The effect of milk collection and storage conditions on the final quality of Trentingrana cheese: Sensory and instrumental evaluation

International Dairy Journal 23 (2012) 105e114

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The effect of milk collection and storage conditions on the final quality of Trentingrana cheese: Sensory and instrumental evaluation Isabella Endrizzi, Alessandra Fabris, Franco Biasioli, Eugenio Aprea, Elena Franciosi, Elisa Poznanski, Agostino Cavazza, Flavia Gasperi* IASMA Research and Innovation Centre, Fondazione Edmund Mach, Food Quality and Nutrition Department, Via E. Mach, 1, 38010 S. Michele, TN, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 March 2011 Received in revised form 14 October 2011 Accepted 28 October 2011

The aim of this research was to study the effects of the modalities of milk collection and storage on the final quality of Trentingrana cheese, an Italian Protected Designation of Origin hard cheese. Two milk collection methods were compared: traditional double milk collection (DMC) and single milk collection (SMC). The latter evaluated as a cost-effective alternative. Experiments were carried out at different milk storage temperatures (ambient temperature and 18
C for DMC; 12
C and 8
C for SMC). The complete experimental design was repeated over two seasons. DMC cheeses had a richer aromatic profile, a lower intensity of yellow colour and a lower ripening index compared to SMC cheeses. From a sensory point of view, Trentingrana Consortium experts evaluated DMC cheeses better in terms of commercial quality whereas the trained panel detected differences only when comparing extreme temperature conditions. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction The Italian Protected Designation of Origin (PDO) Trentingrana is a cheese produced from raw cows’ milk characterized by a slow ripening period longer than 18 months, thus producing a grainy texture typical of Italian Parmesan-like cheeses, e.g., Parmigiano Reggiano and Grana Padano, two of the most well-known Italian PDO cheeses (Battistotti & Corradini, 1993). Trentingrana is a variety of PDO Grana Padano (EC Commission Regulation No. 1107, 1996). Livestock on mountain terrains only in a delimited area and restricted cattle feeding and cheese manufacturing protocols give rise to this distinctive cheese (MiPAF, 2006) justifying its “Trentingrana” trademark embossed on the wheel near the “Grana Padano” label. In Trentingrana Consortium dairies, milk is traditionally obtained by double milk collection (DMC) where evening milk is delivered to the cheese factory in milk churns either without any temperature control or in thermo-regulated tanks (18
C) and left to rest overnight. In the morning, this milk is skimmed (after natural cream separation) and fresh whole milk from the morning milking is added at a 1:1 ratio. Trentingrana Consortium is now considering the substitution of double milk collection (DMC) with single milk collection (SMC) where milk that is refrigerated and stored at the farm is delivered * Corresponding author. Tel.: þ39 0461 615186; fax: þ39 0461 650956. E-mail address: fl[email protected] (F. Gasperi). 0958-6946/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.idairyj.2011.10.004

once daily to the cheese factory. Despite the economical benefits of SMC, its introduction may compromise the linkage between product and territory, as the maintenance of a certain degree of artisanship is crucial for the typicality of a PDO product (De Roest & Dufour, 2000). For this reason, the Trentingrana Consortium has decided to support this experimentation aimed at understanding the effects of milk stored under different conditions on cheese quality. Numerous studies have investigated the influence of storage conditions on milk quality. These report a decrease in bacterial count (Franciosi, Settanni, Cologna, Cavazza, & Poznanski, 2011a; Hartmann, Reis, & Masson, 2008; Raynal & Remeuf, 2000) and in milk whey draining capacity, and an increase in coagulation times and casein dissociation in refrigerated milk (Hartmann et al., 2008; Leitner et al., 2008; Malacarne et al., 2008; Raynal & Remeuf, 2000). Furthermore, milk cold storage modifies several chemical and physical milk properties affecting cheese-making efficiency (De la Fuente, Requena, & Juarez, 1997; Walstra & van Vliet, 1986), even when these changes are partially reversible, e.g., after mild heating or other specific treatments (Creamer, Berry, & Mills, 1977; Puhan, 1989). Despite this, there is little information available on the relationship between milk refrigeration and the final quality of cheese. Dahl, Tavaria, and Malcata (2000) reported that cheeses produced from refrigerated and non-refrigerated raw sheep milk present differences in microbiological profiles during ageing and in volatile patterns at the end of six months of ripening. The same research group (Tavaria, Reis, & Malcata, 2006) reported that cheeses manufactured with refrigerated milk were different in

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their micro-structural characteristics. Even if chemical composition, rheological behaviour and sensory attributes were closely associated with cheese micro-structure, the effect of cold-induced changes in cheeses on the perceptible sensory quality is still undemonstrated. This effect is, however, of utmost importance because it represents the key factor for consumer product acceptance. To examine the effect of DMC and SMC on cheese quality, we measured unspecific (overall) sensory differences between samples using a triangle test carried out by a trained panel (Meilgaard, Civille, & Carr, 1999), sensory quality scores assigned by an expert panel as recommended for PDO product certification (Pérez Elortondo et al., 2007), changes in cheese colour using a colorimeter, an aspect not widely investigated in studies on different storage conditions (Bunka, Stetina, & Hrabe, 2008), physicochemical parameters related to basic composition, the volatile compound profile (Marilley & Casey, 2004; McSweeney & Sousa, 2000) that plays a fundamental role in cheese odour and flavour (Curioni & Bosset, 2002), and finally, proteolytic activity by measuring the soluble nitrogen fraction (Masotti, Hogenboom, Rosi, De Noni, & Pellegrino, 2010), since proteolysis in this prolonged ripening extra-hard cheese strongly contributes to flavour and taste (Panari, Mariani, Summer, Guidetti, & Pecorari, 2003). 2. Material and methods 2.1. Milk collection and cheese making Trentingrana is only produced in a restricted mountain region in northern Italy (Trentino Province) and its annual production (4000 tonnes) accounts for only 3% of the total amount of Grana Padano produced in Italy. The factory where the trials were carried out was chosen among the 17 dairies belonging to the Consortium on the basis of a previous study on the sensory quality of Trentingrana production (Gasperi, Biasioli, Framondino, & Endrizzi, 2004), and as being the most technically suitable for conducting the experimental trials. The raw bovine milk obtained from the same two local farms was stored under four different storage conditions before cheese making, as summarized in Table 1. In the case of double milk collection (DMC), the evening milk was delivered to the cheese factory in milk churns either without any temperature control (trial A) or in thermo-regulated tanks (18
C; trial B). At the cheese factory, milk underwent an overnight creaming in large vats. After skimming, the evening milk was added to the fresh morning milk and used to produce cheese according to the standard cheesemaking procedure of Grana (Salvadori del Prato, 1998), which foresees the transformation of 1000 L of milk into two twin wheels of 35e38 kg of Trentingrana. In single milk collection (SMC), the morning milk was stored at the production farm until the addition of the evening milking, either at 12
C (trial C) or at 8
C (trial D) in cooling tanks under mild stirring conditions to ensure temperature uniformity in the bulk milk and to avoid creaming. Both the morning and the evening milk were delivered to the cheese factory in the evening and were Table 1 The four milk treatments under study and the related sample codes.a Parameter

Milk collection modality Milk temperature

Code A

B

C

D

DMC Ambient

DMC 18
C

SMC 12
C

SMC 8
C

a Milk collection modalities were: DMC, double milk collection; SMC, single milk collection.

left overnight in large vats to allow natural cream separation by gravity. The day after, the partially skimmed milk was processed with the same procedure described above. For each trial, milk was manufactured into cheese over three days (three replicates) into three twin wheels. The complete experimental design (four milk treatments, three cheese-making replicates) was replicated in two periods to take into consideration the milk seasonal variability (winter season in AprileMay 2007 and summer season in JulyeAugust 2007). The cheeses were ripened under controlled temperature and humidity. After 9 and 18 months of ripening, the wheels were inspected by official experts of the Trentingrana Consortium to verify the absence of inner structural defects, such as those caused by manufacturing accidents or improper fermentation. The cheese wheels were analyzed after 19e20 months. 2.2. Milk and clotting analysis Fat and casein content were evaluated by infrared analysis (AOAC, 2000; Milk-o-Scan, Foss Electric, Hillerød, Denmark) according to Biggs (1978). Acidity was determined by titrating with NaOH, using phenolphthalein as indicator (end-point pH 8.30), and the results were expressed in
SH. The coagulation properties, i.e., milk clotting time (r, time from the addition of rennet to the onset of gelation) and the curd firming time (k20, time from the onset of gelation until the signal attained a width of 20 mm), were measured (McMahon & Brown, 1982) by a Formagraph (Italian Foss Electric, Padova, Italy). Total bacterial counts (TBC) were obtained by plating decimal dilutions of milk prepared in peptone water (0.1% mycological peptone, Oxoid, Basingstoke, UK) onto plate count agar added with 1 g L1 skimmed milk and incubated aerobically at 30
C for 24 h. 2.3. Cheese sampling At the end of ripening, for each milk treatment, three wheels were analyzed, one for each cheese-making day (choosing one of the two twin wheels produced). The expert panel of the Consortium analyzed half a wheel for each sample and the remaining half wheel was stored at 4
C until a sensory discriminant analysis was conducted (within two weeks). On the same morning of sensory analysis, colour measurements were taken of fresh cut cheeses, and two samples for chemical analysis were prepared. One sample was delivered for composition and nitrogen fractions analysis and the other one was stored at 20
C until the day before volatile compounds analysis when the samples were thawed at 4
C (overnight). Both analyses were carried out on a grated sub-sample obtained from an inner slice of about 12 cm  6 cm  1.5 cm (after the removal of the rind: 5 cm from the external side, 1 cm from the tip). 2.4. Discriminant sensory analysis Sensory differences in experimental cheeses were tested using an overall difference discriminant method carried out by trained judges according to the standard triangle test procedure (ISO, 2008). The panel consisted of 38 judges (21 males and 17 females); 27 of them had previous experience in discriminant or descriptive sensory analysis. Judges had brief training on the triangle test procedure and sensory panel performance was monitored during the whole analysis period, evaluating the discriminant ability of each judge. For each season, three test sessions were carried out (once per week; six analysis sessions in total). In each test session, cheeses produced in one of the three different cheese-making days were analyzed, and the wheels to be compared were randomly chosen

I. Endrizzi et al. / International Dairy Journal 23 (2012) 105e114

among the three days. Four consecutive triangle tests presented in randomized order were carried out per session; in each set, one DMC cheese was compared with one SMC cheese (i.e., A versus C; A versus D; B versus C; B versus D). Cheese samples were cut into slices from a piece of cheese of 4-5 kg (1/8 of a cheese wheel) and then, from each slice, 16 parallelepipeds (1.5 cm  1.5 cm  3 cm) were prepared and served at 15
C in singular transparent plastic-covered cups coded with a 3-digit random number. The evaluations were carried out in isolated booths under white lights and data were collected using a Fizz computer system (Biosystemes, Couternon, France). For each comparison, we considered the mean values obtained for the same comparison in each season. Statistical analysis of the data to calculate the number of mean correct judgements and their probability of occurrence was based on a binomial distribution with P ¼ 1/3 (Schlich, 1993). 2.5. Sensory evaluation by the expert panel This jury was composed of eight people involved in several aspects of cheese production and marketing. These experts, with several years experience in the quality control of the commercialized Trentingrana cheese, have played a fundamental role in the quality payment system (Bittante et al., 2011). Experts’ performance and reliability of evaluation procedures have been thoroughly investigated by the authors using a large data set collected by the Consortium over 11 years (before and after this experimentation) and a publication on this is in preparation. The experts evaluated the samples in accordance to a standardized protocol based on seven quality parameters (Table 2) scored on a 7-point scale. The jury evaluated the samples in two sessions for each period and in each session, six samples were presented blind in a balanced order. An overall quality score for each cheese was then obtained as a weighted average of jury’s data for each evaluated parameter (using the weights reported in the Table 2). 2.6. Colour measurements Colour measurements were recorded at room temperature on freshly cut quarter part of the cheese wheels using a tri-stimulus colorimeter (CR-400; Minolta Camera Co., Osaka, Japan) calibrated with a white standard plate and that uses the L*a*b* system. The L*-, a*- and b*-parameters describe visual lightness (as values increase

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from 0 to 100), redness to greenness (positive to negative values, respectively), and yellowness to blueness (positive to negative values, respectively) of the samples. For each cheese wheel, six measurements (three from the core and three under the rind) were collected to take into account the expected radial variability (Panari et al., 2003; Tosi, Sandri, Tedeschi, Malacarne, & Fossa, 2008). 2.7. Basic composition and nitrogen fractions analysis Moisture was analyzed by drying the sample at 102
C (IDF, 1982), fat according to the GerbereSiegfeld method (Savini, 1946), and sodium chloride (NaCl) by titration with AgNO3 (IDF, 1988); pH was measured with a potentiometer (Crison Instruments, Barcelona, Spain) with the electrode directly inserted in the cheese paste. Total nitrogen (TN) and soluble nitrogen at pH 4.6 (SN) were measured using the Kjeldahl method (IDF, 1993) and the protein content was calculated as TN  6.38. Ripening index was also calculated as an SN/TN ratio and expressed as a percentage. The latter describes the proportion of casein (casein is not soluble) progressively digested during ripening by proteolytic enzymes into nitrogenous compounds (peptones, peptides and smaller caseinic fractions, until free amino acids) soluble in acidified buffer (pH ¼ 4.6; McSweeney & Fox, 1987). 2.8. Head-space analysis by solid phase micro extractionegas chromatographyemass spectrometry Samples were analyzed using a head-space technique, solid phase micro extraction coupled with gas chromatographyemass spectrometry (SPMEeGCeMS) according to the procedure described in Carlin, Versini, Gasperi, and Endrizzi (2005), which was subsequently optimized. About 3 g of grated cheese was placed into glass vials (20 mL, Supelco, Bellefonte, PA, USA) with 4 mL bidistilled water, 50 mL of a solution of three internal standards with purity not lower than 99% (4-methyl-2-pentanone 0.0509 g L1 and ethyl heptanoate 0.06 g L1, both from Aldrich, Milan, Italy, and isobutyric acid 20.021 g L1, Fluka, Milan, Italy), a magnetic stir bar and capped with a PTFE/silicone septa (Supelco). Cheese samples were equilibrated at 40
C under stirring (750 rpm) for 30 min. A fused-silica fibre coated with divinylbenzene/carboxen/polydimethylsiloxane 50/30 mm (DBV/CAR/PDMS, Supelco) was then introduced and exposed to the head-space environment for 30 min.

Table 2 List of sensory attributes measured by the expert panel of the Trentingrana Consortium.a Type of evaluation

Attributesb

Description

Visual: Judges looked at the external and internal surface of the wheel and evaluated the following characteristics:

External aspects of wheel (1.0)

The regularity of wheel shape, its surface integrity, the uniformity and type of crust colour, the degree of definition of trademark. The colour of paste and its degree of uniformity among various parts of the section. The crust thickness. The degree of grain fineness of paste micro-structure. The quality of odour sensation based on intensity and equilibrium of sensations perceived by smelling (caused by the presence of typical aromas related to ripened Grana cheese and by the absence of anomalous aromas related to defects of fermentation)

By smelling: Judges smelt the wheel in different zones and evaluated the following characteristics: By tasting: Judges sampled some cheese pieces from different zones (corresponding to the middle and the external sides of the wheel), taste it and evaluated the following characteristics:

Interior colour (1.3) Rind thickness (1.0) Texture (1.0) Odour (1.7)

Taste (1.8) Aroma (1.7)

The quality of taste sensation, based on intensity and equilibrium of basic tastes (salty, acid, sweet, bitter) The quality of flavour sensation, based on intensity and equilibrium of sensations perceived during the chewiness and after the swallowing relating to odour retro-nasal perception (caused by presence of typical aromas related to ripened Grana cheese and by absence of anomalous aromas related to defects of fermentation)

a For each parameter, the evaluation instruction and the sensory definition are given. The anchors on the 7-point scale were 4 ¼ very bad/unacceptable and 10 ¼ very good/ excellent. b The weightings applied in calculating the total score are given in parenthesis; the total score was calculated for each sample as a weighted mean by using these weightings.

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Volatile compounds adsorbed on the SPME fibre were desorbed at 250
C in the injector port of a GC interfaced with a mass detector operating in an electron ionization mode (EI, internal ionization source; 70 eV) with a scan range of m/z 30e300 (GC Clarus 500, PerkinElmer, Norwalk, CT, USA). Procedure phases were automatically managed using a self-sampling system (CTC combiPAL, CTC Analysis AG, Zwingen, Switzerland). Separation was achieved on a HP-Innowax fused-silica capillary column (30 m, 0.32 mm ID, 0.5 mm film thickness; Agilent Technologies, Palo Alto, CA, USA). The GC oven temperature program was set at 40
C for 3 min, 40e180
C at 4
C min1, 180
C for 6 min and finally 180e220
C at 5
C min1. Helium was used as carrier gas with a constant column flow rate of 2 mL min1. Compound identification was based on mass spectra matching from the standard NIST-98/Wiley library and retention times (RT) of authentic reference standards. To test the repeatability of the method, we analyzed ten replicates of a reference cheese. The observed averaged variation were 23%, 16%, 23%, and 19% respectively for the classes of acids, esters, ketones, and aldehydes, in agreement with the literature for SPME analysis with this type of matrix (Barbieri et al., 1994; Bellesia et al., 2003). Each sample was measured in triplicate. 2.9. Statistical analysis Summary statistics, two-way analysis of variance (ANOVA) considering four milk treatments (A ¼ DMC and milk uncontrolled temperature; B ¼ DMC and milk temperature of 18
C; C ¼ SMC and milk temperature of 12
C; D ¼ SMC and milk temperature of 8
C) and two seasons (winter and summer trials) as main effects with interaction, and post-hoc Tukey’s test were carried out by using Statistica 8.0 (StatSoft, Inc., Tulsa, OK, USA). Results of the interaction effect was not shown in tables but commented in the text. Explorative multivariate data analyses were computed by means of principal component analysis (PCA) using The Unscrambler 8.5 (Camo Process AS, Oslo, Norway). 3. Results and discussion 3.1. Milk for cheese Composition and coagulation properties of vat milk are reported in Table 3. Refrigerated SMC milk had, in comparison with DMC milk, higher contents of casein and, consequently, lower fat/casein ratios (except for treatments A and C which were not statistically different). In winter, milk showed higher fat contents on average than in summer, whereas no differences were found between milk in terms of coagulation properties among thermal treatments and between the seasons but their interactions were significant (data not shown). In fact, milk refrigerated at 18
C seemed to have longer

clotting and curd firmness times in summer as compared with the other thermal treatments, which behaved in a similar way during the two seasons. Refrigerated SMC milk showed, in comparison with nontemperature controlled DMC milk, slightly (P < 0.05) lower total bacterial counts. A thorough investigation on microbiological dynamics can be found in Franciosi et al. (2011a), where the effects of the four different milk treatments were analyzed using a large data set (evening milk, skim milk and cream, whole milk added in the morning for DMC and vat milk) comprising the milk vat samples of this study. No statistically significant differences were found among the four conditions with the exception of Enterococci and Psychrotrophic bacteria that were higher in unrefrigerated milk, suggesting that keeping the milk without refrigeration before delivery to cheese factory induced the growth of psychrotrophics, compared with milk refrigeration itself. 3.2. Sensory differences Table 4 reports the results of the four triangle test series comparing each DMC cheese replicated in the two seasons. Focussing on the average of three test replicates, no differences were perceptible between DMC cheese wheels from milk stored at 18
C (B) and SMC cheese from milk stored at 12
C (C) and 8
C (D). Comparing DMC cheese from non-refrigerated milk (A) with SMC cheese from milk stored at 12
C (C) two different behaviours were observed according to the season. No differences were noticeable in summer whereas the two milk treatments seemed very different in winter (P ¼ 0.016). DMC cheeses without any temperature control (A) were significantly different from SMC cheese from milk stored at 8
C (D) in both seasons (winter P ¼ 0.016; summer P ¼ 0.042), highlighting a larger difference where the most extreme conditions were compared. 3.3. Expert panel evaluation Mean values and standard deviations for each parameter evaluated by the expert panel are shown in Table 5. All parameters were reported, even if the attribute “external aspects”, based on expert evaluation of wheel external surface, was most likely not related to the investigated treatments but was instead dependent on cheese processing (marking, cleaning, storage conditions, etc.). These results were kept because the overall quality score used by the Consortium to compare cheese quality took into account these aspects. No significant differences existed between winter and summer scores for all parameters, however, the difference was significant among cheeses produced with different milk treatments. With the exception of “external aspects” and “rind thickness”, cheeses showed quality scores which decreased with milk storage

Table 3 Physico-chemical parameters of vat milk related to milk treatment effect (for treatment codes see Table 1) and season effect (winter and summer).a Parameter

Milk treatment effect A

Log cfu mL1 pH Acidity (
SH) Fat (%) Casein (%) Fat/casein r (min) k20 (min)

3.9 5.45 3.7 2.37 2.64 0.89 34 50

Season effect B

(0.4)c (0.06) (0.1)ab (0.07) (0.06)a (0.04)bc (3) (20)

3.7 5.44 3.4 2.4 2.60 0.92 39 70

C (0.3)bc (0.04) (0.2)a (0.2) (0.01)a (0.04)c (8) (30)

2.9 5.48 3.7 2.4 2.74 0.86 33 45

D (0.4)a (0.07) (0.1)ab (0.1) (0.02)b (0.04)ab (4) (6)

3.3 5.47 3.7 2.3 2.74 0.82 35 48

(0.1)ab (0.09) (0.1)b (0.2) (0.01)b (0.06)a (7) (6)

Winter

Summer

3.5 5.42 3.7 2.4 2.7 0.91 36 50

3.5 5.50 3.5 2.3 2.70 0.84 35 50

(0.3) (0.05)a (0.2) (0.1)b (0.1) (0.04)b (5) (20)

(0.7) (0.06)b (0.2) (0.1)a (0.06) (0.05)a (7) (20)

a Values are means with standard deviations in parenthesis; statistical analysis was using ANOVA post-hoc Tukey’s tests. Within the same effect, superscript letters are used for significant results: values with different letters are significantly different from one another at P < 0.05. In the parameter column, r is the time from the addition of rennet to the onset of gelation and the curd firming time, k20 is the time from the onset of gelation until the Formagraph signal attained a width of 20 mm.

I. Endrizzi et al. / International Dairy Journal 23 (2012) 105e114 Table 4 Averages over three replicates of total and correct responses given by the panel in each triangle test and the relative P-values of the triangle tests carried out by the trained panel for each comparison in the two seasons (for codes of the compared treatments see Table 1).

A versus C

Seasona W

S

A versus D

W

S

B versus C

W

S

B versus D

W

S

a b

Day (replicates)

Total responses

Correct responses

1.2 externa l as pect

0.9 S_ D _ 2

0.6

62. 6

60. 0

1 2 3 Average 1 2 3 Average

33 29 34 32.0 26 30 20 25.3

21 15 16 17.3 10 13 7 10.0

0.000* 0.031* 0.067 0.016* 0.357 0.166 0.521 0.304

1 2 3 Average 1 2 3 Average

33 29 34 32.0 26 30 20 25.3

16 16 20 17.3 15 18 7 13.3

0.051 0.013* 0.002* 0.016* 0.009* 0.002* 0.521 0.042*

1 2 3 Average 1 2 3 Average

33 29 34 32.0 26 30 20 25.3

10 14 13 12.3 4 16 9 9.7

0.705 0.068 0.330 0.370 0.989 0.019* 0.191 0.462

1 2 3 Average 1 2 3 Average

33 29 34 32.0 26 30 20 25.3

12 12 16 13.3 15 15 7 12.3

0.419 0.232 0.067 0.243 0.009* 0.043* 0.521 0.092

63. 9

W_ D_ 2

P-valueb

od our a ro m a tas te

0.3 0

W_C _1 63. 7 S_ D _ 1

62. 5

61. 7

S MC

S_ B_ 1

S_ C _ 3

60. 8 60. 4

-0.3

64. 1

64. 8

68. 8

S_ A_1

W_B_1

S_ B _ 3

W_B_ 2 W_A_W 3 _C_2 W_A_ 1 62. 3 W_A_ 2 62. 7 S_ B_ 2 W_D _3

S_ C _ 1

S_ D _ 3

58. 4

S_ C _ 2

W_ D_1

67. 6

W_B_3

61. 6

PC 2 - 17%

Treatment comparison

109

DMC textu re

64. 1

S_A_2

65. 5

S_A_ 3

-0.6

rin d th icknes s

-1.2 -1.00

i nteri or co lour

60. 2

-0.9

W_ C_ 3

-0.8 0

-0.60

-0.40

-0.2 0

0.00

0 .20

0.40

0.6 0

0 .80

1.00

1.2 0

PC 1 - 63% Fig. 1. PCA bi-plot carried out on quality scores assigned by the expert panel to experimental cheeses. For treatment codes refer to Table 1; summer and winter collections designated by the prefixes S_ and W_, respectively, and the suffixed numbers _1, _2 and _3 refer to the three replicates. Open and solid symbols indicate, respectively, the single milk collection (SMC) samples, encompassed by the solid ellipsis, and the double milk collection (DMC) samples, encompassed by the dashed ellipsis. Crosses (þ) indicate the parameters evaluated by the expert panel. The numbers written near to the sample codes are the global quality scores calculated as weighted averages of each parameter.

the map in bold. The best experimental cheeses located on the right side of the plot were different in terms of all parameters: “texture”, “interior colour”, “rind thickness”, “odour”, “taste”, and “aroma”. Moreover, positive values of the second component (17%) identified cheeses with a higher score for “external aspect”, “odour”, “aroma”, and “taste”, whereas cheeses with negative values were characterized by a higher score for “rind thickness” and “interior colour”. 3.4. Colour

S, summer; W, winter. Asterisks indicate P-values < 0.05.

Table 6 reports the data for colour parameters; significant differences were found among cheeses treated with different temperatures for all of the three variables. In particular, for b*values, a noticeable difference was shown between SMC cheese (C and D) and DMC cheeses (B), where the colour for the former cheeses had a higher yellow component. Seasonal effects were also very strong; the colour in winter cheeses was more characterized by white and yellow components and less by the red one in summer cheeses. In order to easily visualize the differences, colours parameters (a* versus b*) were plotted in Fig. 2. Lightness (L*) was not taken into account for this illustration because it was negative correlated with a* and consequently gave similar information. SMC cheeses (open symbols) were mostly located in the upper right part of the plot characterized by slight nuance of redness and higher

temperature for cheeses made from milk stored under the cooler temperature conditions. Three significant interactions were observed: for “rind thickness” and “colour”, cheeses produced with milk refrigerated at 8
C (D) showed a lower quality score in summer than in winter whereas the contrary happened for cheese B. For “aroma”, cheeses A (DMC without any temperature control) showed higher quality scores in winter whereas cheeses C (SMC 12
C) seemed better in summer. The best commercial quality of DMC cheeses (A and B) shown by univariate analysis was well summarized by the bi-plot shown in Fig. 1 obtained by running a PCA on quality scores assigned by the expert panel. The first component (63%) separated SMC (open symbols) from DMC cheeses (solid symbols) on the basis of the quality scores used as a supplementary variable superimposed on

Table 5 Expert panel evaluations shared by milk treatment effect (for treatment codes see Table 1) and season effect (winter and summer).a Evaluation

Milk treatment effect A

External aspects Rind thickness Texture Interior colour Odour Taste Aroma

7.3 6.8 7.1 7.0 7.0 6.4 6.3

Season effect B

(0.5)ab (0.5)ab (0.5)bc (0.6)bc (0.4)b (0.5)b (0.5)b

7.1 6.7 7.2 7.2 6.9 6.4 6.3

C (0.4)a (0.5)b (0.6)c (0.6)c (0.4)ab (0.5)b (0.5)b

7.3 6.5 6.9 6.8 6.7 6.2 6.0

D (0.5)ab (0.5)ab (0.5)b (0.6)b (0.5)ab (0.5)ab (0.5)ab

7.5 6.3 6.5 6.4 6.7 6.0 5.9

(0.5)b (0.6)a (0.6)a (0.7)a (0.5)a (0.4)a (0.3)a

Winter

Summer

7.3 6.5 6.9 6.9 6.8 6.3 6.1

7.3 6.5 6.9 6.8 6.9 6.2 6.1

(0.5) (0.5) (0.6) (0.6) (0.5) (0.6) (0.5)

(0.5) (0.6) (0.6) (0.7) (0.5) (0.5) (0.5)

a Values are means with standard deviations in parenthesis; statistical analysis was using ANOVA post-hoc Tukey’s tests. Within the same effect superscript letters show significant results; values with different letters are significantly different from one another at P < 0.05.

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Table 6 Colour parameters shared by milk treatment effect (for treatment codes see Table 1) and season effect (winter and summer).a Parameter

Milk treatment effect

L* a* b*

Season effect

A

B

C

D

Winter

Summer

73 (2)a 1 (2)b 15 (2)ab

73 (3)ab 2.0 (0.6)a 15 (2)a

74 (4)b 1.8 (0.7)ab 16 (2)b

73 (4)a 1.7 (0.7)ab 16 (2)b

76 (2)b 2 (1)a 16 (2)b

71 (2)a 1.4 (0.6)b 15 (2)a

a Values are means with standard deviations in parenthesis; statistical analysis was using ANOVA post-hoc Tukey’s tests. Within the same effect superscript letters show significant results; values with different letters are significantly different from one another at P < 0.05.

intensities of yellow compared to DMC cheeses (solid symbols), which were in the lower left part of the plot. Summer samples were characterized by low intensities of yellow and high intensity of redness in comparison with winter samples. The sample labelled as W_A_2 (produced during winter season from non-refrigerated milk) presented a reddish coloration in the core of the cheese wheel, a technological defect possibly induced by the Maillard reaction (Demarigny, Hennequin, & Barillier, 2005); the reddish colour component was stronger here, than in all other samples. 3.5. Cheese composition and proteolysis The results of basic compositional analysis and nitrogen fractions are reported in Table 7. No significant differences existed among milk treatments for pH, moisture, protein, sodium chloride, and total nitrogen, however, significant differences were found in fat, soluble nitrogen contents and in the ripening index. Cheeses made by refrigerated SMC milk (C and D) had lower fat contents (except for A and C treatments which were not statistically different), and higher concentrations of soluble nitrogen and consequently higher ripening indices than DMC cheeses (A and B). Winter cheeses had, on average, higher contents of fat, protein, total and soluble nitrogen and thus an advanced level of ripening, whereas pH values were higher in summer. No significant interactions were observed except for soluble nitrogen content; DMC cheeses seemed to have faster ripening in winter than in summer, whereas SMC cheeses had a similar content of soluble nitrogen in both seasons.

18 W _D_3 W _C_2 W _B_W2_D_2

Winter SMC

W _C_3 W _C_1 W _B_3WW _D__A1_3

W

b*

in te rD

MC

16

W _B_1

W _A_1

Summer SMC S_D_2 S_D_1 S_C_2

S_C_1 SS__CA__33 S_A_2 S_D_3

S_A_1

S_B_1

14

W _A_2

Summer DMC

S_B_S3_B_2

-3

-2

-1

0

1

a* Fig. 2. Scatter-plot of colour parameters (a* versus b*). Open and solid symbols indicate, respectively, the single milk collection (SMC) and the double milk collection (DMC) samples. For treatment codes refer to Table 1; summer and winter collections designated by the prefixes S_ and W_, respectively, the suffixed numbers _1, _2 and _3 refer to the three replicates.

3.6. Volatile compounds The GC analysis allowed the separation and quantification of 49 compounds: For each compound, a statistical summary based on milk treatment and season effects are reported in Table 8. The most representative classes of compounds were fatty acids from C2 to C10 with the related ethyl esters and some methyl ketones, especially those with 5, 7 and 9 atoms of carbon. Of these compounds, two aldehydes (2- and 3-methylbutyraldehyde) and some alcohols (3-methylbutanol and heptanol) were of importance. These compounds are recognized as common constituents of cheese flavour and are related to glycolysis, lipolysis and proteolysis processes occurring during ripening (McSweeney, 2004). Fatty acids, which are derived from triglyceride lipolytic degradation, had the typical distribution as expected for cheeses, with a higher concentration of acids with an even number of carbon atoms than those with an odd number of carbon atoms (McSweeney & Sousa, 2000). Volatile fatty acids play an important role in the flavour of several cheeses (Curioni & Bosset, 2002) and for some as predominant components, e.g., in ewes’ milk cheeses as reported by Tavaria, Ferreira, and Malcata (2004). The origins of the second most abundant class of compounds, ethyl esters, are either the esterification reactions occurring between short- and medium-chain fatty acids and alcohols derived from lactose fermentation or from amino acid catabolism during cheese ripening (Curioni & Bosset, 2002; Liu, Holland, & Crow, 2004). High concentration of ethyl esters in Parmigiano Reggiano cheese (Dumont, Roger, & Adda, 1974; Meinhart & Schreier, 1986) and in Grana Padano cheese (Moio & Addeo, 1998) has previously been reported. In particular, ethyl hexanoate is the major ester present in both cheeses, followed in abundance by the ethyl esters of butanoic, octanoic and decanoic acids (Boscaini, Van Ruth, Biasioli, Gasperi, & Märk, 2003; Meinhart & Schreier, 1986; Moio & Addeo, 1998; Qian & Reineccius, 2002). As for Trentingrana cheese, the role of ethyl esters has been reported by Aprea et al. (2007), where Trentingrana samples of different ages were analyzed using a direct head-space analysis and mass spectrometric technique. The series of methyl ketones derive from b-oxidation and decarboxylation of free fatty acids whereas those of the aldehydes derive from the degradation of amino acids via the Strecker reaction (Urbach, 1995). Despite the high variability among replicates, milk treatment effects on the concentration of volatile compounds were significant for 23 out of 49 quantified compounds. All of the fatty acids and related esters increased with milk temperature. Ketones also showed the same trend, which was more evident in winter than in summer. On the contrary, the concentration of few organic compounds decreased with milk treatment temperature. In particular, two aldehydes (2- and 3-methylbutyraldehyde) and one pyrazine (2,6-dimethylpyrazine) tended to increase with milk cooling, showing a more pronounced effect in winter than in summer. On average, winter samples were characterized by higher concentrations of total ketones measured, acetic acid and two esters (ethyl acetate and isobutyrate), whereas two acids (nonanoic

I. Endrizzi et al. / International Dairy Journal 23 (2012) 105e114

111

Table 7 Physico-chemical parameters and nitrogen fraction of cheese shared by milk treatment effect (for treatment codes see Table 1) and season effect (winter and summer).a Parameterb

Milk treatment effect A

pH Moisture Fat Protein Sodium chloride Total nitrogen (TN) Soluble nitrogen (SN) SN/TN (100)

5.45 32.3 26.5 32 1.79 5.1 1.31 26

Season effect B

(0.06) (0.3) (0.8)bc (1) (0.08) (0.2) (0.08)a (2)a

5.44 32.3 26.8 32 1.8 5.1 1.3 26

C (0.04) (0.5) (0.6)c (2) (0.1) (0.3) (0.2)a (2)a

5.48 32.8 25.8 33.4 1.67 5.23 1.52 29

D (0.07) (0.4) (0.8)ab (0.6) (0.07) (0.09) (0.07)b (1)b

5.47 32.4 25.3 33.5 1.7 5.25 1.54 29

Winter (0.09) (0.7) (0.9)a (0.4) (0.1) (0.06) (0.05)b (1)b

5.42 32.3 26.7 33.4 1.73 5.23 1.48 28

(0.05)a (0.5) (0.7)b (0.4)b (0.07) (0.06)b (0.09)b (2)b

Summer 5.50 32.6 25.5 32.4 1.8 5.1 1.4 27

(0.06)b (0.5) (0.8)a (0.6)a (0.1) (0.3)a (0.2)a (3)a

a Values are means with standard deviations in parenthesis; statistical analysis was using ANOVA post-hoc Tukey’s tests. Within the same effect superscript letters show significant results; values with different letters are significantly different from one another at P < 0.05. b Values for moisture, fat, protein, sodium chloride, total nitrogen and soluble nitrogen are g 100 g1 of cheese.

Table 8 Volatile compounds shared by milk treatment effect (for treatment codes see Table 1) and season effect (winter and summer).a Compound

Code

Milk treatment effect

Season effect

A

B

C

D

Winter

Summer

Ethyl acetate 2-Methylbutyraldehyde 3-Methylbutyraldehyde Ethyl isobutyrate 2-Pentanone Ethyl butyrate 2-Butenal 2-Hexanone Ethyl benzene 2-Pentanol 1,2-Dimethyl benzene 1,4-Dimethyl benzene 1-Butanol 2-Heptanone 3-Methylbutanol 3-Heptanol Ethyl hexanoate Pyridine 2-Octanone Acetoin 2-Heptanol 2,6-Dimethylpyrazine 1-Hexanol 2-Nonanone Ethyl octanoate 8-Nonen-2-one 1-Heptanol 2-Ethylhexanol Acetic acid 2-Nonanol Propionic acid 2-Undecanone Ethyl decanoate Butanoic acid Isovaleric acid Benzaldehyde Valeric acid 2-Tridecanone Hexanoic acid b-Phenylic acid Furfuryl acid d-Octalactone Heptanoic acid Octanoic acid d-Decalactone Nonanoic acid Benzilic acid Decanoic acid d-Dodecalactone

C1 C2 C3 C4 C5 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C25 C26 C27 C28 C29 C30 C31 C32 C33 C34 C36 C37 C38 C39 C40 C41 C42 C43 C44 C45 C46 C47 C48 C49 C50 C51 C52

13 (5) 8 (2)a 23 (6)a 120 (30)bc 200 (100)ab 500 (300)b e 9 (4)b 60 (90) 0.3 (0.4) 20 (10) e 0.2 (0.6) 210 (70) 8 (7) e 300 (100)b e 2 (2)b 1.6 (0.3)ab 20 (10) 5 (2)a 1.1 (0.5)b 17 (5) 30 (10)b e e 0.4 (0.7) 5  104 (1  104)b e 200 (200) 0.3 (0.5) 3.0 (0.9)b 1  105 (2  104)b 700 (400)b e 400 (100)b e 11  104 (3  104)b e e 1.1 (0.2) 700 (200)c 22  103 (9  103)b 1.0 (0.2)b 9  102 (1  102) e 3  103 (1  103)b e

13 (3) 9 (3)ab 28 (10)ab 130 (30)c 200 (40)b 400 (200)ab e 7 (2)ab e 0.3 (0.3) 20 (30) e e 200 (80) 6 (5) 0.9 (0.9) 200 (100)ab e 1 (1)b 2 (2)b 15 (8) 6 (2)ab 0.6 (0.2)a 19 (5) 20 (20)ab e e e 5  104 (1  104)b 0.3 (0.5) 40 (60) e 3 (2)b 9  104 (4  104)b 600 (400)ab e 300 (100)b e 8  104 (3  104)b e e 1.2 (0.1) 400 (200)b 17  103 (9  103)b 1.2 (0.1)b 9  102 (2  102) e 3  103 (1  103)b e

15 (7) 8 (1)b 27 (4)a 50 (30)a 150 (40)ab 210 (50)ab e 7 (2)ab 40 (50) 0.3 (0.4) 30 (30) e e 180 (40) 4.0 (0.7) 0.7 (0.8) 120 (30)a e 0.2 (0.2)a 0.8 (0.3)a 21 (8) 8 (3)b 0.6 (0.1)a 19 (4) 6 (2)a e e 0.3 (0.4) 35  103 (4  103)a 0.8 (0.9) e e 0.5 (0.4)a 35  103 (2  103)a 300 (70)ab e 120 (20)a e 32  103 (3  103)a e e 1.1 (0.1) 170 (40)a 6.3  103 (0.6  103)a 1.1 (0.1)b 7  102 (8  102) e 1  103 (4  102)a e

16 (10) 11 (3)b 38 (9)b 90 (20)ab 100 (30)a 180 (60)a 0.4 (0.4) 5 (2)a 40 (30) 0.3 (0.3) 30 (20) 7 (8) e 150 (60) 4 (3) 0.9 (0.5) 110 (50)a e 0.22 (0.03)a 0.8 (0.5)a 20 (10) 8 (3)ab 0.4 (0.1)a 16 (3) 7 (3)a e e 0.3 (0.2) 39  103 (4  103)ab 1.2 (0.4) 10 (20) e 0.5 (0.3)a 33  103 (8  103)a 300 (100)a 0.4 (0.7) 90 (20)a e 28  103 (6  103)a e e 0.8 (0.1) 120 (40)a 5  103 (1  103)a 0.8 (0.1)a 5  102 (5  102) e 9  102 (6  102)a e

19 (5)b 9 (3) 29 (10) 110 (50)b 200 (90)b 300 (100) e 9 (3)b 20 (30) e 20 (20) 3 (6) e 240 (50)b 6 (5) 0.2 (0.4)a 200 (100) e 1 (1)b 2 (1)b 20 (10) 7 (2) 0.7 (0.5) 21 (3)b 10 (10) e e 0.3 (0.5) 5  104 (1  104)b 0.4 (0.6) 100 (100) 0.1 (0.4) 2 (2) 6  104 (4  104) 400 (300) 0.2 (0.5) 200 (200) e 6  104 (4  104) e e 1.1 (0.2) 400 (300) 11  103 (9  103) 1.1 (0.2) 6  102 (5  102)a e 1  103 (1  103)a e

9 (3)a 9 (2) 29 (6) 80 (40)a 130 (30)a 400 (200) 0.2 (0.3) 5 (1)a 50 (70) 0.60 (0.08) 30 (20) e 0.1 (0.4) 140 (30)a 5 (4) 1.0 (0.8)b 200 (200) e 0.26 (0.06)a 0.9 (0.6)a 19 (9) 6 (3) 0.6 (0.2) 15 (2)a 20 (10) e e 0.4 (0.3) 39  103 (7  103)a 0.8 (0.7) 50 (80) e 2 (1) 6  104 (3  104) 500 (400) e 200 (100) e 6  104 (4  104) e e 1.1 (0.2) 400 (300) 1  104 (1  104) 1.0 (0.2) 1.0  103 (5  102)b e 2  103 (1  103)b e

a Values are means (mg kg1) with standard deviations in parenthesis; statistical analysis was using ANOVA post-hoc Tukey’s tests; a dash indicates that the value was below the detection limit (0.02 mg kg1). Within the same effect superscript letters show significant results; values with different letters are significantly different from one another at P < 0.05. Internal standards 4-methyl-2-pentanone (C6), ethyl heptanoate (C24) and isobutyric acid (C35) are not reported in the table. Codes reference to Fig. 3.

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and decanoic acid) were higher in summer samples. Significant interactions between the two factors under investigation were observed for 12 compounds. Cheeses from different milk storage conditions showed different behaviours depending on the season of production. The PCA plot on volatile compounds depicted in Fig. 3 indicates the presence of differences among cheeses, and in particular, that most of the variability originated from different milk storage conditions (35% of variance accounted for on PC1). Seasonal effects were of importance; the second component (accounting for 16% of variance) distinctly separated summer from winter cheeses. Summer samples were well-separated according to milk storage, whereas in winter, the two DMC cheeses overlapped. Furthermore, samples belonging to the SMC cheeses showed lower scattering among replicates in both seasons. This can be associated with the lower variability in the production process due to the lower storage temperatures that inhibit microbiological processes. Findings on volatile compounds were compatible with previous work by Fabris et al. (2010) conducted on a similar sample set but analyzed with a direct head-space analysis based on the new proton transfer reaction time of flight mass spectrometry (PTReTOFeMS) technique, only recently applied to food investigations (Soukoulis et al., 2010). Rapid PTReTOFeMS fingerprinting coupled with data mining methods gave results that were in quantitative and qualitative agreement with the GC/MS analysis presented here. In this work, we focused on the final quality of cheese. It is important to note that storage temperature already induces differences in milk, both in terms of casein content and total bacterial count. Additionally, further differences are introduced by the two creaming technologies adopted for SMC and DMC. It is known that the milk creaming process is crucial for the optimization of the fat to casein ratio of milk, the dynamics of microbial population, and the enzymatic activities, which play a crucial role during hard cooked cheese production (Franciosi, De Sabbata, Gardini, Cavazza, & Poznanski, 2011b). Therefore, what we observed in the final cheese was the consequence of the differences induced by milk treatments through the physico-chemical and microbiological composition of the starting milk, which influences the whole ripening process. Except for treatments A and C which were not statistically different, SMC cheeses contained a slightly lower fat content than DMC cheeses. In agreement with our finding, Raynal and Remeuf (2000) indicated that cold stored milk (4
C) induces greater losses of fat as well as more casein dissociation. The cheeses were also C26

0,80 W inte r SMC

C1 W_C_3 W_C_2 C 2 3 C22 W_C_1

PC 2 - 16%

0,60 0,40

W_D_3 W_D_2

0,20

W_D_1

0,00

S_D_3 S_D_1

-0,20 Sum m e r SMC -0,40 -0,60

C30

C17

W_B_2

C37 C46 C27 C40 C18 C42 C7 C47 S um m e r DMC

C14 C49 C10

S_C_2

W inte r DMC

C38 S_B_2C36

S_B_1 C12

W_A _1

W_A _2

C25 C4 W_B_3 C21 C48 C33 C45

S_D_2 2 S_C_3CC 8 C32 C3

S_C_1

C51

-0,80 -1,00 - 0,60

C13 C39

C35 C9 C24 C15 W_B_1C5 C20 W_A _3 C34 C16 C31

S_B_3

S_A _3 C11

-0,40

- 0,20

0,00

S_A _2

0,20

S_A _1

0,40

0,60

0,80

1,00

1,20

PC 1 - 35 % Fig. 3. PCA bi-plot carried out on volatile compounds identified by SPME-GC-MS. Open and solid symbols indicate, respectively, the single milk collection (SMC) and the double milk collection (DMC) samples. For treatment codes see Table 1; summer and winter collections designated by the prefixes S_ and W_, respectively, the suffixed numbers _1, _2 and _3 refer to the three replicates. Crosses (þ) indicate the identified compounds. For compound codes, see Table 8.

different in terms of soluble nitrogen content and consequently in ripening index, which describes the proportion of casein progressively digested during ripening by proteolytic enzymes into peptones, peptides and smaller caseinic fractions (Fox, Guinee, Cogan, & McSweeney, 2000, chap. 11). This index was higher in SMC cheese than in DMC cheese, suggesting that the degradation process of casein was probably due to a higher proteolytic activity of psychrotrophic bacterial enzymes in cheeses produced from refrigerated milk, where bacteria had more time to work in trials with milk stored at lower temperatures (24 h in the single milk delivery and about 12 h in the double one; Franciosi et al., 2011b). The higher concentrations of two aldehydes (2- and 3-methylbutyraldehyde) in the SMC cheeses are related to the casein breakdown by proteolytic enzymes into peptones, peptides and smaller casein fractions, until free amino acids were converted to the corresponding a-ketoacids, which may be decarboxylated to its corresponding aldehydes (McSweeney, 2004). This advanced proteolysis does not induce a richer aromatic profile; SMC cheeses showed lower concentrations for almost all volatile compounds than DMC cheeses, especially for acids and esters (with the exception of the aldehydes) and mostly in winter samples. The breakdown of lipids is chiefly favoured in DMC cheese as measured microbiologically (higher bacterial load) and physically, and in particular, in cheese from milk delivered in churns (trial A) where the unavoidable turbulence releases lipase enzymes (Liu et al., 2004; Raymond, Morin, Cormier, Champagne & Dubeau, 1990). Our findings seem to suggest that the lipolytic process produces a more intense aroma profile. This is not necessarily a positive aspect because the degradation of fat in cheeses ripened over a long period may sometimes induce the formation of undesired odours due to the high concentrations of specific acids (e.g., 2 mg 100 g1 propionic acid; Tosi et al., 2008). Since the lower development of aroma in SMC cheeses is more evident with lower milk storage temperature, we suggest that in the case of refrigerated milk the final bacterial load could be too low to develop a suitable aromatic profile. With respect to sensory quality, cheeses produced with cold milk were, for the expert panel, worst in terms of commercial quality (especially for texture and interior colour) and also for chemosensory aspects, even when the latter case holds true only for milk stored at the lowest temperature (8
C). In the case of single milk collection at 12
C, the differences were, on the contrary, not significant. The panel of trained judges instead seemed to be less sensitive than both instrumental assessment and the expert panel, and in fact, detected differences only when the most extreme temperature conditions were compared. Understanding whether the effect induced by the above treatments can be perceived by consumers is a further challenging task. The differences highlighted by instrumental analysis and confirmed by the experts of the Consortium were not as clearly detected by the trained judges, who have been trained to develop their sensory skills but are not specialized in Trentingrana cheese. This finding is consistent with Zamora and Guirao (2004), who have demonstrated the better discriminating ability of experts as compared to trained judges probably due to their thorough knowledge of product. It was assumed that consumers were comparable to the trained panel and that they might be less sensitive and therefore not able to identify differences in Trentingrana cheese induced by the different milk storage conditions. Furthermore, a consumers’ opinion is not only affected by the intrinsic sensory properties of cheese but also by the expectations they have about products, that can be influenced by particular external factors, such as brand (Cardello, 1995). An exhaustive conclusion on this important issue goes beyond the goals of the present work and should be addressed by a targeted consumer study.

I. Endrizzi et al. / International Dairy Journal 23 (2012) 105e114

4. Conclusions Instrumental analyses indicated noticeable differences between DMC and SMC cheeses in terms of colour, fat content, soluble nitrogen fraction and aromatic profile. The expert panel perceived DMC cheeses to be different in terms of commercial quality compared to SMC cheeses. The trained panel’s evaluations, however demonstrated that these differences were not clearly noticeable for those who were not product experts and were no better than the average consumer. For these reasons, we can conclude that, if the milk storage temperature is not lower than 12
C, the final quality will be not strongly affected and consequently SMC could be an interesting alternative to traditional DMC without risk of losing the authenticity of this PDO product. Acknowledgements A special thanks goes to the FEM colleagues who helped us; in particular, Giorgio De Ros, the project coordinator, Luisa Demattè for her critical reading and all of the lab technicians and panel judges. We are also very grateful to Mauro Pecorari for his precious consultancy and to Trentingrana Consortium (CONCAST-Trentingrana, Spini di Gardolo, Italy) and its expert panel for the support and collaboration. This work was supported by the Autonomous Province of Trento, Italy, as part of the project called “Qualità della filiera Grana Trentino”. References AOAC. (2000). Dairy-products. Official methods of analysis. Washington, DC, USA: Association of Official Analytical Chemists. Aprea, E., Biasioli, F., Gasperi, G., Mott, D., Marini, F., & Märk, T. D. (2007). Assessment of Trentingrana cheese ageing by proton transfer reactionemass spectrometry and chemometrics. International Dairy Journal, 17, 226e234. Barbieri, G., Bolzoni, L., Careri, M., Mangia, A., Parolai, G., Spagnoli, S., et al. (1994). Study of the volatile fraction of Parmesan cheese. Journal of Agricultural and Food Chemistry, 42, 1170e1176. Battistotti, B., & Corradini, C. (1993). Cheese, chemistry, physics and microbiology. In P. F. Fox (Ed.), Italian cheese (2nd ed.). (pp. 221e233) London: Chapman & Hall. Bellesia, F., Pinetti, A., Pagnoni, U. M., Rinaldi, R., Zucchi, C., Cagliati, L., et al. (2003). Volatile components of Grana-Parmigiano-Reggiano type hard cheese. Food Chemistry, 83, 55e61. Biggs, D. A. (1978). Instrumental infrared estimation of fat, protein and lactose in milk: collaborative study. Journal of the Association of Official Analytical Chemists’, 61, 1015e1034. Bittante, G., Cologna, N., Cecchinato, A., De Marchi, M., Penasa, M., Tiezzi, F., et al. (2011). Monitoring of sensory attributes used in the quality payment system of Trentingrana cheese. Journal of Dairy Science, . doi:10.3168/ids.2011-4319. Boscaini, E., Van Ruth, S., Biasioli, F., Gasperi, F., & Märk, T. D. (2003). Gas chromatographyeolfactometry (GCeO) and proton transfer reactionemass spectrometry (PTReMS): analysis of the flavor profile of Grana Padano, Parmigiano Reggiano, and Grana Trentino cheeses. Journal of Agricultural and Food Chemistry, 51, 1782e1790. Bunka, F., Stetina, J., & Hrabe, J. (2008). The effect of storage temperature and time on the consistency and color of sterilized processed cheese. European Food Research and Technology, 228, 223e229. Cardello, A. V. (1995). Food quality: relativity, context and consumer expectations. Food Quality and Preference, 6, 163e168. Carlin, S., Versini, G., Gasperi, F., & Endrizzi, I. (2005). Caratterizzazione aromatica con tecnica SPME (solid phase micro-extraction) in spazio di testa di formaggi Trentingrana rispetto ad altri similari. In F. Gasperi, & G. Versini (Eds.), Caratterizzazione di formaggi tipici dell’arco alpino: il contributo della ricerca (pp. 231e239). San Michele all’Adige (TN), Italy: Istituto Agrario di San Michele all’Adige. Creamer, L. K., Berry, G. P., & Mills, O. E. (1977). A study of the dissociation of bcasein from the ovine casein micelle at low temperature. New Zealand Journal of Dairy Science and Technology, 12, 58e66. Curioni, P. M. G., & Bosset, J. O. (2002). Key odorants in various cheese types as determined by gas chromatographyeolfactometry. International Dairy Journal, 12, 959e984. Dahl, S., Tavaria, F. K., & Malcata, F. X. (2000). Relationships between flavour and microbiological profiles in Serra da Estrela cheese throughout ripening. International Dairy Journal, 10, 255e262. De la Fuente, M. A., Requena, T., & Juarez, M. (1997). Salt balance in ewe’s and goat’s milk during storage at chilling and freezing temperatures. Journal of Agricultural and Food Chemistry, 45, 82e88.

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