利用近红外透射光谱技术同步测定糙米的多项品质指标初报

利用FOSS TECATOR 公司生产的1241型近红外透射光谱仪,对266份整粒糙米样品进行了光谱扫描并测定了其多项品质指标。借助于近红外定标软件WINISI,建立了利用近红外透射光谱同时测定糙米多项品质指标的模型。结果表明,蛋白质模型的分析效果最好,其外部检验 (经校正的) 工作标准误\[SEP (C) \] 为0.312,检验决定系数(RSQ )为0956,而其他4项指标直链淀粉含量、糊化温度(碱消值)、透明度（透光率）和垩白度模型的SEP (C)分别为1.672、 0.389、 0034和4.024,RSQ分别为0.745、0.838、0.797和0.714。分析了近红外测定的重复性并讨论了该模型在水稻遗传育种等方面的应用前景。     

单粒活体稻谷种子直链淀粉含量的近红外透射光谱分析

利用FOSS Tecator公司的Infratec 1255型带单粒定标器的近红外谷物分析仪,对222粒单粒稻谷进行扫描并测定了直链淀粉含量的参比数据。借助于功能强大的近红外定标器软件(WinISI),采用多种计量数学处理方法和不同的回归统计方法进行定标曲线的开发和比较,优化得到了单粒水稻种子直链淀粉含量测定的近红外定标方程。其定标标准偏差（SEC）、交叉检验标准误差（SECV）、检验工作标准误差（SEP）和定标相关系数（RSQ）分别为2.828、3.088、2.792、0.848。     

水稻细菌性褐条病病原的鉴定

为明确从褐条病稻苗上分离出来的病原细菌并与西瓜果斑病菌相区分, 对该病原特性进行了研究。分离获得6株褐条病致病菌,其中4株经主要细菌学特性、菌落形态、致病性、Biolog、脂肪酸分析（FAME）、电镜观察、Nested PCR鉴定及与3株水稻细菌性褐条病标准菌株和2株西瓜果斑病标准菌株的比较,证实了该病是由单极鞭革兰氏阴性细菌〖WTBX〗Acidovorax avenae〖WTBZ〗 ssp. 〖WTBX〗avenae〖WTBZ〗引起的。FAME将水稻细菌性褐条病菌误鉴定为西瓜果斑病菌,而用Biolog和nested-PCR鉴定能得到准确的鉴定结果。     

绿茶茶多酚含量近红外光谱快速无损检测方法研究

应用现代近红外光谱分析技术,对156份绿茶样品直接进行光谱扫描,采用偏最小二乘法（PLS）建立了茶多酚含量的定标模型,并讨论了不同的散射处理、导数处理和平滑处理等光谱预处理方法对模型的影响,最后对最优模型的预测性能进行了验证。原始光谱在经过多元散射校正、二阶导数和8点平滑光谱预处理下的模型较优,其定标标准差（SEC）为1．33％,定标相关系数（RC）为0．932,预测标准差（SEP）为1．61％,预测相关系数（RV）为0．913,预测偏差（Bias）仅为0．375％。结果表明,应用近红外光谱法可以实现绿茶中茶多酚含量的快速无损检测,建立的定标模型能够达到实际应用中的精度要求。     

小麦籽粒淀粉和直链淀粉含量的近红外漫反射光谱法快速检测

为寻找一种简便易行的小麦籽粒淀粉和直链淀粉含量测定方法,以91个普通小麦品种及高代稳定品系为材料,采用偏最小二乘（PLS）回归法,对利用近红外漫反射光谱（NIRS）法测定小麦完整籽粒淀粉及直链淀粉含量进行了研究。结果表明,采用一阶导数+减去一条直线、矢量归一化光谱预处理,分析谱区为7 501.9~5 450 cm-1、7 501.9～4 597.6 cm-1,分别建立淀粉、直链淀粉含量的校正模型,校正和预测效果最佳。模型的校正决定系数(R2cal）分别为0.894 8和0.920 6,交叉验证决定系数（R2cv）分别为0.690 2和0.827 6,外部验证决定系数（R2val）分别为0.815 1和0.806 7;各项误差为1.479~1.080。表明利用NIRS分析法测定完整小麦籽粒的淀粉和直链淀粉含量是完全可行的。     

基因水稻培矮64S回交后代白叶枯病抗性与育性研究

对筛选到的转[WTBX][STBX]Xa21[WTBZ][STBZ]基因培矮64S后代纯合株系抗性与育性的研究表明,在第5代杂种中,该外源基因仍能稳定遗传表达;以其作父本、培矮64S为母本所配制的F1植株, Xa21基因PCR检测均呈阳性,且绝大多数表现抗白叶枯病,说明该外源基因能够通过常规杂交方式转移利用。这些杂交组合F2群体中抗病与感病株出现3∶1分离,表明转Xa21基因在杂交后代的传递属单基因显性遗传。长日高温下,（培矮64S/转Xa21基因培矮64S）F1雄性完全可育,F2群体可育与不育株出现27∶1分离。     

高油玉米群体油分、蛋白质和淀粉含量近红外分析模型的构建

以BHO高油玉米F2∶3家系为材料,应用主成分空间和傅里叶变换近红外光谱分析技术,采用偏最小二乘回归法(PLS),建立了测定高油玉米子粒的油分、蛋白质和淀粉含量的近红外校正模型。预处理分别采用一阶导数 矢量归一化、一阶导数 多元散射校正及直线相减等方法,主成分维数分别为5、9、9。验证分析表明,所建立的油分、蛋白质和淀粉含量的校正模型的校正和预测效果最好,其校正决定系数(R2cal)分别为0.950、0.973、0.976,交叉验证决定系数(R2cv)和外部验证决定系数(R2val)在0.918～0.948,各项误差(RMSEE、RMSECV、RMSEP)在0.305%～0.721%。结果表明,所建立的高油玉米完整子粒品质性状三成分模型的准确度和精确度均较高,可以满足高油玉米群体大量样品无损品质分析的需要。     

大麦籽粒抗性淀粉含量快速测定方法研究

为探索快速高效测定大麦籽粒中抗性淀粉含量的方法，利用衰减全反射中红外（attenuated total reflection mid-infrared spectroscopy,ATR-MIR）和近红外（near-infrared spectroscopy,NIR）光谱技术，分别用3种不同方法进行预处理，建立大麦样品的抗性淀粉含量快速测定红外模型，通过不同预处理预测模型的校正和内部交叉验证结果的比较，依据决定系数（r）和均方根误差（RMSE）筛选出基于ATR-MIR和NIR光谱的最佳预测模型，再对最佳预测模型进行外部验证。结果表明，经基线位移校正+范围归一化（BOC+RN）预处理后的PLS模型为最佳ATR-MIR预测模型；经标准正态变换+Savitzky-Golay法一阶求导（SNV+1thD）的预处理模型为最佳NIR预测模型。用验证集材料对BOC+RN和SNV+1thD最佳预测模型的预测效果进行外部验证，光谱预测值与化学测定值之间没有显著差异，说明两种方法均可以用于大麦籽粒抗性淀粉含量测定；ATR-MIR光谱比NIR光谱具有更好的预测能力。     

小种红茶茶多酚和咖啡碱近红外定量分析模型的建立

以76份有代表性的小种红茶为研究对象,采用现行国标方法测定的茶多酚和咖啡碱含量作为近红外预测模型的化学值,对应采集样品的近红外光谱值,分别建立小种红茶茶多酚和咖啡碱含量最佳偏最小二乘法（Partial least squares,PLS）模型。结果表明,所构建的茶多酚含量模型校正集决定系数（Coefficient of determination,R²）为97.59%,校正均方差（Root mean square error of calibration,RMSEC）为0.566%,验证集R²为95.06%,预测均方差（Root mean square error of prediction,RMSEP）为0.855%;咖啡碱含量模型校正集R²为96.98%,RESEC为0.110%,验证集R²为95.67%,RESEP为0.148%。茶多酚和咖啡碱含量定量分析模型效果均较好,可实现对小种红茶茶多酚和咖啡碱含量的快速检测。     

基于高光谱的冬小麦不同生育时期地上部生物量监测

为实现冬小麦不同生育时期地上部生物量的高光谱监测,2017-2019年分别在河南省鹤壁市、原阳县和温县布置冬小麦氮肥梯度田间试验,分别于分蘖期、拔节期、抽穗期和灌浆期测定冬小麦地上部生物量及其冠层原位高光谱反射率（400～950 nm）,并采用Pearson相关分析明确两者间定量回归关系,再分别利用支持向量机（support vector machine, SVM）和偏最小二乘回归（partial least square, PLS）建立预测模型并进行精度验证,以确定最优光谱监测时期和有效波段。结果表明,冬小麦地上部生物量与冠层高光谱反射率在可见光区（400～715 nm）呈负相关,在近红外区（715～950 nm）呈正相关,且相关性表现为分蘖期<拔节期<灌浆期≤抽穗期。生育时期间模型精度差异较大,抽穗期效果最优,SVM和PLS模型的验证决定系数分别为0.877和0.859,相对分析误差分别为2.429和2.340;灌浆期次之,决定系数分别为0.835和0.830,相对分析误差分别为2.416和1.814;分蘖期最低,决定系数分别为0.693和0.750,相对分析误差分别为1.063和0.894。同时,冬小麦地上部生物量有效波段在生育时期间具有明显的异同性,分蘖期时有效波段在可见光-近红外区均有明显的均衡分布,至拔节期时产生明显的短波“蓝移”现象,抽穗期“蓝移”现象更显著,而至灌浆期则表现出明显的长波“红移”特征。此后,再次构建基于有效波段的冬小麦不同生育时期地上部生物量SVM和PLS监测模型,决定系数和相对分析误差分别高于0.72和1.40,预测精度较理想,能够满足无损和精准监测需求。     

Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy

This study was conducted to develop near-infrared reflectance spectroscopy (NIRS) equations to predict the amino acid and nitrogen content of milled rice powder. The samples were scanned by NIRS and analyzed for amino acid composition and total nitrogen by HCl hydrolysis–HPLC methodology and Kjeldahl method, respectively. The NIRS equations of 15 different amino acids, except for cystine, methionine and histidine, showed high coefficients of determination (RSQ=84.8–97.5%) and low standard errors in calibration (SEC) with 3 g samples for NIRS scanning, while the calibration models of cystine and histidine could explain less variation (RSQ with 77.7 and 65.0%). Calibration for methionine was not suitable to estimate methionine because of its very low RSQ (10.2%). The equations for total amino acids and nitrogen also showed high RSQ and lower SEC, respectively. Furthermore, calibration equations developed with only about 500 mg samples showed similar accuracy and reliability to those with the full cup by using the same calibration set. The equations developed for relative contents of total amino acids did not show good, effective calibration and cross-validation. Only eight different amino acids can be predicted using the equations because their RSQs of calibration were higher than 50.6% (50.6–73.9%). The others cannot be estimated with confidence by their relative contents due to lower RSQ in calibration. Moreover, their relative contents can be calculated from their absolute contents estimated by NIRS calibration.     

Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy

Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein (CP) content on intact sunflower seeds (Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration (n=250) where the remaining samples (n=50) were used as validation set. Two mathematical treatments (first and second derivative), none (log 1/R) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient of determination in calibration (R_cal²) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96 (SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg⁻¹ on a dry weight basis (second derivative, 400–2500 nm). Prediction models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs.     

近红外反射光谱技术预测花生种子含水量

选取116份大花生为实验材料,应用近红外反射光谱技术,结合偏最小二乘法,采用交叉检验建立了大花生含水量的近红外模型。优化结果表明,原始光谱不经过预处理,光谱范围为4597．7～11988C1TI-1,维数12,此时建立的模型校正结果最佳。模型决定系数（形）为93．62,根均方差（RMSECV）为1．17。用该模型对20个未参与建模的实验材料进行预测,偏差为-1．781～1．902,相对误差为0．122Voo～2．855％。结果表明含水量模型具有很好的预测准确性,可用于鲜食花生种子水分含量快速检测。     

近红外反射光谱测定玉米完整子粒蛋白质和淀粉含量的校正模型

以玉米完整子粒为实验材料,采用偏最小二乘回归法建立近红外反射光谱测定其蛋白质和淀粉含量的数学模型,光谱预处理结果表明采用矢量归一化和一阶导数+多元散射校正分别建立蛋白质含量和淀粉含量的校正模型效果最佳,外部验证结果证明校正后模型预测结果与化学值之间的相关系数分别达到0.946 9和0.924 0。     

Prediction of grain weight,brown rice weight and amylose content in single rice grains using near-infrared reflectance spectroscopy

The potential of near-infrared reflectance spectroscopy (NIRS) for simultaneous analysis of grain weight (mg), brown rice weight (mg) and milled rice amylose content (AC, %) in single rice grains was studied. Calibration equations were developed using 474 single grain samples, scanned as both rice grain and brown rice. An independent set containing 90 F₂ generation grains was used to validate the equations. In general, equations developed using the first derivative resulted in superior calibration and validation statistics compared with the second derivative and those developed using brown rice were superior to those developed from the rice grain. Fitting equations were developed and monitored with an external validation set. The standard error of prediction (corrected for bias) SEP(C) for AC, brown rice weight and rice grain weight for equations developed using brown rice were 2.82, 1.09 and 1.30, with corresponding coefficient of determinations (r²) of 0.85, 0.71 and 0.67, and SEP(C)/S.D. of 0.39, 0.57 and 0.59, respectively. It was demonstrated that NIRS provides a convenient way to screen single intact grains. This will be advantageous in early generation selection in rice breeding programs.     

Measurement of Gliadin and Glutenin Content of Flour by NIR Spectroscopy

Traditional NIR calibration methods rely on assembling a calibration set of samples and using procedures such as multiple linear regression or partial least squares to develop the calibration. The problem with this methodology is to assemble a calibration set which maximises the diversity of samples represented whilst minimising the intercorrelations between constituents, particularly total protein content and moisture content. The application of NIR measurements of grain has moved beyond simply measuring protein and moisture content. There is now considerable interest in using NIR to measure a range of quality parameters such as Extensograph extensibility and maximum resistance. These parameters are not themselves represented in the NIR spectrum, but are a direct result of the protein composition of the sample. Consequently, a method for predicting the protein composition would be useful. In this paper, we present the results of a comparison of a curve fitting methodology and the more usual partial least squares curve fitting of the component protein spectra, using samples obtained from a wheat breeders» trial. Gliadin and glutenin contents were measured by SE-HPLC and used to develop a partial least squares calibration and the results compared with a curve-fitting methodology. For the situation examined here, the curve fitting methodology did not perform as well as partial least squares calibration. For glutenin, SEP=0·65 for the curve fitting compared to SECV=0·38 for a traditional PLS calibration. However, the results from the curve-fitting are independent of the total protein content and show sufficient discrimination for potential use in sample protein ranking.     

Prediction of the nutrient content of botanical fractions from annual legumes by near-infrared reflectance spectroscopy

Procedures used to determine chemical composition and digestible organic matter in dry matter (DOMD) are slow and expensive. The possibility of using near-infrared reflectance spectroscopy (NIRS) as an alternative procedure was investigated with annual legumes. Material from cultivars of Medicago murex, Trifolium balansae, T. resupinatum and T. subterraneum was harvested soon after plants had matured. Samples were sorted into stem, leaf and burr fractions and analysed chemically and by NIRS. Data were then sorted into two similar sets, one of which was for calibration and the other for validation. Data for each chemical fraction, in samples used for calibration, were regressed sequentially against the corresponding reflectance spectral data, the log of there reciprocal of which was transformed to first or second derivatives. Equations of best fit were then used to predict the composition of samples in the validation set.
Standard errors of calibration and validation respectively, expressed as percentages of the mean, were 0·5 and 0·6 for dry matter (DM), 2·0 and 2·6 for organic matter (OM), 4·8 and 4·3 for DOMD, 6·0 and 7·2 for crude protein, 4·1 and 4·4 for acid-detergent fibre (ADF), 2·5 and 3·1 for neutral-detergent fibre (NDF) and 8·9 and 10·9 for lignin.