Detection of Anthracnose in Camellia Oleifera Based on Laser-Induced Breakdown Spectroscopy

Yande Liu; Xue Gao; Mengjie Cheng; Zhaoguo Hou; Xiaodong Lin; Jia Xu

doi:10.3788/LOP57.093006

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 9 >Page 093006 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 9, 093006 (2020)

Detection of Anthracnose in Camellia Oleifera Based on Laser-Induced Breakdown Spectroscopy

Yande Liu^*, Xue Gao, Mengjie Cheng, Zhaoguo Hou, Xiaodong Lin, and Jia Xu

Author Affiliations

Institute of Optics Mechanics Electronics Technology and Application, East China Jiaotong University, Nanchang, Jiangxi 330013, China

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DOI: 10.3788/LOP57.093006 Cite this Article Set citation alerts

Yande Liu, Xue Gao, Mengjie Cheng, Zhaoguo Hou, Xiaodong Lin, Jia Xu. Detection of Anthracnose in Camellia Oleifera Based on Laser-Induced Breakdown Spectroscopy[J]. Laser & Optoelectronics Progress, 2020, 57(9): 093006 Copy Citation Text

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Fig. 1. Sample of camellia oleifera leaves. (a) Healthy camellia oleifera leaves; (b) infected anthracnose camellia oleifera leaves

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Fig. 2. PCR test results of camellia oleifera leaves

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Fig. 3. Original spectrum of camellia oleifera leaves after interception

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Fig. 4. Working curve of standard solution. (a) Healthy camellia oleifera leaves; (b) infected anthracnose camellia oleifera leaves

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Fig. 5. Location of characteristic line of Mn element

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Fig. 6. Comparison before and after data smoothing

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Fig. 7. PLS model and prediction model of Mn element after 7-point data smoothing and first derivative de-noising. (a) PLS model; (b)prediction model

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Fig. 8. Best sub-interval selected by the iPLS model. (a) Spectral graph of the 24th interval with RMSECV; (b) spectral graph corresponding to the 6th sub-interval

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Fig. 9. iPLS modeling set scatter diagram

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Fig. 10. iPLS prediction set scatter diagram

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Testcondition	Wavelength /nm	Lampcurrent /mA	Acetylene flowrate /(L·min^-1)	Airflowrate /(L·min^-1)	Slitwidth /nm
Parameter	279.5	3	1.3	7.5	0.2

Table 1. Determination conditions of Mn element

Sample	Category	Numberof samples	Rangevalue /(mg·mg^-1)	Averagevalue /(mg·mg^-1)
Healthy camelliaoleifera leaves	calibration set	181	1.0600-4.2570	2.2919
Healthy camelliaoleifera leaves	prediction set	59	1.4610-3.7240	2.2767
Camellia oleifera leaves with anthracnose	calibration set	157	0.7990-3.3290	1.4476
Camellia oleifera leaves with anthracnose	prediction set	51	1.0680-2.7880	1.3581

Table 2. Division of samples

Spectral pretreatmentmethod	Evaluationindex	Beforesmoothing	5 pointssmoothing	7 pointssmoothing	9 pointssmoothing
	R_C	0.8461	0.8621	0.8956	0.8760
Original	RMSECV /(μg·mg^-1)	0.2575	0.2499	0.2204	0.2433
	R_P	0.8195	0.8212	0.8540	0.8315
	RMSEP /(μg·mg^-1)	0.2769	0.2658	0.2548	0.2591
	R_C	0.8534	0.8562	0.8648	0.8605
Denoising	RMSECV /(μg·mg^-1)	0.2434	0.2375	0.2403	0.2482
	R_P	0.8162	0.8196	0.8329	0.8142
	RMSEP /(μg·mg^-1)	0.2553	0.2469	0.2373	0.2499
	R_C	0.8345	0.8440	0.8840	0.8396
Baseline correction	RMSECV /(μg·mg^-1)	0.2875	0.2570	0.1770	0.2764
	R_P /(μg·mg^-1)	0.8010	0.8219	0.8333	0.8019
	RMSEP /(μg·mg^-1)	0.3169	0.2722	0.2524	0.2911
	R_C	0.8584	0.8947	0.9025	0.8892
First derivativede-noising	RMSECV /(μg·mg^-1)	0.2414	0.2206	0.2192	0.2227
	R_P	0.8215	0.8519	0.8882	0.8352
	RMSEP /(μg·mg^-1)	0.2692	0.2474	0.2356	0.2339
	R_C	0.8523	0.8521	0.8507	0.8619
Second derivativede-noising	RMSECV /(μg·mg^-1)	0.2528	0.2532	0.2569	0.2354
	R_P	0.8190	0.8150	0.8377	0.8323
	RMSEP /(μg·mg^-1)	0.2784	0.2791	0.2774	0.2802
	R_C	0.8323	0.8389	0.8429	0.8413
Normalization	RMSECV /(μg·mg^-1)	0.2829	0.2705	0.2711	0.2514
	R_P	0.8189	0.8106	0.8254	0.8177
	RMSEP /(μg·mg^-1)	0.3062	0.3098	0.2955	0.3016

Table 3. PLS model results of smoothed data processed by different preprocessing methods

Interval number	Optimum principal component	RMSECV /(μg·mg^-1)	R	Optimum interval
1	7	0.2770	0.8304	1
2	9	0.2580	0.8548	1
3	10	0.2360	0.8807	1
4	12	0.2200	0.8966	1
5	6	0.2510	0.8632	2
6	6	0.2490	0.8660	2
7	6	0.2560	0.8577	2
8	6	0.2550	0.8589	2
9	7	0.2330	0.8843	3
10	8	0.2350	0.8832	3
11	7	0.2380	0.8797	3
12	7	0.2400	0.8769	3
13	7	0.2200	0.8966	4
14	6	0.2260	0.8918	4
15	7	0.2240	0.8931	4
16	7	0.2270	0.8899	4
17	6	0.2220	0.8945	5
18	6	0.2210	0.8961	5
19	7	0.2170	0.8999	5
20	8	0.2200	0.8979	5
21	6	0.2350	0.8816	6
22	8	0.2160	0.9016	6
23	8	0.2160	0.9013	6
24	8	0.2090	0.9076	6
25	6	0.2280	0.8889	7
26	7	0.2100	0.9068	7
27	6	0.2140	0.9027	7
28	6	0.2090	0.9067	7
29	8	0.2700	0.8414	7
30	8	0.2190	0.8996	8

Table 4. iPLS modeling analysis results

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