蜻蜓算法优选小麦粉蛋白质近红外建模校正集OA北大核心CSTPCD
Calibration Set Optimization by Dragonfly Algorithm for Near-Infrared Modeling of Wheat Flour Protein Content
为优选小麦粉蛋白质近红外建模校正集,在传统K/S(Kennard/Stone)方法划分的初始校正集基础上采用二进制蜻蜓算法(binary dragonfly algorithm,BDA)挑选代表性样品,建立小麦粉蛋白质含量偏最小二乘回归(partial least square regression,PLSR)模型,并用预测集检验评估模型的稳定性及预测性能.结果表明:BDA挑选出的最佳校正集样品数量为30个,所建模型的预测决定系数(R2p)为0.…查看全部>>
In order to optimize the calibration set for near-infrared modeling of the protein content in wheat flour,the binary dragonfly algorithm(BDA)was used to select representative samples from the primary calibration set divided by the traditional Kennard/Stone(K/S)method.Based on the representative samples,a partial least square regression(PLSR)model for estimating the protein content in wheat flour was established,and the prediction set was employed to evaluate…查看全部>>
胡云超;刘智健;汪莹;黄浩冉;王红鸿;吴彩娥;熊智新
南京林业大学轻工与食品学院,江苏南京 210037南京林业大学轻工与食品学院,江苏南京 210037南京林业大学轻工与食品学院,江苏南京 210037南京林业大学轻工与食品学院,江苏南京 210037南京林业大学轻工与食品学院,江苏南京 210037南京林业大学轻工与食品学院,江苏南京 210037南京林业大学轻工与食品学院,江苏南京 210037
轻工业
蜻蜓算法近红外光谱校正集优选小麦粉蛋白质含量
dragonfly algorithmnear-infrared spectroscopyoptimization of calibration setprotein content of wheat flour
《食品科学》 2024 (9)
9-15,7
"十三五"国家重点研发计划重点专项(2019YFD1002300)
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