*2.5.2. Microorganisms*

instead of aqua vitae occurs in 1660. Considering the three ethnic groups that formed the Brazilian nation (Native Brazilians, Africans, and Europeans), scarce information is available about the real contribution of each group to the initial production of fermented sugar‐ cane. However, *cachaça*, which is the result of the distillation of fermented sugarcane juice, was certainly "discovered" by Europeans, the most technologically advanced group, who had knowledge and equipment to do so [77]. Nowadays, *cachaça* is the typical and exclusive denomination to Brazilian spirit produced from sugarcane juice with alcohol content ranging 38%–48% (v/v) at 20°C (68°F), which present unique characteristics (Table 2) [78]. Brazil has an estimated installed capacity of *cachaça* production ranging from 1.2 to 1.5 billion litters/year; however, the production is less than 800 million litters/year. Accord‐ ing to the Brazilian Institute of Geography and Statistics (IBGE), almost 15,000 establish‐ ments are currently producing *cachaça*. In 2014, 10.2 million liters of *cachaça* were exported

\*Sum of isobutyl (2-methyl-1-propanol), isoamyl (2-methyl-1-butanol and 3-methyl-1-butanol), and n-propyl (1-

The main raw material for the production of *cachaça* is the juice of sugarcane (*Saccharum* spp.). The first step to prepare of the fermentation medium is the extraction of sugarcane juice. Small producers extract the juice by crushing the sugarcane using a mill. Yet, the large producers use a more complex system of extraction: (i) crushing system, (ii) cutting machines, and (iii) shredders [80]. The resulting sugarcane juice is an opaque (color ranging from brown to dark green), viscous, and sweet liquid. The color is due to different pigments such as chlorophyll

38-48

to 66 countries, generating US\$ 18.33 million in revenue [79].

Source: Ministry of Agriculture, Livestock and Supply - Brazil

propanol) alcohols.

120 Food Production and Industry

*2.5.1. Production*

+ 5-(Hydroxymethyl)furfural

**Compounds Units Limits** Copper mg/L 5.0 Ethyl carbamate μg/L 210.0 Volatile acidity mg/100mL anhydrous ethanol 150.0 Total esters mg/100mL anhydrous ethanol 200.0 Aldehydes mg/100mL anhydrous ethanol 30.0 Total higher alcohols\* mg/100mL anhydrous ethanol 360.0 Furfural+HMF+ mg/100mL anhydrous ethanol 5.0 Methanol mg/100mL anhydrous ethanol 20.0 Acrolein mg/100mL anhydrous ethanol 5.0 Particles in suspension - Absent Dry extract g/L 6.0 Total sugars g/L 6.0

**Table 2.** Components present in *cachaça* and its limits in accordance with the Brazilian law.

As important as the preparation of the medium is the preparation of microorganisms that will ferment the sugarcane juice, the so-called foot-of-vat. Traditionally, *S. cerevisiae* is the most used yeast, but some other species are also utilized, namely, *Pichia* sp. [11, 84]. The cell concentration usually found at the beginning is about 106 –107 cells/mL and about 108 cells/mL at the end of the fermentation. Traditionally, the majority of the *cachaça* is produced through self-inoculation, using different protocols. Usually, a homemade mixture is prepared: (i) diluted sugarcane juice (carbon source), (ii) rice bran and/or corn bran (nutrient sources), and (iii) lemon/orange juice (to reduce initial pH) [85]. All these ingredients are mixed and kept at rest for 24 hours, during which wild yeast present in sugarcane juice will multiply—microor‐ ganism multiplication is verified through foaming. At the end of the first 24 hours, a new volume of diluted sugarcane juice is added, and the process is restarted [85]. The process is repeated until it reaches 20% of the volume of the fermentation vat. At the end of the process, a wild yeast community is obtained, which is adapted to the physical and chemical conditions of the fermentation. Producers, which aim to improved organoleptic characteristics, tend to use selected yeast in fermentative process. There are two basic types of selected yeast: (i) yeast that are used to produce ethanol or other beverages without being specific for *cachaça* production and (ii) yeast selected from their own vats of *cachaça* fermentation. Yeast selected in their own fermentation tanks have advantages over the first because they are more adapted to the *cachaça* fermentation specific conditions and are able to provide unique features to the product [86, 87]. There are numerous advantages to using selected yeast, including (i) rapid and (ii) homogeneity of the fermentation, (iii) higher fermentation yields, (iv) higher quality of the final product, (v) lowest risk of contaminations, and (vi) highest resistance to stress [88]. The improved aroma obtained in the final product is another advantage of using the selected yeast [86]. Given that *cachaça* fermentation develops in an environment with few controls, these vats represent a unique ecological niche. Interesting phenotypes, such as the production of flavor and aroma compounds, can be assessed by the detailed characterization of isolated strains [89]. The use of yeast strains producing aroma compounds, as esters (ethyl acetate, isoamyl acetate, ethyl hexanoate, ethyl octanoate, and ethyl decanoate) and higher alcohols (propyl, isoamyl, and isobutyl alcohol), can significantly increase the final quality of *cachaça*. Thus, such profile can be a differential characteristic between several *cachaça* producers or even distinct producing regions—if all producers in a particular region start using a selected yeast strain.


**2. Results**

MIL-101(Fe) was obtained by a solvothermal method from benzene-1,4-dicarboxylic acid and iron (III) chloride, according to the method of Zhu et al. [43] The powder X-ray diffraction (PXRD) pattern of the material prepared at 3 hours reaction time confirms the identity of the material by comparison with the simulated pattern from the literature (Figure 3). The differences in relative intensity between the low angle peaks (at less than 6 ◦2θ ) and those at higher angle (greater than 8 ◦2θ ) in the measured pattern compared to the simulated pattern is accounted for by the presence of solvent in the as-prepared sample; in contrast, the simulated pattern is for an idealised guest-free framework. These X-ray powder patterns are typical of MIL-101 samples reported in the literature [44–46]. There is also evidence in the PXRD for the presence of an impurity phase in the sample, which we identify below as being MIL-88B (Figure 1) [47]. It is already known that MIL-88B and MIL-101 are "framework isomers" and may form in competition to each other [4,48,49].

**Figure 3.** Powder X-ray diffraction patterns of samples of MIL-101 prepared by solvothermal synthesis at 3 hour reaction times. The simulated pattern of MIL-101 was produced from the published structure of Férey et al. [36] and that of MIL-88B from the refined diffraction profile in this work for MIL-88B(Fe).

Figure 3 also shows PXRD patterns of materials prepared where small amounts of Sc3+ are used to replace some of the Fe3+, or some of the BDC linker is replaced by mono-2-sulfobenzene-1,4-dicarboxylate (MSBDC). In both cases, the crystallinity of the resultant product is considerably less than for the Fe material, and the MIL-88B impurity remains in the samples. SEM was used to detect the presence of the sulfo-modified ligand in the material, but no signal from sulfur was observed. Since we used this method successfully to image sulfur in sulfo-modified UiO-66 [31], we conclude that for MIL-101(Fe) prepared by this method, the incorporation of the sulfo-modified ligand is negligible. Fourier transform infrared (FT-IR) spectra also provided no evidence for any characteristic sulfo bands. Owing to the lack of any sulfo-functionalisation, we excluded these samples from any further study.

The highest scandium amount that can be introduced to MIL-101(Fe) is only 10%. If 50% of the Fe is replaced by Sc (note that this is the nominal metals ratio used in the synthesis) then the material formed is solely MIL-88B under the synthesis conditions that we have used, labelled MIL-88B(Fe0.5Sc0.5), and indeed if longer synthesis times are used then, even for the iron material, only MIL-88B is found (see Figure 4). The powder X-ray pattern of MIL-88B matches that reported by Ma et al. who prepared the iron form by microwave synthesis in the same solvent [50]. Figure 4 also demonstrates the stability of MIL-88B in DMSO upon prolonged heating: although the powder pattern shows some broadening of the peaks, the material clearly has the same structure. The materials are, however, unstable in water: upon heating to 140 ◦C for 3 hours, the MOF structure collapses and the only crystalline

product recovered is the acid of the BDC linker, along with an orange powder, presumably amorphous iron oxide.

**Figure 4.** Powder X-ray diffraction patterns of MOFs transformation from MIL-101(Fe), 3 hour crystallisation) to MIL-88B(Fe), 40 hour crystallisation, along with mixed (Fe,Sc) (nominal metals ratio 1:1), 3 hour crystallisation. The second from top pattern illustrates the stability of the mixed-metal phase on heating in DMSO.

Confirmation of the identity of the more stable MOF structure in the synthetic conditions used was provided by a more detailed analysis of higher resolution powder X-ray diffraction patterns, and Figure 5 shows the profile fits to the MIL-88B(Fe), MIL-88B(Sc0.5Fe0.5) and MIL-88B(Sc) materials. The refined lattice parameters (Figure 5) fitted using the hexagonal space-group *P*62*c* are consistent with those reported in the literature for MIL-88B materials: for example, Ma et al. found *a* ~ 12.7 Å, *c* ~ 18.4 Å and *V* ~ 2570.2 Å3 for MIL-88B(Fe) isolated from *N*,*N*-dimethylformamide (DMF) [50], and Mowat et al. found *a* = 11.2190 Å, *c* = 19.373 Å, *V* = 2112.27 Å3 for MIL-88B(Sc) containing a mixture of water and DMF [39]. MIL-88B, however, is an example of a breathing MOF and the lattice parameters can vary dramatically with the amount, or type, of solvent present [51], so it is difficult to draw conclusions in the trends in the values of the fitted lattice parameters for the three materials that we have studied. Larger unit cell volumes are observed for both the Sc-containing samples we have prepared compared to the iron form, which is consistent with the larger ionic radius of octahedral Sc3+ compared to Fe3+ [52]. However, the presence of different amounts of solvents (or mixtures of water and DMF) cannot be ruled out, which may also adjust the unit cell volume.

**Figure 5.** Profile fits to powder X-ray diffraction patterns of (**a**) MIL-88B(Sc), (**b**) MIL-88B(Fe0.5Sc0.5), and (**c**) MIL-88B(Fe) (Cu *K*α1 radiation) fitted using space-group *P*62*c*. The points are the measured data, the red line is the final fit, the blue line is the difference curve and the pink ticks are the positions of the allowed reflections. Refined lattice parameters and unit cell volumes are provided on each panel.

In addition to the different unit cell of the mixed-metal material, more substantive evidence for the formation of a mixed-metal sample of the MOF, rather than a phase-separated mixture of materials, comes from EDX mapping on the SEM, Figure 6. This shows how the two metals are both present in micro-sized crystallites of MIL-88B(Sc0.5Fe0.5). Proving the degree of mixing of the two metals on smaller length-scales is challenging; in the previous work on MIL-100(Fe,Sc), which contains the same trimeric building units as found in MIL-88B, it was impossible to determine, using extended X-ray absorption fine structure (EXAFS) spectroscopy, whether the individual clusters contained mixtures of metals or whether single metal clusters were present [41].

**Figure 6.** Elemental maps of MIL-88B(Fe0.5Sc0.5) at two magnifications showing the distribution of the two metals, and the presence of chlorine.

The elemental mapping suggests that the concentration of iron in the MIL-88B(Fe,Sc) sample is lower than that of scandium, despite the initial 1:1 molar ratio used in synthesis. This was verified by bulk analysis for metals using ICP-OES, which gave a Sc:Fe molar ratio of 3.1:1. X-ray photoelectron spectroscopy (XPS, Figure 7), was used to quantify the metal ratio at the surface of the material, and integration of the signal gave a Sc:Fe ratio of 6.0:1. The typical depth of the XPS analysis for these elements is around 5–10 nm, which shows how the surface of the particles is scandium-rich. First, this suggests that most of the Fe is present within the crystallites of the MIL-88B, and, second, shows how the surface is terminated substantially by Sc-containing species, which are strongly bound, since they are not removed by the washing used in the sample preparation. One possible explanation for the excess scandium at the surface is the presence of a scandium chloride species, necessary for charge-balance of the terminated crystal surfaces. Some of the XPS signal can indeed be accounted for as ScCl3 (Figure 7a), and this result is also consistent with the presence of chlorine detected in the EDXA mapping on the SEM. The majority of signal (>80%) in the Sc 2*p*3/2 region (Figure 7a), however, can be accounted for by the presence of octahedrally coordinated scandium, assigned using ScOOH as a reference, as expected for the MIL-88B structure. The Fe 2*p*3/2 region of the XPS (Figure 7b) is more complex due to multiplet splitting [53–56]. Careful analysis of the overall Fe 2*p*3/2 envelope necessitated a component at around 709 eV, indicating the presence of a significant amount of Fe2+ as well as Fe3+. This is unlike the spectrum of the single metal MIL-88B(Fe) (Supplementary Materials) that shows >90% Fe3+, as expected, and would suggest that the mixed-metal material contains defects (see below for a further discussion of this).

**Figure 7.** X-ray photoelectron spectra of MIL-88B(Fe,Sc) in (**a**) the Sc 2*p* region and (**b**) the Fe 2*p*3/2 region. The fitted contributions are shown with reference spectra in the literature used for guidance (see text).

The FT-IR spectra of the MIL-88B materials (Figure 8) shows all the vibrational bands characteristic of framework-coordinated carboxylate groups expected in the MIL-88B structure, and further evidence for the isostructural nature of the samples (see Supplementary Materials for assignment of the spectra). Some weak additional bands in the spectrum of the MIL-88(Fe,Sc) sample suggest the presence of some uncoordinated (protonated) carboxylic acid groups. This is not due to the presence of crystalline H2BDC (not seen in the powder XRD, Figure 5, and which also has a different FT-IR spectrum), and therefore suggests a defective structure for the mixed-metal material, with some linkers that do not bridge pairs of clusters. The thermogravimetric analysis (TGA) data (Supplementary Materials) show that all the solvents and water molecules in MIL-88B materials are removed at above ~150 ◦C. On continued heating, a decomposition of the MIL-88B network occurs above 450 ◦C, forming Fe2O3 and/or Sc2O3 as the final product, which is consistent with the literature [47]. The TGA of the mixed-metal material (Supplementary Materials) shows an extra inflection in mass loss in the region corresponding to the combustion of the linker, which is consistent with the presence of some partially coordinated benzene-1,4-dicarboxylate groups. This is consistent with the more defective structure of this material, also indicated by IR, and charge balance in the material may be explained by the presence of some Fe2+ that was seen by XPS, as discussed above.

**Figure 8.** Infrared spectra of MIL-88B materials. The features labelled \* are assigned as due to uncoordinated carboxylic acid groups.

Three samples were selected to examine their effectiveness as catalysts for glucose conversion: MIL-88B(Fe), MIL-88B(Fe,Sc), and MIL-88B(Sc). Given the instability of the materials in water, DMSO was used as the reaction solvent, in which the materials proved to be stable (Figure 4). The catalytic results are summarised in Table 1, which shows the total glucose conversion as well as the three desired products analysed for: fructose, mannose, and 5-HMF. We report results from two different catalyst loadings (based on mass, 30:1 and 7.5:1, glucose to catalyst ratio) and at two different temperatures (120 ◦C and 140 ◦C). Firstly, it is important to note that DMSO alone, in the absence of any catalyst, results in a conversion of just 1.6% at 120 ◦C and 60.3% at 140 ◦C. However, the product mixture contains only a minor amount of 5-HMF at the upper reaction temperature and neither of the intermediate sugars, fructose or mannose, are present, which suggests a decomposition pathway that does not yield these desired products. Indeed, Jia et al. found that anhydrous DMSO alone gave cellobiose as the main dehydration product of glucose at 130 ◦C for 2 hours [57]. In the presence of the MIL-88B solids, a much more significant formation of 5-HMF is seen, along with the intermediate fructose and mannose sugars. DMSO is known to act as a catalyst for the dehydration of fructose to 5-HMF in the absence of any Brønsted acid at 150 ◦C [58]. This implies that the MIL-88B acts as a Lewis acid catalyst that produce the fructose by glucose isomerisation, which can then be converted to 5-HMF by the DMSO solvent.

The mixed-metal MIL-88B(Fe,Sc) gives the highest conversion (70.7%) and 5-HMF yield (24.9%) at 140 ◦C with a 7.5 substrate–catalyst ratio. This catalyst thus produces significant amounts of 5-HMF from glucose. The mixed-metal material is more effective than either the MIL-88B(Fe) or MIL-88B(Sc), which may be due to greater structural disorder leading to a greater concentration of defects, such as coordinatively unsaturated metal sites, implying more Lewis acid sites. As noted above, the degree of mixing of the metals is difficult to prove unambiguously, but since the mixed-metal material always gives a higher glucose conversion than either of the single metal materials, there must be a cooperative effect, which in turn suggests that the two metals are in close proximity. In previous work on MIL-100(Sc), it was noted that upon addition of excess Fe, the presence of small amounts of amorphous Fe2O3 enhanced the Lewis acidity [41], so it is possible that we are seeing the same

beneficial effect here, although the presence of small amounts of an amorphous second phase is difficult to determine.

The effect of temperature is also significant on the catalysis. At the temperature of 120 ◦C and the catalyst-to-substrate ratio of 7.5, the selectivity towards 5-HMF is notably higher over the mixed-metal material compared to Fe-only material (16.8% vs 1.7%). At the lower catalyst-to-substrate ratio of 30:1, the mixed-metal material shows a slightly lower activity than the Fe-only material, but a notably higher 5-HMF selectivity. The varying response of the two materials to temperature at both catalyst loadings is consistent with a different, and more favourable, mode of action in the mixed-metal material. Interestingly, the scandium material has a higher selectivity to 5-HMF than the iron at the catalyst low loadings, yet lower total conversion, providing evidence that each metal has a distinct role in the catalysis. Vlachos and co-workers studied the kinetics and mechanism of tandem Lewis–Brønsted acid catalysis of the same reaction, and reported that relative Lewis–Brønsted acid concentration has an optimum and pronounced effect on 5-HMF yield, which leads to 5-HMF yield being maximised due to a change in the rate-limiting step from fructose dehydration to glucose isomerisation [59]. In this sense, the MIL-88B(Fe) catalyst shows the formation of fructose both at 120 ◦C and 140 ◦C; however, the MIL-88B(Sc) catalyst yields higher 5-HMF yields. On the other hand, the mixed-metal MIL-88B(Fe,Sc) yields the highest 5-HMF yields, which is more clearly pronounced at 140 ◦C. Hence, the origin of the behaviour of our catalysts may be due to the distribution and availability of acid sites.

We considered the recyclability of the MIL-88B(Fe,Sc) catalyst by isolating the solid after a run, washing with DMSO, and adding fresh reagents (Figure 9). This was done at a larger scale than the screening reactions reported in Table 1 so mass transport may be different; however, the material maintains activity after three cycles and only after the fourth use shows a notable drop in glucose conversion. The recycling of MOF catalysts has not always been reported in the literature, but for the results that are available, we note that, although the recyclability of MIL-88B(Fe,Sc) is not as complete as for some sulfonated MIL-101(Cr) materials (albeit under different reaction conditions) [27,29], others have noted that the build-up of humins can make recycling difficult [10]. Nevertheless, the observation of recyclability points towards the use of the materials in continuous reactors, as has been achieved for sulfonated MIL-101(Cr) in a fixed-bed system [26]; here, conversion need not be high to generate useful quantities of the desired product.

**Figure 9.** Recyclability of MIL-88B(Fe,Sc) as a catalyst for glucose conversion (120 ◦C in DMSO for 3 hours).

In comparison to other MOF-based solid acids that have been recently investigated as catalysts for glucose conversion, the MIL-88B(Fe,Sc) shows favourable properties. Tsapatsis and co-workers have recently showed that a composite of MIL-101(Cr) and nanoparticles of Cr(OH)3 gave the highest conversion of glucose in a two-step process with ethanol as the solvent for isomerisation to ethyl fructoside, followed by the addition of water to allow hydrolysis to fructose, each performed at 100 ◦C for 24 hours: the catalyst was more effective than a physical mixture of the two component solids and

various other MIL-101(Cr) materials, but also outperformed the zeolite Sn-beta giving >75% conversion, with almost 60% of the product fructose [29]. Yabushita et al. used the zirconium MOF NU-1000 modified with phosphate to optimise the 5-HMF yield from glucose and also screened various solvent mixtures: for their catalysis, however, water-DMSO gave the lowest overall glucose conversion in comparable conditions to those we have used (at 140 ◦C for 5 hours), and THF or propanols as solvents gave almost complete conversion [30]. Looking at other solid catalysts applied in DMSO solvent, the recent work of Li et al. on MnO2 particles surface modified with Sn4+-containing coordination polymers provides a useful comparison [60]. Their catalyst achieved ~56% conversion at 150 ◦C in 5 hours, with a 60% selectivity towards 5-HMF, which was superior to other Sn-based catalysts reported in the literature in DMSO solvent. Therefore, the MIL-88B(Fe,Sc) catalyst is comparable to the Sn-based catalyst (with higher conversion, although lower selectivity), but has the advantage of being prepared in a single-step, rather than being a surface-modified composite material. Other reported systems that show a higher conversion of glucose to 5-HMF in DMSO involve a multi-step synthesis of the catalyst and strong mineral acids. This includes Sn-containing vanadium phosphate, prepared in three steps starting from a phosphoric acid treatment of vanadium oxide [61]; sulfated mesoporous niobium oxide prepared in several steps, involving ultrasound and hydrothermal treatments, with a final sulfuric acid treatment [62]; and sulfated zirconia materials that are activated using chlorosulfonic acid in ethylene dichloride [63].

The nature and location of the Lewis acid sites in MOF catalysts have not been extensively studied in glucose isomerisation reactions, and the accessibility of active sites will depend on the substrates used. Ammonia temperature-programmed desorption (TPD) was used to characterise the MIL-88B(Fe,Sc) material (Figure 10), and this confirms the presence of surface acid sites at a concentration of 0.36 mmol g−1. The results are complicated by the onset of collapse of the MOF at higher temperatures (see TGA in Supplementary Materials), but a desorption event between 100 and 300 ◦C is characteristic of the interaction of ammonia with surface acid sites. This is similar behaviour to that reported for the Cu-containing MOF-74 by Jiang et al., who observed desorption of ammonia around 100–250 ◦C under similar conditions [64].

**Figure 10.** Temperature-programmed desorption of ammonia from MIL-88B(Fe,Sc), with data from a fresh sample of the material (without ammonia adsorbed) heated under the same conditions as a blank.

While MIL-88B is a porous, breathing framework in the presence of certain solvents [51], it is likely that the bulky glucose does not penetrate into the MOF structure, but the activity is instead associated with the surface of the crystallites. Additional PXRD measurements made on samples immersed in DMSO show no evidence for pore-opening in this solvent at room temperature (Supplementary Materials), so it is probable that the material remains in a contracted form during the catalysis. Indeed, after catalysis and ammonia TPD, there is no evidence for pore swelling in the PXRD (Supplementary Materials). Our measured BET surface area of 16 m2 g−1 for MIL-88B(Fe,Sc) activated in vacuum at 100 ◦C corroborates this, showing the lack of porosity and a surface area as expected for micron-sized powders. This result is similar to that reported for other MIL-88B(Fe) materials: for example, Rahmani and Rahmani measured a BET surface area of 23 m2 g−1 [65], while Vuong et al. found a surface area of 30 m2 g−1 for a hydrated, mixed (Fe, Ni) form of the material with amino-functionalised linkers [66]. Previous work on dense (non-porous) scandium-organic frameworks has shown how surface Lewis acidity is sufficient for the acetalization of aldehydes or the cyanosilylation of carbonyl compounds [67,68]. Although Mitchell et al. found that MIL-88B(Sc) was an inferior Lewis acid catalyst compared to porous MIL-100(Sc) in a variety of organic transformations [41], which they attributed to its low surface area, in DMSO it is clear that the Lewis acidity is sufficient for the glucose isomerisation.


**5. Discussions**

Snow avalanche hazards are computed to be increasing worldwide due to climate changes [128,129]. Among all the climatic contributors, climate extremization is identified as one of the factors influencing the behavior, irregularity, and frequency of snow avalanches [130,131]. In some areas, it causes the thinning and shortening of the duration of snow cover, contributing to an increased irregularity that raises the hazard. As a result, a correct climatic analysis involving investigations of changes in the snow cover and snow avalanche hazard assessment is vital for administering many crucial societal issues concerning territorial planning, risk mitigation, and resilience activities [25,132,133].

Exposure to this hazard may be voluntary, as is the case with skiing, or involuntary, such as on public transportation corridors and settlements. According to the literature and technical reports [31–33], the techniques used to evaluate avalanche hazards and risks are different depending on the circumstances.

Here, we attempted to understand the main interrelationships between climate extremization and environmental risk in a mass movement-prone area, such as the Prati di Tivo area. We discussed the stepwise approach to be followed for a correct snow avalanche assessment by combining the spatial distribution of the snow avalanches and the main climatic features of the study area. It was also essential to compare the findings with the detailed geomorphological features of the Vallone della Giumenta to outline the role of climate extremization in the triggering of the avalanches.

The combination of preliminary results and thematic maps allowed us to better characterize the study area from a morphometric, geomorphological, climatic, and nivological standpoint. In such a complex and mass movement-prone area, it was necessary to activate a risk mitigation protocol to develop land use policies and activities to define a significant snow avalanche assessment. According to the PIDAV project [99], the safety services for ski resorts and facilities at Prati di Tivo were updated by installing 12 Obellx® gas exploders [109,134] to manage short-term avalanche risks better. The installation was realized in correspondence with the main detachment areas at elevations ranging between 2100 and 2250 m a.s.l.

Moreover, as part of the increasingly more frequent processes of climate extremization, on 24–26 March 2020, a heavy snowfall event affected the study area. It was acknowledged as a prevalently stormy snowfall, which brought 90 cm of fresh snow (with a density of 140 kg/m3) over the ski facilities located at Prati di Tivo at elevations of about 1400 m a.s.l. Given the high snow accumulation rates, explosive pitches were performed on 24 March immediately after the beginning of the snowfall event and on March 26 during the main event, inducing moderate detachments of fresh, humid, and low-cohesion snow. Even if the preventive activity of Obellx® gas exploders reasonably mitigated the snow dynamics, on the night of 27 March, around 4:20 a.m., two natural snow avalanche events occurred following new abundant snowfalls and affected the northern escarpment of the Corno Piccolo ridge (Figure 16). A detailed field survey and a specific site investigation were also performed in the early morning of 28 March, thanks to a clear weather improvement. Considering the information gathered from this survey, it was possible to make several essential deductions:

**Figure 16.** Photo documentation of the snow avalanches of March 2020. (**a**) Evidence of the summit detachment area at the base of Corno Piccolo with a clear surface slab and (**b**) a simplified snow avalanche path (in red) affecting the Vallone della Giumenta and involving the main ski facilities.

The presence of interdigitated snow mass accumulations belonging to the March 2020 avalanches, both partially converged into the Vallone della Giumenta, testified the dynamic avalanche framework of the study area. Unfortunately, this peculiar nivological setting makes it impossible to define the temporal evolution of the two different events. For these reasons, preliminary one- and two-dimensional avalanche simulation models (e.g., AVAL-1D and RAMMS [39,112]) were applied to better describe the possible evolution of the documented avalanche events at a particular site (such as the Vallone della Giumenta), as well as to calculate the consequences of possible hazard scenarios. The avalanche modeling was carried out by employing RAMMS software and implemented with GIS techniques. In detail, it was performed both by considering a scenario characterized by a limited thickness of the snow cover mitigated by the Obellx® devices' activity and a scenario in which the downstream slopes were totally covered by a thick snow cover (i.e., not secured by the preventive action of the Obellx® devices). The resulting data defined different stopping distances and paths of the selected avalanche under scenarios driven by other transmitted pressures, as graphically shown in Figure 16. It showed how, in the absence of the preventive action of the Obellx® devices (red stars in Figure 16), an avalanche event can predominantly occur along the Vallone della Giumenta. Moreover, transmitted pressures, which vary from 30 to 150 kPa, and different heights of snow cover (Hc = 0.5, 1.0, and 1.5 m) were accounted for. Under these scenarios, the avalanche path can widely reach the Prati di Tivo area, involving the residence (reported as the hotel in Figure 14) and the four-seat chairlift line (Figures 16b and 17).

Snow avalanches can generally act as geomorphic agents [135]. Snow avalanches can exert considerable erosive forces playing a significant role in landscape development. Evaluating the morphological features of the mass movement-prone area and the main avalanche features is essential to quantify the material entrained by the avalanche and transported to the deposition zone [136].

A semi-quantitative analysis was applied to the modeled avalanche path at Vallone della Giumenta (Figures 13 and 17) to better describe the geomorphic role of snow avalanches at the Prati di Tivo area. This specific site investigation presents peculiar morphometric features and snow avalanche pressures accounted as representative of the main avalanche events in the study area. The analysis focused on evaluating the pre- and post-avalanche setting, highlighting the variations in the contributing area caused by the snow avalanche along the Vallone della Giumenta. This evaluation showed a variation that increased by about two-fold (>50%), as the contribution of each avalanche track and rock gully was significant in the geomorphic action of the avalanche. It is graphically shown in Figure 18 and summarized in Table 4.

**Figure 17.** Results of the avalanche dynamics simulation under different transmitted pressures and heights of snow cover: (**a**) Hc = 0.5 m, (**b**) Hc = 1.0 m, and (**c**) Hc = 1.5 m.

In conclusion, the resulting data allowed us to properly define the main steps of the developed risk mitigation protocol. It was activated following some recent damaging snow avalanches affecting the Prati di Tivo area to better develop mitigation activities and land use policies needed for the management of permanent settlements, recreation infrastructures, and ski facilities. The relevance and the impact of the work are represented by: (1) the provision of new data on the physiography–geomorphology of the study area and the mass movement-prone areas, (2) the outline of a multidisciplinary methodological approach for the definition of snow avalanche critical areas and the configuration of hazard protocols not yet developed for the Central Apennines, and (3) a technical scientific basis to develop the civil protection plans required to increase the knowledge of citizens and interested stakeholders about proper land management considering multi-hazard scenarios (i.e., snow avalanches and landslides).

**Figure 18.** Pre- and post-avalanche landslide contributing area.

**Table 4.** Dimensions of the possible landslide area, modified by snow avalanche dynamics.


**Author details**

Eman Helmy Thabet Center of Excellence in Regenerative Medicine and Its Research Application (CERRMA), Alexandria University, Egypt

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© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Roles of Extracellular Vesicles in Cancer Metastasis DOI: http://dx.doi.org/10.5772/intechopen.103798*


**8. Overall perspective and future work**

204 New Advances in the Basic and Clinical Gastroenterology

The proposed AR-DLac scheme, thoroughly described in §5.3, selects the optimum IMFs. In the reconstruction scenario (R-case) the selected IMFs and the residue reconstruct a new image from which the FV is extracted, while in non reconstruction scenario (NR-case) the FVs from the selected IMFs are concatenated in order to form the final FV. The results of both scenarios are tabulated in Table 2, for all classifiers. The best classification rates for each implementation scenario are notated in bold. The format %±% corresponds to mean acc.,

> **Implementation scenario R-case NR-case**  Acc. Sens. Spec. Acc. Sens. Spec.

LDA 95.5±0.5 94.8±0.8 96.2±0.4 89.2±0.9 84.3±1.7 94.1±0.4 QDA 95.8±0.6 93.9±1.1 97.7±0.3 **91.2±0.9 86.2±1.3 95.9±1.1**  MD 96.3±0.2 96.0±0.1 96.6±0.6 78.7±1.4 98.3±0.9 58.3±2.7 SVM **96.7±0.6 96.5±0.3 96.9±0.9** 89.7±0.8 87.5±1.0 91.9±0.9

Table 2. Mean classification rates (%) for both R-case and NR-case scenarios, for all

The most efficient performance of the proposed AR-DLac scheme is delivered in R-case scenario where the classification accuracy reaches 96.7% by exploiting SVM classifier. However, the difference between the most (SVM) and the least (LDA) efficient classifier is only 1.2 percentage points, in terms of accuracy, implying that the features extracted by the proposed analysis are quite robust, exhibiting advanced overall performance regardless of the classification algorithm engaged. It is also remarkable the fact that the high accuracy rate is accompanied by high and relatively consistent rates for both sensitivity (96.5%) and specificity (96.9%). These results indicate that AR-DLac scheme is capable of equally recognising ulcer and normal data without exhibiting bias towards a specific pattern. This behaviour applies to all examined classifiers since the difference between sensitivity and

The classification results of the NR-case, suggest inferior performance of the AR-DLac scheme during the non reconstruction scenario. The most effective classifier for NR-case is QDA delivering 91.2% accuracy, 86.2% sensitivity and 95.9% specificity. These rates, compared to those of R-case, are 5.5, 10.3 and 1.0 percentage points lower, respectively. Even the worst scenario for R-case (LDA classifier) achieves 4.3 percentage points higher accuracy than QDA in NR-case. Moreover, the balance between sensitivity and specificity rates deteriorated ranging from 4.4 (for SVM) to 40 (for MD) percentage points. MD, as in the individual IMF case, fails to recognize correctly the normal images since the specificity rate is 58.3%. The divergence in performance between R-case and NR-case indicates that the recomposed image by the optimal IMFs and the residue represents more efficiently the intestinal texture information than the individual components of the image. This may be explained by the fact that in NR-case the trend of the image (residue) is ignored. Moreover, the fact that FVNR includes 3 IMFs x 8 features = 24 features in total (33% larger than FVR)

**7.2 Reconstruction (R-case) – Non Reconstruction (NR-case) scenarios**

sens. and spec. ± standard deviation.

classifiers (LDA, QDA, MD, SVM).

specificity does not exceed 2.6 percentage points.

may also affect the classification performance.

**Classifier**

The aforementioned experimental results highlight the potential of the proposed scheme towards ulcer and healthy intestinal tissue discrimination. The optimum image components (IMFs) that contain the majority of texture information include IMFs 5, 6 and 8. Individual IMFs score up to 84% classification accuracy, while their exploitation as a group enhances the detection rate up to 91.2%. On the contrary, the refined image reconstruction process achieves 96.7% successful tissue identification. When compared with other approaches, the proposed scheme seems to be more effective exhibiting increased classification ability by using a smaller feature vector. One of the most efficient ulcer detection approaches (Kodogiannis *et al.*, 2007b), used as a comparison baseline, employs 54 features (instead of 8) and results in 94.5% accuracy when applied to our dataset (Charisis *et al.*, 2011).

In spite of the promising performance of the proposed scheme, there are still some issues for improvements that should be taken under consideration for its use in a future computer aided diagnosis system. Firstly, the number of ulcer and normal images should be increased in order to secure maximum diversity between the ulcer cases, develop more robust algorithms and obtain more accurate conclusions. Secondly, automatic segmentation of the regions of interest (ROI) is mandatory. In our approach, WCE images are manually cropped to the ROI. A potential solution is to divide each WCE image (576x576) into small patches (64x64) and choose the patches that depict mucosa. These patches contain almost all possible ROI by covering the most valid area of the original WCE image. At last, the computational cost of the proposed techniques should be revised. In this work, the introduction of a real time application was not our objective. In this context, the computational cost of the current unoptimized MATLAB code is 9.3 seconds per ROI, considering an average size of 145x145 pixels. BEEMD analysis consumes 83.4% of this time while DLac analysis and classification absorb 16.2% and 0.4%, respectively. When focusing on real time application, dedicated hardware and programming languages, more efficient implementation algorithms (for BEEMD and DLac) and multithreading programming should be considered.

#### **9. Conclusion**

Wireless capsule endoscopy (WCE) is a novel, non-invasive form of endoscopy that has started a new era for the visual inspection of the entire small bowel. A WCE system consists of a pill-shaped, wireless capsule that the patient swallows. The capsule, propelled by the natural bowel movements, captures and transmits images from the internal mucous membranes, along its journey through the digestive tract. Despite the revolution WCE has introduced, there are several limitations that pose serious questions about the competency of WCE compared to probe gastroscopy and colonoscopy. Some of the major challenges include camera speed/quality, power supply, controllable manoeuvring and interventional capabilities. Significant research is conducted towards this direction, various approaches have been proposed and the first achievements have emerged. A new capsule equipped

Enhanced Ulcer Recognition from Capsule Endoscopic Images Using Texture Analysis 207

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with two cameras and increased battery life has been developed for colon and esophagus examination. Magnetically remote controlling and wireless power transmission concepts are in experimental phase. Apart from the hardware-oriented limitations and approaches there is a major issue concerning the vast amount of data produced by a WCE examination. The optimal WCE needs to contain a computerized system for automatic detection of pathologies such as ulcer and polyps in order to overcome the drawback of time-consuming reviewing of the video (Fireman, 2010). In this context, researchers aim to develop automatic diagnosis systems. The main contribution of this work is the presentation of a novel tool for WCE image analysis and classification by exploiting color-texture features. This colortexture perspective was inspired by the one the gastroenterologist usually adopts in clinical practice for successful diagnosis of pathologies and, especially, ulcer. The proposed AR-DLac scheme is based on the ingenious combination of BEEMD and DLac, applied on the green component of WCE images in order to identify ulcerations. BEEMD, apart from an adaptive image denoising tool, was exploited to reveal the intrinsic components (IMFs) of the images in order to achieve data driven, adaptive image refinement (AR), boost the distinctness between normal and ulcer regions and facilitate DLac analysis to extract efficient texture characteristics. AR entailed optimum IMF selection, based on the structure patterns of IMFs disclosed by DLac. The optimum IMFs were used either to reconstruct a new, refined image, or provide separate images. The proposed AR-DLac approach was evaluated on selected WCE images, captured from patients, depicting ulcer and healthy tissue. Experimental results have shown that AR-DLac scheme exhibits quite satisfactory overall classification performance. Intestinal texture information is distributed along IMFs 3 to 8; thus, the utilization of a single IMF for the detection procedure is not recommended. The classification performance of individual IMFs does not exceed 84% (classification accuracy), with IMF5 being the most efficient. When individual images (i.e., optimum IMFs) are employed (non reconstruction case) the performance improves and accuracy reaches 91.2%. However, the best results are delivered in the reconstruction case, where accuracy, sensitivity and specificity exceed 96.5%. The advanced overall classification performance of AR-DLac approach paves the way for its use in a provisional automatic diagnosis system.

#### **10. References**

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**10**

*Russian Federation*

**Methods of Protein Digestive**

Mikhail Akimov and Vladimir Bezuglov

**Stability Assay – State of the Art**

*Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science*

The evaluation of protein stability in digestive tract is an important topic in various fields of biomedical sciences. The most straightforward task within it is the evaluation of food quality, as during the food processing the digestibility of a protein could fall dramatically. Another task, tightly connected with the previous one, is the evaluation of protein allergenicity. This parameter is shown to be dependent on protein's digestibility, the less digestible ones having the highest probability to induce immune response. Finally, the task of development of new peptide drugs also requires digestive stability check for *per os* drug

Current knowledge on protein digestion suggests it to be a multistage process, starting from pepsin cleavage in stomach and proceeding through trypsin and chymotrypsin digestion in intestinal lumen, and finally involving cleavage by intestinal surface and intracellular proteases. The latter two protease groups accomplish the most deep protein degradation. An intriguing point is that in spite of this knowledge the models, which include all stages of

Protein digestion, specific enzymes, involved in this process, as well as research area specific methodology for protein digestibility evaluation is regularly reviewed. However, as a common rule, the authors of latter group of reviews ignore the existence of methods with similar goals in other research areas, as well as the question of a degree to which any given method represents a living organism. The aim of this review is to describe existing models for evaluation of protein digestibility with a special emphasis on biological relevance

Before a discussion of protein digestibility evaluation methods, it seems worthwhile to introduce a reader to the current understanding of protein and peptide digestion process in order to provide him with a holistic picture of what could happen to an ingested protein. That is why this section will give a short description of digestive tract and an evaluation of enzymes involved in protein digestion in stomach and intestine. A special emphasis will be laid on characterization of intestinal wall peptidases (surface as well as intracellular) with

provided by distinct model as well as on productivity of each methodology.

description of their specificities and their role in overall protein digestion.

**1. Introduction**

forms that are the most convenient for a patient.

**2. Protein and peptide digestion**

protein digestion, are used primarily in food quality control.