近红外反射光谱快速评定玉米和小麦营养价值的研究
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摘要
本研究主要目的是分别在全国范围内广泛收集不同种植地区、不同品种的玉米和小麦样品,并评估其营养价值及变异；建立玉米和小麦中常规成分和氨基酸的近红外快速预测模型；以猪消化代谢试验所测得的消化能和代谢能值作为参比数据,探讨使用近红外反射光谱技术快速预测玉米和小麦中猪有效能含量的可行性。本论文包括以下四个试验：
试验一：探讨了使用近红外反射光谱测定玉米常规营养成分和猪有效能的可行性。于2009年-2011年,从全国范围内收集玉米样品117个,并测定其常规成分和猪消化能和代谢能。结果表明：不同来源玉米的营养成分差异较大；水分、粗蛋白、粗脂肪、酸性洗涤纤维、中性洗涤纤维和容重的定标决定系数(RSQcal)为0.89～0.95,交互验证决定系数(1-VR)为0.87～0.93,交互验证相对分析误差(RPDcv)为2.83～3.67,验证决定系数(RSQv)为0.85～0.91,验证相对分析误差(RPDv)为2.67～3.20,其定标方程可用于日常分析；粗灰分、总磷和淀粉尚无法用于实际预测；玉米总能、猪消化能和代谢能的RSQcal、1-VR、RPDcv、RSQv和RPDv分别为0.87～0.94、0.86～0.92、2.78-3.53、0.86-0.90和2.64～3.17,定标方程可用于日常分析。
试验二：探讨了使用近红外反射光谱测定玉米中氨基酸的可行性。于2009年-2011年,从全国范围内收集玉米样品89个,并测定其18种氨基酸含量。结果表明：不同来源玉米中各氨基酸含量的差异较大；除赖氨酸、蛋氨酸、色氨酸和胱氨酸外,其他14种氨基酸的RSQcal、1-VR和RPDcv分别为0.86～0.94、0.84～0.93和2.56～4.44；除精氨酸、赖氨酸、蛋氨酸、色氨酸、胱氨酸、甘氨酸和酪氨酸外,其他11种氨基酸的RSQv和RPDv分别为0.83～0.91和2.51-3.33,定标方程可用于日常分析。除色氨酸外,使用近红外预测氨基酸含量优于使用粗蛋白。
试验三：探讨了使用近红外反射光谱测定小麦常规营养成分和猪有效能的可行性。于2011年-2012年,从全国范围内收集小麦样品58个,并测定其常规成分和猪消化能和代谢能。结果表明：不同来源小麦的营养成分差异较大；使用44个定标样品建立近红外模型,水分、粗蛋白、粗脂肪、中性洗涤纤维、容重和总能的RSQcal为0.87～0.92,1-VR为0.85-0.90, RPDcv为2.68～3.13,RSQv为0.84～40.90,RPDv为2.51-3.16,其定标方程可用于日常分析,而酸性洗涤纤维、粗灰分、总磷和淀粉尚无法用于实际预测。小麦猪消化能和代谢能的RPDv和RPDv均小于2.50,尚无法应用于实际预测；定标样品量增加到58个后,所有营养成分的交互验证效果均得到改善,除粗灰分、总磷、淀粉和代谢能外,其他营养成分的RPDcv超过2.50。
试验四：探讨了使用近红外反射光谱测定小麦中氨基酸的可行性。于2009年-2011年,从全国范围内收集小麦样品450个,通过CENTER和SELECT算法选择381个样品用于近红外定标和验证,并测定其18种氨基酸含量。结果表明：不同来源小麦中各氨基酸含量的差异较大；除蛋氨酸、缬氨酸、色氨酸、胱氨酸和酪氨酸外,其他13种氨基酸的RSQcal、1-VR, RPDcv、RSQv和RPDv分别为0.87～0.96、0.83～0.95、2.53～4.53、0.83～0.94和2.54～3.93,定标方程可用于日常分析。除色氨酸外,使用近红外预测氨基酸含量优于使用粗蛋白。
The objective of the present study was to collect corn and wheat samples from different planting regions and varieties from all over china, respectively, and evaluate their nutritional values and variation. Prediction equations were developed for the rapid determination of proximate nutrients and amino acids in corn and wheat by near-infrared reflectance spectroscopy (NIRS). In addition, according to the reference data (digestible energy and metabolizable energy values) determined by digestion-metabolism experiments using growing pigs, the possibility of using NIRS for the rapid determination of available energy (digestible energy, DE; metabolizable energy, ME) in corn and wheat was also investigated. This thesis includes the following4experiments:
Experiment1:The possibility of using NIRS for quantitative determination of proximate nutrients and swine available energy in corn was investigated. From2009to2011, a total of117corn samples were collected from all over China. The proximate composition were analyzed, and the DE and ME of these corn samples were determined by digestion-metabolism experiments using growing pigs. Results showed that the variations in nutrients were large between different sources of the corn samples. The coefficient of determination for calibration (RSQcal), coefficient of determination for cross-validation (1-VR), ratio of performance to deviation for cross-validation (RPDcv), coefficient of determination for external validation (RSQv) and ratio of performance to deviation for validation (RPDv) of moisture, crude protein (CP), ether extract (EE), acid detergent fiber (ADF), neutral detergent fiber (NDF) and density was0.89-0.95,0.87-0.93,2.83-3.67(>2.50),0.85-0.91and2.67-3.20(>2.50), respectively, which indicates that these NIRS models can be used in routine analysis. The predictive performance of ash, total phosphorus (TP) and starch was poor (RPDv,1.92-2.47,<2.50) and could not be used for routine analysis. The RSQca,,1-VR, RPDcv, RSQv and RPDv for GE, DE and ME was0.87-0.94,0.86-0.92,2.78-3.53(>2.50),0.86-0.90and2.64-3.17(>2.50), respectively, which indicates that good NIRS models were obtained for these three energy constituents and these prediction equations can be used in routine analysis.
Experiment2:The possibility of using NIRS for quantitative determination of amino acids of corn was investigated. From2009to2011, a total of89corn samples were collected from all over China, and the18amino acids contents were analyzed. Results showed that the variations in18amino acids were large between different sources of the collected corn samples. Except for the lysine, methionine, tryptophan and cystine, good NIRS prediction equations were obtained for the other14amino acids with high RSQcal (0.86-0.94),1-VR (0.84-0.93) and RPDv (2.56-4.44,>2.50) values. Excellent predictive performance of the NIRS models were received for most amino acids (RSQv,0.83-0.91; RPDv,2.51-3.33,>2.50) with the exception of arginine, lysine, methionine, tryptophan, cysteine, glycine and tyrosine, and these NIRS equations could be used in routine analysis. Except for tryptophan, compared to the linear regression results of amino acid contents relative to crude protein, NIRS has a better predictive performance.
Experiment3:The possibility of using NIRS for quantitative determination of proximate nutrients and swine available energy in wheat was investigated. From2011to2012, a total of58wheat samples were collected from all over China. The proximate composition were analyzed, and the DE and ME were determined by digestion-metabolism experiments using growing pigs. Results showed that the variations in nutrients were large between different sources of the wheat samples. The RSQcal,1-VR, RPDcv, RSQv and RPDv of the NIRS models developed by44calibration samples for moisture, CP, EE, NDF, density and GE was0.87-0.92,0.85-0.90,2.68-3.13(>2.50),0.84-0.90and2.51-3.16(>2.50), respectively, which indicates that these prediction equations can be used in routine analysis. The predictive performance of ADF, ash, TP and starch was poor (RPDv,1.91-2.43,<2.50) and could not be used for routine analysis. The NIRS models obtained for DE and ME also can not be used for routine analysis with low RPDcv (2.29-2.41,<2.50) and RPDv (2.03-2.11,<2.50). The cross-validation performance of models developed by58calibration samples was improved, and the RPDcv values exceeded2.50with the exception of ash, TP, starch and ME.
Experiment4:The possibility of using NIRS for quantitative determination of amino acids of wheat was investigated. From2009to2011, a total of450wheat samples were collected from all over China,381samples were chosen for NIRS calibration and validation by CENTER and SELECT algorithm, and the18amino acids contents were analyzed. Results showed that the variations in18amino acids were large between different sources of the wheat samples. Except for methionine, valine, tryptophan, cysteine and tyrosine, the RSQcal,1-VR, RPDcv, RSQv and RPDv for the other13amino acids was0.87-0.96,0.83-0.95,2.53-4.53(>2.50),0.83-0.9and2.54-3.93(>2.50), respectively, which indicates that these NIRS models can be used in routine analysis. Except for tryptophan, the predictive performance measuring amino acids by NIRS was better than that obtained by crude protein regression.
试验一：探讨了使用近红外反射光谱测定玉米常规营养成分和猪有效能的可行性。于2009年-2011年,从全国范围内收集玉米样品117个,并测定其常规成分和猪消化能和代谢能。结果表明：不同来源玉米的营养成分差异较大；水分、粗蛋白、粗脂肪、酸性洗涤纤维、中性洗涤纤维和容重的定标决定系数(RSQcal)为0.89～0.95,交互验证决定系数(1-VR)为0.87～0.93,交互验证相对分析误差(RPDcv)为2.83～3.67,验证决定系数(RSQv)为0.85～0.91,验证相对分析误差(RPDv)为2.67～3.20,其定标方程可用于日常分析；粗灰分、总磷和淀粉尚无法用于实际预测；玉米总能、猪消化能和代谢能的RSQcal、1-VR、RPDcv、RSQv和RPDv分别为0.87～0.94、0.86～0.92、2.78-3.53、0.86-0.90和2.64～3.17,定标方程可用于日常分析。
试验二：探讨了使用近红外反射光谱测定玉米中氨基酸的可行性。于2009年-2011年,从全国范围内收集玉米样品89个,并测定其18种氨基酸含量。结果表明：不同来源玉米中各氨基酸含量的差异较大；除赖氨酸、蛋氨酸、色氨酸和胱氨酸外,其他14种氨基酸的RSQcal、1-VR和RPDcv分别为0.86～0.94、0.84～0.93和2.56～4.44；除精氨酸、赖氨酸、蛋氨酸、色氨酸、胱氨酸、甘氨酸和酪氨酸外,其他11种氨基酸的RSQv和RPDv分别为0.83～0.91和2.51-3.33,定标方程可用于日常分析。除色氨酸外,使用近红外预测氨基酸含量优于使用粗蛋白。
试验三：探讨了使用近红外反射光谱测定小麦常规营养成分和猪有效能的可行性。于2011年-2012年,从全国范围内收集小麦样品58个,并测定其常规成分和猪消化能和代谢能。结果表明：不同来源小麦的营养成分差异较大；使用44个定标样品建立近红外模型,水分、粗蛋白、粗脂肪、中性洗涤纤维、容重和总能的RSQcal为0.87～0.92,1-VR为0.85-0.90, RPDcv为2.68～3.13,RSQv为0.84～40.90,RPDv为2.51-3.16,其定标方程可用于日常分析,而酸性洗涤纤维、粗灰分、总磷和淀粉尚无法用于实际预测。小麦猪消化能和代谢能的RPDv和RPDv均小于2.50,尚无法应用于实际预测；定标样品量增加到58个后,所有营养成分的交互验证效果均得到改善,除粗灰分、总磷、淀粉和代谢能外,其他营养成分的RPDcv超过2.50。
试验四：探讨了使用近红外反射光谱测定小麦中氨基酸的可行性。于2009年-2011年,从全国范围内收集小麦样品450个,通过CENTER和SELECT算法选择381个样品用于近红外定标和验证,并测定其18种氨基酸含量。结果表明：不同来源小麦中各氨基酸含量的差异较大；除蛋氨酸、缬氨酸、色氨酸、胱氨酸和酪氨酸外,其他13种氨基酸的RSQcal、1-VR, RPDcv、RSQv和RPDv分别为0.87～0.96、0.83～0.95、2.53～4.53、0.83～0.94和2.54～3.93,定标方程可用于日常分析。除色氨酸外,使用近红外预测氨基酸含量优于使用粗蛋白。
The objective of the present study was to collect corn and wheat samples from different planting regions and varieties from all over china, respectively, and evaluate their nutritional values and variation. Prediction equations were developed for the rapid determination of proximate nutrients and amino acids in corn and wheat by near-infrared reflectance spectroscopy (NIRS). In addition, according to the reference data (digestible energy and metabolizable energy values) determined by digestion-metabolism experiments using growing pigs, the possibility of using NIRS for the rapid determination of available energy (digestible energy, DE; metabolizable energy, ME) in corn and wheat was also investigated. This thesis includes the following4experiments:
Experiment1:The possibility of using NIRS for quantitative determination of proximate nutrients and swine available energy in corn was investigated. From2009to2011, a total of117corn samples were collected from all over China. The proximate composition were analyzed, and the DE and ME of these corn samples were determined by digestion-metabolism experiments using growing pigs. Results showed that the variations in nutrients were large between different sources of the corn samples. The coefficient of determination for calibration (RSQcal), coefficient of determination for cross-validation (1-VR), ratio of performance to deviation for cross-validation (RPDcv), coefficient of determination for external validation (RSQv) and ratio of performance to deviation for validation (RPDv) of moisture, crude protein (CP), ether extract (EE), acid detergent fiber (ADF), neutral detergent fiber (NDF) and density was0.89-0.95,0.87-0.93,2.83-3.67(>2.50),0.85-0.91and2.67-3.20(>2.50), respectively, which indicates that these NIRS models can be used in routine analysis. The predictive performance of ash, total phosphorus (TP) and starch was poor (RPDv,1.92-2.47,<2.50) and could not be used for routine analysis. The RSQca,,1-VR, RPDcv, RSQv and RPDv for GE, DE and ME was0.87-0.94,0.86-0.92,2.78-3.53(>2.50),0.86-0.90and2.64-3.17(>2.50), respectively, which indicates that good NIRS models were obtained for these three energy constituents and these prediction equations can be used in routine analysis.
Experiment2:The possibility of using NIRS for quantitative determination of amino acids of corn was investigated. From2009to2011, a total of89corn samples were collected from all over China, and the18amino acids contents were analyzed. Results showed that the variations in18amino acids were large between different sources of the collected corn samples. Except for the lysine, methionine, tryptophan and cystine, good NIRS prediction equations were obtained for the other14amino acids with high RSQcal (0.86-0.94),1-VR (0.84-0.93) and RPDv (2.56-4.44,>2.50) values. Excellent predictive performance of the NIRS models were received for most amino acids (RSQv,0.83-0.91; RPDv,2.51-3.33,>2.50) with the exception of arginine, lysine, methionine, tryptophan, cysteine, glycine and tyrosine, and these NIRS equations could be used in routine analysis. Except for tryptophan, compared to the linear regression results of amino acid contents relative to crude protein, NIRS has a better predictive performance.
Experiment3:The possibility of using NIRS for quantitative determination of proximate nutrients and swine available energy in wheat was investigated. From2011to2012, a total of58wheat samples were collected from all over China. The proximate composition were analyzed, and the DE and ME were determined by digestion-metabolism experiments using growing pigs. Results showed that the variations in nutrients were large between different sources of the wheat samples. The RSQcal,1-VR, RPDcv, RSQv and RPDv of the NIRS models developed by44calibration samples for moisture, CP, EE, NDF, density and GE was0.87-0.92,0.85-0.90,2.68-3.13(>2.50),0.84-0.90and2.51-3.16(>2.50), respectively, which indicates that these prediction equations can be used in routine analysis. The predictive performance of ADF, ash, TP and starch was poor (RPDv,1.91-2.43,<2.50) and could not be used for routine analysis. The NIRS models obtained for DE and ME also can not be used for routine analysis with low RPDcv (2.29-2.41,<2.50) and RPDv (2.03-2.11,<2.50). The cross-validation performance of models developed by58calibration samples was improved, and the RPDcv values exceeded2.50with the exception of ash, TP, starch and ME.
Experiment4:The possibility of using NIRS for quantitative determination of amino acids of wheat was investigated. From2009to2011, a total of450wheat samples were collected from all over China,381samples were chosen for NIRS calibration and validation by CENTER and SELECT algorithm, and the18amino acids contents were analyzed. Results showed that the variations in18amino acids were large between different sources of the wheat samples. Except for methionine, valine, tryptophan, cysteine and tyrosine, the RSQcal,1-VR, RPDcv, RSQv and RPDv for the other13amino acids was0.87-0.96,0.83-0.95,2.53-4.53(>2.50),0.83-0.9and2.54-3.93(>2.50), respectively, which indicates that these NIRS models can be used in routine analysis. Except for tryptophan, the predictive performance measuring amino acids by NIRS was better than that obtained by crude protein regression.
引文
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