Fluorescence spectroscopy as a non destructive method to predict rheological characteristics of Tilsit cheese

Accepted Manuscript Fluorescence spectroscopy as a non destructive method to predict rheological characteristics of Tilsit cheese

Zhyldyzai Ozbekova, Asylbek Kulmyrzaev PII:

S0260-8774(17)30178-4

DOI:

10.1016/j.jfoodeng.2017.04.023

Reference:

JFOE 8859

To appear in:

Journal of Food Engineering

Received Date:

06 October 2016

Revised Date:

13 April 2017

Accepted Date:

20 April 2017

Please cite this article as: Zhyldyzai Ozbekova, Asylbek Kulmyrzaev, Fluorescence spectroscopy as a non destructive method to predict rheological characteristics of Tilsit cheese, Journal of Food Engineering (2017), doi: 10.1016/j.jfoodeng.2017.04.023

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ACCEPTED MANUSCRIPT “Fluorescence spectroscopy as a non destructive method to predict rheological characteristics of Tilsit cheese” by Zh. Ozbekova and A. Kulmyrzaev >Potential of fluorescence spectroscopy to predict rheological and physico-chemical properties of semi-hard cheeses. >Tryptophan emission, vitamin A emission and excitation spectra were processed with multivariate statistical tools. >The study showed high potential of the fluorescence spectroscopy in the study of rheological and physico-chemical properties of semihard Tilzit cheese.

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1 2 3

FLUORESCENCE SPECTROSCOPY AS A NON DESTRUCTIVE

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METHOD TO PREDICT RHEOLOGICAL CHARACTERISTICS OF

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TILSIT CHEESE

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Zhyldyzai Ozbekova and Asylbek Kulmyrzaev*

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[email protected], [email protected]

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Department of Food Engineering, Kyrgyz-Turkish Manas University, Prospekt Mira, 56, 720038

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Bishkek, Kyrgyz Republic

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RUNNING HEAD: Fluorescence and rheology of Tilsit cheese

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*CORRESPONDING AUTHOR: e-mail: [email protected]; phone: +996-555-098107; fax: +996-312-541935

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ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

ABSTRACT

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The objective of this study was to investigate the potential of fluorescence spectroscopy to

3

predict rheological characteristics of semi-hard cheeses as yield stress (L), flow stress (F),

4

storage modulus (G’) and loss modulus (G”) measured at linear-viscoelastic, yield stress and

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flow stress oscillation regions. Melting temperatures and chemical composition of the semi-hard

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cheeses were also predicted using fluorescence spectra. Principal component analysis (PCA) and

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partial least squares regression (PLSR) were applied to the fluorescence spectra to extract

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information on the rheological properties, chemical composition, and melting temperatures. L

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and F were predicted with R2=0.90 from the vitamin A emission and excitation spectra,

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respectively. Melting temperatures, moisture, protein and fat contents were predicted with

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R2=0.98 from the vitamin A emission spectra. This study demonstrates that fluorescence

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spectroscopy has potential for the accurate, non-destructive and rapid prediction of cheese

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rheology at linear-viscoelastic, yield stress and flow stress oscillation regions simultaneously.

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Keywords

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Cheese, Oscillation rheology, Fluorescence spectroscopy, Multivariate statistics

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ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

1. Introduction

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Cheese is used as a final product in the human diet, as well as an important ingredient in

4

various foods to form desirable texture, taste, flavor and nutritional value. The use of cheese as

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an ingredient is affected by complex physical, thermal and mechanical processes (John, 2008;

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Subramanian et al., 2006). The rheological properties of cheese undergo significant changes in

7

relation to temperature, duration and intensity of mechanical stress, method of transportation as

8

well as changing shear rate (O’Callaghan and Guinee, 2004). Since the cheese is exposed to

9

these different factors, the study of rheological behavior of cheeses in a wide range of the shear

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stress, i.e. in linear visco-elastic (LVE), yield stress, and flow stress regions becomes a task of

11

critical importance.

12

In the LVE range small sinusoidal stresses or strains are applied to a cheese sample at levels

13

that do not cause significant irreversible changes to the cheese internal structure. The LVE range

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is characterized by viscous (G”) and elastic (G’) moduli the values of which remain unchanged

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throughout the LVE region indicating that the microstructure of the cheese is undisturbed.

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Therefore the LVE region is suitable for probing cheese structure and structure development

17

during different processes. The Yield Stress region shows a significant change in the structure of

18

the material. It also indicates the start of plastic deformation when a breakdown occurs,

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consequently, modulus decreases. The Flow Stress region can be used to determine the crossing

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point of two modulus (G’ and G”) at which gelling time and beginning of sample flow are

21

determined (Mezger, 2011). The melting temperature of cheese is also an important quality

22

parameter that characterizes the readiness for implementation and transition of the product from

23

one processing step to another (Karoui et al., 2003).

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The most common methods to determine the rheological properties of cheeses are dynamic

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and transient tests (Venugopal and Muthukumarappan, 2003). Textural and rheological

26

characteristics of cheeses can also be studied using compression (Kulmyrzaev et al., 2005, 3

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

Campanella et al., 1987, Buffa et al., 2001) and extensional (Ak et al., 1993, Edward et al., 2007)

2

tests. Rheological properties of cheeses can be measured using small amplitude oscillatory shear

3

experiments (Joshi et al., 2004) and tube viscometry techniques (Leach et al., 2003). Despite the

4

usefulness of these techniques, a major drawback is that they are tedious, destructive, invasive,

5

time-consuming and require highly skilled operators (Strasburg and Ludescher, 1995; Purna et

6

al., 2005).

7

Fluorescence spectroscopy is a highly sensitive, rapid, non-destructive and easy to use an

8

analytical technique that provides information on the presence of fluorescent molecules

9

(Lakowicz, 2006). Natural compounds that exhibit fluorescence in cheeses are vitamin A and

10

tryptophan residues, which provide specific information on the physical state of triglycerides,

11

and protein conformational changes (Lakowicz, 2006), respectively. For instance, the melting

12

temperature of fat in cheese has been determined from vitamin A fluorescence spectra recorded

13

at different temperatures (Karoui et al., 2003). Fluorescence spectroscopy has been considered to

14

monitor rheological parameters (Karoui and Dufour, 2006), light-induced changes (Andersen et

15

al., 2005), quality and ripening of cheeses (Kulmyrzaev et al., 2005) and their molecular

16

structure and molecular changes throughout the ripening (Kulmyrzaev et al., 2005; Karoui et al.,

17

2007).

18

Studies conducted earlier assessed the potential of front face fluorescence spectroscopy to

19

predict rheological parameters of cheeses exhibited in LVE region only, i.e. the storage modulus

20

G’, loss modulus G”, and the loss factor tg=G”/G’ (Karoui et al., 2003). Stress at fracture (F),

21

strain at fracture (F), work to fracture (WF) and modulus of deformability (E) of soft cheeses

22

obtained conducting uniaxial compression also well correlate with fluorescence spectra

23

(Kulmyrzaev et al., 2005). However, as mentioned earlier, cheese as a functional ingredient can

24

be subjected to mechanical processing (mixing, extrusion, transportation in tubes etc.) at shear

25

stresses that can cause disruption of cheese structure and flow. LVE range of viscoelastic

26

materials (G’=const) is limited by the value of strain (shear) L (Mezger, 2011). L is the strain 4

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

value at which the curve G’ begins to deviate from the LVE plateau value (G’=const). At strain

2

amplitudes higher than L the structure of the sample changes irreversibly. Thus, the limiting

3

value L and corresponding to it the limiting value L (yield stress) characterize the point at which

4

the structure of the sample is deformed irreversibly. Therefore L or L is considered as a critical

5

rheological parameter that provides knowledge on the ability of viscoelastic materials to resist

6

applied external force (stress), i.e. the mechanical strength of the internal structure of materials.

7

Commonly yield stress L is used in industrial practice to characterize structure strength of

8

viscoelastic materials (Mezger, 2011).

9

viscoelastic materials determined by the oscillatory rheology is the flow point F or flow stress

10

F. The flow point occurs as the crossover point G’=G” at which the internal structure of the

11

material is braking to such an extent causing the material to flow. In industrial practice the flow

12

stress F is used in engineering processing of viscoelastic materials at which high rate shear

13

deformation occurs (mixing, extrusion, pumping through channels). Therefore, L (yield stress)

14

and F (flow stress) are significant rheological characteristics of cheeses from both scientific and

15

practical point of view.

Another significant rheological characteristic of

16

The objective of this study was to investigate the potential of fluorescence spectroscopy to

17

predict the rheological characteristics of semi-hard cheeses such as L (yield stress), F (flow

18

stress), modulus G’, and loss modulus G” measured at three oscillation (yield stress, flow stress

19

and LVE) regions using amplitude sweep oscillatory tests. Melting points measured using

20

temperature sweep tests and chemical compositions of the cheeses were also predicted using

21

fluorescence spectra of the cheeses. Principal Component Analysis (PCA) and Partial Least

22

Squares Regression (PLSR) were applied to fluorescence spectra to extract information on

23

rheological, chemical, and melting point of semi-hard cheeses.

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2. Materials and methods

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ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

2.1 Cheese samples

2 3

Three kinds of pre-packed semi-hard cheese Tilsit (n=3) with 20 % (low-fat (LF), 45%

4

(medium-fat (MF)) and 55% (high-fat (HF)) fat in dry matter (FDM) were obtained from a local

5

supermarket. Each cheese piece was cut into three sample portions and one was used for

6

rheological measurements, while second and third portions were used in measuring chemical

7

composition and fluorescence spectroscopy, respectively. The samples were identified, vacuum

8

packed and stored at 4 °C before conducting experiments.

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2.2 Chemical analysis

11 12

The moisture, fat and protein content of cheese samples were measured. Moisture in cheese

13

was determined by the oven drying method at 130 °C (AOAC International 948.12, 2000). Fat

14

content was determined using an extraction procedure with petroleum ether in a Soxhlet

15

apparatus (Distillation System Vapodest 20, Germany) according to AOAC 920.125 (AOAC

16

International, 2000). Kjeldahl method (AOAC International 991.20, 2000) was applied to

17

measure protein content using an Extraction Unit EV6 All/16 (Gerhardt, Germany). All chemical

18

analyses were carried out on three replicates of each cheese sample and average values were

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taken.

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2.3 Rheological measurements

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The cheese samples were sliced into thin disks (2 mm thick and 25 mm diameter) and stored

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at 4 °C until analysis in plastic bags to prevent dehydration. The rheological measurements were

25

conducted using an MCR 302 rheometer (Anton Paar, Graz, Austria) with a parallel plate 6

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

measuring system PP25 (Ø 25 mm), and the Peltier temperature control unit (P-PTD 200/56).

2

The results of the rheological measurements were analyzed using Rheoplus/32 V3.61 (Anton

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Paar Germany GmbH, D-73760 Ostfildern) software.

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Сheese exhibits both elastic (G’) and viscous (G”) behavior depending on deformation

5

conditions (shear rate) (Guinee, 2011). Therefore, oscillatory tests were conducted to obtain

6

additional information on the elastic behavior of the cheese samples. Amplitude sweep (AS) tests

7

were performed at fixed angular frequency =10 rad/s and strain values varied as 0.01%-100 %.

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Temperature was maintained at 25, 30, 35, 40, 45, 50, 55, 60, 65 and 70 °C. Oscillation tests

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were conducted in yield stress, flow stress, and LVE ranges and G’, G” and τ (yield stress) were

10

measured. The oscillatory tests were performed in triplicate at a given temperature and average

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values of the measured characteristics were obtained.

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Melting temperatures of the cheese samples were determined using temperature sweep (TS)

13

test. The angular frequency () and strain (γ) were set constant as 10 rad/s and 1%, respectively.

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The temperature ramp was 20 to 100 °C, in a heating rate of 1 °C per 3 min. Tests were

15

performed in triplicate for each sample and the average values were taken.

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2.4 Fluorescence spectroscopy

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Fluorescence spectra were recorded using a Fluoromax-4 spectrofluorometer (Horiba Jobin

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Yvon, USA) provided with a single-position (56°) thermostatically controlled cell holder

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dedicated to front-face fluorescence. The temperature of the cell holder was controlled by a

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digital temperature controller (VWR, Model 1136D, USA) set at 25, 30, 35, 40, 45, 50, 55, 60,

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65 and 70 °C. The cheese samples were cut into rectangular bar shaped specimens with 1 cm1

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cm cross-section and 4.2 cm length in order to fit into a 5-ml quartz cuvette. The cheese

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specimens were placed in the quartz cuvette, transferred into the cell holder of the

26

spectrofluorometer and upon reaching the desirable temperature fluorescence spectra were 7

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

collected. Emission spectra of tryptophan residues (305-480 nm, excitation: 290 nm) and vitamin

2

A (340-620 nm, excitation: 322 nm) in the cheese samples were recorded. Excitation spectra of

3

vitamin A were also recorded in the wavelength range of 250–350 nm with the emission

4

wavelength set at 410 nm. Each spectrum was recorded in triplicate on different aliquots. In total

5

270 spectra (3 types of spectra, 3 types of cheese, 10 temperatures, and 3 repetitions) were

6

recorded.

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2.5 Multivariate statistical analysis

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The objective of the statistical processing was to derive relevant information from the

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fluorescence spectral data allowing prediction of rheological characteristics, chemical

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composition, and melting point of semi-hard cheeses. In order to reduce scattering effects, the fluorescence spectra were normalized by reducing

13 14

the area under each spectrum to a value of 1 according to Bertrand and Scotter (1992):

ci  Fi norm

15 16

(1)

and norm 

17

n

F j 1

2 j

(2)

18

where ci is the corrected value at wavelength i, Fi is the fluorescence intensity at emission

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wavelength i, Fj is the fluorescence at wavelength j, and n is the number of data points for each

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spectrum. Thus, mainly the shifts of the maximum emission and the width changes of the

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spectra, retaining most of the cheese structural information, were considered. Normalization of

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the fluorescence spectra was conducted using a custom-designed algorithm written in MatLab

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(The MathWorks Inc., MA, USA).

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Rheological and chemical data tables were standardized to assure zero mean and unit

25

variance of each column. The experimental data were arranged in six data tables: (1) emission 8

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

spectra of tryptophan, (2) emission spectra of vitamin A, (3) excitation spectra of vitamin A, (4)

2

rheological properties, (5) chemical composition, and (6) melting point.

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Principal component analysis (PCA) was applied to the normalized fluorescence spectral data

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to obtain a map describing physical and chemical variations between the samples studied. PCA

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finds combinations of variables that describe major trends in the data. Mathematically, PCA

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relies upon an eigenvector decomposition of the covariance or correlation matrix of the process

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variables. PCA reduces the dimensionality of the data, having lost the least amount of

8

information, and describes objects with the few principal components (PC) that explain as much

9

as possible of the total variance contained in the original data without altering their native

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structure (Monfreda, 2012).

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Partial least squares regression (PLSR) was applied in order to predict rheological

12

characteristics, chemical composition and melting point of the cheese samples from fluorescence

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data. PLSR searches the relationship and interdependence of two (X, Y) or more (X, Y, Z etc.)

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random variables and describes their common structure. The accuracy of the regression is

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expressed with a correlation coefficient (R2). It allows establishing the extent of the relationship,

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on which predict the value of a random variable from a large set of independent values (Dijkstra,

17

2010). In PLSR ‘‘leave-one-out’’ cross-validation process was used for validation, that is,

18

leaving one sample of the calibration set at a time for prediction.

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The custom-designed versions of PCA and PLSR programmed in MatLab (The MathWorks Inc., MA, USA) were utilized in the statistical data treatment.

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3. Results and discussion

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3.1 Chemical composition and melting temperature

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9

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1 2

The differences in chemical composition lead to differences in texture, rheological properties, and fluorescence spectra among the cheese samples.

3

The mean results and standard deviations for moisture, fat, protein contents and melting

4

temperatures of the cheese samples are presented in Table 1. Chemical composition, especially

5

fat and protein content, considerably affect the rheology/texture of cheeses (Hort et al., 1997). It

6

is assumed that milk proteins contribute to firmness and milk fats influence smoothness of the

7

cheese. The fat content of the LF cheese was measured as 9.8% and its protein content was

8

29.1%, while the MF cheese contained 24.1% and 24.9% of fat and protein, respectively (Table

9

1). The HF cheese contained 31.1% of fat and 24.3% of protein, while its moisture content was

10

40.3%.

11

Melting points of the cheeses measured using temperature sweep (TS) test are presented in

12

Table 1. Temperature-sweep dynamic shear profiles obtained with the LF, MF and HF cheeses

13

are shown in Fig. 1. The melting points of the MF cheese and HF cheese were 52.18 °C and

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59.57 °C, respectively (Table 1). The temperature sweeps of the MF and HF cheese samples had

15

a crossover of G’ and G” at which solid-like behavior changes into liquid-like behavior (Fig. 1).

16

Increasing temperature induces melting of the cheese fat entrapped in the protein network, and,

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in agreement with earlier studies (Reparet and Noël, 2003; Tunick, 2010), G’ and G”of the MF

18

and HF cheeses gradually decreased, G’ remaining over G” until their cross point. Domination

19

of elastic behavior over viscous one before crossover G’ and G” (Fig. 1) can be explained by the

20

mechanical resistance of the protein network while the cheese fat melts progressively (Reparet

21

and Noël, 2003). At the temperatures above the melting point the cheese fat becomes liquid and

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structural support of the fat globules to the protein network reduces. The melting point of the LF

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cheese was not derived due to the shapes of the TS curves (Fig. 1). The LF cheese was the only

24

variety with the elastic component G’ always higher than the viscous component G”, resulting in

25

values without any crossing-point over the entire temperature range (25-70 °C) (Fig. 1).

26

Moreover, the values of G’ and G” were the highest among all the studied cheeses. This 10

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

observation could be explained by the fact that the low fat content of the LF cheese might reduce

2

the melting of the cheese (Reparet and Noël, 2003; Tunick et al., 1993).

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3.2 Rheological characteristics

5 6

Table 2 presents the values of the storage modulus (G’) and the loss modulus (G”) measured

7

at LVE, yield stress and flow stress regions as well as the values of the yield stress (L) and flow

8

stress (F) measured at 25-70 °C. In agreement with the studies reported earlier, an increase in

9

temperature results in a decrease of all the measured rheological characteristics of the cheese

10

samples (G’, G”, L, F) (Karoui et al., 2003). In addition, as fat content of the cheese samples

11

increase, the magnitudes of its rheological characteristics decrease. For example, at 25 °C the

12

yield stress (L) of the LF cheese containing 9.76% of fat was 8942 Pa, which decreased to 3260

13

Pa in case of the HF cheese with 31.08% of fat at the same temperature (Table 1 and Table 2).

14

G’, G”, and F also decreased when the fat content of the cheese samples increased. In

15

agreement with earlier studies, at low amplitude values, in the LVE range, both the G’ and G”

16

curves display constant plateau values on different levels (data not shown). The overall

17

magnitudes of G’, G”, L, and F decreased from the LF, MF cheese to the HF cheese (Table 2).

18

Such evolution of the rheology of cheeses is believed to be caused, particularly, by the amounts

19

of fat and moisture in cheese, that is both G’ and G” decrease with increasing moisture and fat

20

(Tunick et al., 1993). Our results showed that L and F follow the same pattern as well (Table 2).

21

However, in the case of the cheese samples tested in the present study, the contribution of the fat

22

content to the reduction of G’, G”, L, and F seems to be much greater than that of the moisture

23

content. The LF cheese with the largest moisture content among the cheese samples (Table 1) is

24

supposed to be the softest one, since elevating the water content in cheese results in greater

25

hydration of the casein network to cause a decrease in hardness and G’ (Tunick et al., 1993).

26

However, the magnitudes of G’, G”, L, and F obtained with the LF cheese remain always larger 11

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comparing to those of the MF and HF cheeses at the same temperature (Table 2). Such a

2

consequence could be elucidated by the fact that, at the same time, the amount of fat in cheese as

3

well as the size and distribution of the lipid particles influences the interactions within protein

4

matrix and determine hardness (Van Hekken et al., 2007). In the LF cheese, the quantity of fat

5

dispersed within the protein matrix was relatively low to disrupt the matrix and resulted in a hard

6

cheese matrix, i.e. in the larger magnitudes of G’, G”, L, and F.

7 8

3.3 Fluorescence properties

9 10 11 12 13

At a constant temperature (25 °C), the shapes of the normalized fluorescence spectra of the cheese samples varied depending on their chemical composition. The tryptophan emission spectra exhibited a peak at 382, 392 and 387 nm recorded with the LF, MF and HF cheese sample, respectively (data not shown).

14

The emission spectra of vitamin A of the cheese samples showed the highest normalized

15

emission intensity at 526 nm and two shoulders located at 400-450 nm and 560 nm (data not

16

shown). The excitation spectra of vitamin A of the LF, MF and HF cheeses had the highest

17

absorption at about 325 nm and a shoulder at 312 nm (data not shown). Additionally, the width

18

and intensities of the fluorescence spectra differed from one cheese sample to the other.

19

As it has been concluded in the previous section, G’, G”, L, and F were mainly influenced

20

by the fat content of the cheeses. Temperature alters physical state of fat in the cheese samples,

21

thus, it also modifies rheological properties of the cheeses. The shape of vitamin A spectra are

22

correlated with the physical state of the triglycerides in the fat globules of an emulsion (Dufour

23

et al., 2000) and the protein/fat globule interactions (Herbert et al., 2000). In Fig. 2 the

24

normalized emission spectra of tryptophan (Fig. 2A), emission spectra (Fig. 2B) and excitation

25

spectra (Fig. 2C) of vitamin A for selected temperatures (25, 45 and 70 °C) obtained with the MF

26

cheese are presented. The shapes of the spectra distinctly changed depending on the temperature. 12

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

As the temperature increased, the highest intensities of all obtained spectra decreased (Fig. 2 A,

2

B, C). The variation of the environment polarity affects the maximum intensity of the emission

3

of tryptophan residues (Lakowicz, 2006; Ladokhin, 2000). As the temperature increased, the

4

maximum intensity of the emission of tryptophan residues in the cheese samples decreased and

5

the maximum intensity wavelength shifted from 394 nm at 25 °C to 379 nm and 378 nm at 45 °C

6

and 70 °C (Fig. 2A). The decrease of the tryptophan emission could be explained by the increase

7

in the hydrophobicity of the tryptophan environment (Rampon et al., 2003). The degree of

8

tryptophan environment hydrophobicity is also estimated by the value of the shift of emission

9

spectra (Ladokhin 2000). The blue-shift (decrease) of the maximum emission wavelength to

10

shorter wavelength range with increasing temperature suggested that the tryptophan residues of

11

milk proteins moved into more hydrophobic environment (Fig. 2A). The reason of this could be

12

conformational changes in proteins due to heating. Similar spectral patterns were also found in

13

the tryptophan emission spectra of the LF and HF cheeses (data not shown).

14

Modifications of the maximum intensities and shapes of the emission spectra (Fig. 2B) and

15

excitation spectra (Fig. 2C) of vitamin A obtained with the MF cheese at different temperatures

16

were explained by the decrease in the viscosity of triglycerides of cheese fat with the temperature

17

increase (Dufour and Riaublanc, 1997). Similar spectral patterns were also observed in the

18

emission and excitation spectra of vitamin A of the LF and HF cheeses obtained at different

19

temperatures (data not shown).

20 21

3.4 Multivariate statistical analysis

22 23

The analysis of the obtained spectra conducted in the previous section has demonstrated that

24

the characteristic spectral patterns (maximum photon emission and excitation, shape and width

25

of spectra) have changed depending on the cheese constituents (protein, moisture and fat

26

contents) and temperature. It should be noted that change of temperature caused modification of 13

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

rheological properties of the cheese samples. Principal component analysis (PCA) is a

2

multivariate tool which is normally applied first in order to confirm the results of tentative

3

analysis of spectral patterns observed under effect of different factors. PCA was applied to the

4

normalized spectra to extract information on the influence of chemical composition and

5

temperature on the cheese properties. 270 spectra (3 types of spectra, 3 types of cheese, 10

6

temperatures, and 3 repetitions) were analyzed.

7

The results of PCA applied to the spectra (tryptophan emission, vitamin A emission and

8

excitation) obtained at 25 °C showed discrimination of the cheese samples depending on the

9

cheese chemical composition (data not shown).

10

Since the principal objective of this study was to predict the rheological characteristics of

11

cheeses such as storage modulus G’, loss modulus G”, yield stress L, and flow stress F

12

measured at three oscillation (yield stress, flow stress and LVE) regions and these characteristics

13

significantly affected by temperature, particular attention was drawn to the results of the PCA

14

conducted on the spectra obtained at different temperatures. As an example, the PCA score plot

15

obtained with the vitamin A excitation spectra of the MF cheese samples presented in Fig. 3.

16

Principal component A1 and principal component A2 explained 69.7% and 26.4% of the total

17

variance of the spectra, respectively. Relative to A1, the MF cheese samples at different

18

temperature conditions were discriminated. The cheese samples, the spectra of which were

19

recorded at the temperatures higher than 40-45 °C, were scored positively relative to the

20

principal component A1, while those, the spectra of which recorded at the temperatures below

21

40-45 °C, were scored negatively Fig. 3. As the temperature-sweep dynamic shear experiments

22

demonstrated (Fig. 1), at 40-45 °C the difference between G’ and G” considerably decreased

23

until the two moduli became equal each other at the cross-point. Thus, the cheese samples scored

24

positively along A1 on the PCA scatter-plot (Fig. 3) oppositely differed from those scored

25

negatively because of the difference in the physical state of triglycerides in the two groups of the

26

cheese samples. The cheese samples scored negatively exhibited solid-like behavior (G’  G”), 14

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

while those scored positively because of melting of fat became liquid-like (G’  G”) (Fig. 3).

2

Similar discriminations depending on temperature were observed when PCA was applied to the

3

tryptophan and vitamin A emission spectra of the MF cheese samples, as well as tryptophan

4

emission, vitamin A emission and excitation spectra of the LF and HF cheese samples (data not

5

shown). The results of PCA proved the close relationship between the spectral data and

6

temperature conditions and, consequently, between the spectral data and rheological

7

characteristics of the cheeses.

8

The PLSR algorithm with cross-validation was used to develop regression models of the

9

chemical properties, melting point, and rheological properties of the experimental cheeses. The

10

numbers of PLSR factors used for the final models were the numbers giving first local minimum

11

for the root mean squared error of validation (RMSEV). The results of PLSR applied to the

12

spectral, chemical and rheological data sets are presented in Table 3. Considering LVE region of

13

the oscillation tests, the best regression model including 7 components (R2=0.82) to predict G’

14

and G” was obtained applying PLSR on the tryptophan emission spectra of the cheeses. Vitamin

15

A emission and excitation spectra are actually correlated with the physical state of triglycerides

16

in the fat globules. In connection with that, processing the fluorescence spectra of vitamin A

17

yielded the PLSR models, which were able to predict G’ and G” with relatively high accuracy

18

(Table 3).

19

Yield stress region is characterized by G’ and G” as well, and additionally yield stress L can

20

be derived from this part of the oscillation tests. As mentioned above, L indicates the magnitude

21

of the stress at which the deformation of a sample becomes irreversible. Therefore, L is a critical

22

characteristic to be taken into account when, for example, the ability of any viscoelastic matter to

23

resist applied external forces in order to maintain given shape and sizes is considered. Yield

24

stress L was well predicted with the R2 value of 0.90 using the PLSR model with 10

25

components, which was derived using the vitamin A emission spectra (Table 3). The model

26

taking into account 6 components derived from the vitamin A excitation spectra predicted fairly 15

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

well the values of G’ and G” related to the yield stress region. Corresponding values of R2 for G’

2

and G” were 0.62 and 0.67, respectively (Table 3).

3

Flow stress region yields flow stress F, which indicates the magnitude of the applied

4

external mechanical stress inducing viscous flow of a viscoelastic matter. At this point the

5

viscoelastic matter exhibits liquid-like behavior. The PLSR models allowing to accurately

6

predict G’, G” and F of the cheese samples related to the flow stress region were derived using

7

the spectra of tryptophan and vitamin A. The R2 values of the models developed on the

8

tryptophan emission spectra were 0.95, while those based upon the vitamin A emission and

9

excitation spectra were 0.85 (Table 3).

10

PLSR statistics for the prediction of the moisture, protein and fat contents and melting points

11

of the cheese samples was also conducted and presented in Table 3. Considering the accuracy of

12

prediction, the regression models derived from the tryptophan emission spectra and vitamin A

13

emission spectra demonstrated the highest values of R2. The melting temperatures of the cheeses

14

(Tmelting) and moisture content were predicted with R2=0.99 using the tryptophan emission

15

spectra (Table 3). As the PLSR statistics showed, the vitamin A emission and excitation spectra

16

could also be considered as a reliable source of information on the chemical composition and

17

melting temperatures of cheeses. Melting temperatures and moisture, protein and fat contents

18

were predicted with R2=0.98 applying PLSR on the vitamin A emission spectra (Table 3). The

19

regression with the vitamin A excitation spectra resulted in the prediction of melting

20

temperatures and moisture content with R2=0.89 and protein and fat content with R2=0.88. PLSR

21

analysis proved the fluorescence spectra of tryptophan and vitamin A as useful information

22

source of rheological and chemical properties of cheeses.

23 24

4.

Conclusion

25

16

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

Fluorescence spectroscopy coupled with multivariate statistical tools was successfully used

2

to develop PLS regression models to predict storage modulus (G’), loss modulus (G”), yield

3

stress (L) and flow stress (F) of the semi-hard cheeses at LVE, yield stress and flow stress

4

oscillation regions simultaneously. In addition, melting temperatures, moisture, protein and fat

5

contents were accurately predicted using the regression models derived from the fluorescence

6

spectral data collected from the experimental cheeses. It should be particularly noted that the

7

attempt to predict yield stress (L) and flow stress (F) from the fluorescence spectra of the

8

cheeses was successful. Indeed, yield stress (L) and flow stress (F) were predicted with R2=0.90

9

applying PLSR to the vitamin A emission spectra and vitamin A excitation spectra, respectively.

10

In conclusion, this study demonstrates that fluorescence spectroscopy combined with

11

multivariate statistical tools has potential application for the accurate, non-destructive and rapid

12

prediction of storage modulus (G’), loss modulus (G”), yield stress (L) and flow stress (F) of

13

semi-hard cheeses at LVE, yield stress and flow stress oscillation regions simultaneously. In

14

order to test its robustness the technique developed in this study will be examined using greater

15

number of cheese varieties.

16 17 18 19 20 21 22 23 24 25 26 17

ACCEPTED MANUSCRIPT Fluorescence and rheology of Tilsit cheese, Ozbekova & Kulmyrzaev 1

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ACCEPTED MANUSCRIPT CAPTIONS TO FIGURES

Fig. 1. Temperature-sweep dynamic shear profiles of the experimental cheeses. Fig. 2. (2A) Tryptophan emission spectra of the medium fat (MF) semi-hard cheese at 25, 45 and 70 °C, (2B) Vitamin A emission spectra of the medium fat (MF) semi-hard cheese at 25, 45 and 70 °C, (2C) Vitamin A excitation spectra of the medium fat (MF) semi-hard cheese at 25, 45 and 70 °C. Fig. 3. PCA similarity map obtained using vitamin A excitation spectra of the medium fat (MF) semi-hard cheese.

ACCEPTED MANUSCRIPT

G', G" (Pa)

106

105

104

G' (LF) G" (LF) G' (MF) G" (MF) G' (HF) G" (HF)

103 30

40

50

Temperature (oC)

Fig. 1

60

70

ACCEPTED MANUSCRIPT

Intensity (a.u.)

0,016 0,014

25 oC 45 oC

0,012

70 oC

A

0,010 0,008 0,006 0,004 0,002 0,000 280 300 320 340 360 380 400 420 440 460 480 500 Wavelength (nm)

Fig. 2A

ACCEPTED MANUSCRIPT

0,035

Intensity (a.u.)

B

25 oC o 45 C 70 oC

0,030 0,025 0,020 0,015 0,010 0,005 0,000 300

350

400

450

500

Wavelength (nm) Fig. 2B

550

600

650

ACCEPTED MANUSCRIPT

0,030

C

o

25 C o 45 C o 70 C

Intensity (a.u.)

0,025 0,020 0,015 0,010 0,005 0,000 240

260

280

300

320

Wavelength (nm)

Fig. 2C

340

360

ACCEPTED MANUSCRIPT 3

x 10

-3

MF70 MF70 MF70

2 MF65 MF65

A2 (26.4%)

1

0

MF35 MF35 MF35

MF40 MF40 MF40

MF30 MF30MF30

MF25 MF25 MF25

MF65

MF45 MF45 MF45

-1

MF50 MF50 MF50

MF60 MF60

MF60 MF55 MF55 MF55

-2 -5

-4

-3

-2

-1

A1 (69.7%)

Fig. 3

0

1

2 x 10

-3

ACCEPTED MANUSCRIPT Table 1 Chemical compositions and melting temperatures of the experimental cheese Cheese LF 1

HF 3

SD 4

Mean

Moisture (%)

54.11 0.185

41.7

Protein (%)

29.12 0.045 24.89 0.095 24.27 0.010

Fat (%)

9.76

Mean

Melting point (°C) 1 LF:

MF 2

a

SD

Mean

SD

0.173 40.33 0.073

0.086 24.11 0.110 31.08 0.710 a

52.18

0.11

59.57

0.09

low fat, 2 MF: medium fat, 3 HF: high fat, 4 SD: standard deviation, a: melting temperature

cannot be derived due to the shape of the temperature sweep curves.

ACCEPTED MANUSCRIPT Table 2 Rheological properties of the cheeses at linear viscoelastic (LVE), yield stress, and flow stress regions at 25 - 70 °C

T (°C) 25 30 35 40 45 50 55 60 65 70

T (°C) 25 30 35 40 45 50 55 60 65 70

T (°C) 25 30 35 40 45 50 55 60 65 70

G' (kPa) 119.40 104.80 99.30 86.65 71.02 45.38 20.70 35.22 56.42 39.82

LF Yield stress G'' (kPa) L (kPa) 47.68 8.94 38.97 5.91 37.65 3.87 36.18 1.36 35.50 1.14 29.77 0.41 34.98 0.55 15.24 0.28 47.90 0.22 24.53 0.37

Flow stress G' (kPa) G'' (kPa) F (kPa) 42.00 42.00 41.60 35.50 35.50 30.60 24.49 24.49 24.35 19.12 19.12 13.97 16.79 16.79 8.02 12.97 12.97 3.48 10.18 10.18 2.22 8.51 8.51 0.92 4.73 4.73 0.56 1.58 1.58 0.25

LVE G' (kPa) G'' (kPa) 107.39 35.07 65.45 23.13 39.26 15.74 29.76 12.85 21.62 10.87 14.35 8.57 8.93 6.86 5.82 5.57 3.50 3.62 1.87 2.80

G' (kPa) 88.79 55.76 35.73 27.55 18.78 13.82 8.70 6.50 3.43 1.76

MF Yield stress G'' (kPa) L (kPa) 37.02 4.09 23.38 2.90 15.45 1.74 12.43 1.08 10.87 0.86 8.47 0.34 8.23 0.25 6.84 0.13 5.69 0.12 3.56 0.08

Flow stress G'' (kPa) F (kPa) 24.78 15.95 20.77 8.22 13.26 6.19 9.19 2.98 7.80 1.71 7.13 0.93 6.97 0.34 5.62 0.14 4.22 0.09 1.89 0.05

LVE G' (kPa) G'' (kPa) 97 31.28 55.84 19.10 33.15 11.83 22.53 9.46 15.84 7.84 11.28 6.49 7.55 5.74 3.20 3.20 1.85 2.74 0.86 1.42

HF Yield stress G' (kPa) G'' (kPa) L (kPa) 79.80 33.40 3.26 46.02 20.94 2.08 27.77 12.45 1.23 20.40 9.14 1.12 14.54 8.18 0.67 11.12 5.99 0.45 7.34 4.56 0.29 2.62 3.08 0.20 1.53 2.57 0.13 0.93 1.39 0.04

LVE G' (kPa) G'' (kPa) 115.76 37.38 110.09 33.28 76.70 25.57 63.32 22.91 48.84 20.89 41.47 18.91 37.07 18.51 22.04 15.84 21.89 12.50 17.41 10.75

G' (kPa) 24.78 20.77 13.26 9.19 7.80 7.13 6.97 5.62 4.22 1.89

Flow stress G' (kPa) G'' (kPa) F (kPa) 20.66 20.66 14.18 15.05 15.05 8.52 9.84 9.84 6.66 7.12 7.12 2.58 5.42 5.42 1.36 4.57 4.57 1.08 3.98 3.98 0.51 3.16 3.16 0.16 0.93 0.93 0.19 0.41 0.41 0.09

Table 3 Results of the PLSR statistics to predict rheological and physico-chemical properties of the experimental cheeses from fluorescence spectra Region

Characteristics G' G" G' G" L G' G" F

LVE Yield stress

Flow stress

Triptophan emission spectra 2

Vitamin A emission spectra 2

Vtamin A excitation spectra

R 0.82 0.82 0.53 0.59 0.75 0.90 0.90 0.90

PLS components 7 7 5 5 9 7 7 7

R 0.80 0.81 0.53 0.62 0.90 0.85 0.85 0.85

PLS components 10 10 8 8 10 8 8 8

R2 0.78 0.78 0.62 0.67 0.74 0.85 0.85 0.85

PLS components 6 6 6 6 10 6 6 6

0.99 0.99 0.98 0.98

6 6 6 6

0.98 0.98 0.98 0.98

9 10 10 10

0.89 0.89 0.88 0.88

6 6 6 6

Physico-chemical characteristics Tmelting (oC) Moisture (%) Protein (%) Fat (%)