烟叶成熟度区分及烟碱含量检测模型研究

为探索一种有效的近红外光谱应用于烟叶成熟度区分以及烟碱含量检测模型建立的方法,对56组烟叶样品近红外光谱数据进行系统聚类,分类结果与原始结果基本一致,客观反映了成熟与未成熟烟叶的差异。识别率达到92.86%。采用多种不同的光谱预处理方法,并选择较优的多元散射校正处理原始光谱,再用偏最小二乘回归建立模型。结果表明：所建模型训练集r=0.9852,RMSE=0.0676;交叉验证r=0.9145,RMSE=0.1645。预测值能够较为均匀紧密地分布于拟合线的两侧,预测结果较好。     

用近红外反射光谱法分析稻米3种必需氨基酸含量的研究

采用近红外反射光谱分析技术来测定水稻精米粉样的3种必需氨基酸含量,揭示不同数学处理方法、光谱散射校正和回归统计方法对建立回归方程的影响.研究表明,光谱数据的数学预处理对发展定标回归方程有很大影响,对于不同的氨基酸,通过比较均可找到一些定标效果较理想的数学处理组合,如"2,3,3,1"等.光谱散射处理对氨基酸的定标改     

近红外光谱法测定大米中的淀粉含量

用化学方法测定64个大米样品中的淀粉含量,利用近红外谷物分析仪采集样品的近红外光谱,选择合适的光谱区间和光谱预处理方法。50个定标集样品的近红外光谱经二阶导数及标准多元离散校正(Standard MSC)预处理,结合偏最小二乘法(PLS)建立了大米中的淀粉含量测定的定标模型,其相关系数为0.8780。14个验证集样品用于外部检验,大米中的淀粉含量的模型预测值与化学值之间的相关系数为0.9498。     

云南烤烟填充值近红外光谱预测模型

为研究采用近红外光谱技术快速预测烤烟烟丝填充值的可行性,选取云南12个烤烟品种不同部位3个等级的140份烤烟初烤烟样品,采集样品近红外光谱和烟丝填充值,采用偏最小二乘法,利用100份样品近红外光谱和填充值建立近红外光谱填充值预测模型。用40份样品近红外光谱和填充值进行模型外部验证,对填充值测定值和预测值进行t检验,T＝0.4125,在显著性水平α＝0.05下,t(0.05,39)=1.6849,T     

基于近红外光谱的小麦品质分类研究

为了快速、简便、准确地鉴别小麦品质的类别,本研究提出了应用近红外光谱分析技术结合BP神经网络的鉴别方法对小麦进行品质分类。研究过程中对小麦样品的光谱数据进行了详细分析,采用马氏距离剔除了光谱数据中异常数据,并通过主成分分析说明利用近红外光谱鉴别小麦品质分类的可行性。为了提高所建模型的性能,采用SPXY算法对小麦样品进行合理的划分。并选取了一阶微分加归一化的预处理方法来处理光谱数据,消除无关信息和噪声对小麦光谱数据的影响。运用偏最小二乘法压缩光谱数据,减少了数据量,节省建模时间。最后采用BP神经网络方法建立了小麦品质分类模型。实验结果显示：模型的鉴别效果较好,对强筋样品识别的准确率高达94.4%,弱筋样品识别的准确率高达100%。实现了快速、准确地对小麦品质强筋和弱筋两类的鉴别,对小麦生产、市场交易及食品加工有着非常重要的意义。     

近红外光谱仪与凯氏定氮法测定油菜蛋白质含量的比较

应用近红外光谱仪和凯氏定氮法测定油菜籽中蛋白质含量作比较分析.结果表明:相同样品用两种分析方法测试结果基本吻合,其偏差和相对标准偏差均小于2%,说明近红外光谱仪所建模型稳定性较好,分析结果准确、可靠,用于日常检验和基础材料的筛选是可行的.     

棉子油分近红外光谱测定技术研究

以近红外光谱分析技术无损、快速和高效等优点,建立棉籽油分含量的近红外分析模型,进行棉籽油分快速大批测定,提高分析水平和效率。218份陆地棉的脱绒光籽为定标样品,通过不同的光谱数学预处理和光谱反射方式,优化棉子含油量近红外光谱法所建立的回归方程。结果表明,采用(2,4,4,1)光谱数学预处理和散射校正(SNV)得到的回归方程效果最好,回归决定系数(RSQ=0.9773)和验证决定系数(1-VR=0.9646)最高,回归标准误差(SEC=0.6977)和交互验证标准误差(SECV=0.8714)最小。对30份验证样品的化学测定值和近红外光谱预测值相关系数高达0.9882,平均偏差0.65%,回归标方程具有很好的预测效果,可以在实践中应用。长江流域棉花杂交种棉子含油量平均值为28.05%,含油量在26.50%~30.05%区间的品种居多,具有很大的改良潜力。     

黍稷籽粒淀粉含量的近红外光谱分析

利用FOSS1241型近红外谷物分析仪对100份黍稷籽粒原样进行光谱扫描,分别采用偏最小二乘法和改进偏最小二乘法两种定标方法,并对原始光谱分别进行不同的预处理,建立了黍稷籽粒淀粉定标模型.试验结果显示:对黍稷籽粒原样扫描的光谱采用偏最小二乘法进行定标,在标准正常化+ 趋势变化散射处理、采用一阶导数、每隔4点求导、每隔4...     

利用VIS/NIR反射光谱快速检测柿饼加工中水分含量

水分是柿饼的重要组成成分,也是影响柿饼制作过程的重要因素。利用可见/近红外反射光谱对柿饼制作过程中的水分含量进行检测。首先,获取柿饼在不同加工阶段的可见/近红外反射光谱(400～1 000 nm),采用烘干法测定柿饼水分含量。然后,对光谱进行Mean smoothing (MS)平滑、多元散射校正(MSC)和一阶导数(1-D)预处理。最后,对不同预处理光谱,结合样本水分含量,使用Samples set partitioning based on joint x-y distance (SPXY)方法划分校正集和验证集,基于SPA方法选择特征波长,建立多元线性回归(MLR)预测模型。结果表明,反射光谱经过MS处理后,确定的9个最优波长组合建立水分检测模型的预测结果最好:预测相关系数(Rp)为0.969 0,预测标准残差(SEP)为3.472 9%,可见/近红外反射光谱技术可以较好地预测柿饼制作过程中的的水分含量。研究可为柿饼加工过程中的品质快速检测提供一定的技术支撑。     

近红外分析的样品前处理新技术研究

摘 要：样品的水分含量、粒度等因素对近红外的检测结果会产生较大影响。在近红外实验室分析中,为获得较好的分析准确度,一般需采用干燥和粉碎等处理。本文以云南烤烟样品为实验材料,在近红外分析样品的前处理中创新性的采用微波快速干燥技术以及普通家用食物粉碎机快速粉碎的方法,使整个样品前处理时间可控制在5分钟以内,同传统烘箱干燥、粉碎过筛等方法相比其处理速度可提高几十倍。实验结果表明：当样品的水分含量差异较大时,通过微波快速干燥方法能有效提高和保证模型分析的准确度;烤烟样品经过普通家用食物粉碎机快速粉碎25秒后可消除粒度差异对近红外检测结果的影响。本研究结果可为近红外分析工作者提供一种快速有效的干燥和粉碎技术,从而简化样品的前处理。     

Estimation of seed weight, oil content and fatty acid composition in intact single seeds of rapeseed ( Brassica napus} L.) by near-infrared reflectance spectroscopy

The potential of near-infrared reflectance spectroscopy (NIRS) for the simultaneous analysis of seed weight, total oil content and its fatty acid composition in intact single seeds of rapeseed was studied. A calibration set of 530 single seeds was analysed by both NIRS and gas-liquid chromatography (GLC) and calibration equations for the major fatty acids were developed. External validation with a set of 75 seeds demonstrated a close relationship between NIRS and GLC data for oleic (r = 0.92) and erucic acid (r = 0.94), but not for linoleic (r = 0.75) and linolenic acid (r = 0.73). Calibration equations for seed weight and oil content were developed from a calibration set of 125 seeds. A gravimetric determination was used as reference method for oil content. External validation revealed a coefficient of correlation between NIRS and reference methods of 0.92 for both traits. The performance of the calibration equations for oleic and erucic acid was further studied by analysing two segregating F2 seed populations not represented in the calibration set. The results demonstrated that a reliable selection for both fatty acids in segregating populations can be made by using NIRS. We concluded that a reliable estimation of seed weight, oil content, oleic acid and erucic acid content in intact, single seeds of rapeseed is possible by using NIRS technique. This revised version was published online in July 2006 with corrections to the Cover Date.     

Genetic variation in phytosterol content of winter rapeseed (Brassica napus L.) and development of NIRS calibration equations

Rapeseed oil is one of the richest natural sources of phytosterols, known to reduce the LDL-cholesterol levels, one of the major cardiovascular disease risk factors. Increasing the phytosterol content in rapeseed could give an added value to the oil and derived products. Our objective was to analyse the genetic variation of phytosterol content in modern winter rapeseed cultivars and resynthesized lines following field experiments and to develop a near-infrared reflectance spectroscopic (NIRS) calibration for high throughput estimation of phytosterol content. Phytosterol content as analysed by gas–liquid chromatography ranged from 3565 to 4800 mg/kg seed for modern cultivars and from 2079 to 4329 mg/kg seed for resynthesized lines. The NIRS calibration showed a high fraction of explained variance in cross-validation of 0.81 for total phytosterol content and the standard error of cross-validation was 241 mg/kg. The results show that the cultivars contain already high phytosterol contents. The NIRS calibrations developed for total phytosterol content should be useful for germplasm screening and in breeding programmes aimed at increasing the phytosterol content in rapeseed.     

Genetic variation for sinapate ester content in winter rapeseed (Brassica napus L.) and development of NIRS calibration equations

Increasing the meal and protein quality of winter rapeseed (Brassica napus L.) for food and feed purposes is gaining importance in rapeseed breeding programmes. Rapeseed meal has a high content of phenolic acid esters, mainly sinapate esters, which have been shown to cause a dark colour and a bitter taste in rapeseed meal and derived protein products. The aim of the present study was to analyse the genetic variation for individual and total sinapate ester content, to develop Near Infrared Reflectance Spectroscopic (NIRS) calibrations, and to identify genotypes with a low sinapate ester content after testing in the field. The following sinapate esters were analysed by HPLC: sinapoylcholine (sinapine), sinapoylglucose, and a minor group of ‘other sinapate esters’ which includes free sinapate. A genotypically diverse set of seed samples of winter oilseed rape (old and new cultivars, breeding lines, resynthesized rapeseed) from different years and locations was collected, their NIRS spectra recorded and the samples were further analysed by HPLC. The complete NIRS calibration seed sample set (n = 575) showed a large variation in total sinapate ester content, ranging from 3.2 to 12.7 mg sinapate equivalents per g seeds. The NIRS calibration equations showed high fractions of explained variances in cross validation () ranging from 0.75 (other sinapate esters) to 0.85 (sinapoylglucose). The standard errors of cross validation (SECV) ranged from 0.38 (other sinapate esters) to 0.70 mg/g seed (total sinapate esters). In validation and in independent validations the predicted results were not always acceptable, indicating that the NIRS calibrations need to be extended by analysing samples from new populations. Following replicated field experiments, a doubled haploid line obtained from the old Dutch cultivar Mansholts’ Hamburger Raps, and related DH lines from the cross DH Mansholts’ × Express were confirmed to have a 30–40% lower sinapate ester content compared to check cultivars.     

Estimating the fatty acid composition of the oil in intact-seed rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy

The objective of this work was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid method to estimate the fatty acid composition of the oil in intact-seed samples of rapeseed. A total of 549 samples (3 g intact seed) from selected mutant and breeding lines were scanned by NIRS, and 220 of them were selected and scanned again by using two different adapters, which reduced the sample size to 300 and 60 mg, respectively. Selected samples were analysed by gas liquid chromatography and calibration equations for individual fatty acids were developed. Calibrations for oleic, linoleic, linolenic, and erucic acid were highly accurate, with values of r² in cross validation from 0.95 to 0.98 (samples of 3 g), from 0.93 to 0.97 (300 mg), and from 0.84 to 0.96 (60 mg). Calibrations for palmitic and stearic acid were less accurate, with values of r² in cross validation always lower than 0.8, probably because of the narrow range available for these fatty acids. The accuracy of the calibration equations for eicosenoic acid was very low (r² = 0.69 in 3 g samples), although improved equations were developed (r² from 0.78 to 0.91) when the relationship between erucic and eicosenoic acid was taken into account. We conclude that NIRS is a powerful technique to estimate the fatty acid composition of the oil in rapeseed, provided that samples covering a wide range of fatty acid levels are available, with the advantage that such estimation is possible with few additional costs when NIRS is used for the determination of other seed quality traits. This revised version was published online in July 2006 with corrections to the Cover Date.     

Identification of genetic variation in Brassica napus seeds for tocopherol content and composition using near‐infrared spectroscopy technique

Tocopherol is an essential fat‐soluble nutrient for humans. Increasing the tocopherol content in Brassica napus seeds can add value to rapeseed vegetable oil; this has become an important breeding target. However, there is no efficient and non‐destructive method for selecting rapeseed accessions with high tocopherol contents. Here, we report the first near‐infrared reflectance spectroscopy (NIRS)‐based technique for predicting rapeseed tocopherol content. Individual seed tocopherol compositions were estimated from 373 rapeseed genotypes of different origins. This method and chemical methods produced comparable predicted values of the tocopherol constituents in the seeds. Three equations were generated for the prediction of tocopherol content by using a modified partial least squares (MPLS) model. The total tocopherol content for the determination coefficient of cross‐validation (R²_cv) (0.74), determination coefficient (RSQ) (0.76) and one minus the ratio of unexplained variance to total variance (1‐VR) (0.65) values indicates a strong correlation between the calibration and validation sets. Overall, our model confirmed the NIRS method as feasible for predicting tocopherol content in rapeseed and as an efficient screening tool for future breeding programs.     

可见/近红外光谱技术无损识别苹果品种的研究

为了能快速无损鉴别苹果的品种，基于高光谱成像技术结合模式识别算法，分别建立了判别苹果品种K最近邻（KNN）识别模型与偏最小二乘判别分析（PLS-DA）识别模型。综合比较了不同光谱预处理方法（二阶微分（SD）、标准正态变换（SNV）和多元散射校正（MSC））对各模型识别效果的影响，并利用主成分分析方法对预处理后的光谱数据进行降维，以提取能反映苹果品种的特征光谱。结果表明：采用主成分分析法选择了累计贡献率超过99.9%的前10个主成分作为样本集特征光谱数据，很好地实现了光谱数据的降维；MSC预处理方法对光谱反射率预处理的效果最好；2种判别模型均能基本满足实际要求，且MSC+KNN识别模型的识别性能最优，对预测集样本的正确识别率高达100%。     

Near infrared reflectance spectroscopy analysis of digestibility traits of maize stover

Summary Near infrared reflectance spectroscopy (NIRS) is emerging as a potentially useful tool in breeding plants for quality traits. Information is lacking, however, on its use in forage maize (Zea mays L.). The objectives of the present investigation were to evaluate the prediction of digestibility traits of maize stover using NIRS technique and to study the effect of laboratory (Lab) and NIRS assays on the estimates of variation and covariation. Twelve inbred lines, 66 diallel crosses among them and eight hybrid checks were evaluated at silage and grain harvests for 2 years at two agro-climatically diverse locations in the Federal Republic of Germany. Standard methods were used for Lab analysis of in vitro digestible organic matter (IVDOM), neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL). In NIRS evaluation, calibration equations were developed by modified stepwise regression. The standard error of calibration was 2.5, 1.7, 1.4 and 0.4 for IVDOM, NDF, ADF and ADL, respectively. The coefficient of multiple determination was high (0.9) except for ADL. The validation statistics (standard error and correlation coefficient) were similar. In the diallel crosses, the estimates of variation (heritabilities in broad and narrow sense, genotypic and error coefficients of variation), generally, did not vary appreciably and consistently in the comparisons between Lab and NIRS methods particulary at silage harvest. Simple and rank correlations between Lab and NIRS analyses were positive and significant. These correlation coefficients based on the mean performance of the diallel crosses at silage harvest were >0.9 and at least 16 hybrids were common between the two analyses, among the upper one-third or lower one-third (22) hybrids. The study showed that NIRS analysis should be useful in maize breeding programmes wherein a large number of genotypes need to be evaluated.Abbreviations NIRS near infrared reflectance spectroscopy - Lab laboratory - IVDOM in vitro digestible organic matter - NDF neutral detergent fibre - ADF acid detergent fibre - ADL acid detergent lignin - SEC standard error of calibration - SEV standard error of validation - SD standard deviation - r simple correlation coefficient - r_s rank correlation coefficient - R² coefficient of multiple determination - h_b ² heritability in broad sense - h_n ² heritability in narrow sense - CV_g genotypic coefficient of variation - CV_e error coefficient of variation - GCA general combining ability effect     

近红外光谱技术鉴别猪肉和牛肉的研究

本文以近红外光谱分析技术结合模式判别方法建立一种鉴别猪肉和牛肉的方法为目的。采用近红外漫反射光谱法,通过多元散射校正（MSC）、一阶导数加Norris导数平滑点（5,3）处理光谱,波段范围选取5996.42～5805.84,9058.85～8546.36 cm-1,采用TQ Analyst光谱分析软件中的马氏距离设为1建立判别分析模型。结果表明,模型能够准确的鉴别出猪肉、牛肉。因此,近红外光谱技术用于猪肉、牛肉鉴别分析具有可行性。