Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Fiber quality and fertility in male alpacas in the Cusco region of Peru

Introduction

High testosterone levels might be associated with coarser fiber in alpacas, affecting fiber quality. In the husbandry systems employed in the Andes, males with higher libido might have an increased frequency of successful matings than males with lower libido. This study aimed to determine some of the factors affecting fiber quality in Peruvian alpacas and to evaluate the association between fiber quality and male mating behavior.

Methods

The study population consisted of 189 adult male alpacas at La Raya, Cusco, Peru, at 4,400 m above sea level, belonging to the National University of San Marcos, Lima, and the National University of San Antonio Abad of Cusco. Fiber samples were collected from male alpacas in September 2015; body condition score (BCS) was evaluated. After washing and drying, the fiber samples were analyzed using an Optical Fiber Diameter Analyser 2000; linear regression analysis between predictor variables (location of alpacas, age, BCS, and fiber color) and fiber quality outcomes was conducted. Fertility data were available only for some males belonging to San Marcos University, consisting of hand-written records from the breeding season January to April 2015; individual fertility quotients were calculated for each male.

Results

Age was associated with fiber quality, young alpacas having the finest fibers (p < 0.05). An increased BCS was associated with increased fiber length, fiber diameter and spinning fineness, but decreased comfort factor (p < 0.05). White fibers were thinner (p = 0.05) than colored fibers, with lower comfort factor and spinning fineness. A significant association between the fertility quotient and fiber curvature was observed (p = 0.018).

Discussion

These results suggest that careful selection of breeding individuals and attention to husbandry could result in improved fiber quality among alpaca herds in Peru. However, it would be advisable to increase the number of males studied, using more reliable methods for evaluating male fertility and pregnancy diagnosis than were available for this study.

Colony identity clues for Syntermes grandis (Blattodea: Termitidae) individuals using near-infrared spectroscopy and PLS-DA approach

Termites are social insects with high species diversity in tropical ecosystems. Multivariate analysis with near-infrared spectroscopy (NIRS) and data interpretation can separate social insects belonging to different colonies of the same species. The objective of this study was to propose the use of discriminant analysis by partial least squares (PLS-DA) combined with NIRS to identify the colonial origin of the Syntermes grandis (Rambur, 1842) (Blattodea: Termitidae) in 2 castes. Six ground S. grandis colonies were identified and mapped; 30 workers and 30 soldier termites in each colony were submitted to spectral measurement with NIRS. PLS-DA applied to the termites' spectral absorbance was used to detect a spectral pattern per S. grandis colony by caste. PLS-DA regression with NIRS proved to be an approach with 99.9% accuracy for identifying the colonial origin of S. grandis workers and 98.3% for soldiers. The methodology showed the importance of qualitatively characterizing the colonial phenotypic response of this species. NIRS is a high-precision approach to identifying the colony origin of S. grandis workers and soldiers. The PLS-DA can be used to design ecological field studies to identify colony territorial competition and foraging behavior of subterranean termite species.

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm^-1 and 3017~2823 cm^-1/1805~1734 cm^-1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R²) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R² values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy

Simple Summary

In the Puna region of Argentina, llama fibre production has enormous social, economic and environmental potential, but is still in its early stages of development. For this reason, classification and quality analysis systems used today are still deficient. Near infrared reflectance spectroscopy is a technological resource used in the agroindustry for quality analysis of organic compounds. In this work we studied the feasibility of this technology to evaluate the mean fibre diameter, which is one of the most important quality parameters in the textile industry. Despite some limitations, which are mainly related to fibre heterogeneity, the results obtained were encouraging as spectroscopy could be used in screening programmes as a sustainable, fast and low-cost method to improve fibre quality validation.

Abstract

Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R²: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R² = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.

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

Nowadays, the fibre diameter (FD) is considered the main selection objective in alpaca populations all over the world. International Committee for Animal Recording recommendations define the FD and its CV as the first two traits to be considered in breeding programmes for this specie. In addition to these main criteria, other selection criteria of economic value used are comfort factor (CF) or standard deviation (s.d.); also other less important traits being used as selection objectives are these morphological traits: density (DE), crimp (CR) or lock structure (LS) for, respectively, Huacaya (HU) and Suri (SU) ecotypes, head (HE), coverage (CO) and balance (BA). The goal of this study was to establish how to implement a combined selection index starting from genetic parameters and to study the expected correlation between genetic trends by considering different alternative procedures of weighting all the involved traits, and the consequences of a wrongly proceeding way. Heritabilities and genetic and phenotypic correlations were estimated from the data set belonging to the PACOMARCA experimental farm for SU and HU. Two approaches were used to check the consequences of a set of subjective weights essayed. The coefficients of selection indexes were obtained for two sets of reference weights. In addition, equivalent weights were drawn if applied those reference values as coefficients of hypothetical selection indexes directly on phenotypes; relative expected genetic responses were computed in different cases. Results showed that almost in all cases for both ecotypes, the weight applied to CF should be surprisingly negative. Concerning genetic responses, only CO was compromised in some cases for the HU ecotype. The essayed methodology allowed explaining the differences between ecotypes in the genetic trends. The proposed methodology was shown to be effective to study the relative importance of the traits granted by the manager of a breeding scheme.