OPTIMIZING NEAR INFRARED REFLECTANCE SPECTROSCOPY TO PREDICT NUTRITIONAL QUALITY IN CHICKPEA HAULMS FOR LIVESTOCK FEED | Asian Journal of Advances in Research
The calibration equations for predicting chickpea haulm (Cicer arietinum) feed quality traits and mineral constituents were developed using near infrared reflectance spectroscopy (NIRS). A FOSS 5000 spectrophotometer was used to scan a total of 1348 chickpea cultivars reflecting a wide variety of Ethiopian environments and genotypic diversity (113 cultivars and 7 landraces) used in the Ethiopian National Chickpea Breeding and Genetics Program. For the creation of the calibration equations, 130 samples reflecting the spectral characteristics of chickpea haulms were chemically analysed using WinISI II software V.1.50. To confirm the equations and classify potential spectral outliers (GH-value>3, where GH is the Mahalanobis distance), a modified partial least-squares (MPLS) regression with cross validation was used. The equations were validated using coefficient of determination (R), standard error of prediction (SEP(C), and ratio of output variance (RPD) values. Ash (r =0.97; RPD=3.64), crude protein (r2= 0.99; RPD = 8.09), acid detergent fibre (r2 = 0.99; RPD = 6.43), neutral detergent fibre (r2=0.99; RPD = 6.65), and lignin (r2 = 0.99; RPD = 5) were all found to be important. IVOMD (r=0.99; RPD=26), ME (r=0.99; RPD=24.3). The calibration equations can reliably predict nutritional quality traits of chickpea haulms, according to these findings. Researchers and farmers may use the NIRS approach to make more cost-effective and fast decisions. Please see the link :- https://mbimph.com/index.php/AJOAIR/article/view/2002