近红外光谱技术在青贮饲料品质检测中的研究进展

本文总结了近年来青贮饲料近红外光谱无损检测技术的研究进展，主要从光谱仪种类、化学计量学方法优化以及近红外光谱技术在青贮上的应用进行综述，为进一步利用近红外光谱技术检测青贮饲料质量提供参考。 [关键词] 近红外光谱技术|青贮|无损检测|化学计量学     

近红外光谱分析技术及其在饲料中的应用

近红外反射光谱分析技术(NIRS)正在以产业链的方式应用于多个领域,它可以快速高效地测定样品中的化学组分和物化性质。近年来,近红外光谱技术尤其是在线分析的应用有了显著的发展,本文就近红外光谱分析技术的原理及在饲料中的应用研究进行了介绍。     

近红外光谱技术在牛奶及其制品分析中的应用进展

传统的化学检测方法很难满足奶制品行业快速发展的需求,快速、灵敏、无损伤的近红外光谱检测技术越来越受到重视和应用.本文介绍了近红外光谱技术的基本原理及分析过程,综述了国内外近红外光谱技术在牛奶及其制品中的最新研究成果,分析了近红外光谱技术在牛奶领域的应用现状,并展望了该检测技术的应用前景.     

近红外法与传统法对饲料中营养成分测定的对比分析

牧区放牧绵羊在越冬期间因牧草枯乏且营养价值低而导致采食量减少。适时为羊群进行补饲,可保证羊群安全越冬。运用常规检测法和近红外分析技术对高寒牧区羊群所补饲的饲料分别进行干物质、粗灰分、粗蛋白、粗脂肪等8项营养指标的检测,验证近红外分析技术与实验室传统检测方法的结果是否吻合。试验结果:分别利用传统检测方法和近红外分析技术测定9个饲料样本中的干物质、粗蛋白、粗脂肪、中性洗涤纤维、酸性洗涤纤维、粗灰分、钙、磷等指标,其结果均无显著差异(P>0.05),表明传统方法和近红外分析方法的检测结果基本吻合,同时也证明近红外分析技术可用于饲料成分测定及分析。     

几种草食动物日粮植物组成研究技术和方法的比较

目前，用于评价草食动物日粮的植物组成的方法大致可分为７种：直接观察法、牧草食物法、瘤胃内容物法、瘘管法、粪便分析法、显微技术和红外及近红外反射光谱技术。     

反刍动物饲料中总磷的近红外反射光谱分析研究

利用近红外反射光谱分析技术,采用偏最小二乘回归(PLS)方法,分别对光谱进行附加散射校正、变量标准化、一阶导数和二阶导数处理,建立了反刍动物饲料中总磷的预测模型。附加散射校正和二阶导数处理定标效果最优。定标集化学分析值与预测值之间的决定系数(R2)和标准差(RM-SEC)分别为0.9426和0.0347,相对分析误差为4.32;验证集化学分析值与预测值之间的决定系数(r2)和标准差(RMSEP)分别为0.9321和0.0359,相对分析误差为3.93。结果表明,利用近红外光谱反射分析技术可以定量检测反刍动物饲料中总磷的含量。     

高山草地的光谱反射特征及其估产模型

对祁连山东段高山草地中５种植物群落的光谱反射特征进行了测试研究。结果表明：１）草本植物和灌木种群的光谱反射率在可见光波段上无显著差异，但在近红外波段存在显著差异；２）土壤背景和测试仪器高度对群落的光谱反射产生显著影响；３）植物生长盛期，可用近红外波段的反射差异来区分草地植物群落的空间分布：４）草地植物群落的地上植物量与几种变换的植被指数呈显著或极显著的非线性相关。     

近红外光谱技术研究进展

近红外光谱技术研究进展ＤａｎｉｅｌＪ．Ｄｙｅｒ等著任鹏编译方天昊校近红外反射光谱（ＮＩＲ）及近红外透射光谱（ＮＩＴ）已成为普遍接受的饲料分析方法。２５年前研制出近红外光谱仪,７０年代中期陆续发表了一系列有关ＮＩＲ应用的文献。如玉米、大豆、燕麦中粗蛋白...     

基于近红外反射光谱快速检测豆粕中掺假菜籽粕的研究

试验旨在研究豆粕中掺假菜籽粕的近红外反射（NIR）光谱定量分析方法。在豆粕中掺入0、5%、10%、15%、20%、25%、30%菜籽粕，每个掺杂水平30份重复样品（建模集样本20个，验证集样本10个）。采用傅里叶变换近红外光谱仪获取样品NIR光谱，使用化学计量学软件拟合建模集样品掺假比例的NIR光谱预测模型，验证集样本评价预测模型的准确度。结果表明，豆粕中菜籽粕掺假比例的NIR光谱预测模型的决定系数R2为0.983，交叉验证均方根误差（RMSECV）为1.30。菜籽粕掺假比例为5%、10%、15%、20%、25%、30%的NIR光谱预测值的相对误差分别为8.93%、12.20%、2.21%、1.17%、1.72%、1.69%。研究表明，使用NIR光谱可以对豆粕中菜籽粕的掺假量实现准确定量测定。     

用近红外反射光谱法估测山区牧草的品质

<正> 前言近红外反射光谱技术能快速估测牧草的营养品质(Norris等,1976;Shenk等,1979;Bartoa和Bu rdick,1981;Shenk等,1981)。然而,应用近红外反射光谱(NIRRS)估测含多种牧草的样品品质的     

近红外光谱法与常规化学分析法苜蓿营养成分的对比研究

选取20个不同来源的苜蓿样品作为研究对象,分别使用近红外光谱法和常规化学分析法检测苜蓿样品中水分、粗蛋白、粗脂肪、粗灰分、酸性洗涤纤维和中性洗涤纤维的含量,验证近红外光谱分析法与常规化学分析法检测的符合程度。试验结果表明：与常规化学分析法比较,苜蓿近红外光谱预测模型更适用于苜蓿样品中粗蛋白、粗脂肪、粗灰分、酸性洗涤纤维和中性洗涤纤维含量的检测,但并不适用于苜蓿水分含量的检测,因此,需要对已有的近红外预测模型进行调整和优化。     

Polyethylene glycol determined by near-infrared reflectance spectroscopy to estimate faecal output in sheep fed fresh permanent grassland forage

This study evaluated the feasibility of using different doses of polyethylene glycol (PEG) as an external marker of faecal output in sheep fed permanent grasslands fodder and compared two near-infrared reflectance spectroscopy (NIRS) calibration strategies for determining faecal PEG content. Three levels of PEG (0.25%, 0.75% and 1.5% of total daily intake) were administered to eight wethers, with each level dosed twice daily. Animals were fed forage obtained from two permanent grasslands cut at two dates during the first cycle of growth. Polyethylene glycol recovery rate was higher (P<0.001) at the highest dose (0.78) and decreased as dose level decreased (0.61 and 0.30 for PEG levels of 0.75% and 0.25% of total daily intake, respectively). NIRS calibration equations established on PEG data dosed directly on the faecal samples (0.61) gave higher (P<0.001) PEG recovery rates than NIRS calibration equations performed on mixtures of faeces with different PEG concentrations (0.49). Finally, faecal output estimates were more accurate (P<0.001) when faeces were sampled at 8:00 (0.61) than at 16:00 (0.51). The highest PEG recovery rate (0.88) was achieved using the highest dose on morning samples when PEG content was estimated by NIRS using turbidimetric results as reference values. We conclude that the usefulness of PEG as an external marker for estimating faecal output on permanent grasslands is limited at PEG doses lower than 1.5% of intake.     

近红外反射光谱法(NIRS)分析奶牛精料补充料营养成分的研究

采用滤光片型8620近红外光谱技术(NIRS),结合主成分回归法,以105个不同的奶牛精料补充料样品建立了常规化学成分以及可消化总养分(TDN)含量的近红外定量分析校正模型。常规化学成分中,粗蛋白、粗脂肪、粗灰分、中性洗涤纤维、酸性洗涤纤维、酸性洗涤木质素和可消化总养分含量的校正模型决定系数R2分别为0.9132、0.9016、0.9220、0.9171、0.8928、0.7083和0.8346;研究发现除酸性洗涤木质素之外,其他成分含量的相对分析误差RPD(SD/SEP)均大于2.5,因此除酸性洗涤木质素之外,所建近红外预测模型对奶牛精料补充料常规营养成分以及TDN含量的快速测定具有重要的实际意义。     

The use of near infra‐red reflectance spectroscopy for the determination of plant nitrogen

Abstract

A Near Infra‐Red Reflectance Spectrophotometer was calibrated to analyse Italian ryegrass for protein nitrogen (N). Rye‐grass samples having a wide range in N content were analysed by standard “wet” chemistry techniques and the resulting data used to calibrate the Near Infra‐Red Spectrophotometer for ryegrass N analysis. A correlation (r) of 0,99 and standard error of calibration (SEC) of 0,209 resulted from the initial regression analysis between the Near Infra‐Red Spectroscopy (NIRS) estimated and “wet” chemistry data. In order to further evaluate the accuracy of the NIRS calibration a separate set of ryegrass samples were analysed for N content, by both the “wet” chemistry and NIRS methods, resulting in a correlation (r) of 0,98 and standard error of prediction (SEP) of 0,235. The applicability of the NIRS ryegrass calibration to other species was briefly examined by estimating the N contents of kikuyu (Pennisetum clandestinum) (r = 0,97 and SEP = 0,277).     

Evaluation of prediction of barley digestible nutrient content with near-infrared reflectance spectroscopy (NIRS)

Near-infrared reflectance spectroscopy (NIRS) was evaluated as a tool to predict the contents (ileal digestible) of protein (IDP), lysine (IDLys), cystine (IDCys), methionine (IDMet) and the contents (faecal digestible) of organic (DOM) and dry matter (DDM) in barley samples for pig diets. Twenty barley samples, which had previously been tested for in vivo and in vitro nutrient digestibility in pigs, were scanned. A number of NIRS calibrations were developed using the in vitro digestibility data of fifteen barley samples as a reference. Validation using the five remaining barley samples was used to select the best equations. The robustness of the calibrations was further tested by validating them with the in vivo digestibility data. Despite the limited number of samples used in the calibration, NIRS was able to accurately predict the content of IDP, IDLys, IDMet and DOM. On the other hand, DDM and IDCys contents could not be accurately predicted. Although, larger sample sets will be required to generate calibrations, which could be applied in the feed industry, these results show that NIRS is a potentially good tool to evaluate the digestible nutrient content of barley.     

近红外光谱法测定DDGS中粗蛋白含量的试验研究

试验建立DDGS粗蛋白含量测定的近红外光谱分析定标模型。采用化学分析法测定72个DDGS样品中的粗蛋白含量,利用FOSS InfraXact型近红外光谱分析仪采集样品光谱,光谱经2,4,4,1导数和标准正常化+散射处理(SNV+Detrend),用改进最小二乘法(MPLS)回归,获得了较好的定标模型,校正决定系数(RSQ)、交叉验证决定系数(1-VR)、校正标准误差(SEC)、交叉验证标准误差(SECV)分别为0.982 5、0.932 8、0.266 2、0.389 5。利用30个验证集的DDGS样品进行外部检验,预测值与真实值之间差异不显著(P>0.05)。结果表明,定标模型的预测性能较好,可以替代化学分析法快速测定DDGS中的粗蛋白含量。     

利用近红外漫反射(NIR)技术快速检测多维饲料中维生素E的含量

本文尝试将近红外漫反射技术应用于预测多维预混料中维生素E的含量,并分析此方法的可行性。利用125个多维预混料小规模定标集进行定标,获得定标方程,并将20个多维预混料预测结果与高效液相色谱(HPLC)法的检测结果进行比较,结果表明用近红外漫反射技术预测多维预混料中维生素E含量可行。     

Analysis of bovine udder, plate and viscera using near infrared reflectance spectroscopy

The objective of this study was to evaluate near infrared reflectance spectroscopy (NIRS) as an accurate and inexpensive alternative to conventional chemical analyses of nonconsumer bovine tissue. Udder, plate and visceral samples were collected from mature, Charolais-Angus and Hereford-Angus crossbred beef cows at slaughter, ground and analyzed for concentrations of lipid, protein and dry matter using standard AOAC chemical procedures. Samples were analyzed using NIRS. The collection of samples was randomly split into separate calibration and validation sets. Nine calibration equations representing each constituent and tissue combination were developed, using first- or second-order derivative mathematical transformations, and those calibration equations were validated. Correlation coefficients of calibration (R) and validation (r) ranged from .95 to .98 and from .87 to .97, respectively, for all analyses except plate dry matter (r = .77). Standard errors of calibration and prediction ranged from 1.89 to 5.81%. Results from this study are interpreted to indicate that bovine udder, plate and visceral tissue composition can be accurately, quickly and efficiently predicted using NIRS technology.     

近红外光谱技术在粗饲料分析中的应用现状

近红外光谱分析技术(NIRS)具有快速、准确、成本低的特点,因此,受到普遍关注。近红外光谱分析技术应用于饲料行业不仅有利于饲料质量控制体系的建立,也有利于即时监控饲料品质。本文综述了近红外光谱技术在粗饲料领域的研究现状。     

Technical note: comparison of Raman, mid, and near infrared spectroscopy for predicting the amino acid content in animal meals

The objective of this study was to compare three infrared spectroscopy techniques for routine evaluation of AA in animal meals. Animal meals (n = 54) with known AA contents were scanned with a near (NIRS), mid (FTIR), and Raman infrared spectrometer. For NIRS and Raman, samples were scanned "as is", whereas for FTIR, samples had to be finely ground before scanning to obtain reasonable spectra. Both FTIR and Raman data suffered from noise; for Raman, this prevented the development of calibrations. Using derivatized spectral data and a standardized outlier removal procedure, calibrations for nutritionally relevant AA could be developed that were equivalent for both NIRS and FTIR. The variation across AA tested explained (r2) by these calibrations was 70% for NIRS and 68 + 3% for FTIR. Removing spectral data between 4,000 and 2,000 cm(-1) from the FTIR data improved calibrations (P = 0.09) and explained an average of 77% of the variation with prediction errors lower than obtained with NIRS (P < 0.01). However, FTIR calibrations based on the entire or the shortened spectrum contained fewer samples than did NIRS calibrations (41 and 39 vs. 48, respectively; P < 0.01) because more samples were removed as outliers. In conclusion, Raman did not yield acceptable spectra for animal meals. For FTIR, sample preparation was more time-consuming because the samples required grinding before analysis. Using the entire mid-infrared range, FTIR calibrations were comparable to NIRS calibrations. Calibrations for FTIR were improved by eliminating wave numbers that exhibited more noise, resulting in prediction errors better than those for NIRS. Thus, FTIR has the potential to yield better calibrations for AA in animal meals than NIRS, but it requires greater care in sample preparation and scanning.