Use of LOCAL algorithm with near infrared spectroscopy in forage resources for grazing systems in Colombia

Abstract

This study used a total of 2020 Colombian forage resources of three families (Grass forages, legume forages, and other forage plants) to develop near infrared spectroscopy calibrations for predicting the nutritional value. Spectra were collected at 2 nm increments using a scanning visible/near infrared spectrometer. The reference data used for each forage were crude protein, crude ash, neutral detergent fiber, acid detergent fiber, acid detergent lignin, measured according to the Association of Official Analytical Chemists. Two chemometric tools for developing near infrared spectroscopy prediction models were compared: the GLOBAL modified partial least squares, and the calibration strategy known as LOCAL. The LOCAL procedure is designed to select, from a large database, samples with spectra resembling the sample being analyzed. Selected samples were used as calibration sets after one-tenth of the samples were selected for validation from each database. Predictions of nutrition indicators in validation samples using generic and specific calibrations were compared with both GLOBAL and LOCAL procedures. For all predicted forages, LOCAL resulted in a significant improvement in both standard error of prediction and bias values compared with GLOBAL. Determination coefficient values (r²) also improved using the LOCAL algorithm, exceeding 0.9 for most forage sets. LOCAL calibration was then used with only one database (n 2020) comprising all the forage samples and SEP and r² were similar to those obtained in the three databases using LOCAL algorithm. Therefore, LOCAL can accurately predict the composition of different forages using only one database, and could offer a practical way to develop robust equations taking into account the biodiversity of Colombian forages.