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.     

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350–1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6–6) showed high de-noising ability with good information preservation. The first derivative of db6–6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R²) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.     

Prediction of colour of European Emmental cheeses by using near infrared spectroscopy: a feasibility study

Near infrared (NIR) spectroscopy was used to predict colour of European Emmental cheese samples. Colour values (L, a and b) were measured on 20 Emmental cheese samples using a Hunter-lab D25-D-2 optical head in the system according to Hunter to determine L (brightness), a (green-red component) and b (blue-yellow component). The diffuse reflectance of the investigated cheeses was also determined by a Büchi NIR Lab N-200 spectrometer using a rotating measuring cell in the range of 1000–2500 nm. The best results for L-value (squared correlation coefficient (R ²) = 0.56, root mean square error of cross-validation (RMSECV) = 0.76, ratio of prediction deviation (RPD) = 1.89 and range error ratio (RER) = 7.91), a-value (R ² = 0.72, RMSECV = 0.15, RPD = 1.98 and RER = 7.6) and b-value (R ² = 0.82, RMSECV = 0.52, RPD = 2.56 and RER = 9.42) were obtained when the first 12 principal components (PCs) of the principal component analysis (PCA) applied on normalised NIR spectra were used. It can be concluded that NIR spectroscopy could be used to predict b-value. The a- and L-values can also be predicted from NIR technique with approximate quantitative prediction.     

Prediction of minerals,fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy

Prediction models for the mineral, fatty acid (FA) and cholesterol contents of commercial European cheeses using near infrared transmittance spectroscopy were developed. Cheese samples (n = 145) were from different dairy species and ripening time. Sample spectra were matched with mineral, FA and cholesterol reference data to develop prediction models. Modified partial least squares regressions were validated through cross-validation procedure on the complete dataset (n = 145) and through external validation after dividing the data into calibration (74%) and external validation (26%) sets. Satisfactory models were developed for Ca, P, S, Mg and Zn, and for FA groups (saturated, unsaturated, monounsaturated and polyunsaturated FAs), major FAs (myristic, palmitic and oleic acids) and some minor FAs, whereas cholesterol content could not be predicted with adequate accuracy. Results of the present study are a precursor to at-line utilisation of prediction models for the most abundant cheese minerals and FAs at an industry level.     

Effect of Curd Washing Level on Proteolysis and Functionality of Low-Moisture Mozzarella Cheese Made with Galactose-Fermenting Culture

Abstract: The effect of curd washing on functional properties of low-moisture mozzarella cheese made with galactose-fermenting culture was investigated. A total of 4 curd washing levels (0%, 10%, 25%, 50% wt/wt) were used during low-moisture mozzarella cheese manufacture, and cheeses were stored for 63 d at 4 °C and the influence of curd washing on proteolysis and functionality of low-moisture mozzarella cheese were examined. Curd washing had a significant effect on moisture and ash contents. In general, moisture contents increased and ash contents decreased with increased curd washing levels. Low-moisture mozzarella cheese made with 10% curd washing levels showed higher proteolysis, meltability, and stretchability during storage than other experimental cheeses. In general, galactose contents decreased during storage; however, cheeses made with 25% and 50% curd washing levels had lower galactose contents than those with control or 10%. L*-values (browning) decreased and proteolysis increased in low-moisture mozzarella cheeses during storage.     

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.     

Discrimination of seasonality in cheeses by near-infrared technology

BACKGROUND: Owing to the importance of the season of collection of milk for cheese quality, a study was made of the usefulness of near‐infrared spectroscopy (NIRS) for discriminating the seasonal origin (winter or summer) of milk and quantifying the fat content of cheeses, since fat is one of the components most affected by the season of collection of milk for the elaboration of cheeses. RESULTS: In the internal validation, 96% of samples from winter milk and 97% of samples from summer milk were correctly classified, while in the external validation the prediction rate of samples correctly classified was 92%. Moreover, quantitative models allowed the determination of fat in winter, summer and winter + summer cheeses. CONCLUSION: Rapid prediction of the fat content of cheeses and the seasonal origin (winter or summer) of milk was achieved using NIRS without previous destruction or treatment of samples. Copyright © 2011 Society of Chemical Industry     

A comparison and joint use of NIR and MIR spectroscopic methods for the determination of some parameters in European Emmental cheese

The determination of winter cheese chemical properties, namely, fat, sodium chloride (NaCl), pH, non protein nitrogen (NPN), total nitrogen (TN) and water soluble nitrogen (WSN) was done using spectroscopic technologies with different wavelength zones. The Emmental cheeses provided from different European countries were studied. A total of 91 cheeses produced during the winter time in Austria (n=4), Finland (n=6), Germany (n=13), France (n=30) and Switzerland (n=38) were analysed by near infrared (NIR) and mid infrared (MIR) spectroscopies. The combination of these two spectral regions (sum of their spectra) was also studied. The partial least square (PLS) regression with the leave one-out cross validation technique was used to build up calibration models using data set designated as calibration set. These models were validated with another data set designated as validation set. The obtained results suggest the use of the NIR for the determination of fat and TN contents, and the MIR for NaCl and NPN contents as well as for the pH. Similar results were obtained for WSN using the two techniques together. The combined spectra of both NIR and MIR did improve the results, while providing comparable results to those obtained from either the NIR or MIR spectroscopy.     

Invited review: Fresh pasta filata cheeses: Composition,role, and evolution of the microbiota in their quality and safety

With a global market of around $55 billion (in US dollars; i.e. around the 57% of the global cheese market), pasta filata cheeses sales are rising approximately 2% per year worldwide and are expected to further increase to $65.01 billion by 2028. Among these groups of cheeses, fresh pasta filata cheeses, such as mozzarella and fior di latte, are the most consumed. Herein, we provide an overview of fresh pasta filata cheeses, their commodity-related and technological aspects, focusing on the composition, the role, and evolution of their microbiota along the dairy chain.     

Low-moisture part-skim mozzarella cheese made from blends of camel and bovine milk: Gross composition,proteolysis, functionality,microstructure, and rheological properties

Camel (CM) milk is used in variety of ways; however, it has inferior gelling properties compared with bovine milk (BM). In this study, we aimed to investigate the physicochemical, functional, microstructural, and rheological properties of low-moisture part-skim (LMPS) mozzarella cheese, made from BM, or BM mixed with 15% CM (CM15%) or 30% CM (CM30%), at various time points (up to 60 d) of storage at 4°C after manufacture. Low-moisture part-skim mozzarella cheeses using CM15% and CM30% had high moisture and total Ca contents, but lower soluble Ca content. Compared with BM cheese, CM15% and CM30% LMPS mozzarella cheese exhibited higher proteolysis rates during storage. Adding CM affected the color properties of LMPS mozzarella cheese manufactured from mixed milk. Scanning electron microscopy images showed that the microstructure of CM15% and CM30% cheeses had smooth surfaces, whereas the BM cheese microstructures were rough with granulated surfaces. Low-moisture part-skim mozzarella cheeses using CM15% and CM30% showed significantly lower hardness and chewiness, but higher stringiness than BM cheese. Compared with BM cheese, CM15% and CM30% cheeses showed lower tan δ levels during temperature surges, suggesting that the addition of CM increased the meltability of LMPS mozzarella cheese during temperature increases. Camel milk addition affected the physicochemical, microstructural, and rheological properties of LMPS mozzarella cheese.     

Modelling of sensory and instrumental texture parameters in processed cheese by near infrared reflectance spectroscopy

This study investigated the application of near infrared (NIR) reflectance spectroscopy to the measurement of texture (sensory and instrumental) in experimental processed cheese samples. Spectra (750 to 2498 nm) of cheeses were recorded after 2 and 4 weeks storage at 4 degrees C. Trained assessors evaluated 9 sensory properties, a texture profile analyser (TPA) was used to record 5 instrumental parameters and cheese 'meltability' was measured by computer vision. Predictive models for sensory and instrumental texture parameters were developed using partial least squares regression on raw or pre-treated spectral data. Sensory attributes and instrumental texture measurements were modelled with sufficient accuracy to recommend the use of NIR reflectance spectroscopy for routine quality assessment of processed cheese.     

A comparison and joint use of mid infrared and fluorescence spectroscopic methods for differentiating between manufacturing processes and sampling zones of ripened soft cheeses

Ten traditional M1 (n = 5) and M2 (n = 5) soft cheeses produced from raw milk, and five other stabilised M3 (n = 5) cheeses manufactured from pasteurised milk, were studied using mid infrared (MIR) and front face fluorescence (FFFS) spectroscopies. MIR (3000–900 cm⁻¹), tryptophan (excitation: 290 nm, emission: 305-450 nm), 400-640 emission spectra (excitation: 380 nm) and vitamin A (excitation: 280–350 nm, emission: 410 nm) spectra were recorded at two sampling zones (external (E) and central (C)) of the investigated cheeses. When the factorial discriminant analysis (FDA) was applied to the MIR spectra, the classification was not satisfactory. With tryptophan fluorescence spectra, correct classification of 94.4 and 69.4% was observed for the calibration and validation spectra, respectively. Better classification was obtained using vitamin A fluorescence spectra, since 91.8 and 80.6% of the calibration and validation spectra, respectively, were correctly classified. When the first five principal components (PCs) of the PCA extracted from each data set were pooled into a single matrix and analysed by FDA, the classification was considerably improved, obtaining a percentage of correct classification of 100 and 91.7% for the calibration and validation samples, respectively. It was concluded that concatenation of the physico-chemical and spectroscopic data sets is an efficient technique for the identification of soft cheese varieties.     

Prediction of fatty acid composition of fresh and freeze-dried cheeses by visible–near-infrared reflectance spectroscopy

Visible–near-infrared reflectance spectroscopy was used to predict the fatty acid (FA) composition of 445 cows’ and goats’ fresh and freeze-dried cheeses. The FA composition of samples was analysed by reference method. Samples were scanned (400–2500 nm) and predictive equations were developed using modified partial least squares with both cross-validation and external validation. The coefficient of determination in external validation (R²V) and residual predictive deviation in the validation set (RPDV) were good for total saturated (0.89, 2.84), monounsaturated (0.90, 2.90), polyunsaturated (0.88, 2.54), trans (0.92, 3.24) FA, butyric (0.84, 2.43), capric (0.92, 2.96), myristic (0.89, 2.94), palmitic (0.91, 3.32), vaccenic (0.88, 2.63), and rumenic (0.90, 2.70) acids in fresh cheeses. Approximate or poor predictions were obtained for caproic, caprylic, lauric, stearic, oleic, linoleic, and linolenic acids (R²V0.80; RPDV2.08). The quantification was significantly more accurate for caprilic, capric, and linoleic acids with freeze-dried cheeses compared with fresh cheeses.     

Detection of milk powder and caseinates in Halloumi cheese

Halloumi cheese is traditionally manufactured from fresh milk. Nevertheless, dried dairy ingredients are sometimes illegally added to increase cheese yield. Lysinoalanine and furosine are newly formed molecules generated by heating and drying milk protein components. The levels of these molecular markers in the finished Halloumi have been investigated to verify their suitability to reveal the addition of skim milk powder and calcium caseinate to cheese milk. Because of the severe heating conditions applied in curd cooking, genuine Halloumi cheeses (n = 35), representative of the Cyprus production, were characterized by levels of lysinoalanine (mean value = 8.1 mg/100 g of protein) and furosine (mean value = 123 mg/100 g of protein) unusual for natural cheeses. Despite the variability of the values, a good correlation between the 2 parameters (R = 0.975) has been found in all cheeses, considering both the fresh and mature cheeses as well as those obtained from curd submitted to a prolonged cooking following a traditional practice adopted by a very small number of manufacturers. Experimental cheeses made by adding as low as 5% of skim milk powder, or calcium caseinate, or both, to cheese milk fell outside the prediction limits at ±2 standard deviation of the above-reported correlation regardless of curd cooking conditions or ripening length. This correlation may be adopted as a reliable index of Halloumi cheese genuineness.     

The compositional and functional properties of commercial mozzarella, cheddar and analogue pizza cheeses

The compositional and functional properties of commercial retail and/or wholesale samples (n = 8) of low-moisture mozzarella, cheddar and analogue (pizza) cheeses were compared. Inter-and intravariety differences were evident with intravariety differences in composition being relatively large for the analogue cheese. Cheddar had the lowest mean pH and level of expressible serum and the highest mean levels of proteolysis, expressible fat, and serum calcium and nitrogen (p < 0.05). Compared to mozzarella, the analogue cheeses had significantly lower (p < 0.05) mean levels of total protein and serum calcium, higher levels of total calcium and higher cheese pH. The mean stretchability of the melted mozzarella cheese was significantly higher than that of the melted cheddar or analogue cheeses. The melted cheddar had the highest mean flowability and lowest mean apparent viscosity (p < 0.05). The mean flowability and apparent viscosity of the analogue cheese were numerically lower and higher, respectively, than those of mozzarella.     

Prediction of Inorganic Salt and Moisture Content of Process Cheese Using Dielectric Spectroscopy

The development of on-line sensors for compositional analysis during cheese manufacture is desirable for improved quality control. Dielectric properties of a food product are principally determined by its moisture and salt content. This indicates that dielectric spectroscopy may offer a rapid, on-line and non-destructive method for the determination of moisture and salt content of process cheese. However limited information is available in the literature on the dielectric properties of process cheese. Therefore the aims of this study are to investigate the dielectric properties of process cheese samples over a range of compositional parameters and to assess the potential of dielectric spectroscopy to improve process control during process cheese manufacture. Dielectric spectra of process cheese samples were measured using a coaxial line probe between 300 MHz and 3 GHz. A clear tend was observed between higher moisture content and increases in the dielectric constant. Inorganic salt content was found to have a major influence on the loss factor. The dielectric data obtained was used to develop chemometric models for the prediction of moisture and inorganic salt content of two experimental sets of process cheese samples (exp A and exp B). The root mean square error of prediction (RMSEP) for the models developed to predict moisture content were 0.524% (w/w) (exp A), and 0.423% (w/w) (exp B), while the RMSEP of the inorganic salt models were 0.220% (w/w) (exp A), and 0.263% (w/w) (exp B). It was concluded that dielectric spectroscopy has potential application for compositional analysis in process cheese manufacture.     

Influence of Water-Soluble Nitrogen and Free Amino Groups on Sensory Attributes of Smoked Mozzarella Cheese

The smoking of cheeses contributes to significant changes in their color, aroma, and also content of free amino groups. The aim of the study was to evaluate changes in sensory attributes of smoked and unsmoked mozzarella cheese during storage for four weeks. It was found that inner layers of both cheeses were equally light despite large differences in their outer layers (ΔL*: 26.33 for smoked and ΔL*: 5.33 for unsmoked). Color of the edge layer in smoked cheese was more saturated than in unsmoked cheese (ΔC* = 17.47). The edge layer in smoked cheese after storage became more saturated (ΔC* = 5.12). Desirability of smoked cheese was 1.5-fold greater than that of unsmoked cheese. Smoking contributed to the disappearance of cooked aroma (by 32%), whey, and cowy/phenolic aromas and intensified the perceptibility of acid taste (8.5 times). Changes were higher in smoked mozzarella because of its higher proteolysis susceptibility.     

Detection of milk mixtures in Halloumi cheese

A capillary electrophoresis method has been applied to the detection of illegal addition of milk from goat and/ or cow in Halloumi cheese, traditionally made with sheep milk. The electrophoretic profiles of the casein from Halloumi cheeses have revealed that caprine para-kappa-casein and bovine alphas1-casein peaks point to the presence of low percentages of goat's and/or cow's milk added to Halloumi cheese. Stepwise multiple linear regression has been used to predict these percentages with a standard error of the estimation of 2.14%. The analytical method combined with the statistical application is valid for the prediction of percentages higher than 2% of goat's and percentages of 5% of cow's milk added to the cheese either in fresh or ripened cheese. The standard error of estimation was higher for the prediction of cow's milk than for goat's milk.