【摘要】：為解決快速無損鑒別秈稻霉變程度問題,利用高光譜技術(shù)采集200份霉變樣本可見/近紅外光譜信息,隨機(jī)選取155份樣本作為校正集,剩余45份作為驗(yàn)證集,根據(jù)預(yù)測濃度殘差檢驗(yàn)標(biāo)準(zhǔn)對校正集中異常樣本進(jìn)行剔除。以新校正集建立主成分線性判別分析(PCA-LDA)和簇類獨(dú)立軟模式法(SIMCA)模型,選用正確識別率為指標(biāo),優(yōu)選最佳鑒別模型。并采用連續(xù)投影算法(SPA)提取特征波長,優(yōu)化優(yōu)選的最佳模型構(gòu)建速度。研究結(jié)果表明,PCA-LDA對所有樣本的誤判總數(shù)為15,正確識別率為92.50%;SIMCA和SPASIMCA對所有樣本的未能正確識別總數(shù)分別為6、2,正確識別率分別為97.00%、99.00%,并且經(jīng)SPA篩選的變量數(shù)為20,僅占原始變量數(shù)的7.81%,建模時(shí)長縮短為原始變量的40.93%。因此,SPA-SIMCA鑒別效果最好,該方法在快速、準(zhǔn)確鑒別秈稻霉變程度上具有可行性。
[Abstract]:In order to solve the problem of rapid nondestructive identification of mildew degree of indica rice, 200 musty samples were collected by hyperspectral technique. 155 samples were randomly selected as calibration set, and the remaining 45 samples were used as validation set. According to the test standard of predicted concentration residual, the abnormal samples in correction concentration are eliminated. The principal component linear discriminant analysis (PCA-LDA) and cluster independent soft mode method (SIMCA) model are established by using the new correction set. The best discriminant model is selected by selecting the correct recognition rate as the index. The continuous projection algorithm (SPA) is used to extract the feature wavelength to optimize the optimal model construction speed. The results show that the total number of misjudgments for all samples by PCA-LDA is 15, the correct recognition rate is 92.50% for Simca and SPASIMCA for all samples is 6 / 2, respectively, and the correct recognition rate is 97.00 / 99.00, and the number of variables screened by SPA is 20, accounting for only the original number. The initial variable number is 7.81, and the modeling time is reduced to 40.933 of the original variable. Therefore, SPA-SIMCA is the best method for identification of mildew of indica rice.
【作者單位】： 中南林業(yè)科技大學(xué)機(jī)電工程學(xué)院;華南農(nóng)業(yè)大學(xué)南方農(nóng)業(yè)機(jī)械與裝備關(guān)鍵技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(31401281) 湖南省科技計(jì)劃重點(diǎn)研發(fā)項(xiàng)目(2016NK2151) 湖南省自然科學(xué)基金(14JJ3115) 湖南省高校科技創(chuàng)新團(tuán)隊(duì)支持計(jì)劃(2014207)
【分類號】：O657.3;TS210.7

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