Flavouring of imitation cheese with enzyme-modified cheeses (EMCs): Sensory impact and measurement of aroma active short chain fatty acids (SCFAs)

Enzyme-modified cheeses (EMCs) are used to impart flavour to imitation cheese products. Cheeses (pH 6 or 5.5) were formulated with 5% w/w EMC, having low, medium or high levels of lipolysis and were examined by a sensory panel. Free fatty acid analyses were performed using SPME/GC. The flavour profile of the flavoured cheeses was affected by EMC composition and pH of the cheese base. Cheeses at a pH of 6.0, flavoured with low lipolysis EMCs, were described as ‘bland’. Lowering the pH of the cheese matrix to 5.5 appeared to increase the flavour intensity of the cheese flavoured with low lipolysis EMC and panellists ranked this cheese the highest, describing its flavour as ‘well-balanced and ‘cheesy’. This study shows that the flavours of imitation cheeses are influenced by the level of lipolysis of the EMCs used to flavour them and also by the pH of the cheese base.     

The effect of adding enzyme‐modified cheese on sensory and texture properties of low‐ and high‐fat cream cheeses

The influence of enzyme‐modified cheese (EMC) and fat content on sensory and texture properties of cream cheese was investigated. Enzyme‐modified cheese and fat content were set at three levels each, and organoleptic and texture properties for all experimental cheeses were then determined. Data were analysed using response surface methodology. Both design parameters had significant influence on sensory and texture properties. The EMC did not alter hardness significantly, whereas the higher fat formula had the higher hardness. The results indicated that the optimum level of EMC was less than 1% for high‐fat cream cheeses and at least 5% for low‐fat cream cheeses.     

The compositional properties,proteolytic–lipolytic maturation parameters and volatile compositions of commercial enzyme‐modified cheeses with different cheese flavours

Eight different commercial enzyme‐modified cheeses (EMCs) were analysed, and the distinctive/common features of the products and production methods were investigated. Results showed that the total free fatty acid contents of EMC samples were 10 to 100 times higher than the values reported for the related cheese varieties. A total of 37 volatile compounds were identified, and acids were found as the most dominant group in all EMC samples. While furan compounds and 2‐acetylpyrrole were most intensively detected in the goat cheese EMC, methyl ketones were found in the highest amounts in Blue cheese EMC.     

Metabolomics-based component profiling of hard and semi-hard natural cheeses with gas chromatography/time-of-flight-mass spectrometry, and its application to sensory predictive modeling

Gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) was used to analyze hydrophilic low molecular weight components, including amino acids, fatty acids, amines, organic acids, and saccharides, in cheese, and the sensometric application for practical metabolomic studies in the food industry is described. Derivatization of target analytes was conducted prior to the GC/TOF-MS analysis. Data on 13 cheeses, six Cheddar cheeses, six Gouda cheeses and one Parmigiano-Reggiano cheese, were analyzed by multivariate analysis. The uniqueness of the Parmigiano-Reggiano cheese metabolome was revealed. Principal component analysis (PCA) showed no grouping of the Cheddar cheeses and Gouda cheeses according to production method or country of origin. The PCA loading plot confirms that many amino acids contribute positively to PC1, suggesting that PC1 is closely related to degradation of proteins, and that lactic acid contributed positively to PC2, whereas glycerol contributed negatively to PC2, suggesting that factors regarding degradation of carbohydrates and fats were expressed in PC2. Partial least squares (PLS) regression models were constructed to predict the relationship between the metabolite profile and two sensory attributes, "Rich flavor" and "Sour flavor", which were related to maturation. The compounds that play an important role in constructing each sensory prediction model were identified as 12 amino acids and lactose for "Rich flavor", and 4-aminobutyric acid, ornithine, succinic acid, lactic acid, proline and lactose for "Sour flavor". The present study revealed that metabolomics-based component profiling, focusing on hydrophilic low molecular weight components, was able to predict the sensory characteristics related to ripening.     

Metabolic fingerprinting of hard and semi-hard natural cheeses using gas chromatography with flame ionization detector for practical sensory prediction modeling

Metabolic fingerprinting using gas chromatography with flame ionization detector (GC/FID) was used to generate a practical metabolomics-based tool for quality evaluation of natural cheese. Hydrophilic low molecular weight components, relating to sensory characteristics, including amino acids, fatty acids, amines, organic acids, and saccharides, were extracted and derivatized prior to the analysis. Data on 12 cheeses, six Cheddar cheeses and six Gouda cheeses, were analyzed by multivariate analysis. Prediction models for two sensory attributes relating to maturation, "Rich flavor" and "Sour flavor", were constructed with 4199 data points from GC/FID, and excellent predictability was validated. Chromatograms from GC/FID and gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS) were comparable when the same column was used. Although GC/FID alone cannot identify peaks, the mutually complementary relationship between GC/FID and GC/MS does allow peak identification. Compounds contributing significantly to the sensory predictive models included lactose, succinic acid, l-lactic acid, and aspartic acid for "Rich flavor", and?lactose, l-lactic acid, and succinic acid for "Sour flavor". Since similar model precision was obtained using GC/FID and GC/TOF-MS, metabolic fingerprinting using GC/FID, which is a relatively inexpensive instrument compared with GC/MS, is easy to maintain and operate, and is a valid alternative when metabolomics (especially using GC/MS) is to be used in a practical setting as a novel quality evaluation tool for manufacturing processes or final products.     

Lipolysis,proteolysis and sensory properties of ewe’s raw milk cheese (Idiazabal) made with lipase addition

In this paper, we describe the effect of the addition of pregastric lipase on the composition and sensory properties of Idiazabal cheese. Free fatty acids (FFA), partial glycerides, free amino acids (FAA), gross composition and sensory characteristics were determined at different ripening times in cheeses manufactured with three different amounts of commercial animal lipase or with lipase-containing artisanal lamb rennet paste. The addition of lipase increased the content of total FFA, particularly of short-chain FFA, and that of total partial glycerides in cheeses. Unexpectedly, lipase utilization significantly affected total FAA concentration, which decreased in cheeses elaborated with high lipase amount. In general, Val, Glu and Leu were the major FAA, and their concentrations depended, mainly, on ripening time. Lipase addition had significant influence on the sensory characteristics of the cheeses, increasing scores for most of the flavour and odour attributes of the cheese. Principal component analysis (PCA) was done including dry matter, FFA, FAA, partial glycerides and odour and flavour attributes of the cheeses. It indicated that aroma and flavour parameters of Idiazabal cheese and the content of short-chain FFA and diglycerides were highly correlated to first principal component (PC1), while texture parameters, compositional variables and FAA were correlated to the second principal component (PC2).     

Using a membrane technique (SPM) for high fat food sample preparation in the determination of chlorinated persistent organic pollutants by a GC/ECD method

This paper focuses upon the use of semipermeable membranes (SPM) as a clean-up method for the determination of 4,4′-DDD, 4,4′-DDE, 4,4′-DDT, Aldrin, Dieldrin, Isodrin, Lindane (γ -HCH), 1,2,4-trichlorobenzene (1,2,4-TCB), 1,2,3-trichlorobenzene (1,2,3-TCB), 1,2,3,4-tetrachlorobenzene (1,2,3,4-TCB), 1,2,4,5-tetrachlorobenzene (1,2,4,5-TCB), Pentachlorobenzene (PeCBz), Hexachlorobenzene (HCB), and Hexachlorobutadiene (HCBD) in high fat food samples. Pork fat, beef fat, butter, egg yolks and chocolate were all used as high fat food samples. The procedure consists of three steps: the first is dialysis in an SPM tube, using n-hexane as an external solvent. The second step is a clean-up procedure using a silica gel column, and the third step is GC/ECD analysis. This experiment shows that recovery values obtained for individual compounds were in the range of 55–100%. The conclusion drawn is that the SPM technique is an efficient method of preparation of high fat food samples for the determination of POPs by GC/ECD methods.     

An exploration of angiotensin-converting enzyme (ACE) inhibitory peptides derived from gastrointestinal protease hydrolysate of milk using a modified bioassay-guided fractionation approach coupled with in silico analysis

An improved bioassay-guided fractionation was performed to effectively screen angiotensin-I converting enzyme inhibitory (ACEI) peptides from milk protein hydrolysate. The aqueous normal phase liquid chromatography, namely hydrophilic interaction liquid chromatography (HILIC), was used as a format of solid-phase extraction (SPE) short column for the first fractionation, then the HILIC-SPE fraction with the best ACEI activity (IC₅₀ = 61.75 ± 5.74 µg/mL; IC₅₀ = half-maximal inhibitory concentration) was obtained when eluted by 95% acetonitrile + 0.1% formic acid (fraction F1). The best HILIC-SPE fraction was further fractionated using reversed-phase (RP)-SPE short column. The best RP-SPE fraction was obtained when eluted by 20% acetonitrile + 0.1% formic acid (fraction P3) with an ACEI activity of IC₅₀ 36.22 ± 1.18 µg/mL. After the 2-step fractionation, the IC₅₀ value of fraction P3 significantly decreased by 8.92-fold when compared with the crude hydrolysate. Several peptides were identified from fraction P3 using liquid chromatography-tandem mass spectrometry. The in silico analysis of these identified sequences based on the BIOPEP database predicted that HLPLPLL (HL-7) was the most active peptide against angiotensin-converting enzyme (ACE). The HL-7 derived from β-casein showed a potent ACEI activity (IC₅₀ value is 16.87 ± 0.3 µM). The contents of HL-7 in the gastrointestinal protease hydrolysate and RP-SPE fraction originated from 1 mg of milk proteins were quantified using a multiple reaction monitoring mode upon liquid chromatography-tandem mass spectrometry analysis to give 19.86 ± 1.14 pg and 14,545.8 ± 572.9 pg, respectively. Besides, the kinetic study indicated that HL-7 was a competitive inhibitor and the result was rationalized using the docking simulation. The study demonstrated an efficient screening of ACEI peptides from commercially available milk powders using a simple SPE process instead of a sophisticated instrument such as HPLC. Moreover, the potent ACEI peptide HL-7 uncovered by this method could be a natural ACE inhibitor.