Idriss Miganeh Waiss

André Kimbonguila

Mohamed Fatouma

Laurette Brigelia Abdoul-Latif

Louis Nkeletela

Joel Matos

Jérémy Scher

Petit

Fatouma Mohamed

Abdoul-Latif

Laurette Brigelia Nkeletela

Louis Matos

Joël Scher

Oyelade, O.J O E Adelakun

M Van De Venter

Effect of milling and sieving processes on the physicochemical properties of okra seed powders

Keywords: okra seeds, sieving, particle size, flowability, FT4 powder rhometer, Milling okra seeds, milling, sieving, particle size, FT4 powder rheometer, flowability 36

come

 '7.47"N; 18°3'44.459"E). Okra were shelled (the seeds were removed from 104 the pods), then seeds were sorted to remove impurities and sun-dried in the open air at 30 °C.

105

Dry seeds were stored at 10 °C until powder production. 106 2.2. Powder production by milling and sieving 107 500 g sun-dried okra seeds were milled with an ultra-centrifugal mill ZM 200 (Retsch, France) 108 at three different milling speeds (6 000, 12 000, and 18 000 rpm) at room temperature (25 °C) 109 using a 20-cm sieve drilled with 1-mm trapezoidal holes. Then, obtained powder was collected 110 in sealable polyethylene bags before performing sieving. Okra seed powders issued resulting from the milling step were sieved with an Analyzer 3 113 Spartan sieve shaker (Fritsch, Idar-Oberstein, Germany) with 0.5 mm vibration amplitude for 114 24 min using sieves of 180, 315, and 500 µm mesh sizes. Hence, the following granulometric 115 classes were produced: ≥ 500 µm, 315 -500 µm, 180 -315 µm, and ≤ 180 µm.

116

The material on each sieve was collected in sealable polyethylene bags, weighed to determine 117 the mass yield of each granulometric class, and stored at 10 °C until analysis. The particle size distribution of okra seed powders (unsieved powders and granulometric 120 classes) were determined by laser diffraction granulometry using dry dispersion (Mastersizer 121 3000, Malvern Instruments France, Orsay, France) at room temperature (25 °C). Dispersion 122 conditions for all powders except the ≤ 180 µm powder samples were as follows: 100 % air 123 pressure at 4 bar, 3 mm hopper gap, and 40 % feed rate. For the ≤ 180 µm powder samples, the 124 following dispersion conditions were employed: 100 % air pressure at 4 bar, 4 mm hopper gap, 125 and 70 % feed rate. The chosen size estimator was the equivalent diameter in volume.

126

The d10, d50, and d90, which are characteristic diameters representative of the particle size 127 distribution, were calculated. They represent the diameters for which 10 %, 50 % and 90 %, 128 respectively, of the particle population have lower sizes. Last, the span was used to evaluate the 129 width of the particle size distribution; it was determined according to the following equation 1: The proximate composition of all granulometric classes as well as unsieved powders were 133 evaluated with standard methods. Fat content was determined by Soxhlet method using 5 g For the compressibility test, the powder sample was first placed in the measurement cell (i.e.

169 two overlaid 25 mm × 10 mL glass cylinders) and submitted to three conditioning cycles. After 170 that, the vessel was split to remove any excess powder and the blade was replaced by the vented 171 piston. Then, powder was slowly compressed by the vented piston moving vertically at 0.05 172 mm. s -1 at different normal stresses ranging from 0.5 to 15 kPa, and the percentage change in (Freeman, 2007;Leturia et al., 2014;Gnagne et al., 2017). The powder sample was placed in Cohesion, expressed in kPa, represents the shear stress required to make the powder bed flow 211 in unconfined conditions: indeed, the cohesive interactions between particles must be broken 212 before they can be individually put in motion. Moreover, generally, the higher the 213 interparticular forces, the more the powder bed can be compressed, and the more energy must 214 be supplied to the powder to make it flow.

215

Flow factor (ffc) is defined as the ratio between the consolidation stress and unconfined yield 216 stress: The larger the ffc value, the better the powder flows (Jenike, 1964;Rhodes, 2008).

217

Powder flow behaviors can be classified as follows (Fu et al., 2012) Then, the shift to the left of the particle size distribution at higher milling speed shows that it 240 led to smaller particles, as expected. Besides, when the milling speed was increased, the 241 proportion of fine particles raised to the detriment of the proportion of large particles. This

242

shows that some large particles, presumably having elevated hardness, were converted in finer The authors wish to thank the reviewers for their new helpful comments. Detailed point-by-point response can be found hereafter, with reviewer comments in black (line numbers associated with reviewer comments correspond to submitted manuscript) and answers in blue (line numbers related to manuscript revision correspond to revised manuscript). In revised manuscript, additions were performed in blue and deletions in red crossed words.

Reviewer #1

The revision is satisfactory.

Thank you for your work in reviewing the manuscript.

Reviewer #2

Although the work presents interesting, it is quite hard to understand the paper due to poor communication. therefore i couldn't identify the main goal of this paper.Overall, this is not a well-written manuscript. There were no sufficient information about the previous study findings presented for readers to follow the present study rationale and procedures. Finally, I recommend that the paper should not be accepted for the publication in the present from.

Thank you for your work in reviewing the manuscript, the authors have tried to take into account these remarks in the second revised manuscript.

-L47: this sentence is not required it is repeating

The sentence in l41-43 indicates that okra (in general, i.e. all plant parts being considered) provides carbohydrates, protein, fat, minerals, and vitamins that are usually deficient in staple foods. The sentence in l47 concerns more specifically okra seeds, which is the studied material. Thus, it was decided to keep this sentence giving further information about the composition of okra seeds. L49-50: Seeds are rich in proteins (45%) and lipids (34%), thus use as a food for a malnutrition children in Nigeria. (reference).

In order to reduce the length of this sentence, the authors preferred to delete "classifying is as a nutrient-rich plant". Also, "A research program initiated by Nigeria" was corrected by "A Nigerian research program".

-L52-54: rewrite, English is not good L78: your introduction and the purpose is not matching rewrite. With changes made in the second revised manuscript, the authors hope that the link between bibliographical data and study aim is now clearer.

L92-93: rewrite The authors did not understand why this sentence should be rewritten, so it was kept unchanged.

L94: it is important do proof reading of your paper This sentence appears clear enough, so the authors do not understand why this comment was formulated by reviewer #2. Maybe the fact that the sentence was justified and not aligned to the left? This was changed in l105.

  purchased from farmers in Impfondo locality, in the north of the Republic of 103 the Congo (1°37

  properties of okra seed powders: particle size distribution 119

184-

  the measurement cell (25 mm x 35 mL glass cylinder) fitted with a porous plate and three 185 conditioning cycles were carried out to achieve a homogeneous packing state of the powder 186 sample. The aeration test was then performed by measuring the flow energy of the powder bed 187 during downward moving of the blade through the powder bed at -100 mm.s -1 tip speed for air 188 velocities ranging from 0 to 5 mm s -1 by 0.5 mm. s -1 steps. Between each test cycle, a 189 conditioning cycle was carried out to obtain a steady state of aerated powder. The most 190 important parameters measured by the aeration test are defined below: 191 Aerated energy (AE, mJ), which is the flow energy measured during downward moving of the 192 blade through the powder bed at maximal air velocity (10 mm.s -1

  239

  270

  Figure2

F

409443

  Figure 8

Figure 1 :

 1 Figure1: Particle size distributions of okra seed unsieved powders obtained at 6 000, 12 000, and 18 000 rpm milling speed.

Figure 2 :

 2 Figure 2: Particle size distributions of the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 6 000 rpm.

Figure 3 :

 3 Figure 3: Particle size distributions of the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 12 000 rpm.

Figure 4 :

 4 Figure 4: Particle size distributions of the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 18 000 rpm.

Figure 5 :

 5 Figure 5: Compressibility of the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 6 000 rpm. Error bars represent standard deviations; some were smaller than the marker size and thus not visible.

Figure 6 :

 6 Figure 6: Compressibility of the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 12 000 rpm. Error bars represent standard deviations; some were smaller than the marker size and thus not visible.

Figure 7 :

 7 Figure 7: Compressibility of the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 18 000 rpm. Error bars represent standard deviations; some were smaller than the marker size and thus not visible.

Figure 8 :Figure 9 :

 89 Figure8: Shear test curve obtained after pre-shear at 9 kPa normal stress for the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 6 000 rpm. Error bars represent standard deviations; some were smaller than the marker size and thus not visible.

Figure 10 :

 10 Figure10: Shear test curve obtained after pre-shear at 9 kPa normal stress for the different granulometric classes and unsieved sample of okra seed powders obtained by milling at 18 000 rpm. Error bars represent standard deviations; some were smaller than the marker size and thus not visible.

  Conditioned bulk density (CBD, g/mL), which is a density measurement of the powder bed in 166 conditioned state. It is thus very similar to the classical powder aerated density.

	165 -167 2.6.2. Compressibility test
	168
	F
	o r
	P
	e e r
	R e v
	i e w

Results and discussion 230 3.1. Impact of milling speed on okra seed powder granulometry 231

  :

	224	All experiments were conducted in triplicate and results were expressed as mean ± standard
	225	(Eq. 3) deviation. Statistical analysis was performed to a one-factor analysis of variance using
	226	Microsoft Excel 2016, significant differences were assessed with Tukey's HSD at a 5 %
	227	confidence level with the DSAASTAT add-on.
	228
	229	3. Particle size distributions of okra seed powders obtained at the three milling speeds (6 000,
	232	12 000, and 18 000 rpm) are shown in figure 1.
	233
	234	F	Figure 1
	235 236 237 238	o First, it was observed that okra seed powders were constituted of two populations: one of large r particles between 200 and 3 000 µm, the other of fine particles between 5 and 200 µm. This is P typical for seed powders obtained by milling, as the different organs of seeds (seed coat, radicle, e r e cotyledon, etc.) differ in hardness, thus resulting in various particle sizes (Chen et al., 2015).
			R e v
			i e w
	218	ffc < 1: not flowing;
	219	1 < ffc < 2: very cohesive;
	220	2 < ffc < 4: cohesive;
	221	4 < ffc < 10: easy flowing;
	222	10 < ffc: free flowing.
	223	2.7. Statistical analysis

2. Efficiency of okra seed powder sieving

		Table 1
	259	Table 1 shows that the sieving process allowed an efficient fractionation of okra seed powders
	260	into the different granulometric classes, as significant mass yields (generally above 10 %) were
	261	found for all sieved powders. As expected from particle size distributions of unsieved powders
		F
		o r
		P
		e e r
	243 244 245	R e v ones at higher milling speed. Also, it can be guessed that the employed ultra-centrifugal mill may not allow decreasing the particle size under 10 µm, in agreement with equipment specifications. i e w
	246	Moreover, figure 1 confirms the effect of the milling speed on the particle size distribution of
	247	okra seed powders. It is worth noting that a more intense milling was also associated to a
	248	broader dispersion of the particle size distribution. Indeed, obtained powder was less
	249	monomodal when milling speed was increased, as evidenced by the increase in span. This may
	250	be indicative of the occurrence of different grinding mechanisms in the employed milling
	251	process. From this granulometric analysis, three mesh sizes (180, 315, and 500 µm) were
	252	chosen for sieving powders into granulometric classes so that each granulometric class
	253	represent a significant mass fraction of the unsieved powder.

254

3.

262

(figure

1

), the ≥ 500 µm granulometric class was the most represented (around 50 % mass yield) 263 whatever the milling speed. This is consistent with the fact that seeds are material of rather high 264 hardness, making them difficult to grind, and resulting into powders containing a marked 265 proportion of large particles. 266 3.3. Particle size distribution 267 Particle size distribution of all granulometric classes and unsieved powder are displayed in 268 figures 2, 3, and 4 and associated granulometric parameters (d10, d50, d90, and span) are 269 presented in table 2.

5. Flow properties of okra seed powders 341 3.5.1. Stability and compressibility tests 342

  3, and 4 and table 2), as expected. Both the decrease in particle sizes and the increase 294 in the width of the particle size distribution at higher milling speed were consistent with the 295 more elevated mass fractions of ≤ 180 µm, 180 -315 µm, and 315 -500 µm granulometric a higher proportion of fibers than other parts of okra seeds. Then, as fibers are 319 more difficult to grind, larger particles may be enriched in this component, leading to higher 320 carbohydrate content. Consequently, the proportions of fat and proteins were decreased in larger 321 particles. Previous studies explained that plant parts containing a high proportion of fibers andThe results of stability tests for granulometric classes and unsieved samples of okra seed

	318 is composed of 322 sugars (both belonging to carbohydrates) are harder to grind and thus result in larger particles
	323	after milling, whereas plant parts with higher levels of fat and proteins are easier to grind and
	324	thus lead to smaller particles (Becker et al., 2016; Zaiter et al., 2016). So, proximate
	325	composition of okra seed powders was greatly dependent on granulometric class; in other
	326	words, the mean particle size had a marked impact on proximate composition of okra powders.
		F
		o r o r
		P P
		e e r e e r
		R e v R e v
		i e w i e w
	312	increased. As regards the influence of milling speed, it appeared that the contents in fat and
	313	proteins (resp., carbohydrates and ash) decreased (resp., increased) in ≤ 180 µm and 180 -315
	314	µm granulometric classes and increased (resp., decreased) in 315 -500 µm and ≥ 500 µm

286

Whatever it may be, the results of sieve fractionation (table

1

) and particle size characteristics 287 of powder fractions (table 2) showed that the successive milling and sieving procedure was 288 effective in micronizing okra seeds and separating unsieved powders into sufficiently different 289 size classes. Indeed, the d50 of sieved powders (granulometric classes) was systematically 290 found between the mesh sizes of upper and lower sieves, which confirms the suitability of sieve 291 shaking for separating okra seed powders.

292

Milling speed contributed to reduce mean particle size and span of granulometric classes (cf. 293 figures 2, 296 classes (table 1). 297 Table 2 298 3.4. Proximate composition 299 In table 3, contents in moisture, fat, proteins, ash, and carbohydrates of all granulometric classes

300 and unsieved powders of okra seeds were evaluated to investigate the influence of milling speed 301 and sieving efficiency. Indeed, proximate composition may be indicative of the stickiness of 302 powder surface (for instance, surface fat generally tend to increase powder stickiness) (Langrish 303 et al.

, 2006; Viviane et al., 2007)

, which is known to greatly impair powder flowability.

304

Table

3

305 Moisture content of granulometric classes ≤ 180 µm, 180 -315 µm, and 315 -500 µm was 306 lower than 10 %, whereas unsieved powders and ≥ 500 µm samples had a moisture content 307 ranging between 10 and 13 %. It is generally considered that a moisture content lower than 10 308 % ensures powder stability during storage

(Kaur, Kaushal and Sandhu, 2013)

. Then, 309 granulometric classes composed of smaller particles were expected to have better preservation 310 abilities than unsieved powders and ≥ 500 µm samples. Also, fat and protein contents 311 significantly decreased when the particle size was increased, unlike carbohydrates and ash that 315 granulometric classes. Indeed, it was expected that a more intense milling enhances the size 316 reduction of plant material and thus the release of fat and proteins from plant cells

(Becker et 317 al., 2016;[START_REF] Zaiter | Antioxidant and antiacetylcholinesterase activities of different granulometric classes of 555 Salix alba (L.) bark powders[END_REF]

. This can be related to okra seed structure: indeed, the outer shell 327

The moisture content was decreased for powder samples constituted by small particles, because 328 they probably undergo a higher friction-induced heating during milling. Moreover, statistical 329 analysis permitted to show that large particles were richer in carbohydrates. Plant parts 330 containing a large proportion of fibers are expected to be more difficult to grind and result in 331 large particles. As fibers were included in total carbohydrates in the proximate composition 332 analysis, the larger content in carbohydrates of higher size granulometric classes of okra seed 333 powders may be due to a higher proportion of fibers. Bioactive compounds are often associated 334 with proteic and fatty fractions, thus the smaller granulometric classes of okra seeds are 335 expected to have higher contents in bioactive compounds

[START_REF] Zaiter | Antioxidant and antiacetylcholinesterase activities of different granulometric classes of 555 Salix alba (L.) bark powders[END_REF] Deli, Ndjantou, 336 et al., 2019)

. At higher milling speed, this differential distribution of macronutrients within 337 granulometric classes was enhanced: small particles contained even more proteins and fat and 338 less moisture, carbohydrates, and ashes. Thus, the discriminating power of the sieving process 339 in matter of proximate composition was improved at higher milling speed. 340 3.343 powders are presented in table 4.

344

Table 4 345

 4 Whatever the milling speed, unsieved powders presented significant differences with 346 granulometric classes in terms of BFE and SE. Moreover, the CBD of unsieved powders was granulometric classes, which is consistent with their probable stickiness 351 deriving from their higher fat content as well as with the fact that smaller particles are able to 352 establish more contact points with their surrounding environment (particles, walls), thus 353 impairing their flowability. At first glance, BFE results seemed in contradiction with this 354 conclusion, as BFE decreased with mean particle size and this could be interpreted as better 355 flowability of granulometric classes of smaller mean particle size. However, it should be 356 mentioned that powder bed with poor flow properties may also lead to low BFE values when 357 particle cohesion is so high that the blade only makes flow the small amount of powder located 358 in its vicinity(Leturia et al., 2014; Gnagne et al., 2017). Also, interpretations of powder 359 flowability on the basis of the BFE values may be distorted by important CBD differences.

	F o r P e e r R e v i e w Table 4 and figures 5, 6, and 7 present the results of the compressibility tests. Whatever the cohesion of smaller 377 350
	378	milling speed, compressibility markedly decreased with mean particle size, as expected: indeed,
	379	it has previously been discussed that smaller particles are generally more cohesive, making

347

systematically found within the range of CBD of granulometric classes. The CBD of 348 granulometric classes decreased with particle size, indicating that the powder bed was less 349 compact for samples composed of smaller particles. This could be an indication of higher 360 Indeed, the stability test is performed to evaluate the flowability of a given volume of 361 conditioned powder bed, hence sample mass may vary between samples according to aerated 362 density (i.e. CBD) differences. When comparing powder sample differing in CBD, the SE 363 seems more appropriate to give information on powder flowability, as it corresponds to flow 364 energy normalized against powder mass. All okra seed powders exhibited SE values close to 5 365 mJ/g, indicating that studied powders had correct flow properties. For samples milled at 6 000 366 rpm, the lower SE (indicative of better flowability) was found for the 180 -315 µm 367 granulometric class, while at 12 000 and 18 000 rpm milling speed, the best flow properties on 368 the basis of the SE was found for the highest granulometric class. This is consistent with the 369 fact that larger particles are expected to flow more easily

(Petit et al., 2017; Shi et al., 2018)

. 370 Also, the better flowability of the 180 -315 µm granulometric class for samples milled at 6 000 371 rpm may result from the lower span of this sample (cf. table 2).

372

Moreover, the influence of milling speed on powder flowability can be deduced by comparing 373 unsieved powders. Table

4

shows that the SE and the CBD increased, while the BFE decreased, 374 when the milling speed was increased. This trend was expected, because when the milling speed 375 increases, the mean particle size decreases and the interparticular interactions become so high 376 that powders are much cohesive and have a poorer flowability. 380 them more compressible. This confirms the better flow properties of powders composed of 381 larger particles. Unsieved powders had intermediate compressibility, comprised in the range of 382 compressibilities of granulometric classes. Compressibility of unsieved powders and 394 Aeration results of granulometric classes and unsieved samples of okra seed powders are 395 presented in table 5. 396 Table 5 397 All investigated okra seed powders were not fluidizable in standard conditions of the FT4 398 aeration test (air velocity up to 10 m.s -1 ). Indeed, very low aeration ratios AR and elevated 399 aerated energies AE were found, showing that the fluidized state was not achieved at maximal 400 air velocity employed in the standard FT4 test. This may be explained by the high mean particle 401 size of 180 -315 µm, 315 -500 µm, and ≥ 500 µm granulometric classes and the presumed 402 relatively high cohesion of ≤ 180 µm and 180 -315 µm granulometric classes (Gnagne et al., 403 2017; Petit et al., 2017). The low fluidizability of studied powders may constitute an advantage 404

for handling purposes at industrial scale, as loss of these powders by projection in the 405 surrounding air may be reduced. Aeration properties seemed not to be affected by changing the 406 milling speed, which is logical, as the order of magnitude of mean particle size of powder 407 samples was the same whatever the milling speed.

Table 1 :

 1 Sieved masses and mass fractions of the different granulometric classes and unsieved powders of okra seed powders obtained at 6 000, 12 000, and 18 000 rpm milling speed.

	Page 29 of 39
			6 000		12 000		18 000
	Milling speed (rpm)
	Granulometric class	Sieved mass (g) Mass yield (%)	Sieved mass (g) Mass yield (%)	Sieved mass (g) Mass yield (%)
	≥ 500 µm	317.53	59.13	164.18	50.26	86.83	42.47
	315 -500 µm	101.05	18.82	52.34	16.02	41.75	20.42
	180 -315 µm	51.16	9.53	44.68	13.68	46.32	22.66
	≤ 180 µm	67.26	12.52	65.44	20.04	29.54	14.45
	Unsieved powder (total) 537.00	100.00	326.64	100.00	204.44	100.00

Table 2 :

 2 Granulometric parameters of the different granulometric classes and unsieved samples of okra seed powders obtained at 6 000, 12 000, and 18 000 rpm milling speed. For powder results at each milling speed separately, means ± standard deviations followed by the same superscripted letters in the same column were not significantly different at p < 0.05 according to Tukey's HSD test.

	Milling speed (rpm)

Table 3 :

 3 Proximate composition (contents in moisture, lipids, proteins, ash, carbohydrates on wet basis) of the different granulometric classes and unsieved powders of okra seeds.

	Milling speed (rpm)

Table 4 :

 4 Results of stability and compressibility tests for unsieved samples and granulometric classes of okra seed powders produced at 6 000, 12 000, and 18 000 rpm milling speed.

	Milling speed
	(rpm)

Table 5 :

 5 Aeration and shear cell test results for unsieved powders and granulometric classes of okra seed powders obtained at 6 000, 12 000, and 18 000 rpm milling speed. Means ± standard deviations followed by the same superscripted letters in the same row were not significantly different at p < 0.05 according to Tukey's HSD test.No experiment was performed on humans or animals. So, ethics approval was not required for this research.

	Milling speed (rpm)

Data Availability Statement:

Research data are not shared.

Before proximate analysis and characterization of flow properties of the powders, the efficiency 256 of the milling and sieving processes for the production of different granulometric classes were 257 verified by weighing powder samples after sieving (Table 1).

The authors acknowledge also support of the LIBio by the "Impact Biomolecules" project of Engineering,57,[111][112][113][114]

Conflict of interests:

The authors declare that they have no conflict of interest.

This sentence was rewritten (cf. l51-53).

-L56: this sentence is not clear Indeed, this convoluted sentence was removed.

-L58: you should discuss about this before you discuss the aim of this paper? you need to explain why you need to study this and the importance of this area and its relationship to human health as well as to okra

The link between nutritional quality and granulometric class has been added in l55-61, which permits to better introduce the aim of the study. The link with human health (food supplements) was added in l64-65.

-L59: this sente4nce and previous sentence has a big jump "flow properties" was removed as it was not introduced previously. The link with the distribution of nutritional compounds within granulometric classes was made through the production of food supplements (cf. l64-65). The reason for studying also flow properties has been developed in l74-79.

-L64: not clear why Indeed, this should now be clearer with the mention of "food supplements" in l64-65.

-L66: I what are the bioactive compounds you are going to extract not clear. Indeed, this was out of the scope of the study, and "bioactive" as "replaced" by "nutritional" (cf. l67-68).

-L67: if you introduce new cofounders you need to explain them.

The authors did not understand this comment, nevertheless this sentence was out of the scope of the study and it was removed. L68: I can't understand your English. This sentence was deleted.

L70: not clear

The authors did not understand what is not clear in this sentence, and thus it as kept unchanged.

L73: not clear

This sentence was corrected in l83-84.

L98: this sentence is too long rewrite The authors did not find that this sentence is too long (42 words, 245 characters including spaces), and only one idea is presented in this sentence, so it was decided to keep this sentence unchanged.

L101: what do you mean by issued

This was corrected by "resulting from" L104: you English is very poor so hard to understand.

The authors really did not understand this comment, as concerned sentence cannot be clearer.

L225-226: I can't understand what you mean by two populations.

It is very classical to talk about particle populations when discussing particle size distribution results. A particle population gathers all particles having similar characteristics, like size in the case of granulometric analysis. See here for instance: http://www-odp.tamu.edu/publications/178_SR/Volume/CHAPTERS/SR178_24.PDF


Loïc ; Kern
email: loic.kern@univ-lorraine.fr

Olivier Fabre
email: olivier.fabre@iaa-lorraine.fr

Joel Scher
email: joel.scher@univ-lorraine.fr

Jérémy Petit
email: jeremy.petit@univ-lorraine.fr

Joël ; Scher

Kern Loïc

Fabre Olivier

Scher Joël

and β-caseins

Keywords: Casein, differential precipitation, calcium affinity, HPLC, dynamic light scattering, Rheology

Chemical fractionation of caseins by differential precipitation: influence of pH, calcium addition, protein concentration and temperature on the depletion in αand β-caseins

Introduction

32

From time immemorial, milk was used as raw material for many dairy products 1 such as cheese, 33 yoghurt, cream, etc. Dairy products or milk derivatives (fractionated proteins, peptides, etc.) exhibit 34 many bioactive properties , and functional properties (emulsification, foaming abilities) [START_REF] Wong | Structures and Functionalities of Milk Proteins[END_REF] . These diverse 35 and unique properties [START_REF] Irshad | Antioxidant Activity of Bioactive Peptides Derived from Bovine Casein 525 Hydrolysate Fractions[END_REF][START_REF] Woo | Bioactive Peptides in Milk and Dairy Products: A Review[END_REF] led to the development of new dairy products of high added-value, like 36 protein isolates. Milk is a complex medium composed of water, proteins (caseins, whey proteins), 37 carbohydrates (mainly lactose), lipids, and minerals (calcium, phosphates, etc.). Some milk minerals 38 (sodium, potassium, etc.) are soluble and very available for interaction with other molecules. The rest 39 of the minerals exist as colloidal species involved in casein micelles. The most important is calcium, 40 which is mainly present within casein micelles (65 %) and is essential to their stability. 52 internal structure of casein micelles is mainly composed of α-and β-caseins, because of their higher 53 hydrophobicity. κ-casein, being a glycosylated protein, has an amphiphilic nature making it prone to 54 be located at the water/protein interface constituted by the micelle surface, thus providing stability to 55 the micelle structure. Nevertheless, the interaction between phosphorylated caseins (α-and β-caseins 56 essentially) and calcium phosphate nanoclusters also contributes to strengthen casein binding in 57 micelles.

58

The micellar organisation of caseins is strongly affected by the physical and chemical properties of the 59 surrounding environment, especially temperature, pH, and chemical composition (especially, mineral 60 composition). Temperature mostly influences the structuration of micelles by strongly affecting their 61 size. For instance, heating milk at 50 °C results in a decrease in micelle size from 180 to 140 nm, as 62 hydrophobic interactions are favoured at high temperatures, leading to micelle contraction [START_REF] Noebel | Apparent Voluminosity of Casein Micelles in the Temperature Range 35-70 536 Degrees C[END_REF] and thus 63 reduction of micelle porosity. However, in other milk derivatives (for example when caseins are 64 reconstituted in simulated milk ultrafiltrate [START_REF] Jennes | Preparation and Properties of a Salt Solution Which Simulates Milk 539 Ultrafiltrate[END_REF] which corresponds to a solution of the mineral fraction of 65 milk), heating leads to an increase in casein micelle diameter by a mechanism that is still not totally 66 understood. This shows that interactions with other milk components markedly affect casein micelle 67 features.

68

Several studies have showed that a pH decrease to 4.6 induces the progressive solubilisation of micellar 69 calcium, thus disturbing micelle structure. A further pH decrease to the isoelectric point of caseins may 70 induce their precipitation. pH increase has an inverse effect [START_REF] Vaia | Solvent-Mediated Disruption of Bovine Casein Micelles at Alkaline PH[END_REF] . Indeed, casein alkalisation affects their 71 electric charge: amino acids undergo an increase in negative charge, enhancing casein electrostatic 72 repulsion, which in turns disturbs ionic equilibrium between micellar and soluble phases. [START_REF] Dezhampanah | Milk β-Casein as a Vehicle for 545 Delivery of Bis(Indolyl)Methane: Spectroscopy and Molecular Docking Studies[END_REF] are rarely employed 78 in the food industry, although several academic studies put in light various technofunctional [START_REF] Matias | Poly(n-Butyl Acrylate)-Casein Nanocomposites as Promising Candidates for 548 Packaging Films[END_REF][START_REF] Wang | Casein Gel Particles as Novel Soft Pickering Stabilizers: The Emulsifying 551 Property and Packing Behaviour at the Oil-Water Interface[END_REF] and 79 biological properties that could be used with benefits by dairy industrials. 80 β-casein is surely the most studied casein. First, it is rather abundant, accounting for about 33 % (w/w) 81 of total caseins (table 1). It has the particularity to leave the micelle and partially solubilise at low 82 temperature (around 4 °C), as in these conditions hydrophobic interactions are lowered. Thus, it can 83 be isolated by cold filtration [START_REF] Cragnell | Bovine β-Casein Has a Polydisperse Distribution of Equilibrium Micelles[END_REF] followed by membrane filtration. β-casein has great amphiphilic 84 properties (in spite of the absence of glycosylated group) owing to its amino acid composition, making 85 it suitable for emulsification and foaming applications [START_REF] Atamer | Bovine β-Casein: Isolation, Properties and Functionality. A Review[END_REF] . β-casein can be enzymatically hydrolysed to 86 produce various peptides with a large range of biological activities: for example, antioxidant [START_REF] Irshad | Antioxidant Activity of Bioactive Peptides Derived from Bovine Casein Hydrolysate 559 Fractions[END_REF] , 87 antibacterial [START_REF] Sibel Akalin | Dairy-Derived Antimicrobial Peptides: Action Mechanisms, Pharmaceutical Uses 561 and Production Proposals[END_REF] , and antihypertensive [START_REF] Del | Food-Grade Production of an Antihypertensive Casein Hydrolysate 564 and Resistance of Active Peptides to Drying and Storage[END_REF] effects have been reported.

88

α S -casein, the major type of casein (accounting for 44 % (w/w) of total caseins, cf. table 1), has been 89 well less studied. The few available information deals with reinforcement of egg protein gels [START_REF] Matsudomi | Alpha-Casein Improves the Gel Properties of Dried Egg 567 White[END_REF] or 90 production of peptides with health benefits, like anxyolitic effect, by enzymatic hydrolysis [START_REF] Malkoski | Kappacin, a Novel Antibacterial Peptide from Bovine Milk[END_REF] .

91

The most interesting casein is probably the κ-casein. This glycoprotein is very effective in promoting 92 emulsification and foaming of food products [START_REF] Chen | Particle Size Determines Foam Stability of Casein Micelle Dispersions[END_REF] . Furthermore, enzymatic hydrolysis of κ-casein may lead 93 to various peptides with interesting biological activities, like kappacin presenting a strong antibacterial 94 activity [START_REF] Malkoski | Kappacin, a Novel Antibacterial Peptide from Bovine Milk[END_REF] . The most studied peptide deriving from κ-casein is surely the glycomacropeptide which is The impact of pH on viscosity of NPC at 55 g/kg total proteins was also investigated at 4, 25, and 50 °C 334 (table 5).

335

The decrease in viscosity at higher temperature was also found for the NPC at 55 g/kg total proteins, 336 whatever the pH. NPC viscosity was also increased at higher total protein concentration, which is 337 logical.

338

Regarding the impact of pH on NPC viscosity, it was more important at 55 g/kg total proteins than at The developed HPLC method intended to separate caseins between them, but also from whey 355 proteins, in order to allow accurate quantification of each casein in untreated casein concentrates as 356 well as supernatants resulting from the differential precipitation process. Standards of α-, β-, κ-caseins, 357 α-lactalbumin, β-lactoglobulin, and bovine serum albumin were analysed by the HPLC method in order 358 to identify their retention times (figure 1).

359

The elution order is apparent in the caption of figure 1. As expected with a C18 HPLC column, retention 360 time increased for hydrophobic compounds: α-casein was slightly more retained than β-casein, and 361 both were markedly more retained than κ-casein. This is in agreement with the hydrophobicity of 362 caseins, decreasing in the following order: β-casein > α-casein > κ-casein. Whey protein are known to 363 be more hydrophilic than caseins, thus lower retention times could be expected from this point of 364 view, but their smaller molecular size make them more prone to interact with the stationary phase, 365 leading to retention times close to those of caseins. Nevertheless, a correct separation of all peaks of 366 major caseins and whey proteins was achieved with applied elution conditions.

367

The knowledge of retention times of the major compounds found in NPC (α-, β-, κ-caseins, α- 407 κ-casein precipitation was higher than expected from literature, lowering the efficiency of the 408 separation process. Two hypotheses can be formulated to explain this pitfall. On one hand, the low κ-409 casein may have been sensitive to precipitation induced by acid hydrolysis: indeed, molar HCl, a strong 410 acid at relatively high concentration, was added drop-by-drop in the NPC solution and could locally 411 cause a marked pH decrease susceptible to hydrolyse κ-casein and make it precipitate. On the other 412 hand, the NPC concentrate was prepared by reconstitution of a powder obtained by spray-drying, 413 hence thermal treatments applied in the course of the manufacturing process (during pasteurisation, 414 evaporation, and spray-drying steps) may have affected casein micelle structure and favoured the 415 association of κ-casein with whey proteins, especially β-lactoglobulin. This chemical modification may 416 have increased κ-casein sensitivity to calcium-induced precipitation, as β-lactoglobulin is known to 417 strongly interact with calcium.

418

In order to limit the probable acid hydrolysis of κ-casein, another series of differential precipitation 419 assays was performed with molar citric acid (Figure 5). Indeed, citric acid is a weak acid, thus local pH industry, for instance in cheese factories, and benefits from a better image in food consumers' mind 422 than chlorhydric acid. The last advantage of citric acid resides in its high affinity for calcium, which may 423 compete with caseins for calcium binding and thus decrease the interaction of calcium with κ-casein, 424 limiting its precipitation.

425

As expected, calcium-induced precipitation of caseins was reduced in the presence of citrates, which 426 are able to complex calcium, thus lowering the proportion of free calcium able to interact with caseins.

427

The difference between the action of HCl and citric acid is obvious when comparing figures 4 and 5: as 428 affinity of chloride ions for calcium is very low compared to citrates, interaction of caseins with calcium 429 is favoured upon HCl addition and thus the maximal precipitation of caseins was achieved at a lower 430 casein/calcium molar ratio: 30 when using HCl against 40 when using citric acid. Interestingly, the 431 proportions of precipitated caseins were increased when using citric acid for the acidification step: 88 432 % of α-casein, 68 % of β-casein, and 60 % of κ-casein. This was unexpected, as the weakness of citric 433 acid should reduce acid hydrolysis of caseins and the complexation of calcium by citrates may have 434 increased casein solubility. Actually, there are very few articles dealing with the interaction between 435 citrates and casein but Mekmene and Gaucheron (2011) found that citrates were stronger 436 complexants for calcium than caseins. This is consistent with the need to increase calcium addition to 437 obtain the same level of casein precipitation when citrates were present in solution: indeed, as some 438 calcium ions were complexed by calcium, the concentration of free calcium ions able to interact with 439 caseins was lowered. Whatever it may be, even if more calcium needed to be employed to reach 440 maximal casein precipitation, the use of citric acid instead of HCl permitted to precipitate more α-and 441 β-caseins, while offering a better fractionation of β-and κ-caseins.

442

In order to improve the selectivity of the separation method, as local acid hydrolysis was still thought 443 to be responsible for the excess of κ-casein precipitation, it was decided to try applying the differential 444 precipitation process to a NPC concentrate at higher total protein concentration: 55 g/kg (figure 6).

F

  368lactalbumin, β-lactoglobulin, bovine serum albumin) made possible their quantification from the area 369 of related peaks on the chromatogram of NPC (figure2).

F

  reduced compared to HCl. Also, citric acid is already commonly used in the food 421

Figure 2 : 650 Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 26504567 : A chromatogram of all major milk proteins obtain by our HPLC method. 646 Separation of milk protein by using our method on a NPC solution at a protein concentration 647 of 22,5 g/kg. 648 Figure 3: A chromatographic representation of the impact of calcium on the three major caseins 649 concentration. Evolution of the proportion of individual casein depending to the calcium ratio with NPC 22,5 651 g/kg and molar HCl. 652 Evolution of the proportion of individual casein depending to the calcium ratio with NPC 22,5 653 g/kg and molar citric acid. 654 Evolution of the proportion of individual casein depending to the calcium ratio with NPC 55 655 g/kg and molar citric acid. 656 Evolution of the proportion of individual casein depending to the calcium ratio with NPC 55 657 g/kg and 0,5 M citric acid.

Figure 2 :

 2 Figure 2: Chromatogram of NPC at 22.5 g total proteins/kg. (1) κ-casein (20.2 min) / (2) BSA (21.4 min) / (3) α-casein (26.2 min) / (4) β-casein (28.6 min) / (5) β-lactoglobulin (31.2 and 31.8 min).

Figure 3 :

 3 Figure 3: Chromatogram of the supernatant obtained from selective precipitation of NPC at a total protein concentration of 22.5 g/kg without calcium addition (straight line) and upon calcium addition at calcium/casein molar ratio of 40 (dashed line).

Figure 4 :

 4 Figure4: Evolution of casein precipitation upon calcium addition during the selective precipitation process for NPC at 22.5 g/kg of total proteins, using molar HCl for acidification. Circles, continuous line: α-casein; diamonds, dashed line: β-casein; triangles.

Figure 5 :

 5 Figure5: Evolution of casein precipitation upon calcium addition during the selective precipitation process for NPC at 22.5 g/kg using molar citric acid for acidification. Circles, continuous line: α-casein; diamonds, dashed line: β-casein; triangles, dotted li: κ-casein.

Figure 6 :

 6 Figure6: Evolution of casein precipitation upon calcium addition during the selective precipitation process for NPC at a total protein concentration of 55 g/kg using molar citric acid for acidification. Circles, continuous line: α-casein; diamonds, dashed line: β-casein; triangles, dotted line: κ-casein.

Figure 7 :

 7 Figure7: Evolution of casein precipitation upon calcium addition during the selective precipitation process for NPC at a total protein concentration of 55 g/kg, using 0.5 M citric acid for acidification. Circles, continuous line: α-casein; diamonds, dashed line: κ-casein.

  Figure 1

caseins by differential precipitation 204

  The differential precipitation process of the current study was developed on the basis of Post and al.'s 205 work[START_REF] Post | Effect of Temperature and PH on the Solubility of Caseins: Environmental Influences 606 on the Dissociation of α(S)-and β-Casein[END_REF] . The main differences with the approach of Post et al. resided on one hand in the nature of

	102 127 152 177 199 248	Several chemical and/or physical methods have been developed to separate milk caseins from other eliminate chemicals that are not suitable for food applications. But this method has several drawbacks: supernatant phase is expected to be enriched in κ-casein, and the precipitated phase in α-and β-For HPLC quantification of caseins, acetonitrile (Sigma-Aldrich) and trifluoroacetic acid (Sigma-Aldrich) 50 °C. Prior to analysis, the sample was equilibrated at the target temperature for 10 h and
	103 128 153 178 200 249	proteins. The chemical process is widely used in the dairy industry for caseinate powder production. it requires specific equipments (a large HPLC column and a recycling system for solvents), large caseins, owing to their difference in calcium affinity. The precipitate is then resuspended in water to Material and methods were used for the preparation of the mobile phase. Calibration was performed using casein and whey to the calcium/casein molar ratio. So, the volume of all samples was adjusted with distilled water temperature equilibration of the viscometer was achieved in 20 min.
	104 129 154 179 201 226 250	Acid casein can be obtained by decreasing milk pH 23 , thus making caseins precipitate. Coagulated amounts of solvents should be handled with care and correctly reprocessed, and produced quantities obtain a liquid phase enriched in α-and β-caseins. α-and β-caseins have the highest affinity for Preparation of native phosphocaseinate solution protein standards (Sigma-Aldrich). before performing the last step of the differential precipitation process. All analysed concentrates presented newtonian rheological behaviour and this was modelled with
	105 130 155 202 227 251	caseins are isolated, then neutralised and resolubilised with an alkali, and finally dried to be stored are low in comparison with traditional methods of casein isolate production. calcium, hence the precipitate obtained by the method of Post et al. (2012) is mainly composed by Finally, the precipitated was separated from the supernatant phase by centrifugation ( Megafuge 8R, Microsoft Excel 2013 (Washington, United State) using linear regressions of the shear stress vs. applied
	95 96 97 98 99 100 106 107 108 109 110 111 121 122 123 124 125 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 156 180 157 158 159 160 161 162 163 164 165 166 181 182 198 203 228 229 230 252 253 254 Chemical fractionation of 216 issued from the C-terminus of κ-casein; it presents various functional properties like foaming and emulsification abilities 21 , but it also allows improving health by contributing to nutrition of phenylketonuria patients or intestinal health prevention, etc. Finally, as κ-casein greatly contributes to micellar stabilisation, modifying its concentration in milk and/or its relative proportion toward other caseins may allow controlling acid gelation or enzymatic hydrolysis of milk derivatives 22 . Thus, milk products enriched or depleted in κ-casein may lead to dairy gels with innovative physicochemical and F o r P e e r R e v i e w under the form of a powder. Since the discovery of the process of acid precipitation of caseins, some novel methods have been described, like ethanolic 24 or phosphate precipitation 25 . Physical processes are also available for casein separation. For example, centrifugation enables the physical separation of compounds on the basis of density difference by applying centrifugal force to liquid samples. However, caseins being soluble, it is necessary to precipitate caseins to be isolated by chemical process prior to centrifugation. which retains molecular compounds. After that, sample molecules are gradually eluted by a polar mobile phase (generally acetonitrile or methanol). This method allows to separate the different caseins and obtain isolates of a-, β-, and κ-caseins. Preparative chromatographic columns do now permit to produce greater amounts of isolated caseins than electrophoretic methods, making chromatographic methods suitable for industrial applications. Another advantage lies in the fact that recovered proteins F o r P e e r R e v i e w Last, the most common method for producing casein isolates still remains the membrane filtration process, either by using organic or inorganic membranes 28 . Generally, tangential filtration is operated by pumping the solution to be concentrated and making it circulate under pressure. After the filtration step, two products are obtained: a retentate in which large molecules (caseins for instance in the case of milk microfiltration) are concentrated and a permeate composed of water and soluble molecules of size inferior to the diameter of membrane pores. Membrane cut-off is selected according to the molecules to be concentrated and defines the type of filtration process, for which pore size decreases in the following order: microfiltration > ultrafiltration > nanofiltration > reverse osmosis 29 . Membrane filtration is used to produce a great variety of milk derivatives: milk protein concentrates, native phosphocaseinate (NPC), whey protein concentrate, etc. is the first step of casein purification is generally a microfiltration in order to remove the majority of whey proteins, minerals, and lactose. The second step often consists in an ultrafiltration of the microfiltration retentate to increase casein concentration. Our study aims at adapting the differential precipitation method of Post et al. (2012) for producing two casein concentrates, one being enriched and the other depleted in κ-casein. In this study of Post et al., casein was first alkalised, leading to destabilisation of casein micelles by an increase in negative charges of caseins. At alkaline pH, soluble caseins have a high negative charge, allowing them to interact with divalent ionic calcium. Calcium addition at this point induces calcium-casein interactions resulting in casein solubility decrease. Solubility of calcium-casein complexes being minimal at acid and neutral pH, the alkalised casein solution containing calcium is then acidified up to pH 7. Precipitated calcium-F o r P e e r R e v i e w In general, NPC is industrially obtained from two-step tangential filtration (microfiltration and these caseins. However, κ-casein is not insensible to calcium, so the precipitation of a fraction of κ-casein is also expected too, together with some whey proteins like β-lactoglobulin 30 . In the current work, it was tried to improve the differential precipitation method by using milk protein concentrates instead of milk as feeding material. The objective of the study was to focus on the interactions between caseins and calcium, hence casein concentrate presents many advantages: mainly, it contains a higher proportion of compounds of interest (caseins) than milk, while having reduced amounts of minerals, lactose, and whey proteins that may interfere with casein-calcium interactions. In milk, the casein/whey protein ratio is known to be close to 80/20, but in milk concentrate (by a combination of microfiltration and ultrafiltration) is around 92/8. The impact of calcium addition was thoroughly investigated in order to select the optimal calcium concentration dependently from the composition of milk derivative to be treated. Also, the influences of initial F o r P e e r R e v i e w ultrafiltration) of pasteurised skim milk followed by stabilisation by spray-drying. In this work, the NPC used for differential precipitation of caseins was Promilk 852 B produced by Ingredia (Arras, France). Haën, Sellze, Germany) either at 0.5 and 1 M. F o r P e e r R e v i e w Thermo Scientific, Massachusetts, USA) at 5 000 x g and 25 °C, then collected. Each differential precipitation assay was replicated three times. shear rate curve. In our case, the coefficient of determination (R²) was high enough (between 98 and 100 %) to apply a linear regression. were carried out to reach different molar ratios between added calcium and total caseins: 10, 20, 30, 217 40, and 50. The information about the initial calcium concentration of the casein concentrate were not 218 provided by the supplier. 219 Then, pH was decreased to 7 by addition of HCl or citric acid either at 0.5 or 1 M. Acid addition was 220 performed drop-by-drop to limit acid hydrolysis of caseins, while avoiding exceeding the target pH of 221 o r P e e r R e v i e w 239 240 Rheological characterisation 241 Rheological behaviour of NPC at protein concentrations of 22.5 and 55 g/kg was evaluated with a 242 Kinexus rotational rheometer (Malvern Instruments, Malvern, UK). A cone-plate geometry of 50 mm 243 diameter and 2° angle was used. Shear stress measurements were realised between 0.0001 and 1 000 244 s -1 shear rate (which corresponded to 71 analytical points) during 10 min. o r P e e r R e v i e w 7. F 231 Dynamic light scattering measurement F 255 Reverse-
	101 126 151	textural properties. are obtained in native form. Elution solvents can be evaporated to increase casein concentration and casein complexes can then be recovered by centrifugation, decantation, or filtration. The resulting

112

Electrophoresis is another separation technique, whose principle is to apply an electric field to liquid 113 samples to separate molecular compounds according to their electrophoretic mobility that depends 114 on electric charge and molecular weight

[START_REF]Électrophorèse[END_REF]

. Following this technique, caseins could be separated from 115 other milk proteins, and even each casein type could be isolated, with a high level of purity.

116 Unfortunately, this method involves the use of chemicals incompatible with food safety requirements 117 (urea, acetonitrile, methanol, etc.) and the amounts of isolated caseins that can be produced are too 118 low to envisage production at industrial scale.

119

For better separation efficiency, chromatographic methods are mostly used

[START_REF] Leeb | Fractionation of Dairy Based Functional Peptides Using Ion-Exchange Membrane 593 Adsorption Chromatography and Cross-Flow Electro Membrane Filtration[END_REF]

. Caseins in liquid solution 120 (such as milk) are injected in a column filled with an apolar stationary phase (often, C18-bonded silica), 167 protein concentration and nature of added acid were studied with a view to meet economical and 168 ecological concerns (treatment of samples having the highest possible protein concentration and 169 reduction of added volumes of acid, calcium, and alkaline solutions in order to obtain products as much 170 concentrated as possible while limiting environmental impact). Prior to these assays intending to 171 optimise the differential precipitation process, DLS analyses were performed on the casein 172 concentrate at different pH ranging from 7 to 11 to identify the critical pH at which alkaline micelle 173 disruption occurs. Also, rheological characterisation of casein concentrate at native pH (6.85) and pH 174 11 were carried out at 4, 25, and 50 °C to find the temperature to be used in the differential 175 precipitation process in order to avoid a too high viscosity of the casein concentrate, which may impair 176 casein concentrate agitation and handling. 183 Its composition was provided by Ingredia and checked by HPLC quantification: 81 % (w/w) total 184 proteins, with caseins and whey proteins respectively representing 92 and 8 % (w/w) of total proteins, 185 8.5 % (w/w) minerals, 1.5 % (w/w) fat, and 2 % (w/w) lactose. NPC powder was resuspended in distilled 186 water to reach the following concentrations: 22.5 and 55 g/kg. Appropriate amounts of powder were 187 slowly poured into distilled water at 25 °C under magnetic stirring for 1 h. Then, casein concentrates 188 were stirred at 4 °C overnight, in order to ensure complete powder solubilisation. 189 Prior to differential precipitation assays, temperature of casein concentrates was adjusted to 25 °C 190 with a warming plate and their proteic composition was checked by HPLC following the method 191 described later. 192 193 Chemical and reagents 194 During differential precipitation assays, alkalisation was performed with NaOH 1 M (VWR, Radnor, 195 USA). Calcium addition was achieved with a 0.7 M CaCl 2 solution, prepared by dissolving CaCl 2 .2H 2 O 196 (Carlo Erba reagents, Val de Reuil, France) in distilled water. Acidification was realised either by HCl 197 (Sigma-Aldrich, Saint-Louis, USA) or citric acid (citric acid monohydrate powder, Honeywell Riedel-de-206 feeding material, casein concentrate, which has a greater proportion of compounds of interest, and 207 on the other hand in the proteic concentration of the feeding material: 55 g/kg in our study (with 92 208 % of casein) vs. 27 g/kg (with 89 % of casein) for Post et al.. The NPC solution was alkalised by the drop-209 by-drop addition of molar NaOH under vigorous stirring until reaching pH 11, as measured with a Five 210 Easy Plus pH meter (Mettler-Toledo, Viroflay, France). During this step, the addition of NaOH induces 211 the progressive destructuration of casein micelles, thus affects ionic equilibrium and leading to a small 212 pH reincrease. Consequently, it was necessary to let solution at rest for 2 minutes minutes to reach pH 213 equilibrium after addition of each NaOH drop. 214 Different amounts of 0.7 M CaCl 2 were added to the alkalised NPC solution at pH 11 and the mixtures 215 were stirred for 30 min to allow calcium-casein interaction. Depending on the assay, calcium additions The acidification step leads to calcium complexation by caseins followed by precipitation of 222 complexes, thus inducing a slow pH reincrease during equilibration. Consequently, solution was let at 223 rest for 20 min after each acid drop addition. 232 Dynamic light scattering (DLS) was used to determine the average micelle diameter in the casein 233 concentrate at different pH (7, 8, 9, 10, 11 and 12) with a Zetasizer Nano (Malvern). Prior to analysis, 234 the casein concentrate was diluted at 2 g/kg of total proteins and adjusted to the target pH with the 235 Five Easy Plus pH meter by using molar NaOH under agitation with a magnetic bar. Then, temperature 236 was equilibrated to 25 °C during 5 min and DLS analysis was repeated three times for each sample. 237 Recorded size estimators were mean diameter in number and mean diameter in volume. Casein 238 standards (κ/α/β) were also analysed with the same method. 245 First, 100 g sample was adjusted to the target pH (from 7 to 12) by adding molar NaOH and the dilution 246 induced by alkalisation was compensated between samples by adding an appropriate amount of

phase high performance liquid chromatography 256

	273	The three replicates of differential precipitation supernatants were each analysed three times by HPLC.
	274
	275	Results and discussion
	276	Size of casein micelles upon pH change
	277
		F
		o r
		P
		e e r
		R e v
		i e w
	265	trifluoroacetic acid (eluant B). Gradient elution was carried out as follows: the initial composition of
	266	the mobile phase was 80 % eluant A and 20 % eluant B. After injection, the proportion of eluant B was
	267	linearly increased to 46 % in 35 min. After that step, 100 % eluant B was passed during 10 min for
	268	cleaning purposes. Finally, during the last 20 min, column was re-equilibrated with 80 % eluant A and
	269	20 % eluant B. Total method duration (analysis, column cleaning, equilibration) was 65 min. Flow rate
	270	was set at 1 mL/min and room temperature was regulated at 25 °C.
	271	The supernatant obtained after application of differential precipitation process was analysed by RP-

Chromatographic analysis was performed with a Prominence HPLC apparatus (Shimadzu Tokyo, 257 Japan). The chromatographic system was composed of a 3-way degasser (Degasys Populaire), a SCL-258 10 A system controller (Shimadzu), FCU-10AL proportioning valves (Shimadzu), an LC-10AT pump 259 (Shimadzu), a SPD-M10 autosampler (Shimadzu), and a light diffusion detector EUROSEP Chromachem 260 (VWR, Radnor, USA). Chromatograms were acquired with the LC Solution software (Shimadzu). 261 Separation was achieved with an Agilent (Santa Clara, USA) 300 Extend column (250 mm length x 262 4.6 mm diameter, C18-bonded silica, 5 μm bead size, 30 nm pore size). Mobile phases were: 89.9 % 263 (v/v) ultra-pure water, 10 % (v/v) acetonitrile (Sigma-Aldrich), and 0.1 % (v/v) trifluoroacetic acid 264 (Sigma-Aldrich) (eluant A); 10 % (v/v) ultra-pure water, 89.9 % (v/v) acetonitrile, and 0.1 % (v/v)

Table 2

 2 presents results of DLS analysis of NPC at pH ranging from 7 to 11.278In the investigated pH range, casein micelles are expected to be negatively-charged, as the isoelectric 279 point of caseins has been reported around 4.8[START_REF] Tian | Effective Recovery of Casein from Its Highly Diluted Solution by Using a Technology 609 of Foam Fractionation Coupled with Isoelectric Precipitation[END_REF] . Thus, pH increase may enhance the negative charge 280 of caseins and lead to their disruption. It is apparent from DLS measurements that no significant size 281 change of casein micelles occurred between pH 7 and 9, showing that electrostatic repulsion between 282 caseins was not sufficient to disrupt micelles in this pH range. At pH 10, the mean diameter in number 283 of caseins was markedly decreased to about 30 -40 nm, indicating the presence of a major population 284 of isolated caseins. Also, the mean diameter in volume increased to 282 nm, showing that some casein 297 the mean particle size of casein concentrate alkalised at pH 11 tended to the size of individual caseins.298The higher size of caseins in NPC alkalised at pH 11 may result from the difference in chemical

	285	micelles were still present at pH 10, but their swelling confirms that electrostatic repulsion between
	286	caseins began to induce their destabilisation. When further increasing the pH at 11, two populations
	287	appeared from measurements of size in volume: one constituted of partially-disrupted micelles of high
	288	size (366 nm) and the other of low mean size (55 nm), likely corresponding to individual caseins. These
	289	results show that casein micelles disruption, triggered between pH 9 and 10, significantly occurred
	290	between pH 10 and 11, which justifies the alkalisation at pH 11 in the differential precipitation process.
	291	But when we increase the pH to 12, we could see that the decrease is more significant, and the
	292	population of particles is more homogenous than pH 11.
	293	To confirm these interpretations, the size of individual caseins was measured by analysing casein
	294	standards (table 3).

295

Table

3

shows that the size in number of isolated caseins is found 10 -13 nm. This confirms that the 296 differential precipitation process applied to casein concentrates allows disrupting casein micelles, as 307 So, the significant decrease of mean particle size at pH 11 suggests that casein micelles are disturbed 308 and individual caseins are present as monomers or very small polymers. The pH 12 seems to be more 309 efficient for the casein micelle destructuration, but it didn't mean the differential precipitation will be

of protein concentration, temperature, and pH on rheological behaviour of casein

	322	observation, viscosity of NPC at 22.5 and 55 g/kg total proteins at various pH was evaluated. This was
	323	achieved at 4, 25, and 50 °C to investigate temperature influence, as viscosity is known to decrease at
	324	increased temperature. Table 4 displays viscosity of NPC at 22.5 g/kg total proteins.
	325	At 22.5 g/kg total proteins, no great change in NPC viscosity was denoted upon alkalisation, especially
	326	at 50 °C. At 25 °C, viscosity seemed to slightly decrease upon alkalisation, while at 4 °C it slightly
	327	increased up to pH 10 and slightly decreased above. These results show that pH had a small impact on
	328	NPC viscosity at 22.5 g/kg total proteins whatever the temperature, which could be expected at this
	329	low total protein concentration (inferior to the one of cow's milk that is around 32 g/kg). Moreover,
	330	F table 4 shows that viscosity of NPC decreases with temperature, which is the common behaviour of
	331	o r proteic solutions. It seems that increase the pH to pH 12 lead to a small decrease of viscosity compare
	332	to the pH 11	P
	333		e r e
			R e v
	313	concentrates	i e w
	314	Preliminary differential precipitation assays (data not shown) showed that a tremendous increase in
	315	NPC viscosity occurred around pH 10 at ambient temperature. Also, protein concentration in the NPC
	316	had a strong impact on viscosity: over 55 g/kg total proteins, stirring of the NPC was impaired, which
	317	could lead to a lower degree of homogenization of the NPC and reduce calcium-caseins interactions.
	318	Besides, caseins, like other proteins, are sensitive to acid hydrolysis; hence, during the acidification
	319	step of the differential precipitation process, the poor agitation of the NPC could locally induce a
	320	marked pH decrease upon acid addition, leading to undesired precipitation of caseins (especially κ-

Table 1 :

 1 Typical composition of micellar casein expressed on dry matter basis.

Table 2 :

 2 Influence of pH on casein micelle size (mean diameters in number and volume) in diluted NPC (2 g/kg total proteins).

Table 3 :

 3 Casein standards size measurements (2 g/kg total proteins).

Table 4 :

 4 Newtonian viscosity of NPC at 22.5 g/kg total proteins at different temperatures (4, 25, and 50 °C) and pH (from 7 to 12).

Table 5 :

 5 Newtonian viscosity of NPC at 55 g/kg total proteins at different temperatures (4, 25, and 50 °C) and pH (from 7 to 12).

Table 6 :

 6 Evolution of the proportion of the different caseins in investigated selective precipitation process conditions.

	Figure 1 : Chromatogram of milk protein standards. (1) κ-casein (19.6 min) / (2) BSA (21.7 min) / (3)
	α-lactalbumin (24.5 min) / (4) α-casein (26.2 min) / (5) β-casein (28.6 min) / (6) β-lactoglobulin (31.2
	and 31.8 min).

Table 1

 1

	Total caseins

Table 3 Sample

 3

				Page 32 of 42
		α-casein	β-casein	κ-casein
	Mean
	diameter in	12 ± 3	10 ± 3	13 ± 3
	number (nm)
	Mean	14 ± 4 (96 %)	12 ± 4 (96 %)	16 ± 5 (75 %)
	diameter in	88 ± 25 (1 %)	116 ± 90 (4 %)	81 ± 31 (13 %)
	volume (nm)	424 ± 100 (3 %)	/	266 ± 106 (11 %)

Table 6

 6

				Page 34 of 42
	Table 5
		Dynamic viscosity	Dynamic viscosity	Dynamic viscosity
	pH
		(mPa.s at 4 °C)	(mPa.s at 25 °C)	(mPa.s at 50 °C)
	7	5.1 ± 0.2	2.6 ± 0.0	1.6 ± 0.1
	8	10.4 ± 0.2	6.3 ± 0.1	2.1 ± 0.2
	9	16.1 ± 1.8	13.5 ± 0.7	4.5 ± 1.4
	10	50.7 ± 2.9	37.0 ± 2.5	13.3 ± 0.4
	11	59.9 ± 10.0	12.1 ± 1.9	4.5 ± 0.2
	12	28.3 ± 1.1	9,4 ± 0.4	3,3 ± 0.4

improve HPLC quantification accuracy.

Acknowledgements Page 22 of 42 Institute of Food Science and Technology International Journal of Food Science & Technology The authors wish to thank the LIBio laboratory where experiments were performed, and its members 518 (researchers and technicians) for the advices regarding this work. 519 The authors acknowledge support of the LIBio by the "Impact Biomolecules" project of the "Lorraine 520 Université d'Excellence" (Investissements d'avenir -ANR).