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Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of spectra of the jujube (Zizyphus jujuba Mill.) fruit samples from four geographical regions. Prediction models were developed for the quantitative prediction of the contents of jujube fruit, i.e., total sugar, total acid, total phenolic content, and total antioxidant activity. Four pattern recognition methods, principal component analysis (PCA), linear discriminant analysis (LDA), least squares-support vector machines (LS-SVM), and back propagation-artificial neural networks (BP-ANN), were used for the geographical origin classification. Furthermore, three multivariate calibration models based on the standard normal variate (SNV) pretreated NIR spectroscopy, partial least squares (PLS), BP-ANN, and LS-SVM were constructed for quantitative analysis of the four analytes described above. PCA provided a useful qualitative plot of the four types of NIR spectra from the fruit. The LS-SVM model produced best quantitative prediction results. Thus, NIR spectroscopy in conjunction with chemometrics, is a very useful and rapid technique for the discrimination of jujube fruit.

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95-100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

Enantioselective binding interaction of the pesticides, metolachlor (RAC-metolachlor) and its S-enantiomer (S-metolachlor), with bovine serum albumin (BSA) was investigated by fluorescence and UV-vis absorption spectroscopy. Both RAC- and S-metolachlors quenched the intrinsic fluorescence of BSA via a static mechanism, and various binding parameters indicated that electrostatic forces were involved in the binding of both of these compounds. Site marker competitive experiments demonstrated that S-metolachlor bound to site I of BSA, while R-metolachlor bound to site II, indicating the importance of enantiomeric factors for binding site selection. Further experiments showed that S-metolachlor had a higher binding affinity to BSA than R-metolachlor. The obtained spectral data were resolved with use of the multivariate curve resolution-alternating least squares method (MCR-ALS), and the extracted concentration profiles of the reacting species in the interaction were obtained. These profiles indicated that S-metolachlor was the main active constituent of RAC-metolachlor for binding with BSA, and these findings have significant implications in providing an explanation why S-metolachlor is the preferred herbicide in practice than RAC-metolachlor.

A novel method for the discrimination of the three kinds of Indigowoad Root sample, Radix Isatidis (RI), Rhizoma et Radix Baphicacanthis Cusia (RRBC) and simulated adulterated samples (AD) was researched and developed with the use of near infrared spectroscopy (NIR) and chemometrics. Principal component analysis (PCA) was applied to process the NIR data of 75 collected Indigowoad Root samples, and the three kinds of such sample were discriminated along the first principal component (PC1) axis. In addition, the data pretreatment methods - genetic algorithm-partial least squares (GA-PLS), successive projections algorithm (SPA), and wavelet transform (WT), were employed to select the key analytical wavelengths, and then, these were used as input variables for the three kinds of the pattern recognition method, such as K-nearest neighbor (KNN), radial basis function-artificial neural network (RBF-ANN), least squares-support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN). The WT was the method of choice for data pretreatment, and three pretreatment-prediction method combinations performed well (basis: %recognition rate) - WT-KNN (98.2%) and BP-ANN (97.3%) as well as GA-PLS - LS-SVM (97.2). A BP-ANN calibration model was built for the quantitative discrimination of the three types of the complex Indigowoad Root samples, and it was successfully validated.

Interaction of chlorotetracycline (CTC) with bovine serum albumin (BSA) was investigated under simulated physiological conditions by spectroscopy with the aid of multivariate curve resolution-alternating least squares (MCR-ALS). Eosin Y was selected as an alternative site I marker on the BSA to study the above molecular interaction. The binding of Eosin Y and CTC to BSA showed that CTC was displaced from CTC-BSA complex by Eosin Y, and Eosin Y-BSA complex was formed. However, the recorded fluorescence spectra of Eosin Y and Eosin Y-BSA overlapped and MCR-ALS was applied to resolve the two-way fluorescence spectra. From the resolved equilibrium concentration profiles, it was observed that Eosin Y competed with CTC in the binding process with BSA; it was also shown that the binding site of CTC on BSA was site I, and this was further confirmed by the fluorescence polarization method. Compared with some common site I markers for BSA, the fluorescence and UV-vis spectral shapes of the Eosin Y-BSA complex were quite different from that of Eosin Y, and this feature facilitated the investigation of the small molecule-BSA interaction.

The interaction of quercetin, which is a bioflavonoid, with bovine serum albumin (BSA) was investigated under pseudo-physiological conditions by the application of UV-vis spectrometry, spectrofluorimetry and cyclic voltammetry (CV). These studies indicated a cooperative interaction between the quercetin-BSA complex and warfarin, which produced a ternary complex, quercetin-BSA-warfarin. It was found that both quercetin and warfarin were located in site I. However, the spectra of these three components overlapped and the chemometrics method - multivariate curve resolution-alternating least squares (MCR-ALS) was applied to resolve the spectra. The resolved spectra of quercetin-BSA and warfarin agreed well with their measured spectra, and importantly, the spectrum of the quercetin-BSA-warfarin complex was extracted. These results allowed the rationalization of the behaviour of the overlapping spectra. At lower concentrations ([warfarin]

Raman spectroscopy has been used to investigate raw cotton acetylation using acetic anhydride/4-dimethylaminopyridine (DMAP) catalyst blend without solvent. The Raman data further confirm successful acetylation as shown by FTIR that was demonstrated previously to be highly sensitive for determining the level of acetylation. However, the Raman peaks are much weaker than the FTIR bands. Nevertheless, the variations of the extent of acetylation estimated from both Raman and FTIR spectra with weight percent gain due to acetylation (WPG) were observed to follow the same pattern. The degrees of acetylation calculated from Raman data were also found to increase linearly with that calculated from the more sensitive FTIR technique. Raman technique is thus suitable for further development as an analytical tool for determining the acetylation level of natural cellulose fibres. Raman data have also shown that the acetylation reaction reduces the crystallinity of cotton.