Rapid screening of wood chemical component variations using transmittance near-infrared spectroscopy

A rapid transmittance near-infrared (NIR) spectroscopy method was developed to predict the variation in chemical composition of solid wood. The effect of sample preparation, sample quantity (single versus stacked multiple wood wafers), and NIR acquisition time on the quantification of alpha-cellulose and lignin content was investigated. Strong correlations were obtained between laboratory wet chemistry values and the NIR-predicted values. In addition to the experimental protocol and method development, improvements in calibration error associated with utilizing stacked multiple wood wafers as opposed to single wood wafers are also discussed.     

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.     

Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy

A rapid transmittance near-infrared (NIR) spectroscopic method has been developed to characterize the lignin content of solid wood. Using simple, multiple regression, and partial least-squares statistical analysis the lignin contents of wood wafers, taken from increment cores, and synthetic wood, prepared by blending milled wood lignin and holocellulose, were compared and quantified. Strong correlations were obtained between the predicted NIR results and those obtained from traditional chemical methods. In addition to the experimental protocol and method development, NIR results from wood samples with different particle sizes and various lignin contents are discussed.     

X-ray photoelectron spectroscopy for wheat powders: measurement of surface chemical composition

The functional properties of wheat powders depend largely on the surface characteristics of their particles. X-ray photoelectron spectroscopy (XPS) has been considered to investigate the surface composition of wheat powders. The objective of the present study is to evaluate the ability of XPS to discriminate wheat components and to calculate the surface composition of wheat powders. First, XPS surveys for the main wheat isolated components (starch, proteins, arabinoxylans, and lipids) were determined. XPS results demonstrate that it is able to distinguish wheat proteins, polysaccharides, and lipids, but it is not able to distinguish starch and arabinoxylan because of their similarity in chemical structure. The XPS analyses of simple reconstituted wheat flours based on two components (starch and protein) or three components (by adding arabinoxylan) demonstrated the ability of XPS to measure the surface composition of the wheat flours. The surface composition of native wheat flour demonstrated an overrepresentation of protein (54%) and lipids (44%) and an underrepresentation of starch (2%) compared to the bulk composition. Results are discussed with regard to difficulties in discriminating arabinoxylans and starch components.     

Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy.

NIR transflectance spectroscopy was used to determine polarimetric parameters (direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides) and sucrose in honey. In total, 156 honey samples were collected during 1992 (45 samples), 1995 (56 samples), and 1996 (55 samples). Samples were analyzed by NIR spectroscopy and polarimetric methods. Calibration (118 samples) and validation (38 samples) sets were made up; honeys from the three years were included in both sets. Calibrations were performed by modified partial least-squares regression and scatter correction by standard normal variation and detrend methods. For direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides, good statistics (bias, SEV, and R(2)) were obtained for the validation set, and no statistically (p = 0.05) significant differences were found between instrumental and polarimetric methods for these parameters. Statistical data for sucrose were not as good as those of the other parameters. Therefore, NIR spectroscopy is not an effective method for quantitative analysis of sucrose in these honey samples. However, NIR spectroscopy may be an acceptable method for semiquantitative evaluation of sucrose for honeys, such as those in our study, containing up to 3% of sucrose. Further work is necessary to validate the uncertainty at higher levels.     

Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed to create a reliable quality-prediction model. As multivariate analyses, principal component analysis (PCA) and partial least-squares projections to latent structures (PLS) were used. It was indicated that the wavenumber region from 5500 to 5200 cm(-1) had high correlation with the quality of the tea. In this study, a reliable quality-prediction model of green tea has been achieved.     

基于蒙特卡罗特征投影法的小麦蛋白质近红外光谱测量变量选择

为了实现小麦蛋白质的无损检测,简化便携式小麦蛋白质检测设备的预测模型,提高模型预测精度,该文针对小麦采集波长范围为950～1690nm的近红外漫透反射光谱,结合蒙特卡罗采样(MCS,monte carlosampling)技术与特征投影图(LPG,latent projective graph)方法对波长变量进行选择。根据模型集群分析(MPA,model population analysis)思想,采用MCS技术建立样本子空间,利用主成分分析(PCA,principal componentanalysis)得到LPG,假定LPG中共线性光谱变量对建模作用相同,选出少数波长变量建立子预测模型,选出预测均方根误差(RMSEP,root-mean-square error of prediction)较小的子模型,统计分析其变量的出现频次,选取频次最高的波长变量作为影响变量(IVs,influential variables)。研究结果表明,利用IVs建模可以将RMSEP值由0.5245减小到0.2548,采用蒙特卡罗采样技术的特征投影图方法(MC-LPG,monte carlo-latent projective graph)进行变量选择,对于提高模型预测精度是可行的。     

Nondestructive determination of oil content and fatty acid composition in perilla seeds by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.     

基于土壤参数的冬小麦产量预测模型

为了实现冬小麦的精细田间管理,研究了基于土壤参数的冬小麦产量预测模型。采用灰色理论对冬小麦土壤电导率 EC值,全氮含量,K+、NO3-以及土壤pH值等因子进行灰色关联度分析,结果表明土壤EC值与土壤全氮含量,K+以及土壤 pH 值的灰色关联度较高。在分析不同生长时期土壤 EC 值,全氮含量,K+、NO3-以及土壤pH值和产量间的相关系数的基础上,采用土壤EC值,全氮含量以及K+作为模型的输入,产量作为输出,建立了冬小麦产量预测BP神经网络（BPNN）模型;采用土壤EC值,全氮含量,K+,灰色关联度作为输入,建立了小麦产量的模糊最小二乘支持向量机（FLSSVM）预测模型。建模结果表明,BPNN 模型的预测决定系数达0.8237,验证决定系数达0.7367;FLSSVM模型的预测决定系数达0.8625,验证决定系数达0.8003。BP神经网络以及FLSSVM预测模型的精度都较高,可以用来评估作物产量,为精细农业变量处方管理提供理论与技术支持。     

Prediction of relative tissue proportions in wheat mill streams by fourier transform mid-infrared spectroscopy

Fourier-transform mid-infrared (FTIR) spectroscopy was investigated as a method to quantify the relative wheat grain tissue proportion in milling fractions. Spectra were acquired with a FTIR spectrometer equipped with an attenuated total reflectance device on ground samples, and the relative tissue proportion was determined according to the biochemical marker methodology as the reference method. Partial least-squares models were developed independently to predict the amount of outer pericarp, aleurone layer, starchy endosperm, and an intermediate layer (made up of inner pericarp plus seed coat plus nucellar epidermis). Good quality of prediction was obtained regardless of the target tissue. The standard errors of prediction obtained for the outer pericarp, intermediate layer, aleurone layer, and starchy endosperm quantification were, respectively, 3.4, 1.3, 3.4, and 4.6%.     

Energy from fat determined by near-infrared reflectance spectroscopy

The amount of energy derived from fat in foods is a requirement of U.S. nutrition labeling legislation and a significant factor in diet development by health professionals. Near-infrared (NIR) spectroscopy has been used to predict total utilizable energy in cereal foods for nutrition labeling purposes, and in the current study, was investigated as a method for evaluation of the amount of energy derived from fat. Using NIR reflectance spectra (1104-2494 nm) of ground cereal samples and reference values obtained by calorimetry and by calculation, modified PLS regression models were developed for the prediction of percent energy from fat and energy from fat/g. The models were able to predict the percent of utilizable energy derived from fat with SECV and R(2) of 1.86-1.89% of kcal (n = 51, range 0-43.0) and 0.98, respectively, and SEP and r(2) of 1.74% of kcal (n = 55, range 0-38.0) and 0.98, respectively, when used to predict independent validation samples. Results indicate that NIR spectroscopy provides useful methods for predicting the energy derived from fat in food products.     

Method for determining frying oil degradation by near-infrared spectroscopy

A rapid near-infrared (NIR) spectroscopic method for measuring degradation products in frying oils, including total polar materials (TPMs) and free fatty acids (FFAs), has been developed. Calibration models were developed using both forward stepwise multiple linear regression (FSMLR) and partial least-squares (PLS) regression techniques and then tested with two independent sets of validation samples. Derivative treatments had limited usefulness, especially in the longer (1100-2500 nm) wavelength region. When using a wavelength region of 700-1100 nm, PLS models gave improved results compared to FSMLR models. The best correlations (r) between the NIR and wet chemical methods for TPM and FFA were 0.983 and 0.943, respectively. In the longer wavelength region (1100-2500 nm), FSMLR models were as good or better than PLS models. The best correlations for TPM and FFA obtained in this region were 0.999 and 0.983, respectively.     

Major components of honey analysis by near-infrared transflectance spectroscopy

NIR transflectance spectroscopy was used to analyze fructose, glucose, and moisture in honey. A total of 161 honey samples were collected during 1992 (46), 1995 (58), and 1996 (57). Samples were analyzed by instrumental, enzymatic (fructose and glucose), and refractometric (moisture) methods. Initially, different calibrations were performed for each of the 3 years of sampling. Good predictions were obtained for all three components with equations of the particular year. But good predictions were not always obtained when the equations calculated one year were applied to samples from another year. To perform a lasting calibration, unique calibration (121 samples) and validation (40 samples) sets were built; honeys of the 3 years were included in both sets. Good statistics (bias, standard error of validation (SEV), and R(2)) were obtained for all three components of the validation set. No statistically significant differences (p = 0.05) were found between instrumental and reference methods.     

Analysis of nitrogen fractions in silage by near-infrared spectroscopy

Total nitrogen, soluble nitrogen (SN), nonprotein nitrogen (NPN), and acid-detergent insoluble nitrogen (ADIN) were analyzed in grass silage by near-infrared (NIR) spectroscopy. A set of 144 samples was used to calibrate the instrument by modified partial least-squares regression, and the following statistical results were achieved: standard error of calibration (SEC) = 0.449 and square correlation coefficient (R (2)) = 0.98 for total nitrogen x 6.25, SEC = 0.425 and R (2) = 0.95 for SN x 6.25, SEC = 0.414 and R (2) = 0.94 for NPN x 6.25, and SEC = 0.139 and R (2) = 0.84 for ADIN x 6.25. To validate the calibration performed, a set of 48 silage samples was used. Standard errors of prediction were 0.76, 0.64, 0.63, and 0.25 for total nitrogen, SN, NPN, and ADIN (all of them multiplied by 6.25), respectively, and R (2) for the regression of measurements by reference method versus NIR analysis were 0.94, 0.92, 0.90, and 0.48 for total nitrogen, SN, NPN, and ADIN, respectively. To compare the results obtained by NIR spectroscopy with those obtained by the reference methods for total nitrogen, SN, and NPN of the validation set, linear regression and paired t tests were applied, and the results were not significantly different (p = 0.05). When mean square prediction error analysis was applied, it could be concluded that for total nitrogen, SN, and NPN, a robust calibration model was obtained and that the main error was unexplained error. Statistical data for ADIN were worse than those of the other parameters; as a result NIR spectroscopy is not an effective method for quantitative analyses of ADIN in silage; nevertheless, it may be an acceptable method for semiquantitative evaluation.     

Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: Importance of sample pre-treatment

Near-infrared reflectance spectroscopy (NIRS) is known for its inexpensiveness, rapidity and accuracy and may become a useful tool for the assessment of soil quality. Objectives were (i) to evaluate the ability of NIRS to predict several chemical and biological properties of organically managed arable soils as well as the properties of grain yield from winter cereals for a closed population and (ii) to test whether the use of field-moist and pre-treated (quick-freezing followed by freeze-drying and grinding) samples will generate similar results. One hundred and sixteen soil samples from nine organically managed farms from Germany sampled in 2005 and 2006 were used for this investigation. Spectra of the near-infrared region (including the visible range, 400–2500 nm) from field-moist (<2 mm) or pre-treated soil samples were recorded. A modified partial least-square regression method and cross-validation were used to develop an equation over the whole spectrum (first–third derivation). For the pre-treated soils, good predictions were obtained for pH, contents of organic C, total N, plant-available P (Olsen) and exchangeable K (calcium-acetate-lactate (CAL)), contents of microbial biomass C and N (C_mic and N_mic) and ergosterol, basal respiration, metabolic quotient, the ratio of organic C/total N, the grain yield of winter cereals and grain nitrogen uptake. The RSC (the ratio of standard deviation of laboratory results to standard error of cross-validation) was greater than 2.0, the correlation coefficients (r) of a linear regression (measured against predicted values) were greater than or equal to 0.9 and the regression coefficients (a) ranged from 0.9 to 1.1. Similar good predictions were obtained if field-moist samples were used, with the exception of P (Olsen), K (CAL), metabolic quotient, grain yield of winter cereals and grain nitrogen uptake (satisfactory predictions) and ergosterol content (unsatisfactory prediction). Good predictions of the contents of Mg (CaCl₂) and microbial biomass P (P_mic) were achieved for field-moist but not for pre-treated samples. Despite sample preparation, only satisfactory predictions were obtained for the ratios of C_mic/N_mic and ergosterol/C_mic and grain nitrogen content (1.4RSC2.0, r0.8 and 0.8a1.2). However, unsatisfactory predictions for field-moist and pre-treated samples were achieved for the content of P (CAL), the nitrogen mineralisation rate and the ratios of C_mic/P_mic and basal respiration/nitrogen mineralisation rate. Our results demonstrate that biological soil properties can be predicted with NIRS for closed populations in both sample states. The pre-treatment should be used if samples have to be stored prior to infrared measurements for periods longer than a month.     

基于近红外光谱技术的蜂蜜掺假识别

为了实现蜂蜜掺假的快速识别,应用近红外光谱结合模式识别方法对蜂蜜掺假现象进行了识别分析。该研究收集了中国不同品种、不同地域的典型天然蜂蜜样品,根据目前市场上常见的蜂蜜掺假手段,掺假物质及相对含量情况配制了掺假蜂蜜样品,利用傅立叶近红外光谱仪采集其透反射近红外光谱,分别采用偏最小二乘判别分析（PLS-DA）,独立软模式法（SIMCA）,误差反向传播神经网络（BP-ANN）和最小二乘支持向量机（LS-SVM）等模式识别方法,进行蜂蜜掺假识别研究。研究结果表明：利用这4种方法在蜂蜜中掺入果葡糖浆和果葡糖水的情况下均能很好地识别出掺假蜂蜜样品,其中对于掺入果葡糖浆的掺假情况,校正集的正确判别率均达到95%以上,验证集的正确判别率均达到87%以上,对于掺入果葡糖水的掺假蜂蜜校正集的正确判别率均达到93%以上,验证集的正确判别率均达到84%以上。通过比较4种不同的识别算法,发现采用LS-SVM时,对两种掺假情况下校正集和验证集的正确判别率均达到了100%,表明基于近红外光谱的蜂蜜掺假快速准确识别是可行的。     

利用近红外光谱技术检测掺假豆浆

为了对豆乳内在营养指标及掺假豆乳进行快速检测,试验运用近红外光谱技术,利用偏最小二乘法进行回归分析,分别建立83个真伪豆浆样品的蛋白质和总固形物含量定标模型,并对模型的预测性能进行分析。结果表明:选取主成分数为12和14,蛋白质和总固形物含量的近红外光谱预测值与化学实测值之间的相关系数R分别为0.9756和0.9489,校正均方根误差分别为0.186和0.175,预测集样品的预测值和实测值之间的残差值均较小、接近零,残差之和分别为-0.074和-1.191,说明建立的定标模型可以准确预测豆浆中蛋白质和总固形物含量,且预测性能较好;通过对预测集样品的预测值与豆浆行业标准规定值相比较,确定预测集样品中掺假豆浆的正确判别率为100%,说明建立的蛋白质和总固形物含量定标模型可以应用于掺假豆浆的判别检测,且判别结果准确率高。本试验表明利用近红外光谱技术可实现对豆浆主要品质指标的快速无损检测,也可准确进行真伪豆浆的快速判别,本检测方法可为豆乳行业健康持续发展提供一定的技术支撑。     

Authentication of the botanical origin of honey by near-infrared spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was evaluated for the authentication of eight unifloral and polyfloral honey types (n = 364 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis. The corresponding error rates were calculated according to Bayes' theorem. NIR spectroscopy enabled a reliable discrimination of acacia, chestnut, and fir honeydew honey from the other unifloral and polyfloral honey types studied. The error rates ranged from <0.1 to 6.3% depending on the honey type. NIR proved also to be useful for the classification of blossom and honeydew honeys. The results demonstrate that near-infrared spectrometry is a valuable, rapid, and nondestructive tool for the authentication of the above-mentioned honeys, but not for all varieties studied.     

Acid detergent fiber analysis in oilseed Brassicas by near-infrared spectroscopy

The potential of near infrared spectroscopy (NIRS) for determining the acid detergent fiber (ADF) in the seed of oilseed Brassica (fam. Brassicaceae) was assessed. One hundred and fifty accessions belonging to the species Indian mustard (Brassica juncea L. Czern.& Coss.), Ethiopian mustard (B. carinata A. Braun) and rapeseed (B. napus L.) were scanned by NIRS as intact and ground seed, and their ADF values were regressed against different spectra transformations by modified partial least squares regression. The coefficients of determination in the external validation (r(2)) for intact and ground seed were 0.83 and 0.85, respectively. The standard deviation to standard error of prediction ratio and range to standard error of prediction ratio were 2.40 and 10.75 for intact seed and 2.62 and 11.76 for ground seed. No significant differences in the prediction were found for both sample presentations. Effects of the C-H and O-H groups of lipids and water, respectively, as well as protein and chlorophyll, were most important in modeling these equations.