Reflections on the Methods Used in This Study 1 Phenolic analysis

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CHAPTER 6: GENERAL DISCUSSION, CONCLUSIONS AND FUTURE PERSPECTIVES

6.1 Reflections on the Methods Used in This Study 1 Phenolic analysis

Upon critical observation of the current literature, there is inconsistency in the proportion of soluble and bound phenolic contents of cereal crops, mainly due to the differences in extraction methods used. In fact, there is a huge discrepancy of the soluble and bound phenolic extraction methods in the current literature (Adom and Liu, 2002; Chandrasekara and Shahidi, 2010; Kotaskova et al., 2016; Massaretto et al., 2011; Pihlava et al., 2015), in that

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ultrasonication, and magnetic stirring are being mostly used to assist the solvent and hydrolysis extraction methods of the soluble and bound PCs, respectively. Ultrasonication- assisted solvent extraction of soluble PCs could lead to a false higher soluble but lower bound phenolic content. Indeed, ultrasonication could misleadingly increase the proportion of soluble PCs by releasing naturally bound PCs due its mechanical force and thus breaking linkages of bound PCs with food macromolecules (Gonzales et al., 2014). Moreover, use of solvent-assisted ultraturrax extraction, as done in our experiments, could extract as much as soluble PCs available but not bound phenolics. Due to the short application time of the Ultraturrax treatment (40 sec), compared to 30 min ultrasonic treatment, damage of cell structure is limited. The pretreatments through which the samples gone through like fermentation (as can be seen in part 2.2) could also have a big impact on the extractability of the phenolic compounds. Therefore, it is worthwhile to standardize both the soluble and bound extraction methods for different food matrices, and in particular for cereal crops, so as to clearly understand the real proportion of the PCs in food materials.

Although quantification of the extracted phenolic compounds is preferably done by chromatographic methods, spectrophotometric method is frequently used. This is mainly due to lack of standards of the different phenolic compounds to use in chromatographic methods, and the fact that these analysis are very time consuming. In this respect, spectrophotometric method is used to have an estimation of the total phenolic/flavonoid content and the antioxidant capacity of the extracts. These methods are very useful as screening methods, and to make comparison between different samples/varieties or processing treatments. However conclusions should be drawn carefully as these results cannot be linked directly to an increased or decreased amount of one particular phenolic compound.

Besides, there is inconsistency in the use of standards in the analysis of TPC, TFC and antioxidant capacity methods (ABTS, DPPH and FRAP) which makes comparison of data among different studies difficult. The base for the choice of standards during phenolic content and antioxidant capacity analysis is not clear in the current literature (Bouayed et al., 2011; Chandrasekara and Shahidi, 2010; Min et al., 2012; Nipornram et al., 2018; Pang et al., 2018; Shen et al., 2009). Generally, the fundamentals on how and why to use a particular standard for TPC or TFC, and antioxidant assays of ABTS, DPPH and FRAP analyses need to be justified. Also important to take into account are possible interfering compounds in the food matrix/extracts in these spectrophotometric methods.

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HCl–vanillin method is one of the most frequently cited method for measuring tannin content, is well established and carries validity (Herald et al., 2014). However, the HCl–vanillin assay is not without major drawbacks including: non-tannin phenolic compounds react with vanillin and thus it is not specific and using catechin as a standard may overestimate the level of tannins (Earp et al., 1981; May and Burns, 1971). It also needs a skilled personnel for repeatability but despite all these drawbacks it remains a method of choice for determining tannin content in cereals.

6.1.2 In vitro methods

In vivo digestion and absorption of food is a spatiotemporal and dynamic process involving

complex enzymatic and transport reactions. Reproduction of all these biochemical and physiological events in a single in vitro model still remains difficult. Simulated digestion methods try to mimic physiological conditions in vivo, considering the presence of an array of digestive enzymes and their concentrations, pH, digestion time, and salt concentrations, among other factors. In vitro digestions have the advantage over in vivo methods in terms of low cost and most importantly at short time and no ethical clearance is needed. However, any

in vitro method does not match the accuracy level achieved by actually studying a food

digestion in vivo. Most importantly, it is not possible to simulate influx of endogenous compounds to the digestive tract and their subsequent digestion and absorption, replicate the effect of antinutritional factors and interactions between the host, the food and the bacteria present in the digestive tract (Coles et al., 2005). In vitro digestion in general could be broadly classified into two main categories as dynamic and static. The dynamic in vitro digestion models such as TNO-model (Verhoeckx et al., 2015) use advanced computerized technology which helps to simulate the dynamic features of digestion such as transport of digested meals, variable enzyme concentrations and pH changes over time as much as possible. On the other hand, simulated static in vitro digestions mimic in vivo digestions with constant ratios of meal to enzymes, salt, bile acids etc. at each step of the digestion. Static in vitro models of human digestion have been used to address questions of digestibility and bioaccessibility and/or matrix release of macronutrients (proteins, carbohydrates, lipids), and micronutrients (minerals, trace elements and secondary plant compounds including carotenoids, and

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phenolic compounds) (Bouayed et al., 2011; Hasjim et al., 2010; Kopf-Bolanz et al., 2012; Tavares et al., 2013). Throughout our study, we have used 4 types of simulated static in vitro digestion models, viz. in vitro digestion for digestibility based classification of starch fractions (Englyst et al., 1992), starch hydrolysis procedure to measure glycemic index (Goni et al., 1997), the INFOGEST standardized model to measure the phenolic and mineral bioaccessibility (Minekus et al., 2014) and the pH-drop in vitro method to measure protein digestibility (Hsu et al., 1977). The INFOGEST consensus static in vitro model used in the bioaccessibility measurement of mineral and phenolic compounds is not validated to be used for digestibility of macro molecules such as protein and starch. Analysing free glucose and amino acids to determine the digestibility of macro-nutrients is not appropriate, since the pancreatic digestion is not complete and needs an additional step with brush border enzymes such as amylo-glucosidase or peptidase to complete starch and protein digestion, respectively (Minekus et al., 2014). The additional step mentioned herewith is not given yet in the protocol, therefore, we used the traditional in vitro protein and starch digestibility specific methods. Significant variations in the use of in vitro digestion parameters between the individual models have been reported impeding the possibility to compare results across studies and to deduce general findings (Williams et al., 2012). This type of disagreements could only be avoided by using uniformly agreed methods. However, difference can still arise despite the use of similar

in vitro models, due to differences in sampling techniques following the end of in vitro

digestion as it will be detailed below. We tried to point out the weaknesses/differences of sampling techniques following the end of an in vitro digestion procedure using the INFOGEST standardized model- an internationally agreed static in vitro digestion model (Minekus et al., 2014) which we used in the determination of phenolic (chapter 2.3) and mineral (chapter 3) bioaccessibility measurements. In the INFOGEST standardized model as is used in chapter 2.3, at the end of the gastrointestinal digestion, the digested food was divided into three parts: 1) Liquid sample containing the dialyzed (D) phenolic content, 2) liquid sample containing the soluble nondialyzable (SND) phenolic content and 3) pellet that contained the bound phenolic contents. The current literature shows that the liquid samples containing the D and SND phenolic compounds are directly used in further analysis as if they were phenolic extracts. In our case, we freeze-dried the liquid samples and performed a solid-liquid extraction by using 80% methanol as a solvent, similar as done for the cereal samples as such.

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In a typical in vitro digestion model for mineral bioaccessibility, consisting dialysis bag, the final digestion will generate three fractions i.e. 1) a fraction which contain dialyzed minerals, 2) fraction containing soluble but nondialyzable minerals, and 3) the pellet fraction that contains the insoluble minerals as described in chapter 3. In the literature, there is no uniform description on how to pretreat the fractions before mineral contents are analysed. Clear methods on how to deal with the analysis of the digestion fractions is necessary in order to get reproducible data and have a better prediction of the physiological digestion. Overall, the difference in handling the samples at the last stages of the in vitro digestion could lead to large differences in intra and inter laboratories.

Related to the methods of analyzing the starch fraction and GI analysis, what is commonly lacking or at least not clearly indicated in the literature is the freshness level of the food products used during the experiments. As starch properties in terms of digestibility and GI is highly dependent on the state and freshness level of the food products, the sampling techniques and the storage conditions of the food matrices should be clearly indicated. In the pH drop in vitro method of protein digestibility, cleavage of the proteins by the cocktail of enzymes at alkaline pH, leads to the release of peptides, amino acids and more importantly to the release of protons resulting in a drop of pH (Moyano et al., 2014; Tinus et al., 2012). However, the formula (%IVPD = 65.66 + 18.1 ΔpH10 min ) used to calculate IVPD needs to be

viewed critically. First, this equilibrium will give a IVPD equal to 66%, even if no protein digestion occurs. However, the drop in pH results from the release of amino acids and peptides as proteins are digested in that the release of amino acids during proteolysis is not expected to be linear or of zero order. Second, mathematically, IVPD can have a value that is greater than 100% when pH10 min is <6.1, or ΔpH10 min >1.9 (Tinus et al., 2012). The pH-drop method

can and already was criticized because of its simplicity compared with the complex processes taking place in vivo. Also, food components with a buffering capacity can influence the pH- drop. Although the pH-drop method is criticized, this method was chosen because it still is the most used technique worldwide due to its simplicity and the relatively low cost. Very complex gastrointestinal models which include computer-controlled dynamic models simulating several physiological features of stomach and intestine (pH changes, peristaltic movements, and transit rates, biliary and pancreatic secretions) could better simulate the complex in vivo digestion of proteins.

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Overall, the major disadvantage of in vitro digestion models is their inability of measuring absorption of food components. This drawback can be tentatively partially solved by using dialysis membranes as used in our study or dynamic models but even then they still remain in

vitro. Unlike to the in vivo digestion in which the absorbed food components are constantly

taken up by their target tissues, in the case of static in vitro that uses dialysis membrane, there will be a gradual build of absorbed food components in the dialysis membrane leading into an equilibrium state. Therefore, the absorption of food components into the dialysis membrane will not be same as throughout the digestion time which could be considered as the limitation of dialysis membrane.

6.1.3 Osborne solubility based storage protein classification

Traditionally, seed storage proteins have been classified on the basis of their solubility characteristics. This solubility classification, as originally developed for wheat proteins, seems not to be valid for all cereal types, example tef. There is limited literature in tef regarding its proportion of storage proteins, however, the results in those papers contradict to one another. In our experiments even the total recovery was very low indicating that the solvents used were not efficient in extracting the storage proteins. Advanced laboratory techniques such as amino acid sequencing and mass spectrometry can be used to better quantify the storage proteins. However, these techniques necessitate the accurate annotation, classification, characterization and decoding of the biological function of the amino acid sequences. Application of machine learning algorithms for classification of seed storage proteins needs amino acid or dipeptide compositions or physicochemical properties of the protein or different combinations of these three features as an input to be able to classify storage proteins (Radhika and Rao, 2015). This type of analysis could lead to a better classification of the storage proteins than the traditional Osborne solubility based classification.

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