本文選題：高光譜 + 哈密瓜　； 參考：《光譜學(xué)與光譜分析》2017年07期

【摘要】：可溶性固形物含量(SSC)和硬度是哈密瓜劃分等級(jí)的重要指標(biāo),同時(shí)也是其成熟度的表征因子。因此,為滿足哈密瓜自動(dòng)化分級(jí)和適宜采摘,采用高光譜技術(shù)結(jié)合特征波長篩選的方法,同時(shí)對哈密瓜的可溶性固形物含量、硬度及成熟度進(jìn)行了無損檢測研究。對多元散射校正(MSC)處理后的光譜分別利用連續(xù)投影算法(SPA)、競爭性自適應(yīng)重加權(quán)算法(CARS)和CARS-SPA方法篩選了哈密瓜可溶性固形物和硬度的特征波長,并將原始光譜、MSC預(yù)處理后的光譜和所篩選的特征波長作為輸入變量分別建立哈密瓜可溶性固形物和硬度的支持向量機(jī)(SVM)預(yù)測模型及成熟度判別模型。結(jié)果顯示,MSC-CARS-SPA方法所建立的可溶性固形物和硬度SVM預(yù)測模型最優(yōu),其Rpre,RMSEP和RPD分別為0.940 4,0.402 7,2.94 1和0.825 3,35.22,1.771。同時(shí)對哈密瓜成熟度進(jìn)行了判別分析,并分別建立了基于全光譜、單一的可溶性固形物或硬度特征波長和主成分分析(PCA)特征融合的哈密瓜成熟度SVM判別模型。結(jié)果顯示,CARS-PCASVM模型的判別結(jié)果與全光譜SNV-SVM模型相同,其校正集和預(yù)測集判別正確率分別為95%和94%。研究表明,利用高光譜技術(shù)結(jié)合特征波長篩選方法可實(shí)現(xiàn)同時(shí)對哈密瓜可溶性固形物和硬度的定量預(yù)測及成熟度判別。
[Abstract]:Soluble solids content (SSCS) and hardness are important indexes for the classification of Hami melon, and they are also the characterization factors of the maturity of Hami melon. Therefore, in order to satisfy the automatic grading and suitable picking of Hami melon, the method of hyperspectral technology combined with characteristic wavelength screening was adopted, and the content of soluble solids, hardness and maturity of Hami melon were studied by nondestructive testing. The spectral characteristics of soluble solids and hardness of Hami melon were screened by continuous projection algorithm (SPAS), competitive adaptive reweighting algorithm (CARSs) and CARS-SPA method. The prediction model of soluble solids and hardness of Hami melon by support vector machine (SVM) and the maturity discriminant model were established by using the pre-treated spectrum and the selected characteristic wavelength of MSC as input variables. The results show that the SVM prediction model of soluble solids and hardness developed by MSC-CARS-SPA method is the best. Its RPD and RMS EP are 0.940 4 ~ 0.402 7 ~ 2.941 and 0.825 ~ 3 ~ 35.225.221.771respectively. At the same time, discriminant analysis of Hami melon maturity was carried out, and the SVM discriminant model of Hami melon maturity was established based on full spectrum, single soluble solids or hardness characteristic wavelength and principal component analysis (PCA). The results show that the discriminant result of CARS-PCASVM model is the same as that of full-spectrum SNV-SVM model, and the accuracy of correction set and prediction set are 95% and 94% respectively. The results showed that the quantitative prediction and maturity discrimination of soluble solids and hardness of Hami melon could be realized by using hyperspectral technique combined with characteristic wavelength screening method.
【作者單位】： 石河子大學(xué)食品學(xué)院;石河子大學(xué)機(jī)械電氣工程學(xué)院;中國農(nóng)業(yè)大學(xué)工學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61263041) 國家科技支撐項(xiàng)目(2015BAD19B03)資助
【分類號(hào)】：O657.3;TS255.7

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