Development and Analytical Validation of Robust Near-Infrared Multivariate Calibration Models for the Quality Inspection Control of Mozzarella Cheese

Botelho, Bruno G.; Mendes, Bruna Alves Pereira; Sena, Marcelo M.

doi:10.1007/s12161-012-9498-z

Cited by 35 publications

(21 citation statements)

References 27 publications

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“…The greatest R 2 VAL for each predicted trait is in bold only if it is >0.50. ally, cheese content of WSN was predicted using NIRS (400-1,100 nm spectrum range) also by Kraggerud et al (2014) with modest results (R 2 CV = 0.42; RMSE of cross validation = 0.12%). Botelho et al (2013), analyzing 123 Mozzarella samples with a Fourier transform infrared spectrophotometer (1,000-2,500 nm spectrum range), obtained RMSE of prediction very similar to ours (2.2% for moisture and 3.2% for fat content) by using PLS regression. Considering another dairy product, Madalozzo et al (2015) tested NIRS (1,100-2,500 nm spectrum range) with PLS regression on 19 ricotta samples in duplicate and obtained results for fat and protein predictions very similar to those of Karoui et al (2006) in terms of cross-validated R 2 and to ours in terms of RMSE.…”

supporting

confidence: 78%

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

Stocco

Cipolat‐Gotet

Ferragina

et al. 2019

Journal of Dairy Science

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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 in-strument, 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.

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supporting

confidence: 78%

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

Stocco

Cipolat‐Gotet

Ferragina

et al. 2019

Journal of Dairy Science

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“…The most widespread is Mahalanobis distance (De Maesschalck et al, 2000), which can also be used to discard the "average" spectra (i.e., those closer to the center of the distribution) to identify the samples to be analyzed using gold-standard methods. In a few cases, other strategies were used to identify outlier samples (Čurda and Kukačková, 2004;González-Martín et al, 2011;Oca et al, 2012;Botelho et al, 2013), and some studies identified the outlier samples a posteriori and eliminated on the basis of the residuals from the calibration equation, which forced a reduction in the RMSE VAL and an increase in the R 2 VAL (Lucas et al, 2008). In any case, the effects of different sample selection strategies and outlier detection procedures on the effectiveness of the prediction equations tested by genuinely independent validation procedures is insufficiently understood.…”

Section: Sample Selection and Identification Of Outliersmentioning

confidence: 99%

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

Bittante

Patel

Cecchinato

et al. 2022

Journal of Dairy Science

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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 broadrange 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.

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“…This figure is calculated for the calibration and validation sets according to Eq. , where RMSE is the root mean square error of calibration or validation and SD is the standard deviation of the reference values in the calibration or validation sets .

R P D = S D / R M S E

…”

Section: Methodsmentioning

confidence: 99%

Quantitative analysis of acidity level in virgin coconut oils by Fourier transform infrared spectroscopy and chemometrics

Zicker

Craig

Ramiro

et al. 2015

Euro J Lipid Sci & Tech

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The quality of virgin coconut oil (VCO), together with its health functionalities, is directly related to its freshness and to the levels of free fatty acids (FFA). Thus, the goal of this work was to develop a quantitative model based on partial least squares regression (PLSR) to predict FFA levels (%) in VCO. A total of 72 Brazilian commercial samples classified in accordance to their shelf life was analyzed. FFA levels ranged from 0.3 to 2.3%. FTIR (Fourier transform infrared spectroscopy) spectra were recorded within a range of 3100 to 680 cm−1 and submitted to mathematical preprocessing. Quantitative models were developed by partial least squares regression (PLSR). Excellent predictive results were obtained, indicating that the FFA levels in VCO could be accurately quantified by FTIR. The correlation coefficient (R) and the root mean squared error of prediction (RMSEP) were, respectively, as high as 0.994 and as low as 0.07. Practical applications: According to international standards, acid‐base titration is the reference method for the determination of FFA in oils. This method is laborious and time‐consuming, uses toxic and flammable solvents and it is susceptible to error when detecting the color change of the indicator. Some of the advantages of FTIR over the official acid‐base titration method are the short time and simplicity of analysis. Besides that, it is non‐destructive and it does not require hazardous solvents and reagents. The results shown in this study indicate that FTIR spectroscopy and chemometrics can be successfully applied to the quantitative evaluation of FFA in VCO. The quality of virgin coconut oil (VCO), together with its health functionalities, is directly related to its freshness and to the levels of free fatty acids (FFA). Acid‐base titration is the reference method for the determination of FFA in oils. This method is laborious and time‐consuming, uses toxic and flammable solvents and it is susceptible to error when detecting the color change of the indicator. FTIR is a rapid, non‐destructive, and accurate technique that could overcome some of the drawbacks of titration methodologies and could be easily implemented in routine quality control laboratories. The results shown in this study indicate that FTIR spectroscopy and chemometrics can be successfully applied to the quantitative evaluation of FFA in VCO.

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Development and Analytical Validation of Robust Near-Infrared Multivariate Calibration Models for the Quality Inspection Control of Mozzarella Cheese

Cited by 35 publications

References 27 publications

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

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

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

Quantitative analysis of acidity level in virgin coconut oils by Fourier transform infrared spectroscopy and chemometrics

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