Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram

Near infrared(NIR) spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis(HCA) performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.     

Curing of an epoxy resin modified with poly(methylmethacrylate) monitored by simultaneous dielectric/near infrared spectroscopies

Simultaneous dielectric and near infrared measurements have been performed in “real-time” to follow polymerisation reactions on blends of a diglycidyl ether of bisphenol-A epoxy resin with 4,4^′-diaminodiphenylmethane hardener and different amounts of poly(methylmethacrylate) as modifier. The effect of the modifier amount on the polymerisation reactions has been studied, as well as that of the curing temperature. Epoxy and amine conversions have been followed by near infrared spectroscopy (NIR), while changes in molecular mobility in the reaction mixture have been analysed by dielectric relaxation spectroscopy (DRS). Evolutions of ionic conductivity and α-relaxation have been analysed and vitrification times have been obtained. The relaxational behaviour has been analysed through curing in the frequency domain, being the change of the main relaxation indicative of the cure reaction advancement. DRS data are also presented as complex impedance Z(ω). Vitrification times, obtained by dielectrometry have been compared with those obtained by rheological measurements and gelation times obtained by NIR have been compared with those obtained by solvent extraction.     

Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pretreatments and estimation of multivariate detection limits

Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600 ppm in an industrial ester. The detection limit obtained, round 100 ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes.     

Determination of the degree of N-deacetylation in chitin and chitosan as well as their monomer sugar ratios by near infrared spectroscopy

Chitin and its derivative chitosan are rapidly becoming of commercial interest in the biomedical, wastewater treatment, and fiber industries. One of the most important properties of these copolymers is the degree of N-acetylation. Presently there is no rapid accurate method to determine this value. We propose near infrared spectroscopy for such a method. Using a primary method such as hydrobromic acid titration or ¹H-NMR, standards can be established. Once a model calibration equation has been developed, unknown samples can be analyzed by near infrared spectroscopy in less than 5 min. This is demonstrated by examining representative structural monomers of chitosan, N-acetyl-D-glucosamine and D-glucosamine hydrochloride, as model compounds as well as actual chitin and chitosan samples. © 1993 John Wiley & Sons, Inc.     

Direct orthogonal signal correction as data pretreatment in the classification of clinical lots of creams from near infrared spectroscopy data

Direct orthogonal signal correction (DOSC) is applied to correct for major variance sources such as temperature effects, time influences and instrumental differences in near infrared (NIR) data. The samples analysed are creams containing different concentrations of an active drug. The final aim is to classify the samples according to their concentration of active compound. Having performed DOSC on the data, it is not necessary anymore to apply sophisticated chemometric techniques to correct for temperature or time effects and to attribute the samples to their respective concentration classes. Moreover, the application of DOSC on the NIR spectra recorded on two different instruments shows that this method can be considered as a valuable alternative for the standardisation in classification applications. Since the applied algorithm tends to overfit, in a second part of this paper, a comparison is made with an algorithm designed by Westerhuis, which should overcome this problem. Although the calibration set results show that the overfitting has been partially corrected for by the latter algorithm, the test set results did not improve significantly.     

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.     

Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES

A rapid and nondestructive near infrared (NIR) method using soft independent modeling of class analogy (SIMCA) for the classification of cultivation area (Korea and China) was evaluated and confirmed. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of ginseng samples were also investigated to find out the differences between Korean samples and Chinese samples. These differences make NIR spectroscopic method viable. The average value of each Korean and Chinese ginseng sample for crude fiber, crude protein, starch, and 10 inorganic constituents were measured and compared with F-test and t-test. The inorganic constituents were also measured by induced coupled plasma-atomic emission spectroscopy (ICP-AES). It could be found that the amount of starch and ten inorganic elements for example Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu and Zn in ginseng samples are considerably different based on cultivation area. SIMCA has been applied to the inorganic data to investigate the possibility of ICP-AES as classification tool. However, it was observed that the result was not equal to than NIR spectra data. The overall results showed the availability of NIR method using SIMCA would be adequate for classification of cultivation of ginseng, since NIR spectra includes useful and various information on chemical properties in spite of broad and overlapped bands.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection

This paper developed a rapid method using near infrared spectroscopy (NIRS) to differentiate two species of Cortex Phellodendri (CP), Cortex Phellodendri Chinensis (PCS) and Cortex Phellodendri Amurensis (PAR), and to predict quantitatively the content of berberine and total alkaloid content in all Cortex Phellodendri samples. Three alkaloids, berberine, jatrorrhizine and palmatine were analyzed simultaneously with a Thermo ODS Hypersil column by gradient elution with a new mobile phase under high-performance liquid chromatography-diode array detection (HPLC-DAD). Berberine content determined by HPLC-DAD was exploited as a critical parameter for successful discrimination between them. Multiplicative scatter correction (MSC), second derivative and Savitsky-Golay (S.G.) were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra as well as to enhance spectral features in order to give a better correlation with the results obtained by HPLC-DAD. With the use of principal component analysis (PCA), samples datasets were separated successfully into two different clusters corresponding to two species. Furthermore, a partial least squares (PLS) regression method was built on the correlation model. The results showed that the correlation coefficients of the prediction models were R = 0.996 for the berberine and R = 0.994 for total alkaloid content. The influences of water absorption bands present in the NIR spectra on the models were also investigated in order to explore the practicability of NIRS in routine use. The outcome showed that NIRS possibly acts as routine screening in the quality control of Chinese herbal medicine.     

Determination of the percentage of milk (cow's, ewe's and goat's) in cheeses with different ripening times using near infrared spectroscopy technology and a remote reflectance fibre-optic probe

In the present work we studied the use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the analysis of the percentage of milk (cow's, ewe's and goat's) used in the elaboration of cheeses with different ripening times. To do so, cheeses with known and varying percentages of cow's, ewe's and goat's milk were elaborated (112 samples with milk collected in winter and 112 samples with milk collected in summer) and used as reference material, and ripening controls were performed over 6 months. The method allows immediate control of the cheese without prior sample treatment or destruction by direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). Of all the samples (224), 200 formed to so-called calibration set and the other 24 were used for external validation. The calibration results obtained using 200 samples of cheese allowed the percentage of cow's, ewe's and goat's milk to be measured. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP(C)) obtained were respectively, 0.834 and 11.6% for cow's milk; 0.871 and 9.8% for goat's milk; 0.880 and 10.6% for ewe's milk. The ratio performance deviation (RPD) values obtained indicate that the NIRS equations can be applied to unknown samples.     

Synthesis,pharmacological evaluation,and molecular modeling studies of novel isatin hybrids as potential anticancer agents

A novel series of isatin hybrids 5a-g was designed, synthesized, and characterized spectroscopically. The synthesized compounds were evaluated for their cytotoxic activity against the human breast cancer cell line (MCF-7) by in vitro MTT assay. Amongst the tested compounds, 5e compound bearing benzyl moiety at N₄ piperazine was found to be the most active with the promising IC₅₀ (12.47 µM). Moreover, the active compounds 5e and 5g were subjected to antitumor evaluation (in vivo) against Dalton’s ascitic lymphoma (DAL) cell line and the results suggested that the best active compound 5e can normalize the blood picture in comparison to the standard drug. An in silico molecular docking study using the crystal structure of Hsp90 protein described the role of significant protein–ligand interactions and revealed more insights into the binding mode. The drug-likeliness of the compounds was predicted based on Lipinski's rule of five and pharmacokinetic ADME parameters. Hence, the synthesized isatin hybrids could be novel starting point anticancer lead compounds demonstrating drug-like properties which can be explored further for anticancer drug discovery.