Soft Independent Modeling Of Class Analogy Research Articles

Future foods protection: Supervised chemometric approaches for the determination of adulterated insects’ flours for human consumption by means of ATR-FTIR spectroscopy

In recent years, the possibility of reintroducing edible insects in the human diet in the areas where this habit had fallen in disuse has been revaluated. The reasons lie in the noticeable nutritional properties of some edible insects (generally rich in protein, vitamins, mineral elements, polyphenols and fatty acids) and their sustainable breeding which requires a moderate amount of water, and no peculiar exigencies. Consequently, insect-based snacks, insect flours, and derived products are currently spreading on the market all over the world. Although “future foods” have been a hot and debated topic in the last years, research has not kept pace with the times, and, in the literature, only a few studies on the characterization and the authentication of edible insects (and their derived products) have been published. In the light of this, the present work aims at developing a non-destructive infrared spectroscopy (IR)-based tool for detecting adulterations in insects’ flours for human consumption. Mixtures of cricket and buffalo worm flours, prepared to mimic adulterated samples, together with pure flours of the same insects, have been analysed by FT-IR spectroscopy. Eventually, classification tools for discriminating between pure and adulterated samples, i.e., Sequential Preprocessing through ORThogonalization Discriminant Analysis (SPORT-DA) and Soft Independent Modelling of Class Analogy (SIMCA), have been employed. Both discriminant and class-modelling approaches demonstrated to be suitable for the purpose. The highest correct classification rate on cricket flour was achieved by means of SPORT-DA, which correctly predicted 100% of test samples; whereas, the most accurate predictions on the buffalo worm flour data set were obtained using SIMCA, which allowed the erroneous classification of only three (over thirty) test objects.

Evaluation of FT-IR spectroscopy combined with SIMCA and PLS‑DA for detection of adulterants in pistachio butter

This work scrutinized the adulteration of pistachio butter with three potential edible oils using Fourier transform infrared spectroscopy (FT-IR) and multivariate classification methods. Each of the classes, including non-adulterated samples and adulterated samples consisting of pistachio butter mixed with various concentrations of peanut oil, corn oil and sunflower oil, were classified. For this purpose, multivariate methods, including soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA), were applied to classify the FTIR data. After evaluating the model on unknown samples, the results indicated that PLS-DA was better than the SIMCA model. The classification efficiency of 98% was obtained using the PLS-DA algorithm compared to the SIMCA model with 91% efficiency for prediction. According to the results FTIR spectroscopy, accompanied by multivariate classification methods, can be used as a rapid and reliable method for classifying and predicting adulteration in pistachio butter with edible oils.

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. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

Optimizing the soft independent modeling of class analogy (SIMCA) using statistical prediction regions

The ultimate goal of a one-class classifier like the “rigorous” soft independent modeling of class analogy (SIMCA) is to predict with a certain confidence probability, the conformity of future objects with a given reference class. However, the SIMCA model, as currently implemented often suffers from an undercoverage problem, meaning that its observed sensitivity often falls far below the desired theoretical confidence probability, hence undermining its intended use as a predictive tool. To overcome the issue, the most reported strategy in the literature, involves incrementing the nominal confidence probability until the desired sensitivity is obtained in cross-validation. This article proposes a statistical prediction interval-based strategy as an alternative strategy to properly overcome this undercoverage issue. The strategy uses the concept of predictive distributions sensu stricto to construct statistical prediction regions for the metrics. Firstly, a procedure based on goodness-of-fit criteria is used to select the best-fitting family of probability models for each metric or its monotonic transformation, among several plausible candidate families of right-skewed probability distributions for positive random variables, including the gamma and the lognormal families. Secondly, assuming the best-fitting distribution, a generalized linear model is fitted to each metric data using the Bayesian method. This method enables to conveniently estimate uncertainties about the parameters of the selected distribution. Propagating these uncertainties to the best-fitting probability model of the metric enables to derive its so-called posterior predictive distribution, which is then used to set its critical limit. Overall, the evaluation of the proposed approach on a diversity of real datasets shows that it yields unbiased and more accurate sensitivities than existing methods which are not based on predictive densities. It can even yield better specificities than the strategy that attempts to improve sensitivities of existing methods by “optimizing” the type 1 error, especially in low sample sizes’ contexts.

Study on egg sorting model based on visible-near infrared spectroscopy

To realize the automatic sorting of eggs, the sorting models are established in this paper by using the visible-near infrared spectroscopy technique and taking the eggshell colour, integrity, as well as the feeding mode as sorting indexes. A variety of methods are selected to remove the noise and systematic error by preprocess the spectral information. The backpropagation neural network (BP), the Principal Component Analysis (PCA) coupled with BP and the Soft Independent Modeling of Class Analogy (SIMCA) sorting method are used to identify the eggshell colours (white, pink, green), eggshell integrity (intact, cracked) and laying hen feeding mode (caged and cage-free) by their characteristic band, respectively. The prediction correlation coefficient (Rv), the prediction mean square error (RMSEP), the prediction standard error (SEP), the recognition rate () and the rejection rate () are used to evaluate the established models. The results show that the established classification models have high prediction accuracy and small errors. The non-destructive testing (NDT) technology has great potential for large-scale intelligent laying hen farms.

A green and direct method for authentication of rosewood essential oil by handheld near infrared spectrometer and one-class classification modeling

Rosewood essential oil is a high value-added forest product obtained from the Aniba rosiodora Ducke species, native to the Amazon. The chemical constitution is rich in linalool, an important compound with fixative properties, but is the whole chemical profile that grants rosewood oil its unique aroma highly valued by the perfumery and cosmetic industries. However, due to intense exploitation for decades, A rosiodora is currently an endangered species and is part of Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), being that the trade and export of its essential oil are allowed only from managements authorized areas. Despite its efficiency for chemical characterization, GC–MS analysis is not available for most cooperatives and small producers and does not allow in situ measurements. On the other hand, near infrared spectroscopy (NIRS) can perform a fast, direct, low-cost, nondestructive analysis using handheld equipments. In this work, a new method for the direct and rapid analysis of this essential oil using near-infrared spectroscopy and data-driven soft independent modeling of class analogy (SIMCA) was developed for rosewood essential oil authentication. The sampling was conducted in different Brazillian states, covering industries, small cooperatives, local producers, and commercial products. All samples were characterized by GC–MS analysis to certify their purity and authenticity. NIRS measurements were performed without any sample treatment and data modeling was accomplished to ensure a representative validation. The representative sampling revealed that there is a significant variation among the samples declared as rosewood oil in the Brazilian market. The method has shown 98% efficiency in the analysis of authentic and commercial samples from different origins, indicating that it can be a green alternative for authentication purposes for export and quality control.

In-line spectroscopy combined with multivariate analysis methods for endpoint determination in column chromatographic adsorption processes for herbal medicine

Objective: In a chromatographic cycle, the adsorption process is a critical unit operation that has a significant impact on downstream processes and, ultimately, the quality of the final products. The development of a rapid method to determine the endpoints of adsorption processes in a large-scale manufacturing is of substantial importance for herbal medicine (HM) manufacturers. Methods: In this study, the adsorption of saponins on a macroporous resin column chromatograph, a critical unit operation in Panax notoginseng (Burkill) F.H.Chen injection manufacturing, was considered as an example. The evaluation results of in-line ultraviolet and visible spectra combined with various multivariate analysis methods, including the moving block standard deviation (MBSD), difference between the moving block average and the target spectrum (DMBA-TS), soft-independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLS-DA), were compared. Results: MBSD was unsuitable for adsorption processes. The relative standard errors of prediction between the predicted and experimental endpoints were 13.2%, 4.67%, and 5.71% using DMBA-TS, SIMCA, and PLS-DA, respectively. Conclusions: Among the considered analysis methods, SIMCA and PLS-DA were more effective for endpoint determination. The results of this study provide a more comprehensive overview of the effectiveness of various multivariate analysis methods to facilitate the selection of the most suitable method. This study was also conducive to address the issues of the in-line detection of adsorption endpoints to guide practical HM manufacturing. Graphical abstract: http://links.lww.com/AHM/A23

Traceability Information Model for Sustainability of Black Soybean Supply Chain: A Systematic Literature Review

Traceability information as a solution option becomes an important task for the industry in providing products, preparing sustainable raw materials, and ensuring adequate safety quality. The emergence of these demands makes the industry perform tracking in order to prepare product inventories ranging from raw materials to products that have been produced. Based on these reasons, the scope of this paper is to provide a systematic review of the literature on various aspects of implementing information traceability models and sustainability of supply chain on economic, social, environmental, technological, institutional, and infrastructural dimensions. For this purpose, we use the Scopus, Science Direct, EBSCO Host, and ProQuest databases. We used the PRISMA model to identify, filter, and test for the eligibility of articles to be included. We selected 52 articles contributed by this search engine. We found was that between 2018 to 2021 there was increasing interest in this research. The dominant traceability information model in the article uses blockchain, the rest use operations research (OR), Google Earth Engine (GEE), website-based, Unified Modeling Language (UML), Extensible Markup Language (XML), physical markup language (PML), logit, enterprise resource planning (ERP), soft independent modelling of class analogies (SIMCA), and Spatially Explicit Information on Production to Consumption Systems (SEI-PCS).

Pattern Recognition Approach for the Screening of Potential Adulteration of Traditional and Bourbon Barrel-Aged Maple Syrups by Spectral Fingerprinting and Classical Methods.

This study aims to generate predictive models based on mid-infrared and Raman spectral fingerprints to characterize unique compositional traits of traditional and bourbon barrel (BBL)-aged maple syrups, allowing for fast product authentication and detection of potential ingredient tampering. Traditional (n = 23) and BBL-aged (n = 17) maple syrup samples were provided by a local maple syrup farm, purchased from local grocery stores in Columbus, Ohio, and an online vendor. A portable FT-IR spectrometer with a triple-reflection diamond ATR and a compact benchtop Raman system (1064 nm laser) were used for spectra collection. Samples were characterized by chromatography (HPLC and GC-MS), refractometry, and Folin–Ciocalteu methods. We found the incidence of adulteration in 15% (6 out of 40) of samples that exhibited unusual sugar and/or volatile profiles. The unique spectral patterns combined with soft independent modeling of class analogy (SIMCA) identified all adulterated samples, providing a non-destructive and fast authentication of BBL and regular maple syrups and discriminated potential maple syrup adulterants. Both systems, combined with partial least squares regression (PLSR), showed good predictions for the total °Brix and sucrose contents of all samples.

Combining class-modelling and discriminant methods for improvement of products authentication

Class- and discriminant modelling are two types of classification tools applied to construct models used for prediction of belongingness of samples to the classes studied. Although class- and discriminant methods have similar goals, the areas of their applicability are different. A class-model is constructed individually for each of the classes studied, based on the similarities among samples from the same class. A classical discriminant model is constructed based on differences among classes studied, and a new sample is always assigned to one of these classes. This characteristic differentiates the discriminant approach from class-modelling, where a new sample can be assigned to none of the classes studied. Due to this property, the class-modelling approach is widely applied for authentication purposes. Yet, if more classes similar to one another are authenticated, individual class-models constructed for these classes can lead to poor classification results. In such cases, the discriminant model can provide a better classification outcome since it takes advantage of differences between the classes. However, the discriminant approach alone is inappropriate for authentication, thus a possible solution is to use a method that benefits from both class- and discriminant modelling. In this study, several methods that combine class-modelling with a discriminant approach were tested, i.e., two-step approach, two soft discriminant methods based on PLS-DA (Partial Least Squares Discriminant Analysis), and ROC (Receiver Operating Characteristics) curve-based SIMCA (Soft Independent Modelling of Class Analogy). The methods were compared with respect to their pros, cons and the scope of their applicability for the example of authentication of three Cyclopia species. They are used for the production of honeybush tea (protected in the European Union as a Geographical Indication (GI)). Moreover, several different authentication scenarios were considered to test the methods thoroughly. It was revealed that the two-step approach, soft discriminant method by Calvini et al. and ROC curve-based SIMCA led to the most efficient models.

Rapid identification of raw and sulfur-fumigated Paeoniae Radix Alba based on Heracles NEO ultra-fast gas phase electronic nose

Since the current identification method for Paeoniae Radix Alba is complex in operation and long time-consuming with high requirements for technicians, the present study employed Heracles NEO ultra-fast gas phase electronic nose(E-nose) technology to identify raw and sulfur-fumigated Paeoniae Radix Alba decoction pieces in order to establish a rapid identification method for sulfur-fumigated Paeoniae Radix Alba. The odors of raw Paeoniae Radix Alba and its sulfur-fumigated products were analyzed by Heracles NEO ultra-fast gas phase E-nose to obtain the odor chromatographic information. The chemometric model was established, and the data were processed by principal component analysis(PCA), discriminant function analysis(DFA), soft independent modeling of class analogy(SIMCA), and partial least squares discriminant analysis(PLS-DA). The differential compounds of raw and sulfur-fumigated samples were qualitatively analyzed based on the Kovats retention index and Arochembase. As revealed by the comparison of gas chromatograms of raw and sulfur-fumigated Paeoniae Radix Alba, the heights of several peaks in the chromatograms before and after sulfur fumigation changed significantly. The peak(No.8) produced by ethylbenzene disappeared completely due to sulfonation reaction in the process of sulfur fumigation, indicating that ethylbenzene may be the key component in the identification of Paeoniae Radix Alba and its sulfur-fumigated products. In PCA, DFA, SIMCA, and PLS-DA models, the two types of samples were separated into two different regions, indicating that the established models can clearly distinguish between raw and sulfur-fumigated Paeoniae Radix Alba. The results showed that Heracles NEO ultra-fast gas phase E-nose technology could realize the rapid identification of raw and sulfur-fumigated Paeoniae Radix Alba, which provides a new method and idea for the rapid identification of sulfur-fumigated Chinese medicine.

Integration of near-infrared spectroscopy and aquaphotomics for discrimination of cultured cancerous cells using phenol red

Presently, mammalian cell lines are the most utilized hosts for the production of biopharmaceuticals. Optical spectroscopy is extensively used for the physical or physiological measurements of cellular features to study the diagnosis, prognosis, and treatment of different cancers. Near-infrared (NIR) spectroscopy and aquaphotomics are ubiquitous techniques increasingly used for nondestructive assessment. The key objective of this study is to evaluate the feasibility of absorbance spectra in the NIR region (700–1100 ​nm) coupled with aquaphotomics analysis to classify the different cancerous cell lines. Human cervix adenocarcinoma cells (HeLa) and human prostate carcinoma (DU145) were the cancerous cell lines, while normal mouse skin fibroblast (L929 ​cell line) was used as a reference for the assessment. The NIR absorbance spectra for 50 ​000 to 275 ​000 ​cells of L929, DU145, and HeLa cell lines in culture media prepared with and without phenol red (PhR) were subjected to principal component analysis (PCA) and soft independent modeling of class analogies (SIMCA) models. The PCA of the spectral absorbance data revealed a clear discrimination between the three cell lines, with subgroups detected based on the presence or absence of PhR in the culture medium. The SIMCA method showed a high accuracy of classification, where larger class distances were obtained among the non-stained cells.

Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito.

The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice.

A novel technique for detection of vanaspati ( hydrogenated fat ) in cow ghee ( clarified butter fat ) using flash gas chromatography electronic nose combined with chemometrics

This study aimed to detect various proportions of vanaspati mixed with cow ghee using an electronic nose (e-nose) based on fast gas chromatography. The e-nose apparatus was equipped with a non-polar and medium polar column used to generate two chromatograms simultaneously along with the volatile compound information. Further investigation on the adulteration detection in ghee was carried out using chemometric analysis such as principal component analysis (PCA), discriminant function analysis (DFA), and soft-independent modeling of class analogy (SIMCA). An outstanding differentiation among all the samples was obtained using PCA and DFA with 99.85% and 99.83% classification accuracy in the e-nose dataset. SIMCA presented a validation score of 76, indicating SIMCA could also be a potential method to detect higher levels of adulteration in ghee. The validation study shows good agreement between FGCEN and GC–MS methods. Practical applications Cow ghee is frequently adulterated with vanaspati, due to its high market demand. The existing methods for adulteration detection are time-consuming, and require tedious sample preparation and expertise in these fields. The e-nose based on fast GC combined with chemometric analysis turned out to be a reliable and promising technique for various proportions of adulteration detection in pure cow ghee. The technique was efficient enough to get the volatile compound information from the samples in a fast-performing way. The chemometrics used has shown to be effective to confirm adulterations in ghee.

Direct rapid quality assurance analysis of complex matrix materials: A chemometrics enabled energy dispersive X-ray fluorescence and scattering spectrometry application

Compared to energy dispersive X-ray fluorescence (EDXRF) that is limited to analysis of elements (Z ≥ 13) via fluorescence and which is hardly direct, energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry exploits additionally, scatter radiation to quantify both low- and heavy-Z elements as well as predict various material properties. The goal of this work was to demonstrate proof-of-concept for chemometrics-enabled EDXRFS spectrometry utilizing a weak sample excitation source towards rapid quality assurance (QA) analysis of complex matrix materials. A109Cd source was used to analyse three types of polymer powders - polypropylene (PP), low density polyethene (LDPE) and high density polyethene (HDPE) moulded as ∼ 2.5 g (∅ = 2.5 cm) pellets; and both actual as well as simulate lubricating oils spiked with trace additive metals B, Na, Ca, Mg, Fe and Zn and subjected to thermal degradation between 100 °C and 400 °C for 24 h. EDXRFS combined with principal components analysis (PCA) and soft independent modelling of class analogy (SIMCA) accurately identified polypropylene as well as differentiated it from both low density polyethene (LDPE) and high density polyethene (HDPE). LDPE and HDPE were fairly discriminated on the basis of density. In the second application partial least squares (PLS) modelling successfully determined the viscosity of lubricating oils utilizing the combination of the scatter peak and the Zn fluorescence signature, as well as correlated the lubricating oils viscosity to their thermal degradation. The reported method is applicable in industrial quality assurance due to its non-destructivity and speed.

Differential mobility spectrometry coupled to mass spectrometry (DMS–MS) for the classification of Spanish PDO paprika

Ion mobility spectrometry (IMS) has proved its huge potential in many research areas, especially when hyphenated with chromatographic techniques or mass spectrometry (MS). However, focusing on food analysis, and particularly in classification and authentication issues, very few applications have been reported. In this study, differential mobility spectrometry coupled to mass spectrometry (DMS–MS) is presented for the first time as an alternative and high-throughput technique for food classification and authentication purposes using a fingerprinting strategy. As a study case, 70 Spanish paprika samples (from La Vera, Murcia, and Mallorca) were analyzed by DMS–MS to address their classification —using partial least squares regression-discriminant analysis (PLS-DA)— and authentication —through soft independent modeling of class analogy (SIMCA). As a result, after external validation, complete sample classification according to their geographical origin and excellent La Vera and Mallorca sample authentication were reached.

Detection of Falsified Antimalarial Sulfadoxine-Pyrimethamine and Dihydroartemisinin-Piperaquine Drugs Using a Low-Cost Handheld Near-Infrared Spectrometer.

Falsified drugs are of serious concern to public health worldwide, particularly for developing countries where quality control of drugs is inefficient. In law enforcement against such fake medicines, there is a need to develop reliable, fast, and inexpensive screening methods. In this work, the ability of an innovative low-cost handheld near-infrared spectrometer to identify falsifications among two antimalarial fixed dose combination tablets, dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine, has been investigated. Analyzed samples were collected in Burkina Faso mainly in rural transborder areas that could be infiltrated by illicit drugs. A principal component analysis was applied on the acquired near-infrared spectra to identify trends, similarities, and differences between collected samples. This allowed to detect some samples of dihydroartemisinin/piperaquine and sulfadoxine/pyrimethamine which seemed to be falsified. These suspicious samples were semiquantitatively analyzed by thin-layer chromatography using Minalab® kits. Obtained results allowed to confirm the falsifications since the suspected samples did not contain any of the expected active pharmaceutical ingredients. The capacity of the low-cost near-infrared device to identify specifically a brand name of dihydroartemisinin/piperaquine or sulfadoxine/pyrimethamine has been also studied using soft independent modelling of class analogy (SIMCA) in the classical and data driven versions. The built models allowed a clear brand identification with 100% of both sensitivity and specificity in the studied cases. All these results demonstrate the potential of these low-cost near-infrared spectrometers to be used as first line screening tools, particularly in resource limited laboratories, for the detection of falsified antimalarial drugs.

Rapid identification of lamb freshness grades using visible and near-infrared spectroscopy (Vis-NIR)

In the meat industry, it is essential to monitor and identify meat freshness grades due to its impact on the safety of human diets. This study aimed to identify premium, sub-fresh, and spoiled lamb samples using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) in the range of 400–1000 nm coupled with chemometrics methods. The two-dimensional correlation spectroscopy (2D-CS) was utilized to select effective wavelengths for simplifying the model and increasing the calculation speed. The capabilities of the four models including partial least squares discriminant analysis (PLS-DA), soft independent modelling of class analogy (SIMCA), back propagation neuron network (BP), decision tree (DT) and random forest (RF) were compared to select the best identification model. The results showed that the RF model generated excellent performance, the accuracies of the training and test sets were 93% and 91%, respectively. In summary, this study showed that it was feasible to rapidly and non-destructively identify and evaluate the freshness grades of lamb using Vis-NIR.

Rapid Detection of Avocado Oil Adulteration Using Low-Field Nuclear Magnetic Resonance.

Avocado oil (AO) has been found to be adulterated by low-price oil in the market, calling for an efficient method to detect the authenticity of AO. In this work, a rapid and nondestructive method was developed to detect adulterated AO based on low-field nuclear magnetic resonance (LF-NMR, 43 MHz) detection and chemometrics analysis. PCA analysis revealed that the relaxation components area (S23) and relative contribution (P22 and P23) were crucial LF-NMR parameters to distinguish AO from AO adulterated by soybean oil (SO), corn oil (CO) or rapeseed oil (RO). A Soft Independent Modelling of Class Analogy (SIMCA) model was established to identify the types of adulterated oils with a high calibration (0.98) and validation accuracy (0.93). Compared with partial least squares regression (PLSR) models, the support vector regression (SVR) model showed better prediction performance to calculate the adulteration levels when AO was adulterated by SO, CO and RO, with high square correlation coefficient of calibration (R2C > 0.98) and low root mean square error of calibration (RMSEC < 0.04) as well as root mean square error of prediction (RMSEP < 0.09) values. Compared with SO- and CO-adulterated AO, RO-adulterated AO was more difficult to detect due to the greatest similarity in fatty acids’ composition being between AO and RO, which is characterized by the high level of monounsaturated fatty acids and viscosity. This study could provide an effective method for detecting the authenticity of AO.

A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers

In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.