基于CARS-PLSR的油茶种仁油脂含量和脂肪酸成分的近红外光谱预测模型构建

doi:10.11707/j.1001-7488.LYKX20240779

Abstract

Abstract:

Objective: This study aims to develop a low-cost, non-destructive, accurate, and batch method for detecting the oil content and fatty acid composition of Camellia oleifera kernels, to improve the evaluation efficiency of the oil traits. Method: The oil content in kernels of 220 C. oleifera clones was determined by Soxhlet extraction, and the fatty acid composition was determined by gas chromatography, respectively. The near infrared spectra of the kernels in the wavelength range of 1000?2500 nm were collected. After preprocessing the spectral data using 9 methods, the samples were divided into calibration and prediction sets at a ratio of 4:1 by random sampling (RS) and sample set partitioning based on joint X-Y distance (SPXY), respectively. The competitive adaptive reweighted sampling (CARS) was used to select the key wavelengths that were significantly correlated with the oil traits of C. oleifera from the spectral data, and the partial least squares regression (PLSR) prediction models were established for determining the oil content and fatty acid composition of C. oleifera kernels. Result: The variation ranges of oil content and the content of seven fatty acids (C16:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:1) were in accordance with or close to normal distribution. The established models for predicting oil content had good accuracy and stability. With the RS samples dividing method, the pretreatment method of standard normal variate (SNV) was optimal. With 14 key wavelengths selected, a prediction model of oil content was established with the relative percent deviation (RPD) of 5.2055, prediction set determination coefficient ($R_{\mathrm{p}}^2 $) and root mean square error (RMSE_p) of 0.965 1 and 1.854 8 g·(100 g)^?1, respectively. With the SPXY samples dividing method, the optimal SNV + first derivative (FD) pretreatment, and 25 key wavelengths selected, another prediction model of oil content was established with a RPD of 3.417 0, prediction set $R_{\mathrm{p}}^2 $ and RMSE_p of 0.916 8 and 2.622 4 g·(100 g)^?1, respectively. The models for C18:1, C18:2 and C18:3 contents were optimal under the RS method using second derivative (SD), SNV and continuum removal (CR) pretreatment methods, respectively, with RPD values of 1.939 4, 2.116 4 and 2.338 1, $R_{\mathrm{p}}^2 $ values of 0.738 5, 0.775 4 and 0.831 6, and RMSE_p values of 1.707 1%, 1.370 2% and 0.049 2%, respectively. Conclusion: The prediction model for oil content of C. oleifera kernels has been constructed based on near-infrared spectroscopy in this study. This model has high accuracy and good stability, and can be used for rapid, batch and non-destructive detection of oil content of C. oleifera kernels. The prediction models for C18:1, C18:2 and C18:3 contents can be used for preliminary prediction of unsaturated fatty acid. This study can provide scientific basis for rapid detection of oil content, fatty acid composition and other traits of C. oleifera by near-infrared spectroscopy technology.

Key words: Camellia oleifera kernel, oil content, fatty acid, near infrared spectroscopy, prediction model

CLC Number:

Huiqi Zhong,Jingyu Chai,Kailiang Wang,Jianhua Teng,Wenyu Bi,Anni Wang,Ping Lin. Construction of Near Infrared Spectroscopy Prediction Models Based on CARS-PLSR for Determining Oil Content and Fatty Acid Composition of Camellia oleifera Kernel[J]. Scientia Silvae Sinicae, 2026, 62(2): 111-125.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Table 1

Distribution of oil content and fatty acid content in the calibration and prediction sample sets"

项目Item		RS法划分样本集 Partitioning sample sets with RS		SPXY算法划分样本集 Partitioning sample sets with SPXY
项目Item		校准集Calibration set	预测集Prediction set	校准集Calibration set	预测集Prediction set
样本数量 Sample number		160	40	160	40
油脂含量 Oil content (OC)/ [g·(100 g)^?1]	最小值Minimum	16.01	16.32~19.56	16.01~16.32	16.01~20.11
	最大值Maximum	51.96~55.22	50.33~55.22	55.22	51.90~53.68
	平均值Mean	38.64~39.23	37.37~39.74	38.15~39.16	37.65~41.68
	标准差Standard deviation	8.59~9.16	7.35~9.80	8.80~9.22	6.97~8.96
棕榈酸 Palmitic acid (C16:0) (%)	最小值Minimum	7.09	7.59~8.34	7.09	7.59~8.39
	最大值Maximum	11.30~11.40	10.60~11.40	11.40	10.60~11.20
	平均值Mean	9.41~9.46	9.34~9.54	9.42~9.48	9.26~9.50
	标准差Standard deviation	0.86~0.90	0.56~0.77	0.88~0.90	0.57~0.69
棕榈烯酸 Palmitoleic acid (C16:1) (%)	最小值Minimum	0.05	0.06~0.90	0.06	0.06~0.09
	最大值Maximum	0.31	0.18~0.23	0.32	0.20
	平均值Mean	0.14	0.13~0.14	0.13~0.14	0.12~0.13
	标准差Standard deviation	0.04~0.05	0.02~0.04	0.04~0.05	0.02~0.03
硬脂酸 Stearic acid (C18:0) (%)	最小值Minimum	0.89	1.14~1.34	0.89	1.14~1.23
	最大值Maximum	2.73	2.14~2.30	2.73	2.14~2.29
	平均值Mean	1.73~1.76	1.64~1.75	1.73~1.74	1.68~1.76
	标准差Standard deviation	0.31~0.32	0.21~0.27	0.31~0.32	0.21~0.27
油酸 Oleic acid (C18:1) (%)	最小值Minimum	69.10, 69.30	69.10~73.50	69.10	69.30~73.30
	最大值Maximum	84.60	82.50~84.10	84.60	82.30~82.60
	平均值Mean	77.29~77.52	77.34~78.27	77.35~77.49	77.46~78.05
	标准差Standard deviation	3.32~3.48	2.42~3.31	3.41~3.50	2.44~2.95
亚油酸 Linoleic acid (C18:2) (%)	最小值Minimum	4.71, 5.65	4.71~6.80	4.71	5.65~6.63
	最大值Maximum	18.10	15.10~16.90	18.10	13.80~18.00
	平均值Mean	10.12~10.40	9.84~10.99	10.30~10.52	9.36~10.26
	标准差Standard deviation	2.73~2.96	2.08~2.90	2.84~2.94	2.00~2.58
亚麻酸 Linolenic acid (C18:3) (%)	最小值Minimum	0.13	0.20~0.22	0.13	0.21~0.23
	最大值Maximum	0.92	0.55~0.70	0.92	0.43~0.67
	平均值Mean	0.37~0.38	0.33~0.37	0.37~0.38	0.31~0.35
	标准差Standard deviation	0.13~0.14	0.07~0.17	0.13~0.14	0.05~0.11
顺-11-二十碳烯酸 Cis-11-eicosenoic acid (C20:1) (%)	最小值Minimum	0.43, 0.43	0.43~0.47	0.43	0.44~0.47
	最大值Maximum	0.83	0.62~0.70	0.83	0.636~0.70
	平均值Mean	0.54~0.55	0.52~0.55	0.54~0.55	0.53~0.54
	标准差Standard deviation	0.06~0.07	0.05~0.06	0.06~0.07	0.04~0.06

Table 1

Fig.4

Fig.5

Table 2

Fig.6

Table 3

Table 4

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样本集划分方法 Partitioning sample sets method	预处理方法 Preprocessing method	波长选择数量 Number of wavelengths selected	潜变量数量 Number of latent variables	相对分析误差Relative percent deviation (RPD)	校准集 Calibration set		校准集交叉验证 Cross validation		预测集 Prediction set
样本集划分方法 Partitioning sample sets method	预处理方法 Preprocessing method	波长选择数量 Number of wavelengths selected	潜变量数量 Number of latent variables	相对分析误差Relative percent deviation (RPD)	$R_{\mathrm{c}}^2 $	RMSE_c/ [g·(100 g)^?1]	$R_{\mathrm{cv}}^2 $	RMSE_cv/ [g·(100 g)^?1]	$R_{\mathrm{p}}^2 $	RMSE_p/ [g·(100 g)^?1]
随机抽样法 Random sampling (RS)	RAW	16	12	4.014 1	0.884 4	2.958 8	0.853 0	3.336 6	0.937 0	2.283 9
	SG	23	12	3.884 6	0.878 2	3.037 7	0.848 7	3.385 7	0.934 8	2.377 6
	FD	28	15	3.549 0	0.904 8	2.779 0	0.876 2	3.169 8	0.918 8	2.259 6
	SD	17	13	4.117 2	0.900 0	2.766 4	0.877 9	3.057 0	0.953 1	2.211 4
	SNV	14	13	5.205 5	0.910 1	2.579 9	0.890 5	2.847 1	0.965 1	1.854 8
	SNV+SG	31	14	4.698 3	0.908 7	2.723 1	0.884 6	3.060 5	0.953 6	1.703 2
	SNV+FD	34	15	4.914 1	0.919 0	2.478 7	0.892 7	2.852 5	0.959 3	1.856 2
	SNV+SD	19	14	5.009 5	0.917 5	2.460 4	0.892 1	2.814 3	0.959 4	1.955 9
	CR	22	11	3.227 4	0.892 9	2.989 4	0.849 5	3.543 1	0.902 4	2.276 2
	CR+SG	16	10	3.610 1	0.875 8	3.175 2	0.850 0	3.488 6	0.924 0	2.184 5
光谱?理化值共生距离算法Sample set partitioning based on joint X-Y distance (SPXY)	RAW	18	12	2.696 4	0.914 2	2.693 9	0.894 7	2.984 7	0.859 3	2.621 8
	SG	16	15	2.493 6	0.895 7	2.969 2	0.869 5	3.321 5	0.839 0	2.806 2
	FD	21	11	2.637 5	0.894 0	2.940 7	0.868 7	3.272 6	0.865 6	2.950 8
	SD	21	14	3.359 6	0.912 2	2.679 4	0.883 0	3.092 0	0.915 9	2.338 7
	SNV	12	12	2.920 2	0.910 6	2.740 1	0.894 3	2.978 9	0.882 3	2.467 0
	SNV+SG	16	15	3.092 4	0.922 9	2.553 4	0.906 6	2.808 8	0.893 0	2.253 8
	SNV+FD	25	15	3.417 0	0.924 1	2.416 2	0.902 0	2.745 2	0.916 8	2.622 4
	SNV+SD	19	12	3.432 5	0.925 8	2.429 4	0.904 6	2.755 5	0.913 5	2.268 2
	CR	16	11	2.208 5	0.907 6	2.777 5	0.889 3	3.039 9	0.801 1	3.322 0
	CR+SG	16	8	2.192 2	0.897 4	2.929 6	0.878 6	3.187 4	0.802 8	3.326 9

脂肪酸 Fatty acid	预处理方法 Preprocessing method	波长选择数量 Number of wavelengths selected	潜变量数量 Number of latent variables	相对分析误差Relative percent deviation (RPD)	校准集 Calibration set		校准集交叉验证Cross validation		预测集 Prediction set
脂肪酸 Fatty acid	预处理方法 Preprocessing method	波长选择数量 Number of wavelengths selected	潜变量数量 Number of latent variables	相对分析误差Relative percent deviation (RPD)	$R_{\mathrm{c}}^2 $	RMSE_c (%)	$R_{\mathrm{cv}}^2 $	RMSE_cv (%)	$R_{\mathrm{p}}^2 $	RMSE_p (%)
棕榈酸 Palmitic acid (C16:0)	SD	31	14	1.408 6	0.485 8	0.616 8	0.3162	0.711 3	0.545 3	0.543 9
棕榈烯酸 Palmitoleic acid (C16:1)	SNV	20	8	1.493 5	0.377 5	0.034 7	0.233 5	0.038 5	0.605 8	0.022 1
硬脂酸 Stearic acid (C18:0)	SD	14	11	1.648 9	0.408 7	0.237 8	0.307 5	0.257 3	0.631 7	0.164 2
油酸 Oleic acid (C18:1)	SD	25	12	1.939 4	0.556 1	2.207 4	0.429 0	2.503 3	0.738 5	1.707 1
亚油酸 Linoleic acid (C18:2)	SNV	29	14	2.116 4	0.530 7	1.866 8	0.356 8	2.185 4	0.775 4	1.370 2
亚麻酸 Linolenic acid (C18:3）	CR	20	13	2.338 1	0.622 0	0.083 5	0.508 2	0.095 2	0.831 6	0.049 2
顺-11-二十碳烯酸 Cis-11-eicosenoic acid （C20:1）	SD	16	13	1.683 1	0.489 3	0.046 5	0.374 5	0.051 5	0.644 0	0.034 7

脂肪酸 Fatty acid	预处理方法 Preprocessing method	波长选择数量 Number of wavelengths selected	潜变量数量 Number of latent variables	相对分析误差Relative percent deviation (RPD)	校准集 Calibration set		校准集交叉验证Cross validation		预测集 Prediction set
脂肪酸 Fatty acid	预处理方法 Preprocessing method	波长选择数量 Number of wavelengths selected	潜变量数量 Number of latent variables	相对分析误差Relative percent deviation (RPD)	$R_{\mathrm{c}}^2 $	RMSE_c (%)	$R_{\mathrm{cv}}^2 $	RMSE_cv (%)	$R_{\mathrm{p}}^2 $	RMSE_p (%)
棕榈酸 Palmitic acid (C16:0)	SD	28	15	1.254 2	0.467 5	0.642 0	0.265 1	0.754 3	0.358 0	0.507 8
棕榈烯酸 Palmitoleic acid (C16:1)	FD	14	11	1.242 0	0.340 4	0.036 2	0.248 1	0.038 6	0.413 5	0.023 5
硬脂酸 Stearic acid (C18:0)	SD	16	13	1.265 8	0.471 6	0.228 7	0.362 0	0.251 3	0.374 4	0.188 8
油酸 Oleic acid (C18:1)	SD	30	14	1.946 3	0.596 3	2.160 0	0.459 8	2.498 6	0.735 0	1.513 3
亚油酸 Linoleic acid (C18:2)	CR	24	14	1.864 5	0.553 3	1.928 7	0.412 9	2.211 1	0.733 2	1.182 2
亚麻酸 Linolenic acid (C18:3)	SD	19	14	1.910 4	0.654 6	0.080 8	0.564 8	0.090 7	0.735 6	0.055 3
顺-11-二十碳烯酸 Cis-11-eicosenoic acid (C20:1)	SNV+SD	21	13	1.348 6	0.557 8	0.043 5	0.444 7	0.048 8	0.480 1	0.041 7

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Construction of Near Infrared Spectroscopy Prediction Models Based on CARS-PLSR for Determining Oil Content and Fatty Acid Composition of Camellia oleifera Kernel

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