Cover
Title Page
1. Copyright
List of Contributors
Part I: Ingredients for Dairy Products Manufacturing
1. Chapter 1.1: Milk
2. Chapter 1.1.1: Milk Quality and Processing
  1. Introduction
  2. Milk Composition and Systems of Payment According to Milk Utilization
  3. Milk Payment Systems
  4. Other Parameters of Milk Quality Deriving from a Genomic Approach Proposed for Milk Payment Schemes
  5. Other Aspects of Milk Quality for Specialty Dairy Products
  6. Conclusions
  7. References
3. Chapter 1.1.2: Milk Preparation for Further Processing into Dairy Products
  1. Introduction
  2. Market Milk
  3. Fermented Milk
  4. Cheese
  5. References
4. Chapter 1.2: Starter Cultures
5. Chapter 1.2.1: Probiotics and Prebiotics
  1. Probiotics
  2. The Microbiota of Human GI Tract
  3. Inflamm-Aging
  4. Prebiotics
  5. References
6. Chapter 1.2.2: Starter and Ancillary Cultures
  1. Introduction
  2. Lactic Acid Bacteria
  3. Ancillary Cultures
  4. References
7. Chapter 1.3: Other Ingredients
8. Chapter 1.3.1: Vitamins, Minerals, and Bioactive Compounds
  1. Vitamins
  2. Bioactive Compounds
  3. New Technologies for Dairy Products Fortification
  4. References
9. Chapter 1.3.2: Fruit and Vegetables
  1. Definitions
  2. Manufacturing Jams and Fruit Preparations
  3. Innovation /Trends
  4. High-Pressure Technology
  5. Pulsed Electric Fields (PEF)
  6. Freeze Concentration
  7. References
10. Chapter 1.4: Additives and Processing Aids
11. Chapter 1.4.1: Acidity Regulators, Preservatives, and Antioxidants
  1. Identification and Coding
  2. Nanotechnologies
  3. Microencapsulation
  4. Acidity Regulators, Preservatives, and Antioxidants
  5. References
12. Chapter 1.4.2: Flavors, Colors, Thickeners, and Emulsifiers
  1. Flavors
  2. Colorants
  3. Additives for Appearance, Consistency, and Stability
  4. References
13. Chapter 1.4.3: Enzymes
  1. Introduction
  2. Milk Clotting
  3. Rennet: Definition
  4. Animal Rennet
  5. Fermentation Chymosin or Recombinant Chymosin
  6. Rennet Paste
  7. Vegetable Rennet
  8. Microbial Coagulants
  9. Lipase
  10. Transglutaminase
  11. Lactase
  12. Lysozyme
  13. References
Part II: Processing of Dairy Products
1. Chapter 2.1: Process innovation
2. Chapter 2.1.1: Enzymes Applications for the Dairy Industry
  1. Use in Cheese Making
  2. Applications for Shelf-Life Extension
  3. Applications for Functional and Environmental Purposes
  4. References
3. Chapter 2.1.2: Plant Cleaning and Sanitizing
  1. Introduction
  2. Dirt Features and Classification
  3. Adhesion of Dirt and Bacteria
  4. Dairy Industry Hygiene
  5. Hygiene Management
  6. References
4. Chapter 2.1.3: Membrane Technologies Applied to Cheese Milk
  1. General Aspects
  2. Characteristics of Membranes
  3. Applications in the Cheese-Making Process
  4. Other Membrane Applications Related to Cheese Making
  5. References
5. Chapter 2.1.4: Process/Product Control: Analysis of Cheese by Proteomics Techniques
  1. Introduction
  2. Target Milk Proteins Heterogeneity among the Mammalian Species of Interest for Dairy Industry
  3. Urea Polyacrilamyde Gel Electrophoresis (Urea-PAGE)
  4. Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis
  5. Isoelectric Focusing Electrophoresis
  6. Two-Dimensional Electrophoresis
  7. Liquid Chromatography and Mass Spectrometry
  8. Capillary Electrophoresis
  9. Conclusions
  10. References
6. Chapter 2.2: Product innovation
7. Chapter 2.2.1: Functional Dairy Products Including Pro/Pre/Symbiotics
  1. Definition and Classification of Functional Food
  2. Probiotics, Prebiotics, and Synbiotics
  3. Bioactive Peptides
  4. γ-Aminobutyric Acid
  5. Vegetable Extracts for Enhancing Nutritional Value of Dairy Food
  6. Dairy Food Fortified/Enriched with Micronutrients
  7. Dairy Products with Modified Lipid Composition
  8. Conclusions
  9. References
8. Chapter 2.2.2: Dairy Products and Their Role in Human Health
  1. Dairy Products and Cancer
  2. Dairy Products and MetS
  3. Dairy Products and Osteoporosis
  4. Dairy Products and Lactose Intolerance
  5. New Technologies Applied to Milk and Dairy Products to Reduce Lactose
  6. Dairy Products and Allergies
  7. References
Part III: Shelf Life of Dairy Products
1. Chapter 3.1: Technological Options to Prolong Shelf Life
2. Chapter 3.1.1: Freezing of Dairy Products
  1. Freezing
  2. References
3. Chapter 3.1.2: Antimicrobial Compounds Applied to Dairy Food
  1. Antimicrobial Agents of Plant Origin
  2. Antimicrobial Agents of Microbial Origin
  3. Antimicrobial Agents of Animal Origin
  4. Antimicrobial Agents of Chemical Origin
  5. References
4. Chapter 3.2: Modern packaging systems to prolong shelf life
5. Chapter 3.2.1: Active Packaging Applied to Dairy Products
  1. Introduction
  2. Bacteriocins
  3. Biopolymers
  4. Essential Oils
  5. Oxygen Scavengers
  6. Antioxidant Packaging
  7. Others
  8. Conclusions
  9. References
6. Chapter 3.2.2: Nanotechnology Applied to the Dairy Sector
  1. Nanostructures for Dairy Applications
  2. Nano-Packaging for Dairy Applications
  3. References
7. Chapter 3.2.3: Biodegradable Packaging Applied to Dairy Products
  1. Introduction
  2. Biodegradable Materials for Dairy Product Packaging
  3. Edible Materials for Use in Dairy Product Packaging
  4. Conclusions
  5. Acknowledgments
  6. References
Part IV: Consumer Acceptance
1. Chapter 4.1: Consumer Behavior with Regard to Quality Perception of Food Products and Decision Making
2. Chapter 4.1.1: The Quality Concept
  1. Introduction
  2. Quality Concepts, Emotions, and Consumer Needs and Experience
  3. Quality in the Dairy Sector: Insights
  4. Conclusions
  5. References
3. Chapter 4.1.2: Food Quality Perception
  1. Introduction
  2. The Concept of Food Quality
  3. The Hierarchical Approaches: The Zeithaml Model
  4. The Integrative Approach: The Total Food Quality Model
  5. References
4. Chapter 4.1.3: Consumer Behavior Models Applied to Food Sector
  1. Introduction
  2. Consumer Behavior Models Applied to Food Sector
  3. Conclusions
  4. References
5. Chapter 4.1.4: Evaluation, Choice, and Purchase
  1. Introduction
  2. Factors Influencing Food Choice and Purchase
  3. Conclusion
  4. References
6. Chapter 4.2: Consumer insight in the process of new dairy products development
7. Chapter 4.2.1: The New Product Development Process
  1. Introduction
  2. Definition Settings and New Food Products Classification
  3. Consumer-Oriented New Product Development Process
  4. Key Stages in the New Product Development Process
  5. Risk and Failure of the NPD
  6. Case Studies
  7. Conclusions
  8. References
  9. Sitography
8. Chapter 4.2.2: Market Opportunities
  1. Introduction
  2. Knowledge Resources for Taking Market Opportunities
  3. Six Sigma Approach for Agri-Food Sector
  4. Market Opportunities in the Dairy Sector
  5. Conclusions
  6. References
9. Chapter 4.2.3: Consumer Insight and Approaches in New Dairy Products Development
  1. Introduction
  2. From Naturalness to Hybrid Qualities
  3. New Dairy Product Development
  4. New Dairy Product Development as a Relational Issue between Consumers and Dairy Industries
  5. References
Part V: Environmental and Policy Issues
1. Chapter 5.1.1: The Milk and Dairy Sector in the European Union: Environmental and Policy Issues
  1. Introduction
  2. Milk and Dairy Production in Europe
  3. Environmental Impact of the Dairy Sector
  4. Policy Considerations and Conclusions
  5. References
  6. Sitography
2. Chapter 5.1.2: Policies and Strategies for Eco-Friendly Dairy Product
  1. Introduction
  2. Dairy Production: The Institutional Context
  3. Strategic Attributes of Eco-Friendly Dairy Initiatives
  4. Capabilities for Eco-Friendly Dairy Production
  5. Creating Competitive Advantage
  6. Concluding Remarks
  7. References
3. Index
End User License Agreement

List of Illustrations

Chapter 1: Milk
1. Figure 1.1.2.1 Milk treatment including double-loop microfilter and sterilization of bacteria concentrate together with the cream needed for fat standardization of the cheese milk.
2. Figure 1.1.2.2 Double bactofugation with optional steriliser (TetraPak Engineering, 2014)
3. Figure 1.3.2.1 Batch process flow for fruit preparations with flavors.
4. Figure 1.3.2.2 Process diagram of an HPP plant.
5. Figure 1.3.2.3 Isostatic pressure chamber.
6. Figure 1.3.2.4 Time/Temperature-Pressure of HPP process⁽⁵⁾.
7. Figure 1.3.2.5 Flow chart of a PEF food-processing system with basic component ^(8).
8. Figure 1.3.2.6 Quality parameters for microwave and conventional heating compared using computed values for typical heating situations. F₀ represents the accumulated lethality.
9. Figure 1.3.2.7 Diagram of freeze concentration process.
10. Figure 1.4.3.1 Casein micelle.
11. Figure 1.4.3.2 Contents of casein micelle.
12. Figure 1.4.3.3 Chymosin and bovine pepsin content ratio as a function of the animal's age.
13. Figure 1.4.3.4 Chymosin and pepsin curling times as functions of the pH of milk.
14. Figure 1.4.3.5 The influence of temperature on the activity of rennet.
15. Figure 1.4.3.6 Clerici natural rennet power cromatography profile.
16. Figure 1.4.3.7 Dorsal view of the tongue, pharnyx, and esophagus of the bovine in which the soft palate, pharynx, and origin of the esophagus are cut dorsally and reflected.
17. Figure 1.4.3.8 Effect of pH on triacylglycerol lipase.
18. Figure 1.4.3.9 Effect of temperature on triacylglycerol lipase.
19. Figure 1.4.3.10 The acyl-transfer reaction of transglutaminase.
20. Figure 1.4.3.11 Influence of temperature on Lactase activity; Influence of pH on Lactase activity.
Chapter 2: Process innovation
1. Figure 2.1.2.1 Circle of sinner.
2. Figure 2.1.2.2 pH-scale to classify detergents and their ingredients.
3. Figure 2.1.2.3 Detergents effect, depending on the type of dirt.
4. Figure 2.1.3.1 Passed and rejected milk constituents based on membrane pore size.
5. Figure 2.1.4.1 Alignment of primary structure of cow, buffalo, goat and sheep β-CN. Light grey highlight: signal peptide; underlined characters: positively charged residues; underlined bold grey characters: negatively charged peptide; dark grey highlight: phosphorylated residues.
6. Figure 2.1.4.2 Alignment of bovine, goat, and sheep α_S1-CN. Highlights and characters legend is reported in the caption of Figure 2.1.4.1.
7. Figure 2.1.4.3 Kappa-casein alignment, bold black characters: glycosylation site; the legend of the other character and highlights is in the caption of Figure 2.1.4.1.
8. Figure 2.1.4.4 Primary structure of bovin, goat, and sheep β-lactoglobulin. Highlight and characters legend is reported in the caption of Figure 2.1.4.1.
9. Figure 2.1.4.5 Urea-PAGE of casein fractions of mozzarella cheeses identified as reported by Faccia et al. (2014). The first three lanes are samples of mozzarella cheese made with not fresh curd and the last three are mozzarella cheeses made with fresh milk.
10. Figure 2.1.4.6 Urea-PAGE of (1–3) goat caseins, (4–7) sheep caseins, and (8–9) cow caseins.
11. Figure 2.1.4.7 SDS-PAGE of bovine caseins fraction (lanes 1–3), bovine whey proteins (lanes 4–7), skimmed bovine milk (lane 8).
12. Figure 2.1.4.8 IEF of casein fractions after chymosin action, using a pH range 3–10. Lanes 1, 3 and 4 caprine, 2 bovine, 5 ovine curd.
13. Figure 2.1.4.9 2-DGE of bovine milk, protein spots are identified according to (Holland et al., 2004).
14. Figure 2.2.1.1 Schematic overview of bioactive functions associated with dairy products.
Chapter 4: Consumer Behavior with Regard to Quality Perception of Food Products and Decision Making
1. Figure 4.1.1.1 Good-quality raw milk characteristics.
2. Figure 4.1.2.1 Consumer Perceptions of Price, Quality, and Value.
3. Figure 4.1.2.2 Four Types of Food Quality.
4. Figure 4.1.2.3 The Total Food Quality Model (TFQM).
5. Figure 4.1.3.1 Theory of reasoned action (TRA).
6. Figure 4.1.3.2 Theory of planned behavior (TPB).
7. Figure 4.1.4.1 Factors influencing food preferences. Source: Randall and Sanjur (1981). Reproduced with permission of Taylor and Francis. www.tandfonline.com.
8. Figure 4.2.1.1 Consumer-oriented new product development concept.
9. Figure 4.2.2.1 Features of an organized dairy sector.
10. Figure 4.2.2.2 Percentage share of various dairy products in the total value of dairy exports, 1990 to 2008.
11. Figure 4.2.3.1 The consumer-led new product development process (from Costa and Jongen, 2006)
12. Figure 4.2.3.2 An overview of the interlinked process and considerations in fermented beverage production and development (from Marsh et al., 2014).
Chapter 5: The Milk and Dairy Sector in the European Union: Environmental and Policy Issues
1. Figure 5.1.1.1 Cow's milk production share by world region at 2013
2. Figure 5.1.1.2 Production of cows' milk on farm in EU countries at 1990, 2000, and 2013 (in thousand tonnes).
3. Figure 5.1.1.3 Number of dairy cows at 1990, 2000, 2013 (in thousand heads).
4. Figure 5.1.1.4 Average production of milk per cow at 1990, 2000, 2010, and 2013 (in kg/head).
5. Figure 5.1.1.5 Milk and dairy production in the EU-28 at 2013 (in million tonnes).
6. Figure 5.1.1.6 Share of different livestock in manure nitrogen production in EU28, nitrous oxide and Ammonia, average 2009–2012.
7. Figure 5.1.1.7 Share of different livestock in manure phosphorous production in EU-28, nitrous oxide and Ammonia, average 2009–2012.
8. Figure 5.1.2.1 Factors affecting the benefits of eco-friendly dairy initiatives.

List of Tables

Chapter 1: Milk
1. Table 1.1.1.1 Mean Values of Fat and Protein Content (% w/w) of Milk Produced in EU Countries (Eurostat, 2015).
2. Table 1.1.1.2 Criteria for raw milk, according to European rules.
3. Table 1.1.1.3 Minimun values of parameters used for milk payment below them monetary penalty or demerit point are applied.
4. Table 1.2.1.1 Examples of Microorganisms with Found Beneficial Effects.
5. Table 1.2.1.2 Beneficial Direct and Indirect Effects of GOS.
6. Table 1.2.2.1 Physiological Characteristics of Lactic Acid Bacteria Rods (adapted from Axelsson, 1998).
7. Table 1.2.2.2 Physiological Characteristics of Lactic Acid Bacteria-cocci (adapted from Axelsson, 1998).
8. Table 1.3.1.1 Water-Soluble Vitamins and Their Effects on Health.
9. Table 1.3.1.2 Ingredients for Milk Fortification in Calcium.
10. Table 1.3.1.3 Minerals and Their Effects on Health.
11. Table 1.3.1.4 Botanicals in Dairy Products: Healthy Potential Effects.
12. Table 1.3.1.5 Antioxidant Substances of Plant Origin.
13. Table 1.3.1.6 Sensitivity of Vitamins (adapted from DSM).
14. Table 1.3.2.1 Frequencies Assigned by the FCC for Industrial, Scientific, and Medical Use (US FDA, 2015).
Chapter 2: Process innovation
1. Table 2.1.2.1 Antimicrobial Activity.
2. Table 2.1.2.2 Reception / Raw Milk—CIP Alkaline Single-Phase.
3. Table 2.1.2.8 Yogurt Line—CIP Single-Phase.
4. Table 2.2.1.1 Some Probiotic Dairy Products Developed Worldwide (adapted from Granato et al., 2010).
5. Table 2.2.1.2 Low-Fat Cheeses Produced Using Different Technologies (adapted from Karimi et al., 2015).
Chapter 3: Technological Options to Prolong Shelf Life
1. Table 3.1.2.1 Antimicrobial Effects of Sugar Esters.
2. Table 3.2.2.1 Nanocomposite Systems Tested Against Main Spoilage and Pathogenic Microorganisms of Dairy Products.
3. Table 3.2.3.1 Application of Edible Coatings to Improve the Quality of Cheese Products.
4. Table 3.2.3.2 Application of Edible Films to Improve the Quality of Cheese Products.
Chapter 4: Consumer Behavior with Regard to Quality Perception of Food Products and Decision Making
1. Table 4.2.1.1 Causes of New Product Failure.
2. Table 4.2.2.1 FAO International Dairy Price Index.
Chapter 5: The Milk and Dairy Sector in the European Union: Environmental and Policy Issues
1. Table 5.1.1.1 Production of Cows' Milk on Farm at 1990, 2000, 2005, and 2010–2013 (in million tonnes).
2. Table 5.1.1.2 Number of Dairy Cows at 1990, 2000, 2005, and 2010–2013 (in thousand heads).
3. Table 5.1.1.3 Average Production of Milk per Cow at 1990, 2000, 2010, and 2013 (in kg/head) and Variations (in %) between 1990/2013 and 2010/2013.

Advances in Dairy Products

Edited by

Francesco Contò

Department of Economics, University of Foggia, Italy

Matteo A. Del Nobile

Department of Agricultural Sciences, Food and Environment,
University of Foggia, Italy

Michele Faccia

Department of Soil, Plant and Food Sciences, University of Bari, Italy

Angelo V. Zambrini

Granarolo S.p.A., Bologna, Italy

Amalia Conte

Department of Agricultural Sciences, Food and Environment,
University of Foggia, Italy

This edition first published 2018

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The right of Francesco Contò, Matteo A. Del Nobile, Michele Faccia, Angelo V. Zambrini, and Amalia Conte. to be identified as the author of the editorial material in this work has been asserted in accordance with law.

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List of Contributors

Angiolillo, L.

Department of Agricultural Sciences

Food and Environment

University of Foggia

Italy

Bacak, A.

Department of Quality Assurance & Food Safety

Granarolo S.p.A., Bologna

Italy

Cassone, A.

Department of Soil, Plant and Food Sciences

University of Bari

Via Amendola 165/A, 70126 Bari

Italy

Colantuono, F.

Department of Economics

University of Foggia

Italy

Conte, A.

Department of Agricultural Sciences

Food and Environment

University of Foggia

Italy

Contò, F

Department of Economics

University of Foggia

Italy

Costa, C.

Department of Agricultural Sciences

Food and Environment

University of Foggia

Italy

De Angelis, M.

Department of Soil

Plant and Food Sciences

University of Bari

Italy

De Lucia, C.

Department of Economics

University of Foggia

Italy

Del Nobile, M. A.

Department of Agricultural Sciences

Food and Environment

University of Foggia

Italy

Dzhabarova, Y. V.

Department of Marketing and International Economic Relations

Faculty of Economic and Social Sciences

University of “Paisii Hilendarski”

Plovdiv, Bulgaria

Donati, E.

Department of Research & Development

Granarolo S.p.A.

Bologna, Italy

Faccia, M.

Department of Soil

Plant and Food Sciences

University of Bari

Via Amendola 165/A, 70126 Bari

Italy

Fiore, M.

Department of Economics

University of Foggia

Italy

Galimberti, P.

Department of Quality Assurance & Food Safety

Granarolo S.p.A., Bologna

Italy

Gobbetti, M.

Faculty of Science and Technology

Free University of Bozen

Italy

Ivanova, D.

Department of Economics of Natural Resources

University of National and World Economy, Sofia

Bulgaria

Kizilirmak, E. O.

Ege University

Engineering Faculty

Food Engineering Department

Bornova IZMIR TURKEY

Lucera, A.

Department of Agricultural Sciences

Food and Environment

University of Foggia

Italy

Gerschenson, L. N.

Industry Department

School of Natural and Exact Sciences

Buenos Aires University (UBA) Member of CONICET

Jagus, R.

Laboratory of Industrial Microbiology Department of Chemical Engineering

Engineering School

UBA

La Sala, P.

Department of Economics

University of Foggia

Italy

Loizzo, P.

Department of Soil

Plant and Food Sciences

University of Bari

Via Amendola 165/A 70126 Bari

Italy

Mikkola, M.

Ruralia Institute

University of Helsinki

Mikkeli

Finland

Minervini, F.

Department of Soil

Plant and Food Sciences

University of Bari

Italy

Mishev, P.

Department of Economics of Natural Resources

University of National and World Economy

Sofia

Bulgaria

Mori, G.

Department of Clinical and Experimental Medicine

University of Foggia

Italy

Mucchetti, G.

Dipartimento di Scienze degli Alimenti e del Farmaco

Parco Area delle Scienze

27/A - 43124 PARMA

Italy

Orsi, C.

Department of Research & Development

Granarolo S.p.A., Bologna

Italy

Olle Resa, C. P.

Fellow of the National Research Council of Argentina (CONICET)

Pazienza, P.

Department of Economics

University of Foggia

Italy

Posa, F.

Department of Clinical and Experimental Medicine

University of Foggia

Italy

Porro, C.

Department of Clinical and Experimental Medicine

University of Foggia

Italy

Tavman, S.

Ege University Engineering Faculty Food Engineering Department Izmir/Turkey

Sahin, B.

Ege University

Engineering Faculty

Food Engineering Department

Bornova IZMIR TURKEY

Silvestri, R.

Department of Economic Science

University of Bari

Italy

Tagliabue, C.

Department of Quality Assurance & Food Safety

Granarolo S.p.A.

Bologna

Italy

Trani, A.

Department of Soil

Plant and Food Sciences

University of Bari

Via Amendola 165/A70126 Bari

Italy

Trotta, T.

Department of Clinical and Experimental Medicine

University of Foggia

Italy

Verga, M.

Clerici Sacco Group

Cadorago

Italy

Vecchione, V.

Department of Economics

University of Foggia

Italy

Vasileva, E.

Department of Economics of Natural Resources

University of National and World Economy

Sofia

Bulgaria

Yilmaz, T.

Manisa Celal Bayar University Engineering Faculty Food Engineering Department

Manisa/Turkey

Zambrini, A. V.

Department of Quality

Innovation, Safety, Environment

Granarolo S.p.A., Bologna

Italy

Ziggers, G. W.

Institute for Management ResearchNijmegen School of Management

Radboud University

Nijmegen

The Netherlands

Part I
Ingredients for Dairy Products Manufacturing

Chapter 1.1

Milk

Chapter 1.1.1

Milk Quality and Processing

Germano Mucchetti¹ and Angelo V. Zambrini²

¹Dipartimento di Scienze degli Alimenti e del Farmaco, Parco Area delle Scienze, Parma, Italy

²Institute for Management Research, Nijmegen School of Management, Radboud University, Nijmegen, Netherlands

Introduction

Milk composition is the result of breeding and feeding conditions. The traditional goal of milk producers is the highest yield of milk with the highest amount of protein and fat. The aim of milk processors is to have milk with differentiated composition able to provide nutritional and sensorial quality specific for each milk product (fluid milk, fermented milk, cheese, dried milk or milk ingredients) so as to increase the quality of the products and the efficiency of the processes.

The interest of milk producers may be sometimes in conflict with milk processors, despite the evidence that the supply chain needs a suitable milk to efficiently make a valuable milk product, to be recognized as such by consumers, willing to pay the right price for this reason.

It is first important to define milk protein, internationally considered as the product of total nitrogen by 6.38. IDF (1995) discussed if this definition should discount either the fraction called “nonprotein nitrogen” or the urea fraction, reducing the declared protein for milk and many milk products by 5% or 2.5%. Such a change could have significant implications with respect to payment to milk producers, animal breeding records, nutrition labeling, and the position of dairy products against competitive products such as soya.

The definition of milk protein may also divide the milk industry as the target protein, for most of cheese producers is casein, while for fluid or dried milk producers the values is the total protein content, as this is the content regulated by legal and nutritional issues.

Furthermore, the quality of proteins may be important as nutritional and well-being viewpoints are taken into account.

The method of evaluating the dietary quality of protein in human nutrition could change in the near future, as FAO is advocating the replacement of PDCAAS (Protein Digestibility Corrected Amino Acid Score) with the DIAAS (Digestible Indispensable Amino Acid Score). DIAAS measures the oro-ileal nitrogen balance by calculating the ileal digestibility of individual amino acids. In contrast, PDCAAS uses crude faecal digestibility values in measuring the oro-faecal nitrogen balance, which includes contributions from intestinal secretions and colonic bacteria, thus underestimating the protein available for absorption (FAO, 2014). The use of DIAAS would change the ranking of the protein quality, determining a higher value of milk proteins when compared with other protein sources (Rutherfurd et al., 2015).

The protein content and a better score of the nutritional quality of milk proteins contribute to add value to milk products compared to other food proteins and as a direct consequence to add value to milk. So, today protein content is the first component of milk quality, determining its economic value.

Milk Composition and Systems of Payment According to Milk Utilization

Milk composition determines the technological properties and processability of several milk products, as cheese, butter, yogurt (Glantz et al., 2009) and powders. Milk composition is widely detailed in many books (e.g., Walstra et al., 2006; Mucchetti et al., 2006), with attention both to major and minor components. The amount of each component may vary in a more-or-less narrow range, but the range of variation and the distribution of the values are often unknown.

A robust record of the distribution of the values around the average values is up to date mainly for protein and fat, which measure is provided by legal requirements and by all the systems of milk payment according to quality. The average content of fat and protein of milk produced in 2014 by the 28 countries of the European Union, together with fat to protein ratio, are in Table 1.1.1.1. Differences among the average values are evident, considering that minimal and maximal values represent the average value related to the month of milk production and not the individual samples.

Table 1.1.1.1 Mean Values of Fat and Protein Content (% w/w) of Milk Produced in EU Countries (Eurostat, 2015).

	Fat			Protein			Ratio Fat to Protein
	mean	min	max	mean	min	max	mean	min	max
Austria	4.19	4.09	4.31	3.39	3.32	3.47	1.23	1.21	1.25
Belgium	4.03	3.86	4.19	3.39	3.31	3.45	1.19	1.16	1.21
Bulgaria	3.69	3.63	3.75	3.29	3.23	3.38	1.12	1.10	1.14
Cyprus	3.67	3.41	3.88	3.44	3.35	3.56	1.07	1.02	1.13
Czech Republic	3.87	3.75	3.97	3.42	3.34	3.51	1.13	1.12	1.15
Denmark	4.21	4.05	4.34	3.50	3.40	3.57	1.20	1.18	1.22
Estonia	3.98	3.80	4.10	3.38	3.30	3.50	1.18	1.14	1.21
Finland	4.28	4.10	4.41	3.48	3.40	3.58	1.23	1.20	1.24
France	3.95	3.81	4.08	3.23	3.15	3.29	1.22	1.20	1.25
Germany	4.08	3.93	4.24	3.41	3.32	3.50	1.20	1.18	1.21
Greece	3.89	3.71	4.04	3.32	3.27	3.38	1.17	1.13	1.20
Hungary	3.66	3.54	3.77	3.23	3.14	3.31	1.13	1.12	1.15
Ireland	4.08	3.73	4.55	3.44	3.20	3.85	1.18	1.11	1.28
Italy	3.77	3.69	3.84	3.35	3.28	3.44	1.12	1.10	1.14
Latvia	3.86	3.58	4.06	3.28	3.15	3.45	1.18	1.07	1.26
Lithuania	4.16	3.93	4.42	3.28	3.16	3.45	1.27	1.23	1.30
Luxembourg	4.09	3.91	4.27	3.38	3.30	3.45	1.21	1.18	1.24
Malta	3.35	3.21	3.47	3.15	3.08	3.23	1.06	1.00	1.08
Netherlands	4.34	4.15	4.52	3.51	3.41	3.60	1.24	1.20	1.27
Poland	4.02	3.84	4.13	3.27	3.19	3.35	1.23	1.20	1.25
Portugal	3.78	3.71	3.86	3.27	3.17	3.36	1.16	1.13	1.18
Romania	3.77	3.70	3.85	3.27	3.24	3.29	1.16	1.14	1.17
Slovakia	3.82	3.68	3.98	3.36	3.26	3.43	1.14	1.12	1.17
Slovenia	4.18	4.06	4.28	3.36	3.27	3.46	1.24	1.22	1.26
Spain	3.65	3.53	3.83	3.26	3.19	3.36	1.12	1.09	1.14
Sweden	4.25	4.21	4.28	3.42	3.40	3.44	1.24	1.24	1.25
United Kingdom	4.00	3.84	4.11	3.28	3.23	3.34	1.22	1.19	1.25
Croatia	3.94	3.82	4.02	3.41	3.31	3.60	1.15	1.09	1.19

28 EU Countries mean	3.95			3.35			1.18
SD	0.25			0.11			0.06

In 2014, Italian milk showed an average content of fat and protein of 3.77% and 3.35% respectively (Table 1.1.1.1). Looking at the distribution of the data of milk collected by one of the largest Italian companies (Granlatte, 2014) the average values resulting from more than 52,000 analyses of fat and protein were higher than the Italian values (3.93 and 3.41%, respectively), but more than 15% of the samples showed a fat and protein content lower than 3.70% and 3.20%, respectively.

Looking at the data of each country, the cumulative effect of different breeding, feeding practices and climates are clear. However, these gross data are useful for statistical aims, but they do not permit to discriminate the effect of the variables.

To be used, milk has to satisfy minimum legal requirements (Total Microbial Count TMC, Somatic Cell Count SCC, veterinary drugs and contaminant residues) (Table 1.1.1.2): the dairy must reject noncompliant milk.

Table 1.1.1.2 Criteria for raw milk, according to European rules.

		CE Regulation
Total Microbial Count (cfu/mL)^a)	100000	853/2004
Somatic Cell Count (cell/mL)^b)	400000	853/2004
Drug residues (µg/kg)^c)	4 - 300	2377/1990
Aflatoxin M1 (µg/kg)^d)	50	1881/2006
Lead (mg/kg)^d)	0,02	1881/2006
Dioxins and PCBs (pg/g fat)^d),e)	6	1881/2006

^a rolling geometric average over a two-month period, with at least two samples per month.

^b rolling geometric average over a three-month period, with at least one sample per month.

^c maximum residue limits of veterinary medicinal products in foodstuffs of animal origin.

^d maximum levels for certain contaminants in foodstuffs.

^e sum of dioxins and dioxin-like PCBs (WHOPCDD/ F-PCB-TEQ).

Some dairies apply for some parameters, for example, aflatoxin residues, stricter limits than the legal ones (50 ppt): Fonterra refuses milk with a content higher than 25 ppt and starting from 20 ppt applies a demerit point (Fonterra, 2013), while Parmalat in Italy established an internal limit of 20 ppt for all its consumer products (Pinelli, 2005).

In addition to the minimal legal requirements, milk may be graded (and paid) according to its “quality,” usually measured according to one or more parameters (e.g., protein, casein, fat, lactose, TMC, SCC, thermoduric bacteria, spores, renneting properties, temperature at reception, etc.).

Furthermore, to be effectively graded, milk quality evaluation should consider its utilization, as it is evident that requirements for producing fluid milk, yogurt, cheese or other milk products may be very different.

The use of different rules for fluid milk and cheese milk was traditional, as drinking milk was the reference product and cheese (when not covered by a specific standard of identity) was considered as the vent where the milk with lower characteristics might be convoyed. Up to the end of the last century in the United States, raw milk was differentiated into two standards: grade A milk used for fluid milk and grade B milk for cheese, butter, and dry milk (Chite, 1991). Grade B, as the letter clearly explains, was lower-quality milk, paid accordingly. Today, this distinction is disappearing, as grade A milk is about 99% of US milk produced, and it is used for all the milk products, with prices differently fixed according to four categories or classes of use (USDA 2015).

Class I. Grade A milk used in all beverage milks.
Class II. Grade A milk used in fluid cream products, yogurts, or perishable manufactured products (ice cream, cottage cheese, and others).
Class III. Grade A milk used to produce cream cheese and hard manufactured cheese.
Class IV. Grade A milk used to produce butter and any milk in dried form.

Butterfat, protein, and other nonfat/nonprotein solids represent variables differently affecting the price of each the four US milk classes (Jesse et al., 2008).

In Quebec (Canada), a five-class system is applied, including the class of milk ingredients (Bourbeau, 2010).

Milk Payment Systems

According to Draaiyer and co-authors (2009), each system of milk payment based on quality, even the simplest, should be set as a function of one or more objectives:

To avoid adulteration (e.g., milk dilution with water or other fluids);
To increase yield of dairy products, as the yield of dairy products will depend on the amount of fat, protein and/or lactose and minerals present;
To promote hygienic quality of the milk and increase safety of dairy products, reducing the presence of pathogenic microorganisms, toxins, drugs, antibiotics, and other residues in the milk.

A payment system, which includes testing for selected parameters, with subsequent rejection and/or penalties or bonuses, is considered functional to improving milk quality.

Beside the quantity (volume or weight), the quality parameters considered in a milk payment system are related to composition (fat, protein, lactose, other solids, free fatty acids), to hygienic quality (total microbial count, thermoduric count, spore count, mycotoxins, drugs, and residues), to physical properties (renneting ability, density, freezing point), and to aspects involved in animal health (somatic cell count).

Most dairies use simplified schemes, including only some of the cited parameters, and pay according to a milk standard composition (Table 1.1.1.3).

Table 1.1.1.3 Minimun values of parameters used for milk payment below them monetary penalty or demerit point are applied.

	France	Italy	Denmark	Ireland	Ireland	Switzerland	Australia	New Zealand
Protein (%)	3.2	3.25
Fat (%)	3.8	3.7
Lactose (%)				4.3	4.2
SCC (Somatic Cell Count) /mL	250000	350000	400000	400000	300000	200000	400000	400000
TMC (Total Microbial Count)/mL	50000	100000	50000	50000	50000	60000	100000	50000
Thermoduric count/mL				1000	500		5000	1500
Coliform /mL								500
Spores/L	1000	500*	4000			2500
Freezing Point (°C)	–0.502					–0.516		–0.513
Aflatoxin (ppt)								20
FFA (Free Fatty Acids) (meq/100 g fat)	0.89
Source: WEB documents from:	UCAL (2013)	CLAL (2015). Aral* (2002)	Arla Foods (2015)	DairyGold (2011)	Glanbia (2011)	Prolait (2012)	Murray Goulburn (2014)	Fonterra (2013)

One of the prominent milk payment systems is the A + B – C multiple component pricing system, where A and B are the monetary values per kilogram of milk fat and milk protein and C is the penalty per liter of milk volume, responsible for higher delivery costs (Sneddon et al., 2013). The milk price is calculated according to the product of fat, protein, and other solids masses by their specific price (€/kg) (e.g., in the Netherlands, Ireland, Australia, New Zealand, Canada or United States) (Dairy Ireland, 2013; Dairygold, 2011; Royal Friesland Campina, 2014; Dairy Farmers of Ontario, 2015; CFR, 2015) eventually subtracted by a volume factor linked to delivery costs.

The system A + B – C reduces the importance of the measure of freezing point, as the volume of milk has a negative value so it is senseless to add water to increase the milk volume delivered.

Alternatively, the system can act with a fixed price per liter/kg of milk integrated with a different premium or penalty for each percentage unit fat and protein above or below a base concentration as in Italy, Germany, or France (CLAL, 2015). In Italy, routine analyses carried out by sanitary controls with routine methods showed that about 11% of the samples resulted with a freezing point higher than the official value of –0,52°C (IZSLER 2015). Similar data were reported for Switzerland (Decrauzat, 2011).

When milk powder is the end product of milk processing, lactose becomes a key component, as in order to standardize milk, either lactose or UF milk permeate must be purchased, or fat and protein must be removed through centrifugal separation and membrane processing, adding costs (Sneddon et al., 2013).

Milk payment system used in the area of Parmigiano Reggiano cheese uses several parameters aimed to value the milk for its specific use, which is set, and economic values are fixed by the bargaining among each dairy and the milk producers delivering milk to the dairy.

Beside the usual ones, specific chemical and physical parameters are indexed as casein instead of total protein, aptitude to rennet coagulation, pH and titratable acidity, and urea (Malacarne et al., 2004).

Casein content is related to total protein content by an average index ranging from 0.774 to 0.782 in the years from 2008 to 2014 (IZLER, 2015).

Changes are mainly due to shifts of nonprotein nitrogen and milk urea nitrogen (MUN) content frequently caused by different feeding conditions.

MUN content is used as a diagnostic of protein feeding in dairy cows, as monitoring the adequacy of protein feeding is a tool to optimize the efficiency of nitrogen utilization by cows with respect to both milk protein production and nitrogen emissions into the environment (Nousiainen et al., 2004, Duinkerken et al., 2011). Average MUN content in Italy is usually lower than 23 mg/dL (IZLER, 2015), with a range from 10 to 40 mg/dL (Granterre 2014). These figures are comparable to data from Belgium (Dufresne et al., 2010), but higher than US data (Ishler, 2008).

Acidity of milk, measured as titrable acidity and/or pH, is a contractual parameter applied in France as a pass/nonpass criterion by Danone (FNPL, 2011), which required milk with pH in the range 6.65 to 6.85 or acidity below 16° Dornic (3.55°SH/50 ml).

Milk is often criticized for its low ratio of unsaturated to saturated fatty acids (FA). Composition of milk fat by dairy cow varies both by feeding and breeding. The ratio between unsaturated and saturated FA can be manipulated feeding cows with an increased intake of fresh grass or clover silage, eventually supplemented with high-fat oil seed cakes (Nousianen et al., 2007). This ratio can change from 0.47 to 0.41, as shown by July 2014 data of milk compared to December 2014, because the content of saturated FA decreased in summer while the unsaturated FA were constant (IZSLER, 2015). Also, as the genetic trend for milk fat concentration is declining, the proportion of saturated FA may decrease because the importance of mammary de novo synthesis of FA of milk fat is lowered (Nousianen et al., 2007).

Milk fat content may be manipulated by targeting feeding to increase the conjugated linoleic acid (CLA) and ω-3 FA content. Milk fat from grass-based diets may be poorer in short-chain FA and linoleic acid and richer in α-linolenic acid when compared to corn silage diets (Reviron et al., 2008).

To promote a change toward the increase of the ratio unsaturated to saturated FA, some cow-feeding policies (e.g., see Switzerland) introduced a significant premium for cheese-milk produced without maize silage (SMP-PSL, 2015).

In Italy, milk for Parmigiano Reggiano cheese must be produced without using any silage for feeding cows or detecting at the farm (Ministero Politiche Agricole, 2015). Use of silage may be checked by measure of the content of cyclopropylic acids (Caligiani et al., 2014).

Free fatty acids (FFAs) content of raw milk is an additional parameter present in some milk payment schemes (Table 1.1.1.3), and it is considered an indicator of dairy cow nutrition, bacterial contamination, and storage quality (Hanuš et al., 2008; Nouisianen et al., 2007).

Microbiological quality of milk may be differently evaluated according to milk utilization, considering additional counts beside TMC.

While TMC of 100,000 cfu/ml is the legal limit for most countries, large dairies often use 50,000 cfu/ml or less as the standard TMC value (Table 1.1.1.3). To obtain a bonus, milk should often have a count lower than 30,000 cfu/mL. TMC, however, is a general parameter, easy to detect with rapid and automated methods, but clearly it does not discriminate the presence of specific groups of microrganisms. For this reason, it is not a useful tool to differentiate milk according to its use for processing.

The average TMC content of milk produced in Lombardia (Italy) in 2014 is lower than 40,000 cfu/mL, with only 3.9% of the samples exceeding the limit of 100,000 cfu/mL (IZSLER, 2015). Out of the 52,000 samples analyzed by the dairy Granlatte in 2014, 68% showed a count less than 25,000 cfu/mL (Granlatte, 2014).

Thermoduric bacteria, mainly heat and highly heat resistant aerobic spore forming bacteria as Bacillus spp and Paenibacillus spp (Ranieri et al., 2012; Lücking et al., 2013; Burgess et al.; 2010, Scott et al.; 2007), represent a major concern for long life fluid milk and dried milk quality within the storage time (Gleeson et al., 2013). Anaerobic spore forming bacteria (Clostridium spp) (Vissers et al., 2006) are more relevant for hard cheese production. Some companies foresee as acceptable a count of thermoduric bacteria ranging between 500 and 5,000 cfu/mL (Table 1.1.1.3).

Count of spores, or more specifically of butyric spores (Table 1.1.1.3), is applied by some countries, including Italy, Switzerland, France, and Denmark, with tolerated values ranging from 300 to 4,000 spores/L of milk.

Italian dairies producing hard-cooked long-ripened cheeses as Grana Padano, Parmigiano Reggiano or Provolone Valpadana PDO cheeses, within 2007 paid milk according also to anaerobic spore count (ARAL, 2002), as spore germination of butyric clostridia is responsible of the late-blowing defect (Mucchetti et al., 2006). The limit, crossed when a levy was applied, was 500 spores/L, while a bonus was paid for milk with a count less than 300 spores/L. As the average count of butyric spore in the last years was less than 200 spores/L (Bolzoni, 2012), this parameter is not yet compulsorily applied by Grana Padano and Parmigiano Reggiano dairies, but each dairy opts for its use or not.

Some dairies apply also a limit for coliforms despite of this criterion is no more considered at present time by European legislation (EC Regulation 2073/2005). A count of 500 coliforms bacteria/mL is the limit fixed by Fonterra (New Zealand) before to apply demerit points. Counts performed by IZLER in Lombardy (Italy) showed an average value in the period from 2011 to 2014 around 2,000 cfu/mL (IZLER, 2015).

Surprisingly, psychrotrophic bacteria count is usually not considered as a parameter to be used for milk payment, even though their heat resistant enzymes are responsible for alteration of long life fluid milk (Poffè et al., 1988; Griffiths et al., 1981) and their growth may represent a serious defect both for fresh or ripened cheeses, as for blue discoloration of Mozzarella cheese (RAFFS, 2010, Caputo et al., 2015).

At the same time, temperature of milk delivery, usually less than 6°C, is a requirement common to all the countries, and specific dispensation is requested to deliver milk at higher temperature, according to technological reasons.

Control of temperature before processing is essential for the prevention of excessive microbial growth and/or milk acidification.

A key parameter used to assess and pay the milk quality is the somatic cell count (SCC). SCC of milk of uninfected udder quarters comprises 70% to 80% of white blood cells (neutrophils and mononuclear cells, as macrophages and lymphocytes) and epithelial cells deriving from the udder tissue itself. A change of the somatic cell count may be the indicator of the presence of animals with a poor health status and is usually related with a lower milk production. Other physiological factors as parity, lactation stage, and breed could also be the origin of an increased somatic cell count (Schukken et al., 2003).

SCC, obviously under the legal target value of 400,000/mL, is an additional parameter that could be differently evaluated for milk grading, according to specific milk utilization. It is well known that SCC high counts are negatively correlated with cheese yield, because of changes of milk composition (Politis et al., 1988; Hortet et al., 1998). However, polimorphonuclear leukocytes (PMN), a class of SCC, can interact with cheese proteloysis because of the activity of cathepsin D (Le Roux et al., 2003). Kelly and co-authors (2000) hypothesized that PMN may be used as a marker for certain functional properties of milk, which could be used by processors to screen bulk milks.

Furthermore, SCC is associated with the urokinase type of plasminogen activators, able to convert plasminogen to plasmin, the native milk proteinase. Plasmin is part of a complex protease-protease activator-protease activator inhibitor-protease inhibitor–system in milk (Ismail et al., 2010). Proteolysis is a key quality factor for many ripened cheeses, and plasmin can play a significant role in cooked cheeses, where chymosin could be partially or totally inactivated by the curd-cooking process (Sheehan et al., 2007; Hayes et al., 2002).

The evidence on the role of plasmin in the age gelation of UHT milk is conflicting (Ismail et al., 2010), but UHT milk processors consider the inactivation of plasmin to a residual activity less than 1% (van Asselt et al., 2008) as one of the primary targets of the milk heat treatment. In any case, the ability of plasmin to hydrolyze casein is a factor reducing the shelf life of UHT and pasteurized milk, as showed by the increase of proteose-peptone fraction, also in pasteurized milk stored for 10 days (De Noni et al., 2007).

The presence of plasmin in casein and whey protein products, should be considered, as it could hydrolyze milk and/or other proteins in a system to which casein or whey protein are added as functional ingredients (Ismail et al., 2010).

A count of 200,000 to 250,000 SCC/mL is becoming a limit to avoid penalties, while values lower than 100,000 SCC/mL justify a bonus, as in the schemes of milk payment of several dairies (UCAL 2013, Prolait, 2012, Murray Goulburn, 2014). Italian data showed in 2014 an average value of about 275,000 SCC/mL (IZSLER, 2015), with 10.7% of the samples overpassing the limit of 400,000, while Granlatte (2015) showed an average of 247,000 SCC/mL with more than 35% of samples with a count lower than 200,000.

Other Parameters of Milk Quality Deriving from a Genomic Approach Proposed for Milk Payment Schemes

The design of milk with specific protein characteristics becomes more and more feasible for breeders (Caroli et al., 2009), because of the advances in genomics and proteomics.

An example is the breed selection, according to the ability to produce milk with the B variant of k-casein, characterized by the substitution of Thr 136 and Asp 148 with Ile and Ala, respectively (Grosclaude et al., 1972). These mutations are located in the so-called casein macro peptide (CMP) relatively close to several glycosylation sites (e.g., Thr 131) and probably affect the structure of the protein and glycosylation patterns. Presence of B allele of κ-Cn not only is associated with an increased cheese yield and quality, but also correlates with other valuable parameters of milk productivity as protein content and milk yield (Walsh et al., 1998). The frequency of kB-Cn is naturally higher than 50% in some breeds as Jersey, Bruna Alpina, or Normande (Walsh, 1999). Tests for rapid check of kB-CN are available (Summer et al., 2010).

Even B allele of β-lactoglobulin is associated to a higher casein number, and it is considered a useful parameter for cattle breeding selection (Hallén et al., 2008).