Journal of Agriculture ›› 2020, Vol. 10 ›› Issue (2): 69-74.doi: 10.11923/j.issn.2095-4050.cjas20190800156
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Niu Minglei1, Hao Xin2(), Li Jianhua3, Wang Junwei4, Li Ping’an5, Li Hua6
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
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.
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URL: http://nxxb.caass.org.cn/EN/10.11923/j.issn.2095-4050.cjas20190800156
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 | R2C=0.84, RMSEC=1.19 | R2P=0.94, RMSEP=1.21 | [21] | |||
a* | 400~1850 nm | PLSR | PCA | 11 | R2C =0.83, RMSEC=0.42 | R2P=0.38, RMSEP=0.87 | |
b* | 6 | R2C =0.78, RMSEC=0.66 | R2P=0.80, RMSEP=0.95 | ||||
L* | 400~2500 nm | PLSR | MSC | 6 | R2C =0.62, RMSECV=1.27 | - | [22] |
L* | - | RCV=0.69, RMSECV=1.73 | |||||
a* | 350~1800 nm | PLSR | - | - | RCV =0.88, RMSECV=0.29 | - | [23] |
b* | - | RCV =0.93, RMSECV=1.16 | |||||
L* | 6 | R2C =0.91, RMSEC=1.95 | R2P =0.89, RMSEP=2.02 | ||||
a* | 400~2500 nm | PLSR | Weighted Regression Coefficients | 5 | R2C =0.78, RMSEC=0.50 | R2P =0.72, RMSEP=0.38 | [32] |
b* | 7 | R2C =0.83, RMSEC=1.84 | R2P =0.80, RMSEP=2.06 | ||||
L* | 18 | R2C =0.81, RMSEC=1.67 | R2P=0.69, RMSEP=1.95 | ||||
a* | 400~2500 nm | PLSR | RC | 13 | R2C =0.83, RMSEC=0.38 | R2P=0.73, RMSEP=0.45 | [25] |
b* | 14 | R2C =0.75, RMSEC=0.78 | R2P=0.69, RMSEP=0.99 | ||||
L* | 13 | RC=0.82, RMSEC=1.67 | RP=0.71, RMSEP=2.46 | ||||
a* | 400~1000 nm | PLSR | RC | 10 | RC=0.95, RMSEC=0.26 | RP=0.93, RMSEP=0.29 | [26] |
b* | 18 | RC=0.94, RMSEC=0.52 | RP=0.88, RMSEP=0.98 | ||||
L* | 900~1000 nm | MLR | MSC | 14 | RP=0.894, RMSEP=2.160 | ||
a* | |||||||
b* | O-PLS | 13 | RP=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 | R2C=0.84, RMSEC=1.19 | R2P=0.94, RMSEP=1.21 | [21] | |||
a* | 400~1850 nm | PLSR | PCA | 11 | R2C =0.83, RMSEC=0.42 | R2P=0.38, RMSEP=0.87 | |
b* | 6 | R2C =0.78, RMSEC=0.66 | R2P=0.80, RMSEP=0.95 | ||||
L* | 400~2500 nm | PLSR | MSC | 6 | R2C =0.62, RMSECV=1.27 | - | [22] |
L* | - | RCV=0.69, RMSECV=1.73 | |||||
a* | 350~1800 nm | PLSR | - | - | RCV =0.88, RMSECV=0.29 | - | [23] |
b* | - | RCV =0.93, RMSECV=1.16 | |||||
L* | 6 | R2C =0.91, RMSEC=1.95 | R2P =0.89, RMSEP=2.02 | ||||
a* | 400~2500 nm | PLSR | Weighted Regression Coefficients | 5 | R2C =0.78, RMSEC=0.50 | R2P =0.72, RMSEP=0.38 | [32] |
b* | 7 | R2C =0.83, RMSEC=1.84 | R2P =0.80, RMSEP=2.06 | ||||
L* | 18 | R2C =0.81, RMSEC=1.67 | R2P=0.69, RMSEP=1.95 | ||||
a* | 400~2500 nm | PLSR | RC | 13 | R2C =0.83, RMSEC=0.38 | R2P=0.73, RMSEP=0.45 | [25] |
b* | 14 | R2C =0.75, RMSEC=0.78 | R2P=0.69, RMSEP=0.99 | ||||
L* | 13 | RC=0.82, RMSEC=1.67 | RP=0.71, RMSEP=2.46 | ||||
a* | 400~1000 nm | PLSR | RC | 10 | RC=0.95, RMSEC=0.26 | RP=0.93, RMSEP=0.29 | [26] |
b* | 18 | RC=0.94, RMSEC=0.52 | RP=0.88, RMSEP=0.98 | ||||
L* | 900~1000 nm | MLR | MSC | 14 | RP=0.894, RMSEP=2.160 | ||
a* | |||||||
b* | O-PLS | 13 | RP=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 | RC=0.80, RMSEC=10.75N | RP=0.74, RMSEP=11.15N | [35] |
350~1800 nm | SiPLSR | - | - | RC=0.90 | RP=0.71, RMSEP=0.02% | [42] | |
系水力 | |||||||
400~2500 nm | PLSR | Weighted regression Coefficients | 4 | R2C=0.71, RMSEC=2.36% | R2P=0.65, RMSEP=2.58% | [29] | |
350~1800 nm | PLSR | - | - | RCV=0.71, . RMSECV=0.09 | - | [28] | |
400~1000 nm | PLSR | RC | 22 | RC=0.85, RMSEC=0.07 | RP=0.58, RMSEP=0.11 | [31] | |
400~900 nm | PLSR | CARS | 20 | R2C =0.95, RMSEC=0.05 | R2P =0.94, RMSEP=0.06 | [37] | |
PH | |||||||
400~2500 nm | Weighted regression Coefficients | 9 | R2C =0.84, RMSEC=0.06 | R2P =0.71, RMSEP=0.08 | [29] | ||
Drip loss | 400~2500 nm | PLSR | RC | 16 | R2C =0.53, RMSEC=0.79% | R2P =0.46, RMSEP=0.87% | [30] |
嫩度 | 900~1700 nm | O-PLS | MFS | 20 | Rc =0.968. RMSEC=1.225N | RP=0.948, RMSEP=1.596N | [32] |
弹性 | 400~1000 nm | PLSR | SPA | 10 | RC =0.85. RMSEC=0.16 | RP =0.84, RMSEP=0.16 | [35] |
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 | RC=0.80, RMSEC=10.75N | RP=0.74, RMSEP=11.15N | [35] |
350~1800 nm | SiPLSR | - | - | RC=0.90 | RP=0.71, RMSEP=0.02% | [42] | |
系水力 | |||||||
400~2500 nm | PLSR | Weighted regression Coefficients | 4 | R2C=0.71, RMSEC=2.36% | R2P=0.65, RMSEP=2.58% | [29] | |
350~1800 nm | PLSR | - | - | RCV=0.71, . RMSECV=0.09 | - | [28] | |
400~1000 nm | PLSR | RC | 22 | RC=0.85, RMSEC=0.07 | RP=0.58, RMSEP=0.11 | [31] | |
400~900 nm | PLSR | CARS | 20 | R2C =0.95, RMSEC=0.05 | R2P =0.94, RMSEP=0.06 | [37] | |
PH | |||||||
400~2500 nm | Weighted regression Coefficients | 9 | R2C =0.84, RMSEC=0.06 | R2P =0.71, RMSEP=0.08 | [29] | ||
Drip loss | 400~2500 nm | PLSR | RC | 16 | R2C =0.53, RMSEC=0.79% | R2P =0.46, RMSEP=0.87% | [30] |
嫩度 | 900~1700 nm | O-PLS | MFS | 20 | Rc =0.968. RMSEC=1.225N | RP=0.948, RMSEP=1.596N | [32] |
弹性 | 400~1000 nm | PLSR | SPA | 10 | RC =0.85. RMSEC=0.16 | RP =0.84, RMSEP=0.16 | [35] |
[1] |
褚小立, 陆婉珍 . 近五年中国近红外光谱分析技术研究与应用进展[J]. 光谱学与光谱分析, 2014,34(10):2595-2605.
doi: 10.3964/j.issn.1000-0593(2014)10-2595-11 URL |
[2] | Zabihi M, Rad A B, Kiranyaz S , et al. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation[C]// 43rd Computing in Cardiology Conference, 2016,43:613-616. |
[3] | 牛晓颖, 邵利敏, 赵志磊 , 等. 基于BP-ANN的草莓品种近红外光谱无损鉴别方法研究[J]. 光谱学与光谱分析, 2012,32(8):2095-2099. |
[4] | 王丽杰 . 快速检测牛奶成分的近红外光谱测量方法及系统研究[D]. 哈尔滨:哈尔滨理工大学, 2006 |
[5] | 蔡雪珍 . 基于近红外光谱技术分析的鲜食葡萄果实的无损检测与品质鉴定[D]. 合肥:安徽农业大学, 2015. |
[6] | 郑先哲, 张强, 刘成海 , 等. 用于检测贮藏稻谷中霉菌指标的便携式近红外光谱分析仪,CN204008454U[P]. 2014. |
[7] | 姜小龙 . 云平台下光谱数据快速无损压缩技术的研究[D]. 长沙:湖南师范大学, 2015. |
[8] | 李军 . 基于小波变换的高光谱特征提取的分解尺度的确定方法[D]. 北京:北京大学, 2007. |
[9] | 王世霞 . 金刚石压腔结合拉曼光谱技术进行稳定同位素分馏系数测定的实验研究[D]. 北京:北京大学, 2011. |
[10] | 田琳 . C70分子中的Jahn-Feller效应,BerryPhae和最低激子光谱研究[D]. 北京:北京大学, 1997. |
[11] | 于新洋 . 线性渐变滤光片型近红外水果品质分析仪及应用研究[D]. 长春:中国科学院长春光学精密机械与物理研究所, 2016. |
[12] | 郑明 . 《便携式土壤成分快速测定仪研制开发》研究成果通过科技部验收[J]. 河南农业大学学报, 2002(3):301. |
[13] | 张志敏, 梁逸曾, 郑宜报 , 等. 可靠准确的化学计量学软件开发基础研究[C]// 第十二届全国计算(机)化学学术会议,苏州, 2013: 40-42. |
[14] | 张宁, 张德权, 李淑荣 , 等. 近红外光谱定性分析技术在食品安全中的应用研究进展[J]. 食品科技, 2008,33(8):218-221. |
[15] | 张荣, 吴文娟 . 近红外光谱技术的定性和定量分析[J]. 化工时刊, 2011,25(9):36-38. |
[16] | 杨增玲, 韩鲁佳, 刘贤 , 等. 鱼粉中肉骨粉的可见-近红外光谱快速定性判别方法[J]. 农业工程学报, 2009,25(7):308-311. |
[17] |
Pavino D, Squadrone S, Cocchi M , et al. Towards a routine application of vibrational spectroscopy to the detection of bone fragments in feeding stuffs: Use and validation of a NIR scanning microscopy method[J]. Food Chemistry, 2010,121(3):826-831.
doi: 10.1016/j.foodchem.2009.12.092 URL |
[18] | Cozzolino D . Prediction of chemical composition of ruminant feeds by near infrared reflectance spectroscopy (NIR) in Uruguay[J]. Revista Argentina de production Animal, 2002,22(2):81-86. |
[19] | 牛智有, 韩鲁佳 . 反刍动物饲料中总磷的近红外反射光谱分析研究[J]. 饲料工业, 2008,29(3):42-44. |
[20] | 丁丽敏, 计成, 戎易 .近红外 ( NIRS)和粗蛋白预测氨基酸含量的精度比较研究[J]. 饲料工业, 2002,23(4):15-18. |
[21] |
Decruyenaere V, Lecomtel P, Demarquilly , et al. Evaluation of green forage intake and digestibility in ruminants using near infrared reflectance spectroscopy (NIRS): developing global calibration[J]. Animal Feed Science and Technology, 2009,148(2-4):138-156.
doi: 10.1016/j.anifeedsci.2008.03.007 URL |
[22] |
Stuth J, Jama A, Tolleson D . Direct and indirect means of predicting forage quality through near infrared reflectance spectroscopy[J]. Field Crops Research, 2003,84(1-2):45-56.
doi: 10.1016/S0378-4290(03)00140-0 URL |
[23] | 李鑫, 王康宁 . 近红外快速预测饲料原料有效能值及其影响因素[J]. 饲料工业, 2006,27(11):26-28. |
[24] | 李升生, 顾宪红, 吴华东 , 等. 影响禽肉品质的环境因素[J]. 畜禽业, 2004(2):52-54. |
[25] | 徐玉玲, 韩玲, 余群力 , 等. 部位肉与肌肉纤维走向对牛肉色泽的影响[J]. 甘肃农业大学学报, 2013,48(5):137-140. |
[26] |
Liu Y, Lyon B G, Windham W R , et al. Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy[J]. Poultry Science, 2004,83(8):1467-1474.
doi: 10.1093/ps/83.8.1467 URL pmid: 15339027 |
[27] |
Samuel D, Park B, Sohn M , et al. Visible-near-infrared spectroscopy to predict water-holding capacity in normal and pale broiler breast meat[J]. Poultry Science, 2011,90(4):914-921.
doi: 10.3382/ps.2010-01116 URL pmid: 21406380 |
[28] |
De Marchi M, Penasa, M, Battagin, M , et al. Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits[J]. Poultry Science, 2011,90(7):1594-1599.
doi: 10.3382/ps.2010-01239 URL pmid: 21673177 |
[29] |
Barbin D F, Kaminishikawahara C M, Soares A L , et al. Prediction of chicken quality attributes by near infrared spectroscopy[J]. Food Chemistry, 2015(168):554-560.
doi: 10.1016/j.foodchem.2014.07.101 URL pmid: 25172747 |
[30] |
Yi Y, Hong Z, Yoon S C , et al. Quality assessment of intact chicken breast fillets using factor analysis with Vis/NIR spectroscopy[J]. Food Analytical Methods, 2017,11(5):1356-1366.
doi: 10.1007/s12161-017-1102-0 URL |
[31] | Jiang H Z, Yoon S C, Zhuang H , et al. Non-destructive assessment of final color and pH attributes of broiler breast fillets using visible and near-infrared hyperspectral imaging: a preliminary study[J]. Infrared Physics & Technology, 2018,92:309-317. |
[32] | 蒋圣启, 何鸿举, 王慧 , 等. 近红外高光谱联用Stepwise算法快速无接触评估冷鲜鸡肉色泽及嫩度[J]. 食品工业科技, 2019,40(13):125-133. |
[33] | 张永明, 孙晓蕾 . 鸡肉的营养价值与功能[J]. 肉类工业, 2008(8):57-58. |
[34] | 陈士进, 彭增起, 李景军 , 等. 光谱技术预测牛肉嫩度研究进展[J]. 食品科学, 2013,34(1):333-339. |
[35] | 熊振杰 . 基于高光谱成像技术的鸡肉品质快速无损检测[D]. 广州:华南理工大学, 2015: 20-44. |
[36] | 张英华 . 肉的品质及其相关质量指标[J]. 食品研究与开发, 2005,26(1):39-42. |
[37] |
Jia B B, Yoon S C, Zhuang H , et al. Prediction of pH of fresh chicken breast fillets by VNIR hyperspectral imaging[J]. Journal of Food Engineering, 2017,208:57-65.
doi: 10.1016/j.jfoodeng.2017.03.023 URL |
[38] | 开晗, 孔保华 . 近红外光谱检测技术在肉类工业中的应用[J]. 肉类研究, 2011(9):35-39. |
[39] | 徐霞, 成芳, 应义斌 . 近红外光谱技术在肉品检测中的应用和研究进展[J]. 光谱学与光谱分析, 2009,29(7):1876-1880. |
[40] | 王丽, 励建荣 . 红外光谱技术在肉品品质鉴别中的应用[J]. 中国食品学报, 2010,10(5):232-236. |
[41] | 吴习宇, 赵国华, 祝诗平 . 近红外光谱分析技术在肉类产品检测中的应用研究进展[J]. 食品工业科技, 2014,35(1):371-374,380. |
[42] | 沈杰 . 基于X射线及近红外光谱技术的禽肉品质检测[D]. 南昌:江西农业大学, 2011: 39-46. |
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