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Comparison of cheeses from goats fed 7 forages based on a new health index

Open ArchivePublished:June 06, 2019DOI:https://doi.org/10.3168/jds.2018-15857

      ABSTRACT

      This study proposed the General Health Index of Cheese (GHIC) as an indicator for the presence of health-promoting compounds in cheese and compared the antioxidant capacity and phenolic and fatty acid contents of cheeses from goats consuming 7 forage species. Ninety-one homogeneous Red Syrian goats were randomly assigned to 1 of 7 feeding treatments (Festuca arundinacea, Hordeum vulgare, Triticosecale, Pisum sativum, Trifolium alexandrinum, Vicia sativa, and Vicia faba minor). The housed goat groups received the scheduled forage ad libitum. Forage species affected the antioxidant capacity, the phenolic and fatty acid contents, the Health Promoting Index, and the GHIC. Trifolium alexandrinum, Triticosecale, and Hordeum vulgare showed a clear advantage in terms of beneficial fatty acids content in goat cheese. Cheese from the Triticosecale group also showed a high antioxidant capacity value even if its polyphenol content was intermediate compared with others. Trifolium alexandrinum and Triticosecale had the highest value of the new index GHIC. This comparison suggests that there are important differences in fatty acid profile and polyphenol content among cheeses from goats fed grasses and legumes commonly used in the Mediterranean area. In this first approach, GHIC index, which combines the positive components found in cheese, seems to be a useful tool to provide an indication concerning the general health value of the product.

      Key words

      INTRODUCTION

      There is considerable interest in increasing levels of beneficial fatty acids (FA) and antioxidant compounds of dairy products with the overall aim of improving nutritional quality and the long-term health of consumers. Polyunsaturated FA play an important role in the prevention and treatment of heart diseases (
      • Simopoulos A.P.
      The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases.
      ). With the aim of improving the FA composition of dairy products, many researchers are focusing their studies on the modification of ruminal microbial metabolism of FA through animal diet formulation (
      • Savoini G.
      • Farina G.
      • Dell'Orto V.
      • Cattaneo D.
      Through ruminant nutrition to human health: Role of fatty acids.
      ). However, most studies have focused on the use of fish oil, oilseeds, or rumen-protected or inert lipids in the diet to modify milk FA profile (
      • Sampelayo M.S.
      • Pérez L.
      • Alonso J.M.
      • Amigo L.
      • Boza J.
      Effects of concentrates with different contents of protected fat rich in PUFAs on the performance lactating Granadina goats: Part II. Milk production and composition.
      ), with much less attention paid to single forage species that represent the principal dietary component in extensive and semi-intensive systems (
      • Bonanno A.
      • Di Grigoli A.
      • Di Trana A.
      • Di Gregorio P.
      • Tornambè G.
      • Bellina V.
      • Claps S.
      • Maggio G.
      • Todaro M.
      Influence of fresh forage-based diets and αS1-casein (CSN1S1) genotype on nutrient intake and productive, metabolic, and hormonal responses in milking goats.
      ). However, a high percentage of UFA makes products highly sensitive to oxidation. The presence of antioxidants could help prevent oxidation of milk FA. Additionally, increased antioxidants in milk may provide several health benefits to consumers, including protection against free radicals, which are able to oxidize biomolecules, leading to mutagenic changes, tissue damage, and cell death (
      • Celi P.
      Oxidative stress in ruminants.
      ). The use of synthetic antioxidants was considered an efficient tool for reducing changes in functional, sensory, and nutritive values of milk, but their use has been restricted due to their low stability and their toxic and carcinogenic effects (
      • Milos M.
      • Makota D.
      Investigation of antioxidant synergisms and antagonisms among thymol, carvacrol, thymoquinone and p-cymene in a model system using the Briggs–Rauscher oscillating reaction.
      ). Increasing consumer awareness of food additives and safety has increased interest in the use of antioxidants from natural sources, such as plant-derived polyphenols.
      Phenolic compounds are widespread in the plant kingdom, and their effectiveness as dietary antioxidants in animal feeding has received considerable attention in the last decade (
      • Vasta V.
      • Luciano G.
      The effect of dietary consumption of plant secondary compounds on small ruminants' product quality.
      ). The presence of antioxidant compounds in dairy products has been observed after the intake of some forage species and aromatic plants, thus providing the possibility of enriching milk and cheese with bioactive molecules fed directly to animals (
      • Sepe L.
      • Cornu A.
      • Graulet B.
      • Claps S.
      • Rufrano D.
      Phenolic content of forage, milk, whey and cheese from goats fed Avena sativa.
      ;
      • García V.
      • Rovira S.
      • Boutoial K.
      • López M.B.
      Improvements in goat milk quality: A review.
      ;
      • Di Trana A.
      • Bonanno A.
      • Cecchini S.
      • Giorgio D.
      • Di Grigoli A.
      • Claps S.
      Effects of Sulla forage (Sulla coronarium L.) on the oxidative status and milk polyphenol content in goats.
      ).
      An effect of feeding system on phenolic compounds and antioxidant capacity in goat milk, whey, and cheese was also observed in some studies (
      • Hilario M.C.
      • Puga C.D.
      • Ocana A.N.
      • Romo F.P.G.
      Antioxidant activity, bioactive polyphenols in Mexican goats' milk cheeses on summer grazing.
      ;
      • Chávez-Servín J.L.
      • Andrade-Montemayor H.M.
      • Vázquez C.V.
      • Barreyro A.A.
      • García-Gasca T.
      • Martínez R.A.F.
      • Ramírez A.M.O.
      • de la Torre-Carbot K.
      Effects of feeding system, heat treatment and season on phenolic compounds and antioxidant capacity in goat milk, whey and cheese.
      ). Furthermore, plant secondary compounds, such as tannic polyphenols, can have an effect on the milk FA composition because of their bacteriostatic and bactericidal effects on rumen microbes and consequently on ruminal biohydrogenation (
      • Vasta V.
      • Luciano G.
      The effect of dietary consumption of plant secondary compounds on small ruminants' product quality.
      ).
      Forage plants, such as grasses and legumes, play an essential role in goat feeding, representing a high proportion of the diet. Consequently, studies are required to assess whether commonly used Mediterranean forages can be a natural source of bioactive compounds, such as FA and polyphenols, which can be transferred directly or indirectly to animal products. In addition, no studies have simultaneously measured bioactive compounds, such as FA and polyphenols, and total antioxidant capacity in goat cheese.
      Taking into account the above considerations and that structural differences were observed when crude polyphenolic compounds were added to the cheese, resulting in rough and granular structures (
      • Han J.
      • Britten M.
      • St-Gelais D.
      • Champagne C.P.
      • Fustier P.
      • Salmieri S.
      • Lacroix M.
      Effect of polyphenolic ingredients on physical characteristics of cheese.
      ), the aim of this work was to evaluate and to compare the natural antioxidant capacity, phenolic content, and healthy FA profile of goat cheeses from animals fed 7 forages, 3 grasses, and 4 legumes. In the current study, we also identified for the first time a new index named the General Health Index of Cheese (GHIC) for classifying our products as a function of the bioactive fractions known to have beneficial effects on human health.

      MATERIALS AND METHODS

      Location, Animals, and Diets

      The experiment was carried out at the experimental farm of the CREA Research Centre for Animal Production and Aquaculture, Bella Muro, located 360 m above sea level in the Basilicata region (Southern Italy), from late May to early June. During the experiment, the goats were managed according to European Union guidelines for accommodation and care of experimental animals (
      • European Commission
      Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes.
      ) and a recommendation of the Commission of the European Communities (
      • European Commission
      Commission recommendation of 18 June 2007 on Guidelines for the accommodation and care of animals used for experimental and other scientific purposes.
      ), CREA (Bella-Muro) authorization of Ministry of Health no. 037D8. Ninety-one Red Syrian goats were allotted into 7 homogeneous groups by live weight (42 ± 1.2 kg), BCS (score of 2.3), milk production (1.1 ± 0.3 kg/d), and DIM (90 ± 4 d). To have homogeneous animals for DIM, in September the goats were estrous synchronized and mated so that kidding occurred within the last week of February. The goats were randomly allocated to the 7 feeding treatments with forage species: tall fescue (Festuca arundinacea Schreb.), barley (Hordeum vulgare L.), triticale (Triticosecale Wittm.), pea (Pisum sativum L.), berseem (Trifolium alexandrinum L.), common vetch (Vicia sativa L.), and fava bean (Vicia faba L., var. minor). The goats were housed in group pens (1 pen/group), received the scheduled forage ad libitum with no supplementation, and had access to water and salt blocks. The forage species were sown in the same area at the farm of CREA (Bella-Muro) and grown in the same pedoclimatic conditions. The forages were cut and administered to housed goats at the following phenological stages, which are commonly used for grazing animals: T. alexandrinum, V. faba minor, and V. sativa at bloom; Triticosecale and H. vulgare at milk waxy; P. sativum at early seed; and F. arundinacea at pre-earing. The forages were harvested daily, coarsely cut, and offered ad libitum. The chemical composition of the offered forages is shown in Table 1.
      Table 1Chemical composition of forage species (% of DM)
      ItemGrassesLegumes
      Festuca arundinaceaHordeum vulgareTriticosecalePisum sativumTrifolium alexandrinumVicia faba minorVicia sativa
      DM (% as fed)44.4350.0054.0945.1347.0824.7330.71
      CP12.9712.148.4619.1814.6422.4715.71
      Ether extract0.972.301.181.340.872.091.30
      Ash6.813.506.427.258.314.418.13
      NDF55.3562.0460.7137.1235.4127.3134.13
      ADF36.3225.9339.4020.7432.9016.7427.00
      ADL5.842.956.752.456.991.435.80
      NFC
      NFC = 100 − (% NDF + % CP + % ether extract + % ash).
      23.8920.0223.2335.1240.7743.7240.74
      1 NFC = 100 − (% NDF + % CP + % ether extract + % ash).
      Each experimental period lasted 11 d, with 8 d to allow the rumen to fully adapt to the forage and 3 d for sampling and data collection. During the last 3 d of the experimental period, daily forage intake was measured. The forage sampling was carried out 3 times, and each sample was collected in duplicate. A subsample was immediately frozen (−80°C) and subsequently lyophilized before polyphenols and total antioxidant capacity analyses, and the remaining samples were stored at −20°C for further analyses.

      Cheese-Making

      In the days of the experimental period, goats were milked using a pipeline milking machine and the bulk milk of each group was processed into goat cheese. Two or more caciotta cheeses (350–400 g) per group and day of sampling were manufactured using a small-scale manufacturing facility at the cheese factory of CREA (Bella-Muro). The flowchart of caciotta cheese is presented in
      • Di Trana A.
      • Sepe L.
      • Di Gregorio P.
      • Di Napoli M.A.
      • Giorgio D.
      • Caputo A.R.
      • Claps S.
      The role of local sheep and goat breeds and their products as a tool for sustainability and safeguard of the Mediterranean environment.
      . Briefly, raw whole milk was filtered and heated in a stainless vat to 37°C. Liquid calf rennet was added to milk in the amount of 20 mL/100 kg of milk. Coagulation took place approximately 20 min after the addition of calf rennet, and the curd was broken down until it reached the size of nut pieces. After draining off the whey, the curd was pressed into cylindrical and perforated molds and stewed at 37°C for 3 h. After cooling to 4 to 6°C, molded cheeses were dry salted for 3 d. The average weight of the manufactured cheeses was as follows: F. arundinacea, 2.20 kg/d; H. vulgare, 0.820 kg/d; Triticosecale, 0.940 kg/d; P. sativum, 1.39 kg/d; T. alexandrinum, 1.23 kg/d; V. faba minor, 1.08 kg/d; and V. sativa, 1.48 kg/d. Cheese samples were collected after 20 d of ripening (at 10°C and 80% environmental humidity) and stored at −80°C until chemical analyses. The chemical analyses were performed on each cheese in duplicate.

      Chemical Analysis of Forage and Cheese

      The chemical composition of forages was analyzed by NIRSystem 5000 (Foss Electric, Hillerød, Denmark; Table 1). The cheese samples were analyzed for chemical composition (DM, fat, and protein; Table 2) using the standard methods described by
      • Di Trana A.
      • Di Napoli M.A.
      • Claps S.
      • Sepe L.
      • Caputo A.R.
      • Fedele V.
      Effect of feeding forage species on fatty acid profile of goat milk and cheese.
      .
      Table 2Least squares means of milk yield, chemical composition, polyphenol content, polyphenolic compounds recovery, and total antioxidant capacity of cheese
      ItemFestuca arundinaceaHordeum vulgareTriticosecalePisum sativumTrifolium alexandrinumVicia faba minorVicia sativaSEMP-value
      Milk yield (g/d)1,061.54
      Means within a row with different superscripts differ (P < 0.05).
      338.46
      Means within a row with different superscripts differ (P < 0.05).
      410.26
      Means within a row with different superscripts differ (P < 0.05).
      561.54
      Means within a row with different superscripts differ (P < 0.05).
      497.44
      Means within a row with different superscripts differ (P < 0.05).
      484.61
      Means within a row with different superscripts differ (P < 0.05).
      707.69
      Means within a row with different superscripts differ (P < 0.05).
      30.43
      P ≤ 0.001.
      Cheese DM (%)52.80
      Means within a row with different superscripts differ (P < 0.05).
      56.82
      Means within a row with different superscripts differ (P < 0.05).
      55.64
      Means within a row with different superscripts differ (P < 0.05).
      54.84
      Means within a row with different superscripts differ (P < 0.05).
      59.69
      Means within a row with different superscripts differ (P < 0.05).
      53.17
      Means within a row with different superscripts differ (P < 0.05).
      56.53
      Means within a row with different superscripts differ (P < 0.05).
      0.98
      P ≤ 0.01
      Cheese protein (% of DM)35.84
      Means within a row with different superscripts differ (P < 0.05).
      38.20
      Means within a row with different superscripts differ (P < 0.05).
      39.05
      Means within a row with different superscripts differ (P < 0.05).
      38.51
      Means within a row with different superscripts differ (P < 0.05).
      37.36
      Means within a row with different superscripts differ (P < 0.05).
      35.17
      Means within a row with different superscripts differ (P < 0.05).
      40.05
      Means within a row with different superscripts differ (P < 0.05).
      0.63
      P ≤ 0.01
      Cheese fat (% of DM)55.00
      Means within a row with different superscripts differ (P < 0.05).
      45.77
      Means within a row with different superscripts differ (P < 0.05).
      45.58
      Means within a row with different superscripts differ (P < 0.05).
      54.75
      Means within a row with different superscripts differ (P < 0.05).
      53.60
      Means within a row with different superscripts differ (P < 0.05).
      50.78
      Means within a row with different superscripts differ (P < 0.05).
      47.77
      Means within a row with different superscripts differ (P < 0.05).
      0.62
      P ≤ 0.001.
      Cheese total polyphenols (mg of GAE
      GAE = gallic acid equivalents.
      /100 g of cheese)
      174.92
      Means within a row with different superscripts differ (P < 0.05).
      219.38
      Means within a row with different superscripts differ (P < 0.05).
      194.07
      Means within a row with different superscripts differ (P < 0.05).
      177.36
      Means within a row with different superscripts differ (P < 0.05).
      194.07
      Means within a row with different superscripts differ (P < 0.05).
      146.02
      Means within a row with different superscripts differ (P < 0.05).
      191.17
      Means within a row with different superscripts differ (P < 0.05).
      5.48
      P ≤ 0.001.
      Cheese total polyphenols (g of GAE/kg of DM)3.34
      Means within a row with different superscripts differ (P < 0.05).
      3.94
      Means within a row with different superscripts differ (P < 0.05).
      2.78
      Means within a row with different superscripts differ (P < 0.05).
      3.30
      Means within a row with different superscripts differ (P < 0.05).
      3.19
      Means within a row with different superscripts differ (P < 0.05).
      2.77
      Means within a row with different superscripts differ (P < 0.05).
      3.36
      Means within a row with different superscripts differ (P < 0.05).
      0.106
      P ≤ 0.001.
      Polyphenolic compounds recovery (%)3.49
      Means within a row with different superscripts differ (P < 0.05).
      8.92
      Means within a row with different superscripts differ (P < 0.05).
      3.54
      Means within a row with different superscripts differ (P < 0.05).
      3.30
      Means within a row with different superscripts differ (P < 0.05).
      9.81
      Means within a row with different superscripts differ (P < 0.05).
      0.81
      Means within a row with different superscripts differ (P < 0.05).
      2.77
      Means within a row with different superscripts differ (P < 0.05).
      0.194
      P ≤ 0.01
      Total antioxidant capacity (mol FeSO4 equivalents/kg)3.03
      Means within a row with different superscripts differ (P < 0.05).
      1.73
      Means within a row with different superscripts differ (P < 0.05).
      3.05
      Means within a row with different superscripts differ (P < 0.05).
      3.44
      Means within a row with different superscripts differ (P < 0.05).
      2.85
      Means within a row with different superscripts differ (P < 0.05).
      3.14
      Means within a row with different superscripts differ (P < 0.05).
      3.64
      Means within a row with different superscripts differ (P < 0.05).
      0.27
      P ≤ 0.01
      a–e Means within a row with different superscripts differ (P < 0.05).
      1 GAE = gallic acid equivalents.
      ** P ≤ 0.01
      *** P ≤ 0.001.

      Total Phenolic Content in Forage and Cheese

      The polyphenol content in forage was extracted with water as suggested by
      • Cecchini S.
      • Paciolla M.
      • Caputo A.R.
      • Bavoso A.
      Antioxidant potential of the polyherbal formulation “immuplus”: A nutritional supplement for horses.
      with slight modifications. Briefly, 5 g of forage sample was extracted by soaking in 50 mL of water at room temperature (20°C) for 48 h under gentle shaking. The extracts were centrifuged at 3,000 × g for 20 min at 4°C. The supernatants were collected and filtered progressively up to 0.45 µm before concentrating them using a lyophilizator. Extract was suspended in the water at a concentration of 1 mg/mL before phytochemical analyses. The total polyphenol content of forage extracts was determined using the Folin–Ciocalteu method (
      • ISO (International Organization for Standardization)
      Determination of substances characteristic of green and black tea—Part 1: Content of total polyphenols in tea—Colorimetric method using Folin-Ciocalteu reagent. ISO method 14502-1.
      ). Tannic and nontannic polyphenol contents were determined using the Folin–Ciocalteu method after the addition of the insoluble matrix, polyvinylpolypyrrolidone, according to
      • FAO (Food and Agriculture Organization of the United Nations)/IAEA (International Atomic Energy Agency)
      Quantification of Tannins in Tree Foliage.
      . The total, nontannic, and tannic polyphenol contents were calculated from a standard curve using gallic acid as the standard, and results were expressed as milligrams of gallic acid equivalents (GAE) per gram of extracted sample.
      Cheese total polyphenol (TP-C) was determined using the Folin–Ciocalteu colorimetric method after a methanolic extraction of sample according to the method of
      • Rashidinejad A.
      • Birch E.J.
      • Sun-Waterhouse D.
      • Everett D.W.
      Effects of catechin on the phenolic content and antioxidant properties of low-fat cheese.
      with slight modifications. Briefly, 500 mg of grated cheese was homogenized for 1 min and extracted with 25 mL of solution of methanol (aqueous solution 95%) and HCl (1%) for 30 min at 50°C under shaking (200 rpm). The blend was cooled and filtered with a linen cloth, and the residue was washed with 1 mL of the same solution. Cheese total polyphenol was expressed as milligrams of GAE per 100 grams of sample.

      Antioxidant Capacity of Forage and Cheese

      Total antioxidant capacity of extract samples of cheese (TAC-C) and of forage was measured using the ferric ion reducing antioxidant power assay as indicated by
      • Benzie I.F.
      • Strain J.J.
      The ferric reducing ability of plasma (FRAP) as a measure of “antioxidant power”: The FRAP assay.
      . The standard curve was constructed using iron sulfate heptahydrate (FeSO4·7H2O). Data were expressed as mol FeSO4 equivalents per kg cheese and as µM FeSO4 equivalents for forage samples.

      FA Composition of Forage and Cheese

      Lipid fraction of forage was extracted from 20 g of forage sample using a mixture of chloroform and methanol (2/1 vol/vol) according to
      • Gray I.K.
      • Rumsby M.G.
      • Hawke J.C.
      The variations in linolenic acid and galactolipid levels in Graminaceae species with age of tissue and light environment.
      . Lipid extract was methylated adding 1 mL of hexane and 0.05 mL of 2 N methanolic KOH (
      • IUPAC
      Standard Methods for the Analysis of Oils, Fats and Derivatives.
      ).
      Lipid fraction of cheese was obtained from cheeses samples thawed slowly at refrigerator temperature (4°C) and finely grated. Three grams of grated cheese was extracted with chloroform and methanol mixture (2/1 vol/vol) according to
      • Bligh E.G.
      • Dyer W.J.
      A rapid method of total lipid extraction and purification.
      . Lipid extract was methylated adding 1 mL of hexane and 0.05 mL of 2 N methanolic KOH (
      • IUPAC
      Standard Methods for the Analysis of Oils, Fats and Derivatives.
      ).
      Separation of the methyl esters in forage and cheese samples was performed according to
      • Di Trana A.
      • Cifuni G.F.
      • Impemba G.
      • Braghieri A.
      • Claps S.
      • Rubino R.
      The grazing system and season affect CLA, ω-3 and trans fatty acid contents in goat milk.
      . Fatty acid methyl esters were identified with reference to the retention time of FA standard mixture of Supelco 37 Component FAME Mix (Supelco, Bellafonte, PA). The individual standard of CLA isomers (cis-9,trans-11 97% and trans-10,cis-12 3%) and C18:1 trans-11 standard were obtained from Larodan (Solna, Sweden). The content of FA was quantified using internal standards (C9:0, C19:0, and C23:0; Supelco) added during the methylation step. Briefly, 100 mg of lipid extract was mixed with 50 μL of 2 N methanolic KOH and 1 mL of hexane containing the internal standards (20 mg/mL).

      Nutritional Indexes

      The Health Promoting Index (HPI) was calculated as suggested by
      • Chen S.
      • Bobe G.
      • Zimmerman S.
      • Hammond E.G.
      • Luhman C.M.
      • Boylston T.D.
      • Freeman A.E.
      • Beitz D.C.
      Physical and sensory properties of dairy products from cows with various milk fatty acid compositions.
      : (n-3 PUFA + n-6 PUFA + MUFA)/[C12:0 + (4 × C14:0) + C16:0]. A new index, GHIC, was calculated to focus on both FA and antioxidant capacity of dairy products. The GHIC was calculated taking into consideration the following indicators: polyphenols, CLA isomers, PUFA, omega-3 FA, and total antioxidant capacity. In addition to CLA, PUFA, and omega-3, which are already known as health-promoting compounds, in GHIC calculation we used polyphenols and total antioxidant capacity because of their increasing health interest (
      • O'Connell J.E.
      • Fox P.F.
      Significance and applications of phenolic compounds in the production and quality of milk and dairy products: A review.
      ;
      • Puchau B.
      • Zulet M.Á.
      • de Echávarri A.G.
      • Hermsdorff H.H.M.
      • Martínez J.A.
      Dietary total antioxidant capacity: A novel indicator of diet quality in healthy young adults.
      ). For each of the selected indicators, we defined minimum and maximum benchmarks, enabling us to rescale indicator values into scores between 0 (indicating a low health value) and 10 (indicating a high health value). In this first approach, the minimum and maximum values of these benchmarks were defined according to our data. For intermediate values, an increasing score of 1 was applied to each 5% increase of the value of the variable. For each cheese obtained from goats fed different forage species, we compared the value of each indicator with the minimum and maximum values of the scoring scale (Table 3). Then, the scores of the different indicators were summed for each cheese to obtain the GHIC.
      Table 3Scheme for calculating the General Health Index of Cheese (GHIC) of caciotta cheeses from goats fed different forage species
      ItemMinumum
      Miminimum and maximum values of the parameters considered.
      Maximum
      Miminimum and maximum values of the parameters considered.
      Percentage increase
      Percentage increase from the minimum value observed in cheeses.
      (score
      Score corresponding to each 5% increase.
      )
      5 (0)10 (1)15 (2)20 (3)25 (4)30 (5)35 (6)40 (7)45 (8)50 (9)55 (10)
      Cheese total polyphenols (mg of GAE
      GAE = gallic acid equivalents.
      /100 g of cheese)
      146.02219.38155.53166.50177.46188.43199.40210.37221.34232.31243.28254.25265.21
      Total antioxidant capacity (mol FeSO4 equivalents/kg)1.733.641.892.072.262.442.622.802.983.163.353.533.71
      PUFA (g/100 g of cheese)0.941.521.001.081.161.231.311.391.461.541.611.691.77
      CLA (g/100 g of cheese)0.110.200.110.120.130.140.150.160.170.180.190.200.21
      ∑n-3 (g/100 g of cheese)0.130.260.140.150.170.180.190.200.220.230.240.260.27
      1 Miminimum and maximum values of the parameters considered.
      2 Percentage increase from the minimum value observed in cheeses.
      3 Score corresponding to each 5% increase.
      4 GAE = gallic acid equivalents.

      Statistical Analysis

      The data of FA composition were processed to compute the content of SFA, MUFA, PUFA, and UFA. The odd- and branched-chain FA (OBCFA), Σn-3, Σn-6, Σn-6/Σn-3 ratios, C12:0/OBCFA, HPI, and GHIC were calculated. Statistical analysis of data was performed using the GLM procedure in Systat 13 (Systat Software Inc., Chicago, IL). The model included the feeding treatment with forage species (F. arundinacea, H. vulgare, Triticosecale, P. sativum, T. alexandrinum, V. sativa, and V. faba minor) and day of cheese-making as fixed factors. No significant effect of day was observed. Mean comparison was tested by Tukey test. Pearson correlation coefficients were calculated among variables. Statistical differences were considered significant at P ≤ 0.05.

      RESULTS AND DISCUSSION

      Cheese Characteristics

      The forage species significantly affected the chemical composition of cheese as shown in Table 2. Cheeses from T. alexandrinum exhibited a higher DM percentage (59.69%) compared with V. faba minor and F. arundinacea cheeses (53.17 and 52.80%, respectively). The highest fat percentage was observed in cheeses from goats fed F. arundinacea and P. sativum (55 and 54.75%, respectively). Cheeses from V. sativa and Triticosecale showed a higher protein percentage (40.05 and 39.05%, respectively) compared with F. arundinacea and V. faba minor cheeses (35.84 and 35.17%, respectively). The goat cheese composition varied within the range usually observed for this type of cheese, which is produced in an artisanal way by a local cheese-maker using a small amount of bulk milk. Milk and cheese quality is a result of a complex process that is under the influence of many factors. Among them, the chemical composition, digestibility, and intake level of forage are the most important. Milk production depends on the level of intake, and fat content depends on the indirect effect of dilution; these aspects appear to be more important when diet included only 1 forage species and less important when several species were mixed (
      • Morand-Fehr P.
      • Fedele V.
      • Decandia M.
      • Le Frileux Y.
      Influence of farming and feeding systems on composition and quality of goat and sheep milk.
      ). In fact, goats are able to select their diet when more forage species are available and, as a consequence, to reduce the variations of energy and protein ingested daily.

      Total Phenolic Content

      Significant effects of the forage species were found for cheese total polyphenol (P < 0.001) and polyphenolic compounds recovery (P < 0.01; Table 2). Cheese from milk of goats fed H. vulgare exhibited the highest value of TP-C (3.94 g of GAE/kg of DM). Compared with the H. vulgare cheese, in grasses polyphenols decreased 15.2% for F. arundinacea and 29.4% for Triticosecale, and in legumes decreased 14.7% for V. sativa, 16.2% for P. sativum, 19.0% for T. alexandrinum, and 29.7% for V. faba minor. The lowest TP-C content was found in cheese from goats fed V. faba minor (2.77 g of GAE/kg of DM). There were no significant differences between cheeses obtained from goats fed F. arundinacea, P. sativum, T. alexandrinum, and V. sativa.
      Previous investigations have reported that the diet ingested by goats influences milk and cheese polyphenol content (
      • Hilario M.C.
      • Puga C.D.
      • Ocana A.N.
      • Romo F.P.G.
      Antioxidant activity, bioactive polyphenols in Mexican goats' milk cheeses on summer grazing.
      ;
      • Sepe L.
      • Cornu A.
      • Graulet B.
      • Claps S.
      • Rufrano D.
      Phenolic content of forage, milk, whey and cheese from goats fed Avena sativa.
      ;
      • Boutoial K.
      • Garcìa V.
      • Rovira S.
      • Ferrandini E.
      • Abdelkhalek O.
      • Lòpez M.B.
      Effect of feeding goats with distilled and non-distilled thyme leaves (Thymus zygis subp. gracilis) on milk and cheese properties.
      ;
      • Di Trana A.
      • Bonanno A.
      • Cecchini S.
      • Giorgio D.
      • Di Grigoli A.
      • Claps S.
      Effects of Sulla forage (Sulla coronarium L.) on the oxidative status and milk polyphenol content in goats.
      ;
      • Chávez-Servín J.L.
      • Andrade-Montemayor H.M.
      • Vázquez C.V.
      • Barreyro A.A.
      • García-Gasca T.
      • Martínez R.A.F.
      • Ramírez A.M.O.
      • de la Torre-Carbot K.
      Effects of feeding system, heat treatment and season on phenolic compounds and antioxidant capacity in goat milk, whey and cheese.
      ).
      • Hilario M.C.
      • Puga C.D.
      • Ocana A.N.
      • Romo F.P.G.
      Antioxidant activity, bioactive polyphenols in Mexican goats' milk cheeses on summer grazing.
      found an increase in total polyphenol content in goat milk and cheese obtained from grazing animals compared with an indoor feeding system.
      • Sepe L.
      • Cornu A.
      • Graulet B.
      • Claps S.
      • Rufrano D.
      Phenolic content of forage, milk, whey and cheese from goats fed Avena sativa.
      detected simple phenols in the cheese, milk, and whey of goats fed Avena sativa.
      • Boutoial K.
      • Garcìa V.
      • Rovira S.
      • Ferrandini E.
      • Abdelkhalek O.
      • Lòpez M.B.
      Effect of feeding goats with distilled and non-distilled thyme leaves (Thymus zygis subp. gracilis) on milk and cheese properties.
      showed that the introduction of nondistilled thyme leaves in goat diet increased cheese polyphenol content.
      • Di Trana A.
      • Bonanno A.
      • Cecchini S.
      • Giorgio D.
      • Di Grigoli A.
      • Claps S.
      Effects of Sulla forage (Sulla coronarium L.) on the oxidative status and milk polyphenol content in goats.
      reported an increase in total polyphenol content in goat milk as a consequence of increased intake of Sulla coronarium L. fresh forage containing phenolic compounds. Recently,
      • Chávez-Servín J.L.
      • Andrade-Montemayor H.M.
      • Vázquez C.V.
      • Barreyro A.A.
      • García-Gasca T.
      • Martínez R.A.F.
      • Ramírez A.M.O.
      • de la Torre-Carbot K.
      Effects of feeding system, heat treatment and season on phenolic compounds and antioxidant capacity in goat milk, whey and cheese.
      observed an effect of feeding system (free-range grazing and permanent confinement) on phenolic compounds and antioxidant capacity in goat milk, whey, and cheese.
      In our study, the differences observed in TP-C content could be ascribed to the polyphenolic profiles of forage species rather than the forage polyphenol content and daily forage intake (Table 4). In fact, the polyphenolic profiles of experimental forage species (Table 4) differ from each other in terms of total polyphenols, nontannic polyphenols, and tannic polyphenols. In addition, when plant extracts are used, it is possible to gain information on the effect of their constituent polyphenols as a group (nontannic and tannic polyphenols) but not on the digestive fate and specific effects of individual polyphenols. Unfortunately, in our study, the polyphenol qualitative profile was not investigated and we can only speculate about the similar polyphenol composition between H. vulgare and T. alexandrinum. In the planning of subsequent studies, it would be advisable to investigate both the forage digestibility and polyphenolic profile.
      Table 4Least squares means of polyphenol content (mg of gallic acid equivalents/g of extract), fatty acid content (g/kg of DM), total antioxidant capacity (μM FeSO4) of forage species, and intake (g/head per day)
      ItemGrassesLegumesSEMP-value
      Festuca arundinaceaHordeum vulgareTriticosecalePisum sativumTrifolium alexandrinumVicia faba minorVicia sativa
      Polyphenol content
       Total polyphenols26.85
      Means within a row with different superscripts differ (P < 0.05).
      16.32
      Means within a row with different superscripts differ (P < 0.05).
      37.88
      Means within a row with different superscripts differ (P < 0.05).
      26.32
      Means within a row with different superscripts differ (P < 0.05).
      15.98
      Means within a row with different superscripts differ (P < 0.05).
      39.94
      Means within a row with different superscripts differ (P < 0.05).
      38.23
      Means within a row with different superscripts differ (P < 0.05).
      1.44
      P ≤ 0.001.
       Nontannic polyphenols23.34
      Means within a row with different superscripts differ (P < 0.05).
      12.70
      Means within a row with different superscripts differ (P < 0.05).
      11.64
      Means within a row with different superscripts differ (P < 0.05).
      18.77
      Means within a row with different superscripts differ (P < 0.05).
      8.98
      Means within a row with different superscripts differ (P < 0.05).
      6.11
      Means within a row with different superscripts differ (P < 0.05).
      6.53
      Means within a row with different superscripts differ (P < 0.05).
      0.49
      P ≤ 0.001.
       Tannic polyphenols3.51
      Means within a row with different superscripts differ (P < 0.05).
      3.62
      Means within a row with different superscripts differ (P < 0.05).
      26.24
      Means within a row with different superscripts differ (P < 0.05).
      7.55
      Means within a row with different superscripts differ (P < 0.05).
      6.92
      Means within a row with different superscripts differ (P < 0.05).
      33.83
      Means within a row with different superscripts differ (P < 0.05).
      31.70
      Means within a row with different superscripts differ (P < 0.05).
      1.34
      P ≤ 0.001.
      Fatty acid content
       ∑C10-C130.12
      Means within a row with different superscripts differ (P < 0.05).
      0.60
      Means within a row with different superscripts differ (P < 0.05).
      0.08
      Means within a row with different superscripts differ (P < 0.05).
      0.18
      Means within a row with different superscripts differ (P < 0.05).
      0.08
      Means within a row with different superscripts differ (P < 0.05).
      1.32
      Means within a row with different superscripts differ (P < 0.05).
      0.20
      Means within a row with different superscripts differ (P < 0.05).
      0.08
      P ≤ 0.001.
       ∑C14:0-C18:01.22
      Means within a row with different superscripts differ (P < 0.05).
      4.10
      Means within a row with different superscripts differ (P < 0.05).
      2.36
      Means within a row with different superscripts differ (P < 0.05).
      2.48
      Means within a row with different superscripts differ (P < 0.05).
      1.58
      Means within a row with different superscripts differ (P < 0.05).
      3.73
      Means within a row with different superscripts differ (P < 0.05).
      2.52
      Means within a row with different superscripts differ (P < 0.05).
      0.06
      P ≤ 0.001.
       C16:1 cis0.19
      Means within a row with different superscripts differ (P < 0.05).
      0.63
      Means within a row with different superscripts differ (P < 0.05).
      0.27
      Means within a row with different superscripts differ (P < 0.05).
      0.29
      Means within a row with different superscripts differ (P < 0.05).
      0.12
      Means within a row with different superscripts differ (P < 0.05).
      0.67
      Means within a row with different superscripts differ (P < 0.05).
      0.21
      Means within a row with different superscripts differ (P < 0.05).
      0.04
      P ≤ 0.001.
       ∑C18:1 cis-90.21
      Means within a row with different superscripts differ (P < 0.05).
      0.81
      Means within a row with different superscripts differ (P < 0.05).
      1.33
      Means within a row with different superscripts differ (P < 0.05).
      0.32
      Means within a row with different superscripts differ (P < 0.05).
      0.14
      Means within a row with different superscripts differ (P < 0.05).
      0.49
      Means within a row with different superscripts differ (P < 0.05).
      0.78
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      P ≤ 0.001.
       C18:2 cis-9,cis-120.74
      Means within a row with different superscripts differ (P < 0.05).
      2.55
      Means within a row with different superscripts differ (P < 0.05).
      1.54
      Means within a row with different superscripts differ (P < 0.05).
      1.29
      Means within a row with different superscripts differ (P < 0.05).
      1.11
      Means within a row with different superscripts differ (P < 0.05).
      1.46
      Means within a row with different superscripts differ (P < 0.05).
      1.68
      Means within a row with different superscripts differ (P < 0.05).
      0.17
      P ≤ 0.001.
       C18:3 cis-6,cis-9,cis-120.02
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      Means within a row with different superscripts differ (P < 0.05).
      0.05
      Means within a row with different superscripts differ (P < 0.05).
      0.03
      Means within a row with different superscripts differ (P < 0.05).
      0.01
      Means within a row with different superscripts differ (P < 0.05).
      ND
      ND = not detected.
      0.00
      Means within a row with different superscripts differ (P < 0.05).
      0.001
      P ≤ 0.001.
       C18:3 cis-9,cis-12,cis-154.34
      Means within a row with different superscripts differ (P < 0.05).
      7.53
      Means within a row with different superscripts differ (P < 0.05).
      2.66
      Means within a row with different superscripts differ (P < 0.05).
      4.74
      Means within a row with different superscripts differ (P < 0.05).
      3.06
      Means within a row with different superscripts differ (P < 0.05).
      6.97
      Means within a row with different superscripts differ (P < 0.05).
      3.67
      Means within a row with different superscripts differ (P < 0.05).
      0.19
      P ≤ 0.001.
       ∑C20-C210.17
      Means within a row with different superscripts differ (P < 0.05).
      0.31
      Means within a row with different superscripts differ (P < 0.05).
      0.20
      Means within a row with different superscripts differ (P < 0.05).
      0.31
      Means within a row with different superscripts differ (P < 0.05).
      0.19
      Means within a row with different superscripts differ (P < 0.05).
      0.43
      Means within a row with different superscripts differ (P < 0.05).
      0.30
      Means within a row with different superscripts differ (P < 0.05).
      0.04
      P ≤ 0.01
      Total antioxidant capacity
       TAC-F
      TAC-F = total antioxidant capacity in forage.
      131.19
      Means within a row with different superscripts differ (P < 0.05).
      65.16
      Means within a row with different superscripts differ (P < 0.05).
      99.91
      Means within a row with different superscripts differ (P < 0.05).
      101.65
      Means within a row with different superscripts differ (P < 0.05).
      132.96
      Means within a row with different superscripts differ (P < 0.05).
      288.46
      Means within a row with different superscripts differ (P < 0.05).
      218.08
      Means within a row with different superscripts differ (P < 0.05).
      19.98
      P ≤ 0.001.
      Intake
       DM1,377
      Means within a row with different superscripts differ (P < 0.05).
      1,059
      Means within a row with different superscripts differ (P < 0.05).
      1,091
      Means within a row with different superscripts differ (P < 0.05).
      1,157
      Means within a row with different superscripts differ (P < 0.05).
      1,129
      Means within a row with different superscripts differ (P < 0.05).
      1,123
      Means within a row with different superscripts differ (P < 0.05).
      1,221
      Means within a row with different superscripts differ (P < 0.05).
      61.00
      P ≤ 0.05
       Total polyphenols9.327
      Means within a row with different superscripts differ (P < 0.05).
      1.901
      Means within a row with different superscripts differ (P < 0.05).
      4.891
      Means within a row with different superscripts differ (P < 0.05).
      6.650
      Means within a row with different superscripts differ (P < 0.05).
      2.012
      Means within a row with different superscripts differ (P < 0.05).
      15.817
      Means within a row with different superscripts differ (P < 0.05).
      8.029
      Means within a row with different superscripts differ (P < 0.05).
      0.065
      P ≤ 0.001.
       Nontannic polyphenols8.107
      Means within a row with different superscripts differ (P < 0.05).
      1.479
      Means within a row with different superscripts differ (P < 0.05).
      1.503
      Means within a row with different superscripts differ (P < 0.05).
      4.742
      Means within a row with different superscripts differ (P < 0.05).
      1.137
      Means within a row with different superscripts differ (P < 0.05).
      2.418
      Means within a row with different superscripts differ (P < 0.05).
      1.372
      Means within a row with different superscripts differ (P < 0.05).
      0.040
      P ≤ 0.001.
       Tannic polyphenols1.219
      Means within a row with different superscripts differ (P < 0.05).
      0.421
      Means within a row with different superscripts differ (P < 0.05).
      3.388
      Means within a row with different superscripts differ (P < 0.05).
      1.909
      Means within a row with different superscripts differ (P < 0.05).
      0.875
      Means within a row with different superscripts differ (P < 0.05).
      13.398
      Means within a row with different superscripts differ (P < 0.05).
      6.658
      Means within a row with different superscripts differ (P < 0.05).
      0.033
      P ≤ 0.001.
      a–g Means within a row with different superscripts differ (P < 0.05).
      1 ND = not detected.
      2 TAC-F = total antioxidant capacity in forage.
      * P ≤ 0.05
      ** P ≤ 0.01
      *** P ≤ 0.001.
      It is known that the different polyphenolic profile of forages is closely associated with the different plant species, botanical family, environmental feature, period of harvesting, and their evolution when the plants grow older (
      • Jeangros B.
      • Scehovic J.
      • Schubiger F.X.
      • Lehmann J.
      • Daccord R.
      • Arrigo Y.
      Valeur nutritive des plantes des prairies 4. Composés phénoliques.
      ;
      • Piluzza G.
      • Bullitta S.
      The dynamics of phenolic concentration in some pasture species and implications for animal husbandry.
      ). In addition, the chemical and structural differences of plant polyphenols (i.e., molecular size, degree of polymerization of soluble and insoluble polyphenols, nature of the phenolic bind to other forage components;
      • Bravo L.
      Polyphenols: Chemistry, dietary sources, metabolism, and nutritional significance.
      ) result in a different bioavailability of these compounds in the rumen and gastrointestinal tract. The degradation and absorption of polyphenols within the rumen and the gastrointestinal tract depend both on the nature of the phenolic compound and on the rumen and intestinal microflora (
      • Bravo L.
      Polyphenols: Chemistry, dietary sources, metabolism, and nutritional significance.
      ), thus affecting the transfer of these bioactive molecules from animal diet to milk and cheese. Studying the effect of red clover on isoflavone concentration of goat milk,
      • Sakakibara H.
      • Viala D.
      • Ollier A.
      • Combeau A.
      • Besle J.M.
      Isoflavones in several clover species and in milk from goats fed clovers.
      found that these compounds moved to milk through some biotransformations.
      • De Feo V.
      • Quaranta E.
      • Fedele V.
      • Claps S.
      • Rubino R.
      • Pizza C.
      Flavonoids and terpenoids in goats milk in relation to forage intake.
      demonstrated that the presence of phenolic compounds in milk depends on the animal feed and observed differences in polyphenolic profile between plant and milk; in fact, some plant metabolites were not found in milk. Every single forage, with its own phenolic profile, would seem to uniquely characterize the phenolic profile of goat milk.
      To better compare the polyphenol content of cheeses in relation to forage species, the polyphenolic compound recovery in cheese was calculated on the basis of polyphenol daily intake by goats and those recorded daily in the cheeses (Table 2); these values ranged from 0.81% to 9.81%. Hordeum vulgare cheese (8.92%) and T. alexandrinum cheese (9.81%) had the highest recovery values among grass and legume forages, respectively. A high recovery is considered desirable in the cheese-making process because a high value indicates a reduced loss of functional ingredients. The variability of the polyphenolic recovery seems to be related more to the nature and content of forage polyphenols than to forage intake (Table 4) among goat groups. The legumes V. faba minor and V. sativa show greater tannic polyphenol values compared with other legumes (P. sativum, T. alexandrinum), and these values are 10 times higher than those found in grasses (F. arundinacea and H. vulgare) with the exception of Triticosecale. The latter grass species exhibited the higher tannic polyphenol value compared with other grasses. Conversely, the nontannic polyphenol values were greater in forage grasses and lower in forage legumes with exception of P. sativum.
      • Todaro M.
      • Alabiso M.
      • Scatassa M.L.
      • Di Grigoli A.
      • Mazza F.
      • Maniaci G.
      • Bonanno A.
      Effect of the inclusion of fresh lemon pulp in the diet of lactating ewes on the properties of milk and cheese.
      observed polyphenol recovery in Pecorino cheese that was 5 times higher in fresh lemon pulp diets than in the control diet without fresh lemon pulp. The recovery level of plant polyphenols in milk and cheeses could be linked to digestion; indeed, in ruminant animals, forage polyphenols are subject to a complex metabolism that involves the whole consortium of ruminal microorganisms. Studies on this topic could increase understanding of the interactions between the microbiome and forage polyphenols, to clarify their transfer in dairy products. On the other hand, the cheese-making process could affect cheese polyphenol content because of the solubility and polarity of phenolic compounds and their chemical interactions with milk proteins and fats (
      • Han J.
      • Britten M.
      • St-Gelais D.
      • Champagne C.P.
      • Fustier P.
      • Salmieri S.
      • Lacroix M.
      Effect of polyphenolic ingredients on physical characteristics of cheese.
      ).

      Antioxidant Capacity

      The results obtained for the total antioxidant capacity of the forages and TAC-C are reported in Table 2, Table 4. Regarding forage species, higher values of antioxidant capacity were found in the legumes V. faba minor and V. sativa compared with the other forage species. Pearson correlation analysis showed that total antioxidant capacity of the forages was positively correlated with total polyphenols (r = 0.66; P = 0.056) and tannic polyphenols (r = 0.76; P = 0.048); these relationships are consistent with other reports (
      • Dudonné S.
      • Vitrac X.
      • Coutiere P.
      • Woillez M.
      • Mérillon J.M.
      Comparative study of antioxidant properties and total phenolic content of 30 plant extracts of industrial interest using DPPH, ABTS, FRAP, SOD, and ORAC assays.
      ). Some studies have demonstrated a linear positive correlation between the content of total phenolic compounds and antioxidant capacity (
      • Kaur C.
      • Kapoor H.C.
      Anti-oxidant activity and total phenolic content of some Asian vegetables.
      ).
      As shown in Table 2, experimental forage species affected the TAC-C (P < 0.01). The TAC-C in goat cheeses ranged from 1.73 to 3.64 mol FeSO4 equivalents/kg. The higher values were found in F. arundinacea, Triticosecale, P. sativum, V. faba minor, and V. sativa cheeses (from 3.03 to 3.64 mol FeSO4 equivalents/kg) compared with cheese obtained from milk of goats fed H. vulgare (1.73 mol FeSO4 equivalents/kg). The T. alexandrinum cheese exhibited an intermediate value (2.85 mol FeSO4 equivalents/kg). These TAC-C values are in agreement with those observed in goat cheese by
      • Lucas A.
      • Coulon J.B.
      • Agabriel C.
      • Chilliard Y.
      • Rock E.
      Relationships between the conditions of goat's milk production and the contents of some components of nutritional interest in Rocamadour cheese.
      , ranging between 2.09 and 13.4 mol FeSO4 equivalents/kg. These authors also reported an effect of the nature of the basic fodder ration on cheese antioxidant capacity. Few studies have evaluated the relationship between forage intake and levels of antioxidant activity in goat cheese and reported that grazing management positively affects goat cheese antioxidant activity, providing an increase in its total polyphenol concentration (
      • Hilario M.C.
      • Puga C.D.
      • Ocana A.N.
      • Romo F.P.G.
      Antioxidant activity, bioactive polyphenols in Mexican goats' milk cheeses on summer grazing.
      ;
      • Chávez-Servín J.L.
      • Andrade-Montemayor H.M.
      • Vázquez C.V.
      • Barreyro A.A.
      • García-Gasca T.
      • Martínez R.A.F.
      • Ramírez A.M.O.
      • de la Torre-Carbot K.
      Effects of feeding system, heat treatment and season on phenolic compounds and antioxidant capacity in goat milk, whey and cheese.
      ). Several milk compounds are antioxidants, such as uric acid, ascorbic acid, β-carotene, α-tocopherol, phenols, whey protein, and casein (
      • Fardet A.
      • Rock E.
      In vitro and in vivo antioxidant potential of milks, yoghurts, fermented milks and cheeses: A narrative review of evidence.
      ). It has been reported that microbial activity during cheese manufacturing and time of ripening could generate an increase in compounds having an effect on TAC in cheese (
      • Lucas A.
      • Rock E.
      • Chamba J.F.
      • Verdier-Metz I.
      • Brachet P.
      • Coulon J.B.
      Respective effects of milk composition and the cheese-making process on cheese compositional variability in components of nutritional interest.
      ;
      • Revilla I.
      • González-Martín M.I.
      • Vivar-Quintana A.M.
      • Blanco-López M.A.
      • Lobos-Ortega I.A.
      • Hernández-Hierro J.M.
      Antioxidant capacity of different cheeses: Affecting factors and prediction by near infrared spectroscopy.
      ). The TAC-C values are mainly influenced by the cheese-making process but independently of the type of cheese-making technology (
      • Lucas A.
      • Rock E.
      • Chamba J.F.
      • Verdier-Metz I.
      • Brachet P.
      • Coulon J.B.
      Respective effects of milk composition and the cheese-making process on cheese compositional variability in components of nutritional interest.
      ). In our study TAC-C seem to be associated with the nature of the forage species and not with cheese-making process and time of ripening, which were the same for all cheeses. In goat cheese no significant correlation was found between TAC-C and TP-C, whereas a slight positive correlation was found between TAC-C and total polyphenols in plant (r = 0.66; P = 0.11). On the contrary,
      • Rashidinejad A.
      • Birch E.J.
      • Sun-Waterhouse D.
      • Everett D.W.
      Effects of catechin on the phenolic content and antioxidant properties of low-fat cheese.
      showed the contribution of the total phenolic content to the total antioxidant activity in low-fat hard cheese made with the addition of catechin. In addition,
      • Di Trana A.
      • Bonanno A.
      • Cecchini S.
      • Giorgio D.
      • Di Grigoli A.
      • Claps S.
      Effects of Sulla forage (Sulla coronarium L.) on the oxidative status and milk polyphenol content in goats.
      found a positive correlation between milk polyphenols and milk TAC in goats fed increasing quantities of Sulla coronarium L. forage. The phytoderivatives added to the cheese during cheese-making (
      • Rashidinejad A.
      • Birch E.J.
      • Sun-Waterhouse D.
      • Everett D.W.
      Effects of catechin on the phenolic content and antioxidant properties of low-fat cheese.
      ) and the polyphenolic profile of the Sulla coronarium L. forage (
      • Di Trana A.
      • Bonanno A.
      • Cecchini S.
      • Giorgio D.
      • Di Grigoli A.
      • Claps S.
      Effects of Sulla forage (Sulla coronarium L.) on the oxidative status and milk polyphenol content in goats.
      ) may have induced the different results compared with the results obtained in this present study.

      FA Content

      The FA content of goat cheese was affected by forage plant species (P ≤ 0.001; Table 5). The important role of forage species, characterized by different FA content, and interactions between the nature of forage and oil supplements in modulating the FA composition of milk fat were reported previously for goats (
      • Chilliard Y.
      • Ferlay A.
      • Rouel J.
      • Lambere G.
      A review of nutritional and physiological factors affecting goat milk synthesis and lipolysis.
      ). Although the cheese FA composition reflects almost completely the milk FA composition (
      • Nudda A.
      • McGuire M.A.
      • Battacone G.
      • Pulina G.
      Seasonal variation in conjugated linoleic acid and vaccenic acid in milk fat of sheep and its transfer to cheese and ricotta.
      ), results from the literature are largely focused on goat milk FA while information on FA content of cheese is scarce and needs further investigation. Besides, different technologies and ripening times make the comparison of cheese data from the literature difficult. In our study, the content of SFA was higher in cheese from goats fed F. arundinacea, P. sativum, T. alexandrinum, and Triticosecale than in cheese from other groups (22.77, 22.63, 22.25, and 22.00 g/100 g, respectively; P ≤ 0.05). The highest SFA values were observed in cheeses with the highest fat content except for Triticosecale.
      Table 5Least squares means of fatty acid composition (g/100 g of cheese) and Health Promoting Index of goat cheeses from goats fed different forage species
      ItemGrassesLegumesSEMP-value
      Festuca arundinaceaHordeum vulgareTriticosecalePisum sativumTrifolium alexandrinumVicia faba minorVicia sativa
      SFA22.77
      Means within a row with different superscripts differ (P < 0.05).
      17.21
      Means within a row with different superscripts differ (P < 0.05).
      22.00
      Means within a row with different superscripts differ (P < 0.05).
      22.63
      Means within a row with different superscripts differ (P < 0.05).
      22.25
      Means within a row with different superscripts differ (P < 0.05).
      17.39
      Means within a row with different superscripts differ (P < 0.05).
      20.56
      Means within a row with different superscripts differ (P < 0.05).
      0.33
      P ≤ 0.001.
      MUFA4.54
      Means within a row with different superscripts differ (P < 0.05).
      6.25
      Means within a row with different superscripts differ (P < 0.05).
      6.83
      Means within a row with different superscripts differ (P < 0.05).
      4.75
      Means within a row with different superscripts differ (P < 0.05).
      6.47
      Means within a row with different superscripts differ (P < 0.05).
      5.62
      Means within a row with different superscripts differ (P < 0.05).
      4.02
      Means within a row with different superscripts differ (P < 0.05).
      0.18
      P ≤ 0.001.
      PUFA1.04
      Means within a row with different superscripts differ (P < 0.05).
      1.12
      Means within a row with different superscripts differ (P < 0.05).
      1.41
      Means within a row with different superscripts differ (P < 0.05).
      0.97
      Means within a row with different superscripts differ (P < 0.05).
      1.52
      Means within a row with different superscripts differ (P < 0.05).
      1.17
      Means within a row with different superscripts differ (P < 0.05).
      0.94
      Means within a row with different superscripts differ (P < 0.05).
      0.03
      P ≤ 0.001.
      C18:1 trans-110.03
      Means within a row with different superscripts differ (P < 0.05).
      0.07
      Means within a row with different superscripts differ (P < 0.05).
      0.11
      Means within a row with different superscripts differ (P < 0.05).
      0.05
      Means within a row with different superscripts differ (P < 0.05).
      0.03
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      Means within a row with different superscripts differ (P < 0.05).
      0.07
      Means within a row with different superscripts differ (P < 0.05).
      0.001
      P ≤ 0.001.
      C18:1 cis-93.99
      Means within a row with different superscripts differ (P < 0.05).
      5.65
      Means within a row with different superscripts differ (P < 0.05).
      6.12
      Means within a row with different superscripts differ (P < 0.05).
      4.78
      Means within a row with different superscripts differ (P < 0.05).
      5.78
      Means within a row with different superscripts differ (P < 0.05).
      4.92
      Means within a row with different superscripts differ (P < 0.05).
      3.77
      Means within a row with different superscripts differ (P < 0.05).
      0.22
      P ≤ 0.001.
      C18:2 cis-9,cis-120.46
      Means within a row with different superscripts differ (P < 0.05).
      0.45
      Means within a row with different superscripts differ (P < 0.05).
      0.51
      Means within a row with different superscripts differ (P < 0.05).
      0.49
      Means within a row with different superscripts differ (P < 0.05).
      0.67
      Means within a row with different superscripts differ (P < 0.05).
      0.54
      Means within a row with different superscripts differ (P < 0.05).
      0.45
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      P ≤ 0.001.
      C18:3 cis-9,cis-12,cis-150.08
      Means within a row with different superscripts differ (P < 0.05).
      0.10
      Means within a row with different superscripts differ (P < 0.05).
      0.16
      Means within a row with different superscripts differ (P < 0.05).
      0.13
      Means within a row with different superscripts differ (P < 0.05).
      0.20
      Means within a row with different superscripts differ (P < 0.05).
      0.13
      Means within a row with different superscripts differ (P < 0.05).
      0.12
      Means within a row with different superscripts differ (P < 0.05).
      0.01
      P ≤ 0.001.
      CLA
      CLA contains a mixture of 2 isomers: 97% cis-9,trans-11 and 3% trans-10,cis-12.
      0.13
      Means within a row with different superscripts differ (P < 0.05).
      0.15
      Means within a row with different superscripts differ (P < 0.05).
      0.16
      Means within a row with different superscripts differ (P < 0.05).
      0.13
      Means within a row with different superscripts differ (P < 0.05).
      0.20
      Means within a row with different superscripts differ (P < 0.05).
      0.12
      Means within a row with different superscripts differ (P < 0.05).
      0.11
      Means within a row with different superscripts differ (P < 0.05).
      0.01
      P ≤ 0.001.
      C14:1 cis/C14:00.04
      Means within a row with different superscripts differ (P < 0.05).
      0.05
      Means within a row with different superscripts differ (P < 0.05).
      0.05
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      Means within a row with different superscripts differ (P < 0.05).
      0.03
      Means within a row with different superscripts differ (P < 0.05).
      0.04
      Means within a row with different superscripts differ (P < 0.05).
      0.03
      Means within a row with different superscripts differ (P < 0.05).
      0.00
      P ≤ 0.001.
      ∑OBCFA
      Odd- and branched-chain fatty acids (ΣOBCFA) are Σiso-C13:0 + anteiso-C13:0 + iso-C14:0 + iso-C15:0 + anteiso-C15:0 + C15:0 + iso-C16:0 + iso-C17:0 + anteiso-C17:0 + C17:0.
      0.56
      Means within a row with different superscripts differ (P < 0.05).
      0.65
      Means within a row with different superscripts differ (P < 0.05).
      0.84
      Means within a row with different superscripts differ (P < 0.05).
      0.54
      Means within a row with different superscripts differ (P < 0.05).
      0.81
      Means within a row with different superscripts differ (P < 0.05).
      0.75
      Means within a row with different superscripts differ (P < 0.05).
      0.50
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      P ≤ 0.001.
      C12:0/∑OBCFA2.05
      Means within a row with different superscripts differ (P < 0.05).
      1.32
      Means within a row with different superscripts differ (P < 0.05).
      1.27
      Means within a row with different superscripts differ (P < 0.05).
      2.33
      Means within a row with different superscripts differ (P < 0.05).
      1.29
      Means within a row with different superscripts differ (P < 0.05).
      1.12
      Means within a row with different superscripts differ (P < 0.05).
      1.97
      Means within a row with different superscripts differ (P < 0.05).
      0.04
      P ≤ 0.001.
      ∑n-3
      Σn-3 = α-C18:3 + C20:3 cis-11,cis-14,cis-17 + C22:5 cis-7,cis-10,cis-13,cis-16,cis-19 + eicosapentaenoic acid + docosahexaenoic acid.
      0.13
      Means within a row with different superscripts differ (P < 0.05).
      0.18
      Means within a row with different superscripts differ (P < 0.05).
      0.26
      Means within a row with different superscripts differ (P < 0.05).
      0.14
      Means within a row with different superscripts differ (P < 0.05).
      0.24
      Means within a row with different superscripts differ (P < 0.05).
      0.18
      Means within a row with different superscripts differ (P < 0.05).
      0.15
      Means within a row with different superscripts differ (P < 0.05).
      0.01
      P ≤ 0.001.
      ∑n-6
      Σn-6 = C18:2 cis-9,cis-12 + γ-C18:3 + CLA trans-10,cis-12 + C20:2 cis-11,cis-14 + C20:3 cis-8,cis-11,cis-14 + C20:4 cis-5,cis-8,cis-11,cis-14 + C22:4n-6 cis-7,cis-10,cis-13,cis-16.
      0.59
      Means within a row with different superscripts differ (P < 0.05).
      0.61
      Means within a row with different superscripts differ (P < 0.05).
      0.80
      Means within a row with different superscripts differ (P < 0.05).
      0.58
      Means within a row with different superscripts differ (P < 0.05).
      0.85
      Means within a row with different superscripts differ (P < 0.05).
      0.70
      Means within a row with different superscripts differ (P < 0.05).
      0.56
      Means within a row with different superscripts differ (P < 0.05).
      0.02
      P ≤ 0.001.
      ∑n-6/∑n-34.59
      Means within a row with different superscripts differ (P < 0.05).
      3.57
      Means within a row with different superscripts differ (P < 0.05).
      3.09
      Means within a row with different superscripts differ (P < 0.05).
      4.05
      Means within a row with different superscripts differ (P < 0.05).
      3.53
      Means within a row with different superscripts differ (P < 0.05).
      3.97
      Means within a row with different superscripts differ (P < 0.05).
      3.65
      Means within a row with different superscripts differ (P < 0.05).
      0.14
      P ≤ 0.001.
      HPI
      Health Promoting Index.
      0.43
      Means within a row with different superscripts differ (P < 0.05).
      0.68
      Means within a row with different superscripts differ (P < 0.05).
      0.57
      Means within a row with different superscripts differ (P < 0.05).
      0.41
      Means within a row with different superscripts differ (P < 0.05).
      0.51
      Means within a row with different superscripts differ (P < 0.05).
      0.46
      Means within a row with different superscripts differ (P < 0.05).
      0.37
      Means within a row with different superscripts differ (P < 0.05).
      0.01
      P ≤ 0.001.
      a–e Means within a row with different superscripts differ (P < 0.05).
      1 CLA contains a mixture of 2 isomers: 97% cis-9,trans-11 and 3% trans-10,cis-12.
      2 Odd- and branched-chain fatty acids (ΣOBCFA) are Σiso-C13:0 + anteiso-C13:0 + iso-C14:0 + iso-C15:0 + anteiso-C15:0 + C15:0 + iso-C16:0 + iso-C17:0 + anteiso-C17:0 + C17:0.
      3 Σn-3 = α-C18:3 + C20:3 cis-11,cis-14,cis-17 + C22:5 cis-7,cis-10,cis-13,cis-16,cis-19 + eicosapentaenoic acid + docosahexaenoic acid.
      4 Σn-6 = C18:2 cis-9,cis-12 + γ-C18:3 + CLA trans-10,cis-12 + C20:2 cis-11,cis-14 + C20:3 cis-8,cis-11,cis-14 + C20:4 cis-5,cis-8,cis-11,cis-14 + C22:4n-6 cis-7,cis-10,cis-13,cis-16.
      5 Health Promoting Index.
      *** P ≤ 0.001.
      In our experimental conditions, in which goats in the fourth month of lactation received the scheduled forage without supplementation, the milk FA content (mainly MUFA and C18:1 cis-9; oleic acid, OA) is also linked to changes in energy balance of goats (
      • Chilliard Y.
      • Ferlay A.
      • Rouel J.
      • Lambere G.
      A review of nutritional and physiological factors affecting goat milk synthesis and lipolysis.
      ). When energy balance is negative, animals mobilize lipids stored in adipose tissues (rich in oleic, stearic, and palmitic acids), mainly in the form of nonesterified FA. This explains the 59% of the variability of milk C18:1 + C18:0 content (
      • Bas P.
      • Chilliard Y.
      • Morand-Fehr P.
      • Rouzeau A.
      • Mandran N.
      Composition des principaux tissus adipeux de la chèvre Alpine en fin de lactation.
      ). In our study, a negative energy balance seems to occur in the goats that produced cheeses with higher OA content (Triticosecale, T. alexandrinum, and H. vulgare groups) and that exhibited lower DMI and milk production compared with others. The higher OA content was observed in cheese fat from goats fed T. alexandrinum, H. vulgare, and Triticosecale compared with others. The highest linoleic acid (LA; C18:2 cis-9,cis-12) content was found in cheese fat from goats fed T. alexandrinum. Concerning linolenic acid (LNA; C18:3 cis-9,cis-12,cis-15), the higher values were detected in cheese fat from T. alexandrinum and Triticosecale groups. The nonendogenous fraction of OA together with LA and LNA derive from the diet (
      • Palmquist D.L.
      Milk fat: Origin of fatty acids and influence of nutritional factors thereon.
      ); they are not synthesized by ruminant tissue, and their concentration in milk and consequently in cheese is dependent on their intake and on the amount that flows out of the rumen. In the present study, cheese from goats fed Triticosecale exibited higher OA, LA, and LNA (6.12, 0.51, and 0.16 g/100 g, respectively) than cheese from goats fed other grass plant species. These results are in agreement with the high level of OA and LA observed in Triticosecale forage (Table 4: 1.33 and 1.54 g/kg of DM, respectively). Among legume groups, T. alexandrinum cheese showed higher content of OA, LA, and LNA (5.78, 0.67, and 0.20 g/100 g, respectively) compared with cheese from other legume species; however, a high level of OA and LA was not found in T. alexandrinum forage. The present results may be linked to the complex pathway involving different forage constituent such as plant FA and plant secondary metabolites. The FA profile of milk and cheese is not always directly linked to the forage FA profile; in fact, some studies have highlighted a more complex mechanism that involves the endogenous lipolysis, plant secondary metabolites (e.g., phenols and tannins), and plant polyphenol oxidase enzyme in ruminal biohydrogenation (
      • Cabiddu A.
      • Lee M.R.F.
      • Decandia M.
      • Molle G.
      • Salis L.
      • Vargiu M.
      • Winters A.L.
      Characterization of polyphenol oxidase activity in a range of forage ecotypes with different phenol substrates. A new insight for PPO and protein-bound phenol evaluation.
      ;
      • Lee M.R.F.
      Forage polyphenol oxidase and ruminant livestock nutrition.
      ) and different transfer efficiency of dietary FA in milk and cheese fat (
      • Dewhurst R.J.
      • Shingfield K.J.
      • Lee M.R.F.
      • Scollan N.D.
      Increasing the concentrations of beneficial polyunsaturated fatty acids in milk produced by dairy cows in high-forage systems.
      ). Interactions between polyphenol oxidase, plant secondary metabolites and their profile, endogenous lipolysis, and PUFA biohydrogenation need further study related to forage species. In cheese from goats fed T. alexandrinum, the higher value of PUFA (1.52 g/100 g; P ≤ 0.05) was linked to the higher cheese fat content.
      The C18:2 cis-9,trans-11 isomer (rumenic acid) in milk fat and cheese has a duple origin: a small amount of rumenic acid escaping ruminal biohydrogenation and a major portion of rumenic acid appearing to originate from endogenous synthesis in the mammary gland from rumen-derived vaccenic acid by activity of Δ9-desaturase activity. Differences in concentrations of rumenic acid in milk fat observed in response to dietary treatments may also be explained by differences in Δ9-desaturase activity (
      • Griinari J.M.
      • Bauman D.E.
      Biosynthesis of conjugated linoleic acid and its incorporation into meat and milk in ruminants.
      ). The C18:2 trans-10,cis-12 isomer seems to be exclusively synthesized in the rumen because the existence of a Δ12-desaturase in the mammary gland was not demonstrated (
      • Griinari J.M.
      • Bauman D.E.
      Biosynthesis of conjugated linoleic acid and its incorporation into meat and milk in ruminants.
      ). Both the cis-9,trans-11 and trans-10,cis-12 CLA isomers appear to be active in inhibiting carcinogenesis in animal models (
      • Pariza M.W.
      • Park Y.
      • Cook M.E.
      The biologically active isomers of conjugated linoleic acid.
      ). In the current study, CLA contents are higher in cheese from goats fed T. alexandrinum compared with other legume groups (0.20 g/100 g); in cheeses from goats fed grass, the CLA content was not significantly different. The variability of cheese vaccenic acid content (from 0.02 to 0.11 g/100 g) and CLA content (from 0.11 to 0.20 g/100 g) could be linked to the content of CLA precursors in plant species, their pathways of biohydrogenation, and Δ9-desaturase activity. In fact, C14:1/C14:0 ratio, the best index of Δ9-desaturase activity (
      • Impemba G.
      • Cifuni G.F.
      • Di Trana A.
      Influence of feeding system, stage of lactation and genetic types on Δ9-desaturase activity in caprine milk.
      ), ranged from 0.03 to 0.05 (Table 5).
      The cheeses from goats fed Triticosecale and T. alexandrinum were also richer in OBCFA (0.84 and 0.81 g/100 g, respectively) than other groups. The highest value of OBCFA observed in cheese of Triticosecale was in accordance with the high level of NDF (Table 1), ΣC13:0, C15:0, C17:0 (data not shown), and low CP content of Triticosecale forage (Table 1), contributing to the synthesis of milk OBCFA. The OBCFA in milk fat are largely derived from rumen bacteria and could be useful noninvasive indicators of rumen fermentation as reported by
      • Fievez V.
      • Colman E.
      • Castro-Montoya J.M.
      • Stefanov I.
      • Vlaeminck B.
      Milk odd- and branched-chain fatty acids as biomarkers of rumen function—An update.
      . Indeed, the same authors reported that variations in milk OBCFA could reflect changes of rumen bacterial populations induced by diet composition. In accordance with our study,
      • Povolo M.
      • Pelizzola V.
      • Lombardi G.
      • Tava A.
      • Contarini G.
      Hydrocarbon and fatty acid composition of cheese as affected by the pasture vegetation type.
      found an effect of vegetation types of alpine pasture (Trifolium alpinum L. vs. Festuca nigrescens Lam) on OBCFA content in cow milk and cheese.
      To verify whether classes of FA can discriminate the origin of cheeses on the basis of type of forage, different ratios between FA were studied. The ratio between C12:0, which originates from de novo synthesis in the mammary gland, and OBCFA was computed. Cheeses from goats fed F. arundinacea, P. sativum, and V. sativa showed higher values (P < 0.001) of C12:0/OBCFA ratio (range: 1.97–2.33) compared with other cheeses (range: 1.12–1.32). This ratio provided to be a useful index to distinguish milk and cheese of cows of mountain and plains origins (
      • Povolo M.
      • Pelizzola V.
      • Contarini G.
      Content of odd-and branched-chain fatty acids in milk and cheese of different origin.
      ).
      A significant investigation indicates that excessive amounts of n-6 PUFA (∑n-6) and a very high n-6/n-3 ratio (∑n-6/∑n-3), as is found in today's Western diets, promote the pathogenesis of many diseases, including cardiovascular disease, cancer, and inflammatory and autoimmune diseases (
      • Simopoulos A.P.
      The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases.
      ). As a consequence, opportunities to enhance omega-3 FA (∑n-3) in many foods, including dairy products, are being explored. The optimal ∑n-6/∑n-3 ratio varies from 1/1 to 4/1 depending on the disease under consideration (
      • Simopoulos A.P.
      The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases.
      ). In the current study, cheeses from goats fed Triticosecale, T. alexandrinum, H. vulgare, and V. sativa showed a lower Σn-6/Σn-3 ratio (3.09, 3.53, 3.57, and 3.65, respectively) compared with others.

      Nutritional Indexes

      The health value of ruminant products can be improved by different dietary strategies to lower atherogenic FA, such as some SFA (C12:0, C14:0, C16:0), and favor beneficial PUFA with a particular emphasis on n-3 PUFA. The HPI was proposed by
      • Chen S.
      • Bobe G.
      • Zimmerman S.
      • Hammond E.G.
      • Luhman C.M.
      • Boylston T.D.
      • Freeman A.E.
      • Beitz D.C.
      Physical and sensory properties of dairy products from cows with various milk fatty acid compositions.
      as an indicator of the health value of dietary fat. Dairy products with high HPI value are assumed to be more beneficial to human health. The relative proportion of FA having antiatherogenic (PUFA, CLA) and proatherogenic (SFA and trans FA) effects appears to vary in cheese fat, with consequent changes in HPI value. The effect of forage species on HPI value of milk was previously reported in goats fed 4 fresh forages (Avena sativa L., Lolium multiflorum Lam., T. incarnatum L., and Vicia sativa L.) and 1 silage forage (Triticosecale silage;
      • Di Trana A.
      • Di Napoli M.A.
      • Claps S.
      • Sepe L.
      • Caputo A.R.
      • Fedele V.
      Effect of feeding forage species on fatty acid profile of goat milk and cheese.
      ). In the current study, the highest HPI value of goat cheese was detected in the group fed H. vulgare (0.68), followed by the Triticosecale and T. alexandrinum groups (0.57 and 0.51, respectively).
      The presence of both FA and polyphenol compounds in cheese, derived or transformed from compounds of dietary origin, and antioxidant capacity of cheeses allows us to combine these variables to obtain a new index. Thus, the development of an index that takes into account the contribution of several bioactive components to the health value of the product could represent a valuable tool. Indeed, previous indexes mainly focused on the contribution of 1 component to the health value of the product (
      • Chen S.
      • Bobe G.
      • Zimmerman S.
      • Hammond E.G.
      • Luhman C.M.
      • Boylston T.D.
      • Freeman A.E.
      • Beitz D.C.
      Physical and sensory properties of dairy products from cows with various milk fatty acid compositions.
      ;
      • Pizzoferrato L.
      • Manzi P.
      • Marconi S.
      • Fedele V.
      • Claps S.
      • Rubino R.
      Degree of antioxidant protection: A parameter to trace the origin and quality of goat's milk and cheese.
      ). The HPI index proposed by
      • Chen S.
      • Bobe G.
      • Zimmerman S.
      • Hammond E.G.
      • Luhman C.M.
      • Boylston T.D.
      • Freeman A.E.
      • Beitz D.C.
      Physical and sensory properties of dairy products from cows with various milk fatty acid compositions.
      is largely focused on the effect of some FA on cardiovascular diseases. The degree of antioxidant protection, calculated by Pizzoferrato et al. (2007) as the molar ratio between an antioxidant compound and a selected oxidation target, evaluates goat cheese resistance to oxidative reactions. The GHIC, adopted for the first time in this study, combines in a single value the contribution of several components to animal product healthfulness (Figure 1). It allows the classification of our cheeses, simultaneously taking into account different indicators of human health. Similarly,
      • Azzini E.
      • Maiani G.
      • Turrini A.
      • Intorre F.
      • Lo Feudo G.
      • Capone R.
      • Bottalico F.
      • El Bilali H.
      • Polito A.
      The health-nutrition dimension: A methodological approach to assess the nutritional sustainability of the typical agro-food products and the Mediterranean diet.
      identified suitable and easily measurable indicators to assess the nutritional sustainability of several groups of foods. In the current study, the new index was significantly affected by forage species (Figure 1; P < 0.01). The highest value of GHIC was observed in cheese from goats fed Triticosecale (27.83) and T. alexandrinum (31.67), as expected because of their high polyphenol and beneficial FA contents. The GHIC values provide immediate information about the simultaneous presence of health-promoting compounds in cheese from goats fed Triticosecale and T. alexandrinum. However, in this paper we cannot define minimum and maximum literature values because of limited literature on this topic. Scientific papers that simultaneously analyze the antioxidant capacity and the FA and polyphenol contents in goat cheese (
      • Lucas A.
      • Rock E.
      • Chamba J.F.
      • Verdier-Metz I.
      • Brachet P.
      • Coulon J.B.
      Respective effects of milk composition and the cheese-making process on cheese compositional variability in components of nutritional interest.
      ,
      • Lucas A.
      • Coulon J.B.
      • Agabriel C.
      • Chilliard Y.
      • Rock E.
      Relationships between the conditions of goat's milk production and the contents of some components of nutritional interest in Rocamadour cheese.
      ) are very few. As a consequence, in this first approach we used the ranges recorded in this investigation. The next step will be to broaden our database to data currently available in the scientific literature.
      Figure thumbnail gr1
      Figure 1Comparison of General Health Index of Cheeses (GHIC) from goats fed different forage species. Results are presented as LSM ± SEM. Different letters (a, b) indicate significant differences (P < 0.01) among cheeses.

      CONCLUSIONS

      These results suggest that the coexistence in every single forage species of a specific FA and polyphenol profile and their interactions during their digestive utilization unequivocally characterize dairy products. The GHIC index, which combines the positive components found in cheese, seems to be a tool to provide an indication concerning the health value of our products. The expansion of range margins of parameters considered in the GHIC would allow us to examine a wide type of cheeses and thus provide a global health indication. An adequate and planned use of forage species containing natural bioactive compounds could be a valid means to enhance dairy product quality by modulation of their FA and polyphenol profile without fat or antioxidant supplementation. Although in this study the animal and environment factors have been minimized, the chemical and health composition of the milk and cheese is a result of a complex mechanism in which the forage plays a strategic role that needs to be further explored.

      ACKNOWLEDGMENTS

      This research was supported by the project GRAZING funded by the University of Basilicata, Potenza, Italy (102050101/17). This research was supported by the project IDENTILAT funded by the Italian Ministry of Agricultural, Food and Forestry Policies, Rome, Italy (MIPAAF; D.M.304/7303/05).

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