Identification of lentils (Lens culinaris Medik) from Eglouvi (Lefkada, Greece) based on rare earth elements profile combined with chemometrics

An analytical approach has been developed to verify the authenticity of premium lentils originating from Eglouvi, Lefkada, Greece. The method relies on the digestion of samples followed by the analysis of their rare earth elements (REEs) content. Lentils originating from Eglouvi exhibit higher content in most REEs compared to lentils from other regions as well as distinct Sc/Y and Sc/Yb concentration ratios. Principal component analysis effectively segregates "Eglouvi" lentils into a distinct cluster. Soft Independent Modelling of Class Analogy (SIMCA) successfully models "Eglouvi" lentils. Significant enhancement in model specificity was achieved upon inclusion of Sc/Y and Sc/Yb concentration ratios as additional variables. The model is capable of detecting adulteration in blends of Eglouvi lentils, with a minimum rejection threshold of 4.6% w/w for Greek lentil adulterants and 6.0% w/w for imported lentil adulterants.

Analyzing changes in volatile flavor compounds of soy protein isolate during ultrasonic-thermal synergistic treatments using electronic nose and HS-SPME-GC-MS combined with chemometrics

The beany flavor of soy protein isolate (SPI) creates barriers to their application in food processing. This study investigated the effect of ultrasonic-thermal synergistic treatments, combined with vacuum degassing, on the removal of volatile compounds from SPI. The results revealed that ultrasonic-thermal synergistic treatments altered protein secondary structure and increased fluorescence intensity and surface hydrophobicity, which affected the flavor-binding ability of protein, resulting in reduced electronic nose sensor response values. At synergistic treatment (350 W, 120 ℃ and 150 s), the content of hexanal, (E)-2-hexenal, and 1-octen-3-ol reduced by 70.60 %, 95.60 % and 61.23 %. (E)-2-nonenal and 2-pentylfuran were not detected. Chemometric analysis indicated significant flavor differences between control and treated SPI. Furthermore, α-helix, β-sheet, β-turn, and surface hydrophobicity highly correlated with volatile compounds through correlation analysis, indicating that altered protein structure affected interactions with volatile compounds. The study reduced beany flavor and further expanded the range of applications of plant protein in food industry.

Impact of harvesting seasons on physicochemical properties and volatile compound profiles of Malaysian stingless bee honey analysed using chemometrics and support vector machine

Stingless bee honey's nutritional value is gaining attention, but the impact of harvesting seasons, specifically the rainy (September 2018) and dry (February 2019) seasons in Malaysia on the honey's physicochemical properties and volatile compounds remains insufficiently explored. This research revealed marginal differences in the physicochemical properties between seasons. However, through individual bee species and cumulative data analysis, honey samples were effectively differentiated based on harvesting seasons. A set of seventeen volatile compounds were identified as potential chemical markers for distinguishing H. bakeri, G. thoracica, and T. binghami honey between rainy and dry seasons. For cumulative data, four significant markers were proposed. These discrimination methods and chemical markers can serve as valuable references in distinguishing stingless bee honey, whether its entomological origin is specified or not between rainy and dry seasons.

Chemometrics combined with comprehensive two-dimensional gas chromatography-mass spectrometry for the identification of Baijiu vintage

The identification of baijiu vintage is crucial for quality assessment and economic value determination. However, its complex composition and multifaceted influences pose significant technical challenges, necessitating research into its aging mechanisms and the development of related identification methods. This study utilized Chemometrics in conjunction with GC × GC-TOFMS for Baijiu Vintage identification. Data compression achieved a reduction of over 1000-fold without compromising key information, enabling analysis on many samples and get their changing regular in a big matrix by MCR. Subsequently, MCR-ALS facilitated the extraction of physical and chemical meaningful information related to baijiu vintage. Key MCR principal components suitable for qualitative and quantitative assessments were selected using CARS-PLS. The regression model demonstrated errors of less than one year. Furthermore, a PLS-DA model provided 30 MCR principal components as potential markers. The research results provide technical support for baijiu vintage identification and lay the groundwork for studying the changing patterns of flavor compounds in baijiu.

Chemometric profiling and anti-arthritic activity of aerial parts of Glinus oppositifolius (L.) Aug. DC

ETHNOPHARMACOLOGICAL RELEVANCE

Glinus oppositifolius (L.) Aug. DC. belongs to the family Molluginaceae, an annual prostrate herb traditionally used to treat inflammations, arthritis, malarial, wounds, fevers, diarrhoea, cancer, stomach discomfort, jaundice, and intestinal parasites. However, the anti-arthritic activity of the aerial part has still not been reported.

AIM OF THE STUDY

To investigate the antioxidant and anti-arthritic activity of G. oppositifolius in Complete Freund's Adjuvant (CFA) induced rats.

MATERIALS AND METHODS

The dried aerial parts of this plant material were defatted with n-hexane and extracted by methanol using a soxhlet apparatus. The in vitro anti-arthritic activity of methanolic extract of G. oppositifolius (MEGO) was evaluated in protein denaturation, membrane stabilization, and inhibition of proteinase assay at 25, 50, 100, 200, and 400 μg/ml concentrations. Female Wistar rats were immunized sub-dermally into the right hind paw with 0.1 ml of CFA. Rats were administered with MEGO at doses of 200 and 400 mg/kg once daily for fourteen days after arthritis induction. Assessment of arthritis was performed by measuring paw diameter, arthritic index, arthritic score, body weight, organ weight, and hematological and biochemical parameters, followed by the analysis of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), interleukin-1-beta (IL-1β), cyclooxygenase-2 (COX-2), interleukin 13 (IL-13) and interleukin 10 (IL-10) and histopathological study. In vivo antioxidant effect was investigated in enzymatic assays. The presence of phytoconstituents was analyzed by Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS), respectively. In silico molecular docking study of the compounds was carried out against COX-2, IL-1β, IL-6, and TNF-α using AutoDock 4.2 and BIOVIA-Discovery Studio Visualizer software.

RESULTS

MEGO's in vitro anti-arthritic activity showed dose-dependent inhibition of protein denaturation, membrane stabilization, and proteinase inhibition, followed by significant in vivo anti-arthritic activity. The rats treated with MEGO showed tremendous potential in managing arthritis-like symptoms by restoring hematological, biochemical, and histological changes in CFA-induced rats. MEGO (200 and 400 mg/kg) showed a significant alleviation in the levels of hyper expressed inflammatory mediators (TNF-α, IL-1β, and IL-6) and oxidative stress (SOD, CAT, GSH, and LPO) in CFA-induced rats. Spergulagenin-A as identified by LC-MS analysis, exhibited the highest binding affinity against COX-2 (-8.6), IL-1β (7.2 kcal/mol), IL-6 (-7.4 kcal/mol), and TNF-α (-6.5 kcal/mol).

CONCLUSIONS

Provided with the comprehensive investigation, methanolic extract of G. oppositifolius against arthritic-like condition is a proof of concept that revalidates its ethnic claim. The presence of Spergulagenin-A might be responsible for the anti-arthritic activity.

The chemometrics analysis and integrated pharmacology approach to decipher the effect and mechanism between raw and processed cistanche tubulosa

ETHNOPHARMACOLOGICAL RELEVANCE

Cistanche tubulosa (CT) is the dried fleshy stem with scaly leaves of Cistanche tubiflora (Schenk) Wight, which has the effects of tonifying the kidney-yang, benefiting the vital essence and blood, and moisturizing the intestines and laxatives. There are differences in the activity of CT before and after processing, but the mechanism of processing is not clear.

AIM OF THE STUDY

The study aimed to compare the strength of action of CT before and after yellow-wine processing in the treatment of constipation and kidney yang deficiency and to identify the active ingredients responsible for the differences in activity before and after yellow-wine processing.

MATERIALS AND METHODS

This study established the fingerprints of CT and PCT using HPLC to identify their shared components. Then efficacy of KYDS and FC were carried out to compare the differences between CT and PCT in terms of efficacy. Next, this study established the spectrum-effect relationship between the shared chemical components and the medical effects of CT and PCT using the gray correlation analysis and entropy methods. Ultimately, the activity of the analyzed chemical components was verified using the zebrafish model.

RESULTS

CT was more effective than PCT in promoting intestinal peristalsis, regulating gastrointestinal hormone levels, and thus treating FC. PCT was more effective than CT in improving the level of hormone indexes of the hypothalamus-pituitary-target gland axis, replenishing blood, and enhancing immunity. Through the analysis of the spectrum-effect relationship, it was finally found that 5, 6, 12 (tubuloside A), and 13 (isoacteoside) might be more closely related to the activity of tonifying kidney yang, and peaks 9, 10, and 11 (acteoside) are more closely associated with the treatment of constipation, and peaks 3 (salidroside), 4, 1, 2 (geniposidic acid), and 8 (echinacoside) were associated with both kidney yang tonic and treatment of constipation. At the same time, an activity verification experiment showed that echinacoside, geniposidic acid, and salidroside were effective in the treatment of FC and KYDS, while acteoside was very effective in the treatment of FC, and tubuloside A was significant in supplementing the blood, which validated the spectrum-effect relationship analysis.

CONCLUSION

This study proved that the raw CT had a better laxative effect, while the yellow-wine processed CT had a better kidney-yang tonic effect; moreover, spectrum-effect relationships were established to analyze the chemical components leading to changes in the activity of CT before and after yellow-wine processing.

A chemometric-assisted method for automatic, rapid and non-targeted detection of multi-pesticides in plant-derived foods by gas chromatography-mass spectrometry

An automatic, rapid and non-targeted detection method for multi-pesticides in plant-derived foods was developed by gas chromatography-mass spectrometry and chemometrics. In this method, a novel algorithm named moving window iterative target transformation factor analysis was proposed. Although there are challenges of peak overlapping and background interference, the retention time and corrected mass spectra of unknown pesticides can be automatically obtained through iteration calculation in the 'moving window' with reference to the pesticide mass spectral library. One mixed pesticide standard and nine varieties of plant-derived foods were investigated with the proposed method. By contrast, a fast temperature programme was used to shorten detection time compared to the standard temperature programme. For the mixed standard, the mass spectra and retention times of all 39 pesticides were successfully obtained from the overlapping signal. Furthermore, all spiked pesticides were successfully detected in plant-derived foods within 10 min using a fast temperature programme.

Fast identification of the BmNPV infected silkworms by portable NIR spectroscopy and chemometrics

A convenient, low-cost, and rapid detection of BmNPV-infected silkworms is of great significance for the safety of the sericulture industry. In this study, a portable NIR system was used to collect the spectra of normal silkworms and the infected silkworms induced by the administration of Bombyx mori nuclear polyhedrosis virus (BmNPV). Different spectral pretreatment methods were applied, then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were used for the classification analysis. The results showed that PCA and LDA were unable to achieve the purpose. For the PLSDA calibration, after the pretreatment of SNV combining 2nd derivative, it had a high identification performance, and obtained low classification errors of 0.023, 0.033, and 0.030 for the calibration set, cross-validation set, and test set, respectively, with higher sensitivity and specificity. Therefore, the BmNPV-infected silkworms can be identified by portable NIR spectroscopy, which will effectively reduce losses for the sericulture industry.

Application of UV-Vis-NIR and FTIR spectroscopy coupled with chemometrics for quality prediction of katsuobushi based on the number of smoking treatments

Katsuobushi, a smoked, dried skipjack tuna, is a traditional Japanese food additive with a unique flavor and taste. Gas chromatography mass spectrometry (GC-MS), fourier transform infrared (FTIR), and ultraviolet-visible-near infrared spectroscopy (UV-Vis-NIR) combined with chemometric methods were evaluated the quality of katsuobushi according to the number of smoking treatments. Using GC-MS, 46 metabolites were identified and five metabolites were selected as key compounds. All samples were classified according to their smoking number via principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and hierarchical cluster analysis (HCA) of the FTIR and NIR spectra. Partial least squares regression (PLSR) analysis revealed that the FTIR and NIR spectra were highly correlated with the metabolites by GC-MS. These results demonstrated the potential of using the FTIR and NIR spectroscopy combined with chemometrics to assess the quality of katsuobushi based on the smoking treatments, with NIR spectroscopy showed particularly promising.

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

A novel headspace solid-phase microextraction arrow method employing comprehensive two-dimensional gas chromatography-mass spectrometry combined with chemometric tools for the investigation of wine aging

BACKGROUND

Omics is used as an analytical tool to investigate wine authenticity issues. Aging authentication ensures that the wine has undergone the necessary maturation and developed its desired organoleptic characteristics. Considering that aged wines constitute valuable commodities, the development of advanced omics techniques that guarantee aging authenticity and prevent fraud is essential.

RESULTS

Α solid phase microextraction Arrow method combined with comprehensive two-dimensional gas chromatography-mass spectrometry was developed to identify volatiles in red wines and investigate how aging affects their volatile fingerprint. The method was optimized by examining the critical parameters that affect the solid phase microextraction Arrow extraction (stirring rate, extraction time) process. Under optimized conditions, extraction took place within 45 min under stirring at 1000 rpm. In all, 24 monovarietal red wine samples belonging to the Xinomavro variety from Naoussa (Imathia regional unit of Macedonia, Greece) produced during four different vintage years (1998, 2005, 2008 and 2015) were analyzed. Overall, 237 volatile compounds were tentatively identified and were treated with chemometric tools. Four major groups, one for each vintage year were revealed using the Hierarchical Clustering Analysis. The first two Principal Components of Principal Component Analysis explained 86.1% of the total variance, showing appropriate grouping of the wine samples produced in the same crop year. A two-way orthogonal partial least square - discriminant analysis model was developed and successfully classified all the samples to the proper class according to the vintage age, establishing 17 volatile markers as the most important features responsible for the classification, with an explained total variance of 88.5%. The developed prediction model was validated and the analyzed samples were classified with 100% accuracy according to the vintage age, based on their volatile fingerprint.

SIGNIFICANCE

The developed methodology in combination with chemometric techniques allows to trace back and confirm the vintage year, and is proposed as a novel authenticity tool which opens completely new and hitherto unexplored possibilities for wine authenticity testing and confirmation.

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

Integration of untargeted and pseudotargeted metabolomics reveals specific markers for authentication and adulteration detection of Fritillariae Bulbus using tandem mass spectrometry and chemometrics

Authentication and adulteration detection of closely related herbal medicines is a thorny issue in the quality control and market standardization of traditional Chinese medicine. Taking Fritillariae Bulbus (FB) as a case study, we herein proposed a three-step strategy that integrates mass spectrometry-based metabolomics and multivariate statistical analysis to identify specific markers, thereby accurately identifying FBs and determining the adulteration level. First, an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics method was employed to profile steroid alkaloids in five sorts of FB and screen potential differential markers. Then, the reliability of the screened markers was further verified by the distribution in different FB groups acquired from ultra-high performance liquid chromatography triple quadrupole mass spectrometry-based pseudotargeted metabolomics analysis. As a result, a total of 16 compounds were screened out to be the specific markers, which were successfully applied to distinguish five FBs by using discriminant analysis model. Besides, partial least squares regression models based on specific markers allowed accurate prediction of three sets of adulterated FBs. All the models afforded good linearity and good predictive ability with regression coefficient of prediction (R2p) > 0.9 and root mean square error of prediction (RMSEP) < 0.1. The reliable results of discriminant and quantitative analysis revealed that this proposed strategy could be potentially used to identify specific markers, which contributes to rapid chemical discrimination and adulteration detection of herbal medicines with close genetic relationship.

Rapid identification of Radix Astragali origin by using fluorescence probe combined with chemometrics

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

Non-destructive detection of single corn seed vigor based on visible/near-infrared spatially resolved spectroscopy combined with chemometrics

Seed vigor is an essential quality evaluation index for seed selection. However, accurately detecting the vigor of a single corn seed is challenging. In this study, we constructed a single-fiber spatially resolved detection device using visible/near-infrared spectroscopy to investigate the patterns and correlations between spatially resolved spectroscopy (SRS) at 500-1000 nm and seed vigor. The device collected spectral data at a light source-detector distance of 5-6.6 mm on the embryo side (S1) and endosperm side (S2) of the corn seeds. The proposed spectral ratio method based on SRS and spectral combination analysis achieved an improvement in the detection accuracy of different corn seed vigor. Modeling by SG-CARS-PLSDA using the ratio method showed further improvement in the prediction ability. The highest accuracy for both S1 and S2 in the Zhengdan 958 variety was 91.67 %, while those of S1 and S2 for the Shaandan 650 variety were 86.67 % and 88.33 %, respectively. In addition, SRS was found to be more advantageous in S2 acquisition, verifying the potential of SRS in the non-destructive testing of seed vigor. This provides a favorable reference for the comprehensive evaluation of other internal quality indices of seeds.

Infrared spectroscopy combined with chemometrics in transflectance mode: An alternative approach in the photodiagnosis of COVID-19 using saliva

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

Sex and blood group determination from hair using ATR-FTIR spectroscopy and chemometrics

Examination of hair with its intact root is commonly used for DNA profiling of the donor. However, its use for gathering other types of information is less explored. Using attenuated total reflectance-Fourier transform infrared spectroscopy, the present study aims to explore other relevant aspects in a non-destructive manner for forensics. Determining the sex and blood group of human hair samples were the major goals of the study. Sex determination was accomplished by analyzing the differential vibrational intensities and stretching of various chemical groups associated with hair and its proteins. Statistical inference of spectral data was performed using chemometric algorithms such as PCA and PLS-DA. The PLS-DA model determined sex with 100% accuracy and blood grouping with an average accuracy of 95%. The present study is the first of its kind to determine sex and blood grouping from human scalp hair shafts, as far as the author knows. By acting as a preliminary screening test, this study could have significant implications for forensic analysis of crime scene samples. Human and synthetic hair were used in validation studies, resulting in 100% accuracy, specificity, and sensitivity, with 0% false positives and false negatives. The technique ATR FTIR spectroscopy could complement the currently used methods of hair analysis such as physical examination and mitochondrial or genomic DNA analysis.

Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets

Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.

Application of HPLC-UV combined with chemometrics for the detection and quantification of 'true cinnamon' adulteration

Cinnamon is one of the most popular spices used in cuisines worldwide. Among its different species, Ceylon cinnamon ("true cinnamon") is the one with the most health benefits due to its high concentration in the antioxidant eugenol and the ultra-low content of the hepatotoxic compound coumarin. However, the higher price of Ceylon cinnamon makes it vulnerable to fraudulent adulteration with more economic species of cinnamon, such as Cassia and Saigon. Thus, for the detection of frauds in cinnamon samples, a HPLC-UV method was developed for the determination of 4 characteristic cinnamon compounds: eugenol, cinnamaldehyde, coumarin and cinnamic acid. The obtained data were analyzed by PLS to attain not only the authentication of cinnamon species but also the detection and quantification of partial adulterations. Several mixtures prepared in the laboratory using different cinnamon powder samples considered 'pure' Ceylon, Cassia or Saigon were tested, concluding that the proposed approach allows a clear identification of Ceylon cinnamon and a suitable quantification of the Ceylon: non-Ceylon ratio regardless of the commercial sample selected (RMSE <0.06 for both training and test sets).

Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics

Ginger powder is an important spice that is susceptible to improper sales such as adulteration or geographical fraud. In this study, a portable near infrared spectroscopy was used to quantitatively predict the 6-gingerol content, an important quality index of ginger, as well as to identify the gingers from three origins in China. Specifically, the optimal preprocessing method was first investigated by comparing the predictions of models. Then three feature variable selection methods including PCA, CARS, and RFrog, on the quantitative analysis of 6-gingerol were also compared, respectively. After comparison, the PLS model established on the S-G combined with SNV preprocessing outperformed the others. The PLS regression of 6-gingerol with variables selected by RFrog possessed the Rc2 of 0.9463, Rp2 of 0.9497, and the RPD of 4.2257, respectively. Moreover, the results further verified that the LDA model by SPA variables extraction successfully identify gingers from different origins with 100 % accuracy.

Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.

Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics

Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.

Distinguishment of different varieties of rhubarb based on UPLC fingerprints and chemometrics

Rhubarb, a widely used traditional Chinese medicine (TCM), is primarily used for purging in practice. It is derived from the dried roots and rhizomes of R. tanguticum Maxim. ex Balf. (RT), Rheum officinale Baill. (RO) and R. palmatum L. (RP). To date, although the three varieties of rhubarb have been used as the same medicine in clinical, studies have found that they have different chemical compositions and pharmacological effects. To ensure the stability of rhubarb for clinical use, a simple and effective method should be built to compare and discriminate three varieties of rhubarb. Here, ultra-performance liquid chromatography-diode array detection (UPLC-DAD) fingerprints combined with chemometric methods were developed to evaluate and discriminate 29 batches of rhubarb. Similarity evaluation, hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed that the chemical constituents of the three varieties of rhubarb were significantly different, and the three varieties could be effectively distinguished. Finally, all the 14 common peaks were identified by ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS). In this research, the developed UPLC fingerprints offer a simple, reliable and specific approach for distinguishing different varieties of rhubarb. This research aims to promote the scientific and appropriate clinical application of rhubarb from three varieties.

Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools

In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and β-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and β-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and β-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.

Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted sampling - whale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

Prediction of melanin content of Fonsecaea pedrosoi using Fourier transform infrared spectroscopy (FTIR) and chemometrics

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Antioxidant Activities of Different Peanut (Arachis hypogaea L.) Market Types by Spectrophotometric Techniques Combined with Chemometrics

Peanut is rich in oil and protein and has a large content of bioactive constituents consisting of tocopherols, phytosterols, and so on. Generally, Virginia, Spanish, Valencia and Runner market types are grown of peanut. In this study, it is aimed to determine the antioxidant activity, total phenolic content and total flavonoid content of peanuts from four different market types, for the first time, and group them with principal component analysis (PCA) and hierarchical cluster analysis (HCA). For PCA, PC1 and PC2 explained 87.655 % of the total variation and, according to the HCA of peanut samples, two main groups were determined. The total phenolic content changed 1.556 to 2.899 mg GAE/g. The lowest value have seen at Spanish merket type to determine the antioxidant activities of peanut samples were maked FRAP and DPPH assay, the lowest FRAP value (8.136 μmol FeSO47H2O/g sample) was seen at Valencia market type, the highest (14.004 μmol FeSO47H2O/g sample) was seen at Virginia market type. It was determined that the total flavonoid, total phenolic content, and antioxidant activities of the Virginia, Valencia, Spanish, and Runner market types included in the study were different from each other, and the Virginia market type showed superior characteristics compared to the others. The results obtained suggest that Virginia market type may be preferred more especially in peanut cultivation for food uses. It is thought that this study can be a source for future studies by eliminating a deficiency in the literature.

An optimized methodology for the determination of multiclass organic ultraviolet sunscreens and metabolites in human milk through chromatographic and chemometric resolution

Organic UV filters (UVFS) are used to mitigate the dermal effects associated with health risks from UV radiation, making them essential in personal care products. UVFS are frequently identified in environmental samples due to their high lipophilicity and persistence, underscoring the urgency of comprehensive assessments and regulatory measures aimed at safeguarding ecosystems and human health. The present study reports a multiclass analytical method for determining 16 UV sunscreens and metabolites in breast milk based on an ultrasound-assisted-dispersive liquid-liquid micro-extraction (UA-DLLME) with further chromatographic and chemometric resolution. The experimental conditions of the UA-DLLME were optimized through the implementation of the Design of Experiment tools. To model the responses, least-squares and artificial neural network methodologies were implemented. The optimal conditions were found by employing the desirability function. The samples were analyzed through reverse-phase liquid chromatographic separation, UV diode array, and fast-scanning fluorescence detection. The chromatographic analysis enabled the resolution of 16 analytes in a total time of 13.0 min. Multivariate curve resolution-alternating least-square (MCR-ALS) modelling was implemented to resolve analytes that were not fully resolved and to determine analytes that coeluted with endogenous components of the breast milk samples. An enrichment factor of 5-fold concentration was obtained with this methodology, reaching recoveries between 65 % and 105 % for 13 multiclass UV sunscreens and metabolites in breast milk samples with RSD % and REP % lower than 12 %.

via HS-GC-MS, response surface methodology, and chemometrics

INTRODUCTION

Quisqualis indica L. (QIL) has a long history as a traditional Chinese herb in China, but the study of volatile components in QIL from different geographical sources has been relatively rare.

OBJECTIVES

To establish an optimal headspace gas chromatography-mass spectrometry (HS-GC-MS) method to comprehensively analyse the volatile component profile and screen quality markers of QIL from different origins.

METHODS

Response surface methodology (RSM) was used to optimise the conditions for headspace analysis. The volatile components of QIL from four main origins of southwest China were analysed and identified by HS-GC-MS. The similarity of all samples of QIL was evaluated by fingerprint. The differences of the volatile components in QIL from different origins were distinguished by chemometrics.

RESULTS

According to the optimal conditions of RSM, a total of 31 volatile components were identified, including fatty acids, aldehydes, alcohols, alkyl pyrazines, and other volatile components. Similarity evaluation presented that there were 26 common volatile components with different contents in all samples. Principal component analysis (PCA) showed that QIL from four different origins could be roughly divided into four categories. Hierarchical cluster analysis (HCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) indicated that QIL from different origins had obvious regional characteristics.

CONCLUSION

The optimised HS-GC-MS method provided a strategy to rapidly, effectively, and accurately elucidate the volatile component profile of QIL from different origins, and seven important differential components were screened for quality evaluation and origin traceability.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Determination of the geographical origin of chicken (breast and drumstick) using ICP-OES and ICP-MS: Chemometric analysis

This study aimed to develop a geographical origin discrimination analytical method for chicken breasts and drumsticks based on inductively coupled plasma (ICP). The sixty elements were set as variables, and the geographical origin discrimination analysis was conducted through chemometrics. In orthogonal partial least square discriminant analysis (OPLS-DA), twenty-three variable importance in projection (VIP) elements were selected in chicken breasts, and twenty-eight VIP elements were selected in drumsticks. The importance of the selected elements was displayed by the area under the curve (AUC) value of the receiver operating characteristic (ROC). Verification of OPLS-DA was performed through permutation test and good results were obtained. A heatmap was also used as a method for determining the geographical origin, and each top element discriminant classification was 100 % accurate, as determined through canonical discriminant analysis (CDA). This method shows potential as a food analysis tool and can accurately determine the geographic origin of chicken.

Differentiation of protein types extracted from tilapia byproducts by FTIR spectroscopy combined with chemometric analysis and their antioxidant protein hydrolysates

This research aimed to characterize protein types including sarcoplasmic protein (SP), myofibrillar protein (MP), and alkali-aided protein extract (AP) prepared from tilapia byproducts using water, 0.6 M NaCl, and alkaline solution (pH 11), respectively compared to freeze-dried minced tilapia muscle (CONTROL). Principal component analysis was performed from second derivative FTIR spectra to differentiate protein type. The AP mostly contained β-sheet structure and had low total sulfhydryl content and surface hydrophobicity. SP can be distinguished from MP by the loading plots of the FTIR bands representing the α-helical structure. While the bands for lipids and β-sheet of protein were noted for differentiating AP from CONTROL. After being hydrolyzed by Protease G6, the AP hydrolysate disclosed the highest ABTS radical scavenging activity, while the SP hydrolysate revealed the strongest metal chelating ability. Thus, an understanding of how fish processing waste can be utilized in the production of antioxidant protein hydrolysates has been achieved.

Chemometric Analysis Evidencing the Variability in the Composition of Essential Oils in 10 Salvia Species from Different Taxonomic Sections or Phylogenetic Clades

Essential oil (EO) of Salvia spp. has been widely used for culinary purposes and in perfumery and cosmetics, as well as having beneficial effects on human health. The present study aimed to investigate the quantitative and qualitative variations in EOs in wild-growing and cultivated pairs of samples from members in four Salvia sections or three clades, namely S. argentea L. (Sect. Aethiopis; Clade I-C), S. ringens Sm. (Sect. Eusphace; Clade I-D), S. verticillata L. (Sect. Hemisphace; Clade I-B), S. amplexicaulis Lam., and S. pratensis L. (Sect. Plethiosphace; Clade I-C). Furthermore, the natural variability in EO composition due to different genotypes adapted in different geographical and environmental conditions was examined by employing members of three Salvia sections or two phylogenetic clades, namely S. sclarea L. (six samples; Sect. Aethiopis or Clade I-C), S. ringens (three samples; Sect. Eusphace or Clade I-D), and S. amplexicaulis (five samples; Sect. Plethiosphace or Clade I-C). We also investigated the EO composition of four wild-growing species of two Salvia sections, i.e., S. aethiopis L., S. candidissima Vahl, and S. teddii of Sect. Aethiopis, as well as the cultivated material of S. virgata Jacq. (Sect. Plethiosphace), all belonging to Clade I-C. The EO composition of the Greek endemic S. teddii is presented herein only for the first time. Taken together, the findings of previous studies are summarized and critically discussed with the obtained results. Chemometric analysis (PCA, HCA, and clustered heat map) was used to identify the sample relationships based on their chemical classes, resulting in the classification of two distinct groups. These can be further explored in assistance of classical or modern taxonomic Salvia studies.

Chemometry and Green Chemistry-Based Chromatographic Analysis of Azilsartan Medoxomil, Cilnidipine and Chlorthalidone in Human Plasma Using Analytical Quality by Design Approach

According to the green chemistry approach, the usage of carcinogenic and teratogenic organic solvents should be minimized in the development of the analytical method for the safety of the environment and analysts. According to the literature review, no high-performance thin-layer chromatographic (HPTLC) method has been reported yet for concomitant analysis of azilsartan medoxomil (AZM), chlorthalidone (CTD) and cilnidipine (CDP) in human plasma. Hence, a robust and accurate HPTLC method has been developed using safe and non-toxic organic solvents for the concomitant analysis of AZM, CTD and CDP in human plasma, fixed-dose combinations (FDCs) and laboratory mixtures. The HPTLC method was developed by the implementation of the analytical quality by design approach using principles of quality risk management and design of experiments (DoE) for regulatory compliance. The principal component analysis was applied for the risk assessment and analysis of potential method variables in the method development. The principle of DoE was used for the response surface modeling to link identified critical method risk parameters with critical method performance attributes using full factorial design (FFD). The method operable design region and analytical control space were navigated for the optimization of the method as per quality target analytical profile. The developed method was also applied for concomitant analysis of AZM, CTD and CDP in their FDCs and laboratory mixture and results were found in good agreement with the labeled amount of the respective drug.

and its congener Abutilon indicum: Targeted metabolomics, greenness assessment, and chemometrics analysis

Sida is one of the most diverse genera, with about 200 species distributed in tropical and subtropical regions of the world. Among 18 species distributed in India, Sida acuta, Sida cordifolia, Sida rhombifolia, and Sida cordata are used in traditional medicines along with its possible adulterant Abutilon indicum for several therapeutic uses. The non-availability of marker-based validated methods for the identification and classification of these species leads to adulteration. Indoloquinoline and quinazoline are the major bioactive alkaloids distributed in Sida spp. First time, a simple, economical and high throughput method was developed and validated for the simultaneous determination of 20-hydroxyecdysone (1), vasicine (2), vasicinone (3), cryptolepine (4), quindolinone (5), and cryptolepinone (6) using HPTLC-UV densitometry. The method was validated to meet globally accepted ICH guidelines. The method was sensitive with LOD and LOQ ranging from 0.38-0.63 and 1.57-2.12 µg/band. The samples were spiked at 3 different concentrations, the recovery values were 93.49-98.88%. In addition, the greenness index of the HPTLC method was estimated using four different greenness assessment techniques. Targeted HPTLC analysis indicated the distribution of specialized metabolites in Sida spp. and A. indicum. However, the occurrence of cryptolepine in A. indicum was not reported in the literature, so this was further confirmed by liquid chromatographic studies of the samples from different locations. The chromatographic data was statistically evaluated by principal component analysis (PCA) and hierarchical clustering (HCA). HPTLC-based targeted metabolite quantitation explains the adulteration/substitution in Sida raw material and derived herbal preparations.

Honey fraud detection based on sugar syrup adulterations by HPLC-UV fingerprinting and chemometrics

In recent years, honey-producing sector has faced the increasing presence of adulterated honeys, implying great economic losses and questioning the quality of this highly appreciated product by the society. Due to the high sugar content of honey, sugar syrups are among its most common adulterants, being also the most difficult to detect even with isotope ratio techniques depending on the origin of the sugar syrup plant source. In this work, a honey authentication method based on HPLC-UV fingerprinting was developed, exhibiting a 100% classification rate of honey samples against a great variety of sugar syrups (agave, corn, fiber, maple, rice, sugar cane and glucose) by partial least squares-discriminant analysis (PLS-DA). In addition, the detection and level quantitation of adulteration using syrups as adulterants (down to 15%) was accomplished by partial least squares (PLS) regression with low prediction errors by both internal and external validation (values below 12.8% and 19.7%, respectively).

Discovery and identification of interspecies peptide biomarkers in the seahorse species using liquid chromatography tandem mass spectrometry and chemometrics

Seahorses have important edible and medicinal values including strengthening the body, tonifying the liver and kidneys, and reducing swelling. And there are abundant seahorse species on Earth. Many seahorses have large price differences due to the scarcity of resources, and some seahorses with similar appearances appear to be confused for use. While in market trading, Hippocampus is susceptible to loss of specialized morphology characteristics, making it difficult to distinguish between specific species. Here we report an effective method based on peptide biomarkers for the identification of seahorse species. Peptide biomarkers for each species were predicted using nano-liquid chromatography-tandem mass spectrometry (Nano-LC-MS/MS) combined with chemometrics software. One unique biomarker peptide for each species was synthesized and verified, and finally developed a liquid chromatography tandem mass spectrometry (LC-MS/MS) multiple reaction monitoring method. The results indicate that the method has great potential for species-specific identification of seahorses and their preparations, among others.

Fingerprinting based on spectral reflectance and chemometrics - An analytical approach aimed at combating the illegal trade of stingray meat in the Amazon

The survival of Amazon stingrays is threatened due to excessive fishing and habitat degradation. To address this issue, this study developed a groundbreaking method to authenticate and differentiate Amazon stingray meats using a portable spectrophotometer and chemometrics. Samples were collected from various species, including an endangered one with a commercialization ban and no population reduction records. Principal Component Analysis (PCA), identified natural groupings based on the meat's commercial origin, while Partial Least Squares-Discriminant Analysis (PLS-DA), accurately discriminated the commercial and geographic origins with 100 % accuracy. Moreover, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA), effectively distinguished Amazon stingray meat from other marketable species. This approach offers a rapid, precise, and non-destructive means for monitoring and controlling the illegal trade of these species, thereby supporting decision-making in the field and promoting the conservation and sustainability of freshwater stingrays in the Amazon region.

Quality markers of Guchang Zhixie pills based on multicomponent qualitative and quantitative analysis combined with network pharmacology and chemometric analysis

Traditional Chinese medicine Guchang Zhixie pills(GCZX) is one of the famous varieties of "Qin medicine" that has been extensively applied to treating irritable bowel syndrome(IBS). However, despite the acknowledged clinical advantages of GCZX there are significant constraints on its quality control and evaluation. The present study utilized UHPLC-Q-Exactive-Orbitrap-MS to analyze the chemical composition of GCZX. Additionally, network pharmacology approaches were utilized to explore the underlying mechanism by which blood components exert therapeutic effects in the treatment of IBS. Furthermore, the GCZX samples were evaluated for their quality on the basis of the qualitative results obtained from 25 batches of GCZX samples using fingerprinting; subsequently, multivariate statistical analysis methods were employed for further analysis. The results indicated the presence of 198 individual components. Among them, 17 prototype compounds were detected in the serum of rats that were administered with GCZX. The potential therapeutic mechanism of GCZX in the treatment of IBS may be associated with the modulation of the neurological system, the immunological system, and the inflammatory response. Moreover, a total of seven prominent peaks were identified after fingerprint analysis. The range of fingerprint similarity among the 25 batches of samples varied from 0.843 to 1.000. The application of chemometrics analysis successfully facilitated the categorical classification of 25 batches of GCZX into three distinct groups. Seven components hold significant importance and should be duly considered during the quality control process of GCZX. The present study can establish the Q-Markers of GCZX for IBS, thereby providing a foundation for investigating the theoretical underpinnings and elucidating the mechanisms underlying the therapeutic effects of GCZX in the treatment of IBS.

Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics

In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.

Revealing the adulteration of sesame oil products by portable Raman spectrometer and 1D CNN vector regression: A comparative study with chemometrics and colorimetry

Identification and quantification of sesame oil products are crucial due to the existing problems of adulteration with lower-priced oils and false labeling of sesame proportions. In this study, 1D CNN models were established to achieve discrimination of oil types and multiple quantification of adulteration using portable Raman spectrometer. An improved data augmentation method involving discarding transformations that alter peak positions was proposed, and synchronously injecting noise during geometric transformations. Furthermore, a novel neural network structure was introduced incorporating vector regression to accurately predict each component simultaneously. The proposed method has achieved higher accuracy in detecting multi-component adulteration compared with chemometrics (100 % accuracy in classifying different oils; R2 over 0.99 and RMSE within 2 % in predicting unknown adulterated samples). Finally, commercially available sesame oil products were tested and compared with gas chromatography and colorimetric methods, demonstrating the effectiveness of our proposed model in achieving higher detection accuracy at low-concentration adulteration.

Chemometric Optimization of SPE for the Determination of Multiclass Pesticides in Portable Water Samples by UHPLC-MS/MS

This study aimed on the development of a SPE-UHPLC-MS/MS method for the simultaneous determination of pesticide residues in drinking water samples. A chemometric approach was applied to optimize the efficiency of the SPE pretreatment procedure. This study involved (i) the application of a Full Factorial Design for the screening of the significant factors, (ii) the application of a Central Composite Design for the determination of the optimal conditions and (iii) the evaluation and validation of the significance of the statistically proposed models. Oasis HLB cartridges were used for the extraction. The optimum sample volume was 300 mL and the elution solvent 3 mL of the mixture of methanol:ethylacetate 70:30 v/v. The method was validated according to the international guidelines. Recoveries were ranged from 63 to 116% and the detection limits were between 0.1 and 1.5 pg mL- 1. The validated method could be used in routine analysis for pesticides screening.

Comparison of autoclaving and γ-radiation impact on four spices aroma profiles and microbial load using HS-SPME GC-MS and chemometric tools

Herbal spices are widely consumed as food additives owing to their distinct aroma and taste as well as a myriad of economic and health value. The aroma profile of four major spices including bay leaf, black pepper, capsicum, and fennel was tested using HS-SPME/GC-MS and in response to the most widely used spices´ processing methods including autoclaving and γ-radiation at low and high doses. Additionally, the impact of processing on microbial contamination of spices was tested using total aerobic count. GC-MS analysis led to the identification of 22 volatiles in bay leaf, 34 in black pepper, 23 in capsicum, and 24 in fennel. All the identified volatiles belonged to oxides/phenols/ethers, esters, ketones, alcohols, sesquiterpene and monoterpene hydrocarbons. Oxides/phenol/ethers were detected at high levels in all tested spices at ca. 44, 28.2, 48.8, 61.1%, in bay leaves, black pepper, capsicum, and fennel, respectively of the total blend and signifying their typical use as spices. Total oxides/phenol/ethers showed an increase in bay leaf upon exposure to γ-radiation from 44 to 47.5%, while monoterpene hydrocarbons were enriched in black pepper upon autoclaving from 11.4 in control to reach 65.9 and 82.6% for high dose and low dose of autoclaving, respectively. Cineole was detected in bay leaf at 17.9% and upon exposure to autoclaving at high dose and γ-radiation (both doses) its level increased by 29-31%. Both autoclaving and γ-radiation distinctly affected aroma profiles in examined spices. Further, volatile variations in response to processing were assessed using multivariate data analysis (MVA) revealing distinct separation between autoclaved and γ-radiated samples compared to control. Both autoclaving at 115 °C for 15 min and radiation at 10 kGy eliminated detected bioburden in all tested spices i.e., reduced the microbial counts below the detection limit (< 10 cfu/g).

Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models' accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models' predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.

A Box-Behnken design-based chemometric approach to optimize the sono-photodegradation of hydroxychloroquine in water media using the Fe(0)/S2O82-/UV system

The huge utilization of hydroxychloroquine in autoimmune infections led to an abnormal increment in its concentration in wastewater, which can pose a real risk to the environment, necessitating the development of a pretreatment technique. To do this, we are interested in researching how hydroxychloroquine degrades in contaminated water. The main goal of this investigation is to optimize the operating conditions for the sono-photodegradation of hydroxychloroquine in water using an ultrasound-assisted Fe(0)/S 2 O 8 2 - /UV system. To get adequate removal of HCQ, a chemometric method based on the Box-Behnken design was applied to optimize the influence of the empirical parameters selected, including Fe(0) dose,S 2 O 8 2 - concentration, pH, and initial HCQ concentration. The quadratic regression model representing the HCQ removal rate (η(%)) was evolved and validated by ANOVA. The optimal conditions as a result of the above-mentioned trade-off between the four input variables, with η(%) as the dependent output variable, were captured using RSM methodology and the composite desirability function approach. For HCQ full decomposition, the optimal values of the operating factors are as follows:S 2 O 8 2 - dose, 194.309 mg/L; Fe(0) quantity, 198.83 mg/L; pH = 2.017, and HCQ initial dose of 296.406 mg/L. Under these conditions, the HCQ removal rate, achieved after 60 min of reaction, attained 98.95%.

UPLC-quadrupole time-of-flight-tandem mass spectrometry combined with chemometrics and network pharmacology to differentiate Coreopsis tinctoria Nutt

Coreopsis tinctoria Nutt. (C. tinctoria) is a traditional medicinal plant, primarily found in plateau areas with altitudes exceeding 3000 m. The efficacy of C. tinctoria appears to be intricately tied to its quality. However, there is a scarcity of studies focused on evaluating the quality of C. tinctoria from diverse geographical locations. In this study, we used ultra-performance liquid chromatography-quadrupole time-of-flight-tandem mass spectrometry to analyze and identify the prevalent chemical components in 12 batches of C. tinctoria sourced from Xinjiang, Qinghai, Tibet, and Yunnan provinces in China. By using cluster analysis and discriminant analysis of partial least squares, we assessed the similarity and identified varying components in the 12 batches of C. tinctoria. Subsequently, their quality was further evaluated. Utilizing network pharmacology, we identified potential active components for the treatment of diabetes mellitus. The findings revealed the presence of 16 flavonoids, 3 phenylpropanes, 2 sugars, 2 amino acids, and 7 hydrocarbons in the analyzed samples. Through variable importance screening, 17 constituents were identified as quality difference markers. Marein and flavanomarein emerged as pivotal markers, crucial for distinguishing variations in C. tinctoria. In addition, network pharmacology predicted 187 targets for 9 common active components, including marein and flavanomarein. Simultaneously, 1747 targets related to diabetes mellitus were identified. The drug-component-disease target network comprised 91 nodes and 179 edges, encompassing 1 drug node, 9 component nodes, and 81 target nodes. In summary, marein and flavanomarein stand out as key biomarkers for assessing the quality of C. tinctoria, offering a scientific foundation for the quality evaluation of C. tinctoria Nutt.

Chemometrics-based Chemical Analysis of Myrrh and Its Vinegar-processed Products by UPLC-MS/MS

Myrrh is widely used in clinical practice but accompanied by obvious toxicity. According to traditional Chinese medicines theory, processing with vinegar can effectively reduce its toxicity. However, the detoxification processing technology of Myrrh and the corresponding mechanism have been unclear. The objective of this study is to systematically analyze the variation in chemical composition of raw Myrrh and its processed products using UPLC-Q-TOF-MS/MS coupled with chemometrics. A total of 75 compounds including 56 sesquiterpenoids, 2 diterpenoids, 15 triterpenoids and 2 other types were identified. Raw Myrrh and its processed products were divided into two major groups, and 14 chemical markers were selected out by principal component analysis and partial least square discriminant analysis. Additionally, the exact content of 5 representative chemical markers was determined to be significantly reduced after vinegar-processing by UPLC-QQQ-MS/MS. Moreover, multivariate statistical analysis and the quantitative results comprehensively indicated that the optimized processing method was processing at a ratio of 200 : 5 (Myrrh:vinegar). This research provides not only a reliable foundation for the study of Myrrh, but also a scientific reference for clinical use of this herb.

Polar licit and illicit ingredients in dietary supplements: chemometric optimization of extraction and HILIC-MS/MS analysis

Many dietary supplements claim the ability to enhance sports performance and to improve the fitness of the consumers. Occasionally, along with legal ingredients, illicit compounds may be added without being labelled, leading to unintended doping. Hence, the aim of this study was to develop an analytical method to determine a set of 12 polar (logDpH=7 from -2.0 to +0.3) compounds including diuretics, stimulants, β2-agonists, methylxanthines, and sweeteners. Hydrophilic interaction liquid chromatography was chosen as separation strategy, coupled with tandem mass spectrometry. The instrumental method was optimized using a two-step design of experiments (DoE). Firstly, a Plackett-Burman (PB) DoE was performed to identify the more influencing variables affecting peak areas and chromatographic resolution among temperature, water percentage in the mobile phase, and flow rate, as well as type and concentration of buffers. Secondly, a D-optimal DoE was set, considering only the most significant variables from the PB-DoE results, achieving a deeper understanding of the retention mechanism. Sample processing by salt-assisted liquid-liquid extraction was studied through DoE as well, and the whole method showed recoveries in the range 40-107% and procedural precision ≤11% for all analytes. Finally, it was applied to real samples, in which the four methylxanthines and two artificial sweeteners were detected and quantified in the range of 0.02-192 mg g-1. These values were compared to the quantities declared on the DS labels, when possible. Furthermore, a sequence of MS/MS scans allowed detection of a signal in one of the samples, structurally similar to the β2-agonist clenbuterol.

Synthesis and characterization of TiO2-based supported materials for industrial application and recovery in a pilot photocatalytic plant using chemometric approach

In this contribution, the performance of powdered titanium dioxide (TiO2)-based photocatalysts was evaluated in a pilot photocatalytic plant for the degradation of different dyes, with an investigated volume of 1 L and solar simulated light as irradiation source. Five different samples, synthesized in our laboratories, were tested in the pilot plant, each consisting of TiO2 nanoparticles (NPs) coupled with a different material (persistent luminescent material and semiconductor material) and treated in different thermal conditions. All synthesized samples have been subjected to X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller analysis (BET), and transmission electron microscopy (TEM) characterization, to shed light on the influence of introducing other materials on titania characteristics. To study and evaluate the significance of the parameters affecting the process in the pilot plant, a chemometric approach was applied, by selecting a mathematical model (D-Optimal) to simultaneously monitor a large number of variables (i.e., 7), both qualitative and quantitative, over a wide range of levels. At the same time, the recovery of the synthesized photocatalysts was studied following a novel promising recuperation method, i.e., annulling the surface charge of the suspended samples by reaching the isoelectric point (pHPZC) of each sample, for the quantitative precipitation of TiO2 nanoparticles.

Comprehensive metabolome characterization and comparison between two sources of Dragon's blood by integrating liquid chromatography/mass spectrometry and chemometrics

Dragon's Blood (DB) serves as a precious Chinese medicine facilitating blood circulation and stasis dispersion. Daemonorops draco (D. draco; Qi-Lin-Jie) and Dracaena cochinchinensis (D. cochinchinenesis; Long-Xue-Jie) are two reputable plant sources for preparing DB. This work was designed to comprehensively characterize and compare the metabolome differences between D. draco and D. cochinchinenesis, by integrating liquid chromatography/mass spectrometry and untargeted metabolomics analysis. Offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), by utilizing a powerful hybrid scan approach, was elaborated for multicomponent characterization. Configuration of an XBridge Amide column and an HSS T3 column in offline mode exhibited high orthogonality (A0 0.80) in separating the complex components in DB. Particularly, the hybrid high-definition MSE-high definition data-dependent acquisition (HDMSE-HDDDA) in both positive and negative ion modes was applied for data acquisition. Streamlined intelligent data processing facilitated by the UNIFI™ (Waters) bioinformatics platform and searching against an in-house chemical library (recording 223 known compounds) enabled efficient structural elucidation. We could characterize 285 components, including 143 from D. draco and 174 from D. cochinchinensis. Holistic comparison of the metabolomes among 21 batches of DB samples by the untargeted metabolomics workflows unveiled 43 significantly differential components. Separately, four and three components were considered as the marker compounds for identifying D. draco and D. cochinchinenesis, respectively. Conclusively, the chemical composition and metabolomic differences of two DB resources were investigated by a dimension-enhanced analytical approach, with the results being beneficial to quality control and the differentiated clinical application of DB.