CAJ | 학술논문

采用近红外光谱(NIRS)分析技术和化学计量方法建立稻谷脂肪酸值的近红外分析模型,并对模犁进行预测准确性评价;在建立定标模型的过程中,探讨光谱散射处理、数学(导数)处理等优化处理对定标模犁的影响.结果表明:修正偏最小二乘法是建立稻谷脂肪酸值测定定标模型的最佳回归方法,所建立模型的定标相关系数(RSQ)为0.961,定标标准偏差(SEC)为1.9205;内部交互验证相关系数(1-VR)为0.9474,内部交互验证标准偏差(SECV)为2.2511.外部验证的相关系数(r)为0.951,外部验证标准偏差(SEP)为1.934.标准方法与NIRS测定方法测定的稻谷脂肪酸值含量之间的t检验值为1.403,显示两种方法测定结果无显著性差异(P<0.1),预测值与实测值的平均绝对偏差为0.27,说明所建立的稻谷脂肪酸值的NIRS数学模型预测准确性较好,可用于稻谷脂肪酸值的快速预测.
채용근홍외광보(NIRS)분석기술화화학계량방법건립도곡지방산치적근홍외분석모형,병대모리진행예측준학성평개;재건립정표모형적과정중,탐토광보산사처리、수학(도수)처리등우화처리대정표모리적영향.결과표명:수정편최소이승법시건립도곡지방산치측정정표모형적최가회귀방법,소건립모형적정표상관계수(RSQ)위0.961,정표표준편차(SEC)위1.9205;내부교호험증상관계수(1-VR)위0.9474,내부교호험증표준편차(SECV)위2.2511.외부험증적상관계수(r)위0.951,외부험증표준편차(SEP)위1.934.표준방법여NIRS측정방법측정적도곡지방산치함량지간적t검험치위1.403,현시량충방법측정결과무현저성차이(P<0.1),예측치여실측치적평균절대편차위0.27,설명소건립적도곡지방산치적NIRS수학모형예측준학성교호,가용우도곡지방산치적쾌속예측.
The mathematic models for the prediction of fatty acid content of rice was established with the technique of near-infrared spectroscopy (NIRS). The result showed that the calibration models developed by the partial least square (PLS) regression were optimum. The statistical values of calibration equation were as follows: the coefficient of correlation (RSQ) of 0.961, the standard error of calibration (SEC) of 1.9205, the determination coefficient of cross-validation (1-VR) of 0.9474, the standard error of cross-validation (SECV) of 2.2511, Regression squared (r) of 0.951, square error of prediction (SEP) of 1.934. The t test value between the chemical standard methods and NIRS method was 1.403 (P<0.1), suggesting no significant difference between these two methods. The absolute average deviation was 0.27. This NIRS method could be applied to predict the fatty acid content in rice.