Determination of dry matter and crude protein contents of undried forages by near‐infrared reflectance spectroscopy

Near‐infrared reflectance spectroscopy (NIRS) was used to predict the dry matter (DM) and crude protein (CP) contents of untreated forage samples. Four hundred forage samples were analysed in reflectance mode. Two mathematical treatments based on the order of derivative of log(1/R), the gap in data points and the numbers of data points used in the first and second smoothings were applied. Predictive equations were developed using modified partial least squares (MPLS) with internal cross‐validation. The coefficient of determination of calibration and the standard error of cross‐validation (SECV, in parentheses) for DM were 0.92 (12.4), 0.92 (12.6) and 0.93 (11.7) for the two treatments and log(1/R) respectively on a g kg^?1 fresh weight basis. For CP the NIRS calibration statistics yielded and SECV (in parentheses) values of 0.85 (19.8), 0.85 (19.5) and 0.87 (18.1) for the two treatments and log(1/R) respectively on a g kg^?1 fresh weight basis. It was concluded that NIRS is a suitable method to predict the dry matter and crude protein contents of fresh forage without sample preparation. © 2002 Society of Chemical Industry     

Prediction of Cadmium content in brown rice using near‐infrared spectroscopy and regression modelling techniques

The feasibility of prediction of cadmium (Cd) content in brown rice was investigated by near‐infrared spectroscopy (NIRS) and chemometrics techniques. Spectral pretreatment methods were discussed in detail. Synergy interval partial least squares (siPLS) algorithm was used to select the efficient combinations of spectral subintervals and wavenumbers during constructing the quantitative calibration model. The performance of the final model was evaluated by the use of root mean square error of cross‐validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficients for calibration set and prediction set (R_c and R_p), respectively. The results showed that the optimum siPLS model was achieved when two spectral subinterval and fifty‐two variables were selected. The predicted result of the best model obtained was as follows: RMSECV = 0.232, R_c = 0.930, RMSEP = 0.250 and R_p = 0.915. Compared with PLS and interval PLS models, siPLS model was slightly better than those methods. These results indicate that it is feasible to predict and screen Cd content in brown rice using NIRS.     

The use of near‐infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants

Near‐infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modified partial least squares (MPLS) regression was applied to scatter‐corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coefficients of determination (R²) and standard errors of cross‐validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg⁻¹ on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice. © 2000 Society of Chemical Industry     

Granulation sensing of first‐break ground wheat using a near‐infrared reflectance spectrometer: studies with soft red winter wheats

A near‐infrared reflectance spectrometer, previously evaluated as a granulation sensor for first‐break ground wheat from six wheat classes and hard red winter (HRW) wheats, was further evaluated for soft red winter (SRW) wheats. Two sets of 35 wheat samples, representing seven cultivars of SRW wheat ground by an experimental roller mill at five roll gap settings (0.38, 0.51, 0.63, 0.75 and 0.88 mm), were used for calibration and validation. Partial least squares regression was applied to develop the granulation models using combinations of four data pretreatments (log(1/R), baseline correction, unit area normalisation and derivatives) and subregions of the 400–1700 nm wavelength range. Cumulative mass of size fraction was used as reference value. Models that corrected for path length effects (those that used unit area normalisation) predicted the bigger size fractions well. The model based on unit area normalisation/first derivative predicted 34 out of 35 validation spectra with standard errors of prediction of 3.53, 1.83, 1.43 and 1.30 for the >1041, >375, >240 and >136 µm size fractions respectively. Because of less variation in mass of each size fraction, SRW wheat granulation models performed better than the previously reported models for six wheat classes. However, because of SRW wheat flour's tendency to stick to the underside of sieves, the finest size fraction of these models did not perform as well as the HRW wheat models. © 2003 Society of Chemical Industry     

基于近红外技术测定不同鲜肉中挥发性盐基氮含量

目的 应用近红外光谱技术快速检测猪肉、羊肉和牛肉的挥发性盐基氮含量。方法 本实验采集各种肉类的近红外光谱, 运用偏最小二乘法(partial least squares, PLS), 光谱经多种不同预处理方式并通过比较选择最优处理后, 建立挥发性盐基氮含量的近红外校正模型。结果 猪肉选择一阶导、S-G平滑方式, 羊肉选择二阶导、S-G平滑方式, 牛肉选择一阶导、Norris平滑方式。猪肉、羊肉和牛肉的挥发性盐基氮建模集相关系数分别为0.9069、0.9106和0.9587, 方根误差分别为1.12、1.64和2.20。结论 所建立的模型取得了较好的结果, 验证了近红外光谱技术对猪肉、羊肉和牛肉挥发性盐基氮进行定量分析的巨大应用潜力。     

近红外光谱法快速检测甜菜糖度的模型优化

目的建立起近红外光谱技术关于甜菜糖度的最佳预测模型。方法研究了Savitzky-Golay平滑处理、Savitzky-Golay导数、均值中心化、差分求导、净分析信号、去趋势校正、标准正态变量变换和多元散射校正等8种预处理方法的多方法联用处理进行光谱数据的预处理,结合光谱波段优选,建立甜菜糖度与近红外光谱的预测模型。结果在进行模型的评价时,以误差均方根(SEP)、校正标准误差(SEC)与交叉检验误差(SECV)作为评价指标。结论发现经过光谱波段优选之后,结合Savitzky-Golay平滑、Savitzky-Golay导数、去趋势校正及均值中心化进行光谱数据的预处理得到的模型效果最佳。     

基于近红外漫反射光谱的面包老化过程中非冻结水含量无损检测

目的 建立一种基于近红外光谱技术快速无损测定面包老化过程中的非冻结水含量的方法。方法 应用近红外漫反射光谱技术采集新鲜面包在放置2h、2d、3d、4d、5d、6d、7d时的光谱，对比导数、S-G平滑(Savitzky Golay smooth)、标准正态变量变换(Standard normal variable transformation，SNV)及多元散射校正(Multiplicative scatter correction，MSC)预处理方法，利用偏最小二乘回归法(Partial least square regression，PLSR)和多元线性回归法（Multiple Linear Regression，MLR）建立面包老化过程中的非冻结水含量的预测模型，并对比两种模型预测结果。结果 利用PLSR建模相较MLR建模结果较好，建立的模型预测结果较好，模型的校正集相关系数（Rc）和均方根误差（RMSEC）分别为0.9386和0.0236 , 验证集相关系数（Rv）和均方根误差（RMSEP）分别为0.9271和0.0245。结论 通过近红外光谱技术结合偏最小二乘法建立面包老化过程中的非冻结水含量模型可作为面包老化过程中的非冻结水含量无损快速测定的可行性方法，其含量变化可以有效预测面包老化，为面包老化的无损检测提供了新的可行方案。     

基于近红外光谱的海藻糖浆组分快速定量检测

研究采用近红外光谱(near infrared spectroscopy,NIRS)快速检测单酶法生产海藻糖浆(海藻糖、麦芽糖及葡萄糖)组成的方法。取65个海藻糖浆作为样本,扫描得到近红外光谱图,分为48个样本校正集,17个样本预测集,计算分析结果表明:一阶微分(first derivative,1D)与S-G平滑(savitzky-golay filter)组合处理为最优预处理方法;运用TQ analyst建模软件中主成分回归(principal component regression,PCR)算法和偏最小二乘法(partial least squares,PLS)算法分别对海藻糖浆建模,发现采用偏最小二乘法(partial least squares,PLS)的海藻糖浆组分模型稳定性和预测能力更好;运用PLS、1D、S-G平滑组合预处理海藻糖浆组分模型,不仅降低光谱的背景噪声,同时还提高模型的稳定性。海藻糖浆各组分模型的交叉验证均方差(RMSEC)、交叉验证决定系数(Rc)、预测均方差(RMSEP)、预测决定系数(RP)依次为:海藻糖模型分别为0.188,0.995,0.089,0.989;麦芽糖模型分别为0.143,0.997,0.131,0.969;葡萄糖模型分别为0.147,0.997,0.094,0.999。NIRS检测快速、无损便捷,可用于检测单酶法生产海藻糖浆的组分。     

近红外漫反射光谱法在稻谷质量检测中的应用

采用DA7200型二极管阵列近红外光谱仪对江西地区的284份稻谷样品进行光谱采集,同时按照国家标准方法对试验样品进行化学分析检验。在化学分析检验的基础上,采用偏最小二乘法建立分析模型,并对其准确性进行了评价探讨。结果显示,采用偏最小二乘法所建立的定量分析模型相关性较高。同时,另选取30份样品进行模型验证,其相关系数和预测均方根误差均符合国家标准方法要求,因此近红外漫反射光谱法可以用于稻谷质量评定中的快速无损检测。     

Prediction of fat in intact cereal food products using near‐infrared reflectance spectroscopy

To evaluate the feasibility of an intact product approach to the near‐infrared (NIR) determination of fat content, a rapid acquisition spectrometer, with an InGaAs diode‐array detector and custom built sampling device, was used to obtain reflectance spectra (1100–1700 nm) of diverse cereal food products. Fat content reference data were obtained gravimetrically by extraction with petroleum ether (AOAC Method 945.16). Using spectral and reference data, partial least‐squares regression analysis was applied to calculate a NIR model (n = 89) to predict fat in intact cereal products; the model was adequate for rapid screening of samples, predicting the test samples (n = 44) with root mean square error of prediction (RMSEP) of 11.8 (range 1.4–204.8) g kg^?1 and multiple coefficient of determination of 0.98. Repeated repacking and rescanning of the samples did not appreciably improve model performance. The model was expanded to include samples with a broad range of particle sizes and moisture contents without reduction in prediction accuracy for the untreated samples. The regression coefficients for the models calculated indicated that spectral features at 1165, 1215 and 1395 nm, associated with CH stretching in fats, were the most critical for model development. Published in 2005 for SCI by John Wiley & Sons, Ltd.     

近红外光谱结合偏最小二乘法快速测定糖果中水分含量

采用近红外光谱(NIR)结合偏最小二乘法(PLS)建立了一种糖果中水分含量快速准确的测定方法。在12500~3600 cm^-1光谱范围内采集116批糖果的近红外漫反射光谱,并用减压干燥失重法测定其水分含量。通过比较不同参数对建模的影响,发现用多元散射校正法进行预处理,在11682.2~9826.1、8939.0~6267.9、5378.8~4487.8 cm^-1光谱范围内,主成分数为15时,应用PLS方法建立的糖果水分的定量分析模型效果最佳。所建立模型的相关系数为0.9716,校正均方根误差和验证均方根误差分别为0.97%和1.03%。该方法结果准确可靠、操作简便,可用于糖果中水分含量的快速检测。     

基于近红外光谱技术的老陈醋可溶性固形物定量分析

可溶性固形物含量(SSC)是食品行业的重要技术参数之一。利用近红外光谱技术对不同醋龄的老陈醋SSC进行分析。在不同光谱预处理下,分别采用主成分回归(PCR)和偏最小二乘法(PLS)建立SSC的定量分析模型。结果表明,采用5点平滑预处理后,利用PLS建立的老陈醋SSC的定量分析模型最优,其校正集的相关系数R为0.999 9,校正标准偏差(RMSEC)为0.038 3,预测标准偏差(RMSEP)和交叉验证标准偏差(RMSECV)分别为0.082 1,0.096 4。表明采用近红外光谱技术对不同醋龄的老陈醋SSC进行定量分析建模是可行的。     

Rapid determination of shoot nitrogen status in rice using near infrared reflectance spectroscopy

Near infrared reflectance (NIR) spectroscopy is a rapid, cheap, simple technique which can be used to make quantitative analyses of the concentrations of nutrients in plant tissue. The application of NIR to determine nitrogen in rice was examined. The absorbance spectrum of rice (Oryza sativa L) shoot tissue was similar to that of the temperate cereal wheat even though rice tissue has a much higher silica content. A 19-filter NIR instrument was calibrated to estimate the nitrogen content of rice shoots with between 0·8 and 3·50% N by the Kjeldahl technique. The calibration model developed used three wavelengths to account for 96% of the variation in sample Kjeldahl nitrogen concentration. This model was validated using 67 samples comprising five rice varieties grown on farms in two seasons in southern New South Wales. The standard error of prediction of the model was 0·15% N. A tissue testing service using this NIR calibration is now operational for rice crops in southern New South Wales.     

Usefulness of near infrared spectroscopy to monitor the extent of heat treatment in fish meal

The objective of this study was to explore the use of near infrared (NIR) spectroscopy and chemometrics to monitor the degree of heat treatment of fish meal. Six batches of fish meal (approximately 500 g) were split in sub‐samples of 50 g and heated at constant temperature (60 ± 5 °C) for different periods of time (15 and 30 min, 1, 2, 3, 4, 5, 6, 8, 48 and 72 h) in a force air oven and scanned in the NIR region (1100–2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), stepwise discriminant analysis (SLDA) and partial least square regression (PLS) models were used to interpret, classify and predict the extent to heat treatment in fish meal samples. The SLDA models correctly classified 80% and 100% of fish meal samples belonging to the untreated fish meal and after 4, 5 and 6 h of heat treatment. However, samples heated for 30 min, 1, 2 and 3 h yield poor classification rates (less than 50%). This study demonstrated the potential ability of NIR spectroscopy to predict and classify the extent of heat treatment during the production of fish meal. However, further research must carry out in order to validate the NIR calibrations to predict the degree of heat treatment in fish meal expose to a shorter time.     

Determination of apparent amylose content,pasting properties and gel texture of rice starch by near‐infrared spectroscopy

For breeding rice with improved quality, apparent amylose content (AAC), rapid visco analyser (RVA) pasting viscosities and gel texture properties may be routinely measured. As a direct measurement is time‐consuming and expensive, rapid predictive method based on near‐infrared spectroscopy (NIRS) is useful for measurement of these quality parameters. In this study, calibration models were developed using modified partial least‐squares regression with different mathematical treatments based on the grain and flour spectra of non‐waxy rice alone or in combination with waxy rice. The results showed that calibration models built with flour spectra are more robust than those with grain spectra, and with total rice including waxy rice are superior to those with only non‐waxy rice. Some starch quality parameters, such as AAC, setback viscosity (SB), pasting temperature (PT), hardness (HD) and cohesiveness (COH) could be predicted with sufficient accuracy by NIRS based on flour spectra, whereas only AAC and PT could be predicted with sufficient accuracy based on grain spectra. All the models reported here are usable for rough sample screening (cold paste viscosity and breakdown viscosity), sample screening (SB, PT and COH) and for most applications (AAC and HD) for routine screening of a large number of samples in the early generation selection in breeding programs. However, for accurate assay of the pasting viscosity and gel textural parameters, direct instrumental measurement should be employed in later generations. Copyright © 2007 Society of Chemical Industry     

面粉湿面筋近红外定量分析中的光谱预处理方法研究

采用10种不同的预处理方法对面粉湿面筋含量的近红外光谱数据进行预处理,并比较了10种预处理方法对偏最小二乘法（PLS）建立定量模型效果的影响。结果表明,采用近红外光谱（NIRS）分析技术对面粉中的湿面筋含量进行定量分析时,一阶导数+减去一条直线的预处理方法对面粉湿面筋含量的预测效果最好,内部交叉验证相关系数R为0.901 8,内部交叉验证标准差（RMSECV）为0.708;预测相关系数R为0.920 9,预测标准差（RMSEP）为0.083,提高了预测模型的精度和准确性。