Field-Template, QSAR, Ensemble Molecular Docking, and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors.

Currently, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected people among all countries and is a pandemic as declared by the World Health Organization (WHO). SARS-CoVID-2 main protease is one of the therapeutic drug targets that has been shown to reduce virus replication, and its high-resolution 3D structures in complex with inhibitors have been solved. Previously, we had demonstrated the potential of natural compounds such as serine protease inhibitors eventually leading us to hypothesize that FDA-approved marine drugs have the potential to inhibit the biological activity of SARS-CoV-2 main protease. Initially, field-template and structure-activity atlas models were constructed to understand and explain the molecular features responsible for SARS-CoVID-2 main protease inhibitors, which revealed that Eribulin Mesylate, Plitidepsin, and Trabectedin possess similar characteristics related to SARS-CoVID-2 main protease inhibitors. Later, protein-ligand interactions are studied using ensemble molecular-docking simulations that revealed that marine drugs bind at the active site of the main protease. The three-dimensional reference interaction site model (3D-RISM) studies show that marine drugs displace water molecules at the active site, and interactions observed are favorable. These computational studies eventually paved an interest in further in vitro studies. Finally, these findings are new and indeed provide insights into the role of FDA-approved marine drugs, which are already in clinical use for cancer treatment as a potential alternative to prevent and treat infected people with SARS-CoV-2.

Discovery of Galangin as a Potential DPP-4 Inhibitor That Improves Insulin-Stimulated Skeletal Muscle Glucose Uptake: A Combinational Therapy for Diabetes.

Dipeptidyl peptidase-4 (DPP-4) is a well-known therapeutic drug target proven to reduce blood glucose levels in diabetes mellitus, and clinically, DPP-4 inhibitors are used in combination with other anti-diabetic agents. However, side effects and skeletal muscle health are not considered in the treatment for diabetic patients. Recently, natural compounds have been proven to inhibit DPP-4 with fewer side effects. In this work, initially, molecular docking simulations revealed that a natural compound, Galangin, possess a binding energy of -24 KJ/mol and interaction residues SER 630 and TYR 547, that are responsible for potent DPP-4 inhibition. In vitro studies showed that galangin not only inhibits DPP-4 in a concentration-dependent manner but also regulates glucose levels, enabling the proliferation of rat L6 skeletal muscle cells. The combination of galangin with insulin benefits regulation of glucose levels significantly in comparison to galangin alone (p < 0.05). These findings suggest the beneficial effect of the use of galangin, both alone or in combination with insulin, to reduce glucose levels and improve skeletal muscle health in diabetes mellitus.

New Molecular Insights into the Inhibition of Dipeptidyl Peptidase-4 by Natural Cyclic Peptide Oxytocin.

Protease inhibition has led to treating many diseases and has been successful in producing many commercial drugs by pharmaceutical companies. Among many proteases, serine protease has been attractive in treating metabolic disorder diabetes mellitus (DM). Gliptins have been proven to inhibit dipeptidyl peptidase-4 (DPP4), a serine protease, and are an emerging therapeutic drug target to reduce blood glucose levels, but until now there is no natural cyclic peptide proven to inhibit serine protease DPP4. This study demonstrates the potential mechanism of natural cyclic peptide oxytocin (OXT) as a DPP4 inhibitor. To achieve this, initially, activity atlas and field-based models of DPP4 inhibitors were utilized to predict the possible features of positive and negative electrostatic, hydrophobic, and activity shapes of DPP4 inhibition. Oxytocin binding mode, flexibility, and interacting residues were studied using molecular docking simulations studies. 3D-RISM calculations studies revealed that the stability of water molecules at the binding site are favorable. Finally, an experimental study using fluorescence assay revealed OXT inhibits DPP4 in a concentration-dependent manner in a significant way (p < 0.05) and possess IC50 of 110.7 nM. These new findings significantly expand the pharmaceutical application of cyclic peptides, and in specific OXT, and implicate further optimization of OXT inhibition capacity to understand the effect of DPP4 inhibition. This work highlights the development of natural cyclic peptides as future therapeutic peptides to reduce glucose levels and treat diabetes mellitus.

Effect of the cross-linking agent and drying method on encapsulation efficiency of orange essential oil by complex coacervation using whey protein isolate with different polysaccharides.

Orange essential oil was microencapsulated by complex coacervation with whey protein isolate (WPI): carboxymethylcellulose (CMC), WPI:sodium alginate (SA) and WPI:chitosan (CH). Effect of pH, protein:polysaccharide ratio and solid concentration on coacervation efficiency were selected for the best coacervation conditions. Tannic acid (TA), sodium tripolyphosphate, oxidised tannic acid and transglutaminase enzyme (TG) were used as cross-linking agents. Highest encapsulation efficiency (EE) for wet coacervated microcapsules ranged from 88% to 94%. Microcapsules were freeze and spray dried to evaluate their effect on its integrity. EE was higher than 80% in freeze dried coacervated microcapsules with and without cross-linking agent, but they formed a solid cake. Spray-dried samples formed a free fluid solid (10-20 µm), where the systems WPI:CMC and WPI:CH cross-linked with TA and TG, respectively showed the highest EE (47% and 50% respectively), representing 400% improvement compared to the samples without cross-linking.

Structure⁻Activity Relationship and Molecular Docking of Natural Product Library Reveal Chrysin as a Novel Dipeptidyl Peptidase-4 (DPP-4) Inhibitor: An Integrated In Silico and In Vitro Study.

Numerous studies indicate that diets with a variety of fruits and vegetables decrease the incidence of severe diseases, like diabetes, obesity, and cancer. Diets contain a variety of bioactive compounds, and their features, like diverge scaffolds, and structural complexity make them the most successful source of potential leads or hits in the process of drug discovery and drug development. Recently, novel serine protease dipeptidyl peptidase-4 (DPP-4) inhibitors played a role in the management of diabetes, obesity, and cancer. This study describes the development of field template, field-based qualitative structure⁻activity relationship (SAR) model demonstrating DPP-4 inhibitors of natural origin, and the same model is used to screen virtually focused food database composed of polyphenols as potential DPP-4 inhibitors. Compounds' similarity to field template, and novelty score "high and very high", were used as primary criteria to identify novel DPP-4 inhibitors. Molecular docking simulations were performed on the resulting natural compounds using FlexX algorithm. Finally, one natural compound, chrysin, was chosen to be evaluated experimentally to demonstrate the applicability of constructed SAR model. This study provides the molecular insights necessary in the discovery of new leads as DPP-4 inhibitors, to improve the potency of existing DPP-4 natural inhibitors.

The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics.

Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.

Microencapsulation techniques to develop formulations of insulin for oral delivery: a review.

Oral insulin delivery represents one of the most challenging goals for pharmaceutical industry. In general, it is accepted that oral administration of insulin would be more accepted by patients and insulin would be delivered in a more physiological way than the parenteral route. From all strategies to deliverer insulin orally, microencapsulation or nanoencapsulation of insulin are the most promising approaches because these techniques protect insulin from enzymatic degradation in stomach, show a good release profile at intestine pH values, maintain biological activity during formulation and enhance intestinal permeation at certain extent. From different microencapsulation techniques, it seems that complex coacervation, multiple emulsion and internal gelation are the most appropriate techniques to encapsulate insulin due to their relative ease of preparation. Besides that, the use of organic solvents is not required and can be scaled up at low cost; however, relative oral bioavailability still needs to be improved.

Identification of Variety and Prediction of Chemical Composition in Cocoa Beans (Theobroma cacao L.) by FT-MIR Spectroscopy and Chemometrics.

Cocoa is rich in polyphenols and alkaloids that act as antioxidants, anticarcinogens, and anti-inflammatories. Analytical methods commonly used to determine the proximal chemical composition of cocoa, total phenols, and antioxidant capacity are laborious, costly, and destructive. It is important to develop fast, simple, and inexpensive methods to facilitate their evaluation. Chemometric models were developed to identify the variety and predict the chemical composition (moisture, protein, fat, ash, pH, acidity, and phenolic compounds) and antioxidant capacity (ABTS and DPPH) of three cocoa varieties. SIMCA model showed 99% reliability. Quantitative models were developed using the PLS algorithm and favorable statistical results were obtained for all models: 0.93 < R2c < 0.98 (R2c: calibration determination coefficient); 0.03 < SEC < 4.34 (SEC: standard error of calibration). Independent validation of the quantitative models confirmed their good predictive ability: 0.93 < R2v < 0.97 (R2v: validation determination coefficient); 0.04 < SEP < 3.59 (SEP: standard error of prediction); 0.08 < % error < 10.35). SIMCA model and quantitative models were applied to five external cocoa samples, obtaining their chemical composition using only 100 mg of sample in less than 15 min. FT-MIR spectroscopy coupled with chemometrics is a viable alternative to conventional methods for quality control of cocoa beans without using reagents, and with the minimum sample preparation and quantity.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices.

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers' health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

FT-MIR and Raman spectroscopy coupled to multivariate analysis for the detection of clenbuterol in murine model.

A fast and simple screening method for the determination of clenbuterol at the ppb level in a murine model was demonstrated by Mid Infrared (MIR) and Raman spectroscopy in conjunction with multivariate analysis. In order to build the calibration models to quantify clenbuterol in rat meat, mixtures of rat meat and clenbuterol were prepared in a range of 5-10,000 ppb. Partial Least Square (PLS) analysis was used to build the calibration model. The results shown that Mid Infrared and Raman spectroscopy were efficient, but Mid Infrared (R(2) = 0.966 and SEC = 0.27) were superior to Raman (R(2) = 0.914 and SEC = 1.167). The SIMCA model developed showed 100% classification rate of rat meat samples with or without clenbuterol. The results were confirmed with contaminated meat samples from animals treated with clenbuterol. Chemometric models represent an attractive option for meat quality screening without sample pretreatments which can identify veterinary medicinal products at the ppb level.

Effect of In Vitro Digestion on the Antioxidant Compounds and Antioxidant Capacity of 12 Plum (Spondias purpurea L.) Ecotypes.

Spondias purpurea L. plum is a source of antioxidant compounds. Nevertheless, once they are consumed and go through the digestive system, these compounds may undergo changes that modify their bioaccessibility. This study aimed to evaluate the effect of in vitro gastrointestinal digestion on the total content of carotenoids (TCC), ascorbic acid (AA), phenolic compounds (TPC), flavonoids (TFC), anthocyanins (TAC), and antioxidant capacity (ABTS, DPPH) of 12 plum Spondias purpurea L. ecotypes. The plum samples were subjected to the InfoGest in vitro digestion model. TCC, AA, TPC, TFC, TAC, ABTS, and DPPH were significantly different (p ≤ 0.05) in each in vitro digestion stage. The gastric stage released the highest content of AA (64.04-78.66%) and TAC (128.45-280.50%), whereas the intestinal stage released the highest content of TCC (11.31-34.20%), TPC (68.61-95.36%), and TFC (72.76-95.57%). Carotenoids were not identified in the gastric stage whilst anthocyanins were lost at the end of the intestinal digestion. At the gastric stage, AA presented a positive and high correlation with ABTS (r: 0.83) and DPPH (r: 0.84), while, in the intestinal stage, TPC and TFC presented positive and high correlation with ABTS (r ≥ 0.8) and DPPH (r ≥ 0.8), respectively.

Extraction and Microencapsulation of Bioactive Compounds from Muicle (Justicia spicigera) and Their Use in the Formulation of Functional Foods.

Bioactive compounds (BC) present in muicle leaves were extracted using the best extraction conditions obtained with a Box-Behnken experimental design, extracting 95% of BC. Microencapsulation of muicle BC was carried out by spray drying using DE10 maltodextrin (MD) and soy protein isolate (SPI) as encapsulating agents. The best conditions for the ethanolic extraction of BC from muicle were 30 °C, 40% aqueous ethanol, and one extraction for 1 h. The best spray drying encapsulating conditions for BC and antioxidant capacity (AC) using MD as an encapsulating agent were: 160-80 °C and 10% MD in the feeding solution, and for SPI: 180-70 °C and 5% SPI in the feeding solution. Microcapsules were added to yogurt and a sensory evaluation and retention of BC during 15-day storage at 4 °C was performed. Sensory evaluation showed that yogurt with added MD microcapsules had better acceptance than that with SPI microcapsules. Based on this, a jelly with added muicle MD microcapsules was also prepared which obtained better acceptance by the judges. At the end of the storage period, yogurt with SPI microcapsules showed better retention of BC and AC than yogurts with MD microcapsules; however, products with MD microcapsules had better acceptance.

Microencapsulation of Rambutan Peel Extract by Spray Drying.

Microencapsulation of bioactive compounds (BC) from rambutan peel by spray drying using DE10 maltodextrin as encapsulating agent was performed. The optimal conditions for the ethanolic extraction of BC were 60 °C, with a time of 1 h, 55% aqueous ethanol and three extraction cycles. The best spray drying encapsulating conditions for BC and antioxidant capacity (AC) were: inlet temperature 160 °C, outlet temperature 80 °C, and 10% encapsulating agent concentration in the feeding solution (core:encapsulating agent ratio of 1:4). With these conditions, retention and encapsulation efficiencies obtained were higher than 85%, the water activity value, moisture content and Hausner Index were of 0.25 ± 0.01, 3.95 ± 0.10%, and 1.42 ± 0.00, respectively. The optimized powder presented good solubility and morphological properties, showing microcapsules without ruptures. Based on these results, microencapsulation by spray drying is a viable technique which protects BC of rambutan peel, facilitating its application in the food, pharmaceutical, and cosmetic industries.

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics.

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1-30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39-0.82, and standard error of prediction (SEP) of 0.45-0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.

Phytochemicals in Garlic Extract Inhibit Therapeutic Enzyme DPP-4 and Induce Skeletal Muscle Cell Proliferation: A Possible Mechanism of Action to Benefit the Treatment of Diabetes Mellitus.

Diabetes mellitus is a severe health problem in Mexico, and its prevalence is increasing exponentially every year. Recently, DPP-4 (dipeptidyl peptidase-4) inhibitors have become attractive oral anti-hyperglycemic agents to reduce the pathology of diabetes. Gliptin's family, such as sitagliptin, vildagliptin, and alogliptin, are in clinical use to treat diabetes mellitus but possess side effects. Therefore, there is a specific need to look for new therapeutic scaffolds (biomolecules). Garlic bulb is widely used as a traditional remedy for the treatment of diabetes. The garlic extracts are scientifically proven to control glucose levels in patients with diabetes, despite the unknown mechanism of action. The aim of the study is to investigate the antidiabetic effects of ultrasonication assisted garlic bulb extract. To achieve this, in-vitro assays such as DPP-4 inhibitory and antioxidant activities were investigated. Further, functional group analysis using FTIR and identification of phytochemicals using mass spectrometry analysis was performed. The results showed that 70.9 µg/mL of garlic bulb extract inhibited 50% DPP-4 activity. On top of that, the garlic extract exhibited a 20% scavenging activity, equivalent to 10 µg/mL of ascorbic acid. Molecular docking simulations on identified phytochemicals using mass spectrometry revealed their potential binding at the DPP-4 druggable region, and therefore the possible DPP-4 inhibition mechanism. These results suggest that prepared garlic extract contains phytochemicals that inhibit DPP-4 and have antioxidant activity. Also, the prepared extract induces skeletal muscle cell proliferation that demonstrates the antidiabetic effect and its possible mechanism of action.

Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study.

A drug repurposing strategy could be a potential approach to overcoming the economic costs for diabetes mellitus (DM) treatment incurred by most countries. DM has emerged as a global epidemic, and an increase in the outbreak has led developing countries like Mexico, India, and China to recommend a prevention method as an alternative proposed by their respective healthcare sectors. Incretin-based therapy has been successful in treating diabetes mellitus, and inhibitors like sitagliptin, vildagliptin, saxagliptin, and alogliptin belong to this category. As of now, drug repurposing strategies have not been used to identify existing therapeutics that can become dipeptidyl peptidase-4 (DPP-4) inhibitors. Hence, this work presents the use of bioinformatics tools like the Activity Atlas model, flexible molecular docking simulations, and three-dimensional reference interaction site model (3D-RISM) calculations to assist in repurposing Food and Drug Administration (FDA)-approved drugs into specific nonsteroidal anti-inflammatory medications such as DPP-4 inhibitors. Initially, the Activity Atlas model was constructed based on the top scoring DPP-4 inhibitors, and then the model was used to understand features of nonsteroidal anti-inflammatory drugs (NSAIDs) as a function of electrostatic, hydrophobic, and active shape features of DPP-4 inhibition. The FlexX algorithm was used to infer protein-ligand interacting residues, and binding energy, to predict potential draggability towards the DPP-4 mechanism of action. 3D-RISM calculations on piroxicam-bound DPP-4 were used to understand the stability of water molecules at the active site. Finally, piroxicam was chosen as the repurposing drug to become a new DPP-4 inhibitor and validated experimentally using fluorescence spectroscopy assay. These findings are novel and provide new insights into the role of piroxicam as a new lead to inhibit DPP-4 and, taking into consideration the biological half-life of piroxicam, it can be proposed as a possible therapeutic strategy for treating diabetes mellitus.

Prediction of coumarin and ethyl vanillin in pure vanilla extracts using MID-FTIR spectroscopy and chemometrics.

Fourier transform mid-infrared (MID-FTIR) spectroscopy coupled with chemometric analysis was used to identify and quantify coumarin (CMR) and ethyl vanillin (EVA) adulterations in pure vanilla extracts. Forty samples adulterated with CMR (0.25-10 ppm) and forty with EVA (0.25-10%) were prepared from pure vanilla extracts and characterized by MID-FTIR spectroscopy to develop chemometric models. Additionally, six commercial vanilla samples were analyzed. A soft independent modeling of class analogy (SIMCA) model was developed to identify and classify the purity from EVA-adulterated or CMR-adulterated samples. Prediction models for CMR or EVA content were developed using the principal component regression (PCR), partial least squares with single y-variables (PLS1), and with multiple y-variables (PLS2) algorithms. Moreover, the predictions of the best quantification chemometric model were compared with the results of a high-performance liquid chromatography-diode array detector (HPLC-DAD) method to evaluate the accuracy of the prediction. The PLS1 algorithm had better performance using 3 and 8 factors for EVA and CMR, respectively. The SIMCA model showed 100% recognition and rejections rates. The results demonstrate that adulteration of pure vanilla with EVA and CMR could be successfully predicted by the developed technique.

Preparation of a porous clay heterostructure and study of its adsorption capacity of phenol and chlorinated phenols from aqueous solutions.

A porous clay heterostructure (PCH) from a Mexican clay was prepared and characterized, and its aqueous phenol and dichlorophenols (DCPs) adsorption capacities were studied using a batch equilibrium technique. The PCH displayed a surface area of 305.5 m2/g, 37.2 A average porous diameter, and a basal space of 23.2 A. The adsorption capacity shown by the PCH for both phenol and DCPs from water (14.5 mg/g for phenol; 48.7 mg/g for 3,4-DCP; and 45.5 mg/g for 2,5-DCP) suggests that the PCH has both hydrophobic and hydrophilic characteristics, as a result of the presence of silanol and siloxane groups formed during the pillaring and calcination of the PCH. The values of maximal adsorption capacity for dichlorophenols were higher than those reported for aluminum pillared clays and some inorgano-organo clays and comparable with some ionic exchange resins.

Designed Functional Dispersion for Insulin Protection from Pepsin Degradation and Skeletal Muscle Cell Proliferation: In Silico and In Vitro Study.

Functionalized single-walled carbon nanotubes with polyethylene glycol (PEGylated SWCNTs) are a promising nanomaterial that recently has emerged as the most attractive "cargo" to deliver chemicals, peptides, DNA and RNAs into cells. Insulin therapy is a recommended therapy to treat diabetes mellitus despite its side effects. Recently, functional dispersion made up of bioactive peptides, bioactive compounds and functionalized carbon nanomaterials such as PEGylated SWCNTs have proved to possess promising applications in nanomedicine. In the present study, molecular modeling simulations are utilized to assist in designing insulin hormone-PEGylated SWCNT composites, also called functional dispersion; to achieve this experimentally, an ultrasonication tool was utilized. Enzymatic degradation assay revealed that the designed functional dispersion protects about 70% of free insulin from pepsin. In addition, sulforhodamine B (SRB) assay, the quantification of insulin and glucose levels in differentiated skeletal muscle cell supernatants, reveals that functional dispersion regulates glucose and insulin levels to promote skeletal muscle cell proliferation. These findings offer new perspectives for designed functional dispersion, as potential pharmaceutical preparations to improve insulin therapy and promote skeletal muscle cell health.

Use of ATR-FTIR spectroscopy coupled with chemometrics for the authentication of avocado oil in ternary mixtures with sunflower and soybean oils.

Avocado oil is a high-value and nutraceutical oil whose authentication is very important since the addition of low-cost oils could lower its beneficial properties. Mid-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulteration of avocado oil with sunflower and soybean oils in a ternary mixture. Thirty-seven laboratory-prepared adulterated samples and 20 pure avocado oil samples were evaluated. The adulterated oil amount ranged from 2% to 50% (w/w) in avocado oil. A soft independent modelling class analogy (SIMCA) model was developed to discriminate between pure and adulterated samples. The model showed recognition and rejection rate of 100% and proper classification in external validation. A partial least square (PLS) algorithm was used to estimate the percentage of adulteration. The PLS model showed values of R(2) > 0.9961, standard errors of calibration (SEC) in the range of 0.3963-0.7881, standard errors of prediction (SEP estimated) between 0.6483 and 0.9707, and good prediction performances in external validation. The results showed that mid-FTIR spectroscopy could be an accurate and reliable technique for qualitative and quantitative analysis of avocado oil in ternary mixtures.