Near Infrared Spectroscopy in Poultry Breeding: Application Status

doi:10.11923/j.issn.2095-4050.cjas20190800156

Abstract

Abstract:

Near-infrared spectroscopy is a new method for rapid spectral analysis. With the development of spectroscopy and stoichiometry, near-infrared spectroscopy has been widely used in agriculture, food, medicine and petrochemical industries, which has greatly improved the informatization level of various industries. Based on the application status of near-infrared spectroscopy in poultry breeding, the development of near-infrared spectroscopy and its application in feed ingredient detection and poultry meat detection are summarized in this paper. Within the aspects of feed detection and poultry meat detection, a mathematical model with high precision and high stability for predicting was established by correlating the spectral information of the poultry meat sample with the reference value of the quality index. Compared with traditional methods, near-infrared spectroscopy has the advantages of providing large amount of information and fast calculation. This paper also summarizes the application status of near-infrared spectroscopy in feed quality detection and meat quality detection, and points out the possibility of large-scale application of near-infrared spectroscopy in poultry farming. The problems and future development of near-infrared spectroscopy are discussed; the results show that NIR spectroscopy has good development prospects and application value in feed and animal products’ detection.

Key words: Near Infrared Spectrum, Nondestructive Detection, Feed Detection, Poultry Meat Detection

CLC Number:

Niu Minglei, Hao Xin, Li Jianhua, Wang Junwei, Li Ping’an, Li Hua. Near Infrared Spectroscopy in Poultry Breeding: Application Status[J]. Journal of Agriculture, 2020, 10(2): 69-74.

Figures/Tables 3

References 42

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预处理方法	决定系数	标准差	交互验证决定系数	交互验证标准差	相对分析误差
First derivative	0.8201	0.0641	0.7412	0.0736	2.34
First derivative+ MSC	0.8909	0.0478	0.7456	0.0768	3.14
First derivative+ SNV	0.9043	0.0417	0.7397	0.0775	3.59
Second derivative	0.9399	0.0351	0.5564	0.0972	4.27
Second derivative+ MSC	0.9426	0.0347	0.7748	0.0761	4.32
Second derivative+ SNV	0.9399	0.0354	0.7156	0.0772	4.24

预处理方法	决定系数	标准差	交互验证决定系数	交互验证标准差	相对分析误差
First derivative	0.8201	0.0641	0.7412	0.0736	2.34
First derivative+ MSC	0.8909	0.0478	0.7456	0.0768	3.14
First derivative+ SNV	0.9043	0.0417	0.7397	0.0775	3.59
Second derivative	0.9399	0.0351	0.5564	0.0972	4.27
Second derivative+ MSC	0.9426	0.0347	0.7748	0.0761	4.32
Second derivative+ SNV	0.9399	0.0354	0.7156	0.0772	4.24

色泽参数	光谱范围	建模方法	最优波长筛选方法	最优波长/个	校正模型精度	预测模型精度	文献
L^*				8	R²_C=0.84, RMSEC=1.19	R²_P=0.94, RMSEP=1.21	[21]
a^*	400~1850 nm	PLSR	PCA	11	R²_C =0.83, RMSEC=0.42	R²_P=0.38, RMSEP=0.87
b^*				6	R²_C =0.78, RMSEC=0.66	R²_P=0.80, RMSEP=0.95
L^*	400~2500 nm	PLSR	MSC	6	R²_C =0.62, RMSECV=1.27	-	[22]
L^*				-	R_CV=0.69, RMSECV=1.73		[22]
a^*	350~1800 nm	PLSR	-	-	R_CV =0.88, RMSECV=0.29	-	[23]
b^*				-	R_CV =0.93, RMSECV=1.16
L^*				6	R²_C =0.91, RMSEC=1.95	R²_P =0.89, RMSEP=2.02
a^*	400~2500 nm	PLSR	Weighted Regression Coefficients	5	R²_C =0.78, RMSEC=0.50	R²_P =0.72, RMSEP=0.38	[32]
b^*				7	R²_C =0.83, RMSEC=1.84	R²_P =0.80, RMSEP=2.06
L^*				18	R²_C =0.81, RMSEC=1.67	R²_P=0.69, RMSEP=1.95
a^*	400~2500 nm	PLSR	RC	13	R²_C =0.83, RMSEC=0.38	R²_P=0.73, RMSEP=0.45	[25]
b^*				14	R²_C =0.75, RMSEC=0.78	R²_P=0.69, RMSEP=0.99
L^*				13	R_C=0.82, RMSEC=1.67	R_P=0.71, RMSEP=2.46
a^*	400~1000 nm	PLSR	RC	10	R_C=0.95, RMSEC=0.26	R_P=0.93, RMSEP=0.29	[26]
b^*				18	R_C=0.94, RMSEC=0.52	R_P=0.88, RMSEP=0.98	[26]
L^*	900~1000 nm	MLR	MSC	14		R_P=0.894, RMSEP=2.160
a^*
b^*			O-PLS	13		R_P=0.888, RMSEP=2.408N

色泽参数	光谱范围	建模方法	最优波长筛选方法	最优波长/个	校正模型精度	预测模型精度	文献
L^*				8	R²_C=0.84, RMSEC=1.19	R²_P=0.94, RMSEP=1.21	[21]
a^*	400~1850 nm	PLSR	PCA	11	R²_C =0.83, RMSEC=0.42	R²_P=0.38, RMSEP=0.87
b^*				6	R²_C =0.78, RMSEC=0.66	R²_P=0.80, RMSEP=0.95
L^*	400~2500 nm	PLSR	MSC	6	R²_C =0.62, RMSECV=1.27	-	[22]
L^*				-	R_CV=0.69, RMSECV=1.73		[22]
a^*	350~1800 nm	PLSR	-	-	R_CV =0.88, RMSECV=0.29	-	[23]
b^*				-	R_CV =0.93, RMSECV=1.16
L^*				6	R²_C =0.91, RMSEC=1.95	R²_P =0.89, RMSEP=2.02
a^*	400~2500 nm	PLSR	Weighted Regression Coefficients	5	R²_C =0.78, RMSEC=0.50	R²_P =0.72, RMSEP=0.38	[32]
b^*				7	R²_C =0.83, RMSEC=1.84	R²_P =0.80, RMSEP=2.06
L^*				18	R²_C =0.81, RMSEC=1.67	R²_P=0.69, RMSEP=1.95
a^*	400~2500 nm	PLSR	RC	13	R²_C =0.83, RMSEC=0.38	R²_P=0.73, RMSEP=0.45	[25]
b^*				14	R²_C =0.75, RMSEC=0.78	R²_P=0.69, RMSEP=0.99
L^*				13	R_C=0.82, RMSEC=1.67	R_P=0.71, RMSEP=2.46
a^*	400~1000 nm	PLSR	RC	10	R_C=0.95, RMSEC=0.26	R_P=0.93, RMSEP=0.29	[26]
b^*				18	R_C=0.94, RMSEC=0.52	R_P=0.88, RMSEP=0.98	[26]
L^*	900~1000 nm	MLR	MSC	14		R_P=0.894, RMSEP=2.160
a^*
b^*			O-PLS	13		R_P=0.888, RMSEP=2.408N

指标	光谱范围	建模方法	最优波长筛选方法	最优波长/个	校正模型精度	预测模型精度	文献
WBSF	400~1000 nm	PLSR	SPA	12	R_C=0.80, RMSEC=10.75N	R_P=0.74, RMSEP=11.15N	[35]
	350~1800 nm	SiPLSR	-	-	R_C=0.90	R_P=0.71, RMSEP=0.02%	[42]
系水力
	400~2500 nm	PLSR	Weighted regression Coefficients	4	R²_C=0.71, RMSEC=2.36%	R²_P=0.65, RMSEP=2.58%	[29]
	350~1800 nm	PLSR	-	-	R_CV=0.71, . RMSECV=0.09	-	[28]
	400~1000 nm	PLSR	RC	22	R_C=0.85, RMSEC=0.07	R_P=0.58, RMSEP=0.11	[31]
	400~900 nm	PLSR	CARS	20	R²_C =0.95, RMSEC=0.05	R²_P =0.94, RMSEP=0.06	[37]
PH
	400~2500 nm		Weighted regression Coefficients	9	R²_C =0.84, RMSEC=0.06	R²_P =0.71, RMSEP=0.08	[29]
Drip loss	400~2500 nm	PLSR	RC	16	R²_C =0.53, RMSEC=0.79%	R²_P =0.46, RMSEP=0.87%	[30]
嫩度	900~1700 nm	O-PLS	MFS	20	Rc =0.968. RMSEC=1.225N	R_P=0.948, RMSEP=1.596N	[32]
弹性	400~1000 nm	PLSR	SPA	10	R_C =0.85. RMSEC=0.16	R_P =0.84, RMSEP=0.16	[35]