基于光譜和水分補(bǔ)償方法的鮮棗內(nèi)部品質(zhì)檢測(cè)
發(fā)布時(shí)間：2018-09-10 08:18
【摘要】：為了建立穩(wěn)定可靠的鮮棗品質(zhì)檢測(cè)模型,利用光譜和水分補(bǔ)償方法進(jìn)行鮮棗內(nèi)部品質(zhì)的檢測(cè)。首先,針對(duì)鮮棗各品質(zhì)指標(biāo)(水分含量、可溶性固形物含量、維生素C含量、蛋白質(zhì)含量、硬度值),采用回歸系數(shù)法(RC)提取特征波段并建立最小二乘支持向量機(jī)(LS-SVM)檢測(cè)模型,預(yù)測(cè)集的決定系數(shù)(R2P)均在0.8261以上,預(yù)測(cè)均方根誤差(RMSEP)均在3.324 9以下。在提取各項(xiàng)品質(zhì)指標(biāo)特征波段的基礎(chǔ)上,剔除其他四項(xiàng)單一品質(zhì)特征波段中與水分特征波段(包含利用RC法所提取到的水分特征波長(zhǎng)和鮮棗中具有明顯水分特征的吸收峰)重疊或接近的波段,并與鮮棗水分含量值進(jìn)行數(shù)據(jù)融合建立了各項(xiàng)指標(biāo)的水分補(bǔ)償模型。結(jié)果表明,硬度值的水分補(bǔ)償模型精度有一定提高,R2P和RMSEP分別為0.830 5和0.055 3;可溶性固形物含量、維生素C含量、蛋白質(zhì)含量的水分補(bǔ)償模型精度均有所下降,R2P分別為0.804 1,0.878 2和0.837 8,RMSEP分別為1.347 3,0.638 0和3.503 2。然后,分析各品質(zhì)指標(biāo)間的相關(guān)性,結(jié)果表明,水分含量在0.05水平上與硬度值呈現(xiàn)顯著的相關(guān)性,在0.01的水平上與其余三項(xiàng)品質(zhì)指標(biāo)之間存在極顯著的相關(guān)性,相關(guān)性強(qiáng)弱與水分補(bǔ)償模型的建模結(jié)果相互支持。研究表明,水分補(bǔ)償法所建的預(yù)測(cè)模型可用于鮮棗內(nèi)部品質(zhì)的檢測(cè),水分含量與其他四項(xiàng)品質(zhì)指標(biāo)之間有相互作用并影響其他品質(zhì)指標(biāo)所建立的預(yù)測(cè)模型。該研究為進(jìn)一步探討光譜檢測(cè)中各內(nèi)部品質(zhì)指標(biāo)間交互作用的解耦提供了新思路。
[Abstract]:In order to establish a stable and reliable quality detection model of fresh jujube, the internal quality of fresh jujube was detected by spectrum and moisture compensation method. Firstly, aiming at the quality indexes (water content, soluble solids content, vitamin C content, protein content, hardness value) of fresh jujube, the characteristic bands were extracted by regression coefficient method (RC) and the detection model of least square support vector machine (LS-SVM) was established. The coefficient of determination (R2P) of prediction set is above 0.8261, and the root mean square error (RMSEP) of prediction is below 3.324 9. On the basis of extracting characteristic bands of each quality index, The other four single quality bands overlap or approach the water characteristic bands (including the water characteristic wavelengths extracted by RC method and the absorption peaks with obvious water characteristics in fresh jujube). The moisture compensation model of each index was established by data fusion with water content of fresh jujube. The results showed that the precision of water compensation model of hardness value was improved to some extent, the values of R2P and RMSEP were 0.830 5 and 0.055 3, respectively, the content of soluble solids and vitamin C were increased, The precision of water compensation model for protein content decreased to a certain extent. The R2P values of R2P were 0.804 ~ 0.878 2 and 0.837 ~ 8 ~ (-1) RMSEP of 1.347 ~ 3 ~ 0 ~ 0.638 0 and 3.503 ~ 2, respectively. Then, the correlation between each quality index was analyzed. The results showed that there was a significant correlation between water content at 0.05 level and hardness value, and a very significant correlation between water content and the other three quality indexes at 0.01 level. The modeling results of correlation and moisture compensation model support each other. The results show that the prediction model established by water compensation method can be used to detect the internal quality of fresh jujube, and there is interaction between water content and the other four quality indexes. This study provides a new idea for further exploring the decoupling of internal quality indexes in spectral detection.
【作者單位】： 山西農(nóng)業(yè)大學(xué)工學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(31271973)資助
【分類號(hào)】：O657.3;TS255.7
，
本文編號(hào)：2233887
[Abstract]:In order to establish a stable and reliable quality detection model of fresh jujube, the internal quality of fresh jujube was detected by spectrum and moisture compensation method. Firstly, aiming at the quality indexes (water content, soluble solids content, vitamin C content, protein content, hardness value) of fresh jujube, the characteristic bands were extracted by regression coefficient method (RC) and the detection model of least square support vector machine (LS-SVM) was established. The coefficient of determination (R2P) of prediction set is above 0.8261, and the root mean square error (RMSEP) of prediction is below 3.324 9. On the basis of extracting characteristic bands of each quality index, The other four single quality bands overlap or approach the water characteristic bands (including the water characteristic wavelengths extracted by RC method and the absorption peaks with obvious water characteristics in fresh jujube). The moisture compensation model of each index was established by data fusion with water content of fresh jujube. The results showed that the precision of water compensation model of hardness value was improved to some extent, the values of R2P and RMSEP were 0.830 5 and 0.055 3, respectively, the content of soluble solids and vitamin C were increased, The precision of water compensation model for protein content decreased to a certain extent. The R2P values of R2P were 0.804 ~ 0.878 2 and 0.837 ~ 8 ~ (-1) RMSEP of 1.347 ~ 3 ~ 0 ~ 0.638 0 and 3.503 ~ 2, respectively. Then, the correlation between each quality index was analyzed. The results showed that there was a significant correlation between water content at 0.05 level and hardness value, and a very significant correlation between water content and the other three quality indexes at 0.01 level. The modeling results of correlation and moisture compensation model support each other. The results show that the prediction model established by water compensation method can be used to detect the internal quality of fresh jujube, and there is interaction between water content and the other four quality indexes. This study provides a new idea for further exploring the decoupling of internal quality indexes in spectral detection.
【作者單位】： 山西農(nóng)業(yè)大學(xué)工學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(31271973)資助
【分類號(hào)】：O657.3;TS255.7
，
本文編號(hào)：2233887
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