Authenticity green coffee bean species and geographical origin using near-infrared spectroscopy combined with chemometrics

To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices.     

Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology

This work intends to develop an online experimental system for screening of deoxynivalenol (DON) contamination in whole wheat meals by visible/near-infrared (Vis/NIR) spectroscopy and computer vision coupling technology. Spectral and image information of samples with various DON levels was collected at speed of 0.15 m s⁻¹ on a conveyor belt. The two-type data were then integrated and subjected to chemometric analysis. Discriminant analysis showed that samples could be classified by setting 1000 μg kg⁻¹ as the cut-off value. The best correct classified rate obtained in prediction was 93.55% based on fusion of spectral and image features, with reduced prediction uncertainty as compared to single feature. However, quantification of DON by quantitative analysis was not successful due to poor model performance. These results indicate that, although not accurate enough to provide conclusive result, this coupling technology could be adopted for rapid screening of DON contamination in cereals and feeds during processing.     

Erbium doped Bi2O3-B2O3 glass-ceramics containing Bi3B5O12 and CaF2 nanocrystallites for 1.53 μm fiber lasers

Trivalent erbium ions doped Bi₂O₃-B₂O₃ transparent glass ceramics containing CaF₂ were prepared and characterized through X -ray diffraction, scanning electron microscopy, Fourier transform infrared absorption, optical absorption, and near infrared emission for 1.53 μm fiber lasers. The glass ceramics obtained by applying thermal treatment at 575 °C for 5 h and 575 °C for 10 h contain Bi₃B₅O₁₂ and CaF₂ crystallites. The Judd-Ofelt theory was applied to evaluate various spectroscopic and laser characteristic properties. The NIR emission corresponding to the ⁴I_13/2 → ⁴I_15/2 (∼1.53 μm) transition was studied by exciting the samples at 514.5 nm laser radiation. The stimulated emission cross-sections of ∼1.53 μm luminescence were also obtained applying the Mc Cumber theory. The experimental results confirm that the transparent glass ceramic obtained at a thermal treatment of 575 °C for 10 h is more suitable to design fiber lasers for diverse applications in the fields of industry, medicine and scientific research.     

Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data

The potential of mid-infrared (MIR) and near-infrared (NIR) spectroscopy for their ability to differentiate between apple juice samples on the basis of apple variety and applied heat-treatment was evaluated. The heat-treatment involved exposure of juice samples (15 ml) for 30 s in a 900 W microwave oven and the apple varieties used to produce the juice samples were Bramley, Elstar, Golden Delicious and Jonagold. The chemometric procedures applied to the MIR and NIR data were partial least squares regression (PLS1 for differentiation on the basis of heat-treatment, PLS2 for varietal differentiation) and linear discriminant analysis (LDA) applied to principal component (PC) scores. PLS1 and PLS2 gave the highest level of correct classification of the apple juice samples according to heat-treatment (77.2% for both MIR and NIR data) and variety (78.3–100% for MIR data; 82.4–100% for NIR data), respectively.     

在线近红外光谱分析仪在汽油自动调合系统中的应用

在线近红外光谱分析仪可以实时测定汽油调合组分及成品油的多种物化性质指标如烯烃、芳烃、苯及氧化物含量、辛烷值（RON、MON）、馏程、蒸气压等。文章介绍了用于汽油自动调舍工艺中的在线近红外光谱分析仪的原理、系统组成和特点，以及在国内外的汽油调合优化控制中的实际应用情况。典型的应用实例表明，将该分析仪用于汽油自动调合工艺中，可为炼油厂带来可观的经济效益和社会效益。     

Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data

In this paper we have tried to build effective model for classification of motor oils by base stock and viscosity class. Three (3) sets of near infrared (NIR) spectra (1125, 1010, and 1050 spectra) were used for classification of motor oils into 3 or 4 classes according to their base stock (synthetic, semi-synthetic, and mineral), kinematic viscosity at low temperature (SAE 0W, 5W, 10W, and 15W) and kinematic viscosity at high temperature (SAE 20, 30, 40, and 50). The abilities of three (3) different classification methods: regularized discriminant analysis (RDA), soft independent modelling of class analogy (SIMCA), and multilayer perceptron (MLP) - were also compared. In all cases NIR spectroscopy was found to be quite effective for motor oil classification. MLP classification technique was found to be the most effective one.     

基于SVM和GA-SVM的NIR光谱柑橘产地鉴别模型研究

为了建立更准确、高效的柑橘产地鉴别模型,提出了基于支持向量机(Support Vector Machine,SVM)的遗传算法(Genetic Algorithm,GA)SVM(GA-SVM)。该算法结合基因优化选择,对SVM算法进行了改进,实验结果显示,采用GA-SVM对16个地区柑橘样本开展NIR的产地鉴别,能得到比SVM更高的识别率。     

Rapid evaluation of olive oil quality by NIR reflectance spectroscopy

NIR spectroscopy calibrations have been developed for a range of quality parameters in olive oil, including FFA, PV, polyphenol content, induction time, chlorophyll, and the major FA. A set of 216 olive oil samples from throughout the Australian olive-growing areas were used to provide a representative range of quality. The variation in the oils tested virtually covered the range of the chemical standard limits described by the International Olive Oil Council. A FOSS NIRSystems^® 6500 spectrophotometer with a liquid cell holder was used. Multiple correlation coefficients squared (R ²) for minor components stearic acid (0.86), and linolenic acid (0.85) were relatively low because the concentration range is very narrow compared with the reproducibility of the reference method. However, the major FA, oleic (0.99) and linoleic (1.00), FFA (0.97), and chlorophyll (0.98) provided high levels of accuracy. All of the parameters measured were sufficiently accurate for routine screening of olive oil.     

Measurement of soybean fatty acids by near-infrared spectroscopy: Linear and nonlinear calibration methods

A key element of successful development of new soybean cultivars is availability of inexpensive and rapid methods for measurement of FA in seeds. Published research demonstrated applicability of NIR spectroscopy for FA profiling in oilseeds. The objectives of this study were to investigate the applicability of NIR spectroscopy for measurement of FA in whole soybeans and compare performance of calibration methods. Equations were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods. Validation results demonstrated that (i) equations for total saturates had the highest predictive ability (r ²=0.91–0.94) and were usable for quality assurance applications, (ii) palmitic acid models (r ²=0.80–0.84) were usable for certain research applications, and (iii) equations for stearic (r ²=0.49–0.68), oleic (r ²=0.76–0.81), linoleic (r ²=0.73–0.76), and linolenic (r ²=0.67–0.74) acids could be used for sample screening. The SVM models produced significantly more accurate predictions than those developed with PLS. ANN calibrations were not different from the other two methods. Reduction in the number of calibration samples reduced predictive ability of all equations. The rate of performance degradation of SVM models with sample reduction was the lowest.