Prediction of sheep responses by near infrared reflectance spectroscopy

Prediction of animal response from near infrared reflectance spectra of feeds was compared with predictions from chemical analyses. Sixty samples of pure and mixed forage-based diets were obtained from sheep intake and digestion trials. Sheep responses measured were digestible energy, dry matter intake, and calculated intake of digestible energy. Diets were analyzed chemically for protein, neutral detergent fiber, and in vitro dry matter disappearance. Coefficients of multiple determination and standard errors for fitting the sheep responses to these 60 diverse diets by regression equations developed from chemical analyses (.62 to .70) or spectra (.63 to .72) were similar. The 60 diets were divided into two sets of 30; one set was used to develop calibration equations for each sheep response, and the second set was used to test the equations. Calibration and errors of prediction were similar. When wavelengths chosen for each of the laboratory measurements were used to fit the sheep responses, standard errors were higher than when responses of sheep were predicted directly from spectra. The scanning instrument has the capability of predicting laboratory analyses and shows potential for predicting animal response as accurately as animal response can be predicted from laboratory analyses.     

Discriminant analysis of black tea by near infrared reflectance spectroscopy

A new method for the discrimination of commercial black tea samples using near infrared (NIR) reflectance spectroscopy has been investigated. NIR data at four wavelengths (1660, 1720, 2050 and 2300 nm) corresponding to maximum variation in the intensity of absorption bands in the spectra of black teas were used to calculate Mahalanobis distances. A cut-off point in the values of these distances was determined by means of which two sets of black teas with differing sensory properties could be discriminated with a 91% success rate.     

Identification of some wheat varieties by near infrared reflectance spectroscopy

Variety identification by electrophoresis is not applicable to routine control in industry. In the present work, the feasibility of near infrared reflectance (n.i.r.) analysis was investigated. Two hundred and two wheat samples including 66 samples of six known varieties were collected and their n.i.r. spectra were recorded. Spectral data were mathematically corrected in order to reduce the effect of granularity on n.i.r. spectra, then Principal Component Analysis and Multiple Discriminant Analysis (MDA) were applied to the corrected data. MDA allowed an efficient identification of the genetic origin of unknown samples: on a prediction set, 87% of samples were correctly identified. The computerised identification procedure needed less than 20 records for one sample. Further studies are necessary before recommending n.i.r. as a routine screening method.     

Feasibility of estimating peanut essential minerals by near infrared reflectance spectroscopy

The use of near infrared reflectance spectroscopy (NIRS) to evaluate the nutritional quality of peanut kernels has potential applications in plant breeding as a rapid, non-destructive tool for seed/plant selection, and in quality control. We investigated the feasibility of applying NIRS to the estimation of essential mineral composition in peanut kernels using two sample sets: A, comprising 56 diverse genotypes (N = 163); and B, comprising nine genotypes grown in five distinct environments (N = 156). Essential mineral composition was analyzed by inductively coupled plasma-optical emission spectroscopy (ICP-OES) and -mass spectrometry (ICP-MS). Calibration models were developed by partial least squares (PLS) regression, and explored a variety of data pre-treatments. Models allowing approximate estimation of K (RPD_CV 2.25, r_CV² 0.800, RPD_P 2.22) and Mg (RPD_CV 2.24, r_CV² 0.786, RPD_P 1.74), and to a lesser extent Ca (RPD_CV 1.85, r_CV² 0.649, RPD_P 1.52) and P (RPD_CV 1.77, r_CV² 0.634, RPD_P 1.65), were developed for Set B, but poorer calibrations were obtained for Set A. This level of accuracy does not allow accurate prediction, but permits approximate quantification that may be useful in plant improvement programs for screening breeding populations. The results are remarkable because NIRS is rarely applied to analytes present at such low concentrations, especially inorganic constituents that are not inherently NIR-absorbent. Further analysis of more diverse peanut samples is warranted to confirm batch-to-batch accuracy and to improve the robustness of calibrations.     

Prediction of milk fatty acid composition by near infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) (700–2500 nm) was used to predict milk fatty acid (FA) composition. Broad FA variability was ensured by using experimental cow milk derived from different feeding regimes (pasture and preserved forages with or without lipid supplements). Detailed FA composition was analyzed by gas chromatography. Predictive equations (354 samples) were developed for liquid and oven-dried milk samples using modified partial least squares with cross-validation and external validation (114 samples). Coefficient of determination in external validation (R²V) and residual predictive deviation (RPD) were good (R²V ≥ 0.88; RPD ≥ 3.26) for saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), unsaturated fatty acids (UNSAT), trans FA, trans and cis-C18:1, caproic, caprilic, capric, lauric, myristic, palmitic and oleic acids in oven-dried milk, approximate for polyunsaturated fatty acid (PUFA), stearic, vaccenic and rumenic acids (R²V ≤ 0.81; RPD ≤ 3.23) and poor for linoleic, linolenic, total n-6 and n-3 acids. The quantification was more accurate for oven-dried milk, but good results were also obtained for SFA, MUFA, palmitic and oleic acids in liquid milk.     

Identification of animal meat muscles by visible and near infrared reflectance spectroscopy

Visible (VIS) and near infrared reflectance spectroscopy (NIRS) was used to identify and authenticate different meat muscle species. Samples from beef (n: 100), lamb (n: 140), pork (n: 44) and chicken (n: 48) muscles were homogenised and scanned in the visible (VIS) and near infrared (NIR) region (400-2500 nm) in a monochromator instrument in reflectance. Both Principal Component Analysis (PCA) and dummy partial least-squares regression (PLS) models were developed to identify different meat species. The models correctly classified more than 80% of the meat sample muscles according with the muscle specie. The results showed the potential of VIS and NIR spectra as an objective and rapid method for authentication and identification of meat muscle species.     

Application of reflectance near infrared spectroscopy for bread analyses

The feasibility of using near infrared spectroscopy (NIRS) for prediction of nutrients in a wide range of bread varieties mainly produced from wheat and rye was investigated. Calibration was performed on samples collected over a 3-year-period and the calibration equations were tested on samples collected the subsequent year. Bread samples were dried, crushed, ground and measured in a rotating sample cup in the wavelength range 1100–2500 nm. Full range or segmented reflectance data was used for calibration based on partial least square (PLS) regression. Protein, fat, dietary fibre, sugar, ash, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA) and Na could be determined directly with r² values of 0.99, 0.99, 0.89, 0.96, 0.91, 0.90, 0.91, 0.92 and 0.76, respectively. The total contents of carbohydrates and energy was calculated from NIR data with r² values of 0.98 and 0.99. The ratios between analyte variation range standard deviation (SD) and the root mean square error of cross validation (RMSECV) were 8.3 (protein), 9.1 (fat), 3.0 (dietary fibre), 4.7 (sugar), 3.0 (ash), 3.1 (SFA), 3.3 (MUFA), 3.5 (PUFA), 1.9 (Na), 7.2 (carbohydrates) and 8.4 (energy). Equivalent ratios were obtained on an independent test set. It is concluded that the applied NIRS methodology is suitable for routine analysis of wheat and rye based bread for the investigated organic properties and ash. The technique may also give a rough estimate of the Na content.     

Measurement of the components in meat patties by near infrared reflectance spectroscopy

Near infrared calibrations have been derived and used routinely in the measurement of fat, moisture, protein, collagen free protein and starch in meat patties. The lower standard error of prediction (SEP) values for moisture, protein, fat and starch content determination were recorded with the first derivative calibration than with those of the second derivative treatment. The prediction for the moisture and protein content determinations with first derivative transforms were satisfactory, the correlation coefficients (r) being 0.99 and 0.98, respectively. Determining the fat content with both first and second derivative data showed excellent results, r amounting to more than 0.99. The result obtained for the starch and collagen free protein (CFP) content determination with the first derivative calibration, as well as with the second derivative treatment, showed a deviation from the chemical data and r was less than 0.97 in both cases. It is recommended that a sample preparation, such as demoisturizing or defatting, is needed to get a high correspondence with reference methods for starch and hydroxyproline determination in meat patties.     

Prediction of moisture and bulk density in milled peat by near infrared reflectance

Calibrations have been developed for the prediction of moisture (34–71% w/w) and bulk density (193–402 g litre^?1) in milled peat. Predictive accuracy was satisfactory in the case of moisture (residual standard deviation (r.s.d.)=1.1) but less so for bulk density (r.s.d.=15.1); values for precision (pooled standard deviation between duplicates) were 0.7 and 12.1 respectively. Variations in milled peat colour had no effect on the accuracy of either calibration although variation in sample temperature (+6 to +27°C) resulted in an increased residual standard deviation and the appearance of a small mean bias; precision was also affected.     

Discrimination of European wheat varieties using near infrared reflectance spectroscopy

Near infrared reflectance (NIR) spectroscopy combined with chemometrics was used to discriminate wheat varieties. A total of 249 samples of different wheat varieties from the 2003–2004 harvest were used to develop the best discriminant equation, by applying various scatters and mathematical treatments in the range of 400–2500 nm. Wheat varieties from Spain were ‘Sarina’, ‘Bolero’, ‘Berdún’, ‘Soisson’, ‘Chamorro’, ‘Artur Nick’, ‘Berdun’, ‘Marius’, ‘Anza’, ‘Kalifa’, and wheat varieties from France were ‘Galibier’ and ‘Quality’. The equation developed with the highest accuracy had an applied scatter of weighted multiplicative scatter correction, a math treatment of 2, 15, 8 (order of derivative, gap data points over which the derivative was taken, number of data points used in performing average smoothing). The percentage of correctly identified varieties was 99.5% for the calibration sample set and 94% for the validation sample set. The results demonstrated the usefulness of NIRS combined with chemometrics as a rapid method for discrimination of European wheat varieties. Although the application of the discriminant equation developed for the 2003–2004 harvest yielded a high rate, further test measurements are necessary to evaluate the robustness of the equation.     

近红外光谱技术在糖业的应用及进展

近红外光谱技术是一种快速、高效的分析技术,发展迅速,具有无损检测、无污染、操作简单、分析快速、可在线实时监测、稳定性和重现性好、节约人力成本和试剂费用且易于维护等特点,并在很多领域得到应用。近年来,已有许多国内外学者对近红外技术在传统制糖工业应用的可行性进行研究。我国作为世界蔗糖生产大国,企业应对新技术加以重视和应用。本文从糖料甘蔗和甜菜的收购、作物快速育种、制糖中间制品检测应用和成品糖检测等几个方面重点介绍国内外近红外分析技术在制糖工业上应用的研究。本文分析了近红外技术在制糖企业应用的优势和不足,并对该技术在糖业应用的发展前景和发展方向进行展望,为企业和科研院所应用研究、在线检测、过程控制和结果分析提供帮助。     

利用可见/近红外光谱技术对小米产地进行溯源研究

目的利用可见/近红外光谱技术对产自不同地区的晋谷21号小米进行溯源研究。方法使用近红外光谱仪获取产自洪洞、浮山、沁县3个不同地区的晋谷21号小米400~1004nm波段范围内的漫反射光谱;对光谱分别进行多元散射校正法(multiple scattering correction,MSC)、一阶导数法(first derivative,1St-D)预处理;对预处理光谱进行主成分分析,全交叉验证确定最佳主成分数量,获取主成分;同时选择预处理光谱特征波长。使用马氏距离法、线性判别法建立判别模型,最后用未知样品的验证准确率来表示模型的判别效果。结果原始光谱和MSC处理光谱提取特征波长分别建立的产地判别模型对3个不同产地的小米判别完全准确;1St-D处理光谱基于7个主成分结合马氏距离法和基于9个主成分结合线性判别法建立的2种判别模型对3个不同产地的小米亦实现完全准确判别。结论可见/近红外反射光谱技术用于小米产地的判别具有可行性,本研究可为小米产地的快速判别应用中提供技术基础。     

利用近红外光谱技术快速测定木材水分和气干密度的研究

利用近红外光谱技术对木材水分和气干密度进行了快速测定。结果表明：两个水分模型的决定系数都接近1,RMSECV值小于0.2%,RPD值大于10,模型质量极好,对样品的预测偏差小于0.2%;气干密度模型的决定系数为0.976、RM-SECV值为0.0152g/cm^3、RPD值为6.47,模型质量好,对样品的预测偏差范围为-0.019～0.02g/cm^3。说明可以利用近红外光谱技术对我国造纸木材的水分和气干密度进行快速、准确的预测。     

Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy

This study was implemented to evaluate the potential of visible and near infrared reflectance (NIR) spectroscopy to predict sensory characteristics related to the eating quality of lamb meat samples. A total of 232 muscle samples from Texel and Scottish Blackface lambs was analyzed by chemical procedures and scored by assessors in a taste panel (TP). Then, these parameters were predicted from Vis/NIR spectra. The prediction equations showed that the absorbance data could explain a significant but relatively low proportion of the variability (R(2)<0.40) in the taste panel traits (texture, juiciness, flavour, abnormal flavour and overall liking) of the lamb meat samples. However, a top-tail approach, looking at the spectra of the 25 best and worst samples as judged by TP assessors, provided more meaningful results. This approach suggests that the assessors and the spectrophotometer were able to discriminate between the most extreme samples. This may have practical implications for sorting meat into a high quality class, which could be branded, into a low quality class sold for a lower price for less demanding food use. Regarding the chemical parameters, both intramuscular fat and water could be more accurately predicted by Vis/NIR spectra (R(2)=0.841 and 0.674, respectively) than sensory characteristics. In addition, the results obtained in the present study suggest that the more important regions of the spectra to estimate the sensory characteristics are related to the absorbance of these two chemical components in meat samples.     

利用可见/近红外反射光谱无损检测小米的粘度

目的建立适用于小米粘度无损检测的可见/近红外反射光谱法。方法使用光谱仪获取小米在367～1020 nm波段范围内的漫反射光谱,采用多元散射校正法(multiple scattering correction,MSC)和一阶导数法(first derivation,1~(st)-D)对原始反射光谱进行处理,并且使用主成分分析确定最佳主成分数,建立小米粘度判别模型,使用全交叉验证法进行模型验证。结果使用原始反射光谱、MSC处理光谱和1~(st)-D处理光谱,分别提取了6、12和12个主成分,建立的峰值粘度模型RCV在0.86以上,对验证集的预测结果 Rp在0.82～0.86之间;而使用1~(st)-D处理光谱提取12个最优主成分,建立的模型可较好地预测小米粘度的破损值,RCV为0.8573,对验证集的预测结果 Rp为0.8309。结论该方法适用于小米粘度的无损检测,为小米加工品质的快速检测提供一定的理论支持。     

近红外光谱快速检测石榴果皮多酚与黄酮含量

为实现石榴果皮多酚及黄酮的快速检测,以采摘自陕西临潼石榴产区的102个新鲜石榴为研究对象,采用近红外光谱技术进行检测。为达到最佳数学模型,采用随机、KS和SPXY样本集划分方法,多元散射校正、标准正态变量交换、一阶导数和二阶导数、多元散射校正+二阶导数和标准正态变量交换+二阶导数多种预处理方法,竞争适应性重赋权重采样法和连续投影法对所建模型进行优化比较。结果表明,经SPXY样本划分,多元散射校正+二阶导数（多酚）、二阶导数（2,4,3）（黄酮）,蒙特卡洛交叉验证法异常值剔除;竞争适应性重赋权重采样法波段选取,石榴果皮多酚和黄酮含量的偏最小二乘模型最好,多酚校正集和预测集样品的决定系数为0.9699和0.9341;黄酮校正集和预测集样品的决定系数为0.9775、0.9540。近红外光谱对石榴果皮多酚及黄酮的快速检测是可行的。      

无损检测阔叶材纤维长度的近红外光谱法

比较了近红外光谱分析技术测量木材纤维长度与传统的测量方法的优缺点并详细介绍了应用近红外光谱分析技术测量木材纤维长度,其中包括木材样品选择及制备、近红外光谱采集及纤维长度真值测量、多变量数据分析与模型建立的实验方法与操作步骤.实验表明近红外光谱分析技术可以快速准确地预测阔叶材纤维长度.     

Measurement of fat and sucrose in dry cake mixes by near infrared reflectance spectroscopy

A feasibility study was carried out to assess the potential of near infrared (NIR) reflectance spectroscopy for the measurement of fat and sucrose in dry cake mixes. The calibration of the NIR instrument was carried out in a research laboratory and then assessed under quality control conditions in the factory laboratory. It was possible to measure fat with an accuracy (±2σ) of ±3.4% for products with a fat content of 8–25% compared with +0.76% for the precision of the Soxhlet procedure. In the case of sucrose the accuracy was ±5.4% for products with a sucrose content of 10–40% compared with ±2.0% for a high pressure liquid chromatography (hplc) method. It must be concluded, therefore, that while NIR offers a quicker, simpler method of quality control, this is at the expense of accuracy.     

可见/近红外反射光谱法检测马铃薯抗性 淀粉含量的研究

目的利用可见/近红外反射光谱技术无损检测新鲜马铃薯茎块中抗性淀粉的含量。方法使用光谱仪获取新鲜马铃薯在345~1100 nm波段范围内的漫反射光谱;分别使用Savitzky–Golay(S-G)平滑处理、多元散射校正(MSC)法和一阶导数法(1st-D)对反射光谱进行预处理;对(S-G)反射光谱、MSC处理光谱和1st-D光谱使用逐步回归法判别法选择最优波长组合,建立多元线性回归模型,使用全交叉验证法验证模型。结果结果表明,可见/近红外反射光谱经过一阶导数处理后,确定的8个最优波长(370、569、576、866、868、886、922和963 nm)组合建立模型的校正和验证结果最好:模型的校正结果为相关系数R=0.996,标准差SEC=0.521%;模型交叉验证相关系数Rcv=0.982,验证标准差SECV=0.791%。结论可见/近红外反射光谱技术可以较好地预测新鲜马铃薯茎块的抗性淀粉含量,本研究可为可见近红外光谱技术在马铃薯功能成分的快速检测提供一定的技术基础。