Prediction of fatty acid composition using a NIRS fibre optics probe at two different locations of ham subcutaneous fat

Pérez-Juan, M.; Afseth, Nils Kristian; González, J.; Dı́az, Isabel; Gispert, M.; Font-i-Furnols, Maria; Oliver, M.A.; Realini, C.E.

doi:10.1016/j.foodres.2010.04.006

Cited by 31 publications

(17 citation statements)

References 32 publications

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“…Class Out: circles; class In: diamondsand Guillard(2001). As for the use of fiber optic probe, our results are definitely comparable, in terms of predictive ability of the external validation samples, to those reported byPérez- Juan et al (2010).…”

supporting

confidence: 84%

“…Hence, this sort of representation shows the degree of convergence of the various models computed for each variable. It can be observed that, for all the modeled chemical variables, the most frequently selected region is the one corresponding to the spectral bands centered at 5785 and 5670 cm −1 , in which the absorption bands related to the C-H stretching first overtone of CH 2 and CH 3 groups are located (Sørensen et al 2012;González-Martin et al 2005;Pérez-Juan et al 2010;Li et al 1999).…”

Section: Variable Selectionmentioning

confidence: 99%

“…At variance, for C18:2, PUFA, and IV the recurring selection of the region in the interval at about 8600-8100 cm −1 is observed, in addition to a region at lower wavenumbers in which two sharper peaks centered at 4710 and 4660 cm −1 are observed. Many works attribute the bands present in the 8600-8100 cm −1 region to the C-H stretching second overtone (Ripoche and Guillard 2001;Pérez-Juan et al 2010;Li et al 1999). In the same spectral region, Cox et al (2000) observed the increase of the peak at 8550 cm −1 , according to the increase of the iodine value of different oil samples.…”

Section: Variable Selectionmentioning

confidence: 99%

“…Conversely, in other works the fat samples were analyzed without any preparative step, using different methods: diffuse reflectance (Ripoche and Guillard 2001), transmission (Sørensen et al 2012) and fiber optic probes (Zamora-Rojas et al 2013;González-Martin et al 2005;Pérez-Juan et al 2010). Müller and Scheeder (2008) also measured the samples in diffuse reflectance but they homogenized the fat samples before spectra acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Iodine Value and Fatty Acids Determination on Pig Fat Samples by FT-NIR Spectroscopy: Benefits of Variable Selection in the Perspective of Industrial Applications

et al. 2016

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In this work, FT-NIR spectroscopy was employed to determine iodine value (IV) and fatty acids (FA) content of pig fat samples, through the combined use of signal preprocessing, multivariate calibration, and variable selection methods. In particular, the main focus was on the use of variable selection methods, both in order to improve the predictive performance of the calibration models, and to identify relevant wavelengths that could be subsequently used for the development of simple, fast, and cheap hand-held devices, able to measure IV and FA content directly on the fat without the need of any sample pretreatment. Firstly, for each property of interest, partial least squares (PLS) multivariate calibration models were calculated considering the whole spectral range and testing different signal preprocessing methods. Then, once chosen the optimal signal preprocessing method, a two-step variable selection procedure was applied. In the first step, the interval-PLS variable selection algorithm was used to calculate a set of calibration models, whose outcomes were considered altogether in the second step, in order to select the optimal calibration model. The variable selection procedure allowed to lower the number of spectral variables retained by the model, and often led to an increase of the performance in prediction of the external test set samples

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supporting

confidence: 84%

Section: Variable Selectionmentioning

confidence: 99%

Section: Variable Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iodine Value and Fatty Acids Determination on Pig Fat Samples by FT-NIR Spectroscopy: Benefits of Variable Selection in the Perspective of Industrial Applications

et al. 2016

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“…Collell et al (2011) applied NIRs to predict data on moisture, water activity and NaCl content at the surface of dry-cured ham during processing. The fatty acid compositions of dry-cured ham subcutaneous fat were predicted by NIRs (Pérez-Juan et al, 2010). In lamb or rabbit meat products (Cifuni et al, 2016;Cozzolino et al, 2000), NIRs was applied in order to determine some nutritional parameters.…”

Section: Introductionmentioning

confidence: 99%

Determining Mandatory Nutritional Parameters for Iberian Meat Products Using a New Method Based on Near Infra-Red Reflectance Spectroscopy and Data Mining

et al. 2019

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The new regulation about mandatory labelling on nutrition requires the declaration of specific parameters: protein, lipid, salt and carbohydrate contents. This study reports a fast, accurate method to determine the values of these mandatory nutritional parameters based on near infra-red reflectance spectroscopy (NIRs) technology and data mining techniques, used in an automatic way. For that, two batches of different Iberian pork meat products (dry-cured ham, dry-cured loin, dry-cured shoulder, dry-fermented Salchichón sausage, and dry-fermented Chorizo sausage were used. One batch of each product was used to train the method and the remaining batch was used for validation. To develop the method, prediction equations were obtained from the NIRs, while nutritional data for the training batches were obtained by applying data mining techniques, and the prediction equations were evaluated against the NIRs data from the validation batch. The prediction equations achieved from very good to excellent degrees of relationship (R > 0.75) and accurate results (MAE < 1, RMSEC < 1, RMSEP < 1) from the training batch. These prediction equations were corroborated using the validation batch, which showed very good to excellent correlation coefficients (R > 0.75). This new method is rapid, as it takes around 10 minutes in comparison with traditional methods that take around 6 days.

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Quantification of Fishmeal in Compound Feed Using NIR Spectroscopy

Wang

et al. 2012

Food Anal. Methods

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Prediction of fatty acid composition using a NIRS fibre optics probe at two different locations of ham subcutaneous fat

Cited by 31 publications

References 32 publications

Iodine Value and Fatty Acids Determination on Pig Fat Samples by FT-NIR Spectroscopy: Benefits of Variable Selection in the Perspective of Industrial Applications

Iodine Value and Fatty Acids Determination on Pig Fat Samples by FT-NIR Spectroscopy: Benefits of Variable Selection in the Perspective of Industrial Applications

Determining Mandatory Nutritional Parameters for Iberian Meat Products Using a New Method Based on Near Infra-Red Reflectance Spectroscopy and Data Mining

Quantification of Fishmeal in Compound Feed Using NIR Spectroscopy

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