CAJ | 학술논문

针对目前的模型传递方法研究大多为不同仪器间的近红外光谱模型传递，该文采用高光谱技术建立猪肉含水率定量检测模型，并针对不同品种间的模型传递提出了一种分段直接校正结合线性插值（piecewise direct standardization combine with linear interpolation, PDS-LI）的传递算法。以杜长大、茂佳山黑猪和零号土猪3个品种为研究对象，以杜长大作为主品种，茂佳山黑猪和零号土猪作为从品种，采用偏最小二乘回归（partial least squares regression, PLSR）法建立猪肉含水率主模型，经 PDS-LI 算法对主模型进行传递后，主模型对茂佳山黑猪和零号土猪样品的预测决定系数R2p分别由传递前的0.263和0.507提高到0.832和0.848，预测均方根误差分别由传递前的1.151%和0.857%降低到0.470%和0.440%，剩余预测偏差（residual prediction deviation, RPD）分别由传递前的1.000和1.214提高到2.447和2.364。结果表明，PDS-LI传递算法能够实现杜长大对茂佳山黑猪和零号土猪样品的模型传递。研究结果为提高猪肉含水率模型适配性问题提供参考。
침대목전적모형전체방법연구대다위불동의기간적근홍외광보모형전체，해문채용고광보기술건립저육함수솔정량검측모형，병침대불동품충간적모형전체제출료일충분단직접교정결합선성삽치（piecewise direct standardization combine with linear interpolation, PDS-LI）적전체산법。이두장대、무가산흑저화령호토저3개품충위연구대상，이두장대작위주품충，무가산흑저화령호토저작위종품충，채용편최소이승회귀（partial least squares regression, PLSR）법건립저육함수솔주모형，경 PDS-LI 산법대주모형진행전체후，주모형대무가산흑저화령호토저양품적예측결정계수R2p분별유전체전적0.263화0.507제고도0.832화0.848，예측균방근오차분별유전체전적1.151%화0.857%강저도0.470%화0.440%，잉여예측편차（residual prediction deviation, RPD）분별유전체전적1.000화1.214제고도2.447화2.364。결과표명，PDS-LI전체산법능구실현두장대대무가산흑저화령호토저양품적모형전체。연구결과위제고저육함수솔모형괄배성문제제공삼고。
At present, most studies on model transfer were based on different spectrometers, and models were established using the near infrared spectroscopy. In this study, a hyperspectral detection model of water content of fresh pork was established by partial least squares regression (PLSR) method. In order to enhance model prediction applicability to different breeds of pork samples, a new model transfer method, piecewise direct standardization combined with linear interpolation (PDS-LI) was processed. In this method, the spectra of slave breed were corrected according to the spectra difference between master breed and slave breed, and then the corrected spectra of slave breed were predicted by master model. A function based on the prediction and reference values of slave breed samples was established. This function would be used to correct the prediction values of unknown test samples of slave breed. The specific steps were as followed: 1) Samples of master breed were divided into the calibration set and the test set, and the master model was built based on the calibration set by PLSR method. 2) Samples of slave breed were partitioned into standard sample selection set C2, standard sample set C2std and unknown test set T2un, and C2 was used for the selection of C2std and T2un was used to verify the transferred model. 3) Transfer matrix F was calculated by PDS algorithm according to the spectra difference between calibration set in master breed and C2std in slave breed, and then C2std and T2un were respectively corrected by transfer matrix F. 4) In order to improve the prediction accuracy of master model to the corrected spectrum of slave breed, the prediction value of T2un need to be corrected. For sample i in unknown test set T2un, symbiosis distance D(i) between sample i and every sample else in standard sample set C2std was calculated successively. D(i) was the sum of Euclidean distances between converted spectrum and absolute deviation of the prediction values. Two minimum values of D(i) were selected, so the prediction value of sample i in T2un could be corrected by the prediction and reference values of the 2 minimum samples. Three breeds, Duchangda, Maojia and Linghao pork were researched in this paper. As master breed, Duchangda samples were used to build the master model, and Maojia and Linghao were considered to be slave breeds to test the feasibility of model transfer algorithm. Equations with predictive determination coefficient (R2p) no less than 0.7 and residual prediction deviation (RPD) no less than 1.9 were considered to be applicable to predict pork quality. Model prediction results showed that for Duchangda samples, the coefficient of determination in cross-validation (R2cv) was 0.884, R2p was 0.883, root mean squared error of cross validation (RMSECV) was 0.279%, root mean squared error of prediction (RMSEP) was 0.237%, and RPD was 2.911, but for Maojia and Linghao samples, the prediction results were very poor: R2p only reached to 0.263 and 0.507, RMSEP, 1.151% and 0.857%, RPD, 1.000 and 1.214, respectively. With PDS-LI transfer method, the model prediction accuracies were substantially increased: R2p increased to 0.832 and 0.848, RMSEP decreased to 0.470% and 0.440%, RPD improved to 2.447 and 2.364, respectively, which indicated that PDS-LI transfer algorithm can achieve the model prediction transfer from Duchangda to Maojia and Linghao pork samples.