Suitability of faecal near-infrared reflectance spectroscopy (NIRS) predictions for estimating gross calorific value

A total of 220 faecal pig and poultry samples, collected from different experimental trials were employed with the aim to demonstrate the suitability of Near Infrared Reflectance Spectroscopy (NIRS) technology for estimation of gross calorific value on faeces as output products in energy balances studies. NIR spectra from dried and grounded faeces samples were analyzed using a Foss NIRSystem 6500 instrument, scanning over the wavelength range 400-2500 nm. Validation studies for quantitative analytical models were carried out to estimate the relevance of method performance associated to reference values to obtain an appropriate, accuracy and precision. The results for prediction of gross calorific value (GCV) of NIRS calibrations obtained for individual species showed high correlation coefficients comparing chemical analysis and NIRS predictions, ranged from 0.92 to 0.97 for poultry and pig. For external validation, the ratio between the standard error of cross validation (SECV) and the standard error of prediction (SEP) varied between 0.73 and 0.86 for poultry and pig respectively, indicating a sufficiently precision of calibrations. In addition a global model to estimate GCV in both species was developed and externally validated. It showed correlation coefficients of 0.99 for calibration, 0.98 for cross-validation and 0.97 for external validation. Finally, relative uncertainty was calculated for NIRS developed prediction models with the final value when applying individual NIRS species model of 1.3% and 1.5% for NIRS global prediction. This study suggests that NIRS is a suitable and accurate method for the determination of GCV in faeces, decreasing cost, timeless and for convenient handling of unpleasant samples

Modelling a quantitative ensilability index adapted to forages from wet temperate areas

Forage ensilability mainly depends on dry matter (DM), water soluble carbohydrates (WSC) and buffer capacity (BC) values at harvest time. According to these parameters, and based on a collection of 208 forages of known ensilability characteristics including short and long term meadows for grazing, italian ryegrass, maize, triticale, soybean, faba bean crops, and samples coming from cereal-legume associations, the objective of this study has been to define a quantitative ensilability index (EI) based on a relationship between DM, WSC and BC contents at harvest date, adapted to the characteristics of fodder from wet temperate areas. For this purpose, a discriminant procedure was used to define this EI based on a linear combination of DM, WSC and BC of forages at harvest time. The quantitative calculated indexes distinguish five successive ranges of ensilability: high ensilability (EI > +28), medium high ensilability (+9 < EI . +28), medium ensilability (.28 < EI . +9), medium low ensilability (.47 . EI . .28) and low ensilability (EI < .47). This quantitative index was externally evaluated and 100% of samples were successfully classified

Validation of two discriminant strategies applied to NIRS data spectra for detection of animal meals in feedstuffs

For developing qualitative or quantitative applications with spectroscopic data, such as near infrared spectroscopy (NIRS), different methodologies have been proposed in the mathematical statistical and computer science literature. Useful chemometrical alternatives have emerged, such as support vector machines (SVM), widely used for modeling multivariate and non-linear systems. These methods are usually compared using the classification performance and the success of results. The aim of the present work was to develop and validate a robust, accurate and fast discriminant methodology based on NIRS data to detect presence of animal meals in feedstuffs. A linear method, modified partial least square (PLS) analysis and one non-linear method (SVM) were studied. Results showed that modified PLS model allows obtaining coefficients of determination for cross validation around 0.97. Applying SVM strategy no false negatives were detected during training step. With both strategies the lowest percentage of misclassified samples on external validation was achieved with SVM, 0% with certified standard samples containing from 0.05% to 4% of animal meals. These results show SVM strategy as a robust method of classification for detecting animal meals in feedstuffs using NIRS methodology.Para el desarrollo de aplicaciones cualitativas o cuantitativas con datos espectroscópicos, como los obtenidos mediante espectroscopia de infrarrojo cercano (NIRS), se han propuesto diferentes metodologías basadas en la estadística matemática y la literatura informática. Entre las alternativas quimiométricas, han surgido las máquinas de vectores soporte (SVM), ampliamente utilizadas para el modelado no linear de sistemas de múltiples variables. Estos métodos quimiométricos de clasificación se evalúan en base al porcentaje de aciertos. El objetivo del presente trabajo ha sido desarrollar y validar una metodología sólida, discriminante, precisa y rápida haciendo uso de la información NIRS para detectar la presencia de harinas animales, prohibidas en piensos compuestos para determinadas especies. Para ello, se evaluaron dos estrategias quimiométricas diferentes, un método lineal modificado basado en mínimos cuadrados parciales y un método de análisis no lineal basado en máquinas de vectores soporte. Los resultados mostraron que el modelo modificado PLS permite obtener coeficientes de determinación para la validación cruzada en torno a 0,97. En lo referente al SVM, con esta estrategia no se detectó ningún falso negativo. Con ambas estrategias el porcentaje más bajo de la clasificación errónea de las muestras en una validación externa se logró con SVM, 0% utilizando muestras patrón certificadas con un contenido en harinas animales entre el 0,05% y el 4%. Los resultados obtenidos han demostrado que la estrategia SVM es el método más robusto de clasificación para la detección de harinas animales en piensos mediante metodología NIRS