Authentication of raw and cooked freeze-dried rainbow trout (Oncorhynchus mykiss) by means of near infrared spectroscopy and data fusion

This study investigated the possibility of using near infrared spectroscopy (NIRS) for the authentication of raw and cooked freeze-dried rainbow trout (Oncorhynchus mykiss) fillets. Latent variable models applied on the spectral data were developed and used to estimate proximate composition, fatty acid profile, fillet yield and cooking loss, and to classify the available dataset by the rearing farm and genetic strain of each sample.Results showed that NIR spectra can be used both to accurately estimate several chemical properties and to classify samples by rearing farm. In order to classify samples by genetic strain instead, a data fusion approach in which color and mechanical information were combined with spectral data was used. No major differences were observed between the results obtained from raw freeze-dried fillets and those obtained from cooked freeze-dried fillets, except for the estimation of certain chemical constituents of interest such as C22:6 n − 3 and polyunsaturated fatty acid content, both of which were better estimated from cooked freeze-dried fillets.     

The use of buttermilk as a raw material for cheese production

Buttermilk is dairy by‐product of butter production. The study aimed to analyse the quality properties of commercial buttermilk and its usefulness as a raw material for Quark cheese production. Within the study, 6 kinds of buttermilk were tested and used for cheese production. Evaluation of cheese quality included physico‐chemical, rheological and sensory analyses. Buttermilk properties were varied and corresponded with the diversification of cheese characteristics. Production yield was 17.2–22.7%, water content 68.1–75.8% and pH 4.09–4.43. The texture was diversified; however, all samples had good sensory quality. The research has shown that buttermilk is a good raw material for cheese production.     

Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals

Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100–2498, 850–1048, and 350–1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.     

A study on the texture diversity of the Artisan Ranchero Cheese from Central Mexico

The objective of the present study was to determine the diversity and causative factors in the textural profile of the Artisan Ranchero Cheese (ARC), a popular soft cheese made with raw cow's milk, which is consumed fresh. These factors included climatic conditions, production zone, cheese supplies, cheesemaking procedure and chemical attributes. Texture attributes were determined using texture profile analysis method. The relationship between texture and chemical attributes of the ARC were determined by a Factor Analysis. An Agglomerative Hierarchical Clustering Analysis was used to differentiate the ARC manufactures into groups. Two groups were identified, production zone being the variable which explained the variation in the data. Finally, a Discriminant Analysis was performed to determine standardisation of the ARC attributes through the sampling periods. The 89% of cheese samples were correctly classified into their own cheese manufacture. This last analysis demonstrated that there was an important degree of standardisation of the cheesemaking procedure within each cheese manufacture, something that was not demonstrated before with artisan cheesemakers.     

Use of FT-NIR and XPS techniques to distinguish cell hull fractions prepared by autolysis or HPH from Saccharomyces cerevisiae and Brettanomyces bruxellensis strains

Yeast hulls can be used to adsorb undesirable compounds such as volatile phenols that may be present in wine. To understand this adsorption process, the properties of the cell walls and their chemical composition need to be better understood. A study was conducted using four different yeast fractions of either autolysed or high-pressure homogeniser (HPH)-crushed yeast biomasses of Saccharomyces cerevisiae and Brettanomyces bruxellensis. Near-infrared spectroscopy (FT-NIR) coupled with X-ray photoelectron spectroscopy (XPS) was used and analysed by principal component analysis (PCA). The FT-NIR spectral region of Saccharomyces and Brettanomyces statistically analysed by PCA showed a clear discrimination accounting for 76% of the variation in the data for PC1; moreover, yeast hulls prepared from the same strain and subjected to two different treatments were also separated. These methods classify yeast cell hulls (YCH) according to strain, composition and treatment applied. Our results indicate that yeast hulls obtained by autolysis are less rich in proteins than those resulting from HPH treatment due to the high pressure that releases more proteins and exposes them on the surface of the cell wall. The composition of YCH at the extreme surface is similar to that found deeper in the wall.     

Graduate Student Literature Review: History,technologies of production,and characteristics of ricotta cheese

This review focused on the historical, technological, and analytical characteristics of ricotta cheese available in the literature. Ricotta cheese is a typical dairy product that originated from Italy, used in the preparation of several traditional dishes, both sweet and salted. The available studies pertaining to ricotta cheese revealed a considerable biodiversity in the production with a large number of varieties produced, whose production varies according to the local uses and customs. The review shows the main chemical and microbial characteristics of the product and also the several parameters that affect the mechanism of the production process and the final characteristics of the product, including the raw materials, the processing methods, the season, the animals' diet, the animals' species, and breeds. Ricotta production can be artisanal or industrial, with differences in the making process. New trends in ricotta cheese production have been developed, with particular attention to the functional effect on human health and the novel technologies applied to extend the shelf-life of the products. Currently, it is not easy to find these new developments in the market, probably related to the cost of production, which is not always bearable by the farms. However, despite the large classification reported and the great interest by the cheese industry, just a few numbers of studies were found for artisanal ricotta productions, which still need to be characterized and studied.     

Genotype by environment interaction due to heat stress in Brown Swiss cattle

Due to its geographical position and a highly variable orography, Italy is characterized by several climatic areas and thus, by many different dairy cow farming systems. Brown Swiss cattle, in this context, are a very appreciated genetic resource for their adaptability and low metabolic requirement. The significant heterogeneity in farming systems may consist of genotype by environment (G × E) interactions with neglected changes in animals' rank position. The objective of this study was to investigate G × E for heat tolerance in Brown Swiss cattle for several production traits (milk, fat, and protein yield in kilograms; fat, protein, and cheese yield in percentage) and 2 derivate traits (fat-corrected milk and energy-corrected milk). We used the daily maximum temperature-humidity index (THI) range, calculated according to weather stations' data from 2008 to 2018 in Italy, and 202,776 test-day records from 23,396 Brown Swiss cows from 639 herds. Two different methodologies were applied to estimate the effect of the environmental variable (THI) on genetic parameters: (1) the reaction norm model, which uses a continuous random covariate to estimate the animal additive effect, and (2) the multitrait model, which splits each production pattern as a distinct and correlated trait according to the first (a thermal comfort condition), third (a moderate heat stress condition), and fifth (a severe heat stress condition) mean THI value quintile. The results from the reaction norm model showed a descending trend of the additive genetic effect until THI reached the value of 80. Then we recorded an increase with high extreme THI values (THI 90). Permanent environmental variance at increasing THI values revealed an opposite trend: The plot of heritability and the ratio of animal permanent environmental variance to phenotypic variance showed that when the environmental condition worsens, the additive genetic and permanent environmental component for production traits play a growing role. The negative additive genetic correlation between slope and linear random coefficient indicates no linear relationship between the production traits or under heat stress conditions, except for milk yield and protein yield. In tridimensional wireframe plots, the extreme margin decreases until a minimum of ～0.90 of genetic correlation in the ECM trait, showing that the magnitude of G × E interaction is greater than the other traits. Genetic correlation values in Brown Swiss suggest the possibility of moderate changes in animals' estimated breeding value in heat stress conditions. Results indicated a moderate G × E interaction but significant variability in sire response related to their production level.     

Fatty acid profiling of bovine milk and cheese from six European areas by GC‐FID and GC‐MS

The fatty acid (FA) composition of milk from six European areas, as well as the alteration in the FA profile during cheese production, was studied using both a targeted GC‐FID and an untargeted GC‐MS approach. By applying principal component, partial least square discriminant and chemical similarity enrichment analysis, a discrimination of the geographical areas could be achieved highlighting important FA classes such as odd‐ and branched‐chain FAs for the differentiation. The FA profile remained constant during cheese production, and aroma compounds have been identified as biomarkers for the ripening methods used, namely foil and smear ripening.     

Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk

The aim of this study was to compare the common method of exploiting infrared spectral data in animal breeding; that is, estimating the breeding values for the traits predicted by infrared spectroscopy, and an alternative approach based on the direct use of spectral information (direct prediction, DP) to predict the estimated breeding values (EBV). Traits were pH, milk coagulation properties, contents of the main casein and whey protein fractions, cheese yield measured by micro-cheese making, lactoferrin, Ca, and fat composition. For the DP method, the number of spectral variables was reduced by principal components analysis to 8 latent traits that explained 99% of the original spectral variation. Restricted maximum likelihood was used to estimate variance components of the latent traits. (Co)variance components of the original spectral traits were obtained by back-transformation and EBV of all derived milk traits were then predicted as traits correlated with the genetic information of the spectra. The rank correlation between the EBV obtained for the infrared-predicted traits and those obtained from the DP method was variable across traits. Rank correlations ranged from 0.07 (for the content of saturated fatty acids expressed as g/100 g of fat) to 0.96 (for dry matter cheese yield, %) and, for most traits, was <0.5. This result can be explained by the nature of the principal components analysis: it does not take into account the covariance between the spectral variables and the reference traits but produces latent traits that maximize the spectral variance explained. Thus, the direct approach is more likely to be effective for traits more related to the main sources of spectral variation (i.e., protein and fat). More research is required to study spectral genetic variation and to determine the best way to choose spectral regions and the type and number of considered latent traits for potential applications.     

Characteristics of ripened Tronchón cheese from raw goat milk containing legally admissible amounts of antibiotics

The aim of this study was to evaluate the transfer of the most widely used antibiotics in dairy goats from milk to cheese as well as their effect on the cheese-making process and cheese characteristics during ripening. Antibiotic-free milk was spiked individually with 7 veterinary drugs (amoxicillin, benzylpenicillin, cloxacillin, erythromycin, ciprofloxacin, enrofloxacin, and oxytetracycline) at an equivalent concentration of the European Union maximum residue limit. Spiked goat milk was used to make mature Tronchón cheeses, which were analyzed at 0, 30, and 60 d of maturation to determine pH, chemical composition, proteolytic and lipolytic activities, and color and textural properties. A sensory evaluation of 60-d ripened cheeses was carried out. Cheeses from raw antibiotic-free goat milk were made simultaneously to be used as reference. The cheese-making process was unaffected by the presence of most antibiotics evaluated. Only erythromycin and oxytetracycline significantly increased the time required for cheese production (122 ± 29 and 108 ± 25 min, respectively). However, variable amounts of antibiotics, ranging from 7.4 to 68%, were transferred from milk to cheese, with oxytetracycline and quinolones showing the highest retention rates. In general, antibiotic residues present in the cheeses at the beginning of maturation decrease significantly along time. Thus, β-lactams and erythromycin residues were not detectable after 30 d of ripening. However, relatively high concentrations of enrofloxacin (148 ± 12 µg/kg) and ciprofloxacin (253 ± 24 µg/kg) residues were found in the cheeses after 60 d of maturation. The quality characteristics of the Tronchón cheeses were only slightly affected by such substances, with few significant differences in the free fatty acid concentration and color and textural properties of the cheeses. Results herein indicate that the use of goat milk containing antibiotics, such as quinolones, at the European Union maximum residue limit for cheese production could adversely affect the safety of the final products because relatively high concentrations of these substances could be retained in soft and semi-mature cheeses, making it necessary to assess the risk for consumer health. Studies on the partition of the antibiotic substances during cheese-making, using specific technologies, would be convenient to guarantee the safety of cheese and related products.     

Quality and safety of irradiated food regarding biogenic amines: Ras cheese

Food safety and quality became very important, especially with the challenge to ensure safe and healthy foods in regard to chemical hazards. So, this study was conducted to evaluate the quality and safety of irradiated Ras cheese during the storage period, with respect to biogenic amines (BAs). Ras cheese was manufactured, ripened and irradiated by γ‐irradiation at 0, 5, 10 and 15 kGy. The samples were stored in refrigerator at 5 ± 1 °C from where samples were withdrawn at 0, 2, 4 and 6 months for analysis. The results revealed that most sensory scores and chemical properties showed insignificant differences (P ≤ 0.05). The microbial counts were reduced with different degrees according to both storage period and irradiation dose. Also, irradiation was effective in reducing the content of BAs without harming the chemical properties of Ras cheese. The total content of BAs reflects the safety of irradiated Ras cheese and also indicates a high‐quality product in comparison with nonirradiated samples.     

Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy

Cheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerød, Denmark) over the spectral range, from 5,000 to 900 wavenumber × cm⁻¹. The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole data set, but the coefficient of determination of validation values for the external validation sets were much lower for all traits (0.30 to 0.73), and particularly for fat recovery (0.05 to 0.18), for the training sets compared with the full data set. For each testing subset, the (co)variance components for the measured and FTIR-predicted phenotypes were estimated using bivariate Bayesian analyses and linear models. The intraherd heritabilities for the predicted traits obtained from our internal cross-validation using the whole data set ranged from 0.085 for daily yield of curd solids to 0.576 for protein recovery, and were similar to those obtained from the measured traits (0.079 to 0.586, respectively). The heritabilities estimated from the testing data set used for external validation were more variable but similar (on average) to the corresponding values obtained from the whole data set. Moreover, the genetic correlations between the predicted and measured traits were high in general (0.791 to 0.996), and they were always higher than the corresponding phenotypic correlations (0.383 to 0.995), especially for the external validation subset. In conclusion, we herein report that application of the cross-validation technique to the whole data set tended to overestimate the predictive ability of FTIR spectra, give more precise phenotypic predictions than the calibrations obtained using smaller data sets, and yield genetic correlations similar to those obtained from the measured traits. Collectively, our findings indicate that FTIR predictions have the potential to be used as indicator traits for the rapid and inexpensive selection of dairy populations for improvement of cheese yield, milk nutrient recovery in curd, and daily cheese production per cow.     

Characteristics of traditional Croatian ewe's cheese from the island of Krk

Krk cheese is a hard, full-fat cheese made from raw sheep's milk, characterized by a delicate, full and strong flavour. The aim of this study was to determine farm influence on the chemical composition of sheep's milk for Krk cheese production, and the chemical characteristics of Krk cheese during ripening. Gross composition of the milk used complies with the average sheep's milk composition from the Croatian Adriatic region. During ripening, fat, protein, salt content and lactic acid concentration increased ( P <  0.01), as well as the water-soluble nitrogen fraction and the 12%-trichloroacetic-acid-soluble nitrogen fraction ( P <  0.05). Degradation of β-casein could be an indicator of the ripening quality of Krk cheese.     

Thermo-physical parameters applied for instrumental profiling and statistical evaluation of German Emmental cheese

Generic Emmental cheese from Germany, produced in five different dairies, was evaluated in terms of thermo-physical properties (melting, flowing, oiling-off and browning) with the aim of characterising German Emmental cheese and identifying similarities and/or differences between Emmental samples from different producers. The data set was subjected to principal component analysis and three principal components were calculated accounting for 68.9% of total variance, indicating that thermo-physical properties are very suitable for Emmental cheese characterisation. Furthermore, linear discriminant analysis was conducted to classify the Emmental cheeses according to the producing dairy based on similarities and/or differences of the thermo-physical properties. Linear discriminant analysis revealed a correct classification of 62.2%. The results offer cheese producers the possibility of varying the manufacturing process and producing similar products with the aim of promoting geographical identification or products with greater differentiating attributes, to enhance distinctiveness of each producing dairy.     

Probiotic potential of γ-aminobutyric acid (GABA)–producing yeast and its influence on the quality of cheese

Kazakh cheese is a traditional dairy product in Xinjiang, China. To study the function and potential probiotic characteristics of yeast in Kazakh cheese and its contribution to cheese fermentation, we screened the γ-aminobutyric acid (GABA)–producing yeasts Pichia kudriavzevii 1–21, Kluyveromyces marxianus B13–5, Saccharomyces cerevisiae DL6–20, and Kluyveromyces lactis DY1–10. We investigated the potential probiotic properties of these strains and their use in cheese fermentation (cheeses designated CSP, CSM, CSS, and CSI, respectively); a control with no added yeast was designated CS. The results showed that the 4 yeast strains all showed high self-polymerization (2- and 24-h autoaggregation capacity of >80 and 90%, respectively), hydrophobicity (40–92% variation, low hydrophobicity in xylene, but within the range of probiotics), and the ability to survive the gastrointestinal tract (survival rate >75% after simulation), indicating the probiotic ability of the strains in vitro. The GABA production capacity of the CSM cheese increased (to 95.6 mg/100 g), but its protein content did not change significantly, and amino acid degradation was obvious. The GABA production capacity of the CSS cheese decreased (to 450 mg/kg); its protein content declined, and its amino acid content increased. Except for water and protein, we found no obvious differences in most physical and chemical indicators. Kluyveromyces marxianus B13–5 helped to form the desired texture. Multivariate statistical analysis showed that fermentation of the cheese with the 4 yeasts improved the production of esters and alcohols. The CSS cheese had good aroma production performance, because S. cerevisiae DL6–20 produced high concentrations of isoamyl alcohol, hexanoic acid ethyl ester, benzyl alcohol, octanoic acid ethyl ester, 3-hydroxy-2-butanone, and hexanoic acid; the content of 2-methyl-propanoic acid was low. Compared with the CSP cheese, the CSI and CSM cheeses had a fruitier aroma and a milder odor, but the CSI and CSM cheeses had high concentrations of ethyl acetate, butanoic acid, ethyl ester, 3-methyl-1-butanol-acetate, ethyl hexanoate, ethyl octanoate, acetic acid 2-phenylethyl ester, and ethyl lactate; concentrations of 3-methyl-butanoic acid, propanoic acid, acetic acid, and butanoic acid were low. The CSP cheese had stronger acid-producing ability. The order of fragrance production performance was CSS > CSI, CSM > CSP > CS. Research into the fermentation mechanisms of GABA-producing yeast in cheese will provide a theoretical basis for the quality control and industrial production of Kazakh cheese.     

Estimation of genetic parameters for cheese-making traits in Spanish Churra sheep

The global production of sheep milk is growing, and the main industrial use of sheep milk is cheese making. The Spanish Churra sheep breed is one of the most important native dairy breeds in Spain. The present study aimed to estimate genetic parameters for a wide range of traits influencing the cheese-making ability of Churra sheep milk. Using a total of 1,049 Churra ewes, we studied the following cheese-making traits: 4 traits related to milk coagulation properties (rennet coagulation time, curd-firming time, and curd firmness at 30 and 60 min after addition of rennet), 2 traits related to cheese yield (individual laboratory cheese yield and individual laboratory dried curd yield), and 3 traits measuring curd firmness over time (maximum curd firmness, time to attain maximum curd firmness, and syneresis). In addition, a list of milk traits, including the native pH of the milk and several milk production and composition traits (milk yield; the fat, protein, and dried extract percentages; and the somatic cell count), were also analyzed for the studied animals. After discarding the noncoagulating samples (only 3.7%), data of 1,010 ewes were analyzed with multiple-trait animal models by using the restricted maximum likelihood method to estimate (co)variance components, heritabilities, and genetic correlations. In general, the heritability estimates were low to moderate, ranging from 0.08 (for the individual laboratory dried curd yield trait) to 0.42 (for the fat percentage trait). High genetic correlations were found within pairs of related traits (i.e., 0.93 between fat and dried extract percentages, ?0.93 between the log of the curd-firming time and curd firmness at 30 min, 0.70 between individual laboratory cheese yield and individual laboratory dried curd yield, and ?0.94 between time to attain maximum curd firmness and syneresis). Considering all the information provided here, we suggest that in addition to the current consideration of the protein percentage trait for improving cheese yield traits, the inclusion of the pH of milk as a measured trait in the Churra dairy breeding program would represent an efficient strategy for improving the cheese-making ability of milk from this breed.     

Invited review: perspectives on the basis of the rheology and texture properties of cheese

Physical and chemical properties of cheese, such as texture, color, melt, and stretch, are primarily determined by the interaction of casein (CN) molecules. This review will discuss CN chemistry, how it is influenced by the cheese-making process, and how it impinges on the final product, cheese. We attempt to demonstrate that the application of principles governing the molecular interactions of CN can be useful in understanding the many physical and chemical properties of cheese and, in turn, how this can be used by the cheesemaker to produce the desired cheese. The physical properties of cheese (as well as flavor) are influenced by a number of factors including: milk composition; milk quality; temperature; the rate and extent of acidification by the starter bacteria; the pH history of cheese; the concentration of Ca salts (proportions of soluble and insoluble forms); extent and type of proteolysis, and other ripening reactions. Our hypothesis is that these factors also control and modify the nature and strength of CN interactions. The approach behind the recently proposed dual-binding model for the structure and stability of CN micelles is used as a framework to understand the physical and chemical properties of cheese.     

Effect of octenyl succinylated waxy starch as a fat mimetic on texture,microstructure and physicochemical properties of Minas fresh cheese

The effect of fat reduction and the addition of octenyl succinylated (OS) waxy maize starch as a fat replacer on the physicochemical properties, texture, and microstructure of Minas fresh cheese was studied. The cheeses were produced according to three formulations: full-fat cheese (FC), reduced-fat cheese (RC), and reduced-fat cheese with 0.5 kg/100 L of added starch (SC). Analyses of the chemical composition, titratable acidity, water-holding capacity (WHC), yield, texture, microstructure, and electrophoretic profile of casein were conducted. Fat reduction increased the hardness and decreased the yield of the cheeses. Fat reduction also promoted a denser microstructure and less proteolysis. The concentration of starch that was added was insufficient to improve the yield and texture parameters of the reduced-fat cheese. However, the addition of starch increased the moisture content and the WHC of the reduced-fat cheese. In general, OS waxy maize starch improved the overall quality of the reduced-fat Minas fresh cheese.     

Rapid determination of Swiss cheese composition by Fourier transform infrared/attenuated total reflectance spectroscopy

There is a need for rapid and simple techniques that can be used to predict the quality of cheese. The aim of this research was to develop a simple and rapid screening tool for monitoring Swiss cheese composition by using Fourier transform infrared spectroscopy. Twenty Swiss cheese samples from different manufacturers and degree of maturity were evaluated. Direct measurements of Swiss cheese slices (∼0.5g) were made using a MIRacle 3-reflection diamond attenuated total reflectance (ATR) accessory. Reference methods for moisture (vacuum oven), protein content (Kjeldahl), and fat (Babcock) were used. Calibration models were developed based on a cross-validated (leave-one-out approach) partial least squares regression. The information-rich infrared spectral range for Swiss cheese samples was from 3,000 to 2,800 cm⁻¹ and 1,800 to 900 cm⁻¹. The performance statistics for cross-validated models gave estimates for standard error of cross-validation of 0.45, 0.25, and 0.21% for moisture, protein, and fat respectively, and correlation coefficients r > 0.96. Furthermore, the ATR infrared protocol allowed for the classification of cheeses according to manufacturer and aging based on unique spectral information, especially of carbonyl groups, probably due to their distinctive lipid composition. Attenuated total reflectance infrared spectroscopy allowed for the rapid (∼3-min analysis time) and accurate analysis of the composition of Swiss cheese. This technique could contribute to the development of simple and rapid protocols for monitoring complex biochemical changes, and predicting the final quality of the cheese.