One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts.

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach.

Multivariate qualitative methodology for semi-quantitative information. A case study: Adulteration of olive oil with sunflower oil.

This paper proposes a strategy to assess the performance of a multivariate screening method for semi-quantitative purposes. The adulteration of olive oil with sunflower oil was considered as a case study using fluorescence spectroscopy and two-class Partial Least Squares Discriminant Analysis (PLS-DA). Building the proper screening methodology based on two-class multivariate classification model involve setting the cut-off value for the adulterated class (class 2).

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin.

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented.

Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics.

This paper proposes to use chromatographic fingerprints coupled to multivariate techniques to authenticate the geographical origin of extra-virgin olive oils (EVOO) of the Arbequina botanical variety. This methodology uses the whole or part of the chromatogram as input data for the classification models but does not identify or quantify the chemicals constituents. Arbequina monovarietal EVOOs from three geographical origins were studied: two from adjacent European Protected Designation of Origin areas, Siurana and Les Garrigues, in Catalonia in the northeast of Spain; and the third from the south of Spain (Andalucia and Murcia).

Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies.

There is no any doubt about the importance of food fraud control, as it has implications in food safety and in consumer health. Focusing on fruit beverages, some types of adulterations have been detected more frequently, such as substitution with less expensive fruits. A methodology based on attenuated total reflectance Fourier-transform mid-infrared spectroscopy (ATR-FTIR) and multivariate classification was applied to detect whether grape nectars were adulterated by substitution with apple juice or cashew juice.

Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars.

During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra.

HPLC-UV and HPLC-CAD chromatographic data fusion for the authentication of the geographical origin of palm oil.

Data fusion combined with a multivariate classification approach (partial least squares-discriminant analysis, PLS-DA) was applied to authenticate the geographical origin of palm oil. Data fusion takes advantage of the synergistic effect of information collected from more than one data source. In this study, data from liquid chromatography coupled to two detectors -ultraviolet (UV) and charged aerosol (CAD)- was fused by high- and mid-level data fusion strategies.

Detection of several common adulterants in raw milk by MID-infrared spectroscopy and one-class and multi-class multivariate strategies.

A sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074g.

Determining performance parameters in qualitative multivariate methods using probability of detection (POD) curves. Case study: Two common milk adulterants.

A strategy for determining performance parameters of two-class multivariate qualitative methods was proposed. As case study, multivariate classification methods based on mid-infrared (MIR) spectroscopy coupled with the soft independent modelling of class analogy (SIMCA) technique for detection of hydrogen peroxide and formaldehyde in milk were developed. From the outputs (positive/negative/inconclusive) of the samples, which were unadulterated and adulterated at target value, the main performance parameters were obtained.

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.

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, Youden's 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.

Partial least squares density modeling (PLS-DM) - a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy.

A new class-modeling method, referred to as partial least squares density modeling (PLS-DM), is presented. The method is based on partial least squares (PLS), using a distance-based sample density measurement as the response variable. Potential function probability density is subsequently calculated on PLS scores and used, jointly with residual Q statistics, to develop efficient class models.

Validation of multivariate screening methodology. Case study: detection of food fraud.

Multivariate screening methods are increasingly being implemented but there is no worldwide harmonized criterion for their validation. This study contributes to establish protocols for validating these methodologies. We propose the following strategy: (1) Establish the multivariate classification model and use receiver operating characteristic (ROC) curves to optimize the significance level (α) for setting the model's boundaries.

Plasmonic nanoprobes for real-time optical monitoring of nitric oxide inside living cells.

An optical sensor was developed for the quantitative determination of intracellular nitric oxide. The sensor consists of plasmonic nanoprobes that have a coating of mesoporous silica and an inner gold island film functionalized with a chemoreceptor for NO.

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.

Figures of merit of a SERS method for Sudan I determination at traces levels.

A substrate for Surface-Enhanced Raman Scattering spectroscopy (SERS), electropolished Al, is proposed as a tool for a rapid and low cost determination of Sudan I. This dye has been used as an additive in some foodstuffs but it is now banned because of the health risk associated with its carcinogenic and mutagenic properties. Despite the presence of fluorescence, Raman spectra of Sudan I can be obtained using excitation lasers at 633 and 785 nm.

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.

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.

Analysing the Temperature Effect on the Competitiveness of the Amine Addition versus the Amidation Reaction in the Epoxidized Oil/Amine System by MCR-ALS of FTIR Data.

The evaluation of the temperature effect on the competitiveness between the amine addition and the amidation reaction in a model cure acid-catalysed reaction between the epoxidized methyl oleate (EMO), obtained from high oleic sunflower oil, and aniline is reported. The study was carried out analysing the kinetic profiles of the chemical species involved in the system, which were obtained applying multivariate curve resolution-alternating least squares (MCR-ALS) to the Fourier transform infrared spectra data obtained from the reaction monitoring at two different temperatures (60°C and 30°C). At both experimental temperatures, two mechanisms were postulated: non-autocatalytic and autocatalytic.

Kinetic analysis of C.I. Acid Yellow 9 photooxidative decolorization by UV-visible and chemometrics.

A kinetic study of the C.I. Acid Yellow 9 photooxidative decolorization process, using H(2)O(2) as oxidant, was carried out by chemometric analysis of the UV-visible data recorded during the process.

1H NMR and UV-visible data fusion for determining Sudan dyes in culinary spices.

Two data fusion strategies (variable and decision level) combined with a multivariate classification approach (Partial Least Squares-Discriminant Analysis, PLS-DA) have been applied to get benefits from the synergistic effect of the information obtained from two spectroscopic techniques: UV-visible and (1)H NMR. Variable level data fusion consists of merging the spectra obtained from each spectroscopic technique in what is called "meta-spectrum" and then applying the classification technique. Decision level data fusion combines the results of individually applying the classification technique in each spectroscopic technique.

Determination of sulphate in water and biodiesel samples by a sequential injection analysis--multivariate curve resolution method.

A spectrophotometric sequential injection analysis (SIA-DAD) method linked to multivariate curve resolution-alternating least squares (MCR-ALS) has been developed for sulphate determination. This method involves the reaction, inside the tubes of the SIA system, of sulphate with barium-dimethylsulphonazo (III) complex, Ba-DMSA (III), displacing Ba(2+) from the complex and forming DMSA (III). When the reaction products reach the detector a data matrix is obtained, which allows a second-order calibration to be developed.

Multisyringe chromatography (MSC) using a monolithic column for the determination of sulphonated azo dyes.

A methodology based on multisyringe chromatography with a monolithic column was developed to determine three sulphonated azo textile dyes: Acid Yellow 23, Acid Yellow 9 and Acid Red 97. An ion pair reagent was needed because of the low affinity between the monolithic column and the anionic dyes. The proposed analytical system is simple, versatile and low-cost and has great flexibility in manifold configuration.

Sequential injection titration method using second-order signals: determination of acidity in plant oils and biodiesel samples.

A new concept of flow titration is proposed and demonstrated for the determination of total acidity in plant oils and biodiesel. We use sequential injection analysis (SIA) with a diode array spectrophotometric detector linked to chemometric tools such as multivariate curve resolution-alternating least squares (MCR-ALS). This system is based on the evolution of the basic specie of an acid-base indicator, alizarine, when it comes into contact with a sample that contains free fatty acids.

Modelling of the simultaneous photodegradation of Acid Red 97, Acid Orange 61 and Acid Brown 425 using factor screening and response surface strategies.

In this paper the influence of seven variables that could be relevant in the photodegradation of three textile dyes - Acid Red 97, Acid Orange 61 and Acid Brown 425 - has been studied with the aim of determining the most efficient conditions for this process. The type and concentration of catalyst, the presence and concentration of H(2)O(2), the stirring, the pH and the dye concentration have been studied as variables. In the first stage the more basic variables were analyzed using a screening methodology (saturated fractional factorial design) and it was concluded that the most influential variable was the presence of H(2)O(2).