近红外光谱技术在家禽养殖领域应用现状

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

农学学报 ›› 2020, Vol. 10 ›› Issue (2): 69-74.doi: 10.11923/j.issn.2095-4050.cjas20190800156

• 农业工程农业机械生物技术食品科学 • 上一篇下一篇

近红外光谱技术在家禽养殖领域应用现状

牛明雷¹, 郝炘²(), 李建华³, 王俊伟⁴, 李平安⁵, 李华⁶

¹ 农业农村部工程建设服务中心,北京 100081
² 天津农垦渤海农业集团有限公司,天津 301823
³ 天津市宝坻区朝霞街道办事处,天津 301800
⁴ 北京市植物保护站,北京 100029
⁵ 湖南省益阳市桃江县农业局,湖南益阳 413499
⁶ 内蒙古自治区赤峰市宁城县植保植检站,内蒙古赤峰 024200

收稿日期:2019-08-15 修回日期:2019-08-29 出版日期:2020-02-24 发布日期:2020-02-24
通讯作者: 郝炘 E-mail:wolf_wild@163.com
作者简介:牛明雷,男,1976年出生,北京人,研究生,研究方向：农业农村项目管理。通信地址：100081 北京市朝阳区朝新嘉园7区7号楼,Tel：13811162966,E-mail：26114798@qq.com。

Near Infrared Spectroscopy in Poultry Breeding: Application Status

Niu Minglei¹, Hao Xin²(), Li Jianhua³, Wang Junwei⁴, Li Ping’an⁵, Li Hua⁶

¹ Construction Service Center, Ministry of Agriculture and Rural Areas, Beijing 100081, China
² Tianjin Agricultural Reclamation Bohai Agricultural Group Co., Ltd., Tianjin 301823, China
³ Zhaoxia Street Office, Baodi District, Tianjin 301800, China
⁴ Beijing Plant Protection Station, Beijing 100029, China
⁵ Agricultural Bureau of Taojiang County, Yiyang 413499, Hunan, China
⁶ Plant Protection and Inspection Station of Ningcheng County, Chifeng 024200, Inner Mongolia Autonomous Region, China

Received:2019-08-15 Revised:2019-08-29 Online:2020-02-24 Published:2020-02-24
Contact: Xin Hao E-mail:wolf_wild@163.com

摘要/Abstract

摘要：

近红外光谱是一种快速光谱分析的新方法,伴随着光谱技术与化学计量法的发展,近红外光谱已广泛应用于农业、食品、医药、石油化工等行业,极大地提高了各行业信息化水平。基于近红外光谱在家禽养殖领域的应用现状,本文总结了近红外光谱技术的发展及其在饲料成分检测和禽肉检测中的应用。在饲料检测和家禽肉类检测方面,通过将禽肉样品的光谱信息与质量指标的参考值相关联,建立了预测饲料和待测肉类高精度、高稳定性的数学模型。与传统方法相比,近红外光谱技术具有信息量大、数据计算速度快等优点。本文还归纳了近红外光谱技术在饲料品质检测和肉类品质检测的应用现状,指出了近红外光谱在家禽养殖领域的大规模应用推广的可能性。同时指出近红外光谱技术存在的问题和未来发展方向,结果表明近红外光谱技术在饲料和畜产品检测领域具有良好的发展前景和应用价值。

关键词: 近红外光谱, 无损检测, 饲料检测, 禽肉检测

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

中图分类号:

牛明雷, 郝炘, 李建华, 王俊伟, 李平安, 李华. 近红外光谱技术在家禽养殖领域应用现状[J]. 农学学报, 2020, 10(2): 69-74.

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.

图/表 3

参考文献 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]

近红外光谱技术在家禽养殖领域应用现状

Near Infrared Spectroscopy in Poultry Breeding: Application Status

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[2]	周成河,the,near-infrared,reflection,spectra,analysis,in,fertilizer,applicationdevelopment,direction周成河. 探究近红外反射光谱分析在土壤肥料学的应用及发展方向[J]. 农学学报, 2015, 5(11): 49-52.