Carmen L. Manuelian

Characterization of major and trace minerals, fatty acid composition, and cholesterol content of Protected Designation of Origin cheeses

Cheese provides essential nutrients for human nutrition and health, such as minerals and fatty acids (FA). Its composition varies according to milk origin (e.g., species and breed), rearing conditions (e.g., feeding and management), and cheese-making technology (e.g., coagulation process, addition of salt, ripening period). In recent years, cheese production has increased worldwide. Italy is one of the main producers and exporters of cheese. This study aimed to describe mineral, FA, and cholesterol content of 133 samples from 18 commercial cheeses from 4 dairy species (buffalo, cow, goat, and sheep) and from 3 classes of moisture content (hard, <35% moisture; semi-hard, 35-45%; and soft, >45%). Mineral concentrations of cheese samples were determined by inductively coupled plasma optical emission spectrometry, and FA and cholesterol contents were determined by gas chromatography. Moisture and species had a significant effect on almost all traits: the highest levels of Na, Ca, and Fe were found in cheeses made from sheep milk; the greatest level of Cu was found in cow milk cheese, the lowest amount of K was found in buffalo milk cheese, and the lowest amount of Zn was found in goat cheeses. In all samples, Cr and Pb were not detected (below the level of detection). In general, total fat, protein, and minerals significantly increased when the moisture decreased. Buffalo and goat cheeses had the highest saturated FA content, and sheep cheeses showed the highest content of unsaturated and polyunsaturated FA, conjugated linoleic acid, and n-3 FA. Goat and sheep cheeses achieved higher proportions of minor FA than did cow and buffalo cheeses. Buffalo cheese exhibited the lowest cholesterol level. Our results confirm that cheese mineral content is mainly affected by the cheese-making process, whereas FA profile mainly reflects the FA composition of the source milk. This study allowed the characterization of mineral and FA composition and cholesterol content and revealed large variability among different commercial cheeses.

Prediction of sodium content in commercial processed meat products using near infrared spectroscopy

The present study evaluated the ability of near infrared transmittance (NIT) spectroscopy (FoodScan, 850-1050nm) to predict sodium (Na) content in commercial processed meat products (n=310) as intact and ground samples. Prediction models were built with all samples spectra and with spectra divided in 5 categories according to the manufacturing meat process. Sodium content (%) was determined using inductively coupled plasma optical emission spectrometry. Modified partial least squares regression for the overall samples showed satisfactory predictive ability for intact (coefficient of determination in cross-validation, R2CV=0.93) and ground samples (R2CV=0.95). Despite the low number of samples, good specific prediction models were developed for each commercial meat category. In conclusion, NIT is really promising for at-line application to predict Na in processed meat products which could help industry to accomplish the new labelling regulation.

Factors associated with herd bulk milk composition and technological traits in the Italian dairy industry

The aim of the present study was to investigate sources of variation of milk composition and technological characteristics routinely collected in field conditions in the Italian dairy industry. A total of 40,896 bulk milk records from 620 herds and 10 regions across Italy were analyzed. Composition traits were fat, protein, and casein percentages, urea content, and somatic cell score; and technological characteristics were rennet coagulation time, curd firming time, curd firmness 30 min after rennet addition to milk, and titratable acidity. Data of herd bulk milks were analyzed using a model that included fixed effects of region, herd nested within region, and season of milk analysis. An average good milk quality was reported in the dairy industry (especially concerning fat, protein, and casein percentages), and moderate to high correlations between composition and technological traits were observed. All factors included in the statistical model were significant in explaining the variation of the studied traits except for region effect in the analysis of casein and somatic cell score. Northeast and central-southern Italian regions showed the best performance for composition and technological features, respectively. Traits varied greatly across regions, which could reflect differences in herd management and strategies. Overall, less suitable milk for dairy processing was observed in summer. Results of the present study suggested that a constant monitoring of technological traits in the dairy industry is necessary to improve production quality at herd level and it may be a way to segregate milk according to its processing characteristics.

Short communication: Prediction of milk coagulation and acidity traits in Mediterranean buffalo milk using Fourier-transform mid-infrared spectroscopy

Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity [pH, titratable acidity (TA)], and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.

Feasibility of near infrared transmittance spectroscopy to predict fatty acid composition of commercial processed meat

BACKGROUND The new European Regulation 1169/2011 concerning nutrition declaration of food products compels the addition of saturated fatty acids, whereas the declaration of monounsaturated and polyunsaturated fatty acids remains voluntary. Therefore, the industry is interested in a more rapid, easy and less cost-effective analysis method for accomplishing this labelling regulation. The present study aimed to evaluate the ability of near infrared transmittance spectroscopy (wavelengths between 850 and 1050 nm) to predict the fatty acid (FA) composition of commercial processed meat samples (n = 310). RESULTS Good predictions were achieved for the absolute content of saturated, unsaturated, monounsaturated and polyunsaturated FA, as well as ω-6 groups, and also for a few individual FA (C16:0, C18:0, C18:1n9, C18:2n6 and 18:1n7), with the coefficient of determination in cross-validation being > 0.90 and the residual prediction deviation being > 3.15. Unsatisfactory models were obtained for the relative content of FA. CONCLUSION Near infrared transmittance spectroscopy can be considered as a reliable method for predicting the main groups of FA in processed meat products, whereas predictions of individual FA are less reliable.

Development of Fourier-transformed mid-infrared spectroscopy prediction models for major constituents of fractions of delactosated, defatted milk obtained through ultra- and nanofiltration

Milk filtration procedures are gaining relevance in the dairy industry because milk ultra- and nanofiltrates are used to increase milk processing efficiency, and as additives for products with improved nutraceutical properties. This study aimed to develop Fourier-transformed mid-infrared spectroscopy calibrations for ultra- and nanopermeate and retentate fractions of defatted and delactosated milk. A total of 154 samples from different milk fractions were collected and analyzed using reference methods to determine protein, solids-not-fat, glucose, and galactose content. The obtained values were matched with their respective Fourier-transformed mid-infrared spectroscopy spectra to develop new prediction models. Calibrations for each trait were built following 3 different approaches to get the best prediction models: (1) using the entire data set, (2) using 3 subsets based on component concentrations (level approach), and (3) using hierarchical clusters calculated with pairwise Mahalanobis distance among spectra (cluster approach). Calibrations were developed using partial least squares regression, after removing low signal-to-noise ratio wavelengths, and validated through a leave-one-out cross-validation procedure. In addition, the accuracy of the predicted values within each fraction was checked for each approach. Dividing the data set into subsets improved prediction models for each trait and for the samples in each milk fraction. Without considering milk fraction, the best improvement was observed for glucose and galactose. Glucose ratio performance deviation in cross-validation (RPD) increased from 7.42 to 11.31 and 11.06, for cluster and level approaches, respectively, whereas galactose RPD increased from 8.86 to 11.69 and 11.27 for cluster and level approaches, respectively. Considering milk fractions, the best improvement was observed for protein content, where RPD ranged from 0.08 to 6.06 for the whole data set calibration, whereas it ranged from 0.43 to 40.34 for the subset calibration approaches. Cluster and level approaches to build calibration models were comparable for samples from different fractions, suggesting that the 2 subsetting protocols should be both investigated to get the best prediction performances.

Invited review: Use of infrared technologies for the assessment of dairy products—Applications and perspectives

Dairy products are important sources of nutrients for human health and in recent years their consumption has increased worldwide. Therefore, the food industry is interested in applying analytical technologies that are more rapid and cost-effective than traditional laboratory analyses. Infrared spectroscopy accomplishes both criteria, making real-time determination feasible. However, it is crucial to ensure that prediction models are accurate before their implementation in the dairy industry. In the last 5 yr, several papers have investigated the feasibility of mid- and near-infrared spectroscopy to determine chemical composition and authenticity of dairy products. Most studies have dealt with cheese, and few with yogurt, butter, and milk powder. Also, the use of near-infrared (in reflectance or transmittance mode) has been more prevalent than mid-infrared spectroscopy. This review summarizes recent studies on infrared spectroscopy in dairy products focusing on difficult to determine chemical components such as fatty acids, minerals, and volatile compounds, as well as sensory attributes and ripening time. Promising equations have been developed despite the low concentration or the absence of specific absorption bands (or both) for these compounds.

Fecal microbiota composition changes after a BW loss diet in Beagle dogs

In developed countries, dogs and cats frequently suffer from obesity. Recently, gut microbiota composition in humans has been related to obesity and metabolic diseases. This study aimed to evaluate changes in body composition, and gut microbiota composition in obese Beagle dogs after a 17-wk BW loss program. A total of six neutered adult Beagle dogs with an average initial BW of 16.34 ± 1.52 kg and BCS of 7.8 ± 0.1 points (9-point scale) were restrictedly fed with a hypocaloric, low-fat and high-fiber dry-type diet. Body composition was assessed with dual-energy X-ray absorptiometry scan, before (T0) and after (T1) BW loss program. Individual stool samples were collected at T0 and T1 for the 16S rRNA analyses of gut microbiota. Taxonomic analysis was done with amplicon-based metagenomic results, and functional analysis of the metabolic potential of the microbial community was done with shotgun metagenomic results. All dogs reached their ideal BW at T1, with an average weekly proportion of BW loss of -1.07 ± 0.03% of starting BW. Body fat (T0, 7.02 ± 0.76 kg) was reduced by half (P < 0.001), while bone (T0, 0.56 ± 0.06 kg) and muscle mass (T0, 8.89 ± 0.80 kg) remained stable (P > 0.05). The most abundant identified phylum was Firmicutes (T0, 74.27 ± 0.08%; T1, 69.38 ± 0.07%), followed by Bacteroidetes (T0, 12.68 ± 0.08%; T1, 16.68 ± 0.05%), Fusobacteria (T0, 7.45 ± 0.02%; T1, 10.18 ± 0.03%), Actinobacteria (T0, 4.53 ± 0.02%; T1, 3.34 ± 0.01%), and Proteobacteria (T0, 1.06 ± 0.01%; T1, 1.40 ± 0.00%). At genus level, the presence of Clostridium, Lactobacillus, and Dorea, at T1 decreased (P = 0.028), while Allobaculum increased (P = 0.046). Although the microbiota communities at T0 and T1 showed a low separation level when compared (Anosims R value = 0.39), they were significantly biodiverse (P = 0.01). Those differences on microbiota composition could be explained by 13 genus (α = 0.05, linear discriminant analysis (LDA) score > 2.0). Additionally, differences between both communities could also be explained by the expression of 18 enzymes and 27 pathways (α = 0.05, LDA score > 2.0). In conclusion, restricted feeding of a low-fat and high-fiber dry-type diet successfully modifies gut microbiota in obese dogs, increasing biodiversity with a different representation of microbial genus and metabolic pathways.

Mineral composition of cow milk from multibreed herds

This study estimated the effect of Holstein-Friesian, Brown Swiss, Jersey, Simmental and Alpine Grey cattle breeds on milk mineral contents (Ca, Mg, P, K, and Na) in multibreed herds using data predicted with mid-infrared spectroscopy. The dataset included 139,821 observations from 16,566 cows and 977 herds. Fixed effects considered in the mixed model were breed, parity, stage of lactation and first-order interactions, and random effects were cow, herd-test-date, and the residual. Multiple comparisons of least squares means were performed for the main effect of breed, parity, and stage of lactation using Bonferroni adjustment. Holstein-Friesian yielded milk with the lowest fat, protein, and casein concentration, and Ca, Mg, and P contents, whereas Jersey cows produced milk with the greatest fat, protein, and casein concentration, and Ca and Mg contents. Results of this study suggest that mixing milk from different breeds could enhance milk composition and technological ability, and therefore contribute to improve dairy industry efficiency.

Technical note: Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness

The aim of the study was to evaluate the feasibility of near infrared (NIR) transmittance spectroscopy to predict cheese ripeness using the ratio of water-soluble nitrogen (WSN) to total nitrogen (TN) as an index of cheese maturity (WSN/TN). Fifty-two Protected Designation of Origin cow milk cheeses of 5 varieties (Asiago, Grana Padano, Montasio, Parmigiano Reggiano, and Piave) and different ripening times were available for laboratory and chemometric analyses. Reference measures of WSN and TN were matched with cheese spectral information obtained from ground samples by a NIR instrument that operated in transmittance mode for wavelengths from 850 to 1,050 nm. Prediction equations for WSN and TN were developed using (1) cross-validation on the whole data set and (2) external validation on a subset of the entire data. The WSN/TN was calculated as ratio of predicted WSN to predicted TN in cross-validation. The coefficients of determination for WSN and TN were >0.85 both in cross- and external validation. The high accuracy of the prediction equations for WSN and TN could facilitate implementation of NIR transmittance spectroscopy in the dairy industry to objectively, rapidly, and accurately monitor the ripeness of cheese through WSN/TN.