Fluorescence spectral analysis for the discrimination of complex, similar mixtures with the aid of chemometrics

A Comparison of Near- and Mid-Infrared Spectroscopic Methods for the Analysis of Several Nutritionally Important Chemical Substances in the Chinese Yam (Dioscorea opposita): Total Sugar, Polysaccharides, and Flavonoids

The Chinese yam (Dioscorea opposita) is a basic food in Asia and especially China. Consequently, an uncomplicated, reliable method should be available for the analysis of the quality and origin of the yams. Thus, near-infrared (NIR) and mid-infrared (mid-IR) spectroscopic methods were developed to discriminate among Chinese yam samples collected from four geographical regions. The yam samples were analyzed also for total sugar, polysaccharides, and flavonoids. These three analytes were used to compare the performance of the analytical methods. Overlapping spectra were resolved using chemometrics methods. Such spectra were compared qualitatively using principal component analysis (PCA) and quantitatively using partial least squares (PLS) and least squares-support vector machine (LS-SVM) models. We discriminated among the four sets of yam data using PCA, and the NIR data performed somewhat better than the mid-IR data. We constructed the PLS and LS-SVM calibration models for the prediction of the three key variables, and the LS-SVM model produced better results. Also, the NIR prediction model produced better outcomes than the mid-IR prediction model. Thus, both infrared (IR) techniques performed well for the analysis of the three key analytes, and the samples were qualitatively discriminated according to their provinces of origin. Both techniques may be recommended for the analysis of Chinese yams, although the NIR technique would be preferred.

Fluorescence spectroscopy in process analytical technology (PAT): simultaneous quantification of two active pharmaceutical ingredients in a tablet formulation

Many pharmaceuticals include highly potent active pharmaceutical ingredients (API), which only require a small dosage to obtain the desired therapeutic effect. This leads to a challenge for quantification of the API using process analytical technology, since the standard nondestructive measurement technique, near-infrared spectroscopy, is not able to quantify below 1% (weight/weight (w/w)) API content. In formulations with more than one API, this challenge is further increased. The purpose of this study is to scrutinize the potential of fluorescence spectroscopy for the simultaneous quantification of two APIs: flupentixol (FLU) in low dosage (0.208-0.625% w/w free base) and melitracen (MEL) (4.17-12.5% w/w free base) in a tablet formulation. Despite internal quenching between the ingredients and the two APIs, this paper demonstrates that it is possible to establish calibrations using partial least squares (PLS) regression on unfolded fluorescence landscapes with a root mean square error of prediction and relative error of 0.038% (w/w) and 9.1%, for FLU and 0.344% (w/w) and 4.1% for MEL, respectively.

Combined NIR/MIR analysis: a novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

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.