摘要
马铃薯是与小麦、稻米、玉米协调发展的第四大主粮作物,现阶段我国正积极推进马铃薯主食开发,但马铃薯品质的参差不齐严重制约了马铃薯产业主食化进程,马铃薯品质快速无损检测对其加工产业化进程有着重要意义。国内外学者基于可见/近红外光谱对马铃薯内部品质检测进行了不少相关研究,但迄今为止大部分研究都基于可见/近红外漫反射原理,马铃薯粗糙的表皮对样品漫反射光谱影响较大。近红外透射光谱能较好的反映样品的品质信息,但马铃薯样品全透射光谱因样品大小不同,导致光谱受光程差异的影响较大。考虑到马铃薯样品整体质地较为均匀,根据马铃薯的形状特性搭建了马铃薯局部透射光谱采集系统,局部透射检测方式既能避免马铃薯表皮的影响,又能在保证光程统一的情况下获得样品内部的信息。该光谱采集系统由光谱采集单元(光谱仪、耦合透镜)与光源单元(卤素灯、灯杯)构成。进行光谱采集时,将二者贴紧马铃薯表面以确保光谱采集单元不会接收到来自马铃薯表面的反射光。用该系统采集了120个马铃薯650～1 100 nm范围的局部透射光谱,分别进行去趋势(detrend)、多元散射校正(muliplication scattering correction,MSC)、标准正态变量变换(standard normal variable transformation,SNV)和一阶导数(first Derivative,FD)预处理,并建立了马铃薯干物质、淀粉、还原糖含量的偏最小二乘预测模型(partial least squares regression,PLSR)。结果显示,采用多元散射校正预处理的干物质和淀粉含量预测模型效果较好,其验证集决定系数分别为0. 854 0和0. 851 0,验证集均方根误差分别为0. 521 9%和0. 484 8%;采用一阶导数预处理的还原糖预测模型效果最好,其验证集决定系数为0. 768 6,均方根误差为0. 025 1%。为进一步优化模型采用竞争性自适应重加权采样(competitive adaptive reweighted sampling,CARS)等三种方法进行特征波长的筛选,并建立了偏最小二乘预测模型。结果显示,马铃薯各品质参数的预测效果均得到了较大提升,CARS筛选波长后的干物质、淀粉、还原糖预测模型的验证集决定系数分别为0. 877 6,0. 865 3和0. 887 7,验证集均方根误差分别为0. 449 2%,0. 930 2%和0. 016 7%。采用CARS特征波长提取能够简化模型,去除无关变量和共线性变量,从而提高模型的精度和稳定性,尤其是对低含量组分还原糖的预测模型效果显著。最后,为验证马铃薯各品质参数预测模型的精度及稳定性,选取30个不同批次马铃薯样品对所建预测模型进行了外部验证。马铃薯干物质、淀粉、还原糖含量的模型预测值与标准理化值决定系数分别为0. 849 9,0. 867 1,0. 877 6,均方根误差分别为0. 660 9,0. 480 9,0. 016 9,平均相对误差分别为2. 03%,1. 77%,7. 58%。研究表明,局部透射光谱携带了马铃薯的内部信息,与干物质、淀粉、还原糖含量有显著相关性。该可见/近红外局部透射检测系统可以实现马铃薯多品质参数的快速无损预测,特别是干物质含量及淀粉含量的预测效果较好,但是对个别还原糖含量非常低的样品出现预测相对误差较大现象,下一步研究中需要进一步优化完善。
Potato is the fourth important grain crop coordi nated with wheat,rice and corn.At present,China is actively promoting the dev elopment of potato staple foods,but the uneven quality of potatoes has seriousl y hampered the process of the main food industry of the potatoes.Therefore,rapid non-destructive testing of potato quality is of great significance to the in dustrialization of processing.Domestic and foreign scholars have conducted a number of related researches on the detection of potato internal quality based on the visible/near-infrared diffuse reflectance principle.This method is commonl y used,but the rough skin of the potato has a great impact on the detection.An other detection method is the transmission spectrum.This method can better refl ect the internal quality information of the sample.However,the total transmiss ion spectrum of the potato varies with the size of the sample and results in alarge change in spectral intensity.Considering the above two reasons and average quality of potato,this study uses partial transmission spectrum as the detecti on method.This method can not only avoid the influence of the potato epidermis,but also obtain the internal information of the sample while maintaining the sa me path length.The spectral acquisition system consists of spectral acquisition units(spectroscopes and coupling lenses)and light source units(halogen lamps and lamp cups)which are arranged side by side.During testing,the two parts a re attached to the sample surface to ensure that the spectral acquisition unit does not receive reflected light from the potato surface.Based on this system,partial transmission spectra of 120 potatoes are collected ranging from 650 to 1 100 nm.The prediction model of dry matter,starch and reducing sugar content was established using partial least squares regression after pretreat by detrend,multivariate scattering correction(MSC),standard normal variable transformatio n(SNV)and first-order derivative(FD).The result shows that the prediction m odels of dry matter and starch content using multiple scatter correction pretrea tment are effective.The determination coefficients of validation set are 0.854 0 and 0.85 1 0,respectively,and the root mean square errors are 0.521 9%and 0.484 8%,respectively.The reducing sugar prediction model using first-order derivat ive pretreatment has the best result.The determination coefficients of validation set is 0.768 6 and the root mean square error is 0.025 1%.In order to optimize the model,three methods such as competitive adaptive reweighted sampling(CARS)are used to filter the characteristic wavelengths,and an optimized par tial least-square prediction model is established.The result shows that the prediction effect of potato quality parameters has been greatly improved.The de termination coefficient of validation sets for dry matter,starch,and reducing sugar prediction models after CARS screening are 0.877 6,0.865 3 and 0.88 7 7,respectively.And the root mean square errors of the validation set are 0.449 2%,0.930 2%and 0.016 7%,respectively.The use of CARS feature wavel ength extraction can simplify the model and remove irrelevant variables and coll inearity variables.This will improve the accuracy and stability of the model,especially for low-component content parameters such as reducing sugars.Fina lly,in order to verify the robust of the potato quality parameters prediction model,30 potato samples are selected for external validation of the prediction model.The determination coefficients between model predicted values and standard physicochemical values of potato dry matter,starch,and reducing sugar are 0.849 9,0.867 1,and 0.877 6,respectively.The root mean square errors are 0.660 9,0.480 9,and 0.016 9,respectively.The average relativeerrors are 2.03%,1.77%and 7.58%,respectively.The present study shows that the partial transmission spectrum carries the internal information of the pota to and it is significantly related to the contents of dry matter,starch,and reducing sugar.The visible/near-infrared partial transmission detection system can achieve rapid and non-destructive prediction of multi-parameters of pota toes,especially good prediction results of dry matter content and starch content,but there is a large relative error in the prediction of individual samples with very low levels of reducing sugars.The next step of the study needs further optimization and improvement.
作者
王凡
李永玉
彭彦昆
杨炳南
李龙
刘亚超
WANG Fan;LI Yong-yu;PENG Yan-kun;YANG Bing-nan;LI Long;LIU Ya-chao(College of Engineering,China Agricultural University,National Research and Development Center for Agro-processing Equipment,Beijing 100083,China;Chinese Acadey of Agricultural Mechanization Sciences,Beijing 100083,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2018年第12期3736-3742,共7页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划项目(2016YFD0401300)
国家科技支撑项目(2014BAD04B05)资助。
关键词
马铃薯
干物质
淀粉
还原糖
可见/近红外局部透射光谱
Potato
Dry matter
Starch
Reducing sugar
Visible/near-infrared partial transmission spectrum
