近红外反射技术建立合肥地区精米直链淀粉含量测定模型

以合肥地区种植的203份水稻材料为检测对象,用近红外反射技术采集光谱,常规化学方法测定精米直链淀粉含量。结果表明,定标样品的直链淀粉含量分布范围为3.439%～28.046%,代表性和连续性良好。采用多种计量数学处理方法和偏最小二乘法(PLS),优化建立了精米直链淀粉含量的定量分析预测模型。定标集(C-Set)样品数132个,相关系数(Rc)0.9278,定标标准差(SEC)1.6582;验证集(V-Set)样品数67个,相关系数(Rv)0.8736,预测标准差(SEP)1.9083,并证实所建立的模型在测定精米直链淀粉含量上具有很好的准确性和实用性,对合肥地区水稻品质育种及种质资源相关研究具有实用价值。     

大米直链淀粉含量的近红外光谱分析

大米的直链淀粉含量是影响大米蒸煮和加工特性的最重要因素之一,常被用作蒸煮米质构特性评价指标。该文对不同粒度、不同类型大米样品进行了近红外光谱分析,建立了大米直链淀粉含量的预测模型,（精米样品）预测值与化学分析值的相关系数达0.95。预测标准差、平均相对误差分别为0.56和3.1%。     

烤烟烟叶钾含量的近红外光谱法快速测定

随机选取烤烟建模集样品(150个)和检验集样品(35个),利用傅里叶变换近红外光谱仪测定烤烟样品的近红外光谱,并用常规化学分析法测定烤烟样品的含钾量。采用偏最小二乘法(PLS)把测得的烤烟样品的光谱值与烤烟钾含量的数值拟合建立定标模型,经分析得出:预测模型分析烤烟钾含量的决定系数(R2)为0.909,预测标准差(RMSEP)为0.119%。近红外法测定结果与常规化学分析方法的结果具有较好的相关性,能够应用于烤烟钾含量的快速诊断。     

粳稻七种必需氨基酸含量近红外漫反射光谱分析技术研究

利用化学法测定了粳稻精米粉必需氨基酸的含量,并分别建立了相应的近红外分析预测模型。结果表明,不同光谱预处理方法对近红外分析模型的预测结果有较大影响,采用光谱预处理的校正效果比不采用预处理的好。用偏最小二乘法(PLS)获得的粳稻精米粉缬氨酸、异亮氨酸、亮氨酸、苯丙氨酸、蛋氨酸、苏氨酸、赖氨酸等7种必需氨基酸含量的预测模型和交叉验证结果显示表明,最优校正决定系数(R2)和交叉检验均方误差(RMSECV)分别为0.8868、0.0303;0.8623、0.0237;0.9008、0.0359;0.8993、0.0278;0.5999、0.0256;0.76040、.0238;0.8543、0.0173。因此在水稻品质育种中,近红外光谱分析技术可用于除蛋氨酸、苏氨酸外的其余5种必需氨基酸含量的测定。     

应用FT-IR光谱指纹分析和模式识别技术溯源茶叶产地的研究

利用近红外光谱分析技术,对28份茶样品进行主成分和聚类分析。结果表明,浙江省龙井绿茶近红外原始光谱谱图差异较大,而不同产地龙井绿茶原始光谱间差异不甚明显。对原始光谱数学处理后对其进行主成份分析,发现在主成分空间内第1主成分得分绝大部分为正,继而对不同产地的样品进行主成分分析,西湖龙井有比较明显的主成分特征,区别于浙江龙井;"西湖龙井"主成份空间分布的离散度大于浙江各市县龙井的变异。对龙井绿茶样品进行聚类分析,得出相同产地的绿茶样品可聚为一类。初步表明应用近红外光谱分析技术可准确、快速、低廉地追溯茶叶的产地。     

玉米低植酸突变体的营养品质分析

对比研究了8个玉米低植酸(Low phytic acid,lpa)突变体及其相应野生型(Wild Type,WT)的主要营养品质及其营养组分,结果显示:与野生型相比,各突变体总磷含量基本不变,无机磷含量均成倍增加,总淀粉含量显著下降,直链淀粉和粗脂肪含量变化不显著,粗蛋白含量在部分突变体中显著上升;多数氨基酸含量在突变体中有所提高,赖氨酸含量除Q319-lpa和X178-lpa下降外均不同程度上升;铁、锰、铜、锌、硒等微量元素含量变化不显著。研究表明lpa突变对玉米营养品质的改良和生物学效价的提高具有有益的作用。     

加工型功能早籼稻新品种“浙辐201”的选育与特性

浙辐201是新品系201干种子经60Coγ射线辐射培育而成的高抗性淀粉含量的早籼稻新品种,具有直链淀粉含量高、胶稠度软和抗性淀粉含量高的特点,适合加工专用和功能食品开发,适宜在浙江、江西、安徽、湖南等省作早中熟早籼稻栽培。     

不同生态条件下氮肥优化管理对杂交中稻稻米品质的影响

在四川省温江和射洪试验点,采用单因素随机区组试验设计,以‘F优498’水稻品种为试验材料,研究了不同氮肥处理[普通尿素优化施肥、减氮15%优化施肥、增氮15%优化施肥,PASP（聚天门冬氨酸）尿素1次施肥、2次施和优化施肥]对稻米品质的影响。结果显示,温江的碾米品质、外观品质和籽粒粗蛋白含量较优;射洪的峰值黏度和崩解值较高,消减值较低,蒸煮食味品质较好,同时直链淀粉含量较高。随着氮肥的施用,稻米碾米品质、直链淀粉含量和籽粒粗蛋白含量显著提高,崩解值显著降低;同时导致射洪生态点的峰值黏度增加,消减值减少;温江生态点的稻米外观品质变优,峰值黏度减小,消减值增加。较农民经验性施肥处理,普通尿素优化处理和PASP尿素处理提高了直链淀粉含量和籽粒粗蛋白含量,降低了温江垩白粒率和垩白度,改善了外观品质;氮肥优化处理降低了峰值黏度和崩解值,提高了消减值,使稻米蒸煮食味品质变差,同时提高了射洪精米率和温江整精米率。较优化施肥处理,PASP尿素处理降低了两试验点的精米率、整精米率和温江垩白粒率,增加了射洪的垩白粒率和垩白度,使外观品质变差;同时PASP尿素1次施肥和2次施肥处理降低了直链淀粉含量和籽粒粗蛋白含量;PASP尿素优化施肥处理降低了两试验点的峰值黏度、崩解值和温江直链淀粉含量,提高了两试验点的籽粒粗蛋白含量和射洪直链淀粉含量。较优化施肥处理,减氮15%和增氮15%优化施肥处理降低了两试验点的直链淀粉含量、整精米率及温江垩白粒率,增加了射洪垩白粒率和垩白度。与PASP尿素1次和2次施肥相比,PASP尿素优化施肥显著降低了垩白度、峰值黏度和崩解值,增加了消减值和籽粒粗蛋白含量;同时导致射洪生态点的整精米率降低,垩白粒率和直链淀粉含量增加;温江生态点的垩白粒率降低,整精米率增加。综合稻米碾米品质、外观品质、淀粉RVA、直链淀粉含量和籽粒粗蛋白含量的关系,射洪PASP尿素2次施肥处理稻米综合品质较好,温江优化施肥处理稻米综合品质较好。     

氮肥用量对普通玉米产量和营养品质的影响

利用田间试验研究了施用氮肥对玉米黔兴2302产量和营养品质的影响。结果表明,施用氮肥不仅显著提高了子粒产量,增产9.59%~23.14%,且不同程度上增加了蛋白质、氨基酸、淀粉和脂肪酸的含量;但不改变直链淀粉和支链淀粉的比值,对玉米口感无不良影响。在玉米子粒中,蛋氨酸、苯丙氨酸、赖氨酸、天门冬氨酸、丝氨酸、谷氨酸、甘氨酸、组氨酸、精氨酸、亚麻酸含量比较稳定,主要受遗传基因的控制,难于因施肥而改变;其余营养成分,如清蛋白、球蛋白、醇溶蛋白、谷蛋白、苏氨酸、缬氨酸、异亮氨酸、亮氨酸、丙氨酸、酪氨酸、脯氨酸、直链和支链淀粉、油酸、亚油酸、棕榈酸和硬脂酸含量因施用氮肥而发生变化,它们的含量既受遗传基因的控制,也受施用氮肥的影响。综合考虑玉米的产量和品质,本试验条件下,黔兴2302玉米较为适宜的氮肥用量约为N 150 kg/hm2。     

水稻籽粒碳氮代谢与品质性状间遗传相关性的研究

研究分析水稻籽粒C、N代谢与稻米主要品质性状间遗传相关性结果表明,水稻籽粒C、N代谢与稻米主要品质性状间有一定遗传相关性,提高可溶性糖含量,则糖氮比显著提高,糙米长、千粒重和直链淀粉含量显著降低;提高全N含量可能显著提高糙米厚和直链淀粉含量,而糖氮比、糙米长和千粒重随之显著降低;随糖氮比的提高,糙米厚、千粒重和直链淀粉含量将显著降低。种子、细胞质和母体植株3套遗传体系同时对成对性状的遗传相关性起作用,且各遗传体系在不同成对性状间的作用效果各异,可溶性糖含量与糙米宽、可溶性糖含量与糙米厚、全N含量与糙米长、全N含量与糙米厚等成对性状间遗传相关性主要受种子直接效应的控制;而可溶性糖含量与糙米长宽比、可溶性糖含量与千粒重、可溶性糖含量与直链淀粉含量、全N含量与糙米宽、全N含量与糙米长宽比、糖氮比与糙米宽、糖氮比与糙米厚等成对性状则以母体植株效应为主。     

Determination of chemical composition of commercial honey by near-infrared spectroscopy.

The feasibility of using near-infrared spectroscopy to determine chemical composition of commercial honey was examined. The influences of various sample presentation methods and regression models on the performance of calibration equations were also studied. Transmittance spectra with 1 mm optical path length produced the best calibration for all constituents examined. The regression model of modified partial least squares (mPLS) was selected for the calibration of all honey constituents except moisture, for which the optimal calibration was developed with PLS. Validation of the established calibration equations with independent samples showed that the spectroscopic technique could accurately determine the contents of moisture, fructose, glucose, sucrose, and maltose with squared correlation coefficients (R(2)) of 1.0, 0.97, 0.91, 0.86, and 0.93 between the predicted values and the reference values. The prediction accuracy for free acid, lactone, and hydroxymethylfurfural (HMF) contents in honey was poor and unreliable. The study indicates that near-infrared spectroscopy can be used for rapid determination of major components in commercial honey.     

Determination of the degree of succinylation in diverse modified starches by raman spectroscopy

A method using Raman spectroscopy was recently developed for the determination of the degree of substitution of succinate in waxy maize starch. In this paper it is demonstrated that the method can be generalized to a wide range of starches of different amylose contents and botanical origins. Raman calibration sets were used to form regression equations for five types of succinylated starches, that is, waxy, regular, and two high-amylose maize samples (47 and 66% amylose, respectively) and wheat. The derived calibration curves can be used to find the degree of substitution in samples with unknown levels of succinylation. The Raman calibration lines had linear correlation coefficients of 0.995 or better and enable the fast and nondestructive determination of the degree of substitution of succinate for different types of starches with minimal sample preparation. Also discussed is the potential utility of Raman spectroscopy to simultaneously determine the degree of substitution of succinate and amylose content, using previously determined calibration curves developed for the amylose content of maize starches.     

Quantifying Surface Lipid Content of Milled Rice via Visible/Near-Infrared Spectroscopy

Visible/near-infrared calibrations were developed and tested for surface lipid content (SLC) of milled long-grain rice. Three rice varieties were divided into two sample sets, with one containing two variables (degree of milling and variety) and another containing three variables (degree of milling, variety, and kernel thickness). The reflectance calibration equation from the set with three variables was much more accurate in predicting SLC than was the calibration from the two-variable set. Optimal calibration and prediction were obtained by combining both visible and near-infrared wavelength ranges and using the modified partial least squares technique on spectra pretreated by standard normal variate and first derivative methods. The best calibration yielded a coefficient of determination (R²) of 0.99 and a standard error of prediction of 0.04% SLC, which was approximately 1.5 times the standard error of calibration and also 1.5 times the SLC measurement error.     

Comparison of Amylose Determination Methods and the Development of a Dual Wavelength Iodine Binding Technique

It has long been recognized that limitations exist in the analytical methodology for amylose determination. This study was conducted to evaluate various amylose determination methods. Purified amylose and amylopectin fractions were obtained from corn, rice, wheat, and potato and then mixed in proportion to make 10, 20, 30, 50, and 80% amylose content starch samples for each source. These samples, considered amylose standards, were analyzed using differential scanning calorimetry (DSC), high-performance size-exclusion chromatography (HPSEC), and iodine binding procedures to generate standard curves for each of the methods. A single DSC standard equation for cereal starches was developed. The standard curve of potato starch was significantly different. Amylose standard curves prepared using the iodine binding method were also similar for the cereal starches, but different for potato starch. An iodine binding procedure using wavelengths at 620 nm and 510 nm increased the precision of the method. When HPSEC was used to determine % amylose, calculations based on dividing the injected starch mass by amylose peak mass, rather than calculations based on the apparent amylose/amylopectin ratio, decreased the inaccuracies associated with sample dispersion and made the generation of a cereal amylose standard curve possible. Amylose contents of pure starch, starch mixtures from different sources with different amylose ranges, and tortillas were measured using DSC, HPSEC, iodine binding, and the Megazyme amylose/amylopectin kit. All the methods were reproducible (±3.0%). Amylose contents measured by these methods were significantly different (P < 0.05). Amylose measurements using iodine binding, DSC, and Megazyme procedures were highly correlated (correlation coefficient >0.95). DSC and traditional iodine binding procedures likely overestimated true amylose contents as residual butanol in the amylose standards caused interference. The modified two-wavelength iodine binding procedure seemed to be the most precise and generally applicable method. Each amylose determination method has its benefits and limitations.     

Potential Use of Raman Spectroscopy for Determination of Amylose Content in Maize Starch

An analytical method using Raman spectroscopy was developed for the determination of amylose concentration in maize starches. FT-Raman spectra of four maize starches with amylose content varying from 3.3 to 66% were obtained. A Raman band at ≈1657 cm^-1 correlated linearly with amylose concentration in the four maize starches, and a calibration curve for Raman band intensity versus amylose content was developed. The linear correlation of the I₁₆₅₇/I₉₀₀ integrated areas with amylose content was r = 0.997. The Raman-based calibration curve allows fast and nondestructive determination of the amylose content in maize starches with minimal sample preparation.     

Whole Grain Amylose Analysis in Maize Using Near-Infrared Transmittance Spectroscopy

The development of genetically modified starches has relied on the use of maize (Zea mays L.) endosperm mutant alleles that alter starch structural and physical properties. A rapid method for predicting amylose content would benefit breeders and commercial handlers of specialty starch corn. For this reason, a study was conducted to investigate the use of near-infrared transmittance spectroscopy (NITS) as a rapid and nondestructive technique for predicting grain amylose content (GAC) in maize. Many single- and double-mutant inbreds and hybrids were used to create a calibration set for the development of a predictive model using partial least squares analysis. A validation set composed of similar genetic material was used to test the prediction model. A coefficient of correlation (r) of 0.94 was observed between GAC values determined colorimetrically and those predicted by NITS; however, the predicted values were associated with a large standard error of prediction (SEP = 3.5). Overall, NITS discriminated well among high amylose and waxy genotypes. The NITS calibration was used to determine levels of contamination by normal kernels in waxy and high-amylose (Amy VII) grain samples intended for wet milling. In both cases, a 5% contaminated sample could be detected from pure samples according to predicted NITS values.     

Prediction of Starch Amylose Content Versus Total Grain Amylose Content in Corn by Near-Infrared Transmittance Spectroscopy

A study was conducted to investigate methods of improving a near-infrared transmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining amylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations ranging in starch-amylose content (SAC) from -8.5 to 76%, relative to a standard curve. The influence of three factors were examined including comparing calibrations made against SAC versus grain amylose content (GAC), developing calibrations using partial least squares (PLS) analysis versus artificial neural networking (ANN), and using all samples in the calibrations set versus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PLS analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard error of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all NITS models. The study revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did not improve performance statistics except for PLS models for SAC in which a decrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples with SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be useful when a rough screening method is needed for SAC. For example, NITS may be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples.     

Rapid prediction of acid detergent fiber, neutral detergent fiber, and acid detergent lignin of rice materials by near-infrared spectroscopy

A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs.     

干燥后稻米食味值的预测与分析

为了采用神经网络方法预测干燥后稻米食味值，依据稻米的食味值与其主要成分(水分、蛋白质、直链淀粉和脂肪酸)和干燥温度有关这一研究结论，用近红外光谱谷物成分分析仪测定了稻米的主要成分值，用专家模糊评判方法确定稻米的食味值，建立了稻米食味值与其主要成分之间的网络结构模型，误差分析结果表明：该方法可以较好预测稻米的食味值，并分析了干燥条件对稻米理化指标的影响规律，影响程度为干燥温度：-0.7；水分：0.68、脂肪酸：-0.56、直链淀粉：0.48和蛋白质含量：-0.33。