Application of Fourier-Transform Infrared (FTIR) for Quality Control of Swiss Cheese

Application of Fourier-transform Infrared (FTIR) for Quality Control of Swiss Cheese

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Ni Cheng

Graduate Program in Food Science and Technology

The Ohio State University

2013

Master’s Examination Committee:

Dr. W. James Harper, Advisor

Dr. Luis E. Rodriguez-Saona

Dr. Sheryl. Barringer

Copyrighted by

Ni Cheng

2013

Abstract

Swiss-type cheese has many problems in its manufacturing. Split and variability are two most important problems in Swiss cheese. And traditional cheese grading is time consuming and costs a lot. The objective of this study was to find indicators for split formation and to show the sources of quality variability. Rapid classification of graded cheese has also been studied.

Swiss-type cheese samples were obtained from different factories for study on split, variability and grading, respectively. Fourier-transform infrared spectroscopy (FTIR) was used in different parts of this study.

FTIR collects the mid-infrared spectra (4000-700cm-1), and the resulting wave numbers formed, were used to monitor water extracts of Swiss-type cheese during manufacture, in defective cheeses and in different grades of cheese. Soft independent modeling of class analogy (SIMCA) was used to develop different classifications with infrared spectra.

This study has shown that FTIR, coupled with water extraction, was a useful tool to monitor changes occurring in Swiss cheese making, for split formation and cheese

ii variability. FTIR showed that band 1639- 1612 cm-1, 1158-1151cm-1, 1485-1493cm-1, which belong to the amide I and II of protein degradation compounds, might be characteristic bands for split formation. Lactic acid degradation may also be a factor for split formation.

For variability in Swiss cheese, FTIR showed different patterns amount different factories and within a single factory (at different manufacturing stages, from vat to vat, and from batch to batch). Based on Taylor’s sensory study (Taylor, 2013) on the same 15 cheeses from five different factories, FTIR could be a promising method to study flavor variability in Swiss-type cheese. FTIR data showed different indicators for flavor variability both within a factory and among different factories. FTIR was also a possible method for rapid cheese grading.

iii

Dedicated to My parents B. Cheng and Z. Li

Thank you for your love

Acknowledgments

I would like to thank my parents, whose love, support and encouragement are unreserved and unconditional. I also would like to thank my Aunt Rachel Cheng and

Uncle Lin Hao, for taking care of me in the USA.

I would like to thank Dr. W. James Harper for his wisdom suggestions and thoughts that helped in guiding my research and thesis. I am grateful to Dr. Luis E. Rodriguez-

Saona for his generous support and help. I would like to Dr. Sheryl Barringer for being as my committee member.

I would like to thank my lab members, Cheryl Wick, Taehyun Ji , Kaitlyn Taylor,

Feifei Hu, Hardy Castada and Matt Gardener for their help and encouragement.

I would like to thank Ting Wang and all people in Dr. Rodriguez-Saona’s lab, for their helps for my experiments.

I would like to extend my gratitude to all my friends within and without Food Science program, who lightened up my life in Columbus, Ohio.

I am thankful to my dear friends Xiaoxue Hu, Yan Huang, Qian Li and Danny Yang for their understanding and always send me positive energy.

I wish to thank the faculty and staff in department for their help related with my

Master studies.

I also want to thank the Swiss Cheese Consortium for providing cheese samples.

Vita

June 2007 ...... Chongqing Yucai School, P.R.China

2011...... B.S. Food Science and Engineering, The

Beijing Forestry University, P.R.China

2011 - Present ...... Master Food Science, Department of Food

Science and Technology, The Ohio State University

Fields of Study

Major Field: Food Science vii

Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vii

Fields of Study ...... vii

Table of Contents ...... viii

List of Tables ...... xii

List of Figures ...... xiv

Introduction ...... 1

Chapter 1: Literature Review ...... 3

1.1 Swiss-type Cheese ...... 3

1.2 Swiss-type Cheese Manufacture ...... 4

1.2.1 Starter Cultures and Two Major Fermentations ...... 6

1.2.2 Eye Formation in Swiss-type Cheese ...... 7

1.2.3 Swiss-type Cheese Flavor ...... 9

1.3 Defects of Swiss-type Cheese ...... 10

1.3.1 Split Defect of Swiss-type Cheese ...... 11

viii

1.3.1.1 Factors Contributing to Splits Defect ...... 11

1.3.1.2 Techniques for Evaluation or Prediction of Split or Eye Formation

...... 12

1.4 Fourier-transform Infrared Spectroscopy (FTIR) ...... 12

1.4.1 History ...... 12

1.4.2 FTIR Spectrometer Mechanism...... 13

1.4.3 Principle of Attenuated Total Reflectance (ATR) ...... 15

1.4.4 Principle of Reflection Fourier-transform Infrared Microscopy ...... 16

1.4.5 Advantages and Disadvantages of FTIR ...... 18

1.4.6 Application of FTIR in Cheese Analysis ...... 19

1.5 Analysis of Infrared Spectra ...... 20

Chapter 2: Fourier-transform Infrared Methodology ...... 22

2.1 Sample preparation for FTIR ...... 22

2.2 ATR-FTIR Spectroscopy Conditions ...... 23

2.3 Microscopy-FTIR Reflectance Conditions ...... 24

2.4 Spectra Analysis ...... 24

2.5 Multivariate Analysis ...... 26

Chapter 3: Monitoring Water Soluble Compounds of Swiss-type Cheese during

Manufacturing Using Attenuated Total Reflectance-Fourier Transform Infrared

Spectroscopy (ATR-FTIR) ...... 28 ix

3.1 Abstract ...... 28

3.2 Introduction ...... 29

3.3 Material and Method ...... 30

3.3.1 Swiss-type Cheese Samples...... 30

3.3.2 Methods ...... 33

3.4 Result and Discussion ...... 33

3.4.1 Classification of All Four Manufacturing Stages of Swiss-type Cheese ...... 33

3.4.2 Classifications of Individual Cheeses at Each of the Four Stages ...... 36

3.4.3 Classification of At Cut Swiss-type Cheese Based on Blind, Eye and Split

Areas ...... 47

3.5 Conclusion...... 53

Chapter 4: Variability in Swiss-type Cheese from Five Factories by Attenuated Total

Reflectance Fourier Transform Infrared (ATR-FTIR) Spectroscopy ...... 54

4.1 Abstract ...... 54

4.2 Introduction ...... 55

4.3 Materials and Methods ...... 56

4.4 Result and Discussion ...... 57

4.4.1 Classification of Swiss-type Cheese Based on Factories ...... 57

4.4.2 Classification of Swiss-type Cheese within Each of the Five Factories...... 61

4.5 Conclusion ...... 66

Chapter 5: Fourier Transform Infrared (FTIR)-Microscopy in Swiss-type Cheese Grading

...... 67

5.1 Abstract ...... 67

5.2 Introduction ...... 68

5.3 Materials and Methods ...... 68

5.3.1 Swiss-type Cheese Samples...... 68

5.3.2 Methods ...... 69

5.4 Result and Discussion ...... 70

5.4.1 Classification of Swiss-type Cheese Based on Grades ...... 70

5.5 Conclusion ...... 72

References ...... 73

Appendix A: Discriminating Power Plots for Classification of Four Manufacturing

Stages of Swiss-type Cheese and Classifications of the Individaul Cheeses in Each Stage

...... 81

Appendix B: Discriminating Power Plots for Classification of Three Swiss-type Cheeses from Each of the Five Factories ...... 84

List of Tables

Table 3.1. Vat and split information for at cut cheeses………………………………….32

Table 3.2. Interclass distances of four manufacturing stages in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese………………………….35

Table 3.3. Summary of discriminating wavenumbers and associated functional groups of classifications of four stages Swiss cheeses……………………………………………...36

Table 3.4. Interclass distances of ten non-defective cheeses at our of press stage in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese....39

Table 3.5. Interclass distances of ten non-defective cheeses at pre-cool stage in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese....40

Table 3.6. Interclass distances of ten non-defective cheeses at out of warm room stage in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese...... 41

Table 3.7. Interclass distances of seven non-defective cheeses at cut stage in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese…………………………...... …42

Table 3.8. Summary of interclass distance of the same vat manufactured in two batches...... 43

xii

Table 3.9 Summary of discriminating wavenumbers and associated functional groups of classifications of the individual non-defective Swiss cheeses in each stage...... 45

Table 3.10. Interclass distances of different areas in soft independent modeling of class analogy (SIMCA) classification of the group of three defective Swiss-type cheeses…...48

Table 3.11 Interclass distances of different areas in soft independent modeling of class analogy (SIMCA) classification of defective Swiss-type cheeses...... …………………...50

Table 4.1. Interclass distances among five factories in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese…………………………………..58

Table 4.2. Manufacturing data of five factories cheese………………………………….60

Table 4.3. Interclass distances of 3 cheeses within each factory in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese flavor………..63

Table 4.4. Summary of discriminating wave numbers and these associated function groups for five factories Swiss-type cheese……………………………………………...64

Table 5.1. Grades and Split Information of Swiss cheeses………………………………69

Table 5.2. Interclass distances of different grading in soft independent modeling of class analogy (SIMCA) classification of pink Swiss-type cheeses……………………………71

xiii

List of Figures

Figure.1.1. Swiss-type cheese manufacture flow chat ……………………………………5

Figure 1.2. Main factors that determine the formation of “eye” in Swiss-type cheese…...8

Figure 1.3. Optical layout of a typical FTIR spectrometer………………………………14

Figure 1.4. Optical pathway of a multiple reflection ATR system………………………16

Figure 1.5. Optical diagram of an infrared microscope used for infrared micro spectroscopy……………………………………………………………………………...17

Figure 2.1. FTIR sample preparation flow chart…………………………………………23

Figure 2.2.Raw representative spectra (top) and 2nd derivative spectral transformation

(bottom) of Swiss-type cheese water extraction by ATR-FTIR. AU=arbitrary unit...... 26

Figure 3.1. Sampling stages in Swiss-type cheese manufacturing……………………....31

Figure 3.2. Sampling for FTIR…………………………………………………………..32

Figure 3.3. Soft independent modeling of class analogy (SIMCA) class projections showing four manufacturing stages of Swiss-type cheese……………………………….34

Figure 3.4. Soft independent modeling of class analogy (SIMCA) class projections showing individual non-defective cheeses at each of the four stages of Swiss-type cheese…………...... 37

Figure 3.5. Soft independent modeling of class analogy (SIMCA) classification

xiv projections showing blind, eye and split area of the group of three defective at cut Swiss- type cheeses considered together………………………………………………………...47

Figure 3.6. Discriminating power plots for classification of different areas of three defective Swiss-type cheeses…………………………………………………………….48

Figure 3.7 Soft independent modeling of class analogy(SIMCA) classification projections showing different areas of defective Swiss-type cheese...... …...... …49

Figure 3.8 Discriminating power plots for classification of different areas of defective

Swiss-type cheeses ………………………………………………………...... …..52

Figure 4.1. Soft independent modeling of class analogy (SIMCA) class projection shows the difference of the Swiss-type cheeses among the five factories………………………57

Figure 4.2. Discriminating power plot for classification of Swiss-type cheese flavor among five factories……………………………………………………………………...59

Figure 4.3. Soft independent modeling of class analogy (SIMCA) classification projections showing the difference of Swiss-type cheese within each factory…………..62

Figure 5.1. Soft independent modeling of class analogy (SIMCA) classification projections showing different grade of Swiss-type cheese………………………………70

Figure 5.2 Discriminating power plots for classification of different grading of Swiss- type cheese……………………………………………………………………………….71

Introduction

The production and consumption of Swiss-type cheese in the United States is increasing from year to year. Swiss-type cheese is graded, based on flavor, body, eyes and texture, finish and appearance and color. The cheese grade is usually based on company graders. However, it is time consuming and highly expensive. Splits and cracks are defects that usually happen during the manufacturing. Variability is also one problem in Swiss cheese manufacture. These problems cause down-grading of cheese and economic loss to manufactures. Many factors, such as milk supply, starter cultures and non-starter micro-organism, may lead to these problems. The source of these needs to be found and possible action needs to be taken.

Fourier transform infrared spectroscopy (FTIR) has been used in dairy food analysis for decades. It is a rapid, simple and high sensitive instrument in food analysis and quality control. FTIR has been used to determine cheese composition, relation to texture, sensory, shelf-life, quality control, seasonal variability and different regions. The water extraction method with FTIR has been used to study cheese compounds and its flavor.

Amino acids, fatty acids, organic acids and peptides were the common compounds in the cheese water soluble fraction. FTIR with water extract of cheese had also been used

1 previously in this department to associate flavor with FTIR patterns in both Cheddar and

Swiss cheese.

In this study, FTIR was used to monitor water soluble compounds of Swiss cheese to find indicators for split formation and to show the sources of quality variability.

Additionally, FTIR was used to build a rapid method for Swiss cheese grading.

Chapter 1: Literature Review

1.1 Swiss-type Cheese

Swiss cheese is one kind of hard to semi-hard cheese that was introduced from a river valley in Switzerland. Swiss-type cheese usually has a slight woody texture, a sweet and nutty flavor and eyes in body (Kosikowski and Mistry, 1997). Swiss cheese includes many varieties including Gruyere, Maasdammer, Comt´e, Beaufort and Emmental

(Frohlich-Wyder and Bachmann, 2007). According to the Code of Federal Regulation,

Swiss-type cheese is produced by a process which produces a finished cheese having the same physical and chemical properties as cheese produced by the Swiss process (FDA,

2012). It is prepared from milk and has holes, or eyes (FDA, 2012). The Federal Code standard of identity also requires the cheese to be 60 days old before sale (FDA, 2012).

The production and consumption of Swiss-type cheese in the U.S is increasing from year to year. Swiss-type cheese is graded based on flavor, body, eyes and texture, finish and appearance and color (USDA 2001). High quality Swiss-type cheese shall have a pleasing and desirable sweet and nutty flavor, which is consistent with aging. It shall have a firm and elastic body and a white to light yellow color, naturally and uniformly.

The cheese contains well-developed and uniformly distributed round eyes. The rindless

3 blocks of Swiss cheese, which has been studied in this research, should be reasonably uniform in size, and well-shaped eye (USDA, 2001).

1.2 Swiss-type Cheese Manufacture

The basic processing of Swiss-type cheese is similar to other major hard cheese varieties. However, it uses thermophlic lactic acid bacteria and propionic bacteria to achieve the eye formation in the final product. Additionally, compared with Cheddar cheese manufacture, although Swiss-type cheese will be turned over for several times during making, it has no Cheddaring step. Fig 1.1 shows a general Swiss-type cheese manufacture flow sheet. Raw milk is collected and then goes through clarification and pasteurization to remove impure substances, spoilage microorganism and pathogens.

Clarification also helps to decrease the amount of multiple nuclei in the cheese milk and to limit excess eye formation (Kosikowski and Mistry, 1997b). The pasteurized milk is cooled to a temperature about 30 °C before adding starter cultures. The salt concentration of brining is about 1.6%. The brined cheese is pre-cooled to 9 to 10 °C to provide more uniform salt and moisture distribution. After the pre-cool stage, the cheese is placed in warm room at 21°C to stimulate the growth of propionic acid bacteria, which produce gas

Figure.1.1 Swiss-type cheese manufacture flow chat (Cited from Harper, 2011)

5 to form eyes in cheese. Then, the cheese is transferred into a cold room, at about 0-1°C, and stored there until it reaches 60-days from the date of manufacture.

1.2.1 Starter Cultures and Two Major Fermentations

Fermentation is important to achieve the desired texture and flavor of Swiss-type cheese. Fermentation helps to form cheese curd and eyes in cheese body, and also degrades casein micelles, protein, liquids and lactose to product flavor compounds. The two major fermentations that occur in Swiss-type cheese-making are lactic acid fermentation and propionic acid fermentation. Three types of bacteria are generally used as starter cultures in the Swiss-type cheese manufacture: Streptococcus thermophilus,

Lactobacillus helveticus or Lactobacillus delbruekii subsp.bulgaricus, and

Propionibacterium freudenreichii subspecies shermani.

Lactic acid fermentation begins at the early stages of manufacture, while propionic acid fermentation starts during warm room storage. The lactic acid fermentation is associated with Streptococcus thermophilus and Lactobacillus. Mixed starter cultures are generally used in Swiss-type cheese manufacture, which renders homofermentative catabolism, and lactose is mostly converted into lactate (Frohlich-Wyder and Bachmann,

2004).

After adding starter cultures at 30°C, Streptococcus thermophilus dominates growth and drops the pH of cheese milk from 6.6 to 6.2 during the making stage. The

Lactobacillus primarily grows and produces acid during the press, and takes the pH down to 5.2 at room temperature.

The propionic acid fermentation begins in the later stage of cheese making because the Propionic bacterium requires higher temperature (21°C) and needs to use lactic acid and lactate as substrates. During warm room storage, Propionibacterium freudenreichii subspecies shermani begins to dominate the growth of microorganism and produce propionic acid, acetic acid and carbon dioxide, in a ratio of 2:1:1( Daly et al., 2009).

Carbon dioxide plays a key role in the eye formation and splits/cracks formation of

Swiss-type cheese. And propionic acid and acetic acid contribute to the development of the characteristic flavor of Swiss-type cheese (Polychromiadou A, 2001).

After warm room storage, the cheese is placed in a cold room, at about 0-1°C, for further ripening. Although most starter cultures growth slows down at cold room temperature, the degradation of protein, peptides and lipids keeps changing the flavor and texture of Swiss-type cheese. After 60 days from the initial make, Swiss-type cheese is ready for packaging and sale.

1.2.2 Eye Formation in Swiss-type Cheese

Eye formation is important character in Swiss-type cheese. Multiple factors contribute to the eye formation in Swiss-type cheese as shown in Fig 1.2 (Lawerence,

Creamer and Gilles, 1987). The eye is primarily produced during the warm room storage

7 as the propionic acid bacteria starts to produce carbon dioxide. Besides gas production, critical rheological properties of cheese body and certain amount of nuclei are also required for eye formation in Swiss-type cheese (Daly et al., 2009). Carbon dioxide is produced and then it diffuses into the cheese body and accumulates in the nuclei to form eyes (Steffen et al., 1993). The amount and rate of carbon dioxide production influences the size, number and distribution of eyes (Polychromiadou, 2001). At the same time, proteolysis is occurring, and conversion of other milk components is also going on to change the rheological properties of cheese body (Creamer and Olson,1982).

Figure 1.2 Main factors that determine the formation of “eye” in Swiss-type cheese (Lawrence et al., 1987).

Law and Tamime (2010a) pointed out, that during the warm room storage, cheese had the lowest firmness and a higher elasticity, higher deformability and lower mechanical resistance.

They also mentioned that high deformability is also associated with high cohesion, which helps to

8 support the internal overpressures without splitting. When the cheese body has the desired rheological properties, and the propionic bacteria produces carbon dioxide at the right rate and level, then the nuclei can be converted into eyes the eye varies from 1 to 3 centimeters in diameter

1.2.3 Swiss-type Cheese Flavor

The component balance theory mentions that cheese flavor is produced by a wide range of nonvolatile and aromatic compounds (Mulder, 1952). The cheese flavor compounds can be divided into three categories: volatile, water-soluble volatile and water-soluble non-volatile (Biede and Hammond 1979, Fox et al., 2000a). The volatile and water-soluble volatile compounds contribute to the cheese aroma, whereas the water- soluble non-volatile compounds determine the cheese taste. Headspace methodology is commonly used for monitoring volatile compounds in cheese. GC-MS, Urea-PAGE and

HPLC have been used for study of water-soluble compounds, using different extraction methods (Fox et al., 2000b). Cheese flavor formation is associated with proteolysis, glycolysis and lipolysis (McSweeney and Sousa, 2000). For Swiss-type cheese, lipolysis has limited influence on its flavor profile. Lower molecular weight fatty acids, which are derived from lactose and lactate formation, contribute to Swiss-type cheese flavor.

Swiss-type cheese is characterized by its nutty and sweet flavor (Warmke et al.,

1996). For Swiss-type cheese, many water soluble non-volatile compounds, including peptides, amino acids and median and short-chain fatty acids, can contribute to flavor

(Biede and Hammond 1979). Amino acid, especially Proline and Valine, contributes to the “Sweet” (Hintz et al, 1956). Inter-actions of amino acid and peptides with Ca2+/Mg2+ also influence sweet flavor (Fox et al., 2000c). Small amino acids and peptides are responsible for the nutty flavor in taste. Bitterness comes from tri- and tetra- peptides.

The water-soluble non-volatile compounds are also associated with acid taste of cheese.

For the water-soluble volatile in Swiss-type cheese, di-acetyl contributes to sweet and free fatty acids (eg. propionic, acetic, butyric) contributes to aroma and acidity (Biede and Hammond 1979). Butyric acid is volatile, but it contributes both for aroma and taste.

For volatile compounds, propionic acid, ethyl acetate and 2-pentanones contribute to the

“nutty” flavor (Lawlor et al., 2002). Free Amino acids and peptides can also be converted into volatile flavor compounds by enzymes (Smit et al., 2000).

1.3 Defects of Swiss-type Cheese

Major defects of Swiss-type cheese are discussed in many papers and can be summarized into three kinds: flavor, texture and discoloration (Daly, et al., 2009,2012;

Rychlika and Bosset. 2001; Le Bourhis et al., 2007; Nath and Kostak, 1986; Shannon and

Olson 1977; Schaller et al., 2000). These defects cause down-grading of Swiss-type cheese and economic loss in the industry. Texture defects include eye size and distribution, shell, glass, blowhole, split/crack and whey-spotted (Kosikowski and Mistry.

1997c). In this thesis, splits and cracks formation of Swiss-type cheese have been studied.

1.3.1 Split Defect of Swiss-type Cheese

Splits or cracks are defined as an undesirable opening, which is visible in the cut cheese loaf and may lead to downgrading of the cheese (Daly et al., 2009). In Swiss-type cheese, split or crack usually happen in the cold room storage during manufacture (Daly et al., 2009). The increase of the internal pressure, and a low resistance to fracture, can lead to the formation of splits or cracks (Zoom and Aallersma, 1996). After being transferred into the cold room, the cheese loaf loses the ability of holding carbon dioxide.

Fat crystallization, which occurs during cooling to 1-2°C, may be a factor causing split formation (Law and Tamime, 2010b). Proteolysis continues to degrade protein into peptides and amino acids to change the elasticity of cheese. Over production of carbon dioxide not only requires higher solubility of gas in cheese loaf, but also lowers the pH to cause loss of calcium from the micelle. The cheese loaf holds less amount of gas in lower temperature and the over produced gas forms irregular opening, instead of eyes.

1.3.1.1 Factors Contributing to Splits Defect

Multiple factors may lead to split formation in Swiss-type cheese. This is described in detail by Daly and others (2009). In general, those factors can be divided into two categories: over production of gas (eg. carbon dioxide or hydrogen) and the loss of desired cheese texture. The factors associated with the formation of splits and cracks include: secondary fermentation; changes in solubility of carbon dioxide in different phases with a decrease in temperature; changes in the texture with a change in

11 temperature and continued proteolysis, causing a loss in the elasticity of cheese texture

(Law and Tamine, 2010c). In addition, the milk supply, non-starter lactic bacteria, interactions between starters and production of other types of gas (hydrogen) also need to be taken into consideration (Daly et al., 2009).

1.3.1.2 Techniques for Evaluation or Prediction of Split or Eye Formation

Manufactures traditionally check the split or eye formation in Swiss-type cheese by

tapping the surface of cheese and listening to the sound, or by cutting cheese into

halves, or taking a cylindrical sample by trier (Kraggerud et al., 2009). Non-destructive

methods have been mentioned and used in some studies. Rosenberg (1992) introduced a

non-destructive magnetic resonance imaging (MRI) method to evaluate eye formation in

Swiss-type cheese. Pastorino and others (2003) used scanning electron microscope

(SEM) images of cheese microstructure. An ultrasonic method was used by Eskelinen

and others (2007) to monitor cheese structure and detect eye and split formation. X-ray

computed tomography and X-ray images were used to evaluate eye formation by

Kraggerud and others (2009).

1.4 Fourier-transform Infrared Spectroscopy (FTIR)

1.4.1 History

Fourier-transform infrared (FTIR) is a simple, rapid and sensitive method for chemical compounds analysis. FTIR is a practical tool for qualitative and quantitative analysis. Several reviews of the FTIR history have been published (Christy et al., 2001;

Johnstono, 1991; Subramanian and Rodriguez-Saona, 2009). The discovery of infrared by Sir William Herschel opened the door of IR spectroscopy world. The measurement of infrared light was possible when Dr. Albert A. Michelson invited the interferometer around 1880 (Subramanian, 2009b). Fourier transformation was used to reduce time for interferometer conversion and spectra interpretation (Lin et al., 2009). Application of

FTIR was limited until it was improved by new instrumentation technology and fast computation after World War II. F James Cooley and John Tukey invented the fast-

Fourier transformation (FFT) (Cooley and Tukey, 1965) to decrease interferometer conversion time. Later, application of laser makes FTIR data repeatable and reproducible; the circular aperture enhances a higher radiation and successive scans of FTIR instrument helps to increase the signal-to-noise of spectra. FT system can be used in both near infrared (wavenumber 12,800-4000 cm-1) and mid infrared (4000-200 cm-1) ranges. The development of computing technology and sample techniques also helped to improve

FTIR methodology. For example, the applications of attenuated total reflectance (ATR) and mercury-cadmium-telluride (MCT) detector.

1.4.2 FTIR Spectrometer Mechanism

The FTIR spectrometer is based on the Michelson interferometer theory. An optical layout of a typical FTIR spectrometer is shown in Fig 1.6. The FTIR is consisting of IR source, laser, beam splitter, fixed mirror, moving mirror, laser detector and IR detector

(Rodriguez-Saona, 2011a). IR light is emitted from the IR source (lamp) and goes through an aperture wheel. After the bean direction being changed by a mirror, the IR

13 light meets the beamsplitter. The beamsplitter splits the IR light into two equal energy lights. One light goes through the beamsplitter and hits the fixed mirror, then it is reflected back to the beamsplitter. The other light is changed in direction to meet the moving mirror and then is reflected to the beamsplitter. The moving mirror changes its distance to the beamsplitter and makes optical path differences between two lights. Those two lights are interfered into one light again at the beamsplitter. The light is changed directions by mirrors and goes through the sample compartment and finally hits the defector.

Figure 1.3. Optical layout of a typical FTIR spectrometer (Subramanian and Rodriguez-Saona,

2009).

The laser also goes through the beamsplitter and be split into two light beams with equal energy and combined again after they hit the moving mirror and fixed mirror,

14 respectively. The laser is used as a reference light, which determines the sampling interval of the interferogram.

1.4.3 Principle of Attenuated Total Reflectance (ATR)

Attenuated total reflectance (ATR) is one of the widely used sampling techniques in chemical analysis. ATR extends the IR spectroscopy for solid and liquid samples analysis

(Rodriguez-Saona, 2011b). ATR is also popular in food analysis and quality control. For instance, it is used in dairy food adulteration, monitoring trans-fat in oil, testing water- soluble compounds and powder samples.

In theoretical conditions, when an IR light goes through from a crystal (high refractive index) to a sample (low refractive index) at a certain angle, all light waves are reflected back into the crystal. However, in fact, some amount of the light waves escapes the crystal surface within a small distance (Fig 1.7). This wave is called evanescent wave.

This phenomenon is called attenuated total reflectance. The evanescent wave is absorbed by the sample and is passed back to the IR light. The IR light then is detected by the detector and converted into infrared spectra.

Figure 1.4 Optical pathway of a multiple reflection ATR system. ( Source:

The commonly used material for the ATR crystal are zinc selenide (ZnSe), silicon, diamond and AMTIR. They have various spectral ranges and different refractive index and can be selected for different samples. However, ATR technique requires tight contact of sample on crystal because the evanescent wave only escapes within a small distance.

1.4.4 Principle of Reflection Fourier-transform Infrared Microscopy

The IR microscope increases the sensitivity and speed of detection and makes the microanalysis possible. It is a useful tool for identifying and characterizing chemical and biological samples (Subramanian and Rodriguez-Saona, 2009). Both transmission and reflectance can be used in IR microscope.

The optical diagram of IR microscope is shown in Fig 1.8. The IR microscope has a similar structure to optical microscopes (Katon, 1996). A flip mirror works to convert between the visible observation and IR spectroscope. When using the visible observation,

IR microscope is a regular optical microscope, helping identify and locate the sample.

When doing the IR spectroscope, the flip mirror turns to a certain angle and changes the

IR light direction to hit the sample. The IR microscopes used MCT detector, which requires pre-treatment of liquid nitrogen to maintain a high sensitivity.

Figure 1.5 Optical diagram of an infrared microscope used for infrared micro-spectroscopy

(Katon, 1996).

1.4.5 Advantages and Disadvantages of FTIR

FTIR has been widely used for its advantages; however, it still has limitations. The advantages and limitations of FTIR are summarized briefly in the following paragraphs

(Stuart, 2004, Smith, 1996, 1999; Johnston 1991, Subramanian, 2009).

FTIR has several advantages:

 Simplify sample preparation

 Requires a small amount of amount of samples;

 Non-destructive analysis;

 Simplicity, sensitivity and rapid detection;

 Less use of hazardous solvent and decreasing hazard to environmental and

human;

 Less analyzing time (high-throughput).

 FTIR can analyze several samples in a short time.

 FTIR also has the ability to apply in various sample analysis, such as, solids,

liquids, semi-solids and powder samples;

 Relatively low operational cost.

 It can be used for both quantitative and qualitative methods.

Although FTIR has several advantages, it still has some problems that limit its application in sample analysis. Firstly, most biological samples contain water, which has a strong absorption band that can mask certain important signals. Vacuum drying of the

18 sample is generally adopted to reduce the influence of water. Secondly, since most FTIR are single beam instruments, this makes FTIR spectra is sensitive to environments changes. Many factors can lead to the uncertainties in spectra. For instance, carbon dioxide and water vapor are two major factors that cause this problem. Because it is difficult to maintain an exactly same amount of carbon dioxide and water vapor in the testing environment when collecting the background spectrum.

FTIR is generally used to show functional groups or fingerprint for identification.

FTIR cannot detect atoms and monoatomic ions, elements, or inert gas. FTIR cannot detect diatomic molecules such as nitrogen and oxygen.

1.4.6 Application of FTIR in Cheese Analysis

FTIR, both near-infrared and mid-infrared, has been used in dairy food analysis for decades. Mid-infrared usually is used with an ATR accessory. FTIR has been used in different cheeses analyses, such as Cheddar, Emmental/Swiss-type cheese and blue cheese (Giangiacomo et al., 1979, Wehling & Pierce 1988, Picque 2002). It also has been applied to processed cheese analysis (Blazquez et al., 2004). FTIR has been used to determine cheese composition, relation to texture, sensory, shelf-life, quality control, seasonal variations and different regions (Blazquez et al., 2006, Chen et al., 1998,

Sultaneh and Harald 2007, Irudayaraj etal. 1999, Sinelli et al., 2005, Karoui 2004,2005,

Adamopoulos 2001, Sorensen and Jepsen 1998). The change of protein secondary structure during ripening by FTIR is also been studies in Cheddar and Swiss cheese

(Martin-del-Campo et al., 2009, Wang et al., 2011). The sampling technique of FTIR in cheese was improved by Koca and others in 2007, using a new water extraction sampling method. The cheese was powdered with liquid nitrogen and then mixed with water for extraction. Most fat and protein were removed by using chloroform and ethanol. The water extraction method has been coupled with ATR-FTIR in monitoring short-chain free fatty acids in Swiss-type cheese (Koca et al., 2007), correlation spectra data with Swiss- type cheese sensory (Koca et al, 2009), prediction composition and flavor in Cheddar cheese (Subramanian, 2009). Chemometric analysis methods, both quantitative and qualitative, have been used for data analysis.

1.5 Analysis of Infrared Spectra

Infrared spectra contains a large amount of information. It can be used to identify compounds and determine identities. A detail summary of infrared spectra interpretation has been published by Coats (2000). It mentioned interpretations of both pure compounds and practical samples. Simple rules and guidelines have been given for infrared spectra interpretation. However, simple interpretation is not enough to translate all the information contained by the infrared spectra. Chemometric analysis also commonly used to further interpretation infrared spectra. For spectroscopy, multivariate classification and calibration help with quantitative and qualitative analysis.

Soft independent modeling of class analogy (SIMCA) was introduced in 1980s

(Wold, 1975). It is a supervised classification model based on sample similarity. Infrared

20 spectra includes not only rich information from sample, but also a lot of variables. To simplify data sets and decrease analysis time, Principal component analysis (PCA) is used before SIMCA classification. PCA helps to transfer correlated variables into sets of uncorrelated (independent) principal components using linear combinations of the original variables (Lavine, 2000; Vogt et al., 1986). In SIMCA, each class is represented by a principal component (PC) model. To having a high signal-to-noise ratio, cross- validation is commonly used to optimize the number of PCs for each class. Residual variance of new sample is compared with the average residual variance of the PC class models. If it fits well with a PC class model, it is classified into this model (2001 infometrix). In the 3D class projection, if classes have a close average residual variance, they will lie close to each other.

SIMCA provides several ways to optimize the classification and review sample information. Interclass distance, Cooman’s plot, discriminating power, factors and loading for each class are commonly taken into consideration. Generally, interclass distance larger than 3 means a significant difference in involved classes (Kvalheim and

Karstand, 1992). Discriminating power shows a relation between wavenumber (which is associated with chemical functional groups) and its contribution for classifications. The higher the discriminating power is provided by a wavenumber, the more influence this wavenumber contributes to this classification. For those wavenumber ranges with low discriminating power value, they may be excluded from the dataset to reduce noise.

Chapter 2: Fourier-transform Infrared Methodology

2.1 Sample preparation for FTIR

The water extraction of Swiss-type cheese introduced by Subramanian was adopted and modified in this study. Fig 2.1 showed the sample preparation flow chart. About 30 gram of frozen cheese was shredded into powder, without using liquid nitrogen. Then, about 0.100 gram of the cheese power was weighed into a 1.5 ml centrifuge tube. For each tube, 0.5 ml of distilled water was added, and then mixed for 10 seconds using sonication (Sonic Dismembrator Model 100, Fisher, Pittsburgh, PA). The water soluble fraction was obtained by adding 0.5ml of chloroform and centrifuged for 3 minutes at

13000 rpm. Chloroform helps to remove the long-chain fatty acids. Two hundred ul of the supernatant was transferred to a new 1.5ml centrifuge tube and 200 ul ethanol was added. The new mixture was centrifuged at 13000rpm for 3 minutes. Ethanol helps to precipitate protein, extract slight non-polarity compounds and assist the fast evaporation of water. One hundred ul of supernatant was transferred to a new 1.5mL centrifuge tube, and stored at 0-4 degree C before FTIR analysis. Water extraction of each sample was made and tested within one week. The water extraction of cheese primarily contains amino acids, peptides, organic acids, short- and medium-chain fatty acids and their esters,

22 alcohols (Subramanian, 2009). This water extraction method contains both volatile and non-volatile compounds of cheese (Engels ea al., 1997, Aston and Creamer, 1986).

Figure 2.1 FTIR sample preparation flow chart.

2.2 ATR-FTIR Spectroscopy Conditions

The ATR-FTIR analysis of Swiss-type cheese water extracts was tested by an infrared spectrometer (Varian FTS-3500GX, Varian Inc., Randolph, MA). This infrared spectrometer was equipped with a potassium bromide beam splitter, a deuterated tri- glycine sulfate (DTGS) detector and a Zinc selenide (ZnSe) attenuated total reflection

(ATR) accessory (Pike Technologies, Madison, WI). Twelve ul of a Swiss-type cheese water extract was put on the ATR accessory and vacuum dried for 4minutes 30seconds before the FTIR scan. Spectra was collected from 4000 to 700 cm-1 , with a resolution of

8cm-1 and 64 scans were co-added per spectrum to enhance signal-to-noise ratio. The

Varian Resolution Pro (Version .0, Varinan Inc., Randolph, MA) was used to record all spectra.

2.3 Microscopy-FTIR Reflectance Conditions

The Swiss-type cheese water extract was tested by a Microscopy FTIR (Varian 600

UMA, Varian Inc., Randolph, MA). This infrared spectrometer was equipped with a potassium bromide beam splitter and a liquid nitrogen cooled HgCdTe (MCT) detector.

Five tenths of an ul of the Swiss-type cheese water extract was put on infrared slide and vacuum dried for 4mins 30seconds before making an FTIR scan. Spectra was collected from 4000 to 700 cm-1 with a resolution of 4cm-1 and 128 scans were co-added per spectrum to enhance signal-to-noise ratio. The Varian Resolution Pro (Version 4.0,

Varian Inc., Randolph, MA) was used to record all spectra.

2.4 Spectra Analysis

The FTIR spectra of Swiss-type cheese water extracts were collected in the mid- infrared region ( 4000 to 700 cm-1).To remove baseline shifts, improve resolution, and reduce the variability between replicates, the raw spectra were normalized, standardized and transformed to their 2nd derivatives (Kansiz et al., 1999, Rinnan et al., 2009). In this study, most of fat and protein were removed from samples by using chloroform and ethanol during sample preparation. Both water-soluble non-volatile and water-soluble volatile exists in the water extraction, such as, butyric acid, propionic acid, acetic acid, amino acid and peptides. Water soluble non-volatile compounds contribute to the taste of

24 cheese. Water, which may have a broad band around 1600cm-1, was also removed from sample by vacuum drying.

A typical raw spectra and its 2nd derivative were shown in Fig2.2. The region 4000 to

3100 cm-1 represents O-H stretching vibration of hydroxyl groups (Smith, 1999). The region 3100-2800cm-1 showed the asymmetric and symmetric stretching vibration of C-H groups in methylene groups of long-chain fatty acids (Coates, 2000). Several bands were found in the region 1800 to 900cm-1, which could include amino acids, carboxylic acids

(short chain fatty), peptides, organic acids and hydroxyl groups (Subramanian et al.,

2011). Generally for cheeses, region 1800-1710 cm-1 was associated with C=O stretching of carbonyl group esters (Koca et al.,2007, Rodriguez-Saona et al.,2006. Chen et al.,1998). Region 1698-1540cm-1 was related to amide I and II (Subramanian et al., 2009;

Rodriguez-Saona et al., 2006; Dufour, 2009). Absorption of region 1500-1250cm-1 was contributed from O-C-H, C-CH and C-O-H bending vibration. Those bending vibrations were associated with amino acids, aliphatic chain of fatty acids and amide III (Paradkar and Irudayaraj, 2002; Sicakesava and Irudayaraj, 2002; Barth, 2000; Dufour, 2009).

Amide III, usually appears around 1300 cm-1 (Dufour, 2009). Region 1200-800 cm-1 was associated with C=O and C-C stretching of organic acids (Irudayaraj and Tewari,

2003;Subramanian et al., 2011). In some cases, the wave number for a specific functional group may be assigned to a specific wave number. For instance, Barth (2000) mentioned amino acids had absorption in 1584cm-1, the stretching of S-O bands appeared in

1122cm-1 (Sun and Wang, 2006), aromatic nitrogen compounds showed up at 1543 and

1516cm-1(Subramanian et al., 2009). Details about functional groups will be discussed in subsequent chapters.

Figure 2.2 Raw representative spectra (top) and 2nd derivative spectral transformation (bottom) of

Swiss-type cheese water extraction by ATR-FTIR. AU=arbitrary unit. (Source of typical spectral

range information: Subramanian et al., 2011)

2.5 Multivariate Analysis

The spectra were exported as GRAMS(*.spc) files from Varian Resolutions Pro

(Version 4.0, Varinan Inc., Randolph, MA) and then were imported into Pirouette®

(Version 4.0, Infometrix Inc., Woodville, WA) for multivariate analysis. No pre- processing was done on the raw spectra. The spectra were directly transformed into their second derivative using normalization and 2nd derivative. Then, the spectra were analyzed by soft independent modeling of class analogy (SIMCA). SIMCA contains much statistical information for the samples. In this study, class projection, interclass distance and discriminating power (reported as wavenumber) were used to find the relation

26 between classification and functional groups of water soluble compounds. When interclass distance is larger than 3, there is a difference exists statistically (Kvalheim and

Karstand, 1992). Class projection and interclass distance were both used to identify classification in samples. Discriminating power helps to find functional groups that contribute largely to the classification.

Chapter 3: Monitoring Water Soluble Compounds of Swiss-type Cheese during

Manufacturing Using Attenuated Total Reflectance-Fourier Transform Infrared

Spectroscopy (ATR-FTIR)

3.1 Abstract

Splits and cracks cause large economic loss for the Swiss cheese industry. This defect is usually formed during the cold room storage. Over production of gas and loss of rheological properties have been reported to cause splits defect in Swiss cheese. This study focused on monitoring the water soluble chemical compound profiles of Swiss cheese during making using attenuated total reflection Fourier transform infrared (ATR-

FTIR). Ten vats of Swiss cheese were sampled out of press, out of pre-cool, out of warm room and at cut stages. A water extract of each sample was made by using chloroform and ethanol and then vacuum dried on a triple-reflection ZnSe crystal mounted on an attenuated total reflectance accessory. Four spectra were collected for each sample from

4000 to 700 cm-1 with a resolution of 8cm-1 and 64 scans were co-added per spectrum to enhance signal-to-noise ratio. All spectra were analyzed using soft independent modeling by class analogy. ATR-FTIR analysis of water extraction of Swiss-type cheese in different four stages showed good correlation with manufacturing information for this factory. Variability of cheeses also appeared from vat to vat and from day to day. The

ATR-FTIR analysis water extraction of Swiss-type cheese can monitor compound changes during manufacturing and can be used as rapid non-destructive analysis method for cheese quality control. Protein degradation may contribute most to split formation in

Swiss-type cheese. Band 1639- 1612 cm-1, 1158-1151cm-1, 1485-1493cm-1, which belong to the amide I and II of peptides, might be characteristic bands for split formation.

Lactose degradation products help to form fatty acids and organic acids and may also be factors for split formation. Additionally, the variability in vats and batches had effect on the split defect.

3.2 Introduction

Fourier transform infrared spectroscopy has been used in dairy food analysis for decades. It is a rapid, simple and high sensitive instrument. Multivariate analysis is a powerful statistic tool, which is linked to help infrared data analysis. The sampling technique for FTIR in cheese was updated by Koca and others in 2007, using a new water extraction sampling method. Most fat and protein were removed by using chloroform and ethanol. The water extraction method has been coupled with ATR-FTIR in monitoring short-chain free fatty acids in Swiss-type cheese (Koca et al., 2007), correlation spectra data with Swiss-type cheese sensory (Koca et al, 2009), prediction composition and flavor in Cheddar cheese (Subramanian, 2009). It is a possible tool to detect compound change during the Swiss-type cheese manufacturing and may be a potential method to detect split formation or find split formation indicators.

Variability is one problem in Swiss cheese manufacture. It appears from day to day

(batch to batch) or from vat to vat. Variability largely influences the quality of Swiss cheese and cause cheese down-grading and economic loss to manufactures. Factors, such as milk supply, starter cultures and non-starter microorganisms, may lead to variability.

Splits are an undesirable opening in Swiss-type cheese. It usually happens in the cold room storage during manufacture. Multiple factors may lead to split defect formation in

Swiss-type cheese. Secondary fermentation, the solubility of carbon dioxide, texture properties changes, temperature, proteolysis, fat crystallization, milk, non-starter lactic bacteria, interactions between starters and other gas production may contribute to the split formation. Additionally, it is difficult to predict or detect the split formation.

In this study, ATR-FTIR was used to monitoring variability of Swiss-type cheese by using its water soluble extract during manufacturing. It was also used to find indicators for split formation in defective at cut cheeses.

3.3 Material and Method

3.3.1 Swiss-type Cheese Samples

All 40 cheese samples were obtained from one Swiss-type cheese manufacturer. Ten different vats of Swiss cheese were sampled out of press, out of pre-cool, out of warm room and at cut stages. Cheese samples were vacuum-packed and frozen. The sampling procedure was shown in Fig 3.1. All cheeses at time of cutting had blind and eye areas.

However, only three of the 10 at cut cheeses were found with splits/cracks defect when cutting the cheese block into 2 cm slides.

warm

room

cold

room

Figure 3.1 Sampling stages in Swiss-type cheese manufacturing.

For out of press, out of pre-cool, out of warm room stages, trier samples were taken from each vat in each stage and sent for analysis. For the at cut stage, 8 lb. vacuum- packaged cheese blocks were received for each vat. Every cheese block was cut into 2cm slices to find split areas. Samples were taken form blind, eye and split areas (if the cheese had a split area) for each vat and stored at freezer before analysis. (Fig 3.2)

Figure 3.2. Sampling for FTIR.

Three of out the ten at cut cheeses were found with split defect. Information of vat and split are shown in Table 3.1.

Table 3.1 Vat and split information for at cut cheeses.

Making Oct.18.2012 Oct.22.2012 Date

At cut Vat1 Vat3 Vat5 Vat7 Vat9 Vat1 Vat3 Vat5 Vat7 Vat9

Split • • Y • • • Y Y • •

Y- Split was found in cheese;

• - No split was found in cheese. 32

3.3.2 Methods

The method of extraction, FTIR and multivariate analysis were the same as those described in Chapter 2.

For out of press, pre-cool and out of warm room cheeses, each cheese had 3 extracts without area specification, while for the at cut stage cheese, one cheese may have six or nine extracts depends on if it has a splits area. Four spectra were collected for each sample. A total of 212 spectra (10 vats cheeses * 3stages * 4spectra+7 vats cheeses *

1stage * 2 areas * 4spectra+3 vats cheese * 1stage * 3areas * 4specta) were collected for all 40 individual cheeses.

3.4 Result and Discussion

3.4.1 Classification of All Four Manufacturing Stages of Swiss-type Cheese

Region 1800-900 cm-1 was used to build a soft independent modeling of class analogy

(SIMCA) classification based on four different manufacturing stages of Swiss-type cheese. For all 10 cheeses as a group, Fig3.3 showed that samples from out of press and pre-cool were very different from samples from out of warm room and at cut. This difference occurred after the first two stages (out of press and pre-cool), because the cheese went through propionic acid fermentation during the warm room storage. The propionic acid fermentation converted the lactate/lactic acid into other compounds (eg. propionic acid, acetic acid, carbon dioxide, pyruvic acid) and helped to form eyes in the cheese loaf. The warm room storage largely changed the cheese components and texture.

Out of warm room samples and at cut samples were also located close to each other, but they had larger overlap than the cheeses out of press and after pre-cool. After the warm room, cheese was transferred into a cold room. Cheese temperature decreased from

21°C to about 1°C, which slowed down the activities of microorganisms and enzymes.

The interaction changes in the cheese were limited when the cheese were in the old room.

Additionally, out of warm room samples and at cut samples showed broader clusters than the first two stage samples. This indicated variation began to appear in samples and fermentation interactions were difficult to control.

Figure 3.3 Soft independent modeling of class analogy (SIMCA) class projections showing four manufacturing stages of Swiss-type cheese.

Interclass distance values were also calculated by SIMCA to identify difference among the four manufacturing stages. In Table 3.2, all interclass distance values were

34 larger than 3, except out of press samples vs. pre-cool samples and out of warm room samples vs. at cut samples. Out of press samples vs. pre-cool samples and out of warm room samples vs. at cut samples were statistically the same, while other comparisons indicated there were significant difference in the two classes. However, both out of press samples vs. pre-cool samples and out of warm room samples vs. at cut samples showed interclass distance close to 3, which might suggest potential differences in samples. In all, the interclass distance values have shown the same results as for class projection.

Table 3.2 Interclass distances of four manufacturing stages in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese.

Sample stage Out of press Pre-cool Out of warm room At cut

Out of press 0.00

Pre-cool 2.63 0.00

Out of warm room 17.93 22.64 0.00

At cut 18.98 24.15 2.43 0.00

This discriminating power plot for classification of four manufacturing stages of

Swiss-type cheese was shown in Fig A.1. in Appendix A. The major discriminating wavenumbers and these associated functional groups were summarized in Table 3.3. The major wave bands were1535, 1474, 1423 cm-1 that contributed to this classification. They were associated with amino acids and short chain fatty acids. Amino acids came from protein degradation and fatty acids arose primarily from the formation of acids related to

35 lactose degradation. This indicated the protein degradation and lactose degradation dominated the changes during manufacture.

Table 3.3 Summary of discriminating wavenumbers and associated functional groups of classifications of four stages Swiss cheeses.

Discriminating Functional groups Compounds wavenumber(cm-1)

O-C-H, C-CH and C-O-H 1535, 1474, 1423 Amino acids and fatty acids bending vibration

Overall, ATR-FTIR analysis of water extracts of Swiss-type cheese at four different stages showed good correlation with manufacturing information. ATR-FTIR could be a useful tool to rapid monitoring water soluble compounds changes and might be used for cheese quality control.

3.4.2 Classifications of Individual Cheeses at Each of the Four Stages

Region 1800-900 cm-1 was also used to build a soft independent modeling of class analogy (SIMCA) classification based on individual cheeses at each of the four stages of

Swiss-type cheese. For at cut stage, only non-defective cheeses were included. In Fig

3.4a, ten out of press cheeses formed two separate clusters to show that they were total different and had variability. After going through the pre-cool stage (Fig 3.4b) and out of

Figure 3.4 Soft independent modeling of class analogy(SIMCA) class projections showing the individual non-defective cheeses at each of the four manufacturing stages of Swiss-type cheese

(Batch Otc.18.2012: Red-Vat1, Black-Vat2, Pink-Vat3, Purple-Vat4, Light Green-Vat5; Batch

Otc.22.2012:Deep Blue-Vat1, Brown-Vat3, Deep Green-Vat5, Yellow-Vat7, Light Blue-Vat9).

warm room stage, as shown in Fig 3.4c, most of the ten cheeses still separated from each other, but some cheeses were similar and had overlaps in their clusters. The at cut non- 37 defective cheeses showed good differentiation in Fig 3.4d, which indicated the cheeses were different from each other in the final stage and had more variability.

Data in Table 3.4, 3.5, 3.6 and 3.7 showed the interclass distance values of the individual cheeses at four stages in SIMCA, respectively. If interclass distance value was larger than

3, it meant there was a difference in samples (Kvalheim and Karstand, 1992). Out of press stage, pre-cool stage and at cut stage showed all interclass distance values were larger than 3, while out of warm room stage showed most of the values were larger than

3. Interclass distances showed more differences in samples at each stage than in the class projection. This may because the class projection was only a 3D picture of different classes, which might not enough to show the difference among ten samples. Overall, the majority of interclass distances values in the four tables were larger than 3, which meant those individual cheeses were different from each other in each stage. This indicated variability generally existed in samples from vat to vat.

Table 3.4 Interclass distances of ten non-defective cheeses at out of press stage in soft independent modeling of class analogy (SIMCA)

classification of Swiss-type cheese.

Oct. 18.2012 Batch Oct.22.2012 Batch

Vat Numb. 1 3 5 7 9 1 3 5 7 9

1 0.00

3 15.73 0.00 Oct.18.2012 5 5.39 14.16 0.00 Batch

39 7 44.38 14.64 51.71 0.00

9 7.77 11.98 7.59 32.47 0.00

1 35.57 9.79 38.79 5.52 26.47 0.00

3 25.91 10.45 27.54 27.30 14.29 21.57 0.00 Oct.22.2012 5 42.34 23.69 44.34 16.52 37.09 18.47 33.63 0.00 Batch 7 36.45 22.55 37.09 17.04 33.74 18.61 31.27 4.84 0.00

9 14.38 10.02 14.27 7.86 14.55 7.70 13.77 6.52 5.38 0.00

Table 3.5 Interclass distances of ten non-defective cheeses at pre-cool stage in soft independent modeling of class analogy (SIMCA)

classification of Swiss-type cheese.

Oct. 18.2012 Batch Oct.22.2012 Batch

Vat 1 3 5 7 9 1 3 5 7 9 Numb.

1 0.00

3 11.18 0.00 Oct.18.201 5 15.60 24.85 0.00

40 2 Batch 7 29.66 41.46 23.19 0.00

9 12.30 7.62 19.74 28.14 0.00

1 16.39 12.68 22.58 36.81 13.21 0.00

3 15.19 6.32 24.35 38.40 8.88 7.96 0.00 Oct.22.201 5 19.00 21.88 14.73 14.69 17.58 19.99 22.04 0.00 2 Batch 7 22.43 12.81 41.18 58.55 4.60 19.38 11.69 23.98 0.00

9 15.16 10.48 27.67 37.13 5.11 14.41 11.09 17.59 4.45 0.00

Table 3.6 Interclass distances of ten non-defective cheeses at out of warm room stage in soft independent modeling of class analogy (SIMCA)

classification of Swiss-type cheese.

Oct. 18.2012 Batch Oct.22.2012 Batch

Vat Numb. 1 3 5 7 9 1 3 5 7 9

1 0.00

3 3.13 0.00

Oct.18.2012 Batch 5 10.34 43.28 0.00

41 7 4.38 3.27 36.70 0.00

9 9.95 48.42 29.58 35.55 0.00

1 5.74 7.71 4.43 9.86 2.78 0.00

3 11.66 22.50 14.05 24.96 5.72 3.86 0.00

Oct.22.2012 Batch 5 19.26 24.49 20.05 28.21 18.99 10.66 14.74 0.00

7 25.71 50.08 42.44 49.75 41.33 13.97 23.74 3.37 0.00

9 26.10 53.92 45.80 52.10 44.87 13.92 24.97 2.54 3.07 0.00

Table 3.7 Interclass distances of seven non-defective cheeses at cut stage in soft independent modeling of class analogy (SIMCA)

classification of Swiss-type cheese.

Oct. 18 2012 Batch Oct. 22.2012 Batch 1 3 7 9 1 7 9 Vat Numb. 1 0.00

Oct.18.2012 Batch 3 7.59 0.00

7 6.26 15.62 0.00

42 9 14.20 14.80 21.24 0.00

1 4.37 9.91 9.65 13.77 0.00

Oct.22.2012 Batch 7 16.58 13.92 13.12 12.53 16.04 0.00

9 21.36 22.48 25.91 15.75 19.22 4.59 0.00

Attention was also given to batch difference. The interclass distance of the same vat samples manufactured in two batches were summarized in Table 3.8. Those values were taken from Table 3.4, 3.5, 3.6 and 3.7.

Table 3.8 Summary of interclass distance of the same vat manufactured in two batches.

Out of press Oct. 18.2012 Batch Vat Numb. 1 3 5 7 9 1 35.57 3 10.45 Oct.22.2012 5 44.34 Batch 7 17.04 9 14.55

Pre-cool Oct. 18.2012 Batch Vat Numb. 1 3 5 7 9

1 16.39 3 6.32 Oct.22.2012 5 14.73 Batch 7 58.55 9 5.11

Out of warm Oct. 18.2012 Batch room Vat Numb. 1 3 5 7 9 1 5.74 3 22.50 Oct.22.2012 5 20.05 Batch 7 49.75 9 44.87

At cut Oct. 18.2012 Batch Vat Numb. 1 3 5 7 9 1 4.37 3 - Oct.22.2012 5 - Batch 7 13.12 9 15.75 - means no available data for interclass distance.

The data in Table 3.8 showed all the interclass distance values of the same vats at four stages in two bathes were larger than 3. Besides the vat had split defect, this indicated that all vats went through different changes during making and produced totally different cheeses in the two batches. There was a batch to batch or day to day variability in this factory.

Although the FTIR patterns were generally different for each individual cheese, when they were taken together as a group, they could be differentiated on the basis of the step of manufacture (Fig. 3.3 and Table. 3.2). This suggests that the vat and date of manufacture did not influence the results for the cheeses from this factory.

The discriminating wave numbers were different for the those individual cheeses at each stage. These discriminating power plots for classification were shown in Fig. A.2,

A.3, A.4 and A.5 in Appendix A. The discriminating wavenumbers and these associated functional groups are summarized in Table 3.9.

Table 3.9 Summary of discriminating wavenumbers and associated functional groups of classifications of the individual non-defective Swiss cheeses in each stage.

Discriminating Stage Functional groups wavenumber(cm-1)

Out of press 1061 C=O and C-C stretching of organic acids

Pre-cool 1061, 1042, 988 C=O and C-C stretching of organic acids

O-C-H, C-CH and C-O-H bending vibration of 1,454 amino acids and fatty acids

Out of warm O-C-H, C-CH and C-O-H bending vibration of 1493, 1431, 1358 room amino acids and fatty acids

1196, 1123 C=O and C-C stretching of organic acids

1612 Amide I, II

O-C-H, C-CH and C-O-H bending vibration of At cut 1420, 1346 amino acids and organic acids

For the out of press stage, the variability of ten cheeses came from organic acids and

Lactobacillus dominated the acid production during the press. However, acid production rate, nonstarter microbial growth and milk conditions also influenced the organic acid productions and increased variability in the samples.

For the pre-cool stage, the acid major band was still 1061cm-1, while wave number

1454cm-1 also appeared. The variability of this stage came from both organic acids and

45 amino acid and fatty acids. However, organic acids contributed more. Both acid production and enzymes activity were responsible for variability in the pre-cool stage.

For out of warm room stage, amino acids and fatty contributed most to the variability.

Organic acids also contributed and the amide zone had limited effect. The major wavenumber shift from organic acids zone to amino acids and fatty acids zone and amide product appeared, which indicated compound degradation went further during continued ripening. Other organic acids were also produced and caused wavenumber shift. A lot of changes went on during the warm room and imported multiple factors to cause variability in the samples.

For the at cut stage, the variability of the seven non-defective cheeses primarily came from amino acids and fatty acids. Low temperature limited the growth of microorganism, but enzymes were still active. Because of nonstarter microorganism, milk supply, rate of difference bio-interactions, variability also appeared in this stage.

Overall, the shifts of major wavenumbers were correlated with the changes that happened in the Swiss cheese making. However, variability still appeared to differentiate cheeses in different stages. This indicated the fermentation interactions were difficult to control, from vat to vat and day to day. Multi-factors could lead to variability within vats and days, such as milk supply, nonstarter microorganism, using different starter cultures and equipment cleaning conditions.

3.4.3 Classification of At Cut Swiss-type Cheese Based on Blind, Eye and Split Areas

There were 3 samples (Vat 5-Oct.18.2012 and Vat3, Vat5-Oct.22.2012) found with a split defect in the at cut stage, SIMCA classification was done based on blind, eye and split areas for all 3 defective cheeses.

In Fig 3.5, SIMCA classification was based on different areas for all 3 defective cheeses as a group. Clusters of blind, eye and split areas had overlap of each other, which meant the areas might be similar in water extracted compounds. Interclass distance values of this classification in Table 3.10 were larger than 3. This indicted the three areas were statistically different, but they were not markedly different. Additionally, from the data in

Fig 3.6, the overall discriminating power of this classification was low.

Figure 3.5 Soft independent modeling of class analogy (SIMCA) classification projections showing blind, eye and split area of the group of three defective at cut Swiss-type cheeses considered together.

Table 3.10 Interclass distances of different areas in soft independent modeling of class analogy

(SIMCA) classification of the group of three defective Swiss-type cheeses.

Figure 3.6 Discriminating power plots for classification of different areas of three defective

Swiss-type cheeses.

However, those 3 defective cheeses belonged to two batches and were made in different vats (Vat 5-Oct.18 2012 and Vat 3, Vat5-Oct.22.2012), SIMCA classification was also done to classify blind, eye and split areas for three defective cheeses, respectively. In Fig 3.7, SIMCA classification projections of Vat 5-Oct.18 2012 batch

48 and Vat 3 and Vat 5-Oct.22.2012 batch showed blind, eye and split areas located away from others, showing definite differences.

Figure 3.7 Soft independent modeling of class analogy(SIMCA) classification projections showing different areas of defective Swiss-type cheese (a.-Vat 5, Oct.18.2012; b.- Vat 3,

Oct.22.2012; c. -Vat 5, Oct.22.2012).

Interclass distances values of these two classifications (Table 3.11) were all largely than 3, which indicated significant difference in blind, eye and split areas in each cheese.

Table 3.11 Interclass distances of different areas in soft independent modeling of class analogy

(SIMCA) classification of defective Swiss-type cheeses (a.Vat 5, Oct.18.2012; b. Vat 3

Oct.22.2012; c. Vat 5 Oct.22.2012).

For Vat 5-Oct.18 .2012 (Fig.3.8a), band 1612, 1558, 1489, 1431 and 1134 cm-1 primarily contributed to it areas classification. 1612, 1558 and 1489 cm-1 were associated with amide I and II of peptides; 1431 and 1354 cm-1 were related with amino acids and fatty acids; 1134cm-1 was the C=O and C-C stretching of organic acids. A small peak at

1686 cm-1 also contributed to this classification. The peptide and amino acid patterns indicated more protein degradation went on in the split area. Amino acid is also known to stimulate the growth of propionic acid bacteria in secondary fermentation of Swiss cheese and lead to the over production of carbon dioxide (Daly et al., 2009). The fatty acids

50 probably arise from the formation of acids related to lactic acid degradation and their formation could be associated with over gas production.

For Vat 5, Oct.22.2012 (Fig. 3.8c), bands 1612, 1558, 1493 cm-1 primarily influenced the areas classification. They were associated with amide I and II. 1435 and 1350 cm-1, which belonged to the amino acids and fatty acids, contributed to the classification. Band

1118 cm-1 located in the organic acids range also had effect. Limited influence of fatty acids ester ( 1709 cm-1) appeared in this vat.

For the two batches cheeses of Vat 5, their discriminating power plots shared similar bands for classification of blind, eye and split areas. Those bands were associated with amide I, II (1616- 1612 cm-1, 1158-1151cm-1, 1485-1493cm-1) , amino acids and fatty acids ( 1435-1413 cm-1 and 1354-1350 cm-1) and organic acid s (1134-1118 cm-1). Small band in fatty acids ester range both appeared.

However, for Vat3, Oct.22.2012 (Fig.3.8b), its discriminating power was different from that of Vat 5. Bands 1639, 1616, 1551 and 1485cm-1 were the major bands for Vat

3’ area differentiation. They were associated with amide I and II compounds. Limited influence came from other compounds, like amino acids and fatty acids (1404, 1385 cm-

1) , fatty acids ester (1732 cm-1) and organic acids (1177 cm-1).

Figure 3.8 Discriminating power plots for classification of different areas of defective Swiss-type cheeses (a.Vat 5, Oct.18.2012; b. Vat 3 Oct.22.2012; c. Vat 5 Oct.22.2012).

Overall, for all three defective cheeses, protein degradation (amide I, II compounds) primarily contributed to blind, eye and split areas differentiation. This indicted the protein degradation was important for split formation in Swiss cheese. The specific bands associated with amide I and II appeared at 1639- 1612 cm-1, 1158-1151cm-1, 1485-

1493cm-1. Limited influence came from other compounds, like amino acids and fatty acids, fatty acids ester and organic acids. They could be related with lactic acid degradation. Additionally, when combining 3 defective cheeses, areas differences did not show up. This may suggest that variation between different vat and batch might influence the split defection.

3.5 Conclusion.

ATR-FTIR analysis water extraction of Swiss-type cheese can monitor compounds changes and variability during manufacturing and can be used as rapid non-destructive method for cheese quality control. Standardization of materials and good manufacturing practice (GMP) might help to limit variability in cheese making. Protein degradation may contribute most to split formation in Swiss-type cheese. Band 1639- 1612 cm-1, 1158-

1151cm-1, 1485-1493cm-1, which belong to the amide I and II of peptides, might be characteristic bands for split formation. However, data from different batches and vats suggest different factors may be involved in split formation in the same factory at different times and in different vats. Lactose degradation products were associated with fatty acids and organic acids formation and may also contribute to split formation. The variability between different vat and batch might influence the split defection.

Chapter 4: Variability in Swiss-type Cheese from Five Factories by Attenuated

Total Reflectance Fourier Transform Infrared (ATR-FTIR) Spectroscopy

4.1 Abstract

Variability in the quality of Swiss-type cheese continues to be a problem in industry.

In order to determine the sources of variability in Swiss-type cheese, the water soluble compounds of Swiss-type cheese were tested by attenuated total reflectance-Fourier transform infrared spectroscopy. Each of the 5 different Swiss-type cheese factories provided 3 cheeses for this study. A water extract of each cheese was made by using chloroform and ethanol and then vacuum drying on a triple-reflection ZnSe crystal mounted on an attenuated total reflectance (Pike Technologies, WI) accessory. Four spectra were collected for each sample from 4000 to 700 cm-1 with a resolution of 8cm-1 and 64 scans were co-added per spectrum to enhance signal-to-noise ratio. All spectra were analyzed using soft independent modeling by class analogy (SIMCA, Pirouette 4.0,

Infometrix, Inc, WA). The SIMCA was done to classify 5 factories cheeses and to differentiate cheeses within each factory. Result showed variability in the FTIR patterns for the 5 factories, which was associated with amino acids, fatty acids and organic acids.

Variability was also achieved within 4 of the 5 factories, respectively. The cheeses from each of the 4 factories had its own profile. This was part of a larger study, in which these

54 same cheeses were evaluated for flavor by a consumer panel and by a trained descriptive flavor panel. Based on Taylor’s (2013) study of the sensory flavor difference in the same cheeses from the same factories, it was possible to state the FTIR may be used to differentiate flavor difference among the cheeses from these five factories.

4.2 Introduction

Swiss-type cheese quality is mainly influenced by flavor, body and appearance.

According to the United State cheese grading system, developed by ADSA, flavor contributes about 45% to the score for cheese grading. The variability of flavor is an important problem for cheese quality control for a factory or the whole industry.

However, many factors may affect flavor formation, even cause flavor variability in cheese. The sources of flavor variability need to be found and possible actions need to be taken for cheese quality control.

The water soluble fraction of cheese is a good way to study cheese flavor and many researches have focused on this methodology. (Biede and Hammond 1979; Cliffe et al.,

1993; Fox et al., 2000; Salle et al., 2000; Koca et al., 2007, 2009; Subramanian et al.,

2009, 2011). GC-MS, Urea-PAGE, HPLC and infrared spectroscopy have all been used to study the water soluble fraction of cheese (Fox et al., 2000b). These studies have shown that the water soluble fraction contained both volatile and non-volatile flavor compounds. Volatile compounds played a more important role in aroma than in taste; whereas the non-volatile contributed most to taste and taste intensity in cheese (McGugan

55 et al., 1979). Amino acids, fatty acids, organic acids and peptides were the common compounds in the cheese water soluble fraction. Sensory tests also been used to identify water soluble compounds associated with flavor.

FTIR has been used previously in this department to associate flavor with FTIR patterns in both Cheddar (Subramanian et al., 2009) and Swiss cheese ( Koca et al.,

2009). This investigation is part of a larger study, using the same 15 cheeses from the same factories to evaluation flavor quality by consumer and descriptive sensory methods and odor activity using SIFT-MS (Taylor, 2013)and microbial populations (Gardner and

Ji, 2013). Taylor found an association between the sensory flavor studies and the odor activity values. Therefore, effects on flavor will referenced throughout in this thesis.

4.3 Materials and Methods

Fifteen cheeses were used in this study. Three cheeses were obtained from each of the

5 factories (factory codes: 148, 207, 374, 465 and 528). All cheeses were received in vacuum sealed plastic wrapped packages and were stored at 0 - 4 °C before analysis.

The methods for sample preparation, spectra analysis and multivarient analyses were the same as those described previously in Chapter.2. FTIR classifications were evaluated for the cheese from the five factories and for the 3 chesses within each factory.

Four spectra were collected for each cheese. A total of 60 spectra (15 cheeses *

4spectra) were collected for all 15 cheeses.

4.4 Result and Discussion

4.4.1 Classification of Swiss-type Cheese Based on Factories

The spectral region 1800-900 cm-1 was found to contain information of cheese variability and was used to build this classification as reported by Subramanian (2009).

Soft independent modeling of class analogy (SIMCA) was used to classify cheese based on different factories. Fig. 4.1 shown that the water extraction cheese samples formed five separated clusters, which indicated water soluble components of the cheeses from of the five factories were different from each other.

Figure 4.1 Soft independent modeling of class analogy (SIMCA) class projection ((Brown-

Factory 148; Red-factory 207; Green-factory 374; Pink-factory 465; Blue-factory 528). 57

The data showed good differentiation of the cheeses from the 5 factories, showing all the interclass distances values among five factories were all larger than 3(Table 4.1). This indicated that there was variability in the FTIR spectra and Swiss-type cheeses from the different factories.

Table 4.1 Interclass distances among five factories in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese.

Factory code 148 207 374 465 528

148 0.00

207 9.41 0.00

374 7.53 9.52 0.00

465 13.66 7.90 14.39 0.00

528 14.18 11.21 17.71 8.50 0.00

Fig 4.2 showed the discriminating power of the wavenumbers that contributed to the classification among the five factories. The higher the discriminating power of a particular wavenumber, the more this wavenumber contributes to the classification. The wavenumber is associated with functional groups. Some wavenumber that contributed to the classification of the five factory Swiss-type cheese samples were highlighted in

Fig.4.2. Wave numbers 1427, 1362, 1180 and 1119 cm-1 were primarily responsible for this differentiation. Wave numbers 1427 and 1362 cm-1 were associated with amino acids and fatty acids. The 1427 and 1473 cm-1 were also reported as the C-N bending 58 vibration and -CH2 stretching of proline in a water solution (Barth, 2000). Proline may contribute to the sweet taste of Swiss-type cheese. The wave numbers 1180 and 1119cm-1 were associated with C=O and C-C stretching of organic acid (Coats, 2000). Wave numbers 1528, 1572, 1681 and 1693 cm-1 were associated with amide I and amide II, but their contribution to classification was less than those associated with proline and other amino acids or organic acids.

Figure 4.2 Discriminating power plot for classification of Swiss-type cheese flavor among five factories.

Overall, there was variability among the cheeses from these five factories Swiss cheese. Taylor (2013) found a correlation between odor activity values and flavor for the differentiation of the cheese from the five factories. This study showed a correlation between FTIR wave numbers and the differentiation of the cheeses from the five factories. Previously, Subramanian and others (2009) and Koca and others (2009) have 59 also reported good correlation between FTIR spectra and sensory evaluation (grading and descriptive sensory). Therefore, it may be stated that FTIR classification is also associated with flavor differentiation.

Amino acids, fatty acids and organic acids primarily contributed to this variability in the wave numbers for the cheeses of the five different factories. Amino acids come from protein degradation; short chain fatty acid and organic acids come from the formation of acids related to lactose degradation. Those interactions could be related to a number of factors, including differences in starter cultures (eg. species and ratio), hygiene conditions

(Non-starter lactic bacteria, cleaning agent residue), variable milk supply and differences of time in warm room and cold room storage.

The variability in the FTIR patterns of the cheeses from the five factories may also be related to the fact that the cheeses from these factories were different in ages. (Table 4.2), as shown in Table 4.2

Table 4.2 Manufacturing data of five factories cheese.

Overall, differences in processing conditions and milk may also have contributed to the Swiss-type cheese FTIR variability among five factories. Standardization of processing conditions and cheese milk may help to decrease the flavor variability for the industry.

4.4.2 Classification of Swiss-type Cheese within Each of the Five Factories

SIMCA was also done for the three cheeses from within each factory to show if there was variability within a factory. Fig 4.3 showed that the class projections for the three cheeses from each factory gave3 separated clusters for each factory. This strongly suggested that there was a flavor variability for cheeses within each factory.

Figure 4.3 Soft independent modeling of class analogy (SIMCA) classification projections showing the difference of Swiss-type cheese within each factory. (3 cheeses were involved for each factory; a.-factory 148; b.-factory 207; c.-factory 374; d.-factory465; e.-factory 528) 62

For four factories (148, 207,374 and 528), all interclass distances values for the 3 cheeses in each factory (Table 4.3) were larger than 3. This meant that the 3 cheeses from each factory had different FTIR profiles.The sensory data supported that there was flavor variability between the three cheeses from each of these 4 factories (Taylor, 2013). This could be because of difference in milk supply and nonstarter microorganism and other processing differences in each of these factories. For the fourth factory (465), it did not show consistent differences between the three cheeses in respect to class differences.

Cheeses 465-1 and 465-2 had a class distance of <3, whereas the difference between

4565-1 and 465-3 was >3. This indicates that there was slightly less variability in factory

465 than in the other four factories

Table 4.3 Interclass distances of 3 cheeses within each factory in soft independent modeling of class analogy (SIMCA) classification of Swiss-type cheese flavor.

The discriminating wave numbers were different for the cheese from each factory.

These wave number differences were shown in Fig. B.1, B.2, B.3, B.4 and B.5. in 63

Appendix B. The discriminating wave numbers and these associated function groups were summarized in Table 4.4.

Table 4.4 Summary of discriminating wave numbers and these associated function groups for five factories Swiss-type cheese Discriminating Factory Functional groups wavenumber(cm-1)

148 1628, 1573 Amide I, II 1284 Amide III

C=O, C-C stretching of organic acid or 207 1122 the stretching of S-O bands

374 1608, 1539 Amide I, II 1126 C=O, C-C stretching of organic acid 1740 C=O stretching of carbonyl group esters

465 1681 Amide I, II

528 1573 Amide I, II O-C-H, C-CH and C-O-H bending vibration of amino acids 1416 and fatty acids 1748 C=O stretching of carbonyl group esters

For factory 148, compounds that had amide I and II structures played an important role in its classification or variability in cheeses. Those compounds might be peptides or other protein degradation compounds. Factory 207, organic acid productions caused the variability in cheese. Factory 374, both amide I and II compounds and organic acids 64 contributed. Factory 528, amino acids and fatty acids, ester of fatty acids leaded to variability in 3 cheeses. Factory 465 cheeses only showed a potential to have variability, which might come from the peptides. This variability came from different biochemical interactions (eg. protein degradation, acid production, amino acids catabolism) and the different degree that those interactions went on. Overall, different factory has different reasons for its variability.

With the support of sensory test, FTIR spectra could help to study cheese flavor

(Subramanian 2009; Subramanian et al., 2009; Koca et al., 2009). Descriptive sensory test of the same cheese had also been done (Taylor and Harper, 2013). It showed different flavor variability within each factory, which was correlated or supported the FTIR result.

In this five factories study, the water extracts of Swiss cheese showed different variability within factory might suggest there were particular flavor variability for each factory.

Biochemical interactions, such as protein degradation, acids formation, happened differently in each cheese in a factory.

Although each factory kept using its own processing conditions for cheese making, some factors (eg. changes of milk supply for each batch, non-starter bacteria, cleaning agent residue, cool rate of temperature of each cheese) were difficult to control and still caused flavor variability within a factory. Factors like milk components, non-starter bacteria and equipment cleaning contributes to flavor variability within a factory and within the whole industry. Cheese processing conditions varies in different factories was

65 also important for variability with in industry. Having less flavor variability in one factory was essential to keep its cheese quality and consumers.

Standardization of cheese milk components and good manufacturing hygiene conditions might help to maintain a stable cheese quality. To decrease flavor variability among different factories, standardization of processing was also needed. However, this action might cause loss of diversity of Swiss-type cheese flavors and provided fewer options for consumers. In aspect of the whole industry, it was important to keep cheese in the same quality for a factory; however, it was critical to keep less flavor variability among different factories.

4.5 Conclusion

The FTIR was a useful tool to monitor variability of Swiss cheese from the five different factories. Differentiation was obtained for the FTIR patterns for these cheeses from each of the five factories and for the three cheeses from each of the five factories.

Important discrimination wave numbers were different for each factory and indicated different causes for its variability. Based on Taylor’s (2013) study of the sensory flavor difference in the same cheeses from the same factories, it is possible to state the FTIR may be used to differentiate flavor difference among the cheese from these five different factories.

Chapter 5: Fourier Transform Infrared (FTIR)-Microscopy in Swiss-type Cheese

Grading

5.1 Abstract

Cheese grading is usually based on consumer or panels. It is time consuming and costs much. There is a need to build a rapid cheese grading method. In this study, the

Swiss-type cheese water extraction was tested by a Microscopy FTIR (Varian 600 UMA,

Varian Inc., Randolph, MA). This infrared spectrometer was equipped with a potassium bromide beam splitter and a liquid nitrogen cooled HgCdTe (MCT) detector. Five tenths of an ul of the Swiss-type cheese water extraction was put on infrared slide and vacuum dried for 4minus 30seconds before FTIR scan. Spectra was collected from 4000 to 700 cm-1 with a resolution of 4cm-1 and 128 scans were co-added per spectra to enhance signal-to-noise ratio. The Varian Resolution Pro (Version 4.0, Varian Inc., Randolph,

MA) was used to record spectra. All spectra were analyzed using soft independent modeling by class analogy (SIMCA) based on different grades. The result showed that

Grade C Swiss cheese was significantly different from Grade A or Grade B (A/B,

A/B/C). But the Grade A cheese was statistically the same with Grade B (A/B, A/B/C).

Amino acids and fatty acids primarily contributed to the classification based on cheese grade. Date variability among samples also showed up in all cheeses. The FTIR might be

67 a good and rapid method for cheese grading, especially for Grade A and Grade C cheese.

It had limited ability to differentiate Grade A cheese and Grade B (A/B, A/B/C) cheese.

5.2 Introduction

Panelists or consumers usually participate in the cheese grading. Flavor, body, color and appearance are taken into consideration. However, it is a time consuming work and costs a lot for maintaining grading facility. FTIR has been used to help dairy products quality control for many years. The applications of FTIR in cheese had been summarized in Chapter 1 and water extract of cheese is a widely used sampling technique. In this study, water extract of Swiss cheese was used with FTIR method and effect was made to classify cheese based on their grades.

5.3 Materials and Methods

5.3.1 Swiss-type Cheese Samples

10 blocks of cheeses were obtained from one Swiss-type cheese manufactory. Grades and splits defect information of those cheeses were also received. To include more information about each cheese, the block was sliced and samples were taken from blind, eye and split areas (if presented). The grades and split information of cheeses were shown in Table 5.1. The grade B cheese mentioned in this study included Grade A/B and Grade

A/B/C cheeses.

Table 5.1 Grades and Split Information of Swiss Cheese.

Mfg. date Code Grads Split information

2011/10/10 10 v11 A/B/C Slight split

2011/10/13 13 v12 A Slight split

2011/10/14 14 v18 C Split

2011/10/16 16 v13 A/B Slight split

2011/10/22 22 v13 A Slight split

2011/10/23 23 v17 A/B Slight split

2011/10/25 25 v01 A/B Slight split

2011/10/28 28 v13 A No Split

2011/10/28 28 v17 A No Split

5.3.2 Methods

The methods presented for FTIR Microscopy, spectral analyses and multivarient analyses were the same as those presented in Chapter 2.

Four spectra were collected for sample. A total of 125 spectra (7 cheeses * 3areas*

5spectra+2 cheese *2areas*5 spectra) were collected for ten cheeses.

5.4 Result and Discussion

5.4.1 Classification of Swiss-type Cheese Based on Grades

The classification of Swiss cheese classified as Grade A, Grade B and Grade C were presented in Figure 5.1 Grade A cheese and Grade B cheese had overlap, but both of them were clearly separated from Grade C cheese. For Grade A and Grade B, samples were located in two separate areas of the clusters. For Grade A and B, samples made during Oct 7-14, 2011 located in lower position of either cluster, while samples made during Oct.16-28,2011lied in higher position. Grade C was also made during Oct 7-

14.2011 and its cluster located in lower position.

Figure 5.1 Soft independent modeling of class analogy (SIMCA) classification projections

showing different grade of Swiss-type cheese.

Interclass distance values (Table 5.2) shown the same result of class projection. Both

Grade C vs. Grade A and Grade C vs. Grade B had interclass distance over 3, which indicated significant difference between Grade C cheese and Grade A or Grade B cheeses. However, Grade A vs. Grade B was statistically the same.

Table 5.2 Interclass distances of different grading in soft independent modeling of class analogy

(SIMCA) classification of pink Swiss-type cheeses

From the discriminating power (Fig5.2), wavenumber 1551 and 1609 cm-1 contributed most to this grading based classification. Those two wavenumber were associated with amino acid and fatty acids. That indicated that the difference between

Grade C cheese and Grade A or Grade B was linked with the changes of amino acid and fatty acids.

Figure 5.2 Discriminating power plots for classification of different grading of Swiss-type cheese.

5.5 Conclusion

The Microscopy-FTIR could be used a good and rapid method comparing for cheese grading. Grade C cheese could be significantly differentiated from higher grad cheese.

And Grade A cheese were similar with Grade B (A/B, A/B/C) cheese.

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Appendix A: Discriminating Power Plots for Classification of Four Manufacturing Stages of Swiss-type Cheese and Classifications of the Individual Cheeses in Each Stage.

Figure A.1. Discriminating power plot for classification of all four manufacturing stages of Swiss-type cheese (See Figure 3.3).

Figure A.2. Discriminating power plot for classification of ten non-defective cheeses at out of press stage of Swiss-type cheese (See Figure 3.4 a).

b. 1042 1061

988 1454

Discriminating Power (Arbitrary Units) (Arbitrary Power Discriminating

Wavenumber (cm-1)

Figure A.3. Discriminating power plot for classification of ten non-defective cheeses at pre-cool stage of Swiss-type cheese (See Figure 3.4 b).

c. 1431

1358

)

E+02 1123

1493 1196

1612

Discriminating Power ( Power Discriminating

Wavenumber (cm-1) Figure A.4. Discriminating power plot for classification of ten non-defective cheeses at out of warm room stage of Swiss-type cheese (See Figure 3.4 c).

Figure A.5. Discriminating power plot for classification of seven non-defective cheeses at cut stage of Swiss-type cheese (See Figure 3.4 d).

Appendix B: Discriminating Power Plots for Classification of Three Swiss-type Cheeses from Each of the Five Factories

The discriminating power for the cheese of factory 148 (Fig B.1), indicated that wave numbers 1628, 1573 and 1280 cm-1 primarily contributed its variability Wave numbers

1628 and 1573 cm-1 were associated with amide I and II (Rodriguez-Saona et al., 2006).

The peak about 1280 cm-1 was associated with amide III (Dufour, 2009).

Figure B.1 Discriminating power plot for classification of four manufacturing stages of Swiss- type cheese (See Figure 4.3 a).

In Fig B.2. (cheeses from factory 207), band 1122 cm-1 was the highest in discriminating power. 1122 cm-1 was associated with C=O, C-C stretching of organic acid or the stretching of S-O bands (Sun and Wang, 2006).

Figure B.2. Discriminating power plot for classification of Swiss cheese flavor for factory 207 (See Figure 4.3 b).

Bands 1608, 1539 and 1126 cm-1 primarily contributes to the classification in factory 374

(Fig.B.3). Wave numbers 1608 and 1539 cm-1 were associated with amide I and II, while

1126 cm-1 was linked to organic acids. 1740 cm-1 was the C=O stretching of carbonyl group esters, which was the fatty acid ester. But it contributed less than amide I, II and organic acids.

Figure B.3 Discriminating power plot for classification of Swiss cheese flavor for factory 374 (See Figure 4.3 c).

Factory 465 (Fig B.4.), the variability between sample 465-1 and sample 465-3 was caused by amide I (1681cm-1) of peptides, which was produced by protein degradation.

Figure B.4 Discriminating power plot for classification of Swiss cheese flavor for each factory 465 (See Figure 4.3 d).

Factory 528 (Fig B.5), major wavenumbers for classification were 1573, 1416 and 1748 cm-1, which represented amide I and II, amino acids and fatty acids, ester of fatty acids, respectively.

Figure B.5 Discriminating power plots for classification of Swiss-type cheese flavor for each factory 528 (See Figure 4.3 e).