Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

RAPID DETERMINATION OF PROTEIN IN MILLET BY FOURIER TRANSFORM NEAR-INFRARED(FTNIR)DIFFUSE REFLECTANCE SPECTROSCOPY

In this paper,the Fourier transform near-infrared(FTNIR)diffuse reflectance spectroscopy is applied for the rapid determination of protein in millet.The partial least-squares(PLS)regression is successfully used as an effective multivariate calibration technique.The calibration set is composed of 20 standard millet samples that the protein contents were determined by the traditional Kjeldahl method.The optimal model dimension is found to be 5 by cross-validation.22 millet samples were determined by the proposed FTNIR-PLS method.The correlation coefficient between the concentration values obtained by the FTNIR-PLS method and the traditional Kjeldahl method is 0.9805.The standard error of prediction(SEP)is 0.28% and the mean recovery is 100.2%.The proposed method has been successfully applied for the routine analysis of protein in about 10,000 grain samples.

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Fructus cnidii （Chinese name shechuangzi） is the fruit produced by Cnidium monnieri （L.） Cusson （Umbelliferae）. It is a perennial herb that is used to treat skin-related diseases and gynecopathyell. Recent pharmacological studies have revealed crude extracts or components isolated from fructus cnidii possess antiallergic, antipruritic, antidermatophytic, antibacterial, antifungal, and antiosteoporotic activities. Osthole and imperatorin are the major compounds present in shechuangzi. They are often used as standards for the evaluation of the quality of shechuangzi products.

Titanium dioxide as an adsorbent to enhance the detection ability of near-infrared diffuse reflectance spectroscopy

A method for quantitative determination of fish sperm deoxyribonucleic acid(fsDNA)was developed by using titanium dioxide(TiO2)as an adsorbent and near-infrared diffuse reflectance spectroscopy(NIRDRS).The selective enrichment of fsDNA was proved by comparing the adsorption efficiency of bovine serum albumin,tyrosine and tryptophan,and the low adsorption background of TiO2 was illustrated by comparing the spectra of four commonly-used inorganic adsorbents(alkaline aluminium oxide,neutral aluminium oxide,nano-hydroxyapatite and silica).The spectral feature of fsDNA can be clearly observed in the spectrum of the sample.Partial least squares(PLS)model was built for quantitative determination of fsDNA using 28 solutions,and 13 solutions with interferences were used for validation of the model.The results showed that the correlation coefficient(R)between the predicted and the reference concentration is 0.9727 and the recoveries of the validation samples are in the range of 98.2%-100.7%.

Determination of Cysteine Using Near-infrared Diffuse Reflectance Spectroscopy with Enrichment via Thiol-maleimide Click Reaction

Enrichment technique has been proved to be an efficient way to make the near-infrared diffuse reflectancespectroscopy（NIRDRS） suitable for micro analysis. However, low selectivity presented by conventional enrichmentmethods makes the quantitative analysis easy to be affected by the coexisting components. In this study, a specificenrichment method with chemical bonding via thiol-maleimide click reaction was used to achieve the reduction of theinterferences. Taking cysteine as the analyzing target, maleimide-functionalized SiO2 nanoparticles were prepared forthe enrichment of cysteine. Then determination of cysteine in aqueous solution and human serum was studied usingthe partial least squares model built from the NIRDRS spectra of the adsorbate. The results show that the concentra-tion that can be quantitatively detected is as low as 2.0 μg/mL, and the correlation coefficient（R） between thereference and predicted concentration is 0.9871 for the validation samples. The recoveries are in the range of89.5%-113.8% for human serum samples in the concentration range of 0--16.2 μg/mL.

Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate

Near infrared diffuse reflectance spectroscopy(NIRDRS) has gained wide attention due to its convenience for rapid quantitative analysis of complex samples. A method for rapid analysis of triglycerides in human serum using NIRDRS with silver mirror as the substrate is developed. Due to the even and high reflectance of the silver mirror, the spectral response is enhanced and the background interference is reduced.Furthermore, both linear and nonlinear modeling strategies were investigated adopting the partial least squares(PLS) and least squares support vector regression(LS-SVR), continuous wavelet transform(CWT)was used for spectral preprocessing, and variable selection was tried using Monte Carlo uninformative variable elimination(MC-UVE), randomization test(RT) and competitive adaptive reweighted sampling(CARS) for optimization the models. The results show that the determination coefficient(R) between the predicted and reference concentration is 0.9624 and the root mean squared error of prediction(RMSEP) is 0.21. The maximum deviation of the prediction results is as low as 0.473 mmol/L. The proposed method may provide an alternative method for routine analysis of serum triglycerides in clinical applications.

20种金丝桃属植物的近红外漫反射光谱法鉴别

为了建立近红外漫反射光谱快速鉴别金丝桃属药用植物的方法,本实验采用聚类分析方法对金丝桃同属20种植物 进行定性鉴别。结果表明,本方法有效、简便、快速、低消耗,可用于属内不同种药材的鉴别与质量控制。

Particle size regression correction for NIR spectrum based on the relationship between absorbance and particle size

Based on the effect of sample size on the near- infrared （NIR） spectrum, the absorbance （log（R）） in any wavelength is divided into two parts, and one of them is defined as non-particle-size-related spectrometry （nPRS） because it is not influenced by particle size. To study the relationship between the absorbance and l^article size, the experiment material including nine samples with different particle size was used. According to the regression analysis, the relationship was studied as the reciprocal regression model, y = a ~ bx ＋ c/x. Meanwhile, the model divides absorbance into two parts, one of them forms nPRS. According to the nPRS, a new correction method, particle size regression correction （PRC） was introduced. In discriminate analysis, the spectra from three different samples （rice, glutinous rice and sago）, pretreated by PRC, could be directly and accurately distinguished by principal component analysis （PCA）, while by the traditional correction method, such as multiplicative signal correction （MSC） and standard normal variate （SNV）, could not do that.