Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Abstract

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n 5 291) of different ages and colours were scanned and their visible-near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R 2 CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R 2 CV 5 0.90 and 0.87; RMSECV 5 1.01 and 1.08 mm and RPD 5 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R 2 CV , 0.65 and RPD , 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R 2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 mm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.Keywords: alpaca, fibre quality, diameter, near-infrared spectroscopy, NIRS Implications Near-infrared reflectance spectroscopy (NIRS) was evaluated as a technique to assess alpaca fibre diameter, staple length and other related traits, measured in greasy samples. The best calibration models, developed by a multivariate regression method, were able to obtain useful predictions for fibre diameter and spinning fineness. Other variables were poorly predicted. As diameter of fibre is one of the most important variables related to textile quality, NIRS can be employed as an effective technique to evaluate the product and to assist alpaca breeders in the identification of superior animals.

Weighting fibre and morphological traits in a genetic index for an alpaca breeding programme

“…International Committee for Animal Recording (ICAR), is an institution aiming to promote the development and improvement of the activities of performance recording and the evaluation of farm livestock (ICAR, 2012). Two of the fibre traits considered selection objective by PACOMARCA have been set as the firsts in the list of seven by ICAR recommendations when organizing performance recording (ICAR, 2012); however, there are others in the list that could be sometimes considered of importance (Canaza-Cayo et al, 2013) at the discretion of the breeder. In this recommendation, ICAR does not suggest any relative importance of subjective traits.…”

Desarrollo y evaluación de modelos de predicción de Finura en vellones de vicuña, mediante espectroscopía de reflectancia en el infrarrojo cercano.

“…Asimismo, se observaron picos de absorción a 2170 y 2280 nm, relacionados con la deformación y combinación de grupos C-H asociados a aminoácidos. La banda de absorción alrededor de los 2055 nm puede estar relacionada con la combinación de grupos N-H presentes en los compuestos proteicos de las fibras (Canaza-Cayo et al, 2013). El desvío estándar del espectro (eje Y derecho), muestra valores prominentes en el rango VIS, explicados por las diferencias entre los colores mencionados anteriormente.…”

“…No obstante, la posibilidad de utilizarlas de manera habitual se torna difícil y costosa, ya que no se cuenta con laboratorios especializados en la región. La espectroscopía de reflectancia en el infrarrojo cercano (NIRS por sus siglas en inglés, Near Infrared Reflectance Spectroscopy), es una tecnología versátil, que tiene el potencial para ser utilizada en la determinación de los parámetros de calidad de las fibras (Canaza-Cayo, Alomar, & Quispe, 2013;Zoccola, Lu, Mossotti, Innocenti, & Montarsolo, 2013). Bajo este supuesto se lleva a cabo la tesis doctoral "Desarrollo de metodología NIRS para la evaluación cuantitativa y cualitativa de fibras especiales de origen animal", en el Laboratorio de Aceites y Grasas perteneciente al Instituto Nacional de Tecnología Agropecuaria (INTA), sede Catamarca.…”

Desarrollo de Modelos NIRS de Predicción para el Análisis de la Finura de Fibras Textiles de Vicuña y Llama

“…Comparativamente, los resultados del estadístico R 2 de los mejores modelos de cada grupo (R 2 =0.61 en el grupo 1 y R 2 =0.70 en los grupos 2 y 3) fueron menores a los obtenidos en fibra de alpaca (R 2 = 0.88) (Alomar, Alarcón Buhofer, & Kusanovic, 2015;Canaza-Cayo, Alomar, & Quispe, 2013;Gishen & Cozzolino, 2007), y lana de oveja limpia, (R 2 = 0.90); pero no así en lana sucia (R 2 = 0.50) (Cozzolino et al, 2005). Por otro lado, el RPD≅ 1.7 en los tres grupos, fue mucho menor a 3, que es el umbral mínimo que debería tener un modelo de calibración para considerarse aceptable para realizar predicciones con exactitud (Fearn, 2002;Gishen & Cozzolino, 2007).…”

“…Por otro lado, el RPD≅ 1.7 en los tres grupos, fue mucho menor a 3, que es el umbral mínimo que debería tener un modelo de calibración para considerarse aceptable para realizar predicciones con exactitud (Fearn, 2002;Gishen & Cozzolino, 2007). Finalmente, los SECV del grupo 1 (SECV≅0.7 µm), y grupos 2 y 3 (SECV≅2.2 µm), fueron mayores a los reportados en otros trabajos en alpaca (SECV=2.62 µm) (Alomar, Alarcón Buhofer, & Kusanovic, 2015;Canaza-Cayo, Alomar, & Quispe, 2013;Gishen & Cozzolino, 2007), y lana de oveja sucia (SECV=11.2µm) (Cozzolino et al, 2005).…”