Preliminary Discrimination of Cheese Adulteration by FT-IR Spectroscopy

Cuibus, Lucian; Maggio, Rubén M.; Mureşan, Vlad; Diaconeasa, Zorița; Fetea, Florinela; Socaciu, Carmen

doi:10.15835/buasvmcn-fst:10795

Cited by 11 publications

(21 citation statements)

References 21 publications

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“…The analysis with MIR spectroscopy provides information on fundamental vibrations and associated rotational-vibration structure in the wavenumber range from 4,000 to 700 cm −1 (Cevoli et al, 2013). Cheese samples have to be ground (Cuibus et al, 2014;Kraggerud et al, 2014) or liquated (Margolies and Barbano, 2018) to be scanned with a MIR spectroscopy instrument. On the other hand, NIR spectrum results from the overtones and combinations of the fundamental frequencies in the MIR region detected in the spectral range from 750 to 2,500 nm (McKenna, 2001).…”

Section: Infrared Technologiesmentioning

confidence: 99%

“…Most studies have built prediction models using wavelengths in the NIR region, and only 3 investigated the use of an instrument that worked in the MIR region. Studies that dealt with MIR spectroscopy scanned cheese samples after milling (Cuibus et al, 2014;Kraggerud et al, 2014) or in a particle-free dispersion (Margolies and Barbano, 2018). On the other hand, studies that used NIR spectroscopy usually recorded absorbance in reflectance mode, with no pre-treatment of the sample.…”

Section: Cheesementioning

confidence: 99%

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

Marchi

Penasa

Zidi

et al. 2018

Journal of Dairy Science

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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.

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Section: Infrared Technologiesmentioning

confidence: 99%

Section: Cheesementioning

confidence: 99%

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

Marchi

Penasa

Zidi

et al. 2018

Journal of Dairy Science

View full text Add to dashboard Cite

show abstract

“…This result proved that quantitative identification model for honey sample works. In another study conducted using the PLS method with FT-NIR (Cuibus et al, 2014), palm fat was added to the cheese in different amounts for adulteration, and its consistency value (mean recovery rate) was 108 and R 2 value was found to be 0.9695. In this study, the actual and predicted values of palm fat added to cheese in different proportions were as follows; 4% for 5%, 10.1% for 10%, 20.2% for 15%, 24.2% for 20% and 41.6% for 40%.…”

Section: Discussionmentioning

confidence: 99%

Using FT-NIR Calibration to Determine Soybean Meal Amount in Concentrated Feed Ration Mixtures for Laying Hens

2019

JSTR

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This research was conducted quantitatively to find out the amount of soybean meal used in the concentrate feeds of laying hen by using near infrared spectroscopy (NIR) technique. For this purpose, 30 different concentrated feed ration mixtures of 1 kg each was prepared for laying hens by increasing the concentration up to 30 %. The functional NIR spectra of feed mixtures were obtained at wavelengths of 10000-4000 nm / cm which were evaluated by applying PLS (Partial Least Square) method on the second derivative. In the normalization study, SNV (Standard Normal Variate) method was applied. In addition, data obtained from first order (1st Derivation B Cap 5 Points Gap 2) were prepared for regression. The linear regression method was applied to the normalized spectra by subtracting the Outlier values from the calibration set and in this way the calibration quality parameters were revealed. At this stage, the standard deviations of the R 2 value, validation and calibration set were calculated. According to the reflections, the graphs obtained with regression coefficients were taken. In addition, sum of the squares for estimating residual error (V-Set PRESS) of the validation set was also revealed. Outlier values were not part of study because of low calibration quality. In the research, R value was determined as 0.9947, R 2 value, 0.9894 and standard deviation value as 0.6762 of calibration set. Moreover, R value was determined as 0.9919, R 2 value, 0.9839 and standard deviation value as 0.8398 of validation set and the principal component value (Principal Components) were found to be around 14. The regression consistency was between 80-110 and it was obtained by dividing standard error value (SEC) of calibration to standard error of estimation (SEP). In conclusion it is evident from the results that method of FT-NIR spectroscopy is a reliable way to determine the extent at which the soybean meal is quantitatively used in laying hen feed mixtures.

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“…The actual and estimated values of the adulteration study performed on the honey sample using PLS method are as follows; 0.69% for 0%, .5.27% for 5%, 14.85% for 15%, 24.31% for 25% and 42.63% for 45%. In another study related to the subject (Cuibus et al, 2014), calibration was developed by using FT-NIR and PLS method in cheeses adulterated with solid palm oil in different ratios. In the study, mean recovery rate was found to be 108 and R 2 value was found to be 0.9695.…”

Section: Discussionmentioning

confidence: 99%

The Creation of the FT-NIR Calibration for the Determination of the Amount of Corn Grain in Concentrated Feed

Yildirim¹,

Bayram²

2019

JSTR

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This study was carried out to determine the proportion of corn grain used in laying hen concantrate feeds quantitatively by means of near infrared spectroscopy (NIR) device. For this purpose, different laying hen concentrate feed consisting of 60 pieces of 1 kg mixture was formed by increasing the concentration of 1% in each ration from 1% to 60%. Functional NIR spectra of feed mixtures were obtained at wavelengths of 10000-4000 nm / cm. The spectra were evaluated by using PLS (Partial Least Square) method on the second derivative. In the normalization study, SNV (Standard Normal Variate) method was applied. In addition, data obtained from first order (1st Derivation B Cap 5 Points Gap 2) were prepared for regression. Linear regression was applied to the normalized spectra by subtracting the Outlier values from the calibration set and calibration quality parameters were revealed. At this stage, the standard deviations of the R 2 value, validation and calibration set were calculated. According to the reflections, the graphs obtained with Regression Coefficients were taken. In addition, the sum of the squares of the estimation residual error (V-Set PRESS) of the validation set was also revealed. Outlier values were determined by the program and were not included in the study due to lowering the calibration quality. In the research, R value was determined as 0.9985, R 2 value, 0.9970 and standard error value as 0.9352. The estimated residual error of the validation set obtained was around 14 with the help of V-Set PRESS (Principal Components). The obtained regression consistency was obtained by dividing the standard error value (SEC) of the calibration to the standard error of estimation (SEP). The consistency value was determined between 80 and 110. These results suggest that NIR or FT-NIR spectroscopy can be used to predict the extent to which the corn grain is quantitatively introduced in laying hen feed mixtures. As a result, it was concluded that the percentage of each feed raw material entering the concentrate feed mixes can be quantitatively determined using FT-NIR spectroscopy as a fast and practical method.

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Preliminary Discrimination of Cheese Adulteration by FT-IR Spectroscopy

Cited by 11 publications

References 21 publications

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

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

Using FT-NIR Calibration to Determine Soybean Meal Amount in Concentrated Feed Ration Mixtures for Laying Hens

The Creation of the FT-NIR Calibration for the Determination of the Amount of Corn Grain in Concentrated Feed

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