title: Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses.
abstract: Due to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different statistical analyses (MLR, PCR and PLS) was used to predict the chemical quality of sheepgrass (Leymus chinensis) in Heilongjiang Province, China including the concentrations of crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). Firstly, the linear partial least squares regression (PLS) was performed on the spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the MLR evaluation method for CP has a potential to be used for industry requirements, as it needs less sophisticated and cheaper instrumentation using only a few wavelengths. Results show that in terms of CP, ADF and NDF, (i) the prediction accuracy in terms of CP, ADF and NDF using PLS was obviously improved compared to the PCR algorithm, and comparable or even better than results generated using the MLR algorithm; (ii) the predictions were worse compared to laboratory-based spectra with the MLR algorithmin, and poor predictions were obtained (R2, 0.62, RPD, 0.9) using MLR in terms of NDF; (iii) a satisfactory accuracy with R2 and RPD by PLS method of 0.91, 3.2 for CP, 0.89, 3.1 for ADF and 0.88, 3.0 for NDF, respectively, was obtained. Our results highlight the use of the combined NIRs-PLS method could be applied as a valuable technique to rapidly and accurately evaluate the quality of sheepgrass hay.
authors.affiliation: Department of Grassland Science, China Agricultural University , Beijing , China ; Heilongjiang Academy of Agricultural Science, Institute of Pratacultural Science , Harbin , China.
authors.forename: Jishan
authors.initials: J
authors.lastname: Chen
authors.affiliation: Heilongjiang Academy of Agricultural Science, Institute of Pratacultural Science , Harbin , China.
authors.forename: Ruifen
authors.initials: R
authors.lastname: Zhu
authors.affiliation: Department of Grassland Science, China Agricultural University , Beijing , China.
authors.forename: Ruixuan
authors.initials: R
authors.lastname: Xu
authors.affiliation: Department of Grassland Science, China Agricultural University , Beijing , China.
authors.forename: Wenjun
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Department of Grassland Science, China Agricultural University , Beijing , China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Shen
authors.affiliation: Department of Grassland Science, China Agricultural University , Beijing , China.
authors.forename: Yingjun
authors.initials: Y
authors.lastname: Zhang
keywords: Chemical quality; Near infrared spectroscopy; Root mean squares error of calibration (RMSEC); Root mean squares error of prediction (RMSEP); Sheepgrass (Leymus chinensis)