Abstract:In this study, anear infrared (NIR) quantitative prediction model for the lysine in the freeze-dried chicken breast muscle powder was developed using the partial least squares method. The model was optimized through four aspects, spectrum preprocessing, model characteristic spectrum screening, outlier samples elimination, and sample selection for modeling and verification, in order to improve the prediction accuracy and robustness of the model. Taking a total of 263 freeze-dried chicken breast muscle powders as the research object, the effects of seven different spectral preprocessing methods, four characteristic spectrum screening methods, MCCV outlier samples elimination methods, and two samples selection methods for modeling and verification, SPXY and chicken breeds, on the NIR quantitative prediction model for the lysine of in freeze-dried chicken breast muscle powder. The results showed that (1) the best model was established on the basis of 156 calibration samples and 39 external verification samples after the elimination of 68 outlier samples from the original spectra in the 1000~2502 nm; (2) The spectral region in the 1000~2502 nm used for modeling should be pretreated by SNV+gapsegment (1#,15,7) method when there was no outlier spectrum or model characteristic spectral region for selection; (3) Least impact on the accuracy of the model established using the original spectra, was exerted by outlier sample elimination and samples selection for modeling in 1386~1379 nm, 1329~1323 nm, 1289~1283 nm, 1276~1258 nm, 1240~1235 nm, 1194~1184 nm, 1173~1168 nm、1142~1137 nm, 1103~1099 nm, 1080~1076 nm, 1058~1054 nm, 1012~1009 nm, 1004~1001 nm. Research showed that the establishment of a near-infrared quantitative prediction model for the lysine of in freeze-dried chicken breast muscle powder is greatly affected by outlier samples, samples for modeling and external sample selection for verification. The applicability of the model is greatly affected by characteristic spectra regions for modeling. The preprocessing of the whole spectrum without other pretreatments has a greater impact on the accuracy of model.
