Evaluation of Chemical Parameters in Soft Mold-Ripened Cheese During Ripening by Mid-Infrared Spectroscopy

Identifying Chemical Differences in Cheddar Cheese Based on Maturity Level and Manufacturer Using Vibrational Spectroscopy and Chemometrics

Cheese is a nutritious dairy product and a valuable commodity. Internationally, cheddar cheese is produced and consumed in large quantities, and it is the main cheese variety that is exported from Australia. Despite its importance, the analytical methods to that are used to determine cheese quality rely on traditional approaches that require time, are invasive, and which involve potentially hazardous chemicals. In contrast, spectroscopic techniques can rapidly provide molecular information and are non-destructive, fast, and chemical-free methods. Combined with partner recognition methods (chemometrics), they can identify small changes in the composition or condition of cheeses. In this work, we combined FTIR and Raman spectroscopies with principal component analysis (PCA) to investigate the effects of aging in commercial cheddar cheeses. Changes in the amide I and II bands were the main spectral characteristics responsible for classifying commercial cheddar cheeses based on the ripening time and manufacturer using FTIR, and bands from lipids, including β'-polymorph of fat crystals, were more clearly determined through changes in the Raman spectra.

Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese

Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (R_Pre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.

FT-MIR Analysis of Water-Soluble Extracts during the Ripening of Sheep Milk Cheese with Different Phospholipid Content

The purpose of this work was to study the suitability of the water-soluble extracts (WSE) of semi-hard sheep milk cheese for analysis by diffuse reflectance Fourier transform mid-infrared spectroscopy (FT-MIR) and the development of classification models using discriminant analysis and based on cheese age or phospholipid content. WSE was extracted from three types of sheep milk cheeses (full-fat, reduced-fat and reduced-fat fortified with lyophilized sweet sheep buttermilk) at various stages of ripening from six to 168 days and lyophilized. The first model used 1854–1381 and 1192–760 cm−1 regions of the first-derivative spectra and successfully differentiated samples of different age, based on changes in the water-soluble products of ripening biochemical events. The second model used the phospholipid absorbance spectral regions (3012–2851, 1854–1611 and 1192–909 cm−1) to successfully discriminate cheeses of markedly different phospholipid content. Cheese WSE was found suitable for FT-MIR analysis. According to the results, a fast and simple method to monitor cheese ripening based on water-soluble substances has been developed. Additionally, the results indicated that a considerable amount of phospholipids migrates to the cheese WSE and that FT-MIR can be a useful tool for their assessment.

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Modeling weight loss of cheese during ripening and the influence of dairy system, parity, stage of lactation, and composition of processed milk

The yield, flavor, and texture of ripened cheese result from numerous interrelated microbiological, biochemical, and physical reactions that take place during ripening. The aims of the present study were to propose a 2-compartment first-order kinetic model of cheese weight loss over the ripening period; to test the variation in new informative phenotypes describing this process; and to assess the effects on these traits of dairy farming system, individual farms within dairy system, animal factors, and milk composition. A total of 1,211 model cheeses were produced in the laboratory using individual 1.5-L milk samples from Brown Swiss cows reared on 83 farms located in Trento Province. During ripening (60 d; temperature 15°C, relative humidity 85%), the weight of all model cheeses was measured, and cheese yield (cheese weight/processed milk weight, %CY) was calculated at 7 intervals from cheese-making (0, 1, 7, 14, 28, 42, and 60 d). Using these measures, a 2-compartment first-order kinetic model (3-parameter equation) was developed for modeling %CY during the ripening period, as follows: [Formula: see text] , where %CY_t is the %CY at ripening time t; %CY_i and %CY_f are the modeled %CY traits at time 0 d (%CY_i = initial %CY) and at the end of a ripening period sufficient to reach a constant wheel weight (%CY_f = final %CY after 60 d ripening in the case of small model cheeses); k_CY is the instant rate constant for cheese weight loss (%/d). Cheese weight and protein and fat losses were calculated as the % difference between the model cheeses at 0 and after 60 d of ripening. The variation in cheese pH was calculated as the % difference between pH at 0 and after 60 d. Dairy system, individual herd within dairy system, and the cow's parity and lactation stage (tested with a linear mixed model) strongly affected almost all the traits collected during model cheese ripening. Milk fat, protein, lactose, pH, and somatic cell score also greatly affected almost all the traits, although k_CY was affected only by milk protein. After including milk composition in the linear mixed model, the importance of all the herd and animal sources of variation was greatly reduced for all traits. The proposed model and novel traits could be tested, first, with the aim of establishing new monitoring procedures enabling the dairy industry to improve milk quality-based payment systems at the herd level and, second, with a view to exploring possible genetic improvements to dairy cow populations.

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.

Study of the Interactions Occurring During the Encapsulation of Sesamol within Casein Micelles Reformed from Sodium Caseinate Solutions

A casein micelle is a natural structure found in milk, based on the association between individual caseins and colloidal calcium phosphate, which can be used as vehicle for the encapsulation of hydrophobic compounds. In this project the capacity of micelles to encapsulate sesamol, a powerful antioxidant present in roasted sesame seeds, was evaluated. The micelles were reformed from sodium caseinate solutions at 2% or 5% (w/v) concentration, and then 1 or 2 mg/mL sesamol were added. A significant increase on the encapsulation efficiency was observed as caseinate concentration increased, going from 28% to 35% of sesamol encapsulated, while the encapsulation yield was greater in all cases for micelles from solutions with lower caseinate concentration. The average size of micelles ranged from 150 to 165 nm with an average zeta potential of -27.3 ± 1.86 mV. FTIR and fluorescence analysis confirm interactions within the casein chains and sesamol molecules with a bathochromic shift which suggests a predominant hydrophilic nature of such interactions. Differential scanning calorimetry thermograms showed that denaturation enthalpy tended to decrease as sesamol concentration increased, suggesting that sesamol molecules may be displacing the water molecules associated with the casein chains, reinforcing the idea of predominant hydrophilic interactions. Based on the results from encapsulation efficiency, it is estimated that about 7 g of casein micelles reformed from 2% (w/v) caseinate solutions with 2 mg/mL of added sesamol may provide the recommended daily dose and may be useful for the development of new functional food products.

PRACTICAL APPLICATIONS

The development of a nanodelivery system for different bioactives will allow the enrichment of foods and drinks to develop new functional products that will satisfy consumers' demands. Additionally, the study of interactions between these molecules will allow us to understand how sesamol is being incorporated within the reformed micelles and how this process can even be improved.

The change of fatty acids composition of Polish biscuits during storage

Commercially available Polish biscuits were stored for 10months under different storage conditions i.e. in temperatures of 5°C and 20°C. The chemical quality alteration caused by chemical reactions occurring within biscuits were studied in terms of change of composition of fat extracted from studied samples in one-month intervals. Correlation of data from standard methods e.g. gas chromatography or classic titration with FT-IR spectroscopy, was followed by calculation of four statistical models that accurately predicted peroxide value, oxidative stability, polar fraction content and unsaturated trans fatty acid content in any samples. On the basis of data obtained, scheme of chemical reactions involved in oxidation process was suggested. A critical time of storage was proposed as an indicator of the period of the highest rate of chemical changes. Among factors considered to influence oxidative stability, the following had the greatest impact: initial water content, initial fat content, and time of storage.

The composition of Camembert cheese-ripening cultures modulates both mycelial growth and appearance

The fungal microbiota of bloomy-rind cheeses, such as Camembert, forms a complex ecosystem that has not been well studied, and its monitoring during the ripening period remains a challenge. One limitation of enumerating yeasts and molds on traditional agar media is that hyphae are multicellular structures, and colonies on a petri dish rarely develop from single cells. In addition, fungi tend to rapidly invade agar surfaces, covering small yeast colonies and resulting in an underestimation of their number. In this study, we developed a real-time quantitative PCR (qPCR) method using TaqMan probes to quantify a mixed fungal community containing the most common dairy yeasts and molds: Penicillium camemberti, Geotrichum candidum, Debaryomyces hansenii, and Kluyveromyces lactis on soft-cheese model curds (SCMC). The qPCR method was optimized and validated on pure cultures and used to evaluate the growth dynamics of a ripening culture containing P. camemberti, G. candidum, and K. lactis on the surface of the SCMC during a 31-day ripening period. The results showed that P. camemberti and G. candidum quickly dominated the ecosystem, while K. lactis remained less abundant. When added to this ecosystem, D. hansenii completely inhibited the growth of K. lactis in addition to reducing the growth of the other fungi. This result was confirmed by the decrease in the mycelium biomass on SCMC. This study compares culture-dependent and qPCR methods to successfully quantify complex fungal microbiota on a model curd simulating Camembert-type cheese.

Chemical and instrumental approaches to cheese analysis

Overcoming the complexity of cheese matrix to reliably analyze cheese composition, flavor, and ripening changes has been a challenge. Several sample isolation or fractionation methods, chemical and enzymatic assays, and instrumental methods have been developed over the decades. While some of the methods are well established standard methods, some still need to be researched and improved. This chapter reviews the chemical and instrumental methods available to determine cheese composition and monitor biochemical events (e.g., glycolysis, lipolysis, and proteolysis) during cheese ripening that lead to the formation of cheese flavor. Chemical and enzymatic methods available for analysis of cheese composition (fat, protein, lactose, salt, nitrogen content, moisture, etc.) are presented. Electrophoretic, chromatographic, and spectroscopic techniques are also reviewed in the light of their application to monitor cheese ripening and flavor compounds. Novel instrumental methods based on Fourier-transform infrared spectroscopy that are currently being researched and applied to cheese analysis are introduced.