近红外光谱法测定土壤全氮和碱解氮含量

为探寻采用近红外光谱技术在野外快速测定土壤全氮和碱解氮含量的方法,采集土壤光谱信号,结合偏最小二乘法和主成分分析法,分别建立土壤全氮和碱解氮含量测定的定标模型。结果表明,采用PLS方法建模时,土壤全氮和碱解氮含量测定定标模型的精度较高。为提高模型的预测精度,采用多元散射校正、标准归一化、基线校正、卷积平滑和小波变换5种方法对光谱信号进行预处理,当用小波变换法对光谱信号进行去噪处理,并与PLS方法结合时,模型的预测精度最高,土壤全氮样品校正模型的相关系数为0.838 5,均方根误差为0.153 1,对应验证模型的相关系数为0.754 9,均方根误差为0.184 2,校正集和验证集土壤全氮含量预测值(y)与实测值(x)之间的关系模型分别为y=0.685 8x+0.198 0和y=0.621 4x+0.237 9;土壤碱解氮样品校正模型的相关系数为0.866 5,均方根误差为0.007 7,对应验证模型的相关系数为0.796 1,均方根误差为0.009 4,校正集和验证集土壤碱解氮含量预测值(y)与实测值(x)之间的关系模型分别为y=0.749 8x+0.019 4和y=0.700 7x+0.023 3。综合分析结果表明,应用近红外光谱技术对土壤全氮和碱解氮含量进行定量预测是可行的,且应用小波变换方法对光谱冗余信息进行预处理后,再与偏最小二乘法相结合可有效地提高模型的精度。     

水旱条件下近红外发射光谱预报水稻直链淀粉含量与粗蛋白含量的QTL分析

以水稻珍汕97B和旱稻IRAT109杂交F9代重组自交系群体为材料,分别在水旱两种处理条件下,用近红外光谱回归模型预测群体的直链淀粉含量及粗蛋白含量。分析结果表明,两种处理条件下直链淀粉含量无显著差异,水分胁迫处理条件下粗蛋白含量显著降低。利用QTLMapper共检测到22个主效QTL和66对显著互作位点与直链淀粉含量和粗蛋白含量有关。主效QTL对直链淀粉和蛋白质含量的联合贡献率在非胁迫条件下为35.34%和37.33%,在胁迫条件下分别为53.40%和58.10％;互作位点对直链淀粉和蛋白质含量的联合贡献率在正常水分条件下分别为66.74%和57.49%,在胁迫条件下分别为48.65%和36.59%。     

Rapid quantitative determination of oleuropein in olive leaves (Olea europaea) using mid-infrared spectroscopy combined with chemometric analyses

Oleuropein, the major active compound in olive leaf, is well known for its benefits for human health. Oleuropein is classically quantified by HPLC, which is time and chemical consuming, laborious and expensive. The aim of this work was to examine the potential of mid-infrared spectroscopy, as a rapid tool, to predict oleuropein content in olive leaf from five Tunisian cultivars (Chemlali, Chetoui, Meski, Sayali and Zarrazi) and one French cultivar (Bouteillan). The reference data of oleuropein content were obtained by the HPLC method. Hundred five samples were analyzed by HPLC and mid-infrared spectroscopy. Samples were randomly divided in a calibration set (73 samples) and in a validation set (32 samples). The spectral data sets were correlated with reference data of oleuropein content by using partial least squares (PLS) regression algorithm. The results showed that the PLS model gave satisfactory model for quantitative prediction of oleuropein content in olive leaf (relative error of prediction = 8.5%). The correlation coefficient was 0.91 and 0.74 for calibration set and validation set, respectively. It can be concluded that mid-infrared spectroscopy constitutes a promising technique for rapid quantification of oleuropein in olive leaf.     

近红外光谱技术在农产品品质检测中的应用

近红外光谱技术是一种新型的无损检测技术,在许多领域都得到了很好的应用。本文从农产品中各种物质成分含量预测、分类鉴别、腐烂鉴别、实时监测几个方面综述了近红外光谱技术在农产品品质检测上的应用,并对其在仪器硬件的研究和开发、化学计量学方法的探索与研究以及快速在线检测方法的研究等方面的发展趋势进行了展望。     

基于可见/近红外光谱的柑橘糖度在线检测分选系统的设计与试验

在6GF–4型林果无损检测与分选成套设备中,设计了基于可见/近红外光谱的柑橘糖度在线检测分选系统,系统主要包括传输装置、光谱采集装置、控制系统以及分选装置。系统在柑橘果实运动状态中采集其光谱信息,并通过所建立的果实糖度模型进行同步计算,根据所得糖度值对柑橘果实实现在线分选。在光谱采集装置中设计了双透镜式光路,可改变投射于柑橘果实上的光斑大小,通过研究比较试验参数积分时间和光斑尺寸大小,得出系统的最佳采集参数为积分时间100 ms,光斑尺寸设置为小,样本移动速率为5个/s。建立的SPXY–CARS–PLSR柑橘糖度在线检测模型校准集和预测集的决定系数分别为0.938和0.836,校准集和预测集的均方根误差分别为0.273°Brix和0.418°Brix。使用未参与建模的25个柑橘果实样本进行外部验证集的在线检测和分选,结果在1°Brix的误差范围内,检测糖度的准确率为92%;当样本分为4个等级时,系统分选正确率为92%;当样本分为3个等级时,系统分选正确率可达100%。     

近红外光谱在转基因农产品检测中的研究进展

简述了近年来近红外光谱分析技术在农产品和食品定量分析中的应用,重点介绍了近红外光谱分析在转基因植株筛选以及转基因和非转基因农产品分类和识别等方面的研究现状,比较和分析了近红外光谱与传统的转基因农产品检测方法的特点,并展望了近红外光谱分析技术在转基因农产品检测研究中的发展前景。     

二维相关光谱结合偏最小二乘法测定牛奶中的掺杂尿素

为了检验牛奶中是否掺杂尿素并将其量化测定,配置含有尿素质量浓度范围为1～20g/L之间40个牛奶样品,以掺杂物尿素浓度为外扰,分别研究了掺杂尿素牛奶的二维相关(近红外-近红外,中红外-中红外,近红外-中红外)光谱特性,在此基础上,分别选择随浓度变化大的4200～4800cm-1和1400～1704cm-1为建模区间,采用偏最小二乘方法建立定量分析模型。研究结果表明:4200～4800cm-1建模分析效果优于1400～1704cm-1建模结果,其交叉验证均方根误差为0.266g/L,对未知样品集预测相关系数达到0.999,预测均方根误差为0.219g/L,这表明所建模型具有较好的预测效果。该方法无需样品处理,成本低,为快速判别牛奶是否掺杂提供了一种新的可能的方法。     

Spectrophotometrical characteristics in the near infrared region in beech (<Emphasis Type="Italic">Fagus crenata</Emphasis>) and pine (<Emphasis Type="Italic">Pinus densiflora</Emphasis>) litters at the various decomposing stages

We examined the relationships between the absorptional characteristics in the near infrared region and the chemical changes of decomposing beech (Fagus crenata) and pine (Pinus densiflora) litters. Spectra as well as the concentrations of chemical substances approached each other and converged with decomposition, although both initial characteristics differed markedly between beech and pine. This indicated that the fundamental chemical structures were almost the same, although their organochemical composition differed. Specific absorption bands for lignin, polysaccharide, and protein were identified at 2,140 and 1,670 nm, 2,270, 1,720, 1,590, and 1,216 nm, and 2,350 nm, respectively. Absorbance at 1,670 nm, peculiar band of aromatics, showed a positive correlation with lignin concentration, which suggested the relative increment of aromatics due to condensed lignin in decomposing litters. Absorbance at 2,140 nm, characterized as the C–H bond in HRC = CHR, showed a negative correlation with lignin concentration, which suggested the decrements of some structures such as side-chains in lignin polymers unrelated to aromatics. Absorbance at 2,270, 1,720, and 1,216 nm, specified to O–H/C–O/C–H bonds in saccharide, might reflect the change of polysaccharide during decomposition because they showed a positive correlation to polysaccharide concentration. In the same way, absorbance at 2,350 nm, identified to the C–H/CH₂ bonds in protein, showed a negative correlation to nitrogen concentration in decomposing litters, which might indicate that the C–H/CH₂ bonds in protein decreased with decomposition due to microbial consumption of carbon in protein. Our findings suggested the possibility that the spectral changes indicate the litter digestibility during decomposition and that also explain the compositional change in decomposing litters.     

激光诱导击穿原子光谱在土壤分析中的应用

土壤分析是土壤学研究及应用的前提和基础,传统化学土壤分析方法逐渐不能适应现代土壤学海量信息数据快速获取的需求。激光诱导击穿光谱作为一种全新的、反映土壤组成元素原子信息的光谱技术,其无需对样品进行复杂前处理,可实现原位、快速、多元素连续在线检测,每条光谱记录土壤样本独一无二的特征,可以视为土壤“指纹”,成为现代土壤分析的有效技术之一。首先介绍和分析了激光诱导击穿原子光谱的原理、光谱获取的主要影响因素、光谱数据处理的化学计量学方法等;然后阐述和梳理了基于激光诱导击穿光谱技术在土壤分析方面的应用成果和进展,包括土壤鉴定、土壤养分评估、土壤重金属测定、微观/介观尺度土壤原位表征等;最后讨论和总结了激光诱导击穿光谱技术在土壤分析中所面临的挑战及其应用展望。     

Detection of Frozen-Thawed Cycles for Frozen Tilapia (Oreochromis) Fillets Using Near Infrared Spectroscopy

Due to the ambient temperature fluctuation during the process of storage, transportation, and sale, frozen fillets may go through frozen-thawed cycles that result in quality changes. In order to detect the frozen-thawed cycles in frozen tilapia fillets, near infrared spectroscopy was applied. The thawing loss, cooking loss, moisture content, total volatile basic nitrogen, and texture profile of fillets in seven frozen-thawed cycles were analyzed to reflect the change of quality. A total of 60 fillets were used to collect the spectra on both dorsal and belly flesh in frozen and thawed state from the first to the seventh cycle. The results showed that there is a better discrimination among the once and the repeated frozen-thawed samples in the frozen state than the thawed state. Frozen samples were still conducive to the differentiation and had higher classification accuracy, ranging from 80% to 93.33%. Dorsal flesh was more beneficial for the differentiation than belly flesh and had the highest accuracy, ranging from 86.67% to 93.33% in the frozen state. These results showed that the nondestructive and fast detection of repeated frozen-thawed cycles in frozen tilapia fillets can be accomplished by near infrared spectroscopy, which has enormous potential for practical application.