光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2015年
1期
113-117
,共5页
高光谱成像%三文鱼%水分含量%random frog%最小二乘支持向量机
高光譜成像%三文魚%水分含量%random frog%最小二乘支持嚮量機
고광보성상%삼문어%수분함량%random frog%최소이승지지향량궤
Hyperspectral imaging%Salmon%Water contents%Random frog%Least-squares support vector machines
应用近红外高光谱成像技术实现三文鱼肉水分含量的快速无损检测。采集来自不同部位的三文鱼肉共90个样本的高光谱图像，提取样本感兴趣区域（ROI）的平均光谱。随机取60个样本作为建模集，其余30个样本作为预测集。分别采用偏最小二乘回归（PLSR）和最小二乘支持向量机（LS-SVM ）对全波段和水分含量建立相关性模型，并对预测集样本的水分含量进行预测。再用一种新的变量提取方法random frog选择特征波长，并基于特征波长分别建立水分检测的PLSR和LS-SVM 模型。特征波长模型的预测精度虽然稍逊于全波段模型，但是仅用12个变量代替了全波段的151个变量，大大简化了模型，更便于实际应用。PLSR和LS-SVM特征波长模型的预测相关系数（Rp ）分别为0.92和0.93，预测均方根误差（RMSEP）分别为1.31％和1.18％，取得了满意的结果。研究表明，近红外高光谱成像与化学计量学方法结合可以准确预测三文鱼肉的水分含量，为鱼肉品质的快速监测提供重要的参考。
應用近紅外高光譜成像技術實現三文魚肉水分含量的快速無損檢測。採集來自不同部位的三文魚肉共90箇樣本的高光譜圖像，提取樣本感興趣區域（ROI）的平均光譜。隨機取60箇樣本作為建模集，其餘30箇樣本作為預測集。分彆採用偏最小二乘迴歸（PLSR）和最小二乘支持嚮量機（LS-SVM ）對全波段和水分含量建立相關性模型，併對預測集樣本的水分含量進行預測。再用一種新的變量提取方法random frog選擇特徵波長，併基于特徵波長分彆建立水分檢測的PLSR和LS-SVM 模型。特徵波長模型的預測精度雖然稍遜于全波段模型，但是僅用12箇變量代替瞭全波段的151箇變量，大大簡化瞭模型，更便于實際應用。PLSR和LS-SVM特徵波長模型的預測相關繫數（Rp ）分彆為0.92和0.93，預測均方根誤差（RMSEP）分彆為1.31％和1.18％，取得瞭滿意的結果。研究錶明，近紅外高光譜成像與化學計量學方法結閤可以準確預測三文魚肉的水分含量，為魚肉品質的快速鑑測提供重要的參攷。
응용근홍외고광보성상기술실현삼문어육수분함량적쾌속무손검측。채집래자불동부위적삼문어육공90개양본적고광보도상，제취양본감흥취구역（ROI）적평균광보。수궤취60개양본작위건모집，기여30개양본작위예측집。분별채용편최소이승회귀（PLSR）화최소이승지지향량궤（LS-SVM ）대전파단화수분함량건립상관성모형，병대예측집양본적수분함량진행예측。재용일충신적변량제취방법random frog선택특정파장，병기우특정파장분별건립수분검측적PLSR화LS-SVM 모형。특정파장모형적예측정도수연초손우전파단모형，단시부용12개변량대체료전파단적151개변량，대대간화료모형，경편우실제응용。PLSR화LS-SVM특정파장모형적예측상관계수（Rp ）분별위0.92화0.93，예측균방근오차（RMSEP）분별위1.31％화1.18％，취득료만의적결과。연구표명，근홍외고광보성상여화학계량학방법결합가이준학예측삼문어육적수분함량，위어육품질적쾌속감측제공중요적삼고。
Near-infrared hyperspectral imaging technique was employed in the present study to determine water contents in salm-on flesh rapidly and nondestructively .Altogether 90 samples from different positions of salmon fish were collected for hyperspec-tral image scanning ,and mean spectra were extracted from the region of interest (ROI) inside each image .Sixty samples were randomly selected as calibration set ,and the remaining 30 samples formed prediction set .The full-spectrum and water contents were correlated using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM ) ,which were then applied to predict water contents for prediction samples .A novel variable extraction method called random frog was applied to select effective wavelengths (EWs) from the full-spectrum .PLSR and LS-SVM calibration models were established respec-tively to detect water contents in salmon based on the EWs .Though the performances of EWs-based models were worse than models using full-spectrum ,only 12 wavelengths were used to substitute for the original 151 wavelengths ,thus models were greatly simplified and more suitable for practical application .For EWs-based PLSR and LS-SVM models ,satisfactory results were achieved with correlation coefficient of prediction (Rp ) of 0.92 and 0.93 respectively ,and root mean square error of predic-tion (RMSEP) of 1.31% and 1.18% respectively .The results indicated that near-infrared hyperspectral imaging combined with chemometrics allows accurate prediction of water contents in salmon flesh ,providing important reference for the rapid inspection of fish quality .
