Classification of real farm conditions Iberian pigs according to the feeding regime with multivariate models developed by using fatty acids composition or NIR spectral data

García-Olmo, Juan; Sanz, Emiliano J. de Pedro

doi:10.3989/gya.130408

Cited by 15 publications

(10 citation statements)

References 5 publications

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“…However, as noted by GarridoVaro et al (2004), a major drawback to the procedures involved both in the current field inspection system and in many of the new certification techniques is that they are costly and laborious.…”

Section: Acorn-grass Weight Gain Index For Iberian Pigsmentioning

confidence: 99%

“…Research has demonstrated that the analysis of melted fat samples, of intact subcutaneous tissue, and even of live animals, coupled with the use of various mathematical algorithms, represents a promising approach both for predicting the proportions of the four major fatty acids (oleic, palmitic, stearic and linoleic) (De Pedro et al, 1992;GarcíaOlmo et al, 2001;González Martín et al, 2002GarridoVaro et al, 2004; tissue samples under laboratory conditions at room temperature (wavelength range 4002500 nm with a spectral interval step of 2 nm). Skin free transverse sections of subcutaneous adipose tissue were analyzed for each sample.…”

Section: Acorn-grass Weight Gain Index For Iberian Pigsmentioning

confidence: 99%

“…The new indoor and outdoor intensive production systems, also considered quality added based products, are produced by using compound feeds rather than natural resources of the dehesa for final fattening, what differs from the ideal traditional production system. The current range of commercial categories is covered under Spanish PREDICTING ACORNGRASS WEIGHT GAIN INDEx USING NONDESTRUCTIVE NEAR INFRARED SPECTROSCOPY… FernándezCabanás et al, 2007;PérezMarín et al, 2007PérezMarín et al, and 2010 and for classifying samples by feeding regime simply on the basis of spectral information (Hervás et al, 1994;De Pedro et al, 1995;GarcíaOlmo et al, 2009;Arce et al, 2009: ZamoraRojas et al, 2012.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime

Sanz¹,

Serrano²,

Zamora-Rojas³

et al. 2013

Grasas y Aceites

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RESUMEN Predicción del Índice de Reposición en Montanera para la clasificación de canales de cerdo Ibérico según régimen alimenticio mediante el análisis no destructivo por Espectroscopía del Infrarrojo CercanoLa clasificación en distintas categorías comerciales según régimen alimenticio de canales de cerdo Ibérico fue evaluada mediante el análisis no destructivo de muestras de tejido adi poso subcutáneo por Espectroscopía del Infrarrojo Cercano (NIRS). Partiendo de una aproximación cuantitativa para pre decir el Índice de Reposición en Montanera (IRM) se estable cieron una serie de criterios para proceder a su clasificación comercial. Se analizaron un total de 719 animales pertene cientes a diversas partidas, que recogen una amplia variabili dad de muestras de distintos regímenes alimenticios, campa ñas y sistemas productivos, para el desarrollo y evaluación de los modelos NIRS cuantitativos. Los resultados de validación externa de los modelos indicaron que es posible discriminar con una gran exactitud entre partidas de distintos categorías (Bellota, Recebo y Cebo), en base al espectro medio repre sentativo de cada partida. Además, el análisis individualizado de los animales mostró un amplio consenso entre la informa ción recibida de campo y la clasificación en base a la predic ción del parámetro IRM por NIRS, sobre todo para categorías con características extremas (Bellota y Cebo). PALABRAS CLAVE: Cerdo Ibérico -Clasificación -Espectroscopía Infrarrojo Cercano -Índice de Reposición en Montanera -Intacto -Régimen alimenticio -Tejido adiposo. SUMMARY Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regimeThe classification of Iberian pig carcasses into different commercial categories according to feeding regime was evaluated by means of a nondestructive analysis of the subcutaneous adipose tissue using Near Infrared Spectroscopy (NIRS). A quantitative approach was used to predict the AcornGrass Weight Gain Index (AGWGI), and a set of criteria was established for commercial classification purposes. A total of 719 animals belonging to various batches, reflecting a wide range of feeding regimes, production systems and years, were analyzed with a view to developing and evaluating quantitative NIRS models. Results for the external validation of these models indicate that NIRS made clear differentiation of batches as a function of three feeding regimes possible with high accuracy (Acorn, Recebo and Feed), on the basis of the mean representative spectra of each batch. Moreover, individual analysis of the animals showed a broad consensus between field inspection information and the classification based on the AGWGI NIRS prediction, especially for extreme categories (Acorn and Feed).

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Section: Acorn-grass Weight Gain Index For Iberian Pigsmentioning

confidence: 99%

Section: Acorn-grass Weight Gain Index For Iberian Pigsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime

Sanz¹,

Serrano²,

Zamora-Rojas³

et al. 2013

Grasas y Aceites

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“…The results indicated that NIR data performed somewhat better than the mid-IR data [17]. NIRS has been used extensively to analyze oil content and fatty acid profile in oilseeds such as sunflower [18], rapeseed [19], soybean [20], peanut [21], and castor [22], as well as in other commodities such as cereals [23], forages [24], corn [25], and animal products [26]. Conversely, NIRS has not yet been reported for evaluation of most soybean essential nutrients like tocopherols, saponins, FAs, and flavonoids.…”

Section: Introductionmentioning

confidence: 96%

Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy

Zhang

et al. 2017

Anal Bioanal Chem

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Improvement of the nutritional quality of soybean is usually facilitated by a vast range of soybean germplasm with enough information about their multiple phytonutrients. In order to acquire this essential information from a huge number of soybean samples, a rapid analytic method is urgently required. Here, a nondestructive near-infrared reflectance spectroscopy (NIRS) method was developed for rapid and accurate measurement of 25 nutritional components in soybean simultaneously, including fatty acids palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid, vitamin E (VE), α-VE, γ-VE, δ-VE, saponins, isoflavonoids, and flavonoids. Modified partial least squares regression and first, second, third, and fourth derivative transformation was applied for the model development. The 1 minus variance ratio (1-VR) value of the optimal model can reach between the highest 0.95 and lowest 0.64. The predicted values of phytonutrients in soybean using NIRS technology are comparable to those obtained from using the traditional spectrum or chemical methods. A robust NIRS can be adopted as a reliable method to evaluate complex plant constituents for screening large-scale samples of soybean germplasm resources or genetic populations for improvement of nutritional qualities. Graphical Abstract ᅟ.

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“…NIRS allows fast spectrometric measurement of several traits in a single analysis provided that the corresponding calibration equations are available. NIRS have been extensively used to analyze oil content and fatty acid profile in oilseeds such as sunflower 10, rapeseed 11, soybean 12, peanut 13, and castor 14, as well as in other commodities such a cereals 15, forages 16, or animal products 17. However, little research has been conducted in tree nuts.…”

Section: Introductionmentioning

confidence: 99%

Near‐infrared spectroscopy for analysis of oil content and fatty acid profile in almond flour

Fernández‐Cuesta

Fernández‐Martínez

Company

et al. 2012

Euro J Lipid Sci & Tech

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Almond kernels show large variability for oil content and fatty acid profile. The objective of this research was to evaluate the potential of near infrared (NIR) reflectance spectroscopy (NIRS) for the analysis of these traits in almond flour. Ground kernels of 181 accessions collected in 2009 were used for developing calibration equations for oil content and concentrations of individual fatty acids. Calibration equations were developed using second derivative transformation and modified partial least squares regression. They were validated with samples from 179 accessions collected in 2010. The accuracy of calibration equations was measured through the coefficient of determination (r 2 ) in external validation and the ratio of the SD in the validation set to the standard error of prediction (RPD). Both r 2 and RPD were high for oil content (r 2 ¼ 0.99; RPD ¼ 9.24) and concentrations of oleic (r 2 ¼ 0.97; RPD ¼ 5.37) and linoleic acids (r 2 ¼ 0.98; RPD ¼ 7.35), revealing that calibration equations for these traits are highly accurate. Conversely, the accuracy of the calibration equations for palmitic (r 2 ¼ 0.54; RPD ¼ 1.41) and stearic acids (r 2 ¼ 0.52; RPD ¼ 1.44) was too low for allowing their application in practice. NIRS discrimination of oil content and concentrations of oleic and linoleic acids was mainly based on the spectral region from 2240 to 2380 nm.Practical applications: NIRS is a high-throughput analytical technique that allows fast measurement of several traits in a single analysis without using chemical reagents. We evaluated the feasibility of analyzing oil content and concentrations of palmitic, stearic, oleic, and linoleic acids in almond flour using fruits collected during 2 years from a world germplasm collection. The fruits collected in 2009 were used for NIRS calibration, whereas the fruits collected in 2010 were used for validation. NIRS equations were highly accurate for measuring oil content and concentrations of oleic and linoleic acids, which are important traits defining the quality of almond flour for specific uses in the food industry. These results have applications both in the research laboratory and the food industry, where NIRS is becoming a widely used technique for quality control.

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Classification of real farm conditions Iberian pigs according to the feeding regime with multivariate models developed by using fatty acids composition or NIR spectral data

Cited by 15 publications

References 5 publications

Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime

Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime

Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy

Near‐infrared spectroscopy for analysis of oil content and fatty acid profile in almond flour

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