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基于可見-近紅外光譜的鮮食葡萄成熟品質(zhì)關(guān)鍵指標(biāo)檢測(cè)
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國(guó)家自然科學(xué)基金項(xiàng)目(32201678)和中央高?;A(chǔ)科研業(yè)務(wù)費(fèi)專項(xiàng)資金項(xiàng)目(2452020201)


Detection of Key Indicators of Ripening Quality in Table Grapes Based on Visible-near-infrared Spectroscopy
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    摘要:

    酚類物質(zhì)是評(píng)價(jià)葡萄成熟品質(zhì)的重要指標(biāo),本文利用可見-近紅外光譜技術(shù)結(jié)合化學(xué)計(jì)量學(xué)定量分析方法對(duì)葡萄皮總酚、籽總酚、皮單寧和籽單寧含量開展了無(wú)損檢測(cè)研究。通過手持式可見-近紅外光譜儀采集巨玫瑰葡萄波長(zhǎng)400~1029nm范圍內(nèi)的漫反射光譜,采用SPXY算法將其劃分為校正集和預(yù)測(cè)集,結(jié)合標(biāo)準(zhǔn)正態(tài)變換(Standard normal variate,SNV)、多元散射校正(Multiplicative scatter correction,MSC)、一階導(dǎo)數(shù)(First derivative,1D)、二階導(dǎo)數(shù)(Second derivative,2D)、Savitzky-Golay卷積平滑(Savitzky-Golay smoothing ,SG)和Savitzky-Golay卷積平滑+一階導(dǎo)數(shù)(SG+1D)6種預(yù)處理方法以及偏最小二乘回歸(Partial least squares regression,PLSR)、支持向量機(jī)回歸(Support vector machine regression,SVR)和卷積神經(jīng)網(wǎng)絡(luò)(Convolutional neural network,CNN)3種建模算法,分別建立了基于全波段和特征波長(zhǎng)的葡萄皮總酚、籽總酚、皮單寧和籽單寧定量預(yù)測(cè)模型并進(jìn)行綜合對(duì)比分析。結(jié)果表明,對(duì)于皮總酚、籽總酚和籽單寧,經(jīng)特征波長(zhǎng)篩選后建立的模型效果優(yōu)于全波段,而對(duì)于皮單寧,全波段建立的模型較特征波長(zhǎng)效果更佳;因此,在預(yù)測(cè)皮總酚、籽總酚、皮單寧和籽單寧含量時(shí),最優(yōu)模型分別為RAW-CARS-SVR、1D-CARS-SVR、RAW-CNN和RAW-CARS-PLSR,校正集相關(guān)系數(shù)(Correlation coefficient of calibration set,Rc)分別為0.96、0.99、0.96和0.91,預(yù)測(cè)集相關(guān)系數(shù)(Correlation coefficient of prediction set,Rp)分別為0.95、0.99、0.83和0.89,剩余預(yù)測(cè)偏差(Residual predictive deviation,RPD)分別為3.56、7.30、1.92和2.25。因此,結(jié)合可見-近紅外光譜和合適的回歸模型,可以實(shí)現(xiàn)對(duì)巨玫瑰葡萄的皮-籽總酚、皮-籽單寧含量的無(wú)損檢測(cè)。

    Abstract:

    Phenolic compounds play a crucial role in assessing the internal quality of grapes and hold significant importance in this regard. The capability of visible-near-infrared (Vis-NIR) spectroscopy combined with multivariate regression models was explored to detect the contents of total phenolics and tannins in grape skins and seeds. Reflectance spectra data of Muscat Kyoho grapes were collected within the wavelength range of 400nm to 1029nm, and the samples were divided into correction set and prediction set by SPXY algorithm. Six commonly used preprocessing methods such as standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (1D), second derivative (2D), Savitzky-Golay smoothing (SG) and SG+1D were applied to the spectral data, and the competitive adaptive reweighted sampling algorithm (CARS) was utilized to select informative wavelengths. The quantitative models for comprehensive analysis of total phenolics and tannins in grape skins and seeds based on full spectra and effective wavelengths were established by partial least squares regression (PLSR), support vector machine regression (SVR), and convolutional neural network (CNN). The results showed that for the total phenolics in grape skins, total phenolics and tannins in grape seeds, the models on the basis of effective wavelengths performed better than those with full spectra data. While for the tannins in grape skins, the models constructed with full spectra yielded better results than the feature wavelength-selected models. The optimal models for the total phenolics and tannins in grape skins and seeds were RAW-CARS-SVR, 1D-CARS-SVR, RAW-CNN and RAW-CARS-PLSR, respectively. The correlation coefficent of calibration set (Rc) were 0.96, 0.99, 0.96 and 0.91, the correlation coefficent of prediction set (Rp) were 0.95, 0.99, 0.83 and 0.89, the residual predictive deviation (RPD) were 3.56, 7.30, 1.92 and 2.25, respectively. Therefore, the developed method could realize the non-destructive detection of the contents of total phenolics and tannins in grape skins and seeds.

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劉文政,周雪健,平鳳嬌,蘇媛,鞠延侖,房玉林,楊繼紅.基于可見-近紅外光譜的鮮食葡萄成熟品質(zhì)關(guān)鍵指標(biāo)檢測(cè)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(2):372-383. LIU Wenzheng, ZHOU Xuejian, PING Fengjiao, SU Yuan, JU Yanlun, FANG Yulin, YANG Jihong. Detection of Key Indicators of Ripening Quality in Table Grapes Based on Visible-near-infrared Spectroscopy[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(2):372-383.

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  • 收稿日期:2023-12-25
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  • 在線發(fā)布日期: 2024-02-10
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