Near-infrared quantitative detection of fat in peeled Korean pine seeds based on manifold learning

doi:10.3969/j.issn.1004-1524.20221056

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (8): 1915-1926.DOI: 10.3969/j.issn.1004-1524.20221056

• Biosystems Engineering • Previous Articles Next Articles

Near-infrared quantitative detection of fat in peeled Korean pine seeds based on manifold learning

QIU Xunchao¹^,²(), CAO Jun²^,^*(), ZHANG Yizhuo²

1. Department of Computer Engineering, Harbin Finance University, Harbin 150030, China
2. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Received:2022-07-17 Online:2023-08-25 Published:2023-08-29

Abstract

Abstract:

Quantitative detection of fat in peeled Korean pine seeds is an important indicator of its edible and breeding value. In addition, near-infrared spectroscopy was used to detect nondestructively. Based on the result of standard normalized variate+first derivative+symlet4 (SNV+1^st-Der+Sym4) pretreatment method, considering the traditional dimensionality reduction, principal components analysis (PCA) has some problems, such as insensitive to nonlinear complex structures and removing the linear correlation information between features completely. Isometric mapping (Isomap), locally linear embedding (LLE), modified locally linear embedding (MLLE), local tangent space alignment (LTSA) and Hessian based locally linear embedding (HLLE) were used to reduce dimensions separately. Taking the model which was established by partial least square (PLS) as the calibration model. Furthermore, ridge regression (Ridge), support vector regression (SVR) and extreme gradient boosting (XGBoost) were adopted to establish mathematical models, respectively. As shown by the results, the quality of the parameter optimization model established by MLLE+SVR was the best, and the optimized parameters were as follows: neighborhood number (neighbors) was 30 and dimension (components) was 16, and the mean value of mean squared error of validation (mean-MSEV) was 0.646 4, and the mean relative error (MRE) of test set was 0.999 2%. Therefore, the MLLE+SVR model can be well applied to the quantitative detection of fat in peeled Korean pine seeds.

Key words: peeled Korean pine seeds, fat, manifold learning, near-infrared spectroscopy

CLC Number:

S126
S58

QIU Xunchao, CAO Jun, ZHANG Yizhuo. Near-infrared quantitative detection of fat in peeled Korean pine seeds based on manifold learning[J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1915-1926.

Figures/Tables 13

References 25

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切分次数 Number of segmentation	样本集 Sample set	脂肪Fat/%
切分次数 Number of segmentation	样本集 Sample set	最大值 Maximum	最小值 Minimum	均值 Mean	标准差 Standard deviation
1	训练集Training set	69.93	60.04	63.48	2.27
	验证集Validation set	67.64	60.24	62.86	1.90
2	训练集Training set	69.93	60.04	63.25	2.19
	验证集Validation set	68.87	60.46	63.78	2.27
3	训练集Training set	69.93	60.04	63.33	2.23
	验证集Validation set	69.59	60.55	63.48	2.17
4	训练集Training set	69.93	60.04	63.26	2.20
	验证集Validation set	69.21	60.40	63.75	2.24
5	训练集Training set	69.93	60.04	63.36	2.17
	验证集Validation set	69.59	60.23	63.35	2.46
6	训练集Training set	69.93	60.04	63.25	2.57
	验证集Validation set	69.59	60.37	63.79	2.57
7	训练集Training set	69.93	60.04	63.34	2.24
	验证集Validation set	68.18	60.23	63.45	2.11
8	训练集Training set	69.93	60.04	63.45	2.22
	验证集Validation set	68.87	60.37	62.99	2.15
9	训练集Training set	69.93	60.04	63.38	2.22
	验证集Validation set	68.26	60.24	63.29	2.21
10	训练集Training set	69.93	60.04	63.34	2.15
	验证集Validation set	69.59	60.23	63.43	2.53

切分次数 Number of segmentation	样本集 Sample set	脂肪Fat/%
切分次数 Number of segmentation	样本集 Sample set	最大值 Maximum	最小值 Minimum	均值 Mean	标准差 Standard deviation
1	训练集Training set	69.93	60.04	63.48	2.27
	验证集Validation set	67.64	60.24	62.86	1.90
2	训练集Training set	69.93	60.04	63.25	2.19
	验证集Validation set	68.87	60.46	63.78	2.27
3	训练集Training set	69.93	60.04	63.33	2.23
	验证集Validation set	69.59	60.55	63.48	2.17
4	训练集Training set	69.93	60.04	63.26	2.20
	验证集Validation set	69.21	60.40	63.75	2.24
5	训练集Training set	69.93	60.04	63.36	2.17
	验证集Validation set	69.59	60.23	63.35	2.46
6	训练集Training set	69.93	60.04	63.25	2.57
	验证集Validation set	69.59	60.37	63.79	2.57
7	训练集Training set	69.93	60.04	63.34	2.24
	验证集Validation set	68.18	60.23	63.45	2.11
8	训练集Training set	69.93	60.04	63.45	2.22
	验证集Validation set	68.87	60.37	62.99	2.15
9	训练集Training set	69.93	60.04	63.38	2.22
	验证集Validation set	68.26	60.24	63.29	2.21
10	训练集Training set	69.93	60.04	63.34	2.15
	验证集Validation set	69.59	60.23	63.43	2.53

模型 Model	最优参数 Optimal parameter	维度 Dimension	mean-MSEV	mean-PCCV
PLS	components=9	9	1.519 2	0.813 3
PCA+Ridge	contribution=0.99	15/16	1.771 2	0.788 2
PCA+SVR	contribution=0.85	3	1.481 9	0.824 0
PCA+XGBoost	contribution=0.96	7	1.887 9	0.745 8
Isomap+Ridge	components=18, neighbors=50	18	1.413 3	0.833 5
Isomap+SVR	components=3, neighbors=90	3	1.436 9	0.825 4
Isomap+XGBoost	components=10, neighbors=100	10	1.403 7	0.838 2
LLE+Ridge	components=18, neighbors=70	18	0.797 4	0.874 7
LLE+SVR	components=16, neighbors=60	16	0.682 2	0.883 7
LLE+XGBoost	components=14, neighbors=60	14	0.989 5	0.851 3
MLLE+Ridge	components=18, neighbors=30	18	0.709 3	0.881 7
MLLE+SVR	components=16, neighbors=30	16	0.646 4	0.914 5
MLLE+XGBoost	components=16, neighbors=80	16	1.072 7	0.849 4
LTSA+Ridge	components=18, neighbors=60	18	0.759 9	0.879 6
LTSA+SVR	components=16, neighbors=20	16	0.743 5	0.880 2
LTSA+XGBoost	components=8, neighbors=70	8	1.193 7	0.841 8
HLLE+Ridge	components=10,neighbors=80	10	0.979 4	0.867 9
HLLE+SVR	components=8, neighbors=50	8	0.830 8	0.874 1
HLLE+XGBoost	components=8, neighbors=70	8	1.193 7	0.841 8

模型 Model	最优参数 Optimal parameter	维度 Dimension	mean-MSEV	mean-PCCV
PLS	components=9	9	1.519 2	0.813 3
PCA+Ridge	contribution=0.99	15/16	1.771 2	0.788 2
PCA+SVR	contribution=0.85	3	1.481 9	0.824 0
PCA+XGBoost	contribution=0.96	7	1.887 9	0.745 8
Isomap+Ridge	components=18, neighbors=50	18	1.413 3	0.833 5
Isomap+SVR	components=3, neighbors=90	3	1.436 9	0.825 4
Isomap+XGBoost	components=10, neighbors=100	10	1.403 7	0.838 2
LLE+Ridge	components=18, neighbors=70	18	0.797 4	0.874 7
LLE+SVR	components=16, neighbors=60	16	0.682 2	0.883 7
LLE+XGBoost	components=14, neighbors=60	14	0.989 5	0.851 3
MLLE+Ridge	components=18, neighbors=30	18	0.709 3	0.881 7
MLLE+SVR	components=16, neighbors=30	16	0.646 4	0.914 5
MLLE+XGBoost	components=16, neighbors=80	16	1.072 7	0.849 4
LTSA+Ridge	components=18, neighbors=60	18	0.759 9	0.879 6
LTSA+SVR	components=16, neighbors=20	16	0.743 5	0.880 2
LTSA+XGBoost	components=8, neighbors=70	8	1.193 7	0.841 8
HLLE+Ridge	components=10,neighbors=80	10	0.979 4	0.867 9
HLLE+SVR	components=8, neighbors=50	8	0.830 8	0.874 1
HLLE+XGBoost	components=8, neighbors=70	8	1.193 7	0.841 8

Near-infrared quantitative detection of fat in peeled Korean pine seeds based on manifold learning

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