Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

Rodríguez-Otero, J. L.; Hermida, Marcelo Antonio Martínez; Cepeda, Alberto

doi:10.1093/jaoac/78.3.802

Cited by 69 publications

(24 citation statements)

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“…Reflectance near-infrared spectroscopy has been reported for the determination of chemical constituents in varieties of hard and semihard cheeses. Similar performance statistics were reported for near-IR with R 2 > 0.92 and SEP values ranging from 0.3 to 0.6% for moisture, 0.3 to 0.5% for fat, and 0.3 to 0.5% for protein, depending on the type of cheese and mathematical transformation of the spectral data (Pierce and Wehling, 1994;Rodriguez-Otero et al, 1995;Sorensen and Jepsen, 1998;Wittrup and Nørgaard, 1998;Mazerolles et al, 2000). Improved prediction results have been reported for near-IR analysis of processed cheeses, with SEP of 1.25 and 1.24 g/kg for moisture and fat, respectively, probably due to the less heterogeneous composition of the cheese material (Adams et al, 1999).…”

Section: Resultssupporting

confidence: 72%

“…Near infrared and mid-IR spectroscopy have been extensively used for the analysis of several milk components such as protein, fat, moisture, sugars, casein, cholesterol, and contaminants such as tetracycline (Rodriguez-Otero et al, 1997;O'Sullivan et al, 1999;Tsenkova et al, 1999;Luginbü hl, 2002;Paradkar and Irudayaraj, 2002;Sivakesava and Irudayaraj, 2002;Sorensen et al, 2003). Near-infrared spectroscopy has been widely studied for the rapid determination of major cheese constituents such as protein, fat, moisture, DM, and total solids (Frank and Birth, 1982;Wehling and Pierce, 1988;Pierce and Wehling, 1994;Rodriguez-Otero et al, 1995;McQueen et al, 1995;Wittrup and Noøgaard, 1998;Adamopoulos et al, 2001;Č urda and Kukačková , 2004). On the contrary, few studies have reported the application of midIR spectroscopy for cheese analysis because of difficulties in the sampling procedures due to the lack of homogeneity of the components in the cheese (McQueen et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

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Rapid Determination of Swiss Cheese Composition by Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

Rodríguez-Saona

Koca

Harper

et al. 2006

Journal of Dairy Science

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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 (approximately 0.5 g) 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 (approximately 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.

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Section: Resultssupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Rapid Determination of Swiss Cheese Composition by Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

Rodríguez-Saona

Koca

Harper

et al. 2006

Journal of Dairy Science

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“…Spectra within each treatment group were centred to detect any outliers of data, using 3.0 as the cut-off H value. 19 No outliers were found in the present study. At the end of spectroscopic analysis, minced meat samples were stored in airtight plastic bags at À30°C for further chemical analysis.…”

Section: Spectroscopic Analysiscontrasting

confidence: 43%

Identification of broiler chicken meat using a visible/near‐infrared spectroscopic technique

Ding

Chan

1999

J. Sci. Food Agric.

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A near-infrared spectroscopic method was developed with a dummy regression technique to differentiate meat originating from broilers and Chinese local chickens. Best classi®cation accuracies of 100%, 92%, 96% and 92% were achieved for minced thigh meat, minced breast meat, breast cut without skin and breast cut with skin respectively. Comparison among the regression models of MLR, PCR, PLS and mPLS did not show obvious differences in classi®cation accuracy. Scatter correction and derivative treatment of the spectral data before discriminant analysis often improved the classi®cation accuracy for minced meat, while for meat cuts, spectra without pretreatment produced better classi®cation. In general, using the full spectrum of 400±2500 nm produced satisfactory classi®cation. The spectrum in the visible region of 400±750 nm, the short-wavelength NIR region of 750± 1100 nm or the long-wavelength NIR region of 1100±2500 nm can also produce satisfactory classi®cation depending on sample presentation methods and regression models. The spectroscopic classi®cation was supported by physical and chemical properties of meat samples, which showed signi®cant differences in collagen and fat contents and pH and chromatic values between the two groups of chickens. The results of the present study indicate that NIR spectroscopy can be used to identify broiler meat or carcass from those of local chickens.

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“…The chemical reference methods for measurement of fat (e.g., Babcock and Mojonnier ether extraction), CP (Kjeldahl), moisture (forced air oven-drying), and salt (Volhard silver nitrate titration) are accurate but are too slow and impractical for rapid analysis of large numbers of cheese samples in a commercial quality assurance (QC) laboratory. Therefore, more rapid and cost-effective methods such as near-infrared (NIR) reflectance for fat, protein, and moisture (Rodriguez-Otero et al, 1995;McKenna, 2001) and coulometric methods (Varcoe, 2001) for salt determination are currently used in cheese factories.…”

Section: Introductionmentioning

confidence: 99%

Determination of fat, protein, moisture, and salt content of Cheddar cheese using mid-infrared transmittance spectroscopy

Margolies

Barbano

2018

Journal of Dairy Science

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The objective of our work was to develop and evaluate the performance of a rapid method for measuring fat, protein, moisture, and salt content of Cheddar cheese using a combination mid-infrared (MIR) transmittance analysis and an in-line conductivity sensor in an MIR milk analyzer. Cheddar cheese was blended with a dissolving solution containing pentasodium triphosphate and disodium metasilicate to achieve a uniform, particle-free dispersion of cheese, which had a fat and protein content similar to milk and could be analyzed using a MIR transmittance milk analyzer. Annatto-colored Cheddar cheese samples (34) from one cheese factory were analyzed using reference chemistry methods for fat (Mojonnier ether extraction), crude protein (Kjeldahl), moisture (oven-drying total solids), and salt (Volhard silver nitrate titration). The same 34 cheese samples were also dissolved using the cheese dissolver solution, and then run through the MIR and used for calibration. The reference testing for fat and crude protein was done on the cheese after dispersion in the dissolver solution. Validation was done using a total of 36 annatto-colored Cheddar cheese samples from 4 cheese factories. The 36 validation cheese samples were also analyzed using near-infrared spectroscopy for fat, moisture, and the coulometric method for salt in each factory where they were produced. The validation cheeses were also tested using the same chemical reference methods that were used for analysis of the calibration samples. Standard error of prediction (SEP) values for moisture and fat on the near-infrared spectroscopy were 0.30 and 0.45, respectively, whereas the MIR produced SEP values of 0.28 and 0.23 for moisture (mean 36.82%) and fat (mean 34.0%), respectively. The MIR also out-performed the coulometric method for salt determination with SEP values of 0.036 and 0.139 at a mean level of salt of 1.8%, respectively. The MIR had an SEP value of 0.19 for estimation at a mean level of 24.0% crude protein, which suggests that MIR could be an easy and effective way for cheese producers to measure protein to determine protein recovery in cheese making.

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Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

Cited by 69 publications

References 0 publications

Rapid Determination of Swiss Cheese Composition by Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

Rapid Determination of Swiss Cheese Composition by Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

Identification of broiler chicken meat using a visible/near‐infrared spectroscopic technique

Determination of fat, protein, moisture, and salt content of Cheddar cheese using mid-infrared transmittance spectroscopy

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