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1.
Visible and near-infrared spectroscopy (VIS/NIR) has been used to detect economic adulteration of crab meat samples. Atlantic blue and blue swimmer crab meat samples were adulterated with surimi-based imitation crab meat in 10% increments. Waveform evaluation revealed that the main features seen in the spectral data arise from water absorptions with a decrease in sample absorbance with increasing adulteration level. Prediction and quantitative analysis was done using raw data, a 15-point smoothing average, a first derivative, a second derivative, and 150 wavelength spectral data gathered from a correlogram. Regression analysis included partial least squares (PLS) and principal component analysis (PCR). Both models were able to perform similarly in predicting crab meat adulteration. The best model for both PLS and PCR used the first derivative spectral data gathered from the correlogram, with a standard error of prediction (SEP) of 0.252 and 0.244, respectively. The results suggest that VIS/NIR technology can be successfully used to detect adulteration in crab meat samples adulterated with surimi-based imitation crab meat. 相似文献
2.
漫反射和透射光谱检测马铃薯黑心病的比较 总被引：5，自引：3，他引：2
针对马铃薯黑心病不易检测,提出马铃薯黑心病的光学无损检测方法,并比较了马铃薯黑心病的漫反射光谱和透射光谱检测方法。通过高光谱图像采集系统、透射光谱采集系统和傅里叶变换近红外光谱仪获取合格马铃薯与黑心病马铃薯的可见/近红外漫反射光谱、可见/近红外透射光谱以及近红外漫反射光谱,并采用偏最小二乘-线性判别分析方法建立马铃薯黑心病的识别模型。透射光谱采集系统采集的可见/近红外透射光谱所建模型的判别正确率最高,对测试集样本的识别正确率为98.46%;高光谱图像采集系统获取的可见/近红外漫反射光谱经二阶导与标准化组合预处理后所建模型对测试集样本的识别正确率为92.31%;傅里叶变换近红外光谱仪获取的漫反射光谱经标准正态变量变换与标准化组合预处理后所建模型对测试集样本的识别正确率90.77%。试验结果表明:采用光谱检测马铃薯黑心病,透射光谱系统优于高光谱成像系统,高光谱成像系统优于傅里叶近红外光谱仪。研究结果为马铃薯内部缺陷的光谱定性判别及便携式仪器的研制提供了参考。 相似文献
3.
Visible/near-infrared (vis/NIR) spectroscopy combined with multivariate analysis was used to quantify chlorophyll content in tomato leaves and classify tomato leaves with different genes. In this study, transgenic tomato leaves with antisense LeETR1 (n = 106) and their parent nontransgenic ones (n = 102) were measured in vis/NIR diffuse reflectance mode. Quantification of chlorophyll content was achieved by partial least-squares regression with a cross-validation prediction error equal to 2.87. Partial least-squares discriminant analysis was performed to classify leaves. The results show that differences between transgenic and nontransgenic tomato leaves do exist, and excellent classification can be obtained after optimizing spectral pretreatment. The classification accuracy can reach to 100% using the derivative of spectral data in the full and partial wavenumber range. These results demonstrate that vis/NIR spectroscopy together with chemometrics techniques could be used to quantify chlorophyll content and differentiate tomato leaves with different genes, which offers the benefit of avoiding time-consuming, costly, and laborious chemical and sensory analysis. 相似文献
4.
D. C. Weindorf S. Chakraborty J. Herrero B. Li C. Castañeda A. Choudhury 《European Journal of Soil Science》2016,67(2):173-183
Arid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory‐based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X‐ray fluorescence (PXRF) and visible near‐infrared diffuse reflectance spectroscopy (Vis–NIR DRS)) to scan a diverse set (n = 116) of samples from arid soil in Spain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression (PSR), support vector regression (SVR) and random forest (RF) analysis, which were applied to Vis–NIR DRS data, PXRF data and PXRF and Vis–NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR + RF predicted gypsum with a ratio of performance to interquartile distance (RPIQ) of 5.90 and residual prediction deviation (RPD) of 4.60, electrical conductivity (1:5 soil : water) with RPIQ of 3.14 and RPD of 2.10, Ca content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and Vis–NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non‐destructive scans. 相似文献
5.
Monitoring nitrogen (N) in oil palm is crucial for the production sustainability. The objective of this study is to examine the capability of visible (Vis), near infrared (NIR) and a combination of Vis and NIR (Vis + NIR) spectral indices acquired from different sensors for predicting foliar N content of different palm age groups. The N treatments varied from 0 to 2 kg per palm, subjected according to immature, young mature and prime mature classes. The Vis + NIR indices from the ground level-sensor that is green + red + NIR (G + R + NIR) was the best index for predicting N for immature palms (R2 = 0.91), while Vis indices blue + red (B + R) and Green Red Index from the space-borne sensor were significantly useful for N assessment of young and prime mature palms (R2 = 0.70 and 0.50), respectively. The application of vegetation indices for monitoring N status of oil palm is beneficial to examine extensive plantation areas. 相似文献
6.
Quantifying moisture and roughness with Support Vector Machines improves spectroscopic soil organic carbon prediction 
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下载免费PDF全文 Christoph Römer Andrei Rodionov Jan Behmann Stefan Pätzold Gerhard Welp Lutz Plümer 《植物养料与土壤学杂志》2014,177(6):845-847
The challenges of Vis‐NIR spectroscopy are permanent soil surface variations of moisture and roughness. Both disturbance factors reduce the prediction accuracy of soil organic carbon (SOC) significantly. For improved SOC prediction, both disturbance effects have to be determined from Vis‐NIR spectra, which is especially challenging for roughness. Thus, an approach for roughness quantification under varying moisture and its impact on SOC assessment using Support Vector Machines is presented here. 相似文献
7.
Detection of hazelnut oil adulteration using FT-IR spectroscopy 总被引：1，自引：0，他引：1
Fourier transform infrared spectroscopy (FT-IR) was used to detect the adulteration of hazelnut oil with different types of oils and to detect the adulteration of extra-virgin olive oil with hazelnut oil. Spectra of hazelnut oil, seven other types of oils, extra-virgin olive oil, and the adulterated oils were collected with a FT-IR equipped with a ZnSe-ATR accessory and a MCTA detector. Discriminant analysis and partial least-squares analysis were used to analyze the data. Classification of hazelnut oil, olive oil, and the other types of oils was achieved successfully with FT-IR. The detection level for sunflower oil adulteration of hazelnut oil was 2%, and the correlation coefficient for the PLS model was 0.99. Adulteration of virgin olive oil with hazelnut oil could be detected only at levels of 25% and higher. 相似文献
8.
Fourier transform infrared spectroscopy (FT-IR) methods and common chemometric techniques [including discriminant analysis (DA), Mahalanobis distances, and Cooman plots] were used to classify various types of dietary supplement oils (DSO) and less expensive, common food oils. Rapid FT-IR methods were then developed to detect adulteration of DSO with select common food oils. Spectra of 14 types of DSO and 5 types of common food oils were collected with an FT-IR equipped with a ZnSe attenuated total reflectance cell and a mercury cadmium telluride A detector. Classification of DSO and some common food oils was achieved successfully using FT-IR and chemometrics. Select DSO were adulterated (2-20% v/v) with the common food oils that had the closest Mahalanobis distance to them in a Cooman plot based on the DA analysis, and data were also analyzed using a partial least-squares (PLS) method. The detection limit for the adulteration of DSO was 2% v/v. Standard curves to determine the adulterant concentration in DSO were also obtained using PLS with correlation coefficients of >0.9. The approach of using FT-IR in combination with chemometric analyses was successful in classifying oils and detecting adulteration of DSO. 相似文献
9.
Thin layer polyacrylamide gel isoelectric focusing (TLIEF) is described for characterizing the species-specific, heat-denatured proteins of 8 species of crab: red (Geryon quinquedens), rock (Cancer irroratus), Jonah (Cancer borealis), blue (Callinectes sapidus), king (Paralithodes camtschatica), snow (Chionoectes spp.), European edible (Cancer pagurus), and dungeness (Cancer magister). Protein pattern differences are shown not only among species, but also between 2 modes of heat processing of the crabmeat. Individual variation within the species as to sex, size and maturity, length of frozen storage, and body parts chosen for sampling do not alter the species banding pattern. The reproducible species-specific fingerprint obviates the need to analyze authenticated samples simultaneously with the unknown crabmeat. 相似文献
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11.
Stefanie Heinze Michael Vohland Rainer Georg Joergensen Bernard Ludwig 《植物养料与土壤学杂志》2013,176(4):520-528
The prediction accuracy of visible and near‐infrared (Vis‐NIR) spectroscopy for soil chemical and biological parameters has been variable and the reasons for this are not completely understood. Objectives were (1) to explore the predictability of a series of chemical and biological properties for three different soil populations and—based on these heterogeneous data sets—(2) to analyze possible predictive mechanisms statistically. A number of 422 samples from three arable soils in Germany (a sandy Haplic Cambisol and two silty Haplic Luvisols) of different long‐term experiments were sampled, their chemical and biological properties determined and their reflectance spectra in the Vis‐NIR region recorded after shock‐freezing followed by freeze‐drying. Cross‐validation was carried out for the entire population as well as for each population from the respective sites. For the entire population, excellent prediction accuracies were found for the contents of soil organic C (SOC) and total P. The contents of total N and microbial biomass C and pH were predicted with good accuracy. However, prediction accuracy for the other properties was less: content of total S was predicted approximately quantitatively, whereas Vis‐NIR spectroscopy could only differentiate between high and low values for the contents of microbial N, ergosterol, and the ratio of ergosterol to microbial biomass C. Contents of microbial biomass P and S, basal respiration, and qCO2 could not be predicted. Prediction accuracies were greatest for the entire population and the Luvisol at Garte, followed by the Luvisol at Hohes Feld, whereas the accuracy for the sandy Cambisol was poor. The poor accuracy for the sandy Cambisol may have been due to only smaller correlations between the measured properties and the SOC content compared to the Luvisols or due to a general poor prediction performance for sandy soils. Another reason for the poor accuracy may have been the smaller range of contents in the sandy soil. Overall, the data indicated that the accuracy of predictions of soil properties depends largely on the population investigated. For the entire population, the usefulness of Vis‐NIR for the number of chemical and biological soil properties was evident by markedly greater correlation coefficients (measured against Vis‐NIR predicted) compared to the Pearson correlation coefficients of the measured properties against the SOC content. However, the cross‐validation results are valid only for the closed population used in this study. 相似文献
12.
为探索NIR光谱技术在水稻种子蛋白质含量分析中的应用,本研究细致分析了单粒稻种在不同光谱采集方式下的近红外光谱(NIRS)特征,并利用离子束诱变育种得到的水稻9311突变体库的种子,建立准确性较好的单粒糙米和单粒稻种的蛋白质定量模型。结果表明,与漫反射光谱采集方式下的单粒糙米蛋白质模型相比,透反射和透射光谱采集方式下能得到相关性较好的糙米蛋白质模型,其中单粒糙米蛋白质最优定量模型的决定系数(R2)为0.97,预测均方根误差(RMSEP)为0.27%。在单粒稻种中,由于种壳的反射作用,漫反射光谱采集方式下依然无法建立准确性高的蛋白质模型,透反射光谱采集方式下能够建立具有一定预测能力的蛋白质定量模型(RMSEP=0.81%),透射光谱采集方式下能够建立准确性高的蛋白质定量模型(R2=0.96,RMSEP=0.24%)。本研究结果为无损快速分析单粒稻种提供了一种解决方法。 相似文献
13.
可见/近红外光谱技术无损检测果实坚实度的研究 总被引：9，自引：2，他引：7
该研究的目的是建立可见/近红外光谱与梨果实坚实度之间的数学模型,评价可见/近红外光谱技术无损测量梨果实坚实度的应用价值.在可见/近红外光谱区域(350～1800nm),试验对比分析了不同测量部位、不同光谱预处理方法和不同校正建模算法的梨果实坚实度校正模型.结果表明:赤道部位吸光度一阶微分光谱的偏最小二乘回归所建梨果实坚实度校正模型的预测性能较优,其校正和预测相关系数分别为0.8779和0.8087,校正和预测均方误差分别为1.0804N和1.4455N.研究表明:可见/近红外光谱技术无损检测梨果实坚实度是可行的. 相似文献
14.
基于近红外光谱技术的蜂蜜掺假识别 总被引：7，自引：1，他引：6
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。 相似文献
15.
Discrimination between orange juice and pulp wash by (1)H Nuclear Magnetic Resonance spectroscopy: identification of marker compounds 总被引：6，自引：0，他引：6
The potential of NMR spectroscopy and multivariate analysis methods to detect the adulteration of orange juice with pulp wash is demonstrated. Principal component analysis has been applied to (1)H NMR spectra of >300 orange and pulp wash juices, and stepwise linear discriminant analysis was used to classify the samples. A model with six principal components gave a high success rate of classification (94%) for both training and validation sets. An important principal component loading showed that dimethylproline played a key role in the discrimination between the two types of juice, with higher levels in pulp wash. Dimethylproline was not previously known as a marker compound for orange juice adulteration. An ANOVA test revealed at least 21 other NMR signals that differed significantly between the authentic and pulp wash groups. The compounds they represent could be seen as potential marker compounds in addition to dimethylproline. This makes NMR with chemometrics an attractive screening tool with advantages in terms of rapidity, simplicity, and diversity of information provided. 相似文献
16.
The potential of near-infrared (NIR) spectroscopy to rapidly determine citric and malic acid contents of raw Japanese apricot (Japanese "ume", also known as the Japanese plum) fruit juice was investigated. In total, 314 raw juice samples with different organic acid compositions were collected over a long growth period, and spectra (1100-1850 nm) of these samples were acquired using an NIR spectrophotometer with a 1-mm path length. Calibrations were performed using a partial least-squares regression method based on a calibration sample set (211 samples), while validations were performed based on a validation sample set (103 samples). The results revealed good agreement between NIR spectroscopy and capillary electrophoresis, including the correlation coefficient (r2), standard error of prediction (SEP), and bias; no statistically (p = 0.05) significant differences were found for these parameters. Moreover, standard deviation ratios of reference data in the validation sample set to the SEP were higher than 3, indicating that NIR spectroscopy may represent an acceptable method for quantitative evaluation of citric and malic acids in raw Japanese apricot fruit juice. 相似文献
17.
Stable carbon isotope ratio mass spectrometry (delta13C IRMS) was used to detect maple syrup adulteration by exogenous sugar addition (beet and cane sugar). Malic acid present in maple syrup is proposed as an isotopic internal standard to improve actual adulteration detection levels. A lead precipitation method has been modified to isolate quantitatively malic acid from maple syrup using preparative reversed-phase liquid chromatography. The stable carbon isotopic ratio of malic acid isolated from this procedure shows an excellent accuracy and repeatability of 0.01 and 0.1 per thousand respectively, confirming that the modified lead precipitation method is an isotopic fractionation-free process. A new approach is proposed to detect adulteration based on the correlation existing between the delta13Cmalic acid and the delta13Csugars-delta13Cmalic acid (r = 0.704). This technique has been tested on a set of 56 authentic maple syrup samples. Additionally, authentic samples were spiked with exogeneous sugars. The mean theoretical detection level was statistically lowered using this technique in comparison with the usual two-standard deviation approach, especially when maple syrup is adulterated with beet sugar : 24 +/- 12% of adulteration detection versus 48 +/- 20% (t-test, p = 7.3 x 10-15). The method was also applied to published data for pineapple juices and honey with the same improvement. 相似文献
18.
Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species 总被引：1，自引：0，他引：1
Cozzolino D Chree A Scaife JR Murray I 《Journal of agricultural and food chemistry》2005,53(11):4459-4463
Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to identify and authenticate fishmeal batches made with different fish species. Samples from a commercial fishmeal factory (n = 60) were scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), dummy partial least-squares regression (DPLS), and linear discriminant analysis (LDA) based on PCA scores were used to identify the origin of fishmeal produced using different fish species. Cross-validation was used as validation method when classification models were developed. DPLS correctly classified 80 and 82% of the fishmeal samples. LDA calibration models correctly classified >80% of fishmeal samples according to fish species The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of fish species used to manufacture the fishmeal. 相似文献
19.
Gertraud Spielbauer Paul Armstrong John W. Baier William B. Allen Katina Richardson Bo Shen A. Mark Settles 《Cereal Chemistry》2009,86(5):556-564
Near‐infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings without a specific calibration for the constituent affected. The analyzer does not require a specific kernel orientation to predict composition or to differentiate mutants. The instrument collects a seed weight and a spectrum in 4–6 sec and can collect NIR data alone at a 20‐fold faster rate. The spectra are acquired while the kernel falls through a glass tube illuminated with broad spectrum light. These results show significant improvements over prior single‐kernel NIR systems, making this instrument a practical tool to collect quantitative seed phenotypes at high throughput. This technology has multiple applications for studying the genetic and physiological influences on seed traits. 相似文献
20.
S. Chamizo A. Stevens Y. Cantón I. Miralles F. Domingo B. Van Wesemael 《European Journal of Soil Science》2012,63(1):42-53
Biological soil crusts (BSCs) are increasingly recognized as common features in arid and semiarid ecosystems and play an important role in the hydrological and ecological functioning of these ecosystems. However, BSCs are very vulnerable to, in particular, human disturbance. This results in a complex spatial pattern of BSCs in various stages of development. Such patterns, to a large extent, determine runoff and erosion processes in arid and semiarid ecosystems. In recent years, visible and near infrared (Vis‐NIR) diffuse reflectance spectroscopy has been used for large‐scale mapping of the distribution of BSCs. Our goals were (i) to demonstrate the efficiency of Vis‐NIR spectroscopy in discriminating vegetation, physical soil crusts, various developmental stages of BSCs, and various types of disturbance on BSCs and (ii) to develop a classification system for these types of ground cover based on Vis‐NIR spectroscopy. Spectral measurements were taken of vegetation, physical crusts and various types of BSCs prior to, and following, trampling or removal with a scraper in two semiarid areas in SE Spain. The main spectral differences were: (i) absorption by water at about 1450 nm, more intense in the spectra of vegetation than in those of physical crusts or BSCs, (ii) absorption features at about 500 and 680 nm for the BSCs, which were absent or very weak for physical crusts, (iii) a shallower slope between about 750 and 980 nm for physical crusts and early‐successional BSCs than for later‐successional BSCs and (iv) a steeper slope between about 680 and 750 nm for the most developed BSCs. A partial least squares regression‐linear discriminant analysis of the spectral data resulted in a reliable classification (Kappa coefficients over 0.90) of the various types of ground cover and types of BSC disturbance. The distinctive spectral features of vegetation, physical crusts and the various developmental stages of BSCs were used to develop a classification system. This will be a promising tool for mapping BSCs with hyperspectral remote sensing. 相似文献