Determination of Fiber Component Content in the Residual Branches of Fruit Trees in South Xinjiang Based on Near Infrared Spectroscopy

doi:10.6048/j.issn.1001-4330.2022.08.024

Abstract

Abstract:

【Objective】 The detection of fiber components in fruit tree stumps generally has the problems of time-consuming, complicated operation and high test cost. The research uses near-infrared spectroscopy technology combined with chemometrics to quickly detect the content of cellulose, hemicellulose and lignin in the stumps of fruit trees in southern Xinjiang. 【Method】 Taking 150 samples of fruit tree stumps collected from various parts of southern Xinjiang as the research object, using near-infrared spectroscopy technology combined with partial least squares（PLS）, Using different pre-processing and characteristic waveband screening methods to optimize the prediction model of the content of each fiber component. 【Result】 The three fiber component near-infrared detection models established by the SG convolution smoothing method preprocessing combined with the competitive adaptive weight sampling method（CARS） optimized feature band have the best effect, and the correlation coefficients r are 0.950,3, 0.948,7 and 0.937,1, respectively. The coefficients of determination R² are 0.900,8, 0.896,5, and 0.875,1, the corrected standard deviations RMSEC were 0.007,0, 0.005,4, and 0.005,1, and the predicted standard deviations RMSEP were 0.011,8, 0.008,9, and 0.008,8, respectively. 【Conclusion】 The use of near-infrared spectroscopy technology can achieve rapid quantitative detection of the three components of cellulose, hemicellulose and lignin in fruit tree stumps in South Xinjiang.

Key words: near infrared spectroscopy; fruit tree stumps; quantitative analysis; cellulose; hemicellulose; lignin

摘要：

【目的】研究运用近红外光谱技术结合化学计量学实现快速检测新疆南疆果树残枝中纤维素、半纤维素和木质素含量。【方法】以150个从新疆南疆各地采集的果树残枝样本为材料,利用近红外光谱技术结合偏最小二乘法（PLS）,采用不同的预处理和特征波段筛选方法优化各纤维组分含量的预测模型。【结果】SG卷积平滑法预处理结合竞争性自适应权重取样法（CARS）优选特征波段建立的3种纤维组分近红外检测模型效果最优,相关系数r分别为0.950 3、0.948 7和0.937 1,决定系数R²分别为0.900 8、0.896 5和0.875 1,校正标准偏差RMSEC分别为0.007 0、0.005 4和0.005 1,预测标准偏差RMSEP分别为0.011 8、0.008 9和0.008 8。【结论】采用近红外光谱技术能够实现新疆南疆果树残枝纤维素、半纤维素和木质素三组分的快速定量检测。

关键词: 近红外光谱技术, 果树残枝, 定量分析, 纤维素, 半纤维素, 木质素

CLC Number:

S66-3

GAO Qian, WANG Yamei, WU Pingfan, ZHANG Hongmei, ZHOU Ling. Determination of Fiber Component Content in the Residual Branches of Fruit Trees in South Xinjiang Based on Near Infrared Spectroscopy[J]. Xinjiang Agricultural Sciences, 2022, 59(8): 2025-2032.

高倩, 王亚梅, 吴平凡, 张红美, 周岭. 基于近红外光谱的果树残枝纤维组分含量分析[J]. 新疆农业科学, 2022, 59(8): 2025-2032.

Figures/Tables 9

References 19

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组分Composition	样本集 Sample set	样本数 Number of samples （个）	最小值 Minimum （%）	最大值 Max （%）	变幅 Luffing （%）	平均 Mean （%）	标准差 Standard deviation
纤维素 Cellulose	校正集	100	24.92	50.08	25.16	35.92	4.44
纤维素 Cellulose	验证集	35	28.76	47.48	18.72	36.91	4.32
半纤维素 Hemicellulose	校正集	100	9.92	31.88	21.96	20.08	4.53
半纤维素 Hemicellulose	验证集	37	14.68	27.1	12.42	21.64	3.25
木质素 Lignin	校正集	100	6.33	23.88	17.55	14.55	3.58
木质素 Lignin	验证集	35	10.48	20.63	10.15	14.15	2.95

组分Composition	样本集 Sample set	样本数 Number of samples （个）	最小值 Minimum （%）	最大值 Max （%）	变幅 Luffing （%）	平均 Mean （%）	标准差 Standard deviation
纤维素 Cellulose	校正集	100	24.92	50.08	25.16	35.92	4.44
纤维素 Cellulose	验证集	35	28.76	47.48	18.72	36.91	4.32
半纤维素 Hemicellulose	校正集	100	9.92	31.88	21.96	20.08	4.53
半纤维素 Hemicellulose	验证集	37	14.68	27.1	12.42	21.64	3.25
木质素 Lignin	校正集	100	6.33	23.88	17.55	14.55	3.58
木质素 Lignin	验证集	35	10.48	20.63	10.15	14.15	2.95

组分 Composition	预处理方法 Pretreatment method	相关系数 r	决定系数 R²	校正标准偏差 RMSECV	预测标准偏差 RMSEP	RPD
纤维素 Cellulose	原始光谱	0.830 4	0.680 3	0.012 6	0.019 4	1.77
	SG	0.860 1	0.716 4	0.010 8	0.018 2	1.88
	均值中心化	0.818 1	0.665 0	0.011 2	0.018 8	1.73
	一阶导	0.841 3	0.692 1	0.012 1	0.023 6	1.80
	SNV	0.846 2	0.705 6	0.011 6	0.019 1	1.84
半纤维素 Hemicellulose	原始光谱	0.812 3	0.552 9	0.017 6	0.028 1	1.50
	SG	0.836 4	0.585 5	0.011 2	0.018 2	1.56
	均值中心化	0.708 8	0.406 4	0.018 0	0.029 4	1.30
	一阶导	0.801 4	0.574 8	0.016 3	0.021 6	1.53
	SNV	0.828 6	0.578 1	0.012 1	0.019 6	1.54
木质素 Lignin	原始光谱	0.802 6	0.606 7	0.012 0	0.017 6	1.59
	SG	0.810 4	0.619 7	0.008 9	0.015 3	1.62
	均值中心化	0.758 9	0.430 1	0.011 6	0.019 8	1.32
	一阶导	0.808 7	0.602 3	0.013 4	0.018 9	1.59
	SNV	0.808 9	0.608 3	0.010 3	0.016 2	1.60

组分 Composition	预处理方法 Pretreatment method	相关系数 r	决定系数 R²	校正标准偏差 RMSECV	预测标准偏差 RMSEP	RPD
纤维素 Cellulose	原始光谱	0.830 4	0.680 3	0.012 6	0.019 4	1.77
	SG	0.860 1	0.716 4	0.010 8	0.018 2	1.88
	均值中心化	0.818 1	0.665 0	0.011 2	0.018 8	1.73
	一阶导	0.841 3	0.692 1	0.012 1	0.023 6	1.80
	SNV	0.846 2	0.705 6	0.011 6	0.019 1	1.84
半纤维素 Hemicellulose	原始光谱	0.812 3	0.552 9	0.017 6	0.028 1	1.50
	SG	0.836 4	0.585 5	0.011 2	0.018 2	1.56
	均值中心化	0.708 8	0.406 4	0.018 0	0.029 4	1.30
	一阶导	0.801 4	0.574 8	0.016 3	0.021 6	1.53
	SNV	0.828 6	0.578 1	0.012 1	0.019 6	1.54
木质素 Lignin	原始光谱	0.802 6	0.606 7	0.012 0	0.017 6	1.59
	SG	0.810 4	0.619 7	0.008 9	0.015 3	1.62
	均值中心化	0.758 9	0.430 1	0.011 6	0.019 8	1.32
	一阶导	0.808 7	0.602 3	0.013 4	0.018 9	1.59
	SNV	0.808 9	0.608 3	0.010 3	0.016 2	1.60

组分Composition	波段选择方法 Spectral wavelength selection method	变量数 Number of variables	相关系数 r	决定系数 R²	校正标准偏差 RMSECV	预测标准偏差 RMSEP	RPD
纤维素 Cellulose	全光谱建模	1557	0.860 1	0.716 4	0.0108	0.018 2	1.88
	SPA	44	0.890 4	0.783 1	0.010 5	0.0177	2.15
	CARS	28	0.950 3	0.900 8	0.007 0	0.0118	3.18
半纤维素 Hemicellulose	全光谱建模	1557	0.836 4	0.585 5	0.011 2	0.018 2	1.55
	SPA	52	0.843 8	0.634 0	0.010 5	0.017 1	1.65
	CARS	27	0.948 7	0.896 5	0.005 4	0.008 9	3.11
木质素 Lignin	全光谱建模	1557	0.810 4	0.619 7	0.008 9	0.015 3	1.62
	SPA	58	0.823 0	0.659 4	0.008 5	0.014 5	1.71
	CARS	21	0.937 1	0.875 1	0.005 1	0.008 8	2.83