Interrelationships among physical and chemical traits of cheese: Explanatory latent factors and clustering of 37 categories of cheeses

Cheese presents extensive variability in physical, chemical, and sensory characteristics according to the variety of processing methods and conditions used to create it. Relationships between the many characteristics of cheeses are known for single cheese types or by comparing a few of them, but not for a large number of cheese types. This case study used the properties recorded on 1,050 different cheeses from 107 producers grouped into 37 categories to analyze and quantify the interrelationships among the chemical and physical properties of many cheese types. The 15 cheese traits considered were ripening length, weight, firmness, adhesiveness, 6 different chemical characteristics, and 5 different color traits. As the 105 correlations between the 15 cheese traits were highly variable, a multivariate analysis was carried out. Four latent explanatory factors were extracted, representing 86% of the covariance matrix: the first factor (38% of covariance) was named Solids because it is mainly linked positively to fat, protein, water-soluble nitrogen, ash, firmness, adhesiveness, and ripening length, and negatively to moisture and lightness; the second factor (24%) was named Hue because it is linked positively to redness/blueness, yellowness/greenness, and chroma, and negatively to hue; the third factor (17%) was named Size because it is linked positively to weight, ripening length, firmness, and protein; and the fourth factor (7%) was named Basicity because it is linked positively to pH. The 37 cheese categories were grouped into 8 clusters and described using the latent factors: the Grana Padano cluster (characterized mainly by high Size scores); hard mountain cheeses (mainly high Solids scores); very soft cheeses (low Solids scores); blue cheeses (high Basicity scores), yellowish cheeses (high Hue scores), and 3 other clusters (soft cheeses, pasta filata and treated rind, and firm mountain cheeses) according to specific combinations of intermediate latent factors and cheese traits. In this case study, the high variability and interdependence of 15 major cheese traits can be substantially explained by only 4 latent factors, allowing us to identify and characterize 8 cheese type clusters.

Analysis of Volatile Organic Compounds in Milk during Heat Treatment Based on E-Nose, E-Tongue and HS-SPME-GC-MS

Volatile organic compounds (VOCs) make up milk flavor and are essential attributes for consumers to evaluate milk quality. In order to investigate the influence of heat treatment on the VOCs of milk, electronic nose (E-nose), electronic tongue (E-tongue) and headspace solid-phase microextraction (HS-SPME)-gas chromatography-mass spectrometry (GC-MS) technology were used to evaluate the changes in VOCs in milk during 65 °C heat treatment and 135 °C heat treatment. The E-nose revealed differences in the overall flavor of milk, and the overall flavor performance of milk after heat treatment at 65 °C for 30 min is similar to that of raw milk, which can maximize the preservation of the original taste of milk. However, both were significantly different to the 135 °C-treated milk. The E-tongue results showed that the different processing techniques significantly affected taste presentation. In terms of taste performance, the sweetness of raw milk was more prominent, the saltiness of milk treated at 65 °C was more prominent, and the bitterness of milk treated at 135 °C was more prominent. The results of HS-SPME-GC-MS showed that a total of 43 VOCs were identified in the three types of milk-5 aldehydes, 8 alcohols, 4 ketones, 3 esters, 13 acids, 8 hydrocarbons, 1 nitrogenous compound, and 1 phenol. The amount of acid compounds was dramatically reduced as the heat treatment temperature rose, while ketones, esters, and hydrocarbons were encouraged to accumulate instead. Furfural, 2-heptanone, 2-undecanone, 2-furanmethanol, pentanoic acid ethyl ester, 5-octanolide, and 4,7-dimethyl-undecane can be used as the characteristic VOCs of milk treated at 135 °C. Our study provides new evidence for differences in VOCs produced during milk processing and insights into quality control during milk production.

Major Causes of Variation of External Appearance, Chemical Composition, Texture, and Color Traits of 37 Categories of Cheeses

Cheeses are produced by many different procedures, giving rise to many types differing in ripening time, size, shape, chemical composition, color, texture, and sensory properties. As the first step in a large project, our aim was to characterize and quantify the major sources of variation in cheese characteristics by sampling 1050 different cheeses manufactured by over 100 producers and grouped into 37 categories (16 with protected designation of origin, 4 traditional cheese categories, 3 pasta filata cheese categories, 5 flavored cheese categories, 2 goat milk categories, and 7 other categories ranging from very fresh to very hard cheeses). We obtained 17 traits from each cheese (shape, height, diameter, weight, moisture, fat, protein, water soluble nitrogen, ash, pH, 5 color traits, firmness, and adhesiveness). The main groups of cheese categories were characterized and are discussed in terms of the effects of the prevalent area of origin/feeding system, species of lactating females, main cheese-making technologies, and additives used. The results will allow us to proceed with the further steps, which will address the interrelationships among the different traits characterizing cheeses, detailed analyses of the nutrients affecting human health and sensorial fingerprinting.

Effects of breed, farm intensiveness, and cow productivity level on cheese-making ability predicted using infrared spectral data at the population level

Fourier-transform infrared (FTIR) spectra collected during milk recording schemes at population level can be used for predicting novel traits of interest for farm management, cows' genetic improvement, and milk payment systems. The aims of this study were as follows. (1) To predict cheese yield traits using FTIR spectra from routine milk recordings exploiting previously developed calibration equations. (2) To compare the predicted cheese-making abilities of different dairy and dual-purpose breeds. (3) To analyze the effects of herds' level of intensiveness (HL) and of the cow's level of productivity (CL). (4) To compare the patterns of predicted cheese yields with the patterns of milk composition in different breeds to discern the drivers of cheese-making efficiency. The major sources of variation of FTIR predictions of cheese yield ability (fresh cheese or cheese solids produced per unit milk) of individual milk samples were studied on 115,819 cows of 4 breeds (2 specialized dairy breeds, Holstein and Brown Swiss, and 2 dual-purpose breeds, Simmental and Alpine Grey) from 6,430 herds and exploiting 1,759,706 FTIR test-day spectra collected over 7 yr of milk sampling. Calibration equations used were previously developed on 1,264 individual laboratory model cheese procedures (cross-validation R² 0.85 and 0.95 for fresh and solids cheese yields, respectively). The linear model used for statistical analysis included the effects of parity, lactation stage, year of calving, month of sampling, HL, CL, breed of cow, and the interactions breed × HL and breed × CL. The HL and CL stratifications (5 classes each) were based on average daily secretion of milk net energy per cow. All effects were highly significant (P < 0.001). The major conclusions were as follows. (1) The FTIR-based prediction of cheese yield of milk goes beyond the knowledge of fat and protein content, partially explaining differences in cheese-making ability in different cows, breeds and herds. (2) Differences in cheese yields of different breeds are only partially explained by milk fat and protein composition, and less productive breeds are characterized by a higher milk nutrient content as well as a higher recovery of nutrients in the cheese. (3) High-intensive herds not only produce much more milk, but the milk has a higher nutrient content and a higher cheese yield, whereas within herds, compared with less productive cows, the more productive cows have a much greater milk yield, milk with a greater content of fat but not of protein, and a moderate improvement in cheese yield, differing little from expectations based on milk composition. Finally, (4) the effects of HL and CL on milk quality and cheese-making ability are similar but not identical in different breeds, the less productive ones having some advantage in terms of cheese-making ability. We can obtain FTIR-based prediction of cheese yield from individual milk samples retrospectively at population level, which seems to go beyond the simple knowledge of milk composition, incorporating information on nutrient retention ability in cheese, with possible advantages for management of farms, genetic improvement of dairy cows, and milk payment systems.

The volatile organic compound profile of ripened cheese is influenced by crude protein shortage and conjugated linoleic acid supplementation in the cow's diet

A shortage in crude protein (CP) and supplementation of conjugated linoleic acids (CLA) in the diets of dairy cows could improve the dairy industry's ecological footprint and the nutritional value of milk, but it is not known what effect such a strategy might have on the aroma profiles of dairy products. The aim of this work was to study the effects of reducing the dietary CP content (from 150 to 123 g/kg of dry matter), with or without a supply of rumen-protected CLA (7.9 g/d C18:2 cis-9,trans-11 and 7.7 g/d C18:2 trans-10,cis-12), on the volatile organic compound (VOC) profile of cheeses ripened for 3 mo. Twenty mid-lactation Holstein-Friesian cows were reared in 4 pens (5 to a pen), and fed 4 different experimental diets over 4 periods of 3 wk each, following a 4 × 4 Latin square design. Twice in each period, 10-L milk samples were taken from each group and used to produce 32 cheeses, which we then analyzed for VOC by solid-phase microextraction and gas chromatography-mass spectrometry. We detected 48 VOC belonging to 10 chemical classes (11 alcohols, 8 ketones, 8 esters, 7 acids, 4 aldehydes, 4 sulfurs, 2 lactones, 2 phenolic, 1 monoterpene, 1 hydrocarbon); these were expressed as concentrations in cheese (quantitative data) or as proportions of total VOC (qualitative data). The results of mixed model analysis showed that the majority of VOC families and individual VOC in ripened cheese were affected by the dietary treatments: CP shortage depressed the concentrations of volatile aldehydes and increased the proportions of some esters and limonene, whereas CLA increased the concentration of total VOC, particularly several acids and esters, and decreased the proportions of ketones and phenolic compounds. The interaction between dietary CP and CLA affected the proportions of alcohols and acids. We performed a factor analysis to extract 5 latent explanatory variables from the individual VOC, which represented 79% of total VOC variance for the quantitative data and 78% for the qualitative data. Addition of CLA decreased the first qualitative factor (the "base aroma" of cheese, explaining 44% of total variance), whereas CP reduction increased the second quantitative factor ("ethyl esters," 15% of total variance) and the third qualitative factor ("butan-," 9% of total variance). In summary, the VOC profile of ripened cheese was heavily influenced by CP content and CLA supplementation in the diets of dairy cows, but the effect on sensorial properties of cheese is also worth considering.

Chemometric authentication of farming systems of origin of food (milk and ripened cheese) using infrared spectra, fatty acid profiles, flavor fingerprints, and sensory descriptions

Milk samples from 1264 cows in 85 farms were authenticated for different farming-systems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared spectra (FTIRS) and gas-chromatographic fatty-acid (FA) profiles. FTIRS gave correct classification greater than FAs (97.4% vs. 81.1%) during calibration, but slightly worse in validation (73.5% vs 77.3%) and their combination improved the results. All milk samples were processed into ripened model-cheeses, and analyzed by near-infrared-spectrometry (NIRS), by proton-transfer-reaction time-of-flight mass-spectrometry for their volatile organic compound (VOCs) fingerprint and by panel sensory profiling (SENS). Farming-system authentication on cheese samples was less efficient than on milk, but still possible. The instrumental methods yielded similar validation results, better than SENS, and their combination improved the correct classification rate. The efficiency of the different technics was affected by specific farming systems. In conclusion, dairy products could be discriminated for farming-systems with acceptable accuracy, but the methods tested differ in sampling procedure, rapidity and costs.

Integrated PTR-ToF-MS, GWAS and biological pathway analyses reveal the contribution of cow's genome to cheese volatilome

Volatile organic compounds (VOCs) are small molecules that contribute to the distinctive flavour of cheese which is an important attribute for consumer acceptability. To investigate whether cow's genetic background might contribute to cheese volatilome, we carried out genome-wide association studies (GWAS) and pathway-based analyses for 173 spectrometric peaks tentatively associated with several VOCs obtained from proton-transfer-reaction mass spectrometry (PTR-ToF-MS) analyses of 1,075 model cheeses produced using raw whole-milk from Brown Swiss cows. Overall, we detected 186 SNPs associated with 120 traits, several of which mapped close to genes involved in protein (e.g. CSN3, GNRHR and FAM169A), fat (e.g. AGPAT3, SCD5, and GPAM) and carbohydrate (e.g. B3GNT2, B4GALT1, and PHKB) metabolism. Gene set enrichment analysis showed that pathways connected with proteolysis/amino acid metabolism (purine and nitrogen metabolism) as well as fat metabolism (long-term potentiation) and mammary gland function (tight junction) were overrepresented. Our results provide the first evidence of a putative link between cow's genes and cheese flavour and offer new insights into the role of potential candidate loci and the biological functions contributing to the cheese volatilome.

Integration of GWAS, pathway and network analyses reveals novel mechanistic insights into the synthesis of milk proteins in dairy cows

The quantities and proportions of protein fractions have notable effects on the nutritional and technological value of milk. Although much is known about the effects of genetic variants on milk proteins, the complex relationships among the set of genes and pathways regulating the different protein fractions synthesis and secretion into milk in dairy cows are still not completely understood. We conducted genome-wide association studies (GWAS) for milk nitrogen fractions in a cohort of 1,011 Brown Swiss cows, which uncovered 170 significant single nucleotide polymorphism (SNPs), mostly located on BTA6 and BTA11. Gene-set analysis and the network-based Associated Weight Matrix approach revealed that the milk proteins associated genes were involved in several biological functions, particularly ion and cation transmembrane transporter activity and neuronal and hormone signalling, according to the structure and function of casein micelles. Deeper analysis of the transcription factors and their predicted target genes within the network revealed that GFI1B, ZNF407 and NR5A1 might act as master regulators of milk protein synthesis and secretion. The information acquired provides novel insight into the regulatory mechanisms controlling milk protein synthesis and secretion in bovine mammary gland and may be useful in breeding programmes aimed at improving milk nutritional and/or technological properties.

Detailed fatty acid profile of milk, cheese, ricotta and by products, from cows grazing summer highland pastures

In this research two-dimensional GC was used to analyse, for the first time, the detailed fatty acid (FA) profiles of 11 dairy matrices: raw milk (evening whole, evening partially skimmed, morning whole, and vat milk), cream, fresh cheese, whey, ricotta, scotta, 6- and 12-month-ripened cheeses, obtained across artisanal cheese- and ricotta-making trials carried out during the summer period while cows were on highland pastures. Samples were collected during 7 cheese- and ricotta-making procedures carried out at 2-week intervals from bulk milk to study possible differences in the transfer and modification of FA. Compared with morning milk, evening milk had fewer de novo synthetised FA. The detailed FA profile of partially skimmed milk differed little from that of evening whole milk before skimming, but the cream obtained differed from partially skimmed milk and from fresh cheese in about half the FA, due mainly to higher contents of all de novo FA, and lower contents of n-3 and n-6 FA. Fresh cheese and whey had similar FA profiles. The ricotta manufacturing process affected the partition of FA between ricotta and scotta, the FA profile of the latter differing in terms of groups and individual FA from the former, whereas ricotta and fresh cheese had similar composition of FA. In general, there was an increase in medium-chain saturated FA, and a decrease in many polyunsaturated FA during the first 6 months of ripening, but not during the second 6 months. Two-dimensional GC yielded a very detailed and informative FA profile on all the 11 dairy products and by-products analysed.

From cow to cheese: genetic parameters of the flavour fingerprint of cheese investigated by direct-injection mass spectrometry (PTR-ToF-MS)

BACKGROUND

Volatile organic compounds determine important quality traits in cheese. The aim of this work was to infer genetic parameters of the profile of volatile compounds in cheese as revealed by direct-injection mass spectrometry of the headspace gas from model cheeses that were produced from milk samples from individual cows.

METHODS

A total of 1075 model cheeses were produced using raw whole-milk samples that were collected from individual Brown Swiss cows. Single spectrometry peaks and a combination of these peaks obtained by principal component analysis (PCA) were analysed. Using a Bayesian approach, we estimated genetic parameters for 240 individual spectrometry peaks and for the first ten principal components (PC) extracted from them.

RESULTS

Our results show that there is some genetic variability in the volatile compound fingerprint of these model cheeses. Most peaks were characterized by a substantial heritability and for about one quarter of the peaks, heritability (up to 21.6%) was higher than that of the best PC. Intra-herd heritability of the PC ranged from 3.6 to 10.2% and was similar to heritabilities estimated for milk fat, specific fatty acids, somatic cell count and some coagulation parameters in the same population. We also calculated phenotypic correlations between PC (around zero as expected), the corresponding genetic correlations (from -0.79 to 0.86) and correlations between herds and sampling-processing dates (from -0.88 to 0.66), which confirmed that there is a relationship between cheese flavour and the dairy system in which cows are reared.

CONCLUSIONS

This work reveals the existence of a link between the cow's genetic background and the profile of volatile compounds in cheese. Analysis of the relationships between the volatile organic compound (VOC) content and the sensory characteristics of cheese as perceived by the consumer, and of the genetic basis of these relationships could generate new knowledge that would open up the possibility of controlling and improving the sensory properties of cheese through genetic selection of cows. More detailed investigations are necessary to connect VOC with the sensory properties of cheese and gain a better understanding of the significance of these new phenotypes.

Proton transfer reaction time-of-flight mass spectrometry: A high-throughput and innovative method to study the influence of dairy system and cow characteristics on the volatile compound fingerprint of cheeses

The aim of this work was to study the effect of dairy system and individual cow-related factors on the volatile fingerprint of a large number of individual model cheeses analyzed by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). A total of 1,075 model cheeses were produced using milk samples collected from individual Brown Swiss cows reared in 72 herds located in mountainous areas of Trento province (Italy). The herds belonged to 5 main dairy systems ranging from traditional to modern and the cows presented different daily milk yields (24.6±7.9kg × d(-1)), stages of lactation (199±138 d in milk), and parities (2.7±1.8). The PTR-ToF-MS revealed 619 peaks, of which the 240 most intense were analyzed, and 61 of these were tentatively attributed to relevant volatile organic compounds on the basis of their fragmentation patterns and data from the literature. Principal component analysis was used to convert the multiple responses characterizing the PTR-ToF-MS spectra into 5 synthetic variables representing 62% of the total information. These principal components were related to groups of volatile compounds tentatively attributed to different peaks and used to investigate the relationship of the volatile compound profile obtained by PTR-ToF-MS to animal and farm characteristics. Lactation stage is related to 4 principal components which brought together 52.9% of the total variance and 57.9% of the area of analyzed peaks. In particular, 2 principal components were positively related to peaks tentatively attributed to aldehydes and ketones and negatively related to alcohols, esters, and acids, which displayed a linear increase during lactation. The second principal component was affected by dairy system; it was higher in the modern system in which cows received total mixed rations. The third principal component was positively related to daily milk production. In summary, we report the first application of this innovative, high-throughput technique to study the effects of dairy system and individual animal factors on volatile organic compounds of model cheeses. Individual cheesemaking procedures together with this spectrometric technique open new avenues for genetic selection of dairy species with respect to both milk and cheese quality.