Crops ›› 2021, Vol. 37 ›› Issue (2): 200-206.doi: 10.16035/j.issn.1001-7283.2021.02.029

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Establishment of Near-Infrared Spectroscopy Model for the Contents of Fat and Fatty Acids in Sunflower Husked Seeds

Zhou Fei1,2(), Wang Wenjun2, Liu Yan2, Ma Jun2, Wang Jing2, Wu Liren2, Guan Hongjiang2, Huang Xutang1,2   

  1. 1Heilongjiang Academy of Agricultural Sciences Postdoctoral Programme, Harbin 150086, Heilongjiang, China
    2Institute of Industrial Crops, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, Heilongjiang, China
  • Received:2020-06-04 Revised:2021-02-07 Online:2021-04-15 Published:2021-04-16

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Abstract:

For achieving rapid and non-destructive testing of sunflower quality, 50 representative oil sunflower materials were being selected. Partial least squares (PLS) was used to construct near-infrared spectroscopy (NIRS) model of the contents of fat, linoleic acid, oleic acid, stearic acid, and palmitic acid in husked seeds. The results showed that the correlation coefficients of calibration and validation of the models for fat, linoleic acid, and oleic acid contents were all greater than 0.96, and the relative errors between the predicted values and the chemical values were all less than 10%, which could meet the rapid determination of the samples composition content. The correlation coefficients of calibration and validation of the models for stearic acid and palmitic acid were 0.92 and 0.82, and the verified correlation coefficients were 0.83 and 0.74, respectively. And the relative errors between the predicted values and the chemical values was between 4.66%-17.99%, which could be used for the preliminary prediction of the sample's composition content. The NIRS model constructed in this study is helpful for the quality identification and rapid screening of oil sunflower germplasm resources in the future.

Key words: Oil sunflower, Husked seeds, Fat, Fatty acid, Near-infrared spectrum

Table 1

Statistical analysis of fat and fatty acids by chemical determination in sunflower seed kernels"

成分
Ingredient		 样品数
Sample number		 平均值
Mean
(%)		 变幅
Variation
range (%)		 标准差
SD
脂肪Fat 50 47.27 38.79~57.17 4.59
亚油酸（18∶2）
Linoleic acid		 50 64.16 51.56~70.60 4.31
油酸（18∶1）
Oleic acid		 50 24.92 19.00~38.84 4.90
硬脂酸（18∶0）
Stearic acid		 50 4.69 3.09~6.27 0.85
棕榈酸（16∶0）
Palmitic acid		 50 6.23 4.99~7.25 0.49

Fig.1

Original near-infrared spectroscopy of sunflower seed kernels"

Fig.2

Spectrum after pretreatment by four methods"

Table 2

Results of the models of fat content and four main fatty acids of seed kernels by using different pretreatment methods"

预处理方法
Pretreatment method		 校正相关系数R2
Correlation coefficients
of calibration		 校正均方
根误差
RMSEC		 验证相关系数R2
Correlation coefficients
of validation		 预测均方
根误差
RMSEP		 主成分数
Number of principal
components
脂肪Fat
原光谱The original spectrum 0.95 1.00 0.93 1.18 4
1st 0.96 0.98 0.94 1.16 4
SNV 0.95 0.97 0.93 1.26 5
1st+SNV 0.97 0.83 0.96 0.96 4
MSC+1st 0.98 0.70 0.97 0.88 4
亚油酸Linoleic acid
原光谱The original spectrum 0.96 0.74 0.91 1.10 9
1st 0.95 0.83 0.89 1.20 7
SNV 0.98 0.53 0.96 0.80 7
1st+SNV 0.97 0.61 0.95 0.83 7
MSC+1st 0.98 0.49 0.97 0.71 6
油酸Oleic acid
原光谱The original spectrum 0.91 1.50 0.83 2.12 10
1st 0.93 1.10 0.88 1.55 7
SNV 0.95 1.04 0.91 1.47 8
1st+SNV 0.98 0.68 0.96 0.99 5
MSC+1st 0.93 1.16 0.89 1.55 6
硬脂酸Stearic acid
原光谱The original spectrum 0.93 0.22 0.82 0.37 12
1st 0.92 0.25 0.83 0.37 9
SNV 0.64 0.51 0.46 0.66 8
1st+SNV 0.79 0.41 0.68 0.55 8
MSC+1st 0.75 0.40 0.51 0.59 7
棕榈酸Palmitic acid
原光谱The original spectrum 0.65 0.25 0.41 0.34 8
1st 0.58 0.25 0.41 0.30 5
SNV 0.82 0.18 0.74 0.23 5
1st+SNV 0.82 0.19 0.72 0.26 6
MSC+1st 0.75 0.22 0.62 0.28 6

Fig.3

Correlation diagrams of calibration (left) and prediction (right) for the optimum model of fat and four fatty acids in sunflower seed kernels"

Table 3

Comparison of the chemical values and the predicted values of near-infrared of ten materials %"

成分Ingredient 1 2 3 4 5 6 7 8 9 10
脂肪Fat 化学值Chemical value 42.35 50.47 48.22 55.67 44.58 50.27 49.84 53.16 40.38 45.72
预测值Predicted value 42.88 52.72 49.93 54.19 42.8 52.88 50.93 49.87 39.77 44.01
绝对误差Absolute error 0.53 2.25 1.71 1.48 1.78 2.61 1.09 3.29 0.61 1.71
相对误差Relative error 1.25 4.46 3.55 2.66 3.99 5.19 2.19 6.19 1.51 3.74
亚油酸Linoleic acid 化学值Chemical value 56.08 65.33 64.15 63.22 55.57 67.42 54.34 68.31 60.28 58.66
预测值Predicted value 59.23 69.29 61.87 60.98 54.01 64.88 55.28 66.56 58.44 60.43
绝对误差Absolute error 3.15 3.96 2.28 2.24 1.56 2.54 0.94 1.75 1.84 1.77
相对误差Relative error 5.62 6.06 3.55 3.54 2.81 3.77 1.73 2.56 3.05 3.02
油酸Oleic acid 化学值Chemical value 35.15 26.77 30.76 28.24 25.23 33.78 25.27 28.75 23.24 31.98
预测值Predicted value 34.22 25.45 31.43 29.01 24.32 32.36 23.21 27.69 25.11 31.02
绝对误差Absolute error 0.93 1.32 0.67 0.77 0.91 1.42 2.06 1.06 1.87 0.96
相对误差Relative error 2.65 4.93 2.18 2.73 3.61 4.20 8.15 3.69 8.05 3.00
硬脂酸Stearic acid 化学值Chemical value 5.28 4.61 5.89 3.77 6.01 5.17 4.73 3.58 4.39 4.55
预测值Predicted value 4.33 3.98 5.22 4.41 5.73 4.66 5.01 4.04 3.88 3.83
绝对误差Absolute error 0.95 0.63 0.67 0.64 0.28 0.51 0.28 0.46 0.51 0.72
相对误差Relative error 17.99 13.67 11.38 16.98 4.66 9.86 5.92 12.85 11.62 15.82
棕榈酸Palmitic acid 化学值Chemical value 6.25 5.63 5.66 6.92 5.39 6.57 5.01 6.13 7.04 5.38
预测值Predicted value 5.63 5.01 6.62 5.98 5.98 5.89 5.57 5.24 6.26 5.07
绝对误差Absolute error 0.62 0.62 0.96 0.94 0.59 0.68 0.56 0.89 0.78 0.31
相对误差Relative error 9.92 11.01 16.96 13.58 10.95 10.35 11.18 14.52 11.08 5.76
[1] 刘俊梅. 向日葵生产发展潜力初探. 现代农业, 2015(3):16-17.
[2] 赵坤. 五原县向日葵生产经济研究. 呼和浩特:内蒙古农业大学, 2013.
[3] 联合国粮食及农业组织. (2020-01-10) [2020-05-07]. http://www.fao.org/faostat/en/#data/QC, 2018.
[4] 侯来宝. 世界四大油料作物—向日葵. 呼和浩特: 内蒙古人民出版社, 2005: 39-41.
[5] 李添宝, 吴越, 罗敬. 利用核磁共振法定量分析植物油中多种脂肪酸及水含量. 食品科学, 2014,35(16):212-216.
[6] 黄瑞娟. 红外光谱技术在食品检测中的应用. 现代测量与实验室管理, 2015(1):9-14.
[7] 崔虎亮, 张亚楠, 贺霞, 等. 近红外光谱技术测定油料种子脂肪酸的研究进展. 中国种业, 2020(2):13-18.
[8] 李勇, 魏益民, 王锋, 等. 影响近红外光谱分析结果准确性的因素. 核农学报, 2005,19(3):236-240.
[9] 褚小立, 袁洪福, 陆婉珍. 基础数据准确性对近红外光谱分析结果的影响. 光谱学与光谱分析, 2005,25(6):886-889.
[10] 葛春芳, 张鉴. 气候条件对向日葵脂肪酸组成的影响. 辽宁农业科学, 1996(4):10-12.
[11] 王飞燕, 胡启林, 贾作忱, 等. 气候条件对油用向日葵籽实含油率的影响. 西北农业学报, 1996,5(1) : 63-66.
[12] 陈炳东, 岳云, 黄高宝, 等. 油葵含油率及脂肪酸组成与土壤盐含量的关系. 中国油料作物学报, 2007,29(4):483-486.
[13] 于海峰, 安玉麟, 李素萍, 等. 油用向日葵品质形成规律研究. 黑龙江农业科学, 2010(9):14-18.
[14] 樊双喜, 钟其顶, 李国辉, 等. 近红外光谱法快速检测黄酒中氨基酸态氮的模型优化研究. 酿酒科技, 2015(5):11-14.
[15] 杨传得, 唐月异, 王秀贞, 等. 傅立叶近红外漫反射光谱技术在花生脂肪酸分析中的应用. 花生学报, 2015,44(1):11-17.
[16] 何小三, 李博, 符树根, 等. 茶油脂肪酸组分近红外模型构建研究. 南方林业科学, 2018,46(5):13-19.
[17] Cui H L, Cheng F Y, Peng L P. Determination of the fatty acid composition in tree peony seeds using near-infrared spectroscopy. Journal of the American Oil Chemists' Society, 2016,93(7):943-952.
[18] 周菲, 黄绪堂, 王文军, 等. 食用向日葵粗蛋白含量近红外光谱模型的建立及可行性分析. 宁夏农林科技, 2015,56(7):35-37,47.
[19] Sato T, Takahata Y, Noda T, et al. Nondestructive determination of fatty acid composition of husked sunflower(Helianthus annua L.) seeds by near-infrared spectroscopy. Journal of the American Oil Chemists' Society, 1995,72(10):1177-1183.
[20] Velasco L, Pérez-Vich B, Fernández-Martínez J M. Use of near-infrared reflectance spectroscopy for selecting for high stearic acid concentration in single husked achenes of sunflower. Crop Science, 2004,44:93-97.
[21] Fassio A, Cozzolino D. Non-destructive prediction of chemical composition in sunflower seeds by near infraredSpectroscopy. Industrial Crops and Products, 2004,20(3):321-329.
[22] Pérez-Vich B, Velasco L, Fernández-Martínez J M. Determination of seed oil content and fatty acid compositionin sunflower through the analysis of intact seeds,husked seeds,meal and oil by near-infrared reflectance spectroscopy. Journal of the American Oil Chemists' Society, 1998,75(5):547-555.
[23] 汪磊, 谭美莲, 傅春玲, 等. 利用近红外技术预测向日葵籽仁品质性状. 中国油料作物学报, 2020,42(1):1-7.
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