籼稻品质分析的近红外光谱模型建立及其应用研究

为了满足籼稻品质快速分析的需求,本研究利用籼稻精米粉近红外光谱建立了直链淀粉含量、蛋白质含量、碱消值、垩白度的回归预测模型.结果表明,本研究提供的预测模型具有良好的测定效果,用偏最小二乘法(PLS)获得的籼稻精米粉直链淀粉含量、蛋白质含量、碱消值、垩白度的回归模型和交叉验证显示最优校正决定系数(R~2)和交叉检验均方误差(RMSECV)分别为0.9561、1.55,0.9510、0.258,0.9076、0.283,0.9014、4.14.说明所建的近红外光谱预测模型具有实用价值.     

豌豆品质性状近红外模型建立及区域差异分析

探讨了傅立叶变换近红外光谱技术(FT-NIRS)检测豌豆蛋白质、淀粉、脂肪和总多酚含量的可行性。用化学方法测定190份豌豆种质的蛋白质、淀粉、脂肪以及总多酚含量,采集其子粒与粉末的近红外光谱,采用偏最小二乘法(PLS)分别建立两种光谱与成份含量预测模型。豌豆粉末模型结果优于子粒模型,其中蛋白质和淀粉的粉末模型的预测残差(RPD)为5.88、5.82,相关系数r2达到0.99、0.99,具有很好的预测性能。对其中产地信息详细明确的150份豌豆种质的品质性状与产地进行两步聚类分析,明确得到3种类型,其特点分别为:类群1低蛋白质含量,类群2高总多酚含量,类群3高蛋白质、高淀粉和高脂肪含量。进一步分析了豌豆品质性状随播种期、经度、纬度、海拔高度的变化情况。结果表明,近红外光谱技术可对豌豆种质资源的部分品质性状进行快速筛选鉴定,聚类分析结论、地理坐标与播期对豌豆种质主要品质性状的影响规律,都可为收集高品质性状豌豆种质资源提供可靠依据。     

近红外光谱法快速测定白芍中芍药苷含量

本研究旨在应用近红外光谱法建立一种白芍药材中芍药苷含量的快速测定方法。利用HPLC测定样品中芍药苷含量,并以其作为参考值,运用偏最小二乘法(PLS)建立芍药苷含量与近红外光谱之间的多元校正模型,对未知样品进行含量预测。结果表明,所建芍药苷定量分析模型的相关系数(R2)、内部交叉验证均方差(RMSECV)、校正均方差(RMSEC)分别为0.99395、0.33068、0.0563;经内部验证,模型的预测均方差(RMSEP)和平均回收率分别为0.0756和100.07%。该方法操作简便,无污染,结果准确可靠,可用于白芍中芍药苷含量的快速测定。     

近红外光谱法快速测定木香中木香烃内酯与去氢木香内酯的含量

本文主要研究近红外光谱法在快速测定木香药材中木香烃内酯与去氢木香内酯含量的应用。采用近红外漫反射光谱法采集木香的近红外光谱,以HPLC测量值为参考值,运用偏最小二乘法(PLS)建立木香烃内酯与去氢木香内酯含量的定量模型,并用未知样品验证该模型。结果表明所建定量模型的校正集内部交叉验证相关系数(R2)、校正均方差(RMSEC)和内部交叉验证均方差(RMSECV)分别为0.9783、0.161和0.374;经外部验证的预测相关系数(r2)和预测均方差(RMSEP)分别为0.9546和0.162。该方法操作简便,测定快速,结果准确,无污染,可用于木香药材中木香烃内酯与去氢木香内酯含量的快速测定。     

应用近红外光谱预测水稻叶片氮含量

以水稻(Oryza sativa)新鲜叶片和干叶粉末两种状态的样品为研究对象, 基于近红外光谱(NIRS)技术, 应用偏最小二乘法(PLS)、主成分回归(PCR)和逐步多元回归(SMLR), 建立并评价了水稻叶片氮含量(NC)近红外光谱模型。结果表明, 基于PLS建立的模型表现最好, 鲜叶氮含量近红外光谱校正模型校正决定系数R_C²为0.940, 校正标准误差RMSEC为0.226; 干叶粉末氮含量的近红外光谱校正模型R_C²为0.977, RMSEC为0.136。模型的内部交叉验证分析表明, 预测鲜叶氮含量内部验证决定系数R_CV²为0.866, 内部验证标准误差RMSECV为0.243; 预测干叶粉末氮含量R_CV²为0.900, RMSECV为0.202。模型的外部验证分析表明, 预测水稻鲜叶氮含量的外部验证决定系数R_V²大于0.800, 外部验证标准误差RMSEP小于0.500, 预测干叶粉末氮含量的R_V²为0.944, RMSEP为0.142。说明, 近红外光谱分析技术与化学分析方法一致性较好, 且基于干叶粉末建立的近红外光谱预测模型的准确性和精确度较新鲜叶片高。     

基于近红外光谱法的枳实含量测定北大核心CSCD

本文应用近红外漫反射光谱法快速测定枳实中辛弗林与醇浸出物的含量。采用高效液相色谱法测定辛弗林的含量,以热浸法测定醇浸出物的含量,运用近红外光谱技术结合化学计量学方法建立枳实中辛弗林与醇浸出物的定量分析模型。并用未知样品验证该模型。结果表明辛弗林与醇浸出物的定量分析模型的内部交叉验证相关系数(R2)分别为0.98052、0.98489,校正均方差(RMSEC)分别为0.00915、0.153,预测均方差(RMSEP)分别为0.0119、0.188,验证集的预测相关系数(r)分别为0.9106、0.9484。该方法简便、快速、准确,可用于枳实中辛弗林与醇浸出物含量的快速测定。     

基于近红外光谱法的枳实含量测定

本文应用近红外漫反射光谱法快速测定枳实中辛弗林与醇浸出物的含量。采用高效液相色谱法测定辛弗林的含量,以热浸法测定醇浸出物的含量,运用近红外光谱技术结合化学计量学方法建立枳实中辛弗林与醇浸出物的定量分析模型。并用未知样品验证该模型。结果表明辛弗林与醇浸出物的定量分析模型的内部交叉验证相关系数(R2)分别为0.98052、0.98489,校正均方差(RMSEC)分别为0.00915、0.153,预测均方差(RMSEP)分别为0.0119、0.188,验证集的预测相关系数(r)分别为0.9106、0.9484。该方法简便、快速、准确,可用于枳实中辛弗林与醇浸出物含量的快速测定。     

NIRS结合TQ软件快速测定山茱萸中莫诺苷的含量

应用近红外光谱法对山茱萸中莫诺苷的含量进行快速测定。利用高效液相色谱法测定山茱萸药材中莫诺苷的含量,采用偏最小二乘法建立其含量与近红外光谱之间的模型,并对未知样品进行含量测定。所建莫诺苷的定量模型的相关系数(R2)为0.9857,校正均方差(RMSEC)、内部交叉验证均方差(RMSECV)和预测均方差(RMSEP)分别为0.105、0.2738和0.234。结果表明该方法所测结果准确,且不需复杂的前处理、绿色、无损,适用大批量山茱萸中莫诺苷的快速测定。     

用氨基葡萄糖含量的测定值间接表示红曲菌固态发酵过程中生物量的研究

【目的】为准确快速地了解紫色红曲菌固态发酵中生物量的变化,【方法】采用理化方法测定菌体量和氨基葡萄糖含量,研究了不同培养时间、培养基组成、培养方式下菌体量与氨基葡萄糖含量的关系,建立生物量和氨基葡萄糖含量的换算关系式;构建关联该菌固态培养物近红外光谱数据与实测氨基葡萄糖含量的PLS模型。【结果】建立了可通过近红外光谱法测定氨基葡萄糖来快速预测固态发酵生物量的方法,其中最优近红外模型的校正集内部交叉验证均方根误差(RMSECV)为0.209 4,预测集相关系数(Rp)和均方根误差(RMSEP)分别为0.993 4和0.217 3;同时利用所建的换算关系式也大大提高了生物量计算的准确性。【结论】基于所建立的生物量和氨基葡萄糖的换算关系式,利用近红外光谱法可以快速并且较准确地测定紫色红曲菌固态发酵过程中生物量的变化。     

NIRS结合TQ软件快速测定山茱萸中莫诺苷的含量北大核心CSCD

应用近红外光谱法对山茱萸中莫诺苷的含量进行快速测定。利用高效液相色谱法测定山茱萸药材中莫诺苷的含量,采用偏最小二乘法建立其含量与近红外光谱之间的模型,并对未知样品进行含量测定。所建莫诺苷的定量模型的相关系数(R2)为0.9857,校正均方差(RMSEC)、内部交叉验证均方差(RMSECV)和预测均方差(RMSEP)分别为0.105、0.2738和0.234。结果表明该方法所测结果准确,且不需复杂的前处理、绿色、无损,适用大批量山茱萸中莫诺苷的快速测定。     

Identification and characterization of granule bound starch synthase I (GBSSI) gene of tartary buckwheat (Fagopyrum tataricum Gaertn.)

Tartary buckwheat (Fagopyrum tataricum Gaertn.) is increasingly considered as an important functional food material because of its rich nutraceutical compounds. Reserve starch is the major component of tartary buckwheat seed. However, the gene sequences and the molecular mechanism of tartary buckwheat starch synthesis are unknown so far. In this study, the complete genomic sequence and full-size cDNA coding tartary buckwheat granule-bound starch synthase I (FtGBSSI), which is responsible for amylose synthesis, were isolated and analyzed. The genomic sequence of the FtGBSSI contained 3947 nucleotides and was composed of 14 exons and 13 introns. The cDNA coding sequence of FtGBSSI shared 63.3%–75.1% identities with those of dicots and 56.6%–57.5% identities with monocots (Poaceae). In deduced amino acid sequence of FtGBSSI, eight motifs conserved among plant starch synthases were identified. A cleavage at the site IVC↓G of FtGBSSI protein produces the chloroplast transit sequence of 78 amino acids and the mature protein of 527 amino acids. The FtGBSSI mature protein showed an identity of 73.4%–77.8% with dicot plants, and 67.6%–70.4% with monocot plants (Poaceae). The mature protein was composed of 20 α-helixes and 16 β-strands, and folds into two main domains, N- and C-terminal domains. The critical residues which are involved in ADP and sugar binding were predicted. These results will be useful to modulate starch composition of buckwheat kernels with the aim to produce novel improved varieties in future breeding programs.     

Proteomics Analysis of Embryo and Endosperm from Mature Common Buckwheat Seeds

We used proteomics analysis to generate the profiles of proteins in the endosperm and embryo of common buckwheat grains. These differentially expressed proteins are potentially involved in seed metabolism. Extractions were done by trichloroacetic acid (TCA) precipitation. The resulting proteins were separated using SDS-PAGE coupled to LC-ESI-Q/TOF-MS/MS. This allowed us to detect and identify 67 proteins with isoforms, making this the most inclusive protein profile. The proteins were determined to be functionally involved in the central metabolic pathway of the seed, with metabolic interest being reflected in the occurrence of a tissue-specific enzyme balance. For a case in point, we found a tissue-specific and subcellular compartment-specific isoform of granule-bound starch synthase 1 in the chloroplast/amyloplast. This provided proteomic verification of the presence of a distinct regulatory mechanism for the biosynthesis of glycan and starch, which produce amylase and amylopectin. Furthermore, several previously characterized allergenic proteins such as 11S and 13S globulin seed storage protein were acknowledged in our seed samples, thus representing the potential for proteomics techniques that survey food sources for any incidence of allergens. This protein profile of common buckwheat grain is a new avenue for understanding its seed physiology in dormant stage as well as suggesting commercial applications for the buckwheat industry as buckwheat flour.     

Identification and characterization of granule bound starch synthase (GBSS-I) from common buckwheat (Fagopyrum esculentum Moench)

Starch grains present in the endosperm of grains of common buckwheat (Fagopyrum esculentum Moench) show a monomodal distribution with size ranging from 4 to 10 μm. SDS-PAGE analysis of starch granule bound proteins revealed the presence of a single band corresponding to molecular mass of 59.7 kDa. The protein is localized within the central core of the starch grains. Antisera raised against the 59.7 kDa protein cross reacted with the 61 kDa GBSS-I from endosperm starches of maize and the 60 kDa GBSS-I from endosperm starches of rice and wheat, thereby indicating serological homology between the 59.7 kDa buckwheat starch granule bound protein and GBSS-I of wheat, maize and rice. 2D-PAGE of starch granule bound proteins of common buckwheat resolved the fraction into 7 spots with pI ranging from 5.2 to 5.6. N-terminal amino acid sequence for 25 residues of two immunoreactive proteins separated by 2D PAGE showed 94 % homology with N-terminal amino acid sequence of GBSS-I from Hordeum vulgare, Triticum spp. and Phaseolus vulgaris. Even though analysis of the sequence alignment revealed a clear diversification into monocotyledonous and dicotyledonous groups, the protein from buckwheat showed similarities with GBSS-I from both dicots as well as monocots. As is the case with dicots, the sequence of GBSS-I from buckwheat has valine as the 11th residue. GBSS-I from majority of monocots has methionine at this position. The sequence also showed similarities with monocots with valine at P’5 from the N-terminus. GBSS-I from majority of dicots has isoleucine at this position. The significance of these substitution remains to be ascertained.     

Monitoring complex media fermentations with near-infrared spectroscopy: comparison of different variable selection methods

Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.     

Monitoring nutrition in small ruminants with the aid of near infrared reflectance spectroscopy (NIRS) technology: A review

This review aims to evaluate the contribution of near infrared reflectance spectroscopy (NIRS) to monitor nutrition in small ruminants, with particular emphasis on the use of feed spectra and fecal spectra. NIRS provides satisfactory accuracy in the analysis of the chemical constituents of feeds for small ruminants, e.g., crude protein and cell wall composition, and is sometimes better than in vitro procedures for predicting in vivo digestibility and the available energy in feeds. In addition, in vitro digestibility can be accurately estimated by NIRS. The effective rumen degradability of protein could potentially be accurately predicted by NIRS, which would eliminate the need for rumen-fistulated animals. Good accuracy in the prediction of tannins has been reported for narrow, single-species applications, as well as for broad arrays of browse species. The identification of NIR segments corresponding to undigested entities has potential to help in providing spectral markers of digestibility. Fecal output can easily be evaluated, using the NIRS-aided analysis of polyethylene glycol (PEG) administered as external indigestible marker. Analysis of NIR spectra of the feces enables the accurate prediction of the chemical characteristics of the feed (dry matter digestibility and crude protein, cell wall attributes, PEG-binding tannins) in stall-fed and grazing animals, and to some extent, of the botanical composition of diets at pasture. Thus, fecal NIRS methodology holds the potential to provide nutritional diagnoses for farmers raising small ruminant.     

Portable near-infrared spectroscopy: A rapid and accurate blood test for diagnosis of Haemonchus contortus infection and for targeted selective treatment of sheep

Haemonchus contortus is the most prevalent and important gastrointestinal nematode (GIN) in small ruminants. Since it reduces the packed cell volume (PCV), causing anemia, early diagnosis can be used for targeted selective treatment (TST) of sheep, reducing antiparasitic drug use and anthelmintic resistance. This study aimed to predict PCV values through near-infrared spectroscopy (NIRS) and to develop a classification and diagnosis model of H. contortus infection using PCV values, eggs per gram of feces (EPG) counts and mean daily weight gain (DWG). A total of 1728 spectra were collected from blood samples of 216 lambs with a portable NIR spectroscope. In parallel, other parameters indicative of infection were measured: PCV by hematocrit, FAffa MAlan CHArt (FAMACHA) scores, EPG and DWG. To evaluate the relationship between NIRS spectra and the evaluated parameters, principal component analysis (PCA) was used for an exploratory analysis, regression by the partial least squares method (PLS) for the prediction of PCV values via NIRS, and PCA linear discriminant analysis (PCA-LDA) as a classification model for diagnosis. The absorption peaks in the NIRS region associated with the excitation of overtones of nitrogen-hydrogen (N-H) functional groups of proteins had a strong impact on the principal components (PCs), indicating that blood proteins, especially hemoglobin, can be estimated by the NIRS technique. The model for predicting PCV by PLS presented a standard error of prediction of 2.53, root-mean-square error of 2.48, and coefficient of determination of 0.84, indicating good correlation between the PCV values predicted by the model and the PCV obtained by hematocrit. The PCA-LDA model presented 93.33% sensitivity and 82.18% accuracy, both higher than those of the FAMACHA method, as was expected for resilient Morada Nova lambs. The multivariate models associated with the NIRS technique reported here can be used in the future as a quick and versatile tool for H. contortus infection diagnosis and TST application in lambs.     

Relationships between kernel vitreousness and dry matter degradability for diverse corn germplasm: I. Development of near-infrared reflectance spectroscopy calibrations

Negative correlations between corn vitreousness and ruminal dry matter and starch degradabilities have been widely reported. To measure corn vitreousness and density more rapidly, Correa et al. [Correa, C.E.S., Shaver, R.D., Pereira, M.N., Lauer, J.G., Kohn, K., 2002. Relationship between corn vitreousness and ruminal in-situ starch degradability. J. Dairy Sci. 85, 3008–3012] initiated the development of near-infrared reflectance spectroscopy (NIRS) calibrations from 47 samples derived from 14 US and five Brazilian commercial hybrids. In this study, we generated more data to add to these NIRS calibrations with the objective of making them more robust. We also evaluated the potential of using Stenvert hardness measurements for NIR calibrations. Thirty-three diverse corn germplasm sources were grown at University of Wisconsin West Madison Research Station. These included a wide range of endosperm characteristics from opaque 2 (o2) types to densely packed flint types, and a number of intermediates. Harvest was at 1/2 milkline and black-layer maturity stages. Dried kernels from middle portions of ears from 12 selected inbreds, four each from low (0–30%), medium (30–70%), and high (70–100%) vitreousness classifications were used to determine vitreousness by manual dissection and density by water displacement using a pycnometer. Hardness was determined on all 33 inbreds on a 20 g sample using a Stenvert micro hammer-cutter mill with 2 mm screen size and 3600 rpm to measure time to collect ground sample to a set receptacle height (T); total column height (CH); and height ratio of coarse to fine (C/F) particles. The NIRS equations were selected on the basis of high R²-values (0.90, 0.92, 0.85, and 0.85) and low SEC (4.85, 0.01, 1.39, and 0.19) and SECV (6.04, 0.02, 1.79, and 0.25), for vitroueness, density, T and CH factors, respectively. Calibrations for vitreousness and density were regarded as the best prediction models compared to stenvert hardness measurements as determined by their RPD values (3.73 and 2.50, respectively). These results show that NIRS can be used as a screening tool in large-scale breeding trials to develop corn hybrids of desired endosperm properties for improved ruminal degradabilities.     

In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation

The use of in-situ near infrared spectroscopy (NIRS) as a tool for monitoring four key analytes in a CHO-K1 animal cell culture was investigated. Previous work using on-line NIRS to monitor bioprocesses has involved its application ex-situ where the analyzer is physically outside the fermentor, or to microbial bioprocesses. This novel application of NIRS to monitor analytes within an animal cell culture using a steam sterilizable in-situ fiber optic probe is very important for furthering the use of NIRS within the bioprocessing industry. The method of calibration used to develop the models involved the use of large data sets so that all likely variation in stoichiometry was incorporated within the models. Successful models for glucose, lactate, glutamine, and ammonia were built with Standard Error of Predictions (SEP's) of 0.072 (g/L), 0.0144 (g/L), 0.308 (mM), and 0.036 (mM), respectively of the total concentration range.     

Ecological applications of near infrared reflectance spectroscopy – a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance

Many ecological studies rely heavily on chemical analysis of plant and animal tissues. Often, there is limited time and money to perform all the required analyses and this can result in less than ideal sampling schemes and poor levels of replication. Near infrared reflectance spectroscopy (NIRS) can relieve these constraints because it can provide quick, non-destructive and quantitative analyses of an enormous range of organic constituents of plant and animal tissues. Near infrared spectra depend on the number and type of CH, NH and OH bonds in the material being analyzed. The spectral features are then combined with reliable compositional or functional analyses of the material in a predictive statistical model. This model is then used to predict the composition of new or unknown samples. NIRS can be used to analyze some specific elements (indirectly – e.g., N as protein) or well-defined compounds (e.g., starch) or more complex, poorly defined attributes of substances (e.g., fiber, animal food intake) have also been successfully modeled with NIRS technology. The accuracy and precision of the reference values for the calibration data set in part determines the quality of the predictions made by NIRS. However, NIRS analyses are often more precise than standard laboratory assays. The use of NIRS is not restricted to the simple determination of quantities of known compounds, but can also be used to discriminate between complex mixtures and to identify important compounds affecting attributes of interest. Near infrared reflectance spectroscopy is widely accepted for compositional and functional analyses in agriculture and manufacturing but its utility has not yet been recognized by the majority of ecologists conducting similar analyses. This paper aims to stimulate interest in NIRS and to illustrate some of the enormous variety of uses to which it can be put. We emphasize that care must be taken in the calibration stage to prevent propagation of poor analytical work through NIRS, but, used properly, NIRS offers ecologists enormous analytical power. Received: 10 October 1997 / Accepted: 12 May 1998