Itziar Ruisánchez

Validation of qualitative analytical methods

Abstract This article reviews the state of the art in validating qualitative analytical methods. After introducing the scope of these qualitative methods, their main characteristics and how they differ from quantitative analysis methods, we propose a classification according to the detection system. We discuss the institutions, programs and documents dealing with the validation of qualitative methods, and we present the performance parameters- false positive and negative, sensitivity and specificity rate, cut-off, unreliability region, ruggedness and cross-reactivity. We also briefly describe the various strategies used to validate qualitative analytical methods — Contingency Tables, Bayes’ Theorem, Statistical Hypothesis Tests and Performance Characteristic Curves.

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV–visible spectroscopy and multivariate classification techniques

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L(-1). Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV. Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.

Surface Enhanced Raman Spectroscopy (SERS) and multivariate analysis as a screening tool for detecting Sudan I dye in culinary spices.

Raman spectroscopy combined with multivariate analysis was evaluated as a tool for detecting Sudan I dye in culinary spices. Three Raman modalities were studied: normal Raman, FT-Raman and SERS. The results show that SERS is the most appropriate modality capable of providing a proper Raman signal when a complex matrix is analyzed. To get rid of the spectral noise and background, Savitzky-Golay smoothing with polynomial baseline correction and wavelet transform were applied. Finally, to check whether unadulterated samples can be differentiated from samples adulterated with Sudan I dye, an exploratory analysis such as principal component analysis (PCA) was applied to raw data and data processed with the two mentioned strategies. The results obtained by PCA show that Raman spectra need to be properly treated if useful information is to be obtained and both spectra treatments are appropriate for processing the Raman signal. The proposed methodology shows that SERS combined with appropriate spectra treatment can be used as a practical screening tool to distinguish samples suspicious to be adulterated with Sudan I dye.

Radial basis functions applied to the classification of UV–visible spectra

Abstract This paper describes how to apply a neural network based in radial basis functions (RBFs) to classify multivariate data. The classification strategy was automatically implemented in a sequential injection analytical system. RBF neural network had some advantages over counterpropagation neural networks (CPNNs) when they are used in the same application: the classification error was reduced from 20% to 13%, the input variables (UV–visible spectra) did not have to be preprocessed and the training procedure was simpler.

Uncertainty of results in routine qualitative analysis

Abstract Uncertainty is a performance characteristic that should be estimated in both quantitative and qualitative results in order to improve knowledge of their reliability. Contrary to quantitative results, uncertainty in qualitative analysis cannot be expressed as an interval around the predicted value. The uncertainty is probabilistic in nature as it might express the probability of taking a wrong decision. In this article, we review four different ways of estimating the uncertainty of a qualitative or screening system: contingency tables; Bayes’ theorem; statistical intervals; and, performance curves. We pay particular attention to their advantages and drawbacks, and their main applications.

Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter propagation neural network (CP-ANN) and a radial basis function network (RBFN). These strategies were used to classify soil samples from different geographical regions in Brazil by means of their near-infrared (diffuse reflectance) spectra. The results were better with CP-ANN (classification error 8.6%) than with RBFN (classification error 11.0%).

A tutorial on the validation of qualitative methods: From the univariate to the multivariate approach

This tutorial provides an overview of the validation of qualitative analytical methods, with particular focus on their main performance parameters, for both univariate and multivariate methods. We discuss specific parameters (sensitivity, specificity, false positive and false negative rates), global parameters (efficiency, Youdens index and likelihood ratio) and those parameters that have a quantitative connotation since they are usually associated to concentration values (decision limit, detection capability and unreliability region). Some methodologies that can be used to estimate these parameters are also described: the use of contingency tables for the specific and global parameters and the performance characteristic curve (PCC) for the ones with quantitative connotation. To date, PCC has been less commonly used in multivariate methods. To illustrate the proposals summarized in this tutorial, two cases study are discussed at the end, one for a univariate qualitative analysis and the other for multivariate one.

Multivariate screening in food adulteration: untargeted versus targeted modelling.

Two multivariate screening strategies (untargeted and targeted modelling) have been developed to compare their ability to detect food fraud. As a case study, possible adulteration of hazelnut paste is considered. Two different adulterants were studied, almond paste and chickpea flour. The models were developed from near-infrared (NIR) data coupled with soft independent modelling of class analogy (SIMCA) as a classification technique. Regarding the untargeted strategy, only unadulterated samples were modelled, obtaining 96.3% of correct classification. The prediction of adulterated samples gave errors between 5.5% and 2%. Regarding targeted modelling, two classes were modelled: Class 1 (unadulterated samples) and Class 2 (almond adulterated samples). Samples adulterated with chickpea were predicted to prove its ability to deal with non-modelled adulterants. The results show that samples adulterated with almond were mainly classified in their own class (90.9%) and samples with chickpea were classified in Class 2 (67.3%) or not in any class (30.9%), but no one only as unadulterated.

FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud

Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96-100% and 88-100% for the mid- and high-level data fusion strategies, respectively.

1H NMR variable selection approaches for classification. A case study: The determination of adulterated foodstuffs

Whenever dealing with large amount of data as is the case of a NMR spectrum, carrying out a variable selection before applying a multivariate technique is necessary. This work applies various variable selection techniques to extract relevant information from (1)H NMR spectral data. Three approaches have been chosen, because each is based on very different foundations. The first method, called Xdiff, is based on calculating the normalized differences between the mean spectrum of a class considered to be the reference and the spectra of each sample. The second approach is the interval Partial Least Squares method (iPLS), which investigates the influential zones of the spectra that contains the most discriminating predictors calculating local PLS-DA models on narrow intervals. The last one is Genetic Algorithms (GAs) which finds the optimal variables from a random initial subset of variables by means of an iterative process. The performance of each variable selection strategy is determined by the classification results obtained when multiclass Partial Least Squares-Discriminant Analysis is applied. This study has been applied to NMR spectra of culinary spices that might be adulterated with banned dyes such as Sudan dyes (I-IV). The three techniques give neither the same number nor the same selected variables, but they do select a common zone from the spectra containing the most discriminating variables. All three techniques give satisfactory classification and prediction results, being higher than 95% with iPLS and GA and around 89% with Xdiff, therefore the three variable selection techniques are suitable to be used with NMR data in the determination of food adulteration with Sudan dyes as well as the specific type of adulterant used (I-IV).