近红外光谱法快速测定女贞子中特女贞苷的含量北大核心CSCD

应用近红外漫反射光谱法快速测定女贞子中特女贞苷的含量。运用近红外光谱技术(NIRS)结合偏最小二乘法(PLS)建立不同产地女贞子中特女贞苷含量的定量校正模型。特女贞苷的定量校正模型内部交叉验证决定系数(R2)为0.98075,校正均方根偏差(RMSEC)为0.216,预测均方根偏差(RMSEP)为0.223,交互验证均方根偏差(RMSECV)为0.52276。该方法具有简便快速,准确无损,可用于女贞子中特女贞苷含量的快速测定。     

近红外光谱法快速测定女贞子中特女贞苷的含量

应用近红外漫反射光谱法快速测定女贞子中特女贞苷的含量。运用近红外光谱技术(NIRS)结合偏最小二乘法(PLS)建立不同产地女贞子中特女贞苷含量的定量校正模型。特女贞苷的定量校正模型内部交叉验证决定系数(R2)为0.98075,校正均方根偏差(RMSEC)为0.216,预测均方根偏差(RMSEP)为0.223,交互验证均方根偏差(RMSECV)为0.52276。该方法具有简便快速,准确无损,可用于女贞子中特女贞苷含量的快速测定。     

怀地黄叶片中总环烯醚萜苷及总苯乙醇苷含量快速分析模型的建立

本文旨在建立地黄叶片中总环烯醚萜苷及苯乙醇苷定量分析模型。利用紫外-可见分光光度法测定不同种质怀地黄生育期内的128份地黄叶片中总环烯醚萜苷及总苯乙醇苷的含量,并将其作为基础值,结合地黄叶片的近红外光谱图,利用TQ8.0分析软件结合偏最小二乘法(PLS),分别建立地黄叶片中总环烯醚萜苷及总苯乙醇苷的定量分析模型。地黄叶片中总苯乙醇苷定量校正模型决定系数(R2)为0.998 2,校正均方根偏差(RMSEC)为0.089 9,预测均方决定差(RMSEP)为0.142,交叉验证均方根偏差(RMSECV)为0.707 2;总环烯醚萜苷定量校正模型的内部交叉验证决定系数(R2)为0.972 1,校正均方差(RMSEC)为0.259,预测均方决定差(RMSEP)为0.095 4,交叉验证均方根偏差(RMSECV)为0.869 4。预测值与实测值差异无统计学意义。该定量模型可用于怀地黄叶片中总环烯醚萜苷及总苯乙醇苷含量的快速测定。     

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。     

干旱胁迫对杉木无性系非结构性碳水化合物含量的影响

以杉木Cunninghamia lanceolata 6个优良无性系组培移栽苗为试材,对聚乙二醇(PEG-6000)模拟干旱胁迫条件下各无性系针叶淀粉、可溶性糖及非结构性碳水化合物(NSC)总量进行测定。结果显示,干旱胁迫致使杉木无性系针叶淀粉含量下降、可溶性糖含量有所提高,NSC含量呈下降趋势(除T-cF1无性系外)。干旱胁迫条件下,可溶性糖含量与针叶相对含水量、丙二醛含量间呈显著正相关(P<0.05),而淀粉含量、NSC与过氧化氢酶活性间均为显著负相关(P<0.05)。     

云南两种食用地衣营养成分研究

测定云南两种食用地衣的主要营养成分 ,并与常见蔬菜及食用菌进行比较。测定结果 ,东方肺衣粗蛋白质含量为 11 0 2 g/ 10 0 g .DW ,粗脂肪 5 5 6 g/ 10 0g .DW ,总糖、可溶性糖、灰分含量分别为 0 6 2g/ 10 0 g、0 37g/ 10 0g、2 6 4g/ 10 0 g .DW ;裂髓树花 (新拟 )的粗蛋白质含量为 3 43g/ 10 0g .DW ,粗脂肪3 12 g/ 10 0g .DW ,总糖、可溶性糖、灰分含量分别为 0 79g/ 10 0 g、0 35g/ 10 0 g、2 99g/ 10 0 g .DW ;两种地衣均含有 17种氨基酸 (色氨酸未测 ) ,多种矿物元素 ,具有一定营养价值 ;且两种地衣营养成分的含量存在差异     

基于HPLC和NIRS建立快速检测盾叶薯蓣中3种皂苷含量的方法

建立一种快速检测盾叶薯蓣中三角叶薯蓣皂苷、盾叶新苷和薯蓣皂苷含量的方法。本研究以全国8个产地的盾叶薯蓣药材为研究对象,首先,利用HPLC-ELSD建立同时测定盾叶薯蓣中三角叶薯蓣皂苷、盾叶新苷及薯蓣皂苷含量的方法,并对不同产地的盾叶薯蓣药材进行三种皂苷的含量测定;其次,扫描盾叶薯蓣药材样品的近红外光谱,分别将盾叶薯蓣药材校正集样品的三种皂苷含量作为参考值,结合其近红外光谱图,以内部交叉验证决定系数(R~2)、校正均方根偏差(RMSEC)、预测均方根偏差(RMSEP)及预测性能指数(PI)作为评价所建定量检测模型性能的指标,利用TQ8.0分析软件结合偏最小二乘法(PLS),通过光谱预处理方法筛选、建模波段及主成分数的确定分别建立盾叶薯蓣药材中三种皂苷含量的快速检测模型;最后,分别利用验证集样品对所建三种皂苷检测模型的预测准确性进行检验。盾叶薯蓣样品中三角叶薯蓣皂苷、盾叶新苷和薯蓣皂苷含量测定方法经考察符合定量分析的要求;盾叶薯蓣药材中三角叶薯蓣皂苷定量检测模型的R~2为0.981 17、RMSEC为0.086 3、RMSEP为0.063 8、PI为90.5;盾叶新苷定量检测模型的R~2为0.982 64、RMSEC为0.042 0、RMSEP为0.027 4、PI为91.1;薯蓣皂苷定量检测模型的R~2为0.943 64、RMSEC为0.009 90、RMSEP为0.005 41、PI为85.8;经统计学检验,三个模型对三种皂苷的预测值与实测值之间无显著性差异。该方法可以相对快速、准确测定盾叶薯蓣中三角叶薯蓣皂苷、盾叶新苷及薯蓣皂苷的含量,为盾叶薯蓣药材质量的快速评价提供依据。     

波长选择SCARS和偏最小二乘法建立香菇中多糖含量预测的近红外光谱模型

为实现香菇多糖含量的快速测定,利用近红外光谱漫反射技术采集了60个香菇粉末样本在12000~3800 cm-1范围内的光谱数据,利用紫外可见光谱法测定了香菇粉末样品的多糖含量。采用多种化学计量学方法,剔除掉四个异常样本后,考察了不同的光谱预处理方法以及波长选择对模型的影响,用留一交互检验法建立了偏最小二乘(PLS)模型,并用所建立的校正模型对独立预测集样本进行了预测。结果表明,当采用二阶导数及变量稳定性的竞争自适应加权抽样法(SCARS)选择的波长对光谱进行处理时,所建立的模型预测效果最佳,在隐变量数为10时,模型相关系数为0.9906,校正均方根误差(RMSEC)为0.0523 g/100 g,预测相关系数Rp=0.9781,预测均方根误差(RMSEP)=0.0577 g/100 g,该模型具有较好的预测能力,可用于香菇多糖含量的近红外光谱快速检测。     

蒙古莸幼苗干旱致死过程中非结构性碳水化合物的变化

以一年生蒙古莸幼苗为对象,设置适宜水分、慢速干旱致死和快速干旱致死3个处理,研究不同干旱强度致死下蒙古莸幼苗各器官中非结构性碳水化合物(NSC,包括可溶性糖和淀粉)的含量变化及其分配规律.结果表明:慢速干旱致死胁迫下各器官可溶性糖含量与适宜水分组无显著差异.随时间的推移,茎可溶性糖含量先增加后减少,淀粉和NSC含量增加;粗根可溶性糖含量减少,淀粉和NSC含量增加;叶可溶性糖含量增加,淀粉和NSC含量减少.致死时(80 d),叶、茎、粗根和细根的NSC含量分别为6.2%、7.8%、8.3%和7.4%.快速干旱致死胁迫下,各器官可溶性糖含量均高于适宜水分处理组,而淀粉和NSC含量均低于适宜水分组.随时间的推移,根可溶性糖含量下降,淀粉和NSC含量上升;茎可溶性糖、淀粉和NSC含量均上升;叶可溶性糖含量上升,淀粉和NSC含量下降.致死时(30 d),叶、茎、粗根和细根的NSC含量分别为5.9%、6.6%、8.9%和7.7%.应对不同的干旱致死情况,蒙古莸幼苗各器官间非结构性碳水化合物呈现出不同的动态变化.在慢速干旱致死胁迫下,NSC优先为维持各器官生理代谢活动提供能量;而在快速干旱致死下,NSC主要以可溶性糖形式维持植物代谢,调节渗透势,促进吸水,应对急剧的干旱胁迫.     

基于NIRS法快速检测荞麦蛋白质与淀粉含量的研究

为建立近红外光谱技术测定荞麦蛋白质与淀粉含量的方法,本研究以217份荞麦样品为试验材料,采用最小二乘回归预测和交叉验证构建近红外预测模型。分析表明:前处理采用多元散射校正法(MSC),维数(Rank)分别为5和5,光谱区间6803.9~6094.2/cm所建立的荞麦蛋白质与淀粉含量模型的预测效果较好,其决定系数(R~2)分别为0.9481和0.9167,交叉验证均方根(RMSECV)分别为0.68和2.08,相对分析误差(RPD)分别为4.39和3.46,均大于3.0,外部验证相关系数均大于0.96。本试验所建立的蛋白质与淀粉含量近红外预测模型具有较高的准确度和稳健性,可用于荞麦品质的快速测定。     

近红外光谱分析法测定东北黑土有机碳和全氮含量

以我国东北黑土为研究对象,分析了2004-2005年采集的136个土壤样品在3699～12000 cm^-1范围的近红外光谱,利用偏最小二乘法建立了原始光谱吸光度与土壤有机碳、全氮和碳氮比之间的定量分析模型.结果表明：土壤有机碳和全氮的模型拟合效果良好,决定系数R²分别为0.92和0.91（P<0.001）,相对分析误差RPD分别为3.45和3.36,利用该模型对验证样本土壤有机碳和全氮的预测值与实测值之间的相关系数分别为0.94和0.93（P<0.001）,表明可以用近红外光谱分析法对黑土有机碳和全氮含量进行测定.但是利用近红外光谱分析法对土壤碳氮比的预测并不理想,虽然验证样本集黑土碳氮比模型预测值与实测值呈显著相关（r＝0.74,P<0.001）,但是校正模型的R²为0.61,RPD仅为1.61,建立的模型不能对黑土碳氮比做出合理的估测.     

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R(2)(cv)) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R(2)(val)) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R(2)(cv), RMSECV, R(2)(val) and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.     

Blend uniformity analysis using stream sampling and near infrared spectroscopy

A near infrared spectroscopic method was developed to determine drug content in a 20% (wt/wt) ibuprofen and spray-dried hydous lactose blend. A blending profile was obtained after blending for 0.5, 1, 3, 5, 10, and 20 minutes. Stream sampling was used to collect about 20 blend samples at each of the blending times from a laboratory scale V-blender. The samples collected were used to develop a near infrared calibration model. The calibration model was then used to determine the drug content of unknown samples from 2 validation blends. The validation blends were not included in the calibration model; they were used to evaluate the effectiveness of the calibration model. A total of 45 samples from the 2 validation blends were predicted by the near infrared calibration model and then analyzed by a validated UV spectrophotometric method. The root mean square error of prediction for the first validation blend was 5.69 mg/g and 3.30 mg/g for the samples from the second blend. A paired t test at the 95% confidence level did not indicate any differences between the drug content predicted by the near infrared spectroscopy (NIRS) method and the validated UV method for the 2 blends. The results show that the NIRS method could be developed while the blending profile is generated and used to thoroughly characterize a new formulation during development by analyzing a large number of samples. The new formulation could be transferred to a manufacturing plant with an NIRS method to facilitate blend uniformity analysis.     

Near-infrared spectroscopy with a fiber-optic probe for state variables determination in solid-state fermentation

This work studied the feasibility of near-infrared spectroscopy (NIRS) with a fiber-optic probe for the prediction of state variables in solid-state fermentation (SSF) samples with neither previous treatment nor manipulation. The models were developed using 50 samples and had the following root mean square error of cross-validation (RMSECV): 0.0253 for moisture content, 4.720 mg/g wet medium for biomass, 3.51 FPA/g wet medium for cellulase. Ten external samples were used to test the stability of the prediction models. The maximum absolute error and maximum relative error (%) were 0.0372 and 5.86 for moisture content, −6.51 and 11.63 for biomass, and 2.26 and 8.52 for cellulase. The predicted values using the NIRS technology were similar to the values obtained using chemical methods.     

Comparison of near infra-red spectroscopy, neutral detergent fibre assay and in-vitro organic matter digestibility assay for rapid determination of the biochemical methane potential of meadow grasses

This paper investigates near infra-red spectroscopy (NIRS) as an indirect and rapid method to assess the biochemical methane potential (BMP) of meadow grasses. Additionally analytical methods usually associated with forage analysis, namely, the neutral detergent fibre assay (NDF), and the in-vitro organic matter digestibility assay (IVOMD), were also tested on the meadow grass samples and the applicability of the models in predicting the BMP was studied. Based on these, regression models were obtained using the partial least squares (PLS) method. Various data pre-treatments were also applied to improve the models. Compared to the models based on the NDF and IVOMD predictions of BMP, the model based on the NIRS prediction of BMP gave the best results. This model, with data pre-processed by the mean normalisation method, had an R² value of 0.69, a root mean square error of prediction (RMSEP) of 37.4 and a residual prediction deviation (RPD) of 1.75.     

Rapid estimation of single cell oil content of solid-state fermented mass using near-infrared spectroscopy

Calibration model using near-infrared reflectance spectroscopy (NIRS) for estimation of SCO content in solid-state fermented mass was established. The NIRS calibration model was derived by partial least-squares (PLS) regression and prediction of SCO contents of independent solid-state fermented mass samples fermented by different oleaginous fungi showed the model to be rapid and accurate, giving R(2)-value higher than 0.9552 and root mean standard error of prediction (RMSEP) value lower than 0.5772%. The established NIRS calibration model could be used to estimate the SCO contents of the solid-state fermented masses and will provide much convenience to the research of SCO production in solid-state fermentation.     

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。     

Assessment of ATR‐NIR and ATR‐MIR spectroscopy as an analytical tool for the quantification of the total polyphenols in Dendrobium huoshanense

In this study, the potentiality of applying attenuated total reflectance near‐infrared (ATR‐NIR) and attenuated total reflectance mid‐infrared (ATR‐MIR) techniques combined with a partial least squares (PLS) regression technology to quantify the total polyphenols (TPs) in Dendrobium huoshanense (DHS) was investigated and compared. The real TP contents in the DHS samples were analysed using methods of reference. The capability of the two IR spectroscopic techniques to quantify the TPs in DHS was assessed by the root‐mean‐square error of calibration (RMSEC) and determination coefficients (R²). The results showed that both NIR and MIR might be used as a fast and simple tool to replace traditional chemical assays for the determination of the TP contents in DHS, and the best NIR model showed slightly better prediction performance [root‐mean‐square error of prediction (RMSEP): 0.307, R²: 0.9122, ratio performance deviation (RPD): 4.43] than the best MIR model (RMSEP: 0.440, R²: 0.9069, RPD: 3.09). Results from this study indicated that both the NIR and MIR models could be used to quantify the TP in DHS, and ATR‐NIR appeared to be the more predominant and more robust technique for the quantification of the TP in DHS.     

Use of near-infrared reflectance spectroscopy to predict species composition in tree fine-root mixtures

Assessment of belowground interactions in mixed forests has been largely constrained by the ability to distinguish fine roots of different species. Here, we explored near infrared reflectance spectroscopy (NIRS) to predict the proportion of woody fine roots in mixed samples and analyzed whether the prediction quality of NIRS models is related to the complexity of the fine-root mixture. For model calibration and validation purposes, 11 series of artificial mixed species samples containing known amounts of fine roots of up to four temperate tree species and non-woody plants were prepared. Three types of models with different calibration/validation approaches were developed and tested against external independent data for additional validation. With these models the proportion of each species in root mixtures was predicted accurately with low standard error of prediction (RMSECV/RMSEP <6.5%) and high coefficient of determination (r²?>?0.93) for all fine-root mixtures. In addition, NIRS models also provided satisfactory estimates for samples with low (<15%) or no content of particular components. The predictive power of the NIRS models did not decrease substantially with increasing complexity of the root samples. The approach presented here is a promising alternative to hand sorting of fine roots, which may be influenced substantially by operator variation, and it will facilitate investigating belowground interactions between woody species.