication of leaf water potential. In addition [14] showed that chlorotic canopies could be distinguished from healthy canopies, as biophysical response to a salinity can be seen in low fractional vegetation cover, low leaf-area index (LAI), high albedo, low surface roughness and high surface resistance compared with healthy crops. This is because healthy vegetation absorbs most of the visible light hitting it and reflects a large portion of the near infrared light. However, sparse vegetation (right) reflects more visible light and less near infrared light [15]. Traditionally, electrical conductivity (EC) measured in dS/m is used to determine the soil salinity on a small field with the aid of hand-held conductivity meter, while on a large scale it is measured and mapped using electromagnetic (EM) conductivity meter [15]. These approaches (traditional and geospatial) are used to quantify the density of plant growth on the earth visible radiation minus near-infrared radiation divided by near-infrared radiation plus visible radiation resulting into vegetation indices [16]. **Table 1** gave the criteria for classifying soil salinity.

#### **2.1. Geographical information system in soil salinity modeling**

During the last decade, there has been a proliferation of geospatial data in natural resource management including in the disciplines of forestry, fishery management, geology, geomorphology,

of such measurements to the nature and distribution of surface materials and atmospheric conditions. Remote sensing system operation involves the detection, collection and interpretation of data from distance by mean of sensors. The reflectance of electromagnetic radiations from the features at the earth surface is measured with the aid of a sensor, while the radiated energy is transmitted through space in waveform. For land resources survey using remote sensing, wavelengths between 0.4 and 2.4 nm are commonly used. Generally, the electromagnetic spectrum ranges from gamma rays, with wavelength of less than 0.03 nm, to radio energy with a wavelength of more than 30 cm. According to [12], the presence of salts at the terrain surface can be detected from remotely sensed data either directly on bare soils, with salt efflorescence and crust, or indirectly through vegetation type and growth as these are controlled or affected by salinity. Better understanding of the relationship can advance the use of remote sensing for soil studies between soil properties and surface reflectance. Salt affected soils in arid regions, especially when a salt crust whitish color is formed show a high reflectance. Effective application of remote sensing data requires one to have the technical expertise and understand the spectra characteristics of the particular features to be studied. In addition, an understanding of the behavior of different wavelength regions on different soil materials and surface conditions may increase the efficiency of the study of soil salinity based

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The change in soil color and the change in soil reflectance properties caused excess soil moisture, which can be easily detected by remote sensing. Plant response is one means of detecting poorly drained soils in California mainly because of a build-up of the water table. On the other hand, Salman [24] reported that as result of excess organic matter, soil color is generally darker in poorly drained areas than well-drained soils. The visible bands in Landsat-MSS data can be used to classify this color. According to [25] as cited in [26] pointed out that color infrared photography could indicate drainage problems by soil moisture saturation or plant stress. Shallow water tables exhibit a rise in surface moisture, which can be detected from visible reflectance and microwave emissivity. The information about drainage basin area and drainage pattern can be obtained from satellite imagery. Water logging and problems associated with drainage can be examined through GIS by identifying the drainage network and its characteristics in a basin, besides the report on the presence of high water table, high mor-

phology, soil color, plant stress and drainage water collection in lower spots [27].

salinity levels with errors between moderately saline and normal soils.

For relatively large areas, remote sensing is an essential tool for mapping and surveying saltaffected and waterlogged soils [28]. The understanding of the actual conditions at the earth surface makes it feasible to interpret the satellite images. However, because of lack of specific absorption bands and spectral confusion, it is complex to distinguish the degree of salinity through remote sensing approach [29] that separated different soil salinity level using Landsat imagery. Most authors are capable to differentiate only 2–3 classes (strong and medium) of

on remote sensing [23].

**2.3. Mapping of waterlogging**

**2.4. Application to large areas**

**Table 1.** Soil salinity classification.

hydrology, wildfire, and climate change [17]. Geographic Information System (GIS) enables the measurement and representation of geographic phenomena and thereafter transform this spatial information into various forms while interacting with social structures [18]. GIS has the benefit of its ability to interrelate spatially multiple types of information and data obtained from a range of sources. In addition based on the findings of [19], GIS technique has the capacity as an effective tool for spatial analyses and modeling of concentrations of air pollutants. GIS also has three basic key functions, which include database management, spatial analysis and visualization. Thus with these three components, this system (GIS) provides the capabilities of linking the concentration of the air pollutants with their geographic location [19]. From their study, it was also discovered that GIS technology could effectively represent the spatial relationships between sources and receptors as it can integrate with satellite remote sensing data in order to spatially analyze the relationship between the geographic location of air pollutants and the land use/land cover classes of the area. Models are representation of reality and they are created as a simplified, manageable view of reality, due to the inherent complexity of the earth and the diverse interactions in it. Models help to understand, describe, or predict how things work in the real world. Models are broadly classified into two namely representation and process models. Representation models are those that represent the objects in the landscape while those that attempt to simulate processes in the landscape are process models [20]. Representation models describe the objects in the landscape, such as buildings, streams, or forest while process models describe the interaction of the objects that are modeled in the representation model. There are different types of process models including suitability modeling, distance modeling and hydrological modeling [21].

#### **2.2. Remote sensing for soil salinity mapping**

The essence of remote sensing is the measuring and recording of the electromagnetic radiation emitted or reflected by the earth's surface [22]. For soil salinity investigation, this may be useful where salty soil, salt-affected vegetation, saline water, pond water and high water table area give contrasting reflectance with other landscape features so that they can be unambiguously distinguished. Remote Sensing (RS) is the art and science of obtaining information about an object, area or phenomenon through analysis of the data acquired by such device that is not in contact with the object, area or phenomenon under consideration. It is the measurement of object properties on Earth's surface using data acquired from aircraft and satellites. Rather than in situ, RS attempts to measure something at a distance. It involves measuring, recording and transmission of electromagnetic energy by the sensors mounted on aircraft or satellite reflected from or emitted by object from vantage point above the surface and relating of such measurements to the nature and distribution of surface materials and atmospheric conditions. Remote sensing system operation involves the detection, collection and interpretation of data from distance by mean of sensors. The reflectance of electromagnetic radiations from the features at the earth surface is measured with the aid of a sensor, while the radiated energy is transmitted through space in waveform. For land resources survey using remote sensing, wavelengths between 0.4 and 2.4 nm are commonly used. Generally, the electromagnetic spectrum ranges from gamma rays, with wavelength of less than 0.03 nm, to radio energy with a wavelength of more than 30 cm. According to [12], the presence of salts at the terrain surface can be detected from remotely sensed data either directly on bare soils, with salt efflorescence and crust, or indirectly through vegetation type and growth as these are controlled or affected by salinity. Better understanding of the relationship can advance the use of remote sensing for soil studies between soil properties and surface reflectance. Salt affected soils in arid regions, especially when a salt crust whitish color is formed show a high reflectance. Effective application of remote sensing data requires one to have the technical expertise and understand the spectra characteristics of the particular features to be studied. In addition, an understanding of the behavior of different wavelength regions on different soil materials and surface conditions may increase the efficiency of the study of soil salinity based on remote sensing [23].

#### **2.3. Mapping of waterlogging**

hydrology, wildfire, and climate change [17]. Geographic Information System (GIS) enables the measurement and representation of geographic phenomena and thereafter transform this spatial information into various forms while interacting with social structures [18]. GIS has the benefit of its ability to interrelate spatially multiple types of information and data obtained from a range of sources. In addition based on the findings of [19], GIS technique has the capacity as an effective tool for spatial analyses and modeling of concentrations of air pollutants. GIS also has three basic key functions, which include database management, spatial analysis and visualization. Thus with these three components, this system (GIS) provides the capabilities of linking the concentration of the air pollutants with their geographic location [19]. From their study, it was also discovered that GIS technology could effectively represent the spatial relationships between sources and receptors as it can integrate with satellite remote sensing data in order to spatially analyze the relationship between the geographic location of air pollutants and the land use/land cover classes of the area. Models are representation of reality and they are created as a simplified, manageable view of reality, due to the inherent complexity of the earth and the diverse interactions in it. Models help to understand, describe, or predict how things work in the real world. Models are broadly classified into two namely representation and process models. Representation models are those that represent the objects in the landscape while those that attempt to simulate processes in the landscape are process models [20]. Representation models describe the objects in the landscape, such as buildings, streams, or forest while process models describe the interaction of the objects that are modeled in the representation model. There are different types of process models including suitability modeling, distance modeling and hydrological modeling [21].

**Degree of salinity Salinity ECe (dS/m)**

Slight 4–8 Moderate 8.25 Strong ˃25

Source: [15].

**Table 1.** Soil salinity classification.

68 Multi-purposeful Application of Geospatial Data

The essence of remote sensing is the measuring and recording of the electromagnetic radiation emitted or reflected by the earth's surface [22]. For soil salinity investigation, this may be useful where salty soil, salt-affected vegetation, saline water, pond water and high water table area give contrasting reflectance with other landscape features so that they can be unambiguously distinguished. Remote Sensing (RS) is the art and science of obtaining information about an object, area or phenomenon through analysis of the data acquired by such device that is not in contact with the object, area or phenomenon under consideration. It is the measurement of object properties on Earth's surface using data acquired from aircraft and satellites. Rather than in situ, RS attempts to measure something at a distance. It involves measuring, recording and transmission of electromagnetic energy by the sensors mounted on aircraft or satellite reflected from or emitted by object from vantage point above the surface and relating

**2.2. Remote sensing for soil salinity mapping**

The change in soil color and the change in soil reflectance properties caused excess soil moisture, which can be easily detected by remote sensing. Plant response is one means of detecting poorly drained soils in California mainly because of a build-up of the water table. On the other hand, Salman [24] reported that as result of excess organic matter, soil color is generally darker in poorly drained areas than well-drained soils. The visible bands in Landsat-MSS data can be used to classify this color. According to [25] as cited in [26] pointed out that color infrared photography could indicate drainage problems by soil moisture saturation or plant stress. Shallow water tables exhibit a rise in surface moisture, which can be detected from visible reflectance and microwave emissivity. The information about drainage basin area and drainage pattern can be obtained from satellite imagery. Water logging and problems associated with drainage can be examined through GIS by identifying the drainage network and its characteristics in a basin, besides the report on the presence of high water table, high morphology, soil color, plant stress and drainage water collection in lower spots [27].

#### **2.4. Application to large areas**

For relatively large areas, remote sensing is an essential tool for mapping and surveying saltaffected and waterlogged soils [28]. The understanding of the actual conditions at the earth surface makes it feasible to interpret the satellite images. However, because of lack of specific absorption bands and spectral confusion, it is complex to distinguish the degree of salinity through remote sensing approach [29] that separated different soil salinity level using Landsat imagery. Most authors are capable to differentiate only 2–3 classes (strong and medium) of salinity levels with errors between moderately saline and normal soils.

#### **2.5. Techniques in monitoring soil salinity**

The classification of salt affected soils, assessment of the percentage of severity particularly in its early stage is important in terms of sustainable agricultural management [30]. Various approaches have been employed by researchers to analyze and monitor soil salinity. The three major techniques commonly used in soil salinity determination include traditional method, Electromagnetic Induction method and Remote Sensing and GIS method. The traditional or conventional methods used for detecting soil properties include ground-based geophysics and laboratory analysis methods [31]. Adeniran et al. [5] used this method to determine electrical conductivity of soil in Omi irrigation scheme by carrying out chemical analysis of the soil samples. The disadvantages of traditional method includes; time consuming, costly since dense sampling is required to adequately characterize the spatial variability of an area and demanding when considering large areas [32, 33]. Remote sensing methods are suitable for detecting, monitoring and controlling soil salinity. Researchers have used GIS and RS techniques to model, assessed, and investigate land use and land cover pattern, detect, map, monitor and forecast soil salinity on an irrigation scheme [34]. Ojo et al. [15] stated the advantages of remote sensing and GIS method includes; time saving, wide range of coverage, facilitation of faster and long term monitoring. Electromagnetic Induction (EMI) was first employed in agriculture to detect saline soils by measuring its electrical conductivity [35]. Electromagnetic approaches are reliable means used for rapid determination of soil salinity [36]. Spatially varying soil types and properties are identified easily and map out quickly with the application of EMI as it offers unique benefit over traditional methods.

Landsat TM data with the depth and mineralization rate of groundwater to create soil salinity map. In Israel, hyper-spectral airborne sensor data were processed to yield quantitative

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Landsat image consists of three separate instrument subsystem, each operating in a different spectral region, using a separate optical system [54]. These subsystems are the Visible and Near Infrared (VNIR), the Short Wave Infrared (SWIR), and the Thermal Infrared (TIR), respectively. Landsat data has 14 bands allocated in three spectral regions as VNIR (band 1, 2, 3) with 15 m resolution, SWIR (bands 4–9) with 30 m resolution and TIR (bands 10–14) with 90 m

With the launching of the Landsat satellite in 1972, researchers began to use satellite data for monitoring environmental activities in different parts of the world [56]. Landsat provides basic tools for working with satellite imagery as automated geo-referencing and cloud detection. Landsat consist of functions for radiometric normalization and various approaches to atmospheric correction. It also includes useful functions such as bare soil line and tasseled cap calculations [57]. The physiological condition of a crop is shown best at TM 5 and TM7. Landsat 5 (TM) that was launched on 01/03/1984 and Landsat 7 (ETM+) on 15/04/1999, each revisit a location every 16 days, and the two orbits are staggered for images to be taken every 8 days. Due to a hardware failure on 31/05/2003, Landsat 7 scenes are now missing 22% of the pixels also severe problem occurs toward the edges of an image. Band attributes are largely coherent, but ETM+ added an additional band. Landsat images haves been converted to integer digital numbers (DN) before distribution to facilitates storage and display. Atmospheric correction, topographic correction and conversion to radiance or reflectance may be required. Minimal processing may be needed if a single image or images widely separated in time are used to examine gross changes. However, careful correction is needed to examine detailed comparison of vegetation indices from multiple images. The most accurate atmospheric corrections need ground data collected during the satellite image capturing. For retrospective studies, this is impossible to obtain, and less-accurate image-based correction method must be used [58]. Band Wavelength and resolution for Landsat 5 Thematic Mapper (TM) and Landsat 7 Thematic Mapper (TM) is shown in **Tables 2** and **3** respectively. Panah and Goossens [23] claimed that thermal band of Landsat (TM) imagery is a good source of information that may have a vital role in soil salinity studies and in detecting gypsiferous soils in arid region. The reflective bands 1, 3, 4 and 7 are the best band composition for preparing the color composite

Land-use and land-cover change being one of the major driving forces of global ecological change, is vital to the sustainable development discussion. One of the most accurate methods

maps of soil salinity [53].

**2.7. Landsat image**

resolution [55].

images [59].

**2.9. Land use and land cover (LU/LC)**

**2.8. Landsat platform characteristics**

#### **2.6. Soil mapping**

There are varieties of methods to identify and map surface features using remotely sensed imagery. Techniques for mapping soil surface conditions, such as salinity and waterlogging are based on the presence or absence of spectral absorption features. Soil mapping include locating and identifying the various soils that occur, nature and properties, collecting information about soil location and recording this information on maps and in supporting documents to show their spatial distribution. Seghal et al. [37] applied Landsat MSS data for mapping salt affected soils in the frame of the reconnaissance soil map of Indian. Dwivedi [38] used Landsat MSS and TM data for more detailed mapping and monitoring of salt affected soils in the Indo-Gangetic alluvial plain. Landsat TM data have proved useful for mapping depositional environments on playas Tunisia [39]. Crowley [40] reported that gypsum and halite were likely to be the only evaporate phases detected and mapped on the Chott el Dyerid using TM data. Mehrjardi et al. [41] used Landsat TM+ taken in 2002 to map soil salinity in Ardakane Yazd by using an exponential model. They used band 3 of the images and soil salinity parameter in a regression analysis (R2 = 0.58) and reported a map accuracy of 0.87% and K coefficient equal to 0.47%. Various remote sensing data such as aerial photos, video, images, infrared thermography, visible and infrared multispectral, microwave and airborne geophysical data, is available for monitoring, classification and mapping out of saline soil [42]. According to [43] several authors have dealt with the study of soil salinization using satellite data, among them [12, 44–51]. In China, Peng [52] integrate Landsat TM data with the depth and mineralization rate of groundwater to create soil salinity map. In Israel, hyper-spectral airborne sensor data were processed to yield quantitative maps of soil salinity [53].