A. Fassio

The use of near-infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants

Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n  = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modified partial least squares (MPLS) regression was applied to scatter-corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coefficients of determination (R2) and standard errors of cross-validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg−1 on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice. © 2000 Society of Chemical Industry

Usefulness of near infrared reflectance (NIR) spectroscopy and chemometrics to discriminate between fishmeal, meat meal and soya meal samples

Near infrared refl ectance (NIR) spectroscopy was used in combination with chemometrics to discriminate between fi shmeal, meat meal and soya meal samples. Samples were obtained from commercial feed mills and scanned in the NIR region (1100 - 2500 nm) in a monochromator instrument in refl ectance mode. Principal component analysis (PCA) and linear discriminant analysis were used to classify samples based on their NIR spectra. Full cross-validation was used in the development of classifi cation models. Partial least squares-discriminant analysis (PLS-DA) correctly classifi ed 85.7% of the fi shmeal samples and 100% of the meat meal and soya meal samples. These results demonstrate the usefulness of NIR spectra combined with chemometrics as an objective and rapid method to classify fi shmeal, meat meal and soya meal samples. NIR spectroscopic methods can be easily implemented in food mills and may be most useful for initial screening at early stages in the food production chain, enabling more costly methods to be used selectively for suspected specimens. El objetivo de este trabajo fue investigar el uso de la espectrofotometria de refl ectancia en el infrarrojo cercano (NIR) en combinacion con la quimiometria para discriminar muestras de harinas de pescado, carne y soja. Muestras provenientes de molinos racioneros comerciales fueron leidas en un equipo monocromador NIRS (NIRSystems, Silver Spring, USA) en el rango de longitudes de onda de 400 a 2500 nm, en refl ectancia. Analisis de componentes principales (APC) y de discriminantes utilizando la tecnica de los cuadrados minimos parciales (PLS-DA) fueron usados para clasifi car las muestras de acuerdo a su origen. El metodo de la validacion cruzada fue utilizado para validar los modelos. El 85,7% de las muestras de harina de pescado y el 100 % de las muestras de carne y soja fueron correctamente clasifi cados usando el metodo PLS-DA. Los resultados obtenidos en este estudio demuestran el potencial uso de la refl ectancia en el infrarrojo cercano combinada con la quimiometria como un metodo rapido y de bajo costo para clasifi car muestras de harina de pescado, carne y soja.

USO DE LA ESPECTROSCOPÍA DE REFLECTANCIA EN EL INFRARROJO CERCANO PARA EL ANÁLISIS DE CALIDAD DE ENSILAJE DE MAÍZ

La espectroscopia de reflectancia en el infrarrojo cercano (NIRS) fue utilizada para predecir la composicion quimica del ensilaje de maiz (Zea mays L). Doscientas muestras de un amplio rango de caracteristicas fisico - quimicas y origen, fueron leidas en un equipo monocromador (NIRS 6500, NIRSystems, Silver Spring, Maryland? USA) en el rango de longitudes de onda de 400 a 2500 nm, en reflectancia. Los coeficientes de determinacion en calibracion (R2cal) y el error estandar de la validacion cruzada (SECV) fueron 0,94 (SECV: 0,74%), 0,94 (SECV: 0,54%), 0,91 (SECV: 1,8%), y 0,90 (SECV: 3,8%) para MS, proteina cruda (PC), fibra detergente acida (FDA) y fibra detergente neutra (FDN) en base materia seca. Los resultados demuestran el potencial del NIRS para el analisis de rutina del ensilaje de maiz para MS, PC, y FDA.

Prediction of chemical composition in sunflower whole plant and silage (Helianthus annus L.) by near infrared reflectance spectroscopy

The aim of this study was to investigate the potential use of near infrared (NIR) reflectance spectroscopy to predict chemical composition in both sunflower whole plant (WPSun) and sunflower silage (SunS). Samples of both WPSun (n = 73) and SunS (n = 50) were analysed by reference method and scanned in reflectance using a NIR monochromator instrument (400–2500 nm). Calibration models were developed between NIR data and reference values for dry matter (DM), crude protein (CP), ash, acid detergent fibre (ADFom), neutral detergent fibre (aNDFom), in vitro organic matter digestibility (OMD), ether extract (EE) and pH using partial least squares regression (PLS). Due to the limited number of samples full cross-validation was used to test the calibration models. The best correlations (R 2 cal) and lowest standard errors in cross-validation (SECV) were obtained for DM (R 2 cal > 0.82, SECV: 27.0 and 35.8 g kg−1), CP (R 2 cal> 0.85, SECV: 9.9 and 10.1 g kg−1) and ash (R2cal> 0.85, SECV 11.2 and 8.2 g kg−1) in both WPSun and SunS samples, respectively. For ADFom, aNDFom and OMD the calibrations were considered to be poor (R 2 cal < 0.85). In SunS samples a good correlation was found for EE (R 2 cal = 0.94, SECV: 15.3 g kg−1).

Instrumental Techniques and Methods: Their Role in Plant Omics

Techniques and methods based on vibrational spectroscopy such as near-infrared reflectance (NIR), mid-infrared (MIR) and Raman spectroscopy are known to be non-destructive and low cost. These characteristics are considered as the most important when these methods or techniques are applied in the field of plant omics. This chapter will provide an overview of the most common vibrational spectroscopy techniques used in the field of plant omic analysis (NIR, MIR, Raman). Information about the hardware (instruments) and software (multivariate data methods) will be also presented and discussed.

Determination of oil content in whole corn (Zea mays L.) seeds by means of near infrared reflectance spectroscopy

The combination of near infrared reflectance spectroscopy and chemometrics was evaluated to measure oil in whole corn.The interpretation of the data generated allowed to identify regions in the NIR related to oil.The result of this study showed that NIR spectroscopy can be used to predict oil content in corn. The objective of this study was to evaluate the ability of near infrared reflectance (NIR) spectroscopy to determine oil content in whole corn (Zea mays L.) samples sourced from a breeding program. Kernel samples were analysed in reflectance in the VIS and NIR regions (400-2500nm) at 2nm intervals using a scanning monochromator. Samples were scanned in a circular cell cup and reflectance data were stored as logarithm of the reciprocal reflectance (log1/R). Samples were not rotated when spectra collection was made. The coefficients of determination (R2) and the standard error of cross validation (SECV) obtained for the prediction of oil content in the calibration set were 0.90% and 0.17%, respectively. The residual predictive deviation (RPD=SD/SECV) value obtained was 2.3, indicating that these calibrations can be used for qualitative determination of oil content (e.g. low, medium and high) or preliminary screening in whole corn.