The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility

Abstract

Information on dairy fibre feed digestibility is important in ration formulation to better predict dairy cattle performance. However, its measurement takes time. Near-infrared reflectance spectroscopy (NIRS) is a rapid, precise, and cost-effective method to predict nutrient value, such as chemical content and digestibility of feedstuffs. This study aims to develop a database for an in vitro digestibility prediction model using NIRS, including dry matter digestibility (DMD), neutral and acid detergent fibre digestibility (NDFD and ADFD), and hemicellulose digestibility (HSD). Eighty dietary fibre feeds consisting of Napier grass, natural grass, rice straw, corn stover, and corn-husk were collected from four dairy farming areas in West Java (Cibungbulang District of Bogor Regency, Parung Kuda District of Sukabumi Regency, Pangalengan District of Bandung Regency, and Lembang District of West Bandung Regency). The spectrum for each sample was collected thrice using NIRSflex 500, which was automatically separated by 2/3 for calibration and 1/3 for validation. External validation was conducted by measuring 20 independent samples. Calibration and validation models were carried out by NIRCal V5.6 using the partial least squares (PLS) regression. The results showed that all parameters produce r2 > 0.5 except for ADFD. Relative prediction deviation (RPD) > 1.5 was only found in hemicellulose digestibility prediction. RPL (SEP/SEL) <1.0 were found in DMD and hemicellulose digestibility. It is concluded that hemicellulose digestibility can be predicted using NIRS accurately while other parameters need improvement.

Development of non‐destructive models to predict oil content and fatty acid composition of Gomenzer (Ethiopian mustard) using near‐infrared reflectance spectroscopy

“…On the other hand, the model developed for palmitic, linolenic, and eicosenoic acids were moderately suitable with R 2 value of 0.68, 0.77, and 0.74 respectively. These were in agreement to NIRS models developed for tryptophan in maize 30 and fibers in feeds 23 that reported by Escuredo et al 31 The RPD C values of calibration equations developed for oil, palmitic, stearic, oleic, linoleic, and erucic acids are more than two as shown in Table 2, which were acceptable result in the good range as studied by Aldo et al 32 Furthermore, the RPD C values for palmitic, linolenic, and eicosenoic acid were 1.6, 1.8, and 1.7, respectively, which were lower than the other equations but still adequate for screening, according to Khamchum et al 33 RER values of all equation of this study varied from 10.0 to 23.0 which agreed with the recommended values for screening purposes as reported by Yang et al 10 except models developed for palmitic, linolenic, and eicosenoic acid.…”

“…However, palmitic, stearic, and eicosenoic acid had a coefficient of determination of prediction models (R 2 v) of 0.02, 0.0.04, and 0.3, respectively, which indicated that the reference and NIRS value were not correlated and they were the bad equations. Furthermore, according to Murphy et al 35 and Zahera, 23 the RPDv values of these models were less than the required value of 1.5. In addition, models developed for palmitic, stearic acid, and eicosenoic acid had narrow distribution datasets and showed a poor correlation between reference values and NIRS predicted values.…”

“…The correlations between the SD of the wet chemical analyzed data and the prediction data by NIRS model (SECV or SEP) 10 were used to estimate the prediction ability of the model and calibrations, as well as the accuracy of the equations 5 . Equations with the highest R 2 , RPD, and 1‐VR and the lowest SECV and SEP were considered the best models 21–23 . Samples with NIRS spectra that were significantly different from other samples were considered abnormal and removed from the dataset when the Mahalanobis distance global‐H (GH) value or the neighboring‐H (NH) value was greater than 3.0 or less than 0.6 24,25 …”

“…5 Equations with the highest R 2 , RPD, and 1-VR and the lowest SECV and SEP were considered the best models. [21][22][23] Samples with NIRS spectra that were significantly different from other samples were considered abnormal and removed from the dataset when the Mahalanobis distance global-H (GH) value or the neighboring-H (NH) value was greater than 3.0 or less than 0.6. 24,25 RESULT AND DISCUSSIONS…”

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

“…NIRS has been used to estimate the ‘conventional’ nutrient composition (e.g., crude protein, neutral detergent fiber, etc.) [ 21 ] of feed, but it has also proven useful to predict concentrations of compounds such as tannins and non-starch polysaccharides [ 22 , 23 ], or to predict properties of a feed, such as digestibility or rumen degradability, which are observed after a feed is offered to animals [ 23 , 24 , 25 ]. NIRS has also shown a potential to estimate the yield of CH 4 from biomass digestors for biogas production [ 26 , 27 ] and has been successful in predictions of CH 4 production during rumen batch culture fermentation with over 100 forage species [ 28 ].…”