On-line determination of fatty acid composition in intramuscular fat of Iberian pork loin by NIRs with a remote reflectance fibre optic probe

González-Martı́n, I.; González-Pérez, C.; Álvarez-Garcı́a, Noelia; González-Cabrera, José Miguel

doi:10.1016/j.meatsci.2004.07.003

Cited by 62 publications

(40 citation statements)

References 7 publications

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“…Previous reports on the use of NIR to predict the fatty acid composition of animal products are not consistent in terms of which fatty acids are best predicted. For example, González-Martín et al (2005) report good prediction for myristic, palmitic and stearic acids and approximate prediction for lauric acid in intramuscular fat of Iberian pork loin. Realini et al (2004) reported good prediction for stearic acid and poor prediction of myristic and palmitic acids in ground beef.…”

Section: Pls Regression Model Statistics Of Biomass and Total Lipidmentioning

confidence: 98%

Microalgal fatty acid composition: rapid assessment using near-infrared spectroscopy

2015

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The feasibility of assessing microalgal fatty acid composition using near-infrared spectroscopy (NIRS) is described. The chlamydomonad microalga, Rhopalosolen saccatus (previously known as Characium saccatum), was isolated from the Fitzroy River, Central Queensland, Australia. R. saccatus was grown in batch culture with varying phosphorus nutrition and assessed for dry matter, total lipid and fatty acid composition using gas chromatography (GC). Transmission spectra (1100-2500 nm) were acquired of liquid culture, and reflectance spectra were acquired of wet and dry filtrates of cultures and of methyl esters. Partial least square (PLS) regression models were built on biomass, total lipid and a number of fatty acids. All sample presentation models supported PLS regression model with a cross validation correlation coefficient (R cv ) >0.87 for biomass and R cv >0.68 for total lipid; however, the use of dry filtrates of culture is recommended as the sample presentation mode of choice. Models for fatty acids based on culture transmission spectra, reflectance spectra of wet and dry culture filtrates, or reflectance spectra of methyl esters in solvent were not acceptable. Dry extracts of methyl esters supported adequate models for fatty acids from C8:0 to C22:0, with the exception of capric and behenic acids, with an R cv of 0.89-0.94; however, in practice, samples processed to this stage can be easily analyzed by GC. Near-infrared spectroscopy can be a potential choice for rapid estimation of biomass (dry matter) and lipid content and composition in microalgae, with further work required to demonstrate oping robustness of the calibration model in prediction of unknown samples.

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Section: Pls Regression Model Statistics Of Biomass and Total Lipidmentioning

confidence: 98%

Microalgal fatty acid composition: rapid assessment using near-infrared spectroscopy

2015

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“…Comparisons between studies are difficult due to the use of different equipment, wavelength range, sample preparation and chemical analyses. Fatty acid data were commonly expressed as percentage of the total FA when used for prediction by NIRS in previous studies in rabbit (Pla et al, 2007), beef (Windham and Morrison, 1998) and pork meat (González-Martín et al, 2005). However, more recently studies in beef (Sierra et al, 2008;Prieto et al, 2011) and lamb meat used reference data expressed as concentration (mg or g) in the muscle and obtained higher accuracies of prediction.…”

Section: Nirs Calibrationsmentioning

confidence: 99%

Application of NIRS for predicting fatty acids in intramuscular fat of rabbit

2012

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Accurate predictions were reported for IMF (R 2 CV=0.98; RPDCV=7.57), saturated (R 2 CV=0.96; RPDCV=5.08) and monounsaturated fatty acid content (R 2 CV=0.98; RPDCV=6.68). Lower accuracy was obtained for polyunsaturated fatty acid content (R 2 CV=0.83; RPDCV=2.40). Several individual fatty acids were accurately predicted such as C14:0, C15:0, C16:0, C16:1, C17:0, C18:0, C18:1 n-9, C18:2 n-6 and C18:3 n-3 (R 2 CV=0.91-0.97; RPDCV>3). Long chain polyunsaturated fatty acids and C18:1 n-7 presented less accurate prediction equations (R 2 CV=0.12-0.82; RPDCV<3).

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“…The structure of FA can produce individual spectral characteristics and they are, therefore, very accessible for detection by near infrared spectroscopy (Gonzá lez-Martín et al, 2002). Hence, NIR spectroscopy has been applied to study the FA content of Iberian pig fat (De Pedro et al, 1992;García-Olmo et al, 2001), intact pork loins (Gonzá lez-Martín et al, 2005) and ground beef (Windham and Morrison, 1998;Realini et al, 2004;Sierra et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Prieto

Ross

Navajas

et al. 2011

Animal

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The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA3) and Limousin (LIM3) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA3 and 106 LIM3 were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P , 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA3 meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM3 meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R 2 (SE CV , mg/ 100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM3 samples (R 2 5 0.12 to 0.62; SE CV 5 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA3 beef samples. In particular for LIM3, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.

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On-line determination of fatty acid composition in intramuscular fat of Iberian pork loin by NIRs with a remote reflectance fibre optic probe

Cited by 62 publications

References 7 publications

Microalgal fatty acid composition: rapid assessment using near-infrared spectroscopy

Microalgal fatty acid composition: rapid assessment using near-infrared spectroscopy

Application of NIRS for predicting fatty acids in intramuscular fat of rabbit

Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

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