發(fā)布時間：2018-05-15 12:55

  本文選題：棉粕 + 近紅外反射光譜�。� 參考：《甘肅農業(yè)大學》2016年碩士論文

【摘要】：本試驗研究近紅外反射光譜技術用于棉粕營養(yǎng)價值評定的可行性,選用我國新疆、山東和湖北等不同種植地區(qū)的76個色澤和氣味正常的棉粕樣品作為試驗材料,其中56個樣品用于建立水分、粗蛋白質、粗脂肪、粗纖維、粗灰分、總能值、蛋公雞表觀代謝能(AME)、真代謝能(TME)和氨基酸的近紅外定標模型,對模型進行內部交叉驗證,另外20個樣品作為外部驗證集對模型進行外部驗證,探討了福斯近紅外儀(FOSS XDS)和尼爾光電微型近紅外儀(Nir Smart Eye 1700)定標與驗證模型的穩(wěn)定性與適用性。試驗一應用福斯近紅外儀和尼爾光電微型近紅外儀,研究近紅外反射光譜技術測定棉粕常規(guī)養(yǎng)分含量和代謝能的可行性。從全國范圍內收集76個不同品種和產地,以及不同加工方式的棉粕樣品,測定其常規(guī)營養(yǎng)成分(水分、粗蛋白質、粗脂肪、粗纖維、粗灰分及總能)和蛋公雞代謝能含量,并隨機選取定標集(N=56)和外部驗證集(N=20)樣品,使用改進的偏最小二乘法(MPLS)建立近紅外定標模型。試驗結果表明:(1)不同來源棉粕的營養(yǎng)成分變異較大,常規(guī)營養(yǎng)成分變異系數(shù)為2.52%~84.75%,其中水分、粗脂肪和粗纖維的變異系數(shù)超過10%;粗蛋白質、粗灰分和總能的變異系數(shù)分別為9.58%、9.81%和2.52%;(2)福斯近紅外儀棉粕的水分、粗蛋白質、粗脂肪、粗纖維、粗灰分和總能的定標決定系數(shù)(RSQcal)為0.924~0.976,交互驗證決定系數(shù)(1-VR)為0.8247~0.9303,外部驗證決定系數(shù)(RSQv)為0.879~0.896,定標方程可用于日常分析。(3)尼爾光電微型近紅外儀棉粕的水分、粗蛋白質、粗脂肪、粗纖維和總能的RSQcal為0.905~0.951,定標標準差為(SEC)0.169~1.456,RSQv為0.883~0.959,定標方程可用于日常分析,粗灰分的RSQv為0.524,模型不可用。AME和TME的分布范圍分別為:4.63 MJ/kg~11.90 MJ/kg和5.39 MJ/kg~13.20 MJ/kg。用福斯近紅外儀建模得到的的AME和TME的RSQcal為0.969和0.927,1-VR為0.9170和0.9057,RSQv為0.911和0.892,定標方程可用于日常分析;用尼爾光電微型近紅外儀建模得到的AME和TME的RSQcal分別為0.954和0.949,SEC為0.400和0.475,RSQv為0.915和0.907,定標方程可用于日常分析,模型達到了可實用水平。試驗二探討了使用近紅外反射光譜法測定棉粕氨基酸含量的可行性。從全國范圍內收集76個不同品種和產地,以及不同加工方式的棉粕樣品,隨機選取定標集(N=56)和外部驗證集(N=20)樣品,并測定其16種氨基酸相應的含量。結果表明:不同來源棉粕中各氨基酸含量的差異較大;用福斯近紅外儀建模得到的天冬氨酸(Asp)、蘇氨酸(Thr)、谷氨酸(Glu)、甘氨酸(Gly)、賴氨酸(Lys)、組氨酸(His)、精氨酸(Arg)和色氨酸(Trp)的RSQcal為0.872~0.953,1-VR為0.7813~0.9504,RSQv為0.840~0.887,定標方程可用于日常分析,其它氨基酸的RSQv0.84,尚無法用于實際預測;尼爾光電微型近紅外儀建模得到的天冬氨酸(Asp)、蘇氨酸(Thr)、絲氨酸(Ser)、谷氨酸(Glu)、甘氨酸(Gly)、亮氨酸(Leu)、苯丙氨酸(Phe)、賴氨酸(Lys)、組氨酸(His)、精氨酸(Arg)和色氨酸(Trp)的RSQcal為0.865~0.970,SEC為0.016~0.537,RSQv為0.845~0.899,定標方程可用于日常分析,其它氨基酸的RSQv0.84,尚無法用于實際預測。在各營養(yǎng)成分、代謝能和氨基酸之間最佳的去散射方法不同。
[Abstract]:The feasibility of using near infrared reflectance spectroscopy to evaluate the nutritional value of cottonseed meal was studied. 76 samples of cotton meal with normal color and smell in Xinjiang, Shandong and Hubei were selected as experimental materials, and 56 of them were used to establish water, crude egg white matter, crude fat, coarse fiber, coarse ash, total energy value, and egg public. Chicken epigenetic metabolic energy (AME), true metabolic energy (TME) and amino acid near infrared calibration model, the internal cross validation of the model, and the external validation of the other 20 samples as external verification set, and the stability of the FOSS XDS and the Neal photoelectric micro type near infrared (Nir Smart Eye 1700) calibration and verification model are discussed. The feasibility of determining the conventional nutrient content and metabolic energy of cottonseed meal by near infrared spectroscopy was studied by using FIR near-infrared instrument and Neal photoelectric miniature near-infrared instrument. The samples of 76 different varieties and producing areas and different processing methods were collected from the whole country. The metabolic energy content of crude protein, crude fat, crude fiber, coarse ash and total energy) and egg rooster, and random selection of N=56 and external validation set (N=20) samples, and using the improved partial least squares (MPLS) method to establish the near infrared calibration model. The results showed: (1) the nutrient composition of the cottonseed meal from different sources varied greatly and the conventional nutrient composition changed. The coefficient of variation is 2.52%~84.75%, in which the coefficient of variation of water, crude fat and crude fiber is more than 10%, and the coefficient of variation of crude protein, crude ash and total energy is 9.58%, 9.81% and 2.52%, respectively. (2) the coefficient of determination of the moisture, crude protein, crude fat, crude fiber, coarse ash and total energy of the fir near infrared instrument is 0.924~0.976, and the interaction between the crude protein and the total energy is 0.924~0.976. The determination coefficient (1-VR) is 0.8247~0.9303, the external verification determination coefficient (RSQv) is 0.879~0.896, and the calibration equation can be used for daily analysis. (3) the moisture of the cottonseed meal, crude protein, crude fat, crude fiber and total energy RSQcal of Neal photoelectric miniature near infrared instrument are 0.905~0.951, the standard deviation is (SEC) 0.169~1.456, RSQv is 0.883~0.959, and the calibration square Cheng Ke is used for daily analysis. The RSQv of coarse ash is 0.524, and the distribution of the model is not available for.AME and TME. The AME and TME RSQcal of 4.63 MJ/kg~11.90 MJ/kg and 5.39 MJ/kg~13.20 MJ/kg. are obtained with AME and TME, 0.969 and 0.927,1-VR are 0.9170 and 0.9057, RSQv is 0.911 and 0.892, and the calibration equation can be used for daily analysis. The RSQcal of AME and TME obtained by Neal photoelectric micro near infrared instrument is 0.954 and 0.949, SEC is 0.400 and 0.475, RSQv is 0.915 and 0.907. The calibration equation can be used for daily analysis and the model reaches the practical level. Experiment two discussed the feasibility of using near infrared reflectance spectroscopy to determine the content of amino acid in cottonseed meal. The samples of 76 different varieties and producing areas and different processing methods were collected, and the N=56 and N=20 samples were randomly selected and the corresponding content of the 16 amino acids was measured. The results showed that the content of amino acids in the cottonseed meal of different sources was different, and the aspartic acid (Asp) was modeled by FTIR. Threonine (Thr), glutamic acid (Glu), glycine (Gly), lysine (Lys), histidine (His), arginine (Arg) and tryptophan (Trp) RSQcal are 0.872~0.953,1-VR 0.7813~0.9504, RSQv is 0.840~0.887, the calibration equation can be used for daily analysis, other amino acids are not used for practical prediction; Neal photoelectric miniature near infrared apparatus is modeled. Asp, serine (Thr), serine (Ser), glutamic acid (Glu), glycine (Gly), leucine (Leu), phenylalanine (Phe), lysine (Lys), histidine (His), arginine (Arg) and tryptophan (Trp) RSQcal are used for daily analysis and other amino acids It can not be used for practical prediction. The best method of scattering is different among nutrients, metabolizable energy and amino acids.

【學位授予單位】：甘肅農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：S816.15

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