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This work demonstrates how near-infrared reflectance spectroscopy (NIR) in combination with chemometrics can be used to analyse and discriminate powdered, pure and adulterated sweet potato samples. Thus, the principal component analysis (PCA) method, which is able to produce diagrams of objects as a function of PC scores, was able to distinguish and group the NIRS from the many different types of potato sample. Other methods such as the radial basis function-partial least squares (RBF-PLS) as well the successive projection algorithm (SPA) combination, distinguished the purple and white sweet potato varieties from each other as well as, importantly, from the different adulterated purple sweet potato samples. Furthermore, the total anthocyanin (TA) and total antioxidant activity (TAA) parameters were also analysed quantitatively in these samples, and high residual prediction deviation (RPD) values were noted. Also, the two recognition methods, k-nearest neighbours (KNN) and linear discriminant analysis (LDA), were able to discriminate the sweet potato samples. Thus, overall this work demonstrated that NIRS combined with chemometrics methods can be employed for the identification of purple sweet potato, white sweet potato and their adulterated samples; in addition, quantitative analysis for TA and TAA in such samples can be successfully performed.

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations.

Single-wavelength high performance liquid chromatography-diode-array-detector (HPLC-DAD) and multi-wavelength combined HPLC methods were researched and developed in order to compare their performance for the classification of complex substances. Thus, the aims of this work were: to compare the performance of the single- and multi-wavelength HPLC-DAD methods for analysis of complex substances – in this context, the Radix Paeoniae herbs, which were classified on the basis of their component species and geographical origin. Three classification methods, Linear discriminant analysis (LDA), Radial basis function artificial neural network (RBF-ANN) and Least squares-support vector machine (LS-SVM), were compared on the basis of their performance in discriminating the Radix Paeoniae samples. The results showed that the multi-wavelength data produced better classification results of the two HPLC methods. This was so, irrespective of the chemometrics method used. However, the LS-SVM models were significantly better in classifying the herb samples. Consequently, the multi-wavelength HPLC-DAD approach is a strong alternative to the more common single-wavelength method, and the LS-SVM was the method of choice for classification of the complex substances such as the Radix Paeoniae herbs.

A spectrofluorimetric analytical method was researched and developed for the quantitative, simultaneous analysis of nitrofurazone and nitrofurantoin antibiotics. The method was based on the kinetic analysis of their hydrolysis reaction coupled with multivariate calibration methods. These consisted of several different multiway data models, and their prediction performance was compared on the basis of the above reaction. Nitrofurans do not fluoresce but their alkaline hydrolysis products show strong fluorescence at 245 nm/456 nm (excitation/emission wavelengths) and 240 nm/452 nm. Different multiway data matrices were acquired at various excitation and/or emission wavelengths, and/or reaction times and resolved by multiway calibration methods, i.e. the parallel factor analysis, unfolded principal component analysis–residual bilinearization and unfolded principal component analysis–residual trilinearization methods. All of these procedures facilitate the second-order advantage. The relationship between the concentration of the analytes and the fluorescence output was found to be nonlinear. Hence, the results from the calibration models were submitted to the radial basis function artificial neural networks for prediction. The best performing prediction model, unfolded principal component analysis–residual bilinearization–radial basis function artificial neural networks, was used to analyze the nitrofurans in fish samples and the results compared well with those obtained from the reference HPLC method.

An NIR spectroscopic method was researched and developed for the analysis of potato crisps (chips) chosen as an example of a common, cheap but complex product. Four similar types of the ‘original flavour’ potato chips from different manufacturers were analysed by NIR spectroscopy; as well, the quality parameters — fat, moisture, acid and peroxide values of the extracted oil were predicted. Principal component analysis (PCA) of the NIR data displayed the clustering of objects with respect to the type of chips. NIR spectra were rank-ordered with the use of the sparingly applied multiple criteria decision making (MCDM) ranking methods, PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid), and a comprehensive quantitative description of the data was obtained. The four traditional parameters were predicted on the basis of the NIR spectra; the performance of the Partial Least Squares (PLS), and Kernel Partial Least Squares (KPLS) calibrations was compared with those from Least Squares-Support Vector Machines (LS-SVM) method. The LS-SVM calibrations, which model better data linearity and non-linearity, successfully predicted all four parameters. This work has demonstrated that NIR methodology with the use of chemometrics can describe comprehensively qualitative and quantitative properties of complex, processed substances as illustrated by the potato chips example, and indicated that this approach may be applied to other similar complex samples.

This paper demonstrates the crucial role of chemometrics in facilitating the knowledge transfer of a laboratory derived rating for sugarcane clonal performance (based on GC measurements) towards a rapid near infrared (NIR) spectroscopic methodology for at-field screening applications. NIR sugarcane stalk spectra were collected from 65 sugarcane clones, which were obtained from two widely different geographical regions. An exploratory principal component analysis (PCA) of an NIR spectral data matrix of the sugarcane clones showed a clear trend along PCI similar to that found from the GC measurements, with the resistant clone (Q90) being well discriminated from the susceptible one (NCo310). Biplots related the NIR spectral frequencies with the spectral objects. Cellulose and lignin bands (4248 and 4406 cm(-1) respectively) were associated with the well performing clones and epicuticular wax bands (4346 and 5781 cm(-1)) with the poorly performing ones. Several partial least squares (PLS) regression models were constructed from the spectral objects, and satisfactorily predicted the traditional sugarcane performance ratings (SEP range +/-0.7-0.9 traditional units for the verification data, and the Measured versus Predicted R-2 range 0.880-0.910). In addition, the stalk spectra were investigated with the use of multicriteria decision-making methods, PROMETHEE and GAIA. This demonstrated that the spectral objects could be ranked on the basis of the NIR information alone, and that this rank order was in agreement with traditionally determined ratings. Thus, the performance of sugarcane clones could be assessed from molecular information as from the earlier laboratory GC measurements, and therefore, can be obtained completely independently of any conventional nominal ratings scales. (C) 2006 Elsevier B.V. All rights reserved.

Anti-bacterial drugs such as sulfonamides and trimethoprim, are commonly used in medical and veterinary applications. The tablets often contain mixtures of drugs such as sulfadiazine, sulfadimidine, sulfamethoxazole, sulfanilamide and trimethoprim. Thus, a simple, rapid and inexpensive method for the simultaneous determination of these drugs was researched and developed with the aid of chemometrics methods comparing the classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS) models. UV-spectra were collected in the 200–350 nm range from a set of samples containing aqueous ethanolic solutions (pH = 9.9) of drug mixtures prepared from the above compounds with the aid of an orthogonal array design. Initial studies of the UV-spectra of the individual compounds showed that satisfactory linear regression calibration models could be constructed in the concentration range of approximately 1.0–24.0 mg l− 1 for all compounds with LOD values in the range of ca. 0.2–1.0 mg l− 1 for each of the five drugs. These studies also indicated the significant spectral overlap of the UV- spectra of the drugs showing the need for chemometrics modeling for simultaneous analysis of the mixtures. Calibration models for the three multivariate methods were constructed with the use of the raw and derivative spectral data sets. These were verified with the aid of UV-spectral data obtained from a separately prepared set of drug mixture samples. The %RPE values for analysis of the individual drugs in mixtures were in the range of 2–10 with %Recoveries being close to 100 for the PCR and PLS models while the CLS method performed relatively poorly. The four PCR and the PLS models constructed could not be distinguished on prediction performance for practical purposes, and the PCR calibration based on the raw data matrix was utilised to illustrate successfully the application of the proposed method for analysis of the drugs in commonly available pharmaceutical tablets.

This paper describes a sequential chemometrics study of the use of a phenotypic property of sugarcane, its epicuticular wax, as a potential genetic marker and predictor of desirable plant traits. Epicuticular wax collected from 122 sugarcane clones was analysed by gas chromatography (GC). Fourteen principal GC peaks were identified and confirmed with the aid of Gas Chromatograph-Mass Spectrometer (GC-MS). The 122×14 GC data matrix was submitted to exploratory principal component analysis (PCA), which showed that the GC data separated according to the performance of the sugarcane clones with respect to a particular resistance trait as classified by conventional field-based rating systems. From the PCA biplot, it was possible for the first time to associate the important wax molecular components with the resistance characteristics of various sugarcane clones. This analysis stimulated research to rank the samples according to the GC data with the use of multicriteria decision-making methods (MCDM), PROMETHEE and GAIA. This experiment indicated that the ranking based on epicuticular wax components was similar to, but arguably more refined than, that obtained by the conventional field-based ratings scale. Such studies suggested that the performance of sugarcane clones could be predicted based on the molecular measurements obtained by GC. A partial least squares (PLS) calibration was constructed for the prediction of the conventional field-based performance ratings on the basis of the GC data matrix, and acceptable prediction results were obtained. Thus, it is now possible to express the sugarcane quality parameter based on accurate molecular measurements derived directly from the surface wax–a phenotypic property of the sugarcane. Additionally, this approach should reduce the time taken for breeders to rank plants.

A method for the simultaneous determination of the pesticides, carbofuran, isoprocarb and propoxur in fruit and vegetable samples has been investigated and developed. It is based on reaction kinetics and spectrophotometry, and results are interpreted with the aid of chemometrics. The analytical method relies on the differential rates of coupling reactions between the hydrolysis product of each carbamate and 4-aminophenol in the presence of potassium periodate in an alkaline solution. The optimized method was successfully tested by analyzing each of the carbamates independently, and linear calibration models are described. For the simultaneous determinations of the carbamates found in ternary mixtures, kinetic and spectral data were processed either by three-way data unfolding method or decomposed by trilinear modeling. Subsequently, 10 different RBF-ANN, PARAFAC and NPLS calibration models were constructed with the use of synthetic ternary mixtures of the three carbamates, and were validated with a separate set of mixtures. The performance of the calibration models was then ranked on the basis of several different figures of merit with the aid of the multi-criteria decision making approach, PROMETHEE and GAIA. RBF-ANN and PC-RBF-ANN were the best performing methods with %Relative Prediction Errors (RPE) in the 3–4% range and Recovery of about 97%. When compared with other recent studies, it was also noted that RBF-ANN has consistently outperformed the more common prediction methods such as PLS and PCR as well as BP-ANN. The successful RBF–ANN method was then applied for the determination of the three carbamate pesticides in purchased vegetable and fruit samples.