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Cheese ripening in nonconventional conditions: A multiparameter study applied to Protected Geographical Indication Canestrato di Moliterno cheese

Open AccessPublished:October 27, 2021DOI:https://doi.org/10.3168/jds.2021-20845

      ABSTRACT

      A multiparameter study was performed to evaluate the effect of fondaco, a traditional ripening cellar without any artificial temperature and relative humidity control, on the chemical, microbiological, and sensory characteristics of Protected Geographical Indication Canestrato di Moliterno cheese. Ripening in such a nonconventional environment was associated with lower counts of lactococci, lactobacilli, and total viable bacteria, and higher presence of enterococci, in comparison with ripening in a controlled maturation room. Moreover, fondaco cheese underwent accelerated maturation, as demonstrated by faster casein degradation, greater accumulation of free AA, and higher formation of volatile organic compounds. Secondary proteolysis, as assessed by liquid chromatography-mass spectrometry of free AA and low molecular weight peptides, did not show any qualitative difference among cheeses, but fondaco samples evidenced an advanced level of peptidolysis. On the other hand, significant qualitative differences were observed in the free fatty acid profiles and in the sensory characteristics. Principal component analysis showed a clear separation of the fondaco and control cheeses, indicating that ripening in the natural room conferred unique sensory features to the product.

      Key words

      INTRODUCTION

      The ripening environment plays a fundamental role on cheese quality because it regulates a series of physical-chemical, microbiological, and biochemical events that contribute to “build” the compositional and sensory profile of the product (
      • van den Berg G.
      • Exterkate F.A.
      Technological parameters involved in cheese ripening.
      ;
      • Macedo A.C.
      • Malcata F.X.
      • Oliveira J.C.
      Effect of production factors and ripening conditions on the characteristics of Serra cheese.
      ;
      • Pachlová V.
      • Buňka F.
      • Buňková L.
      • Weiserová E.
      • Budinský P.
      • Žaludek M.
      • Kráčmar S.
      The effect of three different ripening/storage conditions on the distribution of selected parameters in individual parts of Dutch-type cheese.
      ). In ancient times, ripening took place in natural caves or cellars, in which temperature and relative humidity (RH) had limited variations over time. Today, environmental parameters are kept under strict control in artificial rooms that allow standardization of the ripening process, but caves and cellars have not been totally decommissioned. In most cases, they have been modified and equipped with control devices, as happens for some European blue (
      • Fernández-Bodega M.A.
      • Mauriz E.
      • Gómez A.
      • Martín J.F.
      Proteolytic activity, mycotoxins and andrastin A in Penicillium roqueforti strains isolated from Cabrales, Valdeón and Bejes–Tresviso local varieties of blue-veined cheeses.
      ) or semihard cheeses (
      • Gobbetti M.
      • Folkertsma B.
      • Fox P.F.
      • Corsetti A.
      • Smacchi E.
      • De Angelis M.
      • Rossi J.
      • Kilcawley K.
      • Cortini M.
      Microbiology and biochemistry of Fossa (pit) cheese.
      ;
      • Dolci P.
      • Barmaz A.
      • Zenato S.
      • Pramotton R.
      • Alessandria V.
      • Cocolin L.
      • Rantsiou K.
      • Ambrosoli R.
      Maturing dynamics of surface microflora in Fontina PDO cheese studied by culture-dependent and -independent methods.
      ). Indeed, ripening in rooms without any artificial temperature and RH control has become rare; however, it is considered to highly contribute to cheese uniqueness (
      • Bérard L.
      • Marchenay P.
      Local products and geographical indications: taking account of local knowledge and biodiversity.
      ). Several papers have been published on this topic, mainly focusing on microbiological aspects (
      • Torracca B.
      • Nuvoloni R.
      • Ducci M.
      • Bacci C.
      • Pedonese F.
      Biogenic amines content of four types of “Pecorino” cheese manufactured in Tuscany.
      ;
      • Ozturkoglu Budak S.
      • Figge M.J.
      • Houbraken J.
      • de Vries R.P.
      The diversity and evolution of microbiota in traditional Turkish Divle Cave cheese during ripening.
      ;
      • Anelli P.
      • Peterson S.W.
      • Haidukowski M.
      • Logrieco A.F.
      • Moretti A.
      • Epifani F.
      • Susca A.
      Penicillium gravinicasei, a new species isolated from cave cheese in Apulia, Italy.
      ,
      • Anelli P.
      • Haidukowski M.
      • Epifani F.
      • Cimmarusti M.T.
      • Moretti A.
      • Logrieco A.
      • Susca A.
      Fungal mycobiota and mycotoxin risk for traditional artisan Italian cave cheese.
      ).
      Canestrato di Moliterno is an example of cheese ripened in an uncontrolled environment; it is manufactured in the Basilicata Region (southern Italy) from a mixture of ewe and goat milk and has received the Protected Geographical Indication (PGI) acknowledgment in 2010. Ripening takes place in ancient cellars called “fondaci,” whose name derives from the Arabian word funduq meaning “warehouse” (
      • European Union
      Regulation No. 441/2010 of 21 may 2010 entering a name in the register of protected designations of origin and protected geographical indications [Canestrato di Moliterno (PGI)].
      ). Ancient fondaco rooms are present in the largest Italian seaside towns such as Geneva, Naples, and Venice, as well as in some mountain and rural towns, where they represented the basements of noble palaces and were used for both food storage and marketing. The structure of fondaco consists of several wide and communicating basement rooms, with walls at least 40-cm thick, 2 or more windows to allow moderate ventilation, and sloping floors to facilitate drainage (
      • Pinarelli C.
      Il Canestrato di Moliterno stagionato in Fondaco.
      ). Despite the fact that fondaco ripening is considered the basis of the PGI status of Canestrato di Moliterno (
      • European Union
      Regulation No. 441/2010 of 21 may 2010 entering a name in the register of protected designations of origin and protected geographical indications [Canestrato di Moliterno (PGI)].
      ), no investigation has explored its effect on cheese characteristics (
      • Pirisi A.
      • Comunian R.
      • Urgeghe P.P.
      • Scintu M.F.
      Sheep's and goat's dairy products in Italy: Technological, chemical, micro- biological, and sensory aspects.
      ;
      • Trani A.
      • Gambacorta G.
      • Loizzo P.
      • Cassone A.
      • Faccia M.
      Chemical and sensory characteristics of Canestrato di Moliterno cheese manufactured in spring.
      ). As this traditional ripening is expensive and difficult to standardize, the producers wonder if it makes a real difference in the cheese quality compared with a rational process performed in a modern and strictly controlled ripening room. The purpose of the present investigation was to assess the differences in the quality characteristics of Canestrato di Moliterno hard cheeses ripened in fondaco or in an artificial room. As ripened cheese represents a very complex matrix, the study used a multiparameter approach based on the use of several analytical techniques, including gas and liquid MS, which were able to supply information on the evolution of the main macroconstituents and their metabolites.

      MATERIALS AND METHODS

      Cheese Samples

      The investigation considered 8 wheels of Canestrato di Moliterno cheese (4 per type of ripening environment) from 2 different batches manufactured on 2 consecutive days at the beginning of May at a dairy farm located in the PGI geographical area (Gorgoglione, Basilicata, Italy). The cheeses were obtained from a mixture of sheep- and goat-farm milk (70/30) by following the official protocol of production. In brief, milk was thermized in the vat at 60°C × 15 min, cooled down at 38°C, and added with autochthonous starter culture (a lyophilized mix of lactococci and lactobacilli strains, developed and copyrighted by the PGI Consortium). After incubation for about 20 min, lamb rennet paste (0.3 g/L, 1:12,000 strength, Prodor) was added, and coagulation was obtained in about 30 min. The coagulum was cut to the size of 3 to 4 mm; the curd grains were scalded at 42°C under stirring and then were left to sediment to the vat bottom. After about 20 min, the whey was drained off and the compacted curd was cut in square blocks to be molded into plastic baskets. The cheeses, weighing about 3-kg each, were kept at 28 to 30°C for 12 h, salted in saturated brine for about 30 h, and then transferred into the artificial ripening room of the farm, set at 13 ± 2°C and 75 ± 5% RH. After 1 mo, 4 batches were transferred to the fondaco room [coded as F (A1, A2, B1, B2)], where they remained for 60 d, before coming back for continuing ripening in the artificial room, as indicated by the PGI official production protocol. The other 4 batches remained in the controlled room [coded as C (A1, A2, B1, B2)], at the farm until the end of ripening. All cheeses were taken for analyses after 6 mo of ripening.

      Chemical, Microbiological, and Sensory Analyses

      The following chemical analyses were performed: moisture by oven drying, pH by a pH meter equipped with a penetration probe (Hanna Instruments), NaCl by chloride analyzer (Sherwood Scientific Ltd.), fat (Soxhlet method), total nitrogen (Kjeldahl method), and water-soluble nitrogen (
      • Kuchroo C.N.
      • Fox P.F.
      Soluble nitrogen in Cheddar cheese: Comparison of extraction procedures.
      ). For microbiological analyses, 10 g of cheese was diluted in 90 mL of 2% (wt/vol) sodium citrate solution and homogenized in a Waring blender (Waring Commercial). Serial dilutions were made in quarter-strength Ringer's solution and plated on specific medium (Oxoid) as follows: total mesophilic bacteria were counted on plate count agar incubated at 30°C for 48 h, lactobacilli were counted on de Man, Rogosa, and Sharpe agar at 37°C for 48 h under anaerobiosis, lactococci were counted in M17 agar at 37°C for 48 h, and enterococci were counted on Slanetz and Bartley medium at 45°C for 48 h. Sensory evaluation was performed by a panel of 9 trained assessors from the staff of the Section of Food Science and Technology at the Department of Soil, Plant and Food Sciences of the University of Bari (Bari, Italy). They were selected following international standards (

      ISO (International Organization for Standardization). ISO 8586–1. Sensory analysis: General guidance for the selection, training and monitoring of assessors. Part 1: Selected assessors. International Organization for Standardization.

      ), and trained as reported in a previous paper (
      • Trani A.
      • Gambacorta G.
      • Loizzo P.
      • Cassone A.
      • Faccia M.
      Chemical and sensory characteristics of Canestrato di Moliterno cheese manufactured in spring.
      ). The panelists evaluated the samples by quantitative descriptive analysis as reported previously (
      • Trani A.
      • Gambacorta G.
      • Loizzo P.
      • Cassone A.
      • Faccia M.
      Chemical and sensory characteristics of Canestrato di Moliterno cheese manufactured in spring.
      ). The descriptors were quantified on a 6-point scale and were selected based on weight percentage (frequency of citations × perceived intensity). Only descriptors with a weight percentage greater than 30% were considered.

      Proteolysis and Lipolysis

      Primary proteolysis was evaluated by urea-PAGE using the protocol described by
      • Andrews A.T.
      Proteinases in normal bovine milk and their action on caseins.
      . After running the gel, it was stained with blue silver as indicated by
      • Candiano G.
      • Bruschi M.
      • Musante L.
      • Santucci L.
      • Ghiggeri G.M.
      • Carnemolla B.
      • Orecchia P.
      • Zardi L.
      • Righetti P.G.
      Blue silver: A very sensitive colloidal Coomassie G-250 staining for proteome analysis.
      and subjected to image analysis by using Quantity One software (version 4.6.3; BioRad). The main casein fractions were identified by comparison with a milk sample taken from the vat and with the data from the scientific literature. Secondary proteolysis was assessed by investigating the low-molecular-weight (MW) peptide and free AA profiles of the water-soluble extract ultrafiltered on a 10-kDa cutoff membrane (Amicon, Millipore Corp.). Peptides were studied by HPLC-MS using an Ultimate 3000 RS Dionex system (Thermo Fisher Scientific) composed by quaternary pump, autosampler, and column compartment. It was coupled with an LTQ Velos PRO Linear Ion Trap mass spectrometer by an electrospray interface (H-ESI; Thermo Fisher Scientific). The chromatographic conditions were as follows: gradient elution at 0.3 mL/min flow rate, from 10 to 50% solvent B (acetonitrile containing 0.1% formic acid) in A (water containing 0.1% formic acid) in 20 min. The column was a Kinetex C18 50 × 3.0 mm with a particle size of 2.6 µm (Phenomenex), controlled by thermostat at 35°C. All reagents were from Fluka and were LC-MS grade. The electrospray interface temperature was 320°C for capillary and 280°C for probe, sheath gas flow was 30 psi, aux gas was 10 (arbitrary units), spray voltage was 3 kV in positive ion mode, and capillary temperature was 320°C. Data acquisition and analysis were performed using Trace Finder software v. 3.2 (Thermo Fisher). Each sample was acquired twice as follows: full scan mode with mass ranging from 50 to 1,800 amu and data dependent fragmentation with full scan from 520 to 2,000 Da, Zoom scan (the accurate mass measurement operational mode of the mass spectrometer), and tandem MS (MS2) of the first 5 higher signals with minimum signal threshold of 500 counts. The MS2 signals from full scan were obtained by using collision-induced dissociation, with normalized collision energy to 30 arbitrary units. The full scan was used to compare the chromatograms among samples, and the data-dependent scan was used for identification of peptides, their AA sequence, and mother protein throughout PEAK v.9 software (Bioinformatics Solutions Inc.). A tentative quantitation was made by comparing the area of peptide peaks with that of an eledoisin-related peptide standard (Lys-Phe-Ile-Gly-Leu-Met-NH2, Sigma-Aldrich). A calibration curve was performed by serial dilution from a 10 mg/L stock solution. Analysis of free AA was performed using the EZ:faast LC/MS AA analysis kit (Phenomenex) as reported by
      • Trani A.
      • Gambacorta G.
      • Loizzo P.
      • Cassone A.
      • Faccia M.
      Chemical and sensory characteristics of Canestrato di Moliterno cheese manufactured in spring.
      . Lipolysis was assessed by determination of free fatty acids (FFA) extracted, purified, and analyzed by GC as indicated in a previous paper (
      • Trani A.
      • Gambacorta G.
      • Loizzo P.
      • Alviti G.
      • Schena A.
      • Faccia M.
      • Aquilanti L.
      • Santarelli S.
      • Di Luccia A.
      Biochemical traits of Ciauscolo, a spreadable typical Italian dry-cured sausage.
      ).

      Volatile Organic Compounds Analysis

      Volatile organic compounds (VOC) were extracted at 37°C for 15 min, as reported in a previous paper (
      • Faccia M.
      • Trani A.
      • Natrella G.
      • Gambacorta G.
      Short communication: Chemical-sensory and volatile compound characterization of ricotta forte, a traditional fermented whey cheese.
      ), after addition of 3-pentanone (81.3 ng) as internal standard for semiquantitation. A Triplus RSH autosampler was used, equipped with a divinylbenzene/carboxen/polydimethylsiloxane 50/30 mm solid-phase microextraction (SPME) fiber assembly (Supelco). The VOC were desorbed by exposing the fiber at 220°C for 2 min in the injection port of the gas chromatograph operating in splitless mode. The GC-MS analysis was performed using a Trace 1300 chromatograph equipped with a capillary column VF-WAX MS (60 m, 0.25 mm, 0.25 µm) coupled to a mass spectrometer ISQ Series 3.2 SP1 (Thermo Scientific). The operating conditions were as follows: oven temperatures of 50°C for 0.1 min; then, increase at 13°C/min to 180°C and at 18°C/min to 220°C, and held for 1.5 min. Source temperature was 250°C; ionization energy was 70 eV; scan range was 33 to 200 amu. Peak identification was done by means of Xcalibur V2.0 software (ThermoFisher Scientific) by matching with the reference mass spectra of the National Institute of Standards and Technology (NIST) library (https://www.nist.gov/nist-research-library); when available, pure standards were also used.

      Statistical Analysis

      The data were statistically processed by XLSTAT software (version 2020.1.3, Addinsoft). Discrete variables were described by their mode values and continuous variables by their means. For microbiological results, the means ± the standard deviation were calculated; the results of the chemical analyses were subjected to one-way ANOVA followed by Tukey's honestly significant difference test at a critical value for significance of P < 0.05; as for the sensory analysis, the nonparametric variables were compared by using the Kruskal Wallis test. Principal component analysis (PCA) was carried out for finding the correlations of the ripening environment with the microbiological counts and the most important low MW metabolites, whereas the Pearson correlation coefficient was computed to extrapolate the correlations among the sensory (taste and aroma attributes) and chemical variables.

      RESULTS

      Chemical, Microbiological, and Sensory Analyses

      The mean gross composition (Table 1) and the microbiological profile of the 2 types of Canestrato di Moliterno were significantly different. fondaco cheese contained about 2% less moisture, was slightly more salted, and presented higher values of pH and water-soluble nitrogen than the control cheese. As for the fat and protein content, the significance of the differences depended on the calculation method as follows: on the dry basis, only fat was different (about 3% lower value in F samples). As to the microbiological profile, F samples were characterized by slightly higher counts of enterococci with respect to the C samples (8.8 × 108 vs. 2.6 × 108 cfu/g), and lower counts of total mesophilic (8.4 × 107 vs. 7.4 × 108 cfu/g), lactococci (7.6 × 107 vs. 4.8 × 108 cfu/g), and lactobacilli (5.6 × 107 vs. 4.6 × 108 cfu/g). Significant differences were also observed in the sensory characteristics, starting with the appearance of the cheese wheels (Supplemental Figure A, https://osf.io/n39yp/quickfiles). In fact, F samples presented slightly concave faces with darker color and an oily thicker rind. These differences suggested faster moisture loss and some fat sweating during ripening; these findings explained the differences found in the gross composition. The darker color of the rind could be also connected to an early Maillard reaction that can take place in long-ripened cheeses under particular conditions, such as low activity water value and availability of free carbonyl groups from lipid oxidation (
      • Zamora R.
      • Hidalgo F.J.
      The Maillard reaction and lipid oxidation.
      ). As for flavor, 17 sensory attributes were developed based on quantitative descriptive analysis, the most of which significantly differed between the 2 types of samples (Table 2). All attributes were perceived as more intense in F samples, except for acid taste, whereas butter, toasted, and musty attributes were not perceived in C samples. The overall flavor of the cheese ripened in fondaco was described as “rough” and “typical,” whereas that of the control cheese was defined as “mild” and “delicate” (results not shown).
      Table 1Chemical composition of the cheeses ripened under different conditions (%, except pH; mean values ± SD)
      RI = ripening index; C= controlled maturation room; F = fondaco; wb= wet basis.
      ItemC (on wb)F (on wb)C (on DM basis)F (on DM basis)
      pH5.16±0.02
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      5.27±0.02
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      Fat34.31±0.51
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      33.48±0.42
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      51.73±0.77
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      49.03±0.62
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      Protein24.84±0.35
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      26.03±0.43
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      37.45±0.53
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      38.12±0.63
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      NaCl3.87±0.05
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      4.21±0.09
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      5.83±0.08
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      6.17±0.13
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      Moisture33.67±1.05
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      31.72±0.83
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      RI13.87±0.22
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      17.40±0.78
      Values in the same row for the same measure unit are statistically different at P< 0.05.
      a,b Values in the same row for the same measure unit are statistically different at P< 0.05.
      1 RI = ripening index; C= controlled maturation room; F = fondaco; wb= wet basis.
      Table 2Sensory attributes (modal values) for Canestrato di Moliterno ripened under different conditions; C = controlled maturation room; F = fondaco; Sig = statistical significance
      ItemCFSig
      Texture
       Soluble34
      Indicates significant difference at P < 0.05.
       Adhesive33
       Crumbly33
       Greasy33
       Hard23
      Indicates significant difference at P < 0.05.
       Crystals01
      Indicates significant difference at P < 0.05.
      Odor
       Fermented23
      Indicates significant difference at P < 0.05.
       Sheep barn12
      Indicates significant difference at P < 0.05.
       Butter02
      Indicates significant difference at P < 0.05.
       Cheese rind01
       Toasted01
      Indicates significant difference at P < 0.05.
       Musty01
      Indicates significant difference at P < 0.05.
      Taste
       Salty34
      Indicates significant difference at P < 0.05.
       Bitter22
       Pungent12
      Indicates significant difference at P < 0.05.
       Acid21
      Indicates significant difference at P < 0.05.
       Umami01
      * Indicates significant difference at P < 0.05.

      Proteolysis

      The urea-PAGE patterns (Figure 1) gave indications about primary proteolysis. The typical complex casein profile expected in a mixture of ovine and caprine milk was observed, with αS1- and β-fractions resolved as multiple bands. Both fractions were hydrolyzed more rapidly in F samples, and αS1-CN was almost totally degraded, as commonly reported in hard cheeses (
      • Sousa M.J.
      • Ardö Y.
      • McSweeney P.L.H.
      Advances in the study of proteolysis during cheese ripening.
      ). In addition to this, the band corresponding to its primary product of degradation, the αS1-I fragment (24–199), was much less intense. Disappearance of this polypeptide is an indirect index of the level of secondary proteolysis because it tends to undergo further enzymatic hydrolysis as ripening proceeds. Detailed information about secondary proteolysis was derived from the determination of free AA and from the LC-MS study of the soluble nitrogen fraction. The free AA profiles (Figure 2) confirmed the presence of significant differences between the samples under study because the total concentration was higher in F samples (1,392.38 mg/kg vs. 643.85 mg/kg). The difference depended on all AA, except for Arg, and the increase with respect to the C samples varied from about 20 to more than 100%. The greatest increases were observed for Ser, Asn, Gly, Ala, Pro, Ile, Asp, and Glu. The cheese ripened in fondaco also contained higher level of some unconventional compounds that the EZ-fast method typically allows to quantify, such as α-aminobutyric and β-aminoisobutyric acids, citrulline, Gly-Pro dipeptide, and Orn. As for the LC-MS study, although the chromatograms presented very similar profiles, the total peptide peak area was significantly lower in F than in C samples (chromatograms not shown). The identification of peptides is reported in Table 3, Table 4, with details about the mother protein, AA sequence, and MW; the results of tentative quantification are also included. The calibration curve, along with the resulting plot slope, intercept, and correlation coefficient is reported as Supplemental Figure B (https://osf.io/n39yp/quickfiles). It is worth highlighting that, to our knowledge, the quantitation of peptides in the present experiment represents one of the first efforts in this area, as the studies available in the literature on peptide formation during cheese ripening are mostly qualitative. The chromatographic conditions applied and the instrumental features of the mass detector allowed separation and identification of peptides with an upper limit of theoretical mass around 2,000 Da, whereas the lower limit was mainly represented by the background noise. In our conditions, the shortest sequence identified corresponded to a 741-Da MW octapeptide. Overall, 77 peptides were identified, of which 39 originated from β-CN, 33 from αS1-CN, and 5 from αS2-CN. All of them were present in both types of cheese, but the total concentration was about 18% higher in the control (708.60 ± 41.22 vs. 579.12 ± 63.54 mg/kg). However, when the values were normalized to the soluble nitrogen content, the difference appeared as much more relevant (about 62%). The MW distribution was as follows: 7 peptides had MW >2,000 Da, 21 were in the range of 1,500 to 2,000 Da, 44 were in the range of 1,000 to 1,500 Da, and 5 had MW <1,000 Da. Peptides with MW ranging from 1,000 to 2,000 Da were by far the most represented, accounting for 92.8% of total peptides in C samples and 96.6% in F samples. The first source for their formation was β-CN, and for the most part originated from AA regions 70 to 120 and 200 to 220. Higher presence of β-CN-derived peptides in hard cheese with respect to those deriving from other caseins was reported by
      • Singh T.K.
      • Fox P.F.
      • Healy A.
      Water-soluble peptides in Cheddar cheese: Isolation and identification of peptides in the diafiltration retentate of water-soluble fractions.
      in Cheddar and by
      • Faccia M.
      • Picariello G.
      • Trani A.
      • Loizzo P.
      • Gambacorta G.
      • Lamacchia C.
      • Di Luccia A.
      Proteolysis of Cacioricotta cheese made from goat milk coagulated with caprifig (Ficus carica sylvestris) or calf rennet.
      in ripened Cacioricotta, whereas
      • Ferranti P.
      • Itolli E.
      • Barone F.
      • Malorni A.
      • Garro G.
      • Laezza P.
      • Chianese L.
      • Migliaccio F.
      • Stingo V.
      • Addeo F.
      Combined high-resolution chromatographic techniques (FPLC and HPLC) and mass spectrometry-based identification of peptides and proteins in Grana Padano cheese.
      reported αS1-CN as the main source of low MW peptides in 21-mo-old Grana Padano. It must be underlined that the peptide profile of mature hard cheeses depends on a huge number of variables, including the ripening time, gross composition and weight of the cheese, type of rennet used, and salt concentration, and making a comparison among different types is very difficult.
      Figure thumbnail gr1
      Figure 1Urea-PAGE pattern of Canestrato di Moliterno cheeses ripened in controlled maturation room batches (C; A1, A2, B1, B2) and in fondaco batches (F; A1, A2, B1, B2).
      Figure thumbnail gr2
      Figure 2Free AA content of control and fondaco cheeses. Letters (A, B) indicate a significant difference in AA content between control and fondaco cheeses (P < 0.05; error bars indicate SE).
      Table 3Peptides (mg/kg of cheese) detected in the water-soluble fraction of Canestrato di Moliterno cheeses ripened in controlled maturation room (C) and fondaco (F), with corresponding possible fragment identities and quantification (RT = retention time)
      FragmentMother proteinPeptide sequence−log10 P-valueMassMass accuracym/zzRTC, mg/kgF, mg/kg
      MeanSDMeanSD
      60–68
      Indicates significantly different concentration between C and F (P < 0.05).
      αS2 goatVRNANEEEY20.161,122.4941330.8562.4421.090.760.010.440.02
      66–76
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepDQAMEDAKQMK35.511,293.5693−513.2647.4621.1417.771.0213.511.74
      59–68
      Indicates significantly different concentration between C and F (P < 0.05).
      αS2 goatVVRNANEEEY28.161,221.5625111.2611.8621.270.700.030.290.01
      62–68
      Indicates significantly different concentration between C and F (P < 0.05).
      αS2 goatNANEEEY21.98867.3246119.5868.4411.381.030.060.510.03
      56–67
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 sheepSKDIGSESIEDQ40.221,306.588986.6654.3621.940.450.010.740.06
      56–65
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 sheepSKDIGSESIE28.441,063.5033227.7532.8821.980.370.010.440.04
      32–37
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepNENLLR23.49757.408252.5758.4612.490.480.020.310.01
      58–67
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 sheepDIGSESIEDQ33.771,091.461857.1546.7724.530.340.020.910.04
      126–135αS1 goat/sheepIVPKSAEEQL25.861,112.607761.2557.3524.960.640.030.590.13
      192–202β goat/sheepAVPQRDMPIQA29.51,224.6284291.5613.5025.920.300.010.290.02
      99–108
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepEDVPSERYLG25.071,163.5458274.7582.9426.050.340.010.570.07
      181–190
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepSQPKVLPVPQ23.991,091.6339541.9547.1226.171.280.090.710.08
      125–135
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepEIVPKSAEEQL24.81,241.6503109.5621.9026.581.220.040.990.15
      99–107
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepEDVPSERYL23.221,106.5244344.4554.4626.740.750.042.120.33
      179–190
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepSLSQPKVLPVPQ30.091,291.75216.6647.0227.590.690.030.430.05
      100–109
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepDVPSERYLGY27.051,197.5665249.6599.9427.690.530.020.810.15
      29–37
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 sheepEVLNENLLR25.671,098.603390.8550.3627.80.560.030.410.03
      120–129αS1 goat/sheepNVPQLEIVPK36.991,135.66392.2569.0628.221.360.102.580.44
      99–112
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepEDVPSERYLGYLEQ25.221,696.794447.6849.4429.440.240.000.290.01
      100–112
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepDVPSERYLGYLEQ30.891,567.7518114.5784.9729.460.380.010.640.10
      100–111
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepDVPSERYLGYLE22.91,439.6932314.1721.0829.690.360.020.460.04
      30–37
      Indicates significantly different concentration between C and F (P < 0.05).
      αS2 goatNIFQEIYK22.911,053.549446.3527.8129.710.330.010.290.01
      40–48
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepVAPFPEVFR22.861,060.5706100.1531.3529.860.570.040.270.01
      126–134
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepFPKYPVEPF21.671,122.575154.9562.3829.870.720.020.890.08
      207–219β goat/sheepQEPVLGPVRGPFP30.221,391.7561337.2697.1229.880.490.040.480.04
      208–219
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepEPVLGPVRGPFP21.451,263.6975322.8633.0629.890.430.050.590.07
      100–110αS1 goat/sheepDVPSERYLGYL23.581,310.6506255.5656.50210.190.280.010.290.02
      190–197αS2 goat
      Indicates significantly different concentration between C and F (P < 0.05).
      FAWPQYLK25.131,051.5491491527.04210.257.010.9910.221.79
      206–209
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepYQEPVLGPVRGPFP41.681,554.8193100.4778.50210.380.650.050.550.04
      104–113αS1 goat/sheepERYLGYLEQL31.881,282.6558288.3642.52210.390.590.030.660.12
      211–220
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepLGPVRGPFPI20.231,051.6178106.3526.87210.46.980.9810.042.69
      30–38
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 sheepVLNENLLRF28.551,116.6292163.5559.41210.461.440.290.290.02
      103–113αS1 goat/sheepSERYLGYLEQL27.181,369.6877222.5686.00210.50.530.020.620.14
      39–48
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepVVAPFPEVFR29.611,159.638991580.88210.570.690.060.310.02
      210–220β goat/sheepVLGPVRGPFPI34.031,150.6862468.7576.62210.760.490.010.450.06
      212–221
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepGPVRGPFPIL22.111,051.6178138.5526.89210.786.930.9810.072.67
      29–38
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 sheepEVLNENLLRF23.491,245.6718380.3624.08210.960.370.000.280.01
      101–113αS1 goat/sheepVPSERYLGYLEQL25.351,565.809145.6784.03211.150.350.010.350.03
      124–134
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepMPFPKYPVEPF29.461,350.6682382.9676.60211.160.340.010.290.01
      207–220β goat/sheepQEPVLGPVRGPFPI35.51,504.8401254.6753.62211.360.970.110.810.10
      41–47
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepAPFPEVF21.17805.40163.3806.46111.414.400.300.700.07
      212–222β goat/sheepGPVRGPFPILV26.141,150.686294.11,151.80111.640.420.040.470.13
      206–220
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepYQEPVLGPVRGPFPI38.911,667.9034241835.16211.691.090.040.690.07
      99–113
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepEDVPSERYLGYLEQL36.791,809.8784105.9906.04211.820.460.010.530.05
      40–47
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepVAPFPEVF20.14904.4694183905.64111.954.330.550.630.07
      100–113αS1 goat/sheepDVPSERYLGYLEQL27.11,680.835861.5841.48212.041.100.041.280.28
      29–39αS1 sheepEVLNENLLRFV33.31,344.7401−204.3673.24212.190.250.000.240.01
      210–221
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepVLGPVRGPFPIL29.711,263.7703133.5632.98212.220.740.070.450.09
      195–214αS1 sheepSDIPNPIGSENSGKITMPLW24.892,155.06220.21,078.56212.260.240.000.230.00
      99–108
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepVPPFLQPEIM24.061,169.615564.91,170.70112.40.330.010.660.05
      196–214αS1 sheepDIPNPIGSENSGKITMPLW32.062,068.0298−388.91,034.62212.550.220.000.230.01
      207–221
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepQEPVLGPVRGPFPIL30.791,617.924296.6810.05212.615.570.713.200.57
      216–222β goat/sheepGPFPILV21.05741.4425148.7742.56112.710.830.070.910.16
      39–47
      Indicates significantly different concentration between C and F (P < 0.05).
      αS1 goat/sheepVVAPFPEVF24.061,003.537847.81,004.59112.722.600.440.560.07
      208–221β goat/sheepEPVLGPVRGPFPIL27.081,489.8656108.3746.02212.785.601.095.941.15
      210–220β goat/sheepVLGPVRGPFPILV32.21,362.8386105682.50212.892.020.121.800.45
      108–116αS1 goat/sheepGYLEQLLRL25.741,103.6339391553.04212.940.300.020.290.02
      206–221
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepYQEPVLGPVRGPFPIL42.711,780.987588.7891.582131.520.080.760.09
      207–222
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepQEPVLGPVRGPFPILV39.61,716.992699.2859.59213.2128.623.0315.703.87
      208–222β goat/sheepEPVLGPVRGPFPILV37.11,588.9341123.7795.57213.2620.113.4020.405.89
      206–222
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepYQEPVLGPVRGPFPILV49.991,880.0559121.5941.15213.53.360.241.660.25
      77–92
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepFTGPIPNSLPQNILPL22.411,719.9559110.2861.08213.6426.922.8914.643.66
      97–108
      Indicates significantly different concentration between C and F (P < 0.05).
      β sheepVVVPPFLQPEIM25.111,367.752237.2684.91213.90.430.010.980.15
      205–222β goat/sheepLYQEPVLGPVRGPFPILV44.991993.1483.5997.66213.920.320.020.300.01
      93–108
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepTQTPVVVPPFLQPEIM28.411,794.959551.3898.98213.990.890.051.270.13
      93–108β goat/sheepTQTPVVVPPFLQPEIM(+15.99)28.971,810.9539105.8906.58214.020.360.050.420.03
      94–108β goat/sheepQTPVVVPPFLQPEIM27.791,693.911319.7847.98214.10.280.020.310.01
      95–108
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepTPVVVPPFLQPEIM30.51,565.8527120.7784.03214.170.740.351.310.13
      95–108β goat/sheepTPVVVPPFLQPEIM(+15.99)30.871,581.847736.6791.96214.220.300.050.290.01
      38–47αS1 goat/sheepFVVAPFPEVF35.921,150.606226.2576.33214.360.240.010.210.00
      75–92
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepYPFTGPIPNSLPQNILPL27.691980.072506.1991.54214.530.360.040.520.03
      204–222β goat/sheepLLYQEPVLGPVRGPFPILV41.292,106.224176.51,054.20214.580.230.000.230.00
      197–205
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepDMPIQAFLL26.151,046.54712.3524.29214.630.290.010.230.01
      74–92β goat/sheepVYPFTGPIPNSLPQNILPL36.662,079.1404490.11,041.09215.130.300.010.290.02
      73–92β sheepLVYPFTGPIPNSLPQNILPL31.342,192.224455.31,097.18215.50.210.000.220.00
      72–92
      Indicates significantly different concentration between C and F (P < 0.05).
      β goat/sheepSLVYPFTGPIPNSLPQNILPL25.982,279.2566435.61,141.13215.610.220.010.260.01
      203–222β goat/sheepFLLYQEPVLGPVRGPFPILV22.672,253.2925468.6752.46315.630.240.010.230.01
      * Indicates significantly different concentration between C and F (P < 0.05).
      Table 4Number of peptides identified in the water-soluble fraction of Canestrato di Moliterno cheeses ripened in controlled maturation room (C) and fondaco (F), grouped by mother protein from which they are derived and by molecular weight (MW)
      ItemTotal numberConcentration (as mg/kg ± SD)Concentration (as % water-soluble nitrogen ± SD)
      CFCF
      Mother protein
       β-casein39398.92 ± 90.76398.92 ± 90.761.42 ± 0.16
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      0.88 ± 0.20
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
       αS1-casein33179.00 ± 10.88
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      133.24 ± 16.92
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      0.52 ± 0.04
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      0.30 ± 0.04
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
       αS2-casein539.32 ± 2.7646.96 ± 11.280.11 ± 0.000.10 ± 0.01
       Total77708.60 ± 41.22
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      579.12 ± 63.54
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      2.05 ± 0.13
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      1.28 ± 0.14
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      MW range
       >2,000 Da76.64 ± 0.166.72 ± 0.200.02 ± 0.000.02 ± 0.00
       1,500–2,000 Da21378.32 ± 44.8
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      263.64 ± 62.92
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      1.10 ± 0.12
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      0.58 ± 0.12
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
       1,000–1,500 Da44279.32 ± 27.48296.48 ± 53.080.81 ± 0.080.66 ± 0.12
       <1,000 Da544.32 ± 4.00
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      12.28 ± 1.40
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      0.13 ± 0.01
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      0.03 ± 0.00
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
       Total77708.60 ± 41.22
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      579.12 ± 63.54
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      2.05 ± 0.13
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      1.28 ± 0.14
      Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.
      a,b Values of concentration in the same row, for each of the two measured units, bearing different letters, are different at P < 0.05.

      Lipolysis and VOC Analysis

      The quantification of FFA gave information about the status of the lipolysis process (Table 5). Different from free AA, the total FFA content was not significantly different between the 2 cheese types. Nevertheless, fondaco ripening resulted in formation of higher amounts of short-chain (C4–C8) and medium-chain (C10–C15) fatty acids, with an increase with respect to the control of about 40% and 21%, respectively. As for the single compounds, the most relevant differences regarded the concentration of butyric (C4), capric (C10), and linoleic (C18:2 cis-6) acids. Interestingly, 2 “uncommon” compounds, pentadecanoic (C15) and elaidic (C18:1 trans-9) acids, were detected only in F samples. The results of the VOC analysis are shown in Table 6. Sixty compounds were identified in the entire set of samples, 54 of which were present in C samples, and 57 in F samples. In both cheeses, the most important chemical groups, in order of abundance, were acids, ketones, alcohols, terpenoids, and esters. Acids and terpenoids were more abundant in C samples, whereas F samples contained higher amounts of ketones, alcohols, and esters. Acids were by far the most abundant chemical class in both cheeses, with butanoic and hexanoic acids representing about 79% of the total amount. Alcohols discriminated the samples quite well, as 2-heptanol, 1-hexanol, and 2-nonanol were higher in F samples, whereas 2-butanol and 2,3-butanediol were higher in C samples. In addition, 1-pentanol was only detected in F samples. Among esters and ketones, ethyl caproate, hexyl acetate, 2-nonanone, and 2-heptanone were the major characterizing compounds of F samples. Among the less abundant groups, alkanes and alkenes were more represented in C samples. Finally, some VOC were not in common between the 2 cheese groups; other than 1-pentanol, the C samples did not contain 2,2,4,6,6-pentamethylheptane, amyl acetate, heptyl acetate, and 5-hepten-2-1, 6 methyl, whereas the F samples did not evidence the presence of octane and 3-octene.
      Table 5Free fatty acid content (mg/g of fat, ±SD) of cheeses ripened in controlled maturation room (C) and fondaco (F)
      Item
      SCFA = short-chain fatty acid; MCFA = medium-chain fatty acid; LCFA = long-chain fatty acid.
      C% of totalF% of total
      C4:00.75±0.19
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      3.781.09±0.14
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      4.93
      C6:00.19±0.230.960.39±0.011.76
      C8:00.28±0.051.410.43±0.051.94
      C10:00.93±0.05
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      4.691.26±0.05
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      5.70
      C12:00.55±0.072.770.68±0.013.08
      C14:01.10±0.105.541.41±0.066.38
      C15:00.00±0.00
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      0.14±0.20
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      0.63
      C16:06.31±0.9031.797.08±0.1632.02
      C18:05.05±0.4425.445.49±0.1224.83
      C18:1n9t0.00±0.00
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      0.47±0.66
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      2.13
      C18:1n9c4.13±0.0120.812.97±2.9013.43
      C18:2n6c0.56±0.01
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      2.820.69±0.01
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      2.71
      Total19.85±1.1510022.11±3.68100
      SCFA1.22±0.48
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      6.151.91±0.10
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      8.64
      MCFA2.57±0.24
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      12.953.49±0.07
      Values bearing different superscripts in the same row are statistically different at P < 0.05.
      15.78
      LCFA16.06±1.3980.9016.70±3.6675.53
      a,b Values bearing different superscripts in the same row are statistically different at P < 0.05.
      1 SCFA = short-chain fatty acid; MCFA = medium-chain fatty acid; LCFA = long-chain fatty acid.
      Table 6Volatile organic compounds (VOC) found in cheese samples
      C = controlled maturation room; F = fondaco; R = identification method; LRI = linear retention index; LRI ref = values taken from Bianchi et al. (2007) and Natrella et al. (2020).
      VOCConcentration, μg/kg of cheeseRLRILRI ref
      CF
      Acids
       Acetic acid7.73
      Values in the same row bearing different letters are different at P < 0.05.
      7.30
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,4381,480
       Propionic acid0.25
      Values in the same row bearing different letters are different at P < 0.05.
      0.22
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,5241,554
       Isobutyric acid0.24
      Values in the same row bearing different letters are different at P < 0.05.
      0.14
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,552
       Butanoic acid32.71
      Values in the same row bearing different letters are different at P < 0.05.
      29.30
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,6101,630
       Isovaleric acid0.48
      Values in the same row bearing different letters are different at P < 0.05.
      0.27
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,653
       Hexanoic acid26.22
      Values in the same row bearing different letters are different at P < 0.05.
      23.83
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,828
       Heptanoic acid0.10
      Values in the same row bearing different letters are different at P < 0.05.
      0.10
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,932
       Octanoic acid4.75
      Values in the same row bearing different letters are different at P < 0.05.
      4.56
      Values in the same row bearing different letters are different at P < 0.05.
      MS2,036
       Nonanoic acid0.09
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS2,113
       Decanoic acid1.72
      Values in the same row bearing different letters are different at P < 0.05.
      1.46
      Values in the same row bearing different letters are different at P < 0.05.
      MS2,198
       Total74.26
      Values in the same row bearing different letters are different at P < 0.05.
      67.20
      Values in the same row bearing different letters are different at P < 0.05.
      Alcohols
       Ethanol0.34
      Values in the same row bearing different letters are different at P < 0.05.
      0.55
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI906932
       2-Butanol2.30
      Values in the same row bearing different letters are different at P < 0.05.
      0.85
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI998975
       2-Pentanol0.75
      Values in the same row bearing different letters are different at P < 0.05.
      0.79
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1051,142
       1-Butanol0.36
      Values in the same row bearing different letters are different at P < 0.05.
      0.31
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1291,152
       1-Pentanol0.00
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2301,256
       2-Heptanol0.68
      Values in the same row bearing different letters are different at P < 0.05.
      2.28
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2941,334
       1-Hexanol0.44
      Values in the same row bearing different letters are different at P < 0.05.
      0.79
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,3291,354
       2-Nonanol0.18
      Values in the same row bearing different letters are different at P < 0.05.
      0.42
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,4911,528
       1-Octanol0.16
      Values in the same row bearing different letters are different at P < 0.05.
      0.15
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,5311,561
       2,3-Butanediol0.45
      Values in the same row bearing different letters are different at P < 0.05.
      0.15
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,561
       Total5.65
      Values in the same row bearing different letters are different at P < 0.05.
      6.35
      Values in the same row bearing different letters are different at P < 0.05.
      Ketones
       Acetone0.04
      Values in the same row bearing different letters are different at P < 0.05.
      0.03
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI812814
       2-Heptanone3.32
      Values in the same row bearing different letters are different at P < 0.05.
      4.36
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1671,185
       2-Octanone0.10
      Values in the same row bearing different letters are different at P < 0.05.
      0.17
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2651,251
       Acetoin0.16
      Values in the same row bearing different letters are different at P < 0.05.
      0.07
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2771,289
       5-Hepten-2-one, 6 methyl0.00
      Values in the same row bearing different letters are different at P < 0.05.
      0.03
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,3161,340
       2-Nonanone7.02
      Values in the same row bearing different letters are different at P < 0.05.
      12.14
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,3681,394
       8-Nonen-2-one0.17
      Values in the same row bearing different letters are different at P < 0.05.
      0.35
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,422
       2-Undecanone0.26
      Values in the same row bearing different letters are different at P < 0.05.
      0.26
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,5741,606
       Total11.07
      Values in the same row bearing different letters are different at P < 0.05.
      17.41
      Values in the same row bearing different letters are different at P < 0.05.
      Terpenoids
      p-Menth-2-ene2.15
      Values in the same row bearing different letters are different at P < 0.05.
      1.23
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,092
       α-Myrcene0.06
      Values in the same row bearing different letters are different at P < 0.05.
      0.05
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1431,167
       α-Phellandrene0.41
      Values in the same row bearing different letters are different at P < 0.05.
      0.25
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1491,160
      d-Limonene2.62
      Values in the same row bearing different letters are different at P < 0.05.
      2.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1831,194
       Total5.24
      Values in the same row bearing different letters are different at P < 0.05.
      3.57
      Values in the same row bearing different letters are different at P < 0.05.
      Esters
       Ethyl acetate0.11
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI845893
       Methyl lactate0.04
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS938
       Ethyl butyrate0.35
      Values in the same row bearing different letters are different at P < 0.05.
      0.22
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,0121,040
       Butyl acetate0.09
      Values in the same row bearing different letters are different at P < 0.05.
      0.35
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,0511,077
       sec-Butyl butyrate0.13
      Values in the same row bearing different letters are different at P < 0.05.
      0.15
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1141,154
       Butyl propionate0.03
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1221,120
       Amyl acetate0.00
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,1551,180
       Butyl butyrate0.08
      Values in the same row bearing different letters are different at P < 0.05.
      0.42
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2011,175
       Ethyl caproate0.76
      Values in the same row bearing different letters are different at P < 0.05.
      1.13
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2151,238
       Hexyl acetate0.23
      Values in the same row bearing different letters are different at P < 0.05.
      1.00
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2511,269
       Heptyl acetate0.00
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,3501,370
       Butyl caproate0.04
      Values in the same row bearing different letters are different at P < 0.05.
      0.25
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,3891,420
       Ethyl caprylate0.12
      Values in the same row bearing different letters are different at P < 0.05.
      0.22
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,4101,438
       Total1.97
      Values in the same row bearing different letters are different at P < 0.05.
      3.95
      Values in the same row bearing different letters are different at P < 0.05.
      Alkanes
       Hexane0.16
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI600600
       Cyclopentane0.04
      Values in the same row bearing different letters are different at P < 0.05.
      0.02
      Values in the same row bearing different letters are different at P < 0.05.
      MS638
       Isooctane0.05
      Values in the same row bearing different letters are different at P < 0.05.
      0.02
      Values in the same row bearing different letters are different at P < 0.05.
      MS675
       Heptane0.10
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI700700
       Octane0.03
      Values in the same row bearing different letters are different at P < 0.05.
      0.00
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI800800
       2,2,4,6,6-Pentamethylheptane0.00
      Values in the same row bearing different letters are different at P < 0.05.
      0.02
      Values in the same row bearing different letters are different at P < 0.05.
      MS939
       Decane0.06
      Values in the same row bearing different letters are different at P < 0.05.
      0.03
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI9751,000
       Total0.45
      Values in the same row bearing different letters are different at P < 0.05.
      0.20
      Values in the same row bearing different letters are different at P < 0.05.
      Aromatic compounds
       Toluene0.08
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,0221,040
       Styrene0.23
      Values in the same row bearing different letters are different at P < 0.05.
      0.20
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI1,2411,261
       Total0.31
      Values in the same row bearing different letters are different at P < 0.05.
      0.24
      Values in the same row bearing different letters are different at P < 0.05.
      Nitrogen containing compounds
       Methane, isocyano-0.09
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
      MS986
       Total0.09
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
      Sulfur compounds
       Dimethyl sulfone0.08
      Values in the same row bearing different letters are different at P < 0.05.
      0.05
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,898
       Total0.08
      Values in the same row bearing different letters are different at P < 0.05.
      0.05
      Values in the same row bearing different letters are different at P < 0.05.
      Lactones
       Caprolactone0.04
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,693
       Total0.04
      Values in the same row bearing different letters are different at P < 0.05.
      0.06
      Values in the same row bearing different letters are different at P < 0.05.
       Aldehydes
       Valeraldehyde, 3-methyl0.05
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      MS1,063
       Total0.05
      Values in the same row bearing different letters are different at P < 0.05.
      0.04
      Values in the same row bearing different letters are different at P < 0.05.
      Alkene
       3-Octene0.03
      Values in the same row bearing different letters are different at P < 0.05.
      0.00
      Values in the same row bearing different letters are different at P < 0.05.
      MS, LRI824846
       Total0.03
      Values in the same row bearing different letters are different at P < 0.05.
      0.00
      Values in the same row bearing different letters are different at P < 0.05.
      a,b Values in the same row bearing different letters are different at P < 0.05.
      1 C = controlled maturation room; F = fondaco; R = identification method; LRI = linear retention index; LRI ref = values taken from
      • Bianchi F.
      • Careri M.
      • Mangia A.
      • Musci M.
      Retention indices in the analysis of food aroma volatile compounds in temperature-programmed gas chromatography: Database creation and evaluation of precision and robustness.
      and
      • Natrella G.
      • Faccia M.
      • Lorenzo J.M.
      • De Palo P.
      • Gambacorta G.
      Sensory characteristics and volatile organic compound profile of high-moisture mozzarella made by traditional and direct acidification technology.
      .

      PCA and Pearson Correlation

      The microbiological counts and the low MW metabolites (VOC, free amino acids, FFA) were included in a data set for multivariate statistical analysis (Figure 3). The 2 factors extracted from the PCA analysis explained 99.4% of the variance, with a clear separation of F and C samples along principal component 1 (87.17% of variability explained). On the other hand, principal component 2 only explained 7.27% of variability, evidencing a slight separation of the 2 replicates of each type of cheese. The discrimination between the 2 cheeses was attributable to almost all parameters applied as follows: total and single FAA (except for Arg), single FFA (except for C18:1 cis-9), count of enterococci, and several classes of VOC (esters, ketones, alcohols, and lactones) characterized the cheese ripened in fondaco. Lactones, C15, C18, C6, and C18:1 trans-9 fatty acids showed a weak correlation. On the other hand, the counts of the other microbial groups, volatile alkanes, alkenes, aromatic compounds, terpenoids, and acids, were strongly correlated with the cheese ripened in the artificial room.
      Figure thumbnail gr3
      Figure 3Principal component analysis of control (C; A1, A2, B1, B2) and fondaco (F; A1, A2, B1, B2) cheeses. F1 and F2 = principal components 1 and 2, respectively; TotFFA = total free fatty acids; TotAA = total free amino acids; TotVOC = total volatile organic compounds; C18:1n9t = elaidic acid; C18:1n9c = oleic acid; C18:2n6 = linoleic acid; C6 = hexanoic acid; C8 = octanoic acid; C12 = lauric acid; C14 = mirystic acid; C15 = pentadecanoic acid; C16 = palmitic acid; C18 = stearic acid.
      Figure 4 shows the Pearson correlation map including the sensory (only considering taste and aroma attributes) and the chemical data. In the figure, the variables that present strong correlation are in bright green or red (positive and negative correlations, respectively), whereas dull colors represent a less strong correlation based on the Pearson coefficient interval. For control cheese, all sensory descriptors resulted to have a strong positive correlation with VOC (average r = 0.866); the same was found for AA but with lower correlation coefficient (average r = 0.50). In the same way, FFA were positively correlated with all the sensory perceptions except for fatty acids from C10 to C18:1 trans-9. Also, for fondaco cheese, the sensory attributes had a strong positive correlation with VOC, but the correlation coefficient of AA was higher than in control (r = 0.86), except for “acid.” Short-chain FFA (C6:0 and C8:0) were positively correlated with all the descriptors, in particular with the “pungent” descriptor that was indicated as much more intense by the panelists than in control cheese. Also, FFA with carbon chains >8 had positive correlations with the sensory descriptors except for C16:0, C18:1 trans-9, and C18:2 cis-9.
      Figure thumbnail gr4
      Figure 4Pearson correlation map including the sensory (taste and aroma attributes) and the chemical data of the cheeses. Scale indicates r-values.

      DISCUSSION

      The present investigation allowed us to assess the presence of differences in Canestrato di Moliterno cheeses ripened in traditional-nonconventional (fondaco cellar) or artificial conditions (controlled ripening room), and to understand the role of the cellar in determining the microbiological, chemical, and sensory characteristics. The higher values of the ripening index and pH supplied first evidence that fondaco cheese underwent faster maturation than control cheese. The results of the microbiological analyses supported this hypothesis because the prevalence of enterococci (an important representative of the nonstarter lactic acid bacteria group) over starter lactic acid bacteria (LAB) populations is a typical feature of an advanced ripening stage (
      • Anastasiou R.
      • Georgalaki M.
      • Manolopoulou E.
      • Kandarakis I.
      • De Vuyst L.
      • Tsakalidou E.
      The performance of Streptococcus macedonicus ACA-DC 198 as starter culture in Kasseri cheese production.
      ;
      • Colombo F.
      • Borgo F.
      • Fortina M.G.
      Genotypic characterization of non starter lactic acid bacteria involved in the ripening of artisanal Bitto PDO cheese.
      ;
      • Gobbetti M.
      • De Angelis M.
      • Di Cagno R.
      • Mancini L.
      • Fox P.F.
      Pros and cons for using non-starter lactic acid bacteria (NSLAB) as secondary/adjunct starters for cheese ripening.
      ). This is because enterococci are able to survive under the adverse conditions that form in cheese as ripening proceeds (i.e., low water activity, high salt concentration, lack of carbohydrates sources), whereas starter LAB tend to undergo to cell lysis (
      • Crow V.L.
      • Coolbear T.
      • Gopal P.K.
      • Martley F.G.
      • McKay L.L.
      • Riepe H.
      The role of autolysis of lactic acid bacteria in the ripening of cheese.
      ;
      • Serio A.
      • Chaves-López C.
      • Paparella A.
      • Suzzi G.
      Evaluation of metabolic activities of enterococci isolated from Pecorino Abruzzese cheese.
      ). Despite their common presence in artisanal cheeses, enterococci have not yet received “generally recognized as safe” (GRAS) status. Further evidence of accelerated ripening in fondaco was derived from the proteolysis study. The more intense casein degradation and higher level of free AA clearly indicated that both primary and secondary proteolysis proceeded faster. In addition to this, the lower concentration of the peptides found by LC-MS analysis suggested that peptidolysis also reached a more advanced level in fondaco cheese. In fact, the ratio between the concentrations of total peptides and of total FAA was 0.42 in F and 1.10 in C samples. The value of such ratios derives from the balance between proteasic and peptidasic activity that, in turn, depends on the composition, abundance, and metabolic status of microbiota. As such, it is likely that higher peptidolysis was connected to faster lysis of starter LAB under the ripening conditions of fondaco because enterococci have low peptidasic activity (
      • Sarantinopoulos P.
      • Andrighetto C.
      • Georgalaki M.D.
      • Rea M.C.
      • Lombardi A.
      • Cogan T.M.
      • Kalantzopoulos G.
      • Tsakalidou E.
      Biochemical properties of enterococci relevant to their technological performance.
      ).
      As for lipolysis, the absence of significant differences in the total FFA amounts was not consistent, with accelerated ripening found in the proteolysis study. It is known that triglycerides degradation in hard cheeses is caused by indigenous and exogenous lipases, as well as from the lipolytic activity of microorganisms. The results suggested that the environmental conditions of the 2 ripening rooms did not significantly influence the lipase enzymes activity. As for the role of microorganisms, the weak lipolytic activity of starter LAB (
      • Collins Y.F.
      • McSweeney P.L.
      • Wilkinson M.G.
      Lipolysis and free fatty acid catabolism in cheese: A review of current knowledge.
      ) and the limited difference in the counts of enterococci, which are much more lipolytic than LAB (
      • Sarantinopoulos P.
      • Andrighetto C.
      • Georgalaki M.D.
      • Rea M.C.
      • Lombardi A.
      • Cogan T.M.
      • Kalantzopoulos G.
      • Tsakalidou E.
      Biochemical properties of enterococci relevant to their technological performance.
      ), might have accounted for the similar intensity of lipolysis. Nevertheless, the qualitative differences observed in the FFA patterns are worth deepening because they appear as potentially useful in discriminating the cheeses.
      The different sensory characteristics of the 2 types of Canestrato di Moliterno demonstrated that fondaco ripening has a deep influence on the “perceivable quality” of the product. The “rough” and more intense flavor of the cheese ripened in fondaco matched well with the higher concentration of many compounds that are reported to exert strong effects on taste and aroma, such as butyric and caproic acids (
      • Chávarri F.
      • Angeles Bustamante M.
      • Santisteban A.
      • Virto M.
      • Barrón L.J.R.
      • de Renobales M.
      Changes in free fatty acids during ripening of Idiazabal cheese manufactured at different times of the year.
      ), ketones, alcohols, esters, and FAA (
      • Collins Y.F.
      • McSweeney P.L.
      • Wilkinson M.G.
      Lipolysis and free fatty acid catabolism in cheese: A review of current knowledge.
      ). On the other hand, the abundance of acids in the VOC profile of the C samples matched well with the higher value of the acid perception reported by the panelists. Our findings agree with those reported in a recent study published by
      • Bettera L.
      • Alinovi M.
      • Mondinelli R.
      • Mucchetti G.
      Ripening of Nostrano Valtrompia PDO cheese in different storage conditions: Influence on chemical, physical and sensory properties.
      on Nostrano Valtrompia Protected Designation of Origin cheese ripened under conventional and nonconventional conditions. In this work, the authors found differences in the characteristics of the cheeses and connected them to the seasonal fluctuations of the temperature in the uncontrolled ripening room. Our investigation did not contemplate constant monitoring of the temperature in fondaco, but the measurements done during the periodical inspections of the cheeses (always performed in the morning) gave values ranging from a minimum of 14°C in June to a maximum of 17°C in July, versus the constant temperature of 13°C in the artificial room. Consequently, it is highly probable that the differences observed in the present study were also caused, or at least strongly influenced, by temperature fluctuations.
      In conclusion, the results obtained demonstrated that ripening Canestrato di Moliterno in fondaco is responsible for the development of particular quality characteristics. Overall, the nonstandardized environmental conditions tended to accelerate maturation, with a related increase in the formation of low MW compounds responsible for flavor. However, this result must be considered within the context of the present experiment, in which the storage in fondaco took place in summer. In this period, the average temperature of the fondaco tends to be higher than that normally present in controlled rooms. As Canestrato di Moliterno is manufactured from January to May, it is likely that different results would be obtained when the cheese is transferred to fondaco in the cold season. In this case, we cannot exclude the possibility that slower maturation occurs, suggesting that the well-known seasonal variability of the cheese quality is caused not only by different quality of the milk but also by the ripening environment. The multiparameter approach allowed us to depict in detail the effect of ripening the cheese under different conditions and could be very useful to validate the authenticity of the product.

      ACKNOWLEDGMENTS

      This work was financially supported by the project PSR Regione Basilicata “Razionalizzazione e innovazione della filiera del formaggio Canestrato di Moliterno IGP—RICaMo” (Basilicata Region, Potenza, Italy). The authors have not stated any conflicts of interest.

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