A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses

Pérez‐Marín, Dolores; Sanz, Emiliano J. de Pedro; Guerrero-Ginel, José Emilio; Garrido‐Varo, Ana

doi:10.1016/j.meatsci.2009.07.012

Cited by 64 publications

(53 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Spanish feed is generally high in C18:1n-9 and low in C18:2n-6 and these pigs, which are used for dry ham production (Fernandez et al, 2003), have a lower LMP and higher age than the Norwegian pigs in our study. The SEP and R 2 for the calibration and CV were of the same magnitude in both our study and the Spanish studies (Fernandez et al, 2003;Garcia-Olmo et al, 2005;Perez-Marin et al, 2009).…”

Section: Discussionsupporting

confidence: 86%

“…A larger variation in fatty acids could be obtained if pigs were picked randomly from different herds, although the calibrations in our work were designed for the prediction of values in terms of breeding value estimations on purebred animals (Landrace and Duroc pigs) tested for carcass and meat quality parameters in a breeding programme. Even so, the variation of fatty acids for pigs in this study were similar to those used to make calibrations for fatty acids in other studies (Fernandez et al, 2003;Garcia-Olmo et al, 2005;Perez-Marin et al, 2009). These studies were conducted on Iberian pigs in Spain fed several diets on the basis of extensive feeding programmes that were different from standard Norwegian feeding strategies.…”

Section: Discussionsupporting

confidence: 80%

“…NIRS has the best predictive ability for organic components with large volumes, and the C16:1n-7 and C18:3n-3 fatty acids are often left out of similar publications (Fernandez et al, 2003;Perez-Marin et al, 2009) or have an R 2 similar to our study for a study that also uses a microwave melting technique (Gonzalez-Martin et al, 2002). Shenk and Westerhaus (1996) indicated that NIRS equations with R 2 values .0.9 may have an excellent precision, whereas those with R 2 values between 0.5 and 0.9 have values with good precision, meaning that all of our equations from both NIRS methods have good future potentials.…”

Section: Discussionmentioning

confidence: 99%

“…Different spectra shapes with different GRs made it necessary to include a repeatability file with different GRs in the models. A repeatability file serves to minimise the influence of such unwanted effects on the results of NIRS calibrations (Tillmann and Paul, 1998) and that is specially important when working with liquid fat samples (PerezMarin et al, 2007;Perez-Marin et al, 2009). The repeatability file in this study contains only spectra of three samples measured repeatedly with four different GRs.…”

Section: Animalsmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Gjerlaug-Enger

Kongsro

Aass

et al. 2011

Animal

View full text Add to dashboard Cite

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R 2 cv ) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n 5 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R 2 val ) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R 2 cv, RMSECV, R 2 val and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (a-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.Keywords: fat moisture, fatty acids composition, near-infrared spectroscopy, gold reflector, repeatability file ImplicationsThis study shows that it is possible to establish simple routine methods for measuring the quality of pig fat, as well as using preparation methods and instruments that are safe, user and environmentally friendly and do not require chemical solvents. The results reveal that several parameters for fat quality can be predicted by near-infrared spectroscopy (NIRS) technology, and a continuation of this study documents the use of NIRS predicted values in the estimation of genetic parameters for breeding purposes. The high heritability obtained in the follow-up study clearly shows the power of NIRS and the high prediction ability of the calibrations developed in this study.

show abstract

Section: Discussionsupporting

confidence: 86%

Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Animalsmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Gjerlaug-Enger

Kongsro

Aass

et al. 2011

Animal

View full text Add to dashboard Cite

show abstract

“…Pérez-Marín et al [141] applied NIR for all stages of pigs: the live animal, carcass in slaughterhouse, subcutaneous fat sample with skin, skin-free subcutaneous fat sample, and transverse section. They develop prediction model for the four main fatty acids in Iberian pig fat, obtaining for palmitic acid SECV values of 1.24% for in vivo analysis and 0.82% for carcass analysis, for stearic acid 0.67% and 0.94%, for oleic acid 1.42% and 1.48%, and for linoleic 0.36% and 0.55%.…”

Section: B15 Dietmentioning

confidence: 99%

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Alander

Bochko

Martinkauppi

et al. 2013

International Journal of Spectroscopy

129

View full text Add to dashboard Cite

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

show abstract

Near‐Infrared Spectroscopy in Laboratory and Process Analysis

Alcalà

Blanco

Menezes

et al. 2012

Encyclopedia of Analytical Chemistry

View full text Add to dashboard Cite

Near‐infrared (NIR) is a spectroscopic method based on the absorption of light in the wavelength region between 700 and 2500 nm due to vibrations of molecular functional groups in the sample. These spectral features arise from overtones and combinations of the fundamental molecular vibrations of these groups that occur in the infrared (IR) at longer wavelengths. NIR is rich with features arising predominantly from vibrations of C–H, O–H, and N–H. (1) NIR spectra are acquired by determining the absorbance, A , of light over a continuous range of wavelengths in spectrophotometers or at several specific wavelengths in fixed‐filter photometers. Absorbance is defined by the following equation: where I is the signal intensity from the sample relative to the signal intensity from a reference, I 0 . The reference can be an empty transmission sample cell for liquids, or it can be a broadband spectral reflector, such as a white ceramic, for reflectance measurements of solids. Absorbance is usually linearly related to concentration, following Beer's Law (Equation ): where ɛ represents the molar extinction coefficient, C is the concentration of the species, and l is the optical pathlength of light through the sample. For a particular species and fixed experimental setup, ɛ and l are constants. Letting a constant, k = (ɛ l ) −1 , then (Equation ) A principal advantage of NIR over mid‐IR for process applications is the ability to use fiber optics to transmit light between the instrument and the sample over considerable distance with NIR. This permits the instrument, a potential source of ignition, to be placed in safe areas where flammable vapors exist. Only the fiber‐optic probe in contact with the sample and the optical fibers that connect to the instrument need be placed in the hazardous location. A disadvantage of NIR is the characteristics of the spectra, which are typically composed of broad, overlapping peaks in comparison to IR spectra. This often requires the use of sophisticated data analysis methods that place additional demands on vendors to provide stable NIR instruments and upon manufacturers and end‐users to have highly trained staff, often at the PhD level, for calibration model development, implementation, and maintenance.

show abstract

A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses

Cited by 64 publications

References 9 publications

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Near‐Infrared Spectroscopy in Laboratory and Process Analysis

Contact Info

Product

Resources

About