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.     

近红外光谱法定性检测餐桌废弃油脂的掺伪

目前餐桌废弃油脂的违法掺伪行为时有发生。探索快速而有效的分析手段,保障百姓餐桌安全是当前的重要任务。本实验采用近红外光谱分析法对餐桌废弃油脂掺伪食用油进行定性鉴别。采用计量学软件,运用距离判别法和BP神经网络算法,对经过标准化与主成分分析处理过的油脂光谱数据建立模式识别模型。距离判别法的判别准确率为89.71%,BP神经网络准确率最高达到97.06%。预测结果说明,分析油脂的近红外光谱信息可以有效鉴别回收油的掺伪行为。      

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.     

Potential of near infrared spectroscopy for the analysis of volatile components in cheeses

Near Infrared Spectroscopy (NIRS) was used for the determination of volatile compounds in cheeses allowed to ripen for different times using a remote fibre-optic reflectance probe. To do so, cheeses with known and varying percentages of cow's, ewe's, and goat's milk were elaborated and used as reference material. The volatile compounds determined were: acetaldehyde, ethanol, 1-propanol, 2-butanol, 2-pentanol, 3-methyl-1-butanol, 2-butanone, 2-pentanone, 2-heptanone and 2-nonanone. The regression method employed was the modified partial least squares (MPLS). The calibration results using 67–72 samples of cheese had a correlation coefficients (RSQ) between 0.600 for the 3-methyl-1-butanol and 0.903 for the 2-nonanone. The robustness of the method was confirmed by applying it to twenty new samples of different compositions and ripening times which did not belong to the calibration group. Likewise, the correlations between the factors of influence studied and the volatile compounds were carried out. The results of the NIRS method are comparable with those of the purge-and-trap-gas chromatography-mass spectrometry.     

近红外光谱法快速分析甲糖膏的应用研究

研究应用近红外光谱法快速分析甘蔗制糖生产中甲糖膏的锤度和蔗糖分。采用NIRSystems 6500型近红外多功能分析仪,对采自生产线的甲糖膏进行光谱扫描和测定,用时小于2min。锤度和蔗糖分模型的光谱预处理方法以"SNV+Detrend、一次求导和一次平滑联合处理"效果最佳,改进型偏最小二乘法建立的模型效果最佳。锤度模型的SEC为0.522,SEP为0.96;蔗糖分模型的SEC为0.576,SEP为0.86。锤度和蔗糖分的偏差都小于1,满足糖厂要求,近红外光谱法应用在甲糖膏锤度和蔗糖分的快速测定是可行的。     

Characterisation of grain quality in diverse sorghum germplasm using a Rapid Visco-Analyzer and near infrared reflectance spectroscopy

BACKGROUND: Twenty‐two diverse sorghum landraces, classified as normal and opaque types obtained from Ethiopia, were characterised for grain quality parameters using near infra‐red spectroscopy (NIRS), chemical and Rapid Visco‐Analyzer (RVA) characteristics. RESULTS: Protein content ranged from 77 to 182 g kg^?1, and starch content from 514 to 745 g kg^?1. The NIRS analysis indicated the pig faecal digestible energy range from 14.6 to 15.7 MJ kg^?1 as fed, and the ileal digestible energy range from 11.3 to 13.9 MJ kg^?1 as fed. The normal sorghums had higher digestible energy than the opaque sorghums, which exhibited lower RVA viscosities, and higher pasting temperatures and setback ratios. The RVA parameters were positively correlated with the starch content and negatively correlated with the protein content. The normal and opaque types formed two distinct groups based on principal component and cluster analyses. CONCLUSION: The landraces were different for the various grain quality parameters with some landraces displaying unique RVA and NIRS profiles. This study will guide utilisation of the sorghum landraces in plant improvement programs, and provides a basis for further studies into how starch and other constituents behave in and affect the properties of these landraces. Copyright © 2011 Society of Chemical Industry     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.     

Quantitative analysis of stevioside in the leaves of Stevia rebaudiana by near infrared reflectance spectroscopy

Stevioside, a diterpenic glycoside extracted from the leaves of Stevia rebaudiana (Bert) Bertoni, is a natural sweetener 300 times sweeter than sucrose. HPLC is routinely used to measure stevioside content in Stevia leaves. Our objectives were to investigate the use of near infrared reflectance spectroscopy (NIRS) to analyse stevioside concentration in Stevia leaves. The NIRS calibrations were developed from 64 samples covering the range normally found in Stevia leaves (4-13%). Another 30 samples were used for validation. Standard error of calibration and coefficient of multiple determination (R²) were 1.47 and 0.90 respectively, using the first derivative mathematical treatment. The validation results, although less accurate, suggest that NIRS is a precise and simple method for routine stevioside determination in Stevia leaves.     

Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS)

Near infrared reflectance spectroscopy (NIRS) was evaluated as a tool to segregate different types of bovine meat and predict several chemical fractions on samples from two breeds, three muscles and six grading (Chilean system) categories. Samples previously minced, frozen and thawed, were scanned (400–2500 nm) and then analyzed for dry matter, crude protein, ether extract, total ash and collagen content, after freeze drying. Discriminant analysis using a partial least squares regression technique and cross validation, correctly identified breed and muscle type for most samples, but carcass grades, with the exception of samples from calves, were not successfully predicted. Best calibrations for chemical composition tested by cross-validation, showed R² and standard errors of cross validation of 0.77 and 0.58% (dry matter), 0.82 and 0.48% (crude protein), 0.82 and 0.44% (ether extract). Calibrations for total ash showed a poor, and for collagen, a very poor prediction ability.     

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。     

在线近红外光谱在保健酒酒精度检测上的应用

采用短波近红外光谱仪器在线检测保健酒调配液生产线上产品的酒精度。通过使用一阶倒数(First derivative,FD)和平滑处理(Norris derivative filter,ND),对近红外图谱进行预处理,使用偏最小二乘法(Partial least square,PLS)建立了酒精度检测近红外模型。模型的校正集均方根误差(Root mean square error of calibration,RMSEC)为0.737,交互验证相关系数为0.9189;预测集均方根误差(Root mean square error of prediction,RMSEP)为0.788,交互验证相关系数为0.9425。实验数据显示,近红外计算酒精度数值与标准法测量数值相对偏差主要集中在±2%之间,该方法可以满足生产过程中在线检测酒精度的要求。      

近红外光谱技术在线测定白砂糖色值

采用国产近红外在线分析系统,对广西某糖厂的白砂糖色值进行了在线测定研究。采用SupNIR-4000型近红外在线分析仪,直接对成品传送带上的白砂糖进行扫描获得光谱数据,测定时间仅需90s。光谱预处理方法"标准化+Savitzky-Golay一阶求导+正交信号校正"最佳,人工神经网络(ANN)建模最佳。结果显示,ANN模型的校正集相关系数(Rc)和标准偏差(RMSEC)分别为0.9215和5.1984,预测集相关系数(Rp)和标准偏差(RMSEP)分别为0.6771和9.3042,色值预测偏差基本在±10,满足糖厂需要。此法在制糖行业具有广泛的应用推广前景。     

Development of near infrared reflectance spectroscopy to predict chemical composition with a wide range of variability in beef

A total of 182 beef samples were minced and divided into calibration set (n = 140) and independent validation set (n = 42). Calibration models of NIRS (1000–1800 nm) were built using partial least squares regression (PLSR) on the calibration set of samples. Both the coefficient of determination in calibration (R²_C) and the coefficient of determination in prediction (R²_P) were over 0.98 for all chemical compositions. The ratio performance deviation (RPD) was 17.37, 5.12 and 10.43 for fat, protein and moisture, respectively. The results of the present study indicate the outstanding ability of NIRS to predict chemical composition in beef.     

Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy

Visible and near infrared reflectance spectroscopy (VIS-NIRS) was used to discriminate meat and meat juices from three livestock species. In a first trial, samples of Longissimus lumborum muscle, corresponding to beef (31) llamas (21) and horses (27), were homogenised and their spectra collected in reflectance (NIRSystems 6500 scanning monochromator, in the range of 400-2500 nm). In the second trial, samples of meat juice (same muscle) from the same species (20 beef, 19 llama and 19 horse) were scanned in folded transmission (transflectance). Discriminating models (PLS regression) were developed against “dummy” variables, testing different mathematical treatments of the spectra. Best models indentified the species of almost all samples by their meat (reflectance) or meat juice (transflectance) spectra. A few (three of beef and one of llama, for meat samples; one of beef and one of horse, for juice samples) were classified as uncertain. It is concluded that NIRS is an effective tool to recognise meat and meat juice from beef, llama and horses.     

Quality assessment of tomato landraces and virus-resistant breeding lines: quick estimation by near infrared reflectance spectroscopy

BACKGROUND: Several tomato landraces are very popular in south‐eastern Spain for their organoleptic fruit quality, but these cultivars are highly susceptible to several viruses. A breeding programme is being carried out for the introduction of virus resistances into these landraces. In the last steps of our breeding programme a high number of breeding lines must be evaluated for agronomic and organoleptic quality parameters. Near infrared reflectance spectroscopy (NIRS) technology shows considerable promise and is ideally suited to the requirements of breeders. RESULTS: Significant differences between a set of 35 tomato breeding lines, seven landraces and one commercial hybrid were observed for quality and mineral content parameters, suggesting that there are considerable levels of genetic diversity between the cultivar groups studied. Using NIRS on dry samples of tomato constitutes a feasible technique to estimate the content of several minerals (C, N, P, K, Ca, Na, Mn, Mg and Cu) according to the coefficient of determination for calibration (R² > 0.90). NIRS estimations of soluble solids content and titratable acidity obtained were considered useful only for general screening purposes. CONCLUSIONS: NIRS technology may be a useful tool in the selection of lines coming out of tomato breeding programs, allowing a quick estimation of mineral content. However, the estimation of soluble solids content and titratable acidity by NIRS must be improved. Copyright © 2011 Society of Chemical Industry     

Combination and comparison of multivariate analysis for the identification of orange varieties using visible and near infrared reflectance spectroscopy

A study of multivariate analysis for orange varieties was carried out, and the potential of visible and near infrared reflectance spectroscopy (Vis/NIRS) for its ability to nondestructively differentiate orange varieties was evaluated. A total of 320 orange samples (80 for each variety) were investigated for Vis/NIRS on 325–1075 nm using a field spectroradiometer. Multivariate classification methods including principal component analysis (PCA), back propagation neural network (BPNN) and partial least squares discriminant analysis (PLSDA) were adopted to classify oranges. Sixteen principal components from PCA were used as the input of BPNN model, and the identification accuracy of four orange varieties reached 100%. The prediction result of PLSDA, i.e., standard error of prediction (SEP) 0.24497, correlation coefficient (R) 0.97843, root mean square error of prediction (RMSEP) 0.24268, and identification accuracy 90% indicate that PLSDA is an alternative model for orange identification. With the comparison of these two models, it shows that BPNN combined with PCA obtained better classification effect than that of PLSDA. The overall results demonstrate that Vis/NIRS technology with multivariate analysis models is promising for the rapid and reliable determination for identification of orange varieties.