Department of Molecular Sciences Variation in milk composition and its correlation to rennet induced coagulation A case study from northern Sweden Mathilda Bergman Master s thesis 30 credits Agricultural Programme - Food Science Molecular Sciences, 2018:37 Uppsala 2019
Variation in milk composition and its correlation to rennet induced coagulation A case study from northern Sweden Mathilda Bergman Supervisor: Assistant supervisor: Examiner: Hasitha Priyashantha, Swedish University of Agricultural Sciences, Department of Molecular Sciences Monika Johansson, Swedish Univeristy of Agricultural Sciences, Department of Molecular Sciences Åse Lundh, Swedish University of Agricultural Sciences, Department of Molecular Sciences Credits: Level: Course title: Course code: Programme/education: Course coordinating department: 30 credits Second cycle, A2E Självständigt arbete i livsmedelsvetenskap - magisterarbete EX0727 Agricultural Programme - Food Science Department of Molecular Sciences Place of publication: Year of publication: Uppsala 2019 Title of series: Molecular Sciences Part Number: 2018:37 Online publication: Keywords: https://stud.epsilon.slu.se Coagulation properties, rennet coagulation time, gel firmness, gross composition, casein micelle size Swedish University of Agricultural Sciences Faculty of Natural Resources and Agricultural Sciences Department of Molecular Sciences
Abstract The coagulation of milk is a basic requirement in cheese making and determines yield, texture and aromas. The coagulation time and the resulting gel is due to the milk properties, variation and quality. As the industry strives to optimize production and have a consistent quality of cheese, it is of utter importance to know the factors influencing on coagulation properties. The aim of the study was to evaluate variation of milk composition and properties of farm milk from northern Sweden to determine correlations with rennet induced coagulation. Fat, protein, ph, casein micelle size (CMS) and milk fat globule (MFG) size of the raw milk was recorded. Gel strength (G20) and rennet coagulation time (RCT) was studied. Farm milk was divided into three different pooled silos based on farm factors. The study showed increased CMS from November to September as well as an increase in coagulation time and decrease in gel firmness and ph. A principal component analysis showed a positive correlation between CMS and G20 in the trial in November, contradicting to the literature. Thus, the result show correlations between milk composition and coagulation properties. However, no clear patterns can be observed from the study but need further research where a broader perspective and factors should be considered. Keywords: Milk coagulation properties, gel firmness, rennet coagulation time, gross composition, casein micelle size
Sammanfattning Koagulering av mjölk är ett grundläggande krav vid osttillverkning och avgör utbyte, konsistens och aromer. Koaguleringstiden och den resulterande gelen är beroende på mjölkens egenskaper, variation och kvalitet. Eftersom branschen strävar efter att optimera produktionen och ha en jämn kvalitet på producerad ost är det viktigt att känna till de faktorer som påverkar koaguleringsegenskaperna. Syftet med studien var att utvärdera egenskaper och variationen i mjölksammansättningen i mjölk från norra Sverige för att fastställa korrelationer med löpeinducerad koagulering. De studerade parametrarna omfattade fett- och proteininnehåll, ph samt storlek på kaseinmicelle och fettkulor. Gårdarna delades in i tre grupper baserat på gårdsfaktorer och mjölken slogs samman till i tre olika silos. Kaseinmicellstorleken och koaguleringstiden ökade från november till september samtidigt som det var en minskning av gelfasthet och ph. Principalkomponentanalysen från mätningen i november visade på en positiv korrelation mellan ökad kaseinstorlek med ökad gel styrka vilket motsäger resultat i litteraturen. Studien visar således på samband mellan mjölksammansättning och koaguleringsegenskaper. Inga tydliga korrelationer eller mönster kan definieras från studien utan kräver ytterligare forskning där ett bredare perspektiv och fler faktorer bör övervägas. Nyckelord: Koaguleringsegenskaper, gelstyrka, koaguleringstid, mjölksammansättning, kaseinmicellestorlek, mjölkfettkulor
Table of contents List of tables 5 List of figures 7 Abbreviations 9 1 Introduction 12 2 Background 14 2.1 Milk composition and coagulation 14 2.1.1 Protein in milk 15 2.1.2 Fat in milk 16 2.1.3 Calcium in milk 17 2.1.4 Changes in composition 17 3 Literature review 19 3.1 Gross composition 19 3.2 ph in milk 21 3.3 Casein micelle size 22 3.4 Milk fat globule distribution 24 3.5 Milk coagulation properties 25 3.6 Effect of bulking milk samples 27 4 Method and material 28 4.1 Gross composition and ph 29 4.2 Rheology properties 29 4.3 Casein micelle size 30 4.4 Fat globule size distribution 30 5 Result and data 31 5.1 Gross composition 31
5.1.1 Fat content in farm cluster and silo milk 31 5.1.2 Protein and ph content in farm cluster and silo milk 33 5.1.3 Lactose content in farm cluster and silo milk 36 5.2 Milk fat globule size in farm cluster and silo milk 38 5.3 Casein micelle size in farm cluster and silo milk 40 5.4 Rheology 42 5.4.1 Rennet coagulation time of farm cluster and silo milk 42 5.4.2 Gel firmness in farm cluster and silo milk 44 5.5 Principal component analysis 46 6 Discussion 48 7 Conclusion 52 Referenslista/References 53 Acknowledgements 62 Appendix 1 63 Appendix 2 64
List of tables Table 1. Overview of studies performed on gross composition of cow milk 19 Table 2. Overview of studies performed on ph of milk 21 Table 3. Overview of studies performed on casein micelle size in milk 22 Table 4. Overview of studies performed on milk fat globule distribution 24 Table 5. Overview of studies performed on milk coagulation properties in milk with focus on gel firmness and coagulation time 25 Table 6. The experimental design of three trails, each with the farm clusters of A, B and C and the triplicate measurements. n= number of farms included in cluster 29 Table 7. Mean fat content in cluster A, B and C, volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown 32 Table 8. Mean protein content in cluster A, B and C, volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown 35 Table 9. Mean lactose content in cluster A, B and C, volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown. 37 Table 10. Average milk fat globule size in cluster A, B and C, volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown 39 Table 11. Casein micelle size average for farms with standard deviation, casein micelle size with volume corrected average based on farm milk volume 5
contribution to silo and observed average casein micelle size in silo. All values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown for farm average 41 Table 12. Average rennet coagulation time of milk in cluster A, B and C, volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown 43 Table 13. Average gel firmness after 20 minutes measured in Pa for cluster A, B and C, volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values November, February and September. Significant difference by the Tukey method with 95 % confidence is shown 45 Table 14. Farms divided into cluster of A, B and C based on farm properties and management 63 6
List of figures Figur 1. Average fat content in in percentage for silo A, B and C with standard deviation bars for November, February and September. 31 Figur 2. Average protein content in percentage for silo A, B and C with standard deviation bars for measurements in November, February and September. 33 Figur 3. ph for farm silo A, B and C with standard deviation bars for measurements made in November, February and September. 36 Figur 4. Average lactose content in percentage for farm cluster A, B and C with standard deviation bars for November, February and September. 36 Figur 5. Average milk fat globule size in µm for silo A, B and C with standard deviation bars for February and September. 38 Figure 6. Average casein micelle size in nm for silo A, B and C with standard deviation bars for November, February and September. 40 Figure 7. Average rennet coagulation time in seconds for silo A, B and C with standard deviation bars for November, February and September. 42 Figur 8. Average gel firmness after 20 minutes measured in pascal for silo A, B and C with standard deviation bars for November, February and September. 44 Figur 9. PCA biplot shows overall correlation between parameters for trail 1 (November), 2 (February) and 3 (September). RCT: Rennet coagulation time. G20: gel firmness after 20 min. CMS: Average casein micelle size. FA1-: Farm cluster A, trail 1. FB1-: Farm cluster B, trail 1. FC1: Farm cluster C, trail 1. DA1-T: Silo A. DB1-T: Silo B. DC1-T2: Silo C. Enlarged versions can be seen in appendix 2. 46 Figur 10. PCA biplot shows overall correlation between parameters for trail 1. RCT: Rennet coagulation time. G20: gel firmness after 20 min. CMS: Average casein micelle size. FA1-: Farm cluster A, trail 1. FB1-: Farm cluster B, trail 1. FC1: Farm cluster C, trail 1. DA1-T: Silo A. DB1-T: Silo B. DC1-T2: Silo C. 64 7
Figur 11. PCA biplot shows overall correlation between parameters for trail 2. RCT: Rennet coagulation time. G20: gel firmness after 20 min. CMS: Average casein micelle size. FA1-: Farm cluster A, trail 1. FB1-: Farm cluster B, trail 1. FC1: Farm cluster C, trail 1. DA1-T: Silo A. DB1-T: Silo B. DC1-T2: Silo C. 65 Figur 12. PCA biplot shows overall correlation between parameters for trail 3. RCT: Rennet coagulation time. G20: gel firmness after 20 min. CMS: Average casein micelle size. FA1-: Farm cluster A, trail 1. FB1-: Farm cluster B, trail 1. FC1: Farm cluster C, trail 1. DA1-T: Silo A. DB1-T: Silo B. DC1-T2: Silo C. 66 8
Abbreviations ANOVA CCP CMS CN DLS FFA GMP G20 LG MCP MFG NTA Analysis of variance Colloidal calcium phosphate Casein micelle size Casein Dynamic light scattering Free fatty acids Glycomacropeptide Gel firmness after 20 min β-lactoglobulin Milk coagulation properties Milkfat globules Nanoparticle tracking analysis PC1 Principal component 1 PC2 Principal component 2 PFR RCT SD SLB Protein to fat ratio Rennet coagulation time Standard deviation Swedish Friesian 9
SLU SRB TAG Swedish University of Agricultural Sciences Swedish Red-and-White Triacylglycerides 10
11
1 Introduction Cheese is a high value product which can be found in different varieties around the world (Fox & McSweeney, 2004). Cheesemaking is dated back to the earliest civilizations and has been employed as a preservative method of milk. There are several classifications of cheese as there are up to 1000 types and the classifications are mainly based on texture, coagulation agent and microflora (Fox, 1993; Fox et al., 2017). A popular type of cheese in Sweden is a semi-hard and long ripened cheese and the overall consumption increase every year. The Swedish production of cheese is however decreasing, and the Swedish cheese industry has not been able to meet the demand (Lindén & Åkesson, 2015). In the production of cheese there are important requirements on the raw material and the milk quality that is correlated to the coagulation ability of the milk (Malacarne et al., 2014; Katz et al., 2016). To optimize the cheese yield is economically important in production of cheese as around 100 kg milk is needed to yield approximately 10 kg of cheese (Walstra et al., 2006). Maximizing the cheese yield obliges a deep knowledge of the composition of the raw milk and its influence on coagulation and gel formation in cheesemaking (Fox et al., 2016). Poor or non-coagulating milk is a fairly common and unwelcome problem for cheese producers as yield and quality of the resulting cheese decrease. It is still not fully known what the factors are behind the non-coagulating milk but it has been seen that only a small amount of this milk type is needed to compromise coagulation and cheese quality (Gustavsson et al., 2014a). The cheesemaking technology strives towards two principal goals. Firstly, to achieve the desired flavour and texture and secondly, to be able to routinely reproduce this at the production of every batch and have a consistent quality (Law & Tamime, 2010). With increasing knowledge of chemistry and microbiology, science has been able to perform a more controlled cheesemaking that is standardized 12
and centralized (Fox & McSweeney, 2004). Cheese is, however, a biochemically dynamic food item with many consecutive processes leading to desired or undesired texture and aromas which makes no batches identical. The processability is due to several reasons such as composition, chemical properties and relative proportions of the raw milk. The milk and cheese properties depend on genetic and environmental factors, such as breed, management and feed, which cause variation in gross composition and several other components of the milk (Robitaille et al., 1993; Hallén, 2008). The factors are of interest as they have an effect on the yield, output and quality which has a direct influence on the economic aspects on the processing of milk. Of special industrial interest and importance is the casein micelle and its characteristics. The structure is essential in the coagulation process of milk into a curd and finally a cheese (Glantz et al., 2010). The modern cheese production has not reached full potential of yield output as the recovery of fat and protein is not to optimal (Fox et al., 2016). There is a knowledge gap in the understanding of the optimisation and production of cheese in the aspects of coagulation and gel firmness. This study may contribute to the understanding of how milk composition and properties contribute to the presented aspects. The aim of the study is to evaluate variation of milk composition and properties of farm milk from northern Sweden to determine correlations with rennet induced coagulation. Here, the rheological properties are the rennet coagulation time and gel firmness after 20 minutes. The main milk components investigated are the gross composition of fat and protein together with milk fat globule size, casein micelle size and ph. The hypothesis is that the milk composition affects the rheological properties of milk coagulation. The study is part of a larger project in collaboration between the Swedish University of Agricultural Sciences, SLU, Norrmejerier and Växa Sverige. The ultimate objective within the bigger project is to understand how raw milk composition affect texture, microstructure and flavour development during the ripening of Swedish long-ripened hard cheese. 13
2 Background 2.1 Milk composition and coagulation Cheese yield and the final product depends on the raw milk composition and quality. The composition and nutrient content of bovine milk differs between breeds and individuals and is also depending on the stage of lactation, season, feed, production system and health of the cows (Walker et al., 2004; Kelly & Bach Larsen, 2010). Average concentrations of bovine milk components range between 4.5-5.0 % for lactose, 3.0-5.0 % for fat and approximately 3.5 % for protein (Hallén, 2008; Kelly & Bach Larsen, 2010; Liu et al., 2017). Kelly & Bach Larsen (2010) pointed out that all the underlying mechanisms to the changes in composition of the milk are not yet fully comprehended. The basic concept of the first steps of rennet based cheesemaking can be divided into two phases (Fox & McSweeney, 1998). The primary phase is an acidification of the milk with a starter culture to a lower ph and followed by a hydrolysis of the κ-casein micelle where the proteolytic cleavage at Phe 105 and Met 106 result in reduced and altered charge of the casein micelle due to split off of the negatively charged gluco-macro-peptide (GMP) (Deeth & Lewis, 2015; Fox et al., 2016). The second phase is aggregation of the para-k-casein into a three-dimensional protein network, as a result of neutralizing the negative charges and repulsions, while facilitate the hydrophobic interactions among the para-k-casein particles. The network of casein micelles entraps fat, water and other elements (Hallén, 2008). The subsequent curd that is formed loses whey through syneresis as the hydrophobic micelles contract to a firmer cheese curd. As the coagulation of milk is a 14
complex procedure many factors such as ph, calcium content, genetic variances of proteins and protein content, as well as the health status of the cow may affect the coagulation time and gel firmness (Hallén, 2008; Fox et al., 2016). The coagulation or precipitation of caseins micelles occurs when the colloidal stability of micelles is disrupted. In cheesemaking this is achieved by adding rennet or acid, where rennet, a combination of pepsin and chymosin, is usually used for manufacturing of hard cheese (Hellmuth & van den Brink, 2013). This results in the release of casein glycomacropeptide (GMP) into the whey. At a 80 % hydrolysis of the GMP, the micelles with the remaining para-κ-casein start to coagulate and the result is a gel out of the micelles. (Eskin, 1990; Horne & Lucey, 2017). Nonspecific proteolysis can occur and result in weaker coagulum and loss of yield but calf rennet, containing chymosin and pepsin, is the least non-specific (Banks & Horne, 2003). The coagulation process is dependent on the gross composition of the milk as well as temperature and acidity (Hallén, 2008). 2.1.1 Protein in milk The composition and concentration of milk components are not constant over the seasons, but subject to variation due to differences in lactation stage and number, season and genetics between breeds and individuals (Kelly & Bach Larsen, 2010). Out of 3.5 % bovine milk protein content, about 80 % is casein (CN) and 20 % whey protein (Hallén, 2008). The four major groups of casein in a casein micelle are α s1-cn, α s2-cn, β-cn and κ-cn, in the approximate proportions 38 %, 10 %, 36 % and 12 %, respectively (Fox & Kelly, 2004). Breed and genetic alterations of proteins have an impact on the composition of the milk. Within the four casein groups there is polymorphism, where α s1-cn exists as five haplotype variants (A, B, C, D, E), α s2- CN have four (A, B, C, D), β-cn seven (A 1, A 2, A 3, B, C, D, E) and of κ-cn three variants (A, B and E) (Amenu & Deeth, 2007; Poulsen et al., 2017). The caseins are subject to microheterogeneity, that is, variation in the chemical structure without major change in properties. However, up to 32 different genetic variants giving rise to differences in phenotype have been identified with an important effect on milk properties and suitability for cheese processing (Amenu & Deeth, 2007). The characteristics of casein micelle have had a growing interest as they are of utter importance to the milk processing industry. The caseins have a direct impact on yield, curd firmness and structure in cheesemaking (Jenkins & McGuiret, 2006; Fox & Brodkorb, 2008). 15
Both rheological and physical properties are affected by factors and interactions between casein micelles (Law & Tamime, 2010). There is though no general understanding of the micelle structure but however some accepted properties. Caseins are found in spherical micelles and the micelles consist of all four major groups of caseins (Slattery, 1976; Huppertz et al., 2018). The micelles are kept stabilized by the calcium binding capacity and the glycosylated κ-cn on the surface of the micelle which gives it amphiphilic characteristics. κ-cn is the smallest of the four major groups with a single chain of 169 amino acids (Holland, 2008; Huppertz et al., 2018)). α s1-cn contain 199 amino acids, α s2-cncontain 207 amino acids and β-cn 209 amino acids (Huppertz et al., 2018). 2.1.2 Fat in milk Milk fat consist of 95-98 % triglycerides (TAG) and the total fat content ranges from 3.0 % to 6.0 % (Kelly & Bach Larsen, 2010; Liu et al., 2017). The TAG:s have a great diversity in fatty acid length and are classified as short-, medium- and long-chained fatty acids, and there is alo variation in their degree of saturation. They are arranged in milk fat globules (MFG) with a nonpolar core and polar lipids of phospholipids and sphingolipids in the membrane. The size of the MFG range from 0.1 to 15 µm (Jensen, 2002; Lu et al., 2016). The MFG are formed in the endoplasmic reticulum of the mammary gland at a size of <0.5 µm and fuse into larger droplets in the cytosol (Truong et al., 2015). The mechanism behind the fusing of small MFG is thought to be associated with calcium and protein complexes but the mechanism behind larger formations are not known. Studies have shown that the lipid and fat content of bovine milk vary with season, lactation stage, breed and feed (Auldist et al., 1998; Walker et al., 2013; Liu et al., 2017). 80 % of the MFG:s in counts has a diameter of 1 µm whereas the MFG of 4 µm stands for most of the mass (Jensen, 2002; Kelly & Bach Larsen, 2010). Milk with smaller MFG contain more phospholipids due to a larger total surface area than milk with larger MFG. The levels of phospholipids and triglycerides can be affected by the feed. The MFG size is affected by several factors and Lu et al. (2016), pointed out the role of the difference in protein and phospholipid composition of the membrane in the formation and secretion of MFG. 16
2.1.3 Calcium in milk Calcium can be found in the casein micelles as colloidal calcium phosphate (CCP), Ca 2+ or as soluble calcium in the milk serum (Holt et al., 1982; Gustavsson et al., 2014b). Two thirds of the total calcium, of about 30 mm, is found in the form of CCP (Deeth & Lewis, 2015). It has been shown that the levels of calcium is correlated to the milk coagulation and gel firmness (Glantz et al., 2011; Deeth & Lewis, 2015). About 2 mm of the total 10 mm soluble calcium is found as ionic calcium. The concentration varies during lactation with the highest concentration in the beginning and in the end of the lactation (Maciel et al., 2015). Calcium concentration is often positively associated with high protein content of the milk but an important factor in the correlation is the ratio of micellar and soluble calcium. 2.1.4 Changes in composition The health status of the cow, the breed, the lactation stage, feed and season are some factors that influence the composition of milk. Within stage of lactation the highest values of nutrients, in terms of protein and minerals, can be found during the first 72 hours, in the colostrum, which is not seen as regular milk (Kelly & Bach Larsen, 2010). The levels of casein and total protein content is high in the early stages of lactation and drop to the lowest levels within 5 to 10 weeks. The concentration of fat and total protein content then increase in the milk the longer the lactation stages proceeds (Auldist et al., 1998). This is of less concern in bulk milk as the calving of individual cows are usually spread over the year and dilutes the effect. The ratio of casein to protein is at highest in mid-lactation which is favourable for cheesemaking. At this stage, also the ratio of γ-cn, which is degradation products of β-cn, is at the lowest (Kelly & Bach Larsen, 2010). Proportion of κ-cn decreases throughout the lactation and is at the lowest at the end of the lactation. At the end of lactation there is also an increase in ph. The composition of milk is changing with change of feed of silage, roughage and concentrates and during periods of grazing (Kelly & Bach Larsen, 2010). The feed has a larger impact on the fatty acid profile than the protein content. Toledo et al. (2002) compared the milk of organic and conventional farms in Sweden during a period of 12 months and found small or no differences in gross composition between the two production systems. The study concluded that the effect on gross composition could be a combination of breed and feeding regimen and that these don t differ to a large extent between conventional and organic in Sweden. 17
Lindmark-Månsson et al. (2003) found regional differences in Swedish dairy milk with regard to total protein and casein but no systematic geographical difference could be detected. The results also showed a variation of the milk composition over the year as well as an effect of grazing season. From the 1970 s to 1996 there was a significant decrease in casein protein content from 2.61 g/100 g to 2.56 g/100 g and an increase in whey protein from 0.73 g/100g to 0.81 g/100 g whereas the total protein content remained on the same level (Lindmark-Månsson et al., 2003). Similar results with increased total protein but changing ratio has been reported in European studies which also describes decreased cheese yield despite increased milk yield (Coulon et al., 2001; Verdier-Metz et al., 2001). These effects could be explained by the genetic breeding programs and payment systems which had a focus on increased milk yield while casein levels in relation to total protein did not receive the same attention (Lindmark-Månsson et al., 2003; Kriščiunaite et al., 2012). The change could also be due to the increased yearly milk yield from 4060 kg in 1970 to 7112 kg in 1996 to todays yearly milk yield of 9743 kg (Lindmark-Månsson et al., 2003; Växa Sverige, 2018). Today the price is based on fat and protein content. 18
3 Literature review 3.1 Gross composition Table 1. Overview of studies performed on gross composition of cow milk Gross composition Protein: 3.84 % Fat: 4.37 % Technique Infra-red technique State (Individual/silo) Individual Factors influenced Breed Swedish Red Reference (Gustavsson et al., 2014b) Protein % S: 3.57. L: 3.54 n.s. Fat % S: 3.83. L: 3.87 n.s. CombiFossFT, combining Fourier transform infrared and flow cytometry technology Individual Small (S) vs large (L) micelles (Bijl et al., 2014) Protein 3.19 % Fat 3.96 %, Fourier transform mid-infrared spectrophotometer Silo Feed and farm management. (Woolpert et al., 2017) Protein: TMR:3.38 % Grass:3.65 % Clover: 3.56 % Fat: Infrared absorption spectroscopy, FT6000 Milkoscan. Silo Pasture vs. indoor feeding system (Total mixed ration, Grass, Clover) (O Callaghan et al., 2016) TMR:4.39 % Grass: 4.65 % Clover:4.30 % Decreased fat %: 3.80 3.55. Infrared analysis Individual Red clover silage (Moorby et al., 2009) Decreased protein %: 3.08 2.93 19
Gross composition Protein %: 3.49 Fat %: 4.3 Lactose %: 4.75 Protein %: 3.69 Protein %: 3.29 (highest in spring, lowest in autumn) Fat %: 4.08 (highest in autumn, lowest in winter) Lactose %: 4.59 n.s Urea (mm): 3.95 n.s Technique Mid-infrared spectroscopy (MilkoScan FT120) Protein to fat ratio (0.70-1.15) State (Individual/silo) Individual Silo Factors influenced Milk with good coagulation properties Different protein to fat ratio from 0.70-1.15 Lactoscope Silo Seasonal variation Reference (Hallén et al., 2010) (Guinee et al., 2007) (Chen et al., 2014) n.s: Not significant PFR: protein to fat ratio Table 1 shows a schematic overview of selected studies made on gross composition of cow milk. Studies have shown the impact of milk with different protein to fat ratios (PFR) on cheddar cheese composition, yield and rheology (Guinee et al., 2007). The PFR vary with season, feed and on farm management as well as region and country. The result from the study of Guinee et al. (2007) in Table 1 are not directly comparable to Swedish production as the study was performed on Irish milk production which has a concentrated calving compared to Swedish production and this affects the milk composition. However, the study shows the significance of standardizing PFR within milk to obtain a continuous quality. Standardization of the milk PFR is done at the dairy. On farm management and feed affect the fat content and composition of the milk as these factors affect the ph of the rumen which in turn influence the fatty acid composition and production (Allen, 1997; Woolpert et al., 2017). O Callaghan et al. (2016) showed the effect of feeding system on milk composition where the fat content was significantly higher for the outdoor feeding system with perennial ryegrass. The two outdoor feeding systems with perennial ryegrass and ryegrass/ white clover respectively, showed a higher protein content compared to the indoor feeding system. The same study showed that highest content of fat and protein in the milk was recorded during October, non-regarding feeding system, which may be due to late lactation stage. These trends have been shown 20
within previous results and studies (Auldist et al., 1998; Kelly et al., 1998; O Callaghan et al., 2016). The higher content of protein, in outdoor feeding system with ryegrass, was also associated to a higher casein content (O Callaghan et al., 2016). Moorby et al. (2009) showed however that protein and fat content in milk decreased with increased intake of red clover in silage which may be due to increased yield and a dilution effect. The season affects the milk gross composition. Chen et al. (2014) showed higher fat content in milk during the autumn. Benedet et al. (2018) showed that the summer months of July and August has the least favourable effects on traits such as lower fat and casein content resulting in yield loss. The factors behind the change in gross composition is put forward as the access to pasture but also exposition to heat stress with higher temperatures (Das et al., 2016). This isn t only associated with tropical areas and is an increasing challenge in the northern regions due to increasing global temperature (Polsky & von Keyserlingk, 2017). Heat stress is associated with decreased feed intake and increased water consumption which affects the rumen and decrease the bacterial activity which leads to reduced milk yield as well as protein, casein and fat content (Das et al., 2016). 3.2 ph of milk Table 2. Overview of studies performed on ph of milk ph Technique State (individual/silo) 6.79 Fresh milk sample Individual at 8 C 6.69 (n.s.) Skim milk, Orion Individual 8102BN ph electrode 6.58 Meterlab (PHM Individual 83) 6.73-6.87 (highest in spring, lowest in autumn) n.s: Not significant Stentron 3001 ph meter Factors influenced Breed Swedish Red Small vs. large micelles Milk with good coagulation properties Reference (Gustavsson et al., 2014b) (Bijl et al., 2014) (Hallén et al., 2010) Silo Season (Chen et al., 2014) 21
Table 2 shows a schematic overview of selected studies made on ph of cow milk. The natural ph of milk is set to 6.6 and altering the ph to above or below the natural ph may influence the characteristics of milk components such as casein micelles (Sinaga et al., 2017). Rennet induced coagulation takes place at an optimum of ph 6.0-6.4 (20-30 C) (Li & Wang, 2016; Sinaga et al., 2017). 3.3 Casein micelle size Table 3. Overview of studies performed on casein micelle size in bovine milk Casein micelle size (nm) Swedish red: 172 Swedish Holstein: 173 Technique z-average hydrodynamic diameter using photon correlation spectroscopy 130 Dynamic light scattering Mastersizer 2000 Small: 170.6 n.s. Large: 206.7 n.s. AE: 155.20 A: 140.92 B:137.68 Dynamic light scattering Zetasizer Nano ZS Dynamic light scattering Mastersizer 2000 State (individual/silo) Silo Individual Individual Individual Factors influenced Breed Swedish Red and Swedish Holstein Breed Swedish Red Small vs large micelles κ-cn (AE, A & B) Reference (Glantz et al., 2011) (Gustavsson et al., 2014b) (Bijl et al., 2014) (Poulsen et al., 2017) 172.3 Photon correlation spectroscopy, Zetasizer 3000H) Individual Milk with good coagulation properties (Hallén et al., 2010) Small: 159.6 Large: 193 Zetasizer Nano ZS Silo Small vs large micelles (Logan et al., 2015) 154-230 No effect Dynamic light scattering ALV Compact Gonimeter system Individual Age, lactation stage, fat and protein content (de Kruif & Huppertz, 2012) 22
Casein micelle size (nm) ph 6.6: 160-170 ph 5.5: 154 ph 7.5:194 Technique Dynamic light scattering Zetasizer Nano 171 Dynamic light scattering Zetasizer Nano ZS State (individual/silo) Silo Individual Factors influenced ph adjustment and restortion: alkalination (ph 7-10.5) and acidification (ph 5-6) Variation over one year in Estonian Holstein 163 n.s. Zeta Master Silo Variation over one year Reference (Sinaga et al., 2017) (Mootse et al., 2014) (Chen et al., 2014) n.s: Not significant Table 3 shows a schematic overview of selected studies made on casein micelle size (CMS) in cow milk. The average casein micelle size, CMS, varies between individual samples as well as silo samples in the studies shown in Table 3. This can be due to several different factors that are still not fully known (Bijl et al., 2014). de Kruif & Huppertz (2012) found that CMS of individual cows do not change as a function of time or lactation. This was confirmed in a study of Hristov et al., (2016) who also concluded that the fat and protein content do not correlate to the CMS. Glantz et al. (2011), showed an impact of the casein alleles on technological properties and also an impact of the whey protein β-lactoglobulin on improved gelation. Studies have suggested a correlation between β-lg type B content with increased content of CN (Wedholm et al., 2006). The whey proteins are in high degree lost in the whey in cheese production but have also been found in hard cheese (Glantz et al., 2011). In silo milk the average casein micelle size is relatively constant with a size of 200 nm (Bijl et al., 2014). There is, however, a difference between individual cows with a variation of diameter between 50 to 500 nm with an average of 120 nm (Fox & Kelly, 2004). The size of the casein micelle has shown to have an impact on the coagulation firmness (Glantz et al., 2010). Smaller sized micelles have shown to create a better developed structure of coagulant (Hallén, 2008). Different techniques to determine the casein micelle size may give diverse results (Mootse et al., 2014). Several studies in Table 3 have used dynamic light scattering (DLS) to measure CMS which is a well-established technique for the last 40 years (Carr & Wright, 2013). The technique is using 23
the signal for all the present particles in a milk sample to retrieve the distribution of the particle sizes from 5 nm to 5 µm (Tran Le et al., 2008). The light signal from the particles in a sample varies as a function of radius which will have an effect on the result of the DLS as larger particles have a higher scattering of light. The nanoparticle tracking analysis (NTA) measures particle sizes of 10 nm to 600 nm. NTA has an advantage of giving both quantitative information as well as a visual result which can be used to determine onset of aggregation in samples. The NTA technique is a relatively recent developed technique and gives an opportunity to acquire information on concentration and relative intensity of light scattering of particles (Carr & Wright, 2013). DLS has shown to be more reproducible to NTA but need to be performed with pure samples at low concentrations (Mootse et al., 2014). 3.4 Milk fat globule size Table 4. Overview of studies performed on milk fat globule distribution Fat globule size State (individual/siloenced Factors influ- Technique (µm) Swedish red: Breed Swedish 3.9 Light diffraction Individual red and Swedish Swedish Holstein: Coulter LS 130 Holstein 3.7 Small: 3.58 Large: 4.76 Small MFG: 3.3± 1.2 Large MFG: 7.6 ± 0.9 Control: 1.7 C16:0: 2.5 C18:1: 4.2 Dynamic light scattering Mastersizer 2000 Mastersizer 2000 Olympus BX40 fluorescence microscope Silo Individual Individual Small vs large MFG Protein and phospholipid content Phospholipid composition of mammary epithelial cell and FFA-induced changes in membrane composition Reference (Glantz et al., 2011) (Logan et al., 2015) (Lu et al., 2016) (Cohen et al., 2015) Table 4 shows a schematic overview of selected studies made on milk fat globule (MFG) size of cow milk. MFG size vary over season and lactation stage and have a heterogeneous size distribution (Logan et al., 2015; Lu et 24
al., 2016). The MFG membrane and its composition of phospholipids and fatty acids has shown to differ between small and larger MFG (Cohen et al., 2015). Lu et al. (2016) observed differences in the protein and lipid content of MFG membrane which affects the size of MFG. The size of MFG has several important industrial implications, where cheese production is mentioned as the size of MFG has shown to impact the rennet induced gel (Logan et al., 2014). Larger MFG enhanced the properties of the gel when it was formed with smaller sized casein micelles but had no effect when the casein micelle network was formed by larger caseins. This is explained by the size of the large MFG, fitting the pores of the micelle network formed by smaller casein micelles (Logan et al., 2014, 2015). MFG size can be affected by the membrane composition of the mammary epithelial cells. When cells where cultured in oleic acid in an experiment by Cohen et al. (2015), the cells contained larger MFG, >3 µm, and 40 % of the droplets were larger than 5 µm. 3.5 Milk coagulation properties Table 5. Overview of studies performed on milk coagulation properties in milk with focus on gel firmness and coagulation time Gel firmness (Pa) G60 Friesian: 50.2 Jersey: 52.7 1.No effect 2.Decreased development 3.No effect Coagulation time (s) Friesian: 1884 Jersey: 1932 1.No effect 2.Increased 3.No effect Technique State (farm/silo) Factors influenced Formagraph (Foss Electic) 265.2 Turbidimetric sensor G 40 90.9 840 Stresstech rheometer, 32 C for 40 min followed by stress sweep for 5 min Silo Jersey and Friesian Rheometer Silo 1.Fertilized pasture 2.Access to clover grass. 3.Supplement feed Silo Holstein herd and season Reference (Auldist et al., 2004) (Hermansen et al., 1994) (Chládek et al., 2011) Individual Swedish Red (Gustavsson et al., 2014a) 25
Gel firmness (Pa) Swedish Red: 93 Swedish Holstein: 102 Coagulation time (s) Swedish Red: 960 Swedish Holstein: 432 Technique State (farm/silo) Factors influenced Gelation: with low-amplitude oscillation measurement Stresstech rheometer. G 15: 42.04 288,6 Bohlin VOR Rheometer Median G max: AE: 150 A: 200 B: 250 AE: 1300 A: 1100 B: 1000 Free oscillation rheometerty Reference Silo Breed (Glantz et al., 2011) Individual Individual Milk protein composition κ-cn (AE, A & B) (Wedholm et al., 2006) (Poulsen et al., 2017) 190 190 Oscillation test using Bohlin VOR Rheometer Individual Milk with good coagulation properties (Hallén et al., 2010) 260 50 Bohlin VOR Rheometer Individual Milk with good coagulation properties + 0.05 % added (Hallén et al., 2010) CaCl2 G final: 77,1 Control: 49.7 792,6 Anton Paar- Physica rheometer Silo Large MFG and small CMS (Logan et al., 2015) G final/g max: gel strength final G 60: gel strength after 60 minutes G 40: gel firmness after 40 minutes G 15: gel firmness after 15 minutes Table 5 shows a schematic overview of selected studies made on milk coagulation properties of cow milk. Coagulum development was reduced and coagulation time was increased with a high access to clover grass (Hermansen et al., 1994). Wedholm et al. (2006) identified non- or poorly coagulating milk in 30 % of the samples with a low content of κ-cn in total and in relation to total CN. The B allele of κ-cn was thought to improve the coagulation properties whereas the E allele has the opposite effect (Schaar, 1984). Poulsen et al. (2017) identified three haplotypes of κ-cn (AE, A and B) to have an impact on the coagulation time and curd firming rate where type AE had significantly poorer properties than A and B. Lactation has shown to have an impact on the coagulation of milk, where milk from first lactation has poor properties for coagulation and curd firmness (Schaar, 26
1984; Wedholm et al., 2006). Several studies have shown samples of noncoagulating milk and Wedholm et al. (2006) suggested it was related to genetic factors and that there was a correlation between low content of κ-cn with low concentration of calcium. The total calcium content, protein content and CMS had the largest impact on the rheology properties of the breed Swedish Red (Gustavsson et al., 2014b; a). Other factors that influenced the coagulation was genetics and certain allele of κ-cn. κ-cn B have in studies shown to be correlated with improved coagulum which could be related to the smaller size of the micelles (Glantz et al., 2011). κ-cn variant B has shown to improve gel firmness, decrease coagulation time and has a smaller casein micelle size compared to the A allele (Wedholm et al., 2006; Glantz et al., 2011). The smaller sized B variant of κ-cn contained more κ-cn than the A and AE variant of κ-cn (Dalgleish, 2011). The study of Poulsen et al. (2017) showed similar results and included the AE haplotype. The CMS of the AE variant was larger and an explanation to less favourable properties in the milk coagulation. The coagulation time has a positive relation to the casein concentration, i.e. the coagulation time wil increase with casein content (Murtaza, 2016). Increased fat concentration has bee reported to decrease the coagulation time but enhance the gel firmness. 3.6 Effect of bulking milk samples The composition of the milk on farm is the combination of milk from all individual and depends on several factors such as genetics, season and feeding regimen (Katz et al., 2016). At the dairy, the milk is generally standardized considering fat content to minimize variation (Maciel, 2016). Kriščiunaite et al. (2012) found that the correlations between milk protein composition and coagulation properties in individual milk samples also existed in bulk milk samples on farm level. The study found that bulked milk had a longer rennet coagulation time which could be explained by partial degradation of casein and the changes in whey to casein ratio. 27
4 Method and material All the data has been pre-collected from an ongoing project at the Åse Lundh lab as a part of a PhD project. Milk from 18 farms from northern Sweden was collected on three occasions in November (trial 1), February (trial 2) and September (trial 3). In the on-going projects, the farms were already selected into three clusters (A, B and C) based on farm factors which can be seen in table 15 in Appendix 1. The aim of creating the 3 clusters was to create milk for cheese making trials with very different composition. Milk samples were collected from the individual farms as well as pooled milk representing the different clusters. Analyses were performed with emphasis on gross composition, ph, rennet coagulation time (RCT), gel firmness after 20 minutes (G20) and CMS (casein micelle size). Milk sampling was performed every second day (a, b, c) during one week in November, February and September with a calculated average and a volume corrected average based on the percentage contribution of a given farm bulk milk to the pooled silos. An overview of the experimental design can be seen in Table 6. The data was statistically analysed with One-Way ANOVA, Minitab Express. Based on the data a principal component analysis (PCA) was performed for all three trials to have a dimension reduction and to compress and attain a data set of variables where correlations between factors could be detected. 28
Table 6. The experimental design of three trials, each with the farm clusters of A, B and C and triplicate samplings on day a-c. n= number of farms included in cluster A n =3 B n =9 C n =5 November February September a a a A A b b b n =4 n =4 c c c a a a B B b b b n =9 n =9 c c c a a a C C b b b n =4 n =5 c c c 4.1 Gross composition and ph The gross composition measurements of the milk samples where performed by Eurofins. Measurements were performed for contents of fat, protein, lactose, urea and free fatty acids. Urea and free fatty acids are not included or discussed in the study due to missing and inconsistent values. The ph was measured on fresh samples by using a ph meter (Seven Compact S210, Mettler-Toledo, Switzerland). 4.2 Rheology properties The rennet induced coagulation properties was measured, with emphasis on rennet coagulation time (RCT) and gel firmness after 20 minutes (G20), with the method of Johansson et al., (2015) with Bohlin CVOR-150-900 rheometer (Malvern Instruments Nordic AB, Uppsala Sweden). Starting time was set to the adding of rennet and the coagulation time (RCT) to when the storage modulus, or gel firmness (G ) reached 1 Pa and after 20 minutes (G20). The rennet used was chymosin:pepsin 75:25, 0.18 IMCU/ml (Kemikalia, Sweden). 29
4.3 Casein micelle size A nanoparticle tracking analysis (NTA) system was used to determine casein micelle size by using the Stokes Einstein equation which calculate the hydrodynamic diameter assuming spherical shape (Malvern Panalytical Ltd, 2018). Skimmed milk was diluted with distilled water 2000 times and injected to a NanoSight NS500 (Malvern Instruments, UK) by a four-way connected, 1 ml syringe at a constant speed of 150 (~15.6µL/min). The connected temperature sensor (NTA Temperature Comms) held a constant temperature at 35 C. The results were recorded at a level of 13 for 90 seconds by a scientific complementary metal-oxide semicunductor (CMOS) camera at a 90 angel and a 658 nm wavelength. Replicate analyses were done with 2 seconds delay in between 4 consecutive measurements for every analysis to achieve an average. The NanoSight 2.3 NTA software was used to process the recording with a screening gain up to 17 and threshold detection up to 14. 4.4 Fat globule size distribution The fat globule size distribution was analyzed by light scattering and diffraction (Mastersizer 3000, Malvern Instrument) with a range of 0.010-3500 µm according to Mie theory by RISE Research Institute of Sweden AB (Uppsala). 30
5 Result and data 5.1 Gross composition 5.1.1 Fat content in farm cluster and silo milk % 5,50 5,00 4,50 4,00 3,50 3,00 2,50 Fat content A B C A B C A B C November February September Figur 1. Average fat content in in percentage for silo A, B and C with standard deviation bars for November, February and September. Figure 1 shows the average fat content for the triplicate milk samples of silo A, B and C for November, February and September. The higher fat content of silo B was due to farm F28 in cluster B who had Jersey breed which is known to have a higher fat and protein content. Cluster B had significantly higher fat content for February compared to September. The same can be seen for silo A but not for silo C. 31
Table 7. Mean fat content in cluster A, B and C; volume corrected mean fat content in cluster A, B and C, observed mean in silo A, B and C. Values for November, February and September. Significant difference by the Tukey method with 95 % confidence is shown Sample A B C Farm average±sd 4.39±0.08 ab A 4.86±0.7 a A 4.36±0.7 b AB n 3 9 5 November Volume corrected average Silo average±sd Farm average±sd 4.36 4.77 4.36 4.50±0.2 4.82±0.1 4.34±0.04 4.28±0.2 b A 4.96±0.9 a A 4.45±0.2 ab A n 4 9 4 February Volume corrected average Silo average±sd Farm average±sd 4.25 4.66 4.42 4.26±0.1 4.9±0.1 4.44±0.1 3.97±0.1 b B 4.74±0.8 a A 4.23±0.3 b B n 4 9 5 September Volume corrected average Silo average±sd 3.96 4.26 4.29 4.01±0.01 4.59±0.02 4.36±0.02 a, b= Means that do not share a latter in a row are significantly different. A, B = Means that do not share a latter in a column are significantly different. n = number of individual farm milk samples contributed into silo. SD= standard deviation. Table 7 shows an overview of the average fat content variation in clusters A, B and C as well as the mean volume corrected value and observed average value for silo A, B and C for all three trials. There is significant difference in fat content between the clusters B and C within measurements in November as well as between cluster A and B for February and cluster B for September. 32
Within cluster A there was a significant difference for the third trail in September which had a lower fat content compared to measurements in November and February. There is no significant difference within cluster B but for cluster C there is a significant difference between measurement in February and a decrease in fat for September. The observed values for the silos differ from the arithmetic mean and the volume corrected values for some cluster and trials. The difference is however small and there is no continuous pattern between the values. 5.1.2 Protein and ph content in farm cluster and silo milk % 3,8 3,75 3,7 3,65 3,6 3,55 3,5 3,45 3,4 Protein A B C A B C A B C November February September Figur 2. Average protein content in percentage for silo A, B and C with standard deviation bars for measurements in November, February and September. Figure 2 shows an overview of the average protein content in silo milk from silo A, B and C for the three trials. Silo B shows a higher protein content compared to silo A and B. Measurements in November and September had significantly higher protein value for silo B compared to measurements in February. Silo A had significantly higher protein content in November compared to February and September. The same trends in the measurements is seen for silo C. Table 8 shows an overview of the average protein content variation in cluster A, B and C as well as the mean volume corrected value and observed average value for silo A, B and C for all three trails. There was no significant difference between the clusters within measurements made in November and February. There was no significant difference within clusters B and C 33