油桐籽不同年份和含油率差异对其含油率NIR检测模型影响的研究
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摘要
收集了2014和2015年的油桐籽样本,用偏最小二乘法(PLS)分别建立了单一年份、混合年份及单一年份不同含油率范围的油桐籽含油率近红外光谱(NIR)检测模型,并验证。结果显示单一年份模型对本年份样本有较好预测,而对另一单一年份样本的预测精度明显下降,而混合年份模型对各年份样本都有较好预测;同年份不同含油率范围样本所建模型,含油率范围大,则模型预测精度下降,但稳定性更好。利用竞争性自适应重加权(CARS)算法筛选出30个变量,并结合PLS对混合年份样本建模,既简化了模型,又提高了模型预测性能,验证集相关系数为0.929,均方根误差为1.765,相对标准偏差为3.31%。因此,建立油桐籽含油率NIR检测模型时,应收集不同年份、不同含油率范围样本,并结合特征波长,以建立预测精度更好、稳定可靠且适应范围广的检测模型。
The samples of Vernicia fordii Seeds from 2014 to 2015 were collected.Partial least squares(PLS)was used to establish NIR calibration models for oil content of Vernicia fordii seeds by the samples from single year,hybrid years and the samples of the different oil content range from the same year,respectively,and then the models were used to predict the different validation samples.The results showed that the models developed by the samples from particular year could be effectively applied to the samples from same year but not another year,however,the models developed by the samples from hybrid years could effectively applied to all the samples and the models developed by the samples of the larger oil content range showed worse prediction,but better robustness than that developed by the samples of the smaller range.The model could not only be effectively simplified,but also be taken for improvement using 30 characteristic variables selected by Competitive adaptive reweighted sampling(CARS).The correlation coefficient(Rp),root mean square error(RMSEP)and relative standard deviation(RSDp)of validation set were 0.929,1.765 and 3.31% for oil content.It was found that the robust and effective model of oil content of Vernicia fordii seeds could be developed by more samples of different year and larger oil content range when combined with characteristic variables.
The samples of Vernicia fordii Seeds from 2014 to 2015 were collected.Partial least squares(PLS)was used to establish NIR calibration models for oil content of Vernicia fordii seeds by the samples from single year,hybrid years and the samples of the different oil content range from the same year,respectively,and then the models were used to predict the different validation samples.The results showed that the models developed by the samples from particular year could be effectively applied to the samples from same year but not another year,however,the models developed by the samples from hybrid years could effectively applied to all the samples and the models developed by the samples of the larger oil content range showed worse prediction,but better robustness than that developed by the samples of the smaller range.The model could not only be effectively simplified,but also be taken for improvement using 30 characteristic variables selected by Competitive adaptive reweighted sampling(CARS).The correlation coefficient(Rp),root mean square error(RMSEP)and relative standard deviation(RSDp)of validation set were 0.929,1.765 and 3.31% for oil content.It was found that the robust and effective model of oil content of Vernicia fordii seeds could be developed by more samples of different year and larger oil content range when combined with characteristic variables.
引文
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[2]文珊娜.油桐36个优良家系主要经济性状研究[D].长沙:中南林业科技大学,2013:31WEN S N.The study of major economic traits in the 36 Tung oil tree excellent families[D].Changsha:Central South University of Forestry&Technology,2013:31
[3]刘金龙,郑小江,郑威,等.油桐品种五爪桐含油量及桐油质量研究[J].湖北农业科学,2011,50(10):2031-2035LIU J L,ZHENG X J,ZHENG W,et al.Study on the oil content and quality of Vernicia fordii cv.five Fingernail Tung[J].Hubei Agricultural Sciences,2011,50(10):2031-2035
[4]高建芹,张洁夫,浦惠明,等.NIR法在测定油菜籽含油量及主要脂肪酸中的应用[J].江苏农业学报,2007,23(3):189-195GAO J Q,ZHANG J F,PU H M,et al.Analysis of oil,oleic acid and erucic acid contents in rapeseed by near infrared reflectance spectroscopy(NIR)[J].Jiangsu Journal of Agricultural Science,2007,23(3):189-195
[5]杨小红,郭玉秋,傅旸,等.利用NIR法分析玉米籽粒主要脂肪酸含量的研究[J].光谱学与光谱分析,2009,29(1):106-109YANG X H,GUO Y Q,FU Y,et al.Measuring fatty acid concentration in maize grain by near-infrared reflectance spectroscopy[J].Spectroscopy and Spectral Analysis,2009,29(1):106-109
[6]HAN S I,CHAE J H,BILYEU K,et al.Non-destructive determination of high oleic acid content in single soybean seeds by near infrared reflectance spectroscopy[J]. Journal of the American Oil Chemists'Society(JAOCS),2014,91(2):229-234
[7]ALFRED Q,HUANG Z G,WU J G,et al.Estimation of oil content and fatty acid composition in cottonseed kernel powder using near infrared reflectance spectroscopy[J].Journal of the American Oil Chemists'Society(JAOCS),2012,89:567-575
[8]TILLMAN B L,GORBET D W,PERSON G.Predicting oleic and linoleic acid content of single peanut seeds using nearinfrared reflectance spectroscopy[J].Crop Science,2006,46(5):2121-2126
[9]原姣姣,王成章,陈虹霞,等.近红外漫反射光谱法测定油茶籽含油量的研究[J].林产化学与工业,2011,31(3):28-32YUAN J J,WANG C Z,CHEN H X,et al.Determination of oil content of Camellia oleifera Abel.seeds by near infrared reflectance spectroscopy[J].Chemistry and Industry of Forest Products,2011,31(3):28-32
[10]奚如春,钟燕梅,邓小梅,等.基于NIR的油茶种子含油量定标模型构建[J].林业科学,2013,49(4):1-6XI R C,ZHONG Y M,DENG X M,et al.Models for determining oil contents in Camellia oleifera seeds by using near infrared spectroscopy[J].Scientia Silvae Sinicae,2013,49(4):1-6
[11]李水芳,李一帆,付红军,等.油桐籽含油率近红外光谱检测模型的构建[J].林业工程学报,2017,2(6):45-49LI S F,LI Y F,FU H J,et al.Determination of Oil Content of Vernicia fordii Seeds by Near Infrared Spectroscopy[J].Journal of Forestry Engineering,2017,2(6):45-49
[12]诸小立.化学计量学方法与分子光谱分析技术[M].北京:化学工业出版社,2011:61,263ZHU X L.Molecular spectroscopy analytical technology combined with chemometrics and its applications[M].Beijing:Chemical Industry Press,2011:61,263
[13]LI H D,LIANG Y Z,XU Q S,et al.Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J].Analytica Chimica Acta,2009,64:77-84
[14]GARCIA-ALVAREZ M,HUIDOBRO J F,HEMIDA M,et al.Major components of honey analysis by near-infrared transflectance spectroscopy[J].J.Agric.Food Chem.,2000,48:5154-5158
[15]陆婉珍.现代近红外光谱分析技术[M].第二版,北京:中国石化出版社,2007:30LU W Z.Modern near infrared spectroscopy analytical technology[M].Second edition,Beijing:Chemical Industry Press,2007:30.