Chemometric Tools Research Articles

Differentiation and prediction of the in vitro antifungal activity of Brazilian propolis against Fonsecaea pedrosoi using chemometrics and diffuse reflectance infrared Fourier Transform spectroscopy

The in vitro antifungal activity of ethanolic extracts of propolis was evaluated at five different concentrations in twelve Fonsecaea pedrosoi strains using agar dilution. Eight propolis extracts were collected from towns located in the central region of the state of Rio Grande do Sul, Brazil. Diffuse reflectance infrared Fourier Transform spectroscopy (DRIFTS) was employed to analyze the spectra of lyophilized propolis extracts. The use of chemometric tools allowed for the development of models with more relevant factors and with fewer variables. Partial least squares (PLS) regression was applied, allowing for the development of PLS-DRIFTS models to predict antifungal activity of all fungal strains with root mean square error of cross validation (RMSECV) lower to 5.00 mm2 and coefficient correlation of cross-validation (R2) upper to 0.99 with five to eight latent variables at 4000-2400 and 2200-600 cm-1. Concluding, DRIFTS models associated with chemometrics are powerful screening tools of propolis samples with fungistatic properties against Fonsecaea pedrosoi.

New Facts on the Antimicrobial Essential Oil of Satureja kitaibelii.

The objective of the present study was to assess the difference in antimicrobial activity of Satureja kitaibelii Wierzb. ex Heuff. essential oil at three growth stages. In addition, using statistical and chemometric tools, we tried to explain why the essential oil from a certain vegetation stage shows the highest antimicrobial activity. S. kitaibelii essential oils demonstrated minimum inhibitory concentration values from 160 to 10000 μg mL-1 , and minimum microbicidal concentration values from 630 to 20000 μg mL-1 . Geraniol, borneol, limonene and p-cymene are the dominant compounds of S. kitaibelii essential oil. The most abundant compound, geraniol, possesses antimicrobial activity in a range of MIC values from 40 to 5000 μg mL-1 and MMC values from 80 to 10000 μg mL-1 . The highest activity of essential oil for all tested strains of microorganisms was recorded in November. Results of statistical analysis indicate that the percentage of dominant compounds of essential oils does not affect the antibacterial activity of essential oils. Chemometric analyses leads to the conclusion that borneol, spathulenol, caryophyllene oxide and limonene can be the main contributors to the antibacterial activity of essential oil from November and that their mutual ratio is important. These results may represent a new methodological approach for future research on essential oils.

Effects of Indoor Dust Exposure on Lung Cells: Association of Chemical Composition with Phenotypic and Lipid Changes in a 3D Lung Cancer Cell Model.

Indoor dust is a key contributor to the global human exposome in urban areas since the population develops most of its activities in private and public buildings. To gain insight into the health risks associated with this chronic exposure, it is necessary to characterize the chemical composition of dust and understand its biological impacts using reliable physiological models. The present study investigated the biological effects of chemically characterized indoor dust extracts using three-dimensional (3D) lung cancer cell cultures combining phenotypic and lipidomic analyses. Apart from the assessment of cell viability, reactive oxygen species (ROS) induction, and interleukin-8 release, lipidomics was applied to capture the main lipid changes induced as a cellular response to the extracted dust compounds. The application of chemometric tools enabled the finding of associations between chemical compounds present in dust and lipidic and phenotypic profiles in the cells. This study contributes to a better understanding of the toxicity mechanisms associated with exposure to chemical pollutants present in indoor dust.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BackgroundProstate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.MethodsA metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).ResultsValidated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.ConclusionsThe metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran

Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several Fusarium species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling. Forty-two samples with DON contamination in the 40–1650 µg/kg range were considered. A chemometric processing of spectroscopic data allowed distinguishing of samples based on their DON content using a cut-off level set at 400 µg/kg DON. Although much lower than the EU limit of 750 µg/kg for wheat bran, this cut-off limit was considered useful whether accepting the sample as safe or implying further inspection by means of more accurate but also more expensive standard analytical techniques. Chemometric data processing using Principal Component Analysis and Quadratic Discriminant Analysis demonstrated a classification rate of 79% in cross-validation. To the best of our knowledge, this is the first time that a pocket-size fluorometer was used for DON screening of wheat bran.

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics.

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

Application of multivariate chemometrics tools for spectrophotometric determination of naphazoline HCl, pheniramine maleate and three official impurities in their eye drops

This work is concerned with exploiting the power of chemometrics in the assay and purity determination of naphazoline HCl (NZ) and pheniramine maleate (PN) in their combined eye drops. Partial least squares (PLS) and artificial neural network (ANN) were the chosen models for that purpose where three selected official impurities, namely; NZ impurity B and PN impurities A and B, were successfully determined. The quantitative determinations of studied components were assessed by percentage recoveries, standard errors of prediction as well as root mean square errors of prediction. The developed models were constructed in the ranges of 5.0–13.0 μg mL−1 for NZ, 10.0–60.0 μg mL−1 for PN, 1.0–5.0 μg mL−1 for NZ impurity B and 2.0–14.0 μg mL−1 for two PN impurities. The proposed models could determine NZ and PN with respective detection limits of 0.447 and 1.750 μg mL−1 for PLS, and 0.494 and 2.093 μg mL−1 for ANN. The two established models were compared favorably with official methods where no significant difference observed.

Discrimination of baru oil adulteration with soy oil using portable near infrared spectroscopy, Fourier-transform infrared spectroscopy and Partial Least-Squares Discriminant

Considered an important source of bioactive compounds and unsaturated fatty acids, Baru oil comes from the Brazilian Cerrado ecoregion and is sold with high value. Methods to detect tampering to certify the authenticity of this type of product are in great demand for quality control. Herein, this study used partial least squares for discriminant analysis (PLS-DA) combined with Fourier-transform infrared spectroscopy (FTIR) and portable near-infrared spectroscopy (NIR) for the detection of adulteration in Baru oil, using soybean oil as an adulterant. Adulteration concentrations were 10, 30, 50, 60, and 80% for analysis by NIR and 10, 30, 50, 60, 70, and 90% for FTIR. After the spectral range selection and pre-processing steps, discrimination models showed good discrimination, and the efficiency was confirmed from the figures of merit, which highlighted percentages above 99% with both techniques used. Anomalous samples were identified in discrimination models obtained with a portable NIR spectrometer in the outlier detection step. The methodology evaluated is direct, fast, and non-destructive, in which the PLS-DA models obtained from the sets of spectra by FTIR presented superior results than those evaluated with the portable NIR spectrometer; however, the latter was also efficient in discriminating as adulterated, reinforcing the feasibility of using spectroscopic techniques and chemometric tools.

Independent components–discriminant analysis for discrimination of Brazilian Canephora coffees based on their inorganic fraction: A preliminary chemometric study

Essential element composition of new Brazilian Canephora coffees of specific origins, including both Robusta and Conilon varieties is reported for the first time. A sample set comprising 100 Canephora samples of different geographical origins (Conilon from Espírito Santo, Amazonian Robusta from indigenous and non-indigenous producers from Rondônia, and Conilon from Bahia) and Arabica coffee (25 samples) was analyzed in terms of their essential mineral content by flame atomic absorption spectrometry using diluted nitric acid to characterize their content in Fe, Ca, Zn, Mg, Mn, Cu, and K. A recent chemometric tool, independent components-discriminant analysis (IC-DA), was applied for the first time to analyze mineral composition data. IC-DA discriminated the samples according to the predetermined classes (geographical origins of Canephora and species – Arabica vs. Canephora), identifying the minerals responsible for the discriminations. IC-DA sensitivity and specificity were higher when compared to the partial least square-discriminant analysis (PLS-DA). The method is shown to be suitable to achieve supervised discrimination of samples based on their essential elemental composition, even when principal component analysis had failed. It can thus be used as a tool for origin and species discrimination based on the inorganic fraction of coffee.

Chemometric-enhanced metabolic profiling of different plant parts belonging to two Bougainvillea species using UPLC-MS/MS spectrometry: Correlation to in vitro anti-inflammatory activity

Bougainvillea is considered as an important source of environment-friendly safe pigmenting agents, namely anthocyanins that are widely used in food, textile, cosmetic, and pharmaceutical industries. This study aimed to investigate the resemblance and variation in the chemical profiles of B. spectabilis Willd and B. glabra Choisy flowers and leaves extracts utilizing UPLC-MS/MS analysis in combination with chemometric tools. Forty-four compounds were tentatively annotated in the tested Bougainvillea extracts. Flavonoids, anthocyanins, mono, di, and triterpenes in addition to phenolic acids were the major classes of the annotated compounds. B. spectabilis flowers samples showed significant variations in their chemical profiles compared to the other tested extracts. The downregulating effect of the tested extracts on the expression of four pro-inflammatory markers (TNF-α, IL-6, IL-1β, and INF-γ) genes was assessed using real-time-PCR technique. All tested extracts abolished pro-inflammatory genes upregulation to levels comparable to those exhibited by piroxicam, on top of which were B. spectabilis leaves that exhibited the strongest anti-inflammatory activity due to their enrichment of bioactive metabolites that significantly influenced the expression of the proinflammatory biomarkers. Orthogonal projection to latent structure-discriminant analysis (OPLS) and coefficient plots unveiled the biomarkers contributing to the anti-inflammatory efficacy including Caffeic acid hexoside, p-hydroxybenzoic acid, sinapinic acid, quercetin 3-O-(4-caffeoylrhamnosyl-rhamnosyl-hexoside), rutin, neoeriocitrin, luteolin robinobioside, quercetin glucuronide (miquelianin) that seemed to be profoundly dominant in B.spectabilis leaves samples. This study showed that Bougainvillea plants can serve as important sources of valuable anti-inflammatory compounds besides their pronounced industrial applications.

Fast and reliable characterization of egg triacylglycerol profiles using atmospheric solids analysis probe tandem mass spectrometry (ASAP-MS/MS) combined with chemometric tools

In this study, the potential of atmospheric solid analysis probe tandem mass spectrometry (ASAP-MS/MS) was assessed as a green and rapid method combined with gas chromatography with flame ionization detection (GC-FID) and chemometrics to determine the triacylglycerol (TAG) profile of eggs from various housing systems, including indoor, cage-free, free-range, organic, and enriched systems. The most abundant ions were identified using the product ion and neutral loss scan. The fatty acid composition, mainly unsaturated, palmitic, and stearic acids of eggs was influenced by the housing systems. The optimal conditions of ASAP-MS for cone voltage, desolvation gas temperature, and desolvation gas flow were determined to be 40 V, 600 °C, and 1100 L h−1, respectively. The main TAGs (50:2, 50:1, 52:4, 52:3, 52:2, 52:1, 54:4, 54:3, 54:2, 54:1) were observed in the mass range m/z 830–890. Notable differences were observed for TAGs 54:1 and 52:4, which were found in significantly higher levels on WE and CE. The proposed methodology enables rapid and efficient TAG profiling of egg yolks, providing a solid foundation for future research in this field.

Innovative non-targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal.

Tigernut is a typical foodstuff from a specific region of Valencia (Spain) called 'L'Horta Nord', where it is commercialized under a Protected Designation of Origin (PDO) as Chufa de Valencia ('Valencia's tigernut'). PDO-recognized tigernuts present unique characteristics associated with their particular production region. Increasing demand and the associated expansion of its cultivation area has made necessary an exhaustive quality control to check the geographical origin and quality seal. In this work, a new multivariate analytical method capable of authenticating the PDO quality seal of tigernut samples was developed. Tigernut fat fraction was extracted under optimal conditions, applying the methodology of design of experiments. The analytical method combined fingerprinting methodology and chemometric tools to observe the natural grouping of samples using the exploratory analysis method and to develop classification models (partial least squares-discriminatory analysis; PLS-DA) to discriminate between two sample categories: (i) PDO tigernuts; and (ii) NON-PDO tigernuts. The built PLS-DA model demonstrated 100% accuracy, high sensitivity and specificity, revealing that the tigernut fat fraction can be applied to authenticate the PDO quality seal. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Aroma-based discrimination of Egyptian versus Indian guava fruits and in response to probiotics as analyzed via SPME/GC–MS and chemometric tools

Guava tree (Psidium guajava L., Myrtaceae) is an economic grown worldwide, particularly in tropical and subtropical regions. Guavas encompass numerous cultivars (cvs.) that were discriminated in previous studies based on leaf morphological features and profile of volatile organic compounds (VOCs). Nevertheless, fruit VOCs have also shown outstanding potential for discrimination of other plant taxa, which has not been utilized in guava. Hence, the current study investigates the various guava cvs. harvested from India and Egypt. A total of 5 samples were analyzed by solid phase microextraction coupled to gas chromatography/mass spectrometry. Results led to the detection of 42 VOCs belonging to aldehydes, alcohols, esters, ketones, aliphatic and aromatic hydrocarbons, in addition to monoterpene and sesquiterpene hydrocarbons. Butylated hydroxytoluene and β-caryophyllene were predominant reaching 77% and 41% in Egyptian and Indian guava, respectively. The impact of probiotic fermentation, i.e., Lactobacillus acidophilus and L. plantarum on aroma profile was not significantly different (p > 0.05). Multivariate data analyses were further applied for samples classification and markers determination, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA score plot showed clear segregation of Egyptian from Indian specimens, whereas OPLS-DA revealed that β-caryophyllene was associated with white fruit versus 3-butenyl isothiocyanate and muurolol in red fruit type in the case of Indian guava. The richness of Egyptian guava in butylated hydroxytoluene in addition to the presence of vitamin C may potentiate its antioxidant activity, to be followed in subsequent studies regarding its health effects.

Spectrophotometry and chromatography analyses combined with chemometrics tools to differentiate green coffee beans into special or traditional.

Green coffee is the hulled coffee bean, rich in chemical compounds indicative of quality before roasting, making the classification special or traditional. This work aimed to determine compounds in green coffee beans and find the differentiation of green coffee beans into special or traditional ones through chemometrics. For that, the levels of phenolic compounds, reducing, nonreducing, and total sugars were quantified by spectrophotometry: caffeine, trigonelline, 5-hydroxymethylfurfural (5-HMF), 3-hydroxybenzoic, 4-hydroxybenzoic, chlorogenic, caffeic, and nicotinic acids (NAs) by high-performance liquid chromatography-UV-Vis; acetaldehyde, acetone, methanol, ethanol, and isoamyl by HS-GC-FID. Principal component analysis (PCA) was used to differentiate green coffee beans through the levels obtained in spectrophotometric and chromatographic analyses. Statistically, the contents of total phenolic compounds, caffeine, nonreducing sugars, total sugars, NA, 5-HMF, acetaldehyde, ethanol, and ethanol/methanol showed significant differences. The PCA made it possible to classify green coffee beans into special and traditional, in addition to understanding the attributes that influenced the differentiation between coffees. In addition, it was possible to classify green coffee beans into special and traditional, either using all parameters evaluated or only using spectrophotometric analyses. In this way, some advantages allow classification without using a trained and experienced evaluator as their previous experience can influence the results due to their expertise in a certain type of coffee, in addition to being faster and cheaper, especially regarding spectrophotometric analyses.

Comparative GC–MS based nutrients profiling of less explored legume seeds of Melilotus, Medicago, Trifolium, and Ononis analysed using chemometric tools

Exploring novel sources of plant protein for nutrition of both humans and animals is motivated mainly by its growing demand worldwide, besides identifying healthy alternatives for animal protein. The present study evaluates metabolome diversity within 15 legume seed species. The examined samples comprised three Melilotus, four Medicago, four Trifolium, and four Ononis seed species. A holistic approach for metabolites profiling using gas chromatography-mass spectrometry (GC–MS) led to the annotation and quantification of 87 metabolites comprising alcohols, free amino acids, aromatics, fatty acids/esters, nitrogenous compounds, organic acids, sugar alcohols, sugars, terpenes, and steroids. Fatty acids represented the major metabolite class represented by palmitic, stearic, oleic, linoleic, and linolenic acids. Sucrose and pinitol were the major sugars and sugar alcohols among seeds. Ononis seeds (OR, OS and OA) were the most abundant in fatty acids, sugars, sugar alcohols, and free amino acids, whereas Melilotus species (MO and MS) were least enriched in these key nutrients posing Ononis as potential food source for humans and animals. The examined seeds were generally low in sulfur-containing free amino acids and lacking many of the essential free amino acids. Multivariate data analysis aided in the identification of Ononis metabolite markers belonging to various classes i.e., (alcohol) glycerol, (sugar) allofuranose, and (sugar alcohol) pinitol, although the differentiation between Medicago, Melilotus, and Trifolium genera was not attained suggestive for other analytical platforms for its classification.

Valorization of Prunus Seed Oils: Fatty Acids Composition and Oxidative Stability.

Prunus fruit seeds are one of the main types of agri-food waste generated worldwide during the processing of fruits to produce jams, juices and preserves. To valorize this by-product, the aim of this work was the nutritional analysis of peach, apricot, plum and cherry seeds using the official AOAC methods, together with the extraction and characterization of the lipid profile of seed oils using GC-FID, as well as the measurement of the antioxidant activity and oxidative stability using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free-radical scavenging method. Chemometric tools were required for data evaluation and the obtained results indicated that the main component of seeds were oils (30-38%, w). All seed oils were rich in oleic (C18:1n9c) and linoleic (C18:2n6c) acids and presented heart-healthy lipid indexes. Oil antioxidant activity was estimated in the range IC50 = 20-35 mg·mL-1, and high oxidative stability was observed for all evaluated oils during 1-22 storage days, with the plum seed oil being the most antioxidant and stable over time. Oxidative stability was also positively correlated with oleic acid content and negatively correlated with linoleic acid content. Therefore, this research showed that the four Prunus seed oils present interesting healthy characteristics for their use and potential application in the cosmetic and nutraceutical industries.

Towards the rapid detection of haze‐forming proteins

Protein haze in white wine can be a serious quality defect because consumers perceive hazy wines as “spoiled”. Unfortunately, a specific method for the detection, or selective treatment, of such proteins in affected wines does not exist. Herein we investigate on the development of an easy‐to‐use sensor device that allows detection of haze-forming proteins (HFPs). Such a device is expected to overcome the limitations of the “heat test” currently used to assess the protein content in wine and the amount of bentonite needed to remove such proteins. To this aim, three different approaches were explored. Firstly, an impedimetric immunosensor against chitinases was developed and its performance assessed. Secondly, the exploitation of the dual role of HFPs as biorecognition element and analyte to develop an impedimetric biosensor was evaluated, in what can be considered a very unique strategy, representing a new paradigm in biosensing. Lastly, Fourier transform infrared (FT-IR) spectra were collected for various wine samples and chemometric tools such as discrete wavelet transform (DWT) and artificial neural networks (ANNs) were used to achieve the quantification of HFPs. Detection of HFPs at the μg/L level was achieved with both impedimetric biosensors, whereas the FT-IR-based approach allowed their quantification at the mg/L level in wine samples directly. The sensitivity of the developed methods may enable the rapid assessment of wine protein content.

Quality control of Ganoderma lucidum by using C, H, O, and N stable isotopes and C and N contents for geographical traceability.

Ganoderma lucidum (G. lucidum) is a popular medicinal fungus that has been used in traditional medicine for decades, with its provenance influencing its medicinal and commercial worth. The amount of active ingredients and the price of G. lucidum from different origins vary significantly; hence, fraudulent labeling is common. Reliable techniques for G. lucidum geographic verification are urgently required to safeguard the interests of consumers, producers, and honest dealers. A stable isotope is widely acknowledged as a useful traceability technique and could be developed to confirm the geographical origin of G. lucidum. G. lucidum samples from various sources and in varying stages were identified by using δ 13C, δD, δ 18O, δ 15N, C, and N contents combined with chemometric tools. Chemometric approaches, including PCA, OPLS-DA, PLS, and FLDA models, were applied to the obtained data. The established models were used to trace the origin of G. lucidum from various sources or track various stages of G. lucidum. In the stage model, the δ 13C, δD, δ 18O, δ 15N, C, and N contents were considered meaningful variables to identify various stages of G. lucidum (bud development, growth, and maturing) using PCA and OPLS-DA and the findings were validated by the PLS model rather than by only four variables (δ 13C, δD, δ 18O, and δ 15N). In the origin model, only four variables, namely δ 13C, δD, δ 18O, and δ 15N, were used. PCA divided G. lucidum samples into four clusters: A (Zhejiang), B (Anhui), C (Jilin), and D (Fujian). The OPLS-DA model could be used to classify the origin of G. lucidum. The model was validated by other test samples (Pseudostellaria heterophylla), and the external test (G. lucidum) by PLS and FLDA models demonstrated external verification accuracy of up to 100%. C, H, O, and N stable isotopes and C and N contents combined with chemometric techniques demonstrated considerable potential in the geographic authentication of G. lucidum, providing a promising method to identify stages of G. lucidum.

Rapid detection of argan oil adulteration by frying oils using laser induced fluorescence spectroscopy combined with chemometrics tools

There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods. The aim of this study was to detect and quantify argan oil adulteration using Laser Induced Fluorescence (LIF) spectroscopy coupled with chemometric methods. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were used to assess argan oil authenticity; PCA was used to classify samples according to their quality and the PLS model to determine the amount of adulterants in pure argan oil. The correlation coefficient of the obtained model was about 0.99, with Root Mean Square Error of Prediction (RMSEP) and Standard Error of Prediction (SEP) of 2%. This study demonstrated the feasibility of LIF spectroscopy combined with chemometric tools to identify adulterants in pure argan oil from a percentage of adulteration, of 0.35 % without the need to destruct samples.

Statistical reliability of a relative standard deviation of chromatographic peak area estimated by a chemometric tool based on the FUMI theory

In pharmaceutical analysis using a high-performance liquid chromatography (HPLC) system, repeatability assessment is significant to obtain reliable and precise quantitative results. The purpose of the present study is to experimentally show the statistical reliability of a relative standard deviation (RSD) of peak area estimated by a chemometric tool based on probability theory, called the function of mutual information (FUMI) theory, which stochastically provided an RSD of peak area and SD of baseline areas with width k (s(k)) from noises and a signal on a single chromatogram. An ultra-high-performance liquid chromatography with ultraviolet detection (UHPLC-UV) for determining ergosterol was applied as an example of the repeatability assessment. In addition, the statistical reliability of an RSD of peak area in the UHPLC-UV system was certified according to a chi-square (χ2) distribution. The 712 values of s(k) were experimentally obtained from a data series of 1001 points in the noise regions of 712 chromatograms. The histogram of χ2 of s(k) was well-fitted to the χ2 distribution curve (freedom degree, ν = 50), indicating that the statistical reliability of an RSD of the peak area in the UHPLC-UV estimated by the FUMI theory (n = 1) was equivalent to that estimated by 50 runs of chromatographic measurements.