近红外光谱技术定量分析玉米杂交种纯度

摘要：应用近红外光谱分析技术结合定量偏最小二乘法对先玉335杂交种纯度进行了定量分析,将不同年份和来源的杂交种和其母本种子粉碎后混合,按0.5%的梯度获得纯度80～100%范围内的样本123份（每梯度按年份和来源设置3个重复）后采集光谱。结果表明：采用散射校正预处理,4 000～8 000 cm-1光谱范围时建模效果较适宜（建模集∶检验集=3∶1）,建模集内部交叉决定系数达96.06%,校正标准差1.18%,平均相对误差1.03%;检验集的决定系数均达到95.02%,校正标准差1.28%,平均相对误差1.12%。采用不同比例的建模样品和检验样品时,建模集和检验集的决定系数均在94%以上,证明了近红外光谱技术定量测定玉米杂交种纯度的可行性以及所建模型的稳定性。     

Compositional analysis of nonfat dry milk by using near infrared diffuse reflectance spectroscopy

Proximate data from 82 nonfat dry milk (NFDM) samples were correlated with near infrared reflectance (NIR) measurements. The best wavelengths for determining constituent concentrations were chosen from 19 preselected filters by using linear regression analysis. The correlation coefficient (r) was 0.971 and the standard error of prediction (SEP) was 0.274 when the predicted values (from NIR measurements) using the 3 wavelengths selected for determining moisture content were compared with laboratory values; r and SEP were 0.961 and 0.099, respectively, when the predicted values using the 4 wavelengths selected for fat content were compared with laboratory results; 0.887 and 0.594, respectively, using the 4 wavelengths selected for lactose content; 0.905 and 0.438 using the 8 wavelengths selected for protein (micro-Kjeldahl) content; and 0.911 and 0.509 using the 7 wavelengths selected for protein (dye binding). These data indicate that NIR can be used to estimate moisture, fat, lactose, and protein content of NFDM.     

Compositional and conformational analysis of yam proteins by near infrared fourier transform Raman spectroscopy

Fourier transform (FT)-Raman spectroscopy was employed to study the molecular structure of yam proteins isolated from three commonly consumed yam species including Dioscorea alata L., D. alata L. var. purpurea, and Dioscorea japonica. Although D. alata L. and D. alata L. var. purpurea consisted of similar amino acid residues, they still exhibited significant differences in conformational arrangement. The secondary structure of D. alata L. was mainly an alpha-helix, while D. alata L. var. purpurea was mostly in antiparallel beta-sheets. In contrast, D. japonica, which belongs to a different species, exhibited explicit differences in amino acid compositions and molecular structures of which the conformation was a mixed form of alpha-helices and antiparallel beta-sheets. FT-Raman directly proved the existence of S-S in yam proteins, implying that oligomer formation in yam proteins might be due to disulfide linking of dioscorin (32 kDa). The microenvironment of aromatic amino acids and the state of S-S in yam proteins were also discussed.     

Characterization of fungal melanins and soil humic acids by chemical analysis and infrared spectroscopy

Summary Humic acids from two Brazilian topsoils under savanna grassland and five soil fungal melanins were characterized by elemental, functional group and infrared analysis. C, N, total acidity, COOH, and phenolic OH contents were within the ranges reported for several other fungal melanins and soil humic acids. Compared with the soil humic acids, the infrared spectra of the fungal melanins showed greater detail, indicative of higher aliphaticity. They were similar to the type III infrared spectra of humic acids, which are characteristically high in proteinaceous material and polysaccharides. The infrared spectra of the humic acids from the two Brazilian soils studied were classified as type I, which includes most soil humic acids. Notwithstanding the greater detail, in some areas the fungal melanin spectra were similar to those reported for other fungal melanins and humic acids of different origins. The probable contribution of the melanic fungi to the formation of soil humic polymers is discussed.     

Quantitative analysis of cotton (Gossypium hirsutum) lint trash by fluorescence spectroscopy

The presence of cotton plant botanical components, or trash, embedded in lint subsequent to harvesting and ginning is an important criterion in the classification of baled cotton by the U.S. Department of Agriculture Agricultural Marketing Service. The trash particles may be reduced in size to the point that specific trash types are not identifiable by image or gravimetric analysis, and it is desirable to quantify different trash types so that processing lines may be optimized for removal of the most problematic trash to enhance processing performance and cotton lint quality. Currently, there are no methods available to adequately quantify cotton lint trash based on botanical origin. The present work attempts to address this issue through the analysis by fluorescence spectroscopy of dimethyl sulfoxide extracts of mixtures of six botanical trash types. The fluorescence data are subsequently subjected to chemometric analysis. The resulting 6 partial least-squares calibration models obtained from 128 mixtures are demonstrated in the case of leaf and hull to be capable of predicting individual trash component concentrations with a high degree of confidence.     

Formation of metal-humic acid complexes by titration and their characterization by differential thermal analysis and infrared spectroscopy

Humic acid (HA) extracted from a Eustis loamy sand (Psammentic Paleudult, Red Yellow Podzolic soil) was flocculated by titration with Al³⁺-, Fe³⁺-, Cu²⁺-, Zn²⁺-, Mn²⁺-, Ba²⁺-, Ca²⁺-, and Mn²⁺-chloride solutions, respectively, to determine possible development of metal-HA complexes, as reported by Flaig et al. (1975), and Tiurin and Kononova (1962). Titration was conducted with HA solutions with an initial pH 11.5 or 7.0. The results indicated that the cations used, except Mg²⁺, yielded insoluble complexes with HA, irrespective of initial pH. After titration, the pH of the metal-HA flocs was 6.0–7.0, which was expected in view of the presence of cation exchange and buffering capacity of HA compounds. More complex formation through electrovalent and covalent bonding by COO^? and phenolic OH groups of the HA molecule was only attained by the use of HA solutions with pH 11.5. On the other hand, less complex formation occurred by the use of HA solutions with an initial pH 7.0, through electrovalent bonding by COO^? groups. Differential thermal analysis (d.t.a.) curves of HA showed shifts in temperatures of the main decomposition peak as a result of flocculation with the different metals. Based on the type of the cations involved, the metal-humic acid flocs could be listed in the following decreasing order of thermal stability: Al³⁺ = Zn²⁺ = Mg²⁺ ≥ HA > Ca²⁺ > Ba²⁺ > Fe³⁺ > Cu²⁺ > Mn²⁺. A systematic relationship could not be found indicating that trivalent ions resulted in the formation of thermally less stable metal-humic acid flocs than divalent ions, as has been reported for HA-metal complexes. Physical mixtures of HA and metal hydroxides exhibited d.t.a. features resembling those of original (nontreated) HA, but not those of the HA-metal flocs.Infrared spectroscopy revealed increased absorption for COO^? vibrations at 1620 and 1400cm^?1 in spectrograms of metal-HA flocs compared to that of original humic acid, a phenomenon explained by many authors to be caused by bonding of the metal ions in hydrated form to the carboxyl or phenolic hydroxyl groups or both of the humic acid molecule. HA-flocs formed from solutions with an initial pH 11.5 had identical i.r. spectra compared with those formed from solutions with an initial pH 7.0.     

Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction

Reducing large spectral datasets to parsimonious representations of wavelengths is of value for efficient storage and easing analysis, in addition to the potential to use a simpler and cheaper spectrophotometer. This study evaluated the potential of calibrating visible and near infrared (vis‐NIR) spectra to total nitrogen (N), total carbon (C), organic C and inorganic C in soil on a 15‐ha farm, with the aim of comparing several wavelength reduction algorithms and rates in terms of model prediction accuracy. We explored the uninformative variables elimination (UVE), UVE coupled with successive projections algorithm (SPA) and two uniform‐interval wavelength reduction approaches (UWR‐I and UWR‐II) with successive wavelength reduction rates (WRRs) of 2, 5, 10, 20, 50, 100, 200, 500 and 1000. The standard normal variate (SNV)‐transformed absorbance spectra of soil samples recorded from 400 to 2499 nm at 1‐nm intervals were used. The calibration sets were subjected to a partial least squares regression (PLSR) with leave‐one‐out cross‐validation. Prediction results showed that UVE can reduce wavelength variables significantly while retaining good model prediction accuracy. The UVE‐SPA produced only three or four wavelengths, with which PLSR models achieved competitive prediction performance, compared with those based on all 2100 wavelengths, with coefficient of determination (R²) of 0.91, 0.89, 0.91 and 0.53 and residual prediction deviation (RPD) of 3.53, 2.95, 3.27 and 1.53 for soil total N, total C, organic C and inorganic C, respectively. The UWR tests showed that PLSR models responded insensitively to various WRRs from 2 to 100. The models calibrated for the 100‐nm interval spectra (21 remaining wavelengths) performed almost as well as those for the 1‐nm interval spectra. Although these findings might be valid only at the farm scale, it is recommended that the proposed wavelength reduction algorithms for more soil types and soils originated from larger areas should be examined.     

柑桔黄龙病近红外光谱无损检测

为探讨快速无损检测柑桔黄龙病的可行性,应用近红外光谱技术结合机器学习方法进行研究。在4000~9000cm-1光谱范围内,采集黄龙病、缺素和健康3类叶片样本的近红外光谱。采用一阶导数、平滑和多元散色校正组合的光谱预处理方法,消除光谱的基线漂移和散射效应。分别对偏最小二乘判别模型(PLS-DA)的主成分因子数和最小二乘支持向量机(LS-SVM)的输入变量数量、核函数类型及其参数进行了优化,建立了PLS-DA和LS-SVM模型。采用预测集样本,评价模型的预测能力,经比较,采用11个主成分得分向量为输入、线性核函数和惩罚因子为2.25的LS-SVM模型预测效果最佳,模型误判率为0。结果表明采用近红外光谱技术结合最小二乘支持向量机进行柑桔黄龙病无损检测是可行的。     

A preliminary study of fungal melanin by infrared spectroscopy

Infrared spectra of melanin and methylated melanin have been interpreted to show the existence of carboxyl groups in two different environments in the melanin structure. In one, there are a small number of carboxyl groups with an absorption band at about 1700 cm^?1; these are relatively free and are capable of interaction with alkali to produce carboxylate in the normal way. In the other environment, the carboxyl groups, absorbing at 1615 cm^?1, are conjugated and strongly hydrogen bonded to presumably phenolic hydroxyl groups, and cannot be neutralized with alkali. They do, however, react with diazomethane, as do the free carboxyls, to produce methyl ester absorbing at 1728 cm^?1.The significance of these conclusions for the survival of melanin in soil and its reactions therein, is briefly discussed.     

棉纤维根数的近红外测量

为准确测量棉纤维的根数,该文建立了一个近红外光电检测系统,其光源为940 nm波长的LED。试验中使用了8个年份、颜色等级有差异的棉纤维试样,测量不同纤维根数以及对应的相对消光量。结果表明,棉纤维的消光特性与输入光强有关。对同样数量的棉纤维,输入光强增大,相对消光量减小。数据分析表明,纤维根数是相对消光量的正指数函数,它远比经典朗伯定律描述的纤维光学特性要好。将该函数作为预测方程,纤维根数预测在95%的置信水平上有R2=0.99。试验中,棉纤维的颜色等级与成熟年份对其近红外光学特性没有表现出显著影响。     

Discrimination of intact and injured Listeria monocytogenes by Fourier transform infrared spectroscopy and principal component analysis

Fourier transform infrared spectroscopy (FT-IR, 4000-600 cm(-)(1)) was used to discriminate between intact and sonication-injured Listeria monocytogenes ATCC 19114 and to distinguish this strain from other selected Listeria strains (L. innocua ATCC 51742, L. innocua ATCC 33090, and L. monocytogenes ATCC 7644). FT-IR vibrational overtone and combination bands from mid-IR active components of intact and injured bacterial cells produced distinctive "fingerprints" at wavenumbers between 1500 and 800 cm(-)(1). Spectral data were analyzed by principal component analysis. Clear segregations of different intact and injured strains of Listeria were observed, suggesting that FT-IR can detect biochemical differences between intact and injured bacterial cells. This technique may provide a tool for the rapid assessment of cell viability and thereby the control of foodborne pathogens.     

Discrimination of cheese products for authenticity control by infrared spectroscopy

Quality and authenticity control serve as the customers' and manufacturers' insurance, and thus the development of analytical tools providing these tasks represents an important step of each product development. The control of authenticity in food manufacturing is even more important due to the direct influence of its products on the health of the population. This study sought to develop an easy to use and robust method for the authenticity control of cheese products. The method is based on the measurement of infrared spectra of the gas phase obtained by heating of selected cheese under controlled conditions. Two different procedures, that is, treatment of samples in a desiccator and their freeze-drying, were compared, and also various temperatures and heating times were studied. It was found that suitable fingerprint infrared spectra can be obtained by both techniques; however, freeze-drying offered faster analysis times. The sample heating temperature and time were evaluated using advanced statistical approaches, and it was found that suitable results could be obtained using 120 °C heating for 90 min. This method was tested for the authenticity control of two cheese families, Tvaruzky and Romadur, for which four cheese products were evaluated and successfully discriminated for each family. This method can be potentially used as a cheap and easy to use alternative to other commercially available options.     

Dose detection of radiated rice by infrared spectroscopy and chemometrics

Infrared spectroscopy based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate the nine different radiation doses (0, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 Gy) of rice. Samples ( n = 16 each dose) were selected randomly for the calibration set, and the remaining 36 samples ( n = 4 each dose) were selected for the prediction set. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavelength bands including near-infrared (NIR) regions and mid-infrared (MIR) regions. The best PLS models were achieved in the MIR (400-4000 cm (-1)) region. Furthermore, different latent variables (5-9 LVs) were used as inputs of LS-SVM to develop the LV-LS-SVM models with a grid search technique and radial basis function (RBF) kernel. The optimal models were achieved with six LVs, and they outperformed PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs (756, 895, 1140, and 2980 cm (-1)) selected by ICA and had better performance than PLS and LV-LS-SVM with the parameters of correlation coefficient ( r), root-mean-square error of prediction, and bias of 0.996, 80.260, and 5.172 x 10 (-4), respectively. The overall results indicted that the ICA was an effective way for the selection of SWs, and infrared spectroscopy combined with LS-SVM models had the capability to predict the different radiation doses of rice.     

基于近红外分析技术检测大豆脂肪酸含量的研究

为探索近红外光谱技术在大豆脂肪酸测试中的应用,寻找一种快速的检测方法。以黑龙江省各地的25份大豆品种为材料,采用Perten8620型近红外光谱仪对搜集到的样品进行扫描并得到光谱数据,采用多元线性回归(MLR)和偏最小二乘法(PLS)对试验数据进行了多元统计分析。结果表明：在1700～2300 nm范围内检测大豆脂肪酸含量是可靠的,并且PLS模型的性能优于MLR模型。该文还对近红外仪中的滤光片组合作了初步探讨,表明不同的滤光片组合对测量精度有一定影响。     

Application of near‐infrared spectroscopy in analysis of soil mineral nutrients

Abstract

The feasibility of using near‐infrared reflectance spectroscopy (NIRS) was investigated for the analysis of pH, electrical conductivity (EC), phosphorus (P), sulfur (S), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), iron (Fe), and manganese (Mn) in 28 Canadian soil samples from three boreholes down to 10 m in depth. Field moist soil samples were scanned for pH and EC, and air‐dry samples were scanned for the analysis of the elements. Calibrations were developed between the near‐infrared spectral data and results obtained by conventional analyses. The NIR‐predicted values were highly correlated to the measured values obtained by the conventional methods (r²>0.9) for P, Ca, Mg, K, Fe, and Mn, and almost as highly correlated (r²>0.8) for S and Na Results for pH were somewhat less successful (r²>0.6), and appeared to be useful only for screening purposes, whereas EC was not successfully predicted by NIRS in this study. It appeared that NIRS could be a useful method for the rapid, non‐destructive, simultaneous analysis of elemental concentrations in dry soils, useful in routine analysis.     

Fourier transform infrared spectroscopy and chemometric analysis of white wine polysaccharide extracts

Monovarietal white wines from Maria Gomes and Bical Portuguese Bairrada varieties were prepared according to different maceration and pectic enzyme clarification procedures. The polysaccharide-rich extracts, obtained by wine concentration, dialysis, and lyophilization, were fractionated by graded ethanol precipitation. A wide range of fractions rich in polysaccharides were obtained. Using the spectral region between 1200 and 800 cm(-)(1) of the FTIR spectra of the wine polysaccharide dry extracts, using PCA and CCA chemometric methods, it was possible to discriminate the extracts on the basis of their polysaccharide composition. Moreover, it was possible to identify the wine-making processes involved and their influence on the wine polysaccharides. Furthermore, a calibration model using a PLS1 was proposed for the quantification of mannose in the samples obtained by precipitation with 60% ethanol aqueous solutions. This information will allow an expeditious assessment and monitoring of the polysaccharide composition and modifications that occur during the wine-making processing and evolution.     

牛肉质构特性的近红外光谱无损检测

为了建立基于近红外光谱技术的牛肉质构特性快速检测方法,该试验采集了202个新鲜牛肉样品在800～2500 nm波长范围内的漫反射光谱,测定了牛肉的硬度、弹性、咀嚼性和黏附性,经小波消噪后,分别采用平滑、一阶微分、二阶微分等6种方法预处理,建立了牛肉质构特性的偏最小二乘回归模型,并用最优模型进行预测。结果表明：经小波消噪后采用二阶微分预处理方法建立的牛肉硬度、弹性、咀嚼性的检测模型效果最好,其校正集相关系数 r 均在0.9以上,校正集均方根误差（root means square error of calibration,RMSEC）分别为6.247 N、0.760 mm、14.954 mJ,预测集相关系数均在0.664以上,预测集均方根误差（root means square error of prediction,RMSEP）分别为8.887 N、0.951 mm、22.117 mJ,相对预测误差（ratio of prediction to deviation,RPD）值分别为2.43、1.88、2.32,预测精度较高,能够有效地预测牛肉的硬度、咀嚼性,可以检测精度要求不高的牛肉弹性;试验所建立的牛肉黏附性检测模型的预测性能不是很理想,虽然其校正集和预测集相关系数较高（分别为0.720、0.694）,RMSEC 和 RESEP 均较小（分别为0.302、0.243 N·mm）,但其 RPD 值小于1.5,模型预测精度较差,不可以用于预测未知样品的黏附性,此方法还需进一步研究。研究结果为牛肉质构特性的快速无损评价提供了理论依据。     

Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis

Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.     

Determination of moisture in Cheddar cheese by near infrared reflectance spectroscopy

Near infrared reflectance (NIR) spectroscopy was used to determine the moisture content of Cheddar cheese. Through multiple linear regression analysis, a 3-wavelength calibration was developed for use with a commercial filter monochromator instrument. For a validation set of 47 samples, the correlation coefficient squared (r2) between the NIR and oven moisture methods was 0.92, with a standard error of performance (SEP) of 0.38%. Sample temperature was found to significantly affect the spectral response; therefore, it was necessary to equilibrate all samples to a uniform temperature prior to NIR analysis. Aging may also affect the NIR characteristics of cheese, although it was possible to develop a successful calibration that encompassed a wide range of aging times.