Research on Quality Prediction of Forages from Pennisetum by Using Near-Infrared Spectrum Model Based on Two Algorithms
To predict the quality of forages from Pennisetum in Chongqing by near-infrared spectrum(NIRS)technique,a total of 165 samples of 10 cultivars of Pennisetum cultivated in Chongqing as research materials,while 70％samples were selected as calibration set and 30％samples as verification set,to establish the NIRS prediction models of moisture(M),crude protein(CP),crude fibre(CF),acid detergent lignin(ADL),acid detergent fibre(ADF),neutral detergent fibre(NDF)and crude ash(Ash)contents of forages from Pennisetum based support vector machine(SVM)algorithm and partial least square(PLS)algorithm,respec-tively.The results showed that the SVM algorithm improved the prediction accuracy of prediction models.A-mong the seven nutritional quality indexes,SVM model obtained five indexes with good prediction effect[rela-tive prediction error(RPD)≥3.0],two indexes with acceptable prediction effect(2.5＜RPD＜3.0),and there were no indexes with unacceptable prediction effect(RPD≤2.5),while PLS model obtained only two indexes with good prediction effect(RPD ≥3.0),there is only one index with acceptable prediction effect(2.5＜RPD＜3.0),and there were four indexes with unacceptable prediction effect(RPD≤2.5),indicating that the predic-tion effect of the model is unacceptable and could not be applied to NIRS quantitative analysis.Overall,the quality prediction model of forages from Pennisetum established by SVM algorithm was better than the predic-tion model established by PLS algorithm,and the prediction determination coefficients of some indexes were greater than 0.80.To sum up,SVM algorithm has good fitting ability for the nonlinear data of NIRS,which can be applied to forage grass quality classification of Pennisetum.[Chinese Journal of Animal Nutrition,2024,36(6):3984-3994]
near infrared spectrumforages from Pennisetumconventional nutrientssupport vector ma-chinepartial least square
万方数据
维普
