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

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.

Near-Infrared Reflectance Spectroscopy (NIRS) detection to differentiate morning and afternoon milk based on nutrient contents and fatty acid profiles

Milking time is one of the factors that affect milk quality. The objective of this study was to differentiate morning milk from afternoon based on milk fatty acid profile and create a prediction model using Near-Infrared Reflectance Spectroscopy (NIRS). This study used explorative research and post-observation analysis. Milk sampling was collected from three different dairy farm locations in West Java Provinces (Pangalengan district of Bandung Regency, Cibungbulang District of Bogor Regency, and Tanah Sareal District of Bogor Municipality). Milk quality observed in this study included milk fat, protein, lactose, solid non-fat (SNF), and fatty acid compositions. Milk fat, protein, lactose, and SNF were analyzed using Lactoscan. Fatty acid compositions were identified using gas chromatography (GC). Sample spectrums were collected using NIRSflex 500. The difference between morning and afternoon milking was tested using a t-test carried out by SPSS ver. 25. Qualitative calibration of milk quality was conducted using NIRSCal v5.6 by applying the cluster (CLU) method. The results from lactoscan and GC showed that milk fat, caprylic acid, and myristoleic acid, and total SFA were significantly different (Sig. (2-tailed) < 0.05) in morning and afternoon milk. However, NIRS failed to generate a sophisticated model for the milk quality differentiation, which shows a low Q-value (0.0011231). The quantitative analysis accurately produced milk fat and total SFA predictions but failed to accurately predict caprylic acid and myristoleic acid. This study concluded that morning milk could be differentiated from afternoon milk based on milk fat, caprylic acid, myristoleic acid, and total SFA content. The NIRS technology can differentiate between morning and afternoon milk based on quantitative calibration of total fat and SFA.

Optimizing Near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding

Multidimensional improvement programs of chickpea require screening of a large number of genotypes for straw nutritive value. The ability of near infrared reflectance spectroscopy (NIRS) to determine the nutritive value of chickpea straw was identified in the current study. A total of 480 samples of chickpea straw representing a nation-wide range of environments and genotypic diversity (40 genotypes) were scanned at a spectral range of 1108 to 2492 nm. The samples were reduced to 190 representative samples based on the spectral data then divided into a calibration set (160 samples) and a cross-validation set (30 samples). All 190 samples were analysed for dry matter, ash, crude protein, neutral detergent fibre, acid detergent fibre, acid detergent lignin, Zn, Mn, Ca, Mg, Fe, P, and in vitro gas production metabolizable energy using conventional methods. Multiple regression analysis was used to build the prediction equations. The prediction equation generated by the study accurately predicted the nutritive value of chickpea straw (R2 of cross validation >0.68; standard error of prediction <1%). Breeding programs targeting improving food-feed traits of chickpea could use NIRS as a fast, cheap, and reliable tool to screen genotypes for straw nutritional quality.

Environmental assessment of soil quality indices using near infrared reflectance spectroscopy

The main purpose of this preset study is to assess soil quality indices in form of potassium (K) and phosphorus (P) contents using a non-invasive and environmental friendly approach namely near infrared reflectance spectroscopy. Soil samples were obtained from Aceh Besar district in rice field land-use. Near infrared spectral data of soil samples were acquired and recorded as absorbance in wavelength range from 1000 to 2500 nm. On the other hand, actual P and K were measured using standard laboratory procedures by means of Kjeldahl methods. Spectral data were corrected and pre-treated using mean centering approach and applied to all dataset. Prediction models were developed using principal component regression and validated using leverage cross validation. The results showed that both soil quality indices can be predicted with maximum correlation coefficient (r) of 0.98 and ratio prediction to deviation (RPD) index of 3.47 for P, and r of 0.91, RPD of 2.68 for K respectively. It may conclude that environmental assessment, particularly for soil quality determination can be conducted rapidly and non-invasively using near infrared spectroscopy approach.