Independent Modeling Of Class Analogy Research Articles

1H NMR and Chemometric Methods to Estimate the Octane Number in Brazilian C Gasolines

Brazilian gasoline type C can be purchased with octane number as the main difference. This quality parameter directly affects the price. Intermediate formulations may not be easily distinguished from conform samples due to similarity in visual appearance and physicochemical properties. The use of anhydrous ethanol as an additive also influences the octane values of the product. In this context, the present study describes the use of 1H nuclear magnetic resonance spectroscopy (1H NMR) associated with chemometrics in the characterization and distinction of gasolines by different octane number. Conform samples of common, premium and blends of common-premium were used. Models of NMR-PCA (principal component analysis) and NMR-SIMCA (soft independent modelling of class analogies) showed a good correlation with the values determined by the standard method. The octane values predicted by the NMR-PLS (partial least squares) model achieved a good correlation root mean square errors of prediction (RMSEP = 0.50), with the values determined by the standard method as well.

Classification of Ceramic Tableware by Laser Induced Breakdown Spectroscopy and Chemometrics

Ceramics manufactured in Brazil and in other countries were analyzed by laser induced breakdown spectroscopy (LIBS). The LIBS spectra were employed to identify the origin of the tableware as produced in Brazil or abroad using soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA). Three sets of spectral variables were evaluated aiming at discrimination of the ceramics: the entire LIBS spectrum, a set of selected spectral ranges, and the maximum of emission intensity of the elements Al, Ca, Mg, Si, Na and Ti. The results for the three sets of variables show good performance, achieving correct classification from 88.6 to 94.3%, and 86.7 to 100% for the external training and test, respectively.

Attenuated total reflection fourier transform infrared spectroscopy based methods for identification of chromatography media formulations used in downstream processes

Chromatographic media play a crucial role in the downstream processing of biotechnology products. The physical and chemical properties of these processing aids are mostly monitored by expensive and time‐consuming preparative tests, but spectroscopic techniques may also be used to measure chromatographic media samples.In this study, chromatographic media formulations used in downstream processes were investigated using attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy. Samples were measured both in original suspension form and after drying to examine the possibilities of a potential spectroscopic method without sample preparation. Principal component analysis (PCA) was employed to identify the spectral differences among the formulations with distinct support matrices and functional groups and soft independent modeling of class analogy (SIMCA) was performed to creating classification models for identification of chromatography media. To increase the number of samples in the SIMCA, simulated spectra were generated based on the experimental spectra.PCA models indicated that spectra of samples in original suspension form and after drying contained similar information about the chemical properties of chromatographic media samples. Moreover, during the classification of spectra based on SIMCA, both measurement techniques gave high sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) results.These results show that ATR FT-IR could be applied as a simple alternative method for monitoring the chromatography media samples. This technique is also feasible without sample preparation. Thereby the multi-hours drying steps may be omitted, the measurements can be performed in a few minutes, and the potential effects of sample preparations can be eliminated.

Paper-based SERS substrate and one-class classifier to monitor thiabendazole residual levels in extracts of mango peels.

The assessment of pesticide residue levels demands fast, low cost and easy-to-use procedures which are not found in conventional methods. In this work, SERS substrates based on the deposition of gold nanoparticles (GNPs) on common office paper were prepared using a wax printer. These substrates combined with Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA), a one-class classifier algorithm, were used for detection of pesticide residues in water extracts of mango peels. Paper-based substrates made sample collection easier compared with conventional SERS methods, since few microliters of the pesticide aqueous extract from fruit peels needed to be deposited onto the substrate. Moreover, one-class classifiers dismiss the need for quantification or calibration curves. Classification of a fruit with residue levels in accordance to regulatory bodies' limits is based on a mathematical threshold. Just as in an authentication problem, all the possibilities for a given analysed fruit are now restricted to agreeing or not agreeing with current regulations. The performance of the one-class model was demonstrated by detecting thiabendazole (TBZ) residues at various mango samples, with all results being confirmed by HPLC-DAD analysis. The final model could distinguish samples with TBZ levels above the ones allowed by the Brazilian Health Regulatory Agency with 94% of selectivity and 92% of sensitivity, even in the presence of other pesticides.

Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy

The maturity of seeds at harvest determines their intrinsic quality characteristics such as longevity and vigor, and these characteristics are dominant factors for seed quality evaluation in the seed industry. However, little information is available on how to identify and further classify the maturation stage of seeds in a way that is nondestructive, precise, rapid, and inexpensive, while also exactly meeting the need for the uniform control of seed performance in the seed industry to improve crop yield. This study demonstrated a nondestructive method for detecting seed maturity by using the single-kernel near-infrared spectroscopy (SK-NIRS) technique. The results showed that five classes of cucumber seeds with different maturation levels can be distinguished successfully. A tree-structured hierarchical classification strategy consisting of one soft independent modeling of class analogy (SIMCA) model and three partial least squares discriminant analysis (PLS-DA) models were proposed ending up with 99.69% of the overall classification accuracy and 0.9961 of Cohen’s kappa in the test set, and its predictive performance was superior to both SIMCA and PLS-DA for direct multiclass classification. SK-NIRS in combination with a multiclass hierarchical classification strategy was proved to be both intuitive and efficient in classifying cucumber seeds according to maturation levels.

Authentication of the Geographical Origin of Margarines and Fat-Spread Products from Liquid Chromatographic UV-Absorption Fingerprints and Chemometrics.

Fat-spread products are a stabilized emulsion of water and vegetable oils. The whole fat content can vary from 10 to 90% (w/w). There are different kinds, which are differently named, and their composition depends on the country in which they are produced or marketed. Thus, having analytical solutions to determine geographical origin is required. In this study, some multivariate classification methods are developed and optimised to differentiate fat-spread-related products from different geographical origins (Spain and Morocco), using as an analytical informative signal the instrumental fingerprints, acquired by liquid chromatography coupled with a diode array detector (HPLC-DAD) in both normal and reverse phase modes. No sample treatment was applied, and, prior to chromatographic analysis, only the samples were dissolved in n‑hexane. Soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA) were used as classification methods. In addition, several classification strategies were applied, and performance of the classifications was evaluated applying proper classification metrics. Finally, 100% of samples were correctly classified applying PLS-DA with data collected in reverse phase.

Detection of formaldehyde in raw milk by time domain nuclear magnetic resonance and chemometrics

This study aimed to evaluate the efficacy of time-domain nuclear magnetic resonance (TD-NMR) to determine the formaldehyde in milk samples (from 1.75 to 21% v/v) using chemometric methods (principal component analysis, partial least squares regression, and soft independent modeling of class analogy, PCA, PLS, and SIMCA), when compared with the official colorimetric method. The analysis was performed at different times of refrigerated storage (0 h and 48 h). Increased formaldehyde concentration (17.5 and 21%) led to a decrease in the whiteness index [65.3–72.8] and the lightness values [70.5–85.3] and an increase in the yellowing index [36.31–38.73] compared to the control [77.8–78.7, 85.2–90.3, and 27.40–29.92, respectively]. Regarding the TD-NMR values, higher T2 values were observed, with the formation of T2,1 and T2,2 throughout the refrigerated storage. PCA and PLS proportionated adequate models while SIMCA was able to discriminate the samples regards the level of adulteration with adequate discrimination and predictive index. Overall, TD-NMR has proven to be an effective method for the dairy industry to check the formaldehyde levels in raw milk.

Screening Malaysian edible bird’s nests for structural adulterants and geographical origin using Mid-Infrared – Attenuated Total Reflectance (MIR-ATR) spectroscopy combined with chemometric analysis by Data-Driven – Soft Independent Modelling of Class Analogy (DD-SIMCA)

Edible bird’s nest (EBN) is widely consumed in South-East Asia as a health food product. In recent years, the adulteration of EBN is quite common due to its high economic value and limited supplies of the natural authentic product. Authentication and traceability of EBN demands time-consuming sequential analytical measurements to confirm that the product quality meets technical and regulatory requirements. Mid Infrared spectroscopy combined with chemometric analysis by Data-Driven Soft Independent Modelling of Class Analogy (DD-SIMCA) was used in this study to screen Malaysian EBNs for structural adulterants and for confirmation of geographical origin. The samples used were authentic processed EBNs of different grades (2A, 3A, 4A and 5A) from the swiftlet species Aerodramus fuciphagus supplied by Ministry of Health, Malaysia. Similarly, adulterants commonly used in EBN namely agar, Karaya gum, bovine gelatin, fish skin, melamine and porcine gelatine were also characterized. Adulterant mixtures were prepared gravimetrically at concentrations of 1, 5 and 10%w/w by combining appropriate quantities of adulterants with individual authentic EBN samples. Samples were then analyzed by Mid-Infrared – Attenuated Total Reflectance (MIR-ATR) spectroscopy. All test samples are properly categorized as members of the target class. However, 61% of the tested adulterated EBNs were correctly classified as ‘alien’ or non-authentic samples. The sensitivity and specifity values for DD-SIMCA models applied to origin determination ranged from 90 to 100% for production regions from Kelantan, Perak and Malacca. MIR-ATR spectroscopy in combination with DD-SIMCA modelling has shown potential as a rapid screening technique to detect the presence adulterants and confirm the provenance of EBN produced in Malaysia. Further work is required to validate the approach with a larger set of authentic samples and for a wider range of adulterants including flavour enhancers and preservatives.

NIR reflectance spectroscopy and SIMCA for classification of crops flour

The potential of SIMCA technique for crops flour classification was studied based on FT-NIR spectroscopy in this research. A total of 72 spectra of flour samples taken from 6 types of crops, i.e. of banana, breadfruit, taro, arrowroot, purple sweet potato, and modified cassava (mocaf). The reflectance data were measured using the NIRFlex N500 Fiber Optic Solids Cell at 4000 – 10, 000 cm−1. The spectral obtained were pre-processed and analyzed using The Unscrambler X version 10.5.1. A 2nd derivative Savitzky-Golay (polynomial order 2, 25 smoothing points) followed by a Standard Normal Variate (SNV) were used for pre-treatment data. Characterization of the flours was done using chemometric models based on principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) explained for each group of flour samples of banana, breadfruit, taro, arrowroot, purple sweet potato, and modified cassava (mocaf). SIMCA calibration models were constructed using 6 spectral measurements for each type of flours; classification set were constructed using 6 spectral measurements. The SIMCA accuracy classification were 100% for mocaf, banana, arrowroot, bread fruit, and taro, and 67% for purple sweet potato.

Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts.

Authentication of Cannabis products is important for assuring the quality of manufacturing, with the increasing consumption and regulation. In this report, a two-stage pipeline was developed for high-throughput screening and chemotyping the spectra from two sets of botanical extracts from the Cannabis genus. The first set contains different marijuana samples with higher concentrations of tetrahydrocannabinol (THC). The other set includes samples from hemp, a variety of Cannabis sativa with the THC concentration below 0.3%. The first stage applies the technique of class modeling to determine whether spectra belong to marijuana or hemp and reject novel spectra that may be neither marijuana nor hemp. An automatic soft independent modeling of class analogy (aSIMCA) that self-optimizes the number of principal components and the decision threshold is utilized in the first pipeline process to achieve excellent efficiency and efficacy. Once these spectra are recognized by aSIMCA as marijuana or hemp, they are then routed to the appropriate classifiers in the second stage for chemotyping the spectra, i.e., identifying these spectra into different chemotypes so that the pharmacological properties and cultivars of the spectra can be recognized. Three multivariate classifiers, a fuzzy rule building expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector machine tree type entropy (SVMtreeH), are employed for chemotyping. The discriminant ability of the pipeline was evaluated with different spectral data sets of these two groups of botanical samples, including proton nuclear magnetic resonance, mass, and ultraviolet spectra. All evaluations gave good results with accuracies greater than 95%, which demonstrated promising application of the pipeline for automated high-throughput screening and chemotyping marijuana and hemp, as well as other botanical products.

Authentication of P.G.I. Gragnano pasta by near infrared (NIR) spectroscopy and chemometrics

Pasta is a typical Italian food item obtained by durum wheat semolina/flour well-known and widely consumed all over the world. Since 2013, Gragnano Pasta, a typical aliment produced in a specific area in the South of Italy, has been awarded with the P.G.I. mark, remarking the high value of this product. Due to its peculiarity and its market value, it is important to characterize and authenticate the Gragnano Pasta. Considering this rationale, the present study aims at developing a non-destructive analytical methodology suitable for this goal. Consequently, the possibility of coupling Near Infrared spectroscopy (NIR) with two different classifiers has been tested. In particular, 949 samples of pasta were analysed, and then classified into categories Gragnano and non-Gragnano by Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogies (SIMCA). In order to externally validate models, samples were divided into a training and a test set of 749 and 200 objects, respectively. Both approaches provided excellent results; PLS-DA correctly classified all the Gragnano samples (and it misclassified only 1 object belonging to the other category), while SIMCA analysis (modelling Class Gragnano) led to 96.57% sensitivity and 100% specificity.

Comparative study of multivariative analysis methods of blood Raman spectra classification

AbstractThe pathological state of a human body leads to altered biochemical composition of body fluids. Conventional biochemical analysis of body fluids is notable for its low‐informative value in localizing a particular pathology. As an alternative, Raman spectroscopy provides detailed evaluation of blood characteristics at the molecular level. Raman blood spectra are characterized by multicollinearity as well as by the presence of autofluorescence background and noises of different nature. Choice of a proper method for experimental data processing of blood spectra is crucial for obtaining statistically reliable information regarding a pathological process in the body. This study examines different approaches to multidimensional analysis of the various‐size Raman spectral dataset of human blood samples by a cost‐effective Raman setup in a clinical setting. To discriminate blood samples by the pathology type, statistical processing of experimental data is performed by factor analysis, logistic regression, discriminant analysis, classification tree, projection to latent structures discriminant analysis (PLS‐DA), and soft independent modeling of class analogies. Comparative analysis of the discussed multivariate methods demonstrates that the PLS‐DA method (sensitivity 0.75, specificity 0.81, and accuracy 0.76) proved to be the most effective for the classification of blood samples by cancer localization. In terms of classification for the presence of hyperproteinemia, the most efficient are the logistic regression method (sensitivity 0.89, specificity 0.99, and accuracy 0.97) and the discriminant analysis method (sensitivity 0.83, specificity 1.0, and accuracy 0.97). In general, the selected multivariate methods could serve as a reliable tool for analyzing spectral characteristics of body fluids.

Diagnosis of pregnancy based classification of embryo culture medium samples by infrared spectrometry and chemometrics

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric procedures was utilized to examine embryo culture medium (ECM) samples in in vitro fertilization (IVF) to differentiate between positive and negative pregnancy.Principal component analysis (PCA) method was successfully applied to mid-IR spectra as outlier detection by application of Hotelling T2 statistic procedure. Unsupervised and supervised pattern recognition techniques, such as hierarchical agglomerative cluster algorithm (HACA), soft independent modeling of class analogy (SIMCA) as a linear supervised pattern recognition technique, binned nearest neighbors (BNN) as a local classifier and counter-propagation artificial neural networks (CP-ANN) as a non-linear technique were used in spectral data analysis for classification and differentiation. Also, the genetic algorithm (GA) as the variable reduction was employed to develop the diagnostic model.A total of 46 ECM samples were examined in the 3000–1000 cm−1 spectral region. Classification efficiency parameters including accuracy (ACC), error rates (ER), sensitivity (Sens), specificity (Spec) and non-error rate (NER) were calculated. Classification ACC of 92.00% and ER of 8.300% was acquired using GA-BNN algorithm, while GA-CP-ANN and GA-SIMCA algorithms provided 93.00% and 5.00% of ACC and ER for the test set, respectively. The results of this study presented that the GA-SIMCA and the GA-CP-ANN chemometric approaches using the infrared data, achieved acceptable results. Also, both of methods could provide the Sens rate of 100.00% and 91.00%, and the Spec rate of 91.00% and 100.00% for positive pregnancy samples (class 1) and negative ones (class 2) in prediction test, respectively.In general, the main advantages of the proposed method could be represented as a reliable, fast, simple, and accurate method for the classification and discrimination of the ECM samples based on their serum beta- subunit human chorionic gonadotropin (β-hCG) levels after embryo transfer (ET) about 15 days later.

Rapid Detection of Live and Dead Escherichia coli in a Suspension Using Spectroscopy and Chemometrics

Viability assessment of bacteria is critical in monitoring of food or environmental samples. Existing methods are time-consuming, labour-intensive or require trained manpower and costly chemicals. Potential of commonly used UV-visual spectrometer was explored for rapid viability detection of Escherichia coli (ATCC 8739). Spectra of samples (live and dead cells) mixed in different proportion revealed clear differences. Live bacterial suspension showed absorption peak at 260 nm with decreasing amplitude as the proportion of live bacteria was reduced in the suspension and vice-versa. Principal component analyses (PCA) of spectral data showed clear clustering of samples based on the level of live bacterial cells (5 % significance level). Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Square Discriminant Analysis (PLSDA) yielded 100 % correct classification with test samples. The percentage of live and dead bacteria in a suspension could be predicted with coefficient of determination (R2 ) of 0.980 and 0.977 for calibration and validation sample sets, respectively, in the range of 259-261 nm using Multiple Linear Regression (MLR). Low standard errors of calibration (4.5), prediction (4.8) and high R2 (0.98) indicated the potential of UV visual spectrometer to detect and predict live and dead cells of E. coli in a suspension.

Geographical discrimination of garlic (Allium Sativum L.) based on Stable isotope ratio analysis coupled with statistical methods: The Italian case study

Stable isotope ratio analysis of the major bioelements (δ2H, δ13C, δ15N, δ18O, δ34S), considered for the first time as a whole, was used to attempt a geographical characterization of red garlic (Allium sativum L.) cultivated throughout different Italian territories (Abruzzo, Lazio and Sicily). Up to now, no official methods are available to determine the geographical origin of this type of product. In this context, the Isotope Ratio Mass Spectrometry method (IRMS) represents a powerful analytical technique. The characteristic ranges of variability of the five isotope ratios in 56 red Italian garlic samples are here presented as well as their relationships. The geographical origin has some influence over the different ratios, although their data distribution shows some overlap when Principal Component Analysis (PCA) is applied. In spite of the relative closeness of the sampling sites, a model with very good predictive performance of the geographical classification was achieved byLinear Discriminant Analysis (LDA) and k-nearest neighbours (k-NN) method. Moreover, preliminary class modelling based on Soft Independent Modelling of Class Analogy (SIMCA) supports the ability of stable isotope ratios analysis for the geographical traceability of garlic.

Automatic soft independent modeling for class analogies

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics.

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds. Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples. The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

1H NMR spectroscopy combined with multivariate data analysis for differentiation of Brazilian lager beer according to brewery

In this study, 1H NMR spectroscopy was used to classify samples of beer, considering three categories (Ambev, Heineken, and Grupo Petropolis), employing chemometric methods: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and soft independent modeling of class analogies (SIMCA). The full NMR spectra were evaluated, although only the aliphatic region (0–3 ppm) was used for multivariate analysis, since it provided superior results, compared to the use of other regions or the full spectrum. It was necessary to use an alignment procedure to eliminate small deviations in the chemical shifts caused by variations of pH and intermolecular interactions. Organic acids (lactic, acetic, and succinic acids) were the chemical compounds most susceptible to these variations. In the PCA, the first two components explained 82.1% of the variability of the dataset, while PLS-DA and SIMCA both provided accuracy higher than 92% in the prediction sets.

Application of chemometrics for detection and modeling of adulteration of fresh cow milk with reconstituted skim milk powder using voltammetric fingerpriting on a graphite/ SiO2 hybrid electrode

In the present work, an analytical approach for the voltammetric detection and prediction of adulteration of fresh cow milk with reconstituted skim milk powder is developed. After precipitation of milk proteins upon addition of ethanol and centrifugation, the supernatant liquid of the samples was analyzed by cyclic voltammetry on a novel graphite/SiO2 hybrid working electrode (GSiHE) using LiClO4 as electrolyte. Under these conditions, fresh milk samples gave broadened peaks/plateaus in both forward and backward potential scanning, attributed mainly to oxidases. Such peaks were not evident in the case of reconstituted skim milk powder samples due to inactivation of enzymes and breakdown of certain antioxidants caused by heat and pressure-treatments. The differences between fresh and reconstituted skim milk powder samples in their voltammetric profile were exploited for the detection of fresh milk adulteration by submitting voltammetric data to chemometrics. As datapoints, the differences between forward and backward current values, recorded at the same potentials, were determined and submitted to multivariate analysis. Principal Component Analysis (PCA) provided a clear differentiation between fresh milk and reconstituted skim milk powder samples. Soft independent modeling of class analogy (SIMCA) was employed to model the class of fresh milks, using samples from 12 commercially available fresh milk brands. Prediction of fresh milk adulteration with reconstituted skim milk powders was achieved by means of Partial Least Squares (PLS) regression analysis. Detection limit of the technique was found to be below 6% (v/v) and the linearity of model in terms of observed/predicted values was confirmed up to 100% (v/v). Validation and applicability of both SIMCA and PLS models were confirmed using a suitable test set, consisting of commercial fresh milk and skim milk powder samples as well as synthetic adulterated fresh milk samples.

Geographical discrimination of saffron (Crocus sativus L.) using ICP-MS elemental data and class modeling of PDO Zafferano dell’Aquila produced in Abruzzo (Italy)

PDO (protected designation of origin) Zafferano dell’Aquila (AQ), Iranian (IR), and commercial (CS) saffron samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The ICP-MS data of 30 elements were handled by unsupervised (principal component analysis (PCA)) and supervised (linear discriminant analysis (LDA)) multivariate statistical methods to identify a subset of discriminant variables useful for geographical classification. Moreover, class modeling of AQ saffron was performed by both UNEQ (unequal disperses classes) and SIMCA (soft independent modeling of class analogy) methods. A good differentiation of the AQ, IR, and CS samples was obtained by the LDA based on four selected elements: Sr, Ca, Mo, and Fe. A UNEQ class model for the PDO AQ saffron based on the above four elements provided 100% sensitivity (all authentic AQ saffron were accepted) and 100% specificity (all IR and CS were rejected), while slightly worse results were obtained using the SIMCA (89% sensitivity and 96% specificity).