Chemometrics of the composition and antioxidant capacity of essential oils obtained from six Cupressaceae taxa.

Essential oils (EOs) are complex and susceptible to environmental conditions, they have a wide range of biological activities and are often used to differentiate between similar species. In this study, gas chromatography-mass spectrometry (GC-MS) coupled with chemometric analysis was applied to systematically analyse and evaluate EOs constituents and antioxidant activity of six Chinese Cupressaceae taxa (Platycladus orientalis Franco, P. orientalis Franco 'Sieboldii', P. orientalis Franco 'Aurea', Juniperus chinensis Roxb., J. chinensis Roxb. 'Kaizuca', and J. sabina L.) under identical conditions. The antioxidant activity of the EOs was evaluated using 2,2 -diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), and ferric reducing power (FRAP), and the total phenolic content (TPC) of the EOs was determined by Folin-Ciocalteau reagent. In total, seventy individual constituents were identified with the main components being α-pinene, sabinene, D-limonene, bornyl acetate, δ-3-carene and ß-myrcene. Principal component analysis (PCA) and hierarchal cluster analysis (HCA) successfully discriminated the six taxa into three chemotypes and the unique chemotype revealed that J. chinensis 'Kaizuca' may be a species rather than a cultivar of J. chinensis. The results of OPLS-DA analysis showed that the three compounds screened, namely, α-pinene, sabinene, and δ-3-carene, can completely distinguish Platycladus spp. from Juniperus spp. The DPPH assay results ranged from 576.14 (J. chinensis 'Kaizuca') to 1146.12 (J. sabina) µmol eq Trolox/mL EO, while the ABTS values ranged from 1579.62 (P. orientalis 'Aurea') to 5071.82 (J. sabina) µmol eq Trolox/mL. In the FRAP assay, the values ranged from 1086.50 (J. chinensis 'Kaizuca') to 1191.18 (J. sabina) µmol eq Trolox/ml and the TPC of the EOs studied ranged from 15.17 (J. chinensis 'Kaizuca') to 39.37 (J. sabina) mg GAE/mL EO. The results consistently showed that J. sabina possessed the strongest antioxidant activity and can be preferentially used as a rich source of potentially natural antioxidants.

Chemometrics-based analysis of the phytochemical profile and antioxidant activity of Salvia species from Iran.

In recent years, the exploration of the therapeutic potential of Salvia has gained considerable attention, leading to a growing number of scientific studies emphasizing its pharmacological properties. Despite this, therapeutic applications of Salvia remain underexploited, requiring further investigation. Iran is a major center for sage diversity in Asia, boasting 60 Salvia species, 17 of which are unique to the area. This study aimed to comprehensively explore and compare the extracts of 102 Salvia samples belonging to 20 distinct Salvia species from Iran, providing a deeper understanding of their specific polyphenol content and, consequently, their antioxidant capabilities and potential therapeutic uses. All samples were analyzed to determine the contents of total phenolics, total flavonoids, total tannin, photosynthetic pigments, and ascorbic acid, along with their antioxidant activity. These data were then combined with the forty distinct chemical fingerprints identified by ultrafast high-pressure liquid chromatography coupled with high-resolution mass spectrometry. Multivariate data analysis was employed to find correlations and differences among the huge number of data obtained and to identify Salvia species with similar phytochemical and/or antioxidant properties. The results show that each Salvia species is characterized by a distinct class of polyphenols recognized for their antidiabetic, anti-inflammatory, cardioprotective and neuroprotective properties. Overall, our findings reveal the potential of some Salvia species for targeted therapeutic applications and provide a rational basis for the development of Salvia-derived nutraceuticals, ultimately improving the prospects for the use of Salvia in medicine.

Characterization of volatile profile from different coriander (Coriandrum sativum L.) varieties via HS-SPME/GC-MS combined with E-nose analyzed by chemometrics.

Headspace-solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) and electronic nose (E-nose) technologies were implemented to characterize the volatile profile of aerial part from 40 coriander varieties. A total of 207 volatile compounds were identified and quantified, including aldehydes, alcohols, terpenes, hydrocarbons, esters, ketones, acids, furans, phenols and others. E-nose results showed that W5S and W2W were representative sensors responding to coriander odor. Among all varieties, the number (21-30 species) and content (449.94-1050.55 µg/g) of aldehydes were the highest, and the most abundant analytes were (Z)-9-hexadecenal or (E)-2-tetratecenal, which accounted for approximately one-third of the total content. In addition, 37 components were determined the characteristic constituents with odor activity values (OAVs) ≥ 1, mainly presenting citrusy, fatty, soapy and floral smells. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) could effectively distinguish different varieties. This study provided a crucial theoretical basis for flavor evaluation and quality improvement of coriander germplasm resources.

Rapid and ultrasensitive detection of thiram and carbaryl pesticide residues in fruit juices using SERS coupled with the chemometrics technique.

A gold nanogap substrate was used to measure the thiram and carbaryl residues in various fruit juices using surface-enhanced Raman scattering (SERS). The gold nanogap substrates can detect carbaryl and thiram with limits of detection of 0.13 ppb (0.13 µgkg-1) and 0.22 ppb (0.22 µgkg-1). Raw SERS data were first preprocessed to reduce noise and undesirable effects and, were later used for model creation, implementing classification, and regression analysis techniques. The partial least-squares regression models achieved the highest prediction correlation coefficient (R2) of 0.99 and the lowest root mean square of prediction value below 0.62 ppb for both pesticide-infected juice samples. Furthermore, to differentiate between juice samples contaminated by both pesticides and control (pesticide-free), logistic-regression classification models were produced and achieved the highest classification accuracies of 100% and 99% for contaminated juice containing thiram and 100% accurate results for contaminated juice containing carbaryl. This indicates that the gold nanogap surface has significant potential for achieving high sensitivity in detecting trace contaminants in food samples.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Characterization of the flavor profile of UHT milk during shelf-life via volatile metabolomics fingerprinting combined with chemometrics.

Ultra-high temperature (UHT) milk is popular among consumers. However, its flavor and texture change in its shelf life. Flavor is highly determinative for the success of dairy products and for consumers' willingness to buy. It is important to milk producers to ensure the optimal flavor of their products in the shelf life. In order to be able to control and predict the flavor quality of UHT milk during the shelf life, this study compared the variations in sensory quality, volatile aroma release and backbone flavor factors and developed a discriminant model to assess flavor quality based on flavouromics data of five competing milk sample during storage. Using partial least squares discriminant analysis (PLS-DA) with Electronic-nose (E-nose) data excellent classification sensitivity and specificity were achieved compared to models based on gas chromatography-mass spectrometry (GC-MS) data. The PLS-DA model using E-nose data exhibited a 100% correct classification rate for the storage period, and a 92% correct rate based on the eight variable importance in the projection (VIP) elements screened for volatile components from different groups. The discriminative model developed herein based on E-nose combined with chemometrics demonstrated advantages such as speed, efficiency, and environmental friendliness. This method shows promise as a precise tool for analyzing aroma changes in UHT milk during its shelf life, and provide support for controlling the flavor substances and milk product development.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging.

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics.

BACKGROUND: As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile. METHODS: XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored. RESULTS: The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid. CONCLUSIONS: The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Optimal harvest period and quality control markers of cultivated Flos Chrysanthemi Indici using untargeted/targeted metabolomics, chemometric analysis and in vivo study.

ETHNOPHARMACOLOGICAL RELEVANCE: Flos Chrysanthemi Indici (FCI), the flower of Chrysanthemum Indicum L., is a popular traditional Chinese medicine (TCM) for treatment of inflammatory diseases in China. FCI is also a functional food, and is widely used as herbal tea for clearing heat and detoxicating. AIM OF THE STUDY: To explore quality control markers of FCI based on the optimal harvest period. MATERIALS AND METHODS: First, UPLC-Q-TOF/MS based untargeted metabolomics was applied to explore the chemical profiles of FCIs collected at bud stages (BS), initial stages (IS), full bloom stages (FS) and eventual stages (ES) from eight cultivated regions in China. Subsequently, lipopolysaccharide (LPS)-induced RAW264.7 cell inflammatory model and carrageenan-induced rat paw edema model were used to confirm the anti-inflammatory effect of FCIs collected at IS/FS. Then, UPLC-PDA targeted metabolomics was used to quantitatively analyze 9 constituents with anti-inflammatory activity (7 flavonoids and 2 phenolic acids) changed significantly (VIP > 4) during flowering stages. Finally, ROC curves combined with PCA analysis based on the variation of 9 active constituents in FCIs from different flowering stages were applied to screen the quality markers of FCI. RESULTS: FCIs at IS/FS had almost same chemical characteristics, but quite different from those at BS and ES. A total of 32 constituents in FCIs including flavonoids and phenolic acids were changed during flowering development. Most of the varied constituents had the highest or higher contents at IS/FS compared with those at ES, indicating that the optimal harvest period of FCI should be at IS/FS. FCI extract could effectively suppress nitric oxide (NO) production in LPS-induced RAW264.7 cells and regulate the abnormal levels of cytokines and PGE2 in carrageenan-induced paw edema model rat. The results of quantitatively analysis revealed that the variation trends of phenolic acids and flavonoids in FCIs were different during flowering development, but most of them had higher contents at IS/FS than those at ES in all FCIs collected from eight cultivated regions, except one sample from Anhui. Finally, linarin, luteolin, apigenin and 3,5-dicaffeoylquinic acid were selected as the Q-markers based on the contribution of their AUC values in ROC and clustering of PCA analysis. CONCLUSIONS: Our study demonstrates the optimal harvest period of FCI and specifies the multi-constituents Q-markers of FCI based on the influence of growth progression on the active constituents using untargeted/targeted metabolomics. The findings not only greatly increase the utilization rate of FCI resources and improve quality control of FCI products, but also offer new strategy to identify the Q-markers of FCI.

Exploring accurate mass measurements in pixel-based chemometrics: Advancing coffee classification with GC-HRMS-A proof of concept study.

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.

Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils.

Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, two training datasets were prepared from literature on antibacterial essential oils to classify active/inactive oils. Random forest classifiers constructed from the data showed improved prediction performance in both test datasets. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.

1H NMR profiling and chemometric analysis for ripening and production characterization of Grana Padano cheese.

Grana Padano (GP) cheese is a renowned PDO Italian cheese whose nutritional characteristics and market price are influenced by the ripening stage. In this work, it was demonstrated that the combined use of untargeted 1H NMR profiling and chemometric analysis can be used as a powerful tool to quantitatively characterize GP ripening and production, focusing on both aqueous and lipid fractions. An initial exploratory analysis revealed substantial variations in the aqueous fraction attributable to aging time, year and season of production. Multivariate analysis was adopted to show these differences, mainly attributable to amino acids. In contrast, the lipid fraction analysis highlighted the impact of production season on fatty acid unsaturation, influenced by feed variations. As regards the production process, this study focuses on the variations induced by bactofugation. In this respect, the aqueous fraction was found to be extensively influenced by this centrifugation step, affecting compounds crucial to organoleptic characteristics.

Component analysis using UPLC-Q-TOF/MS and quality evaluation using fingerprinting and chemometrics for hops.

Hops, extensively cultivated in China for their food and medicinal applications, currently lack well-defined chemical markers to evaluate variations in their quality. The study aimed to explore variations in the quality of Chinese hops by the chemical characteristics of hops, employing UPLC-Q-TOF/MS, integrated with chemical fingerprinting and chemometrics. The results indicated that Chinese hops are abundant in polyphenols and bitter acids. The integration of UPLC-Q-TOF/MS, Chemical fingerprinting, and chemometrics revealed to be an accurate and effective approach for assessing the quality of Chinese hops. In this study, ten important chemical markers were found to be useful in differentiating various hop varieties. Moreover, the support vector machine showed a prediction accuracy of 92.3077% in identifying Chinese hop varieties. The strategy of the study lays the groundwork for classifying Chinese hop varieties and serves as a prerequisite for future quality control studies, particularly focusing on chemical compositions.

Traceability Research on Geographic Erigeron breviscapus Based on High-Resolution Mass Spectrometry and Chemometric Analysis.

A method was developed to identify and trace the geographic sources of Erigeron breviscapus using high-resolution mass spectrometry and chemometrics. The representative samples were collected from the geographic area of Honghe Dengzhanhua and other areas in Yunnan province and Guizhou province. The data points could be determined well using the PCA and PLS-DA diagram. A total of 46 characteristic compounds were identified from Honghe Dengzhanhua and within Guizhou province, but 37 compounds were different from Honghe Dengzhanhua and other counties in Yunnan province. Two biomarkers were found from three regions. Their structures were inferred as 8-amino-7-oxononanoic acid and 8-hydroxyquinoline, and they had the same molecular composition. This may suggest that a possible synthesis pathway can be proven in the future.

Non-linear responses via agglomeration and aggregation of gold nanoparticles for surface-enhanced Raman spectroscopy (SERS) coupled with chemometric analysis for chlorpyrifos detection.

This study investigates the behaviour of gold nanoparticles (AuNPs) when exposed to chlorpyrifos, an agricultural pesticide, and its application in detecting the pesticide via surface-enhanced Raman spectroscopy (SERS). Under synergistic addition of NaCl, AuNPs undergo agglomeration at lower chlorpyrifos concentrations but aggregation at higher concentrations, resulting in a distinctive nonlinear SERS response. A linear relationship is obtained between 0.001 and 1 ppm with detection limit (LOD) of 0.009 ppm, while an inverse response is observed at higher concentrations (1-1000 ppm) with a LOD of 1 ppm. Combining the colorimetric response of AuNP solutions, their absorbance spectra, and principal component analysis can improve detection reliability. The assay, coupled with a simple recovery method using acetonitrile swabbing, achieves high reproducibility in detecting chlorpyrifos in cucumber, even at concentrations as low as 0.11 ppm. This approach can be tailored for various chlorpyrifos concentrations not only in cucumbers but also in different food matrices.

Rapid analysis of bioactive compounds from citrus samples by direct analysis in real-time mass spectrometry combined with chemometrics.

Renowned for their nutritional benefits, citrus fruits are harvested at various stages in China for functional food production. This study introduces an innovative analytical method, DART-MS, enabling direct qualitative analysis of citrus samples without the need for preprocessing. Simultaneously, the combination of chemometrics can be applied to distinguish between three different citrus samples: Citri Reticulatae Pericarpium, Citri Reticulatae Pericarpium Viride, and Citri Reticulatae "Chachi". Notably, given the international regulatory concerns surrounding synephrine, a precise quantitative analysis method for synephrine was developed. The limit of detection (LOD) and the limit of quantification (LOQ) were 39 ng mL-1 and 156 ng mL-1, respectively. The recovery rates obtained varied from 98.46% to 100.71%. Furthermore, the intra-day and inter-day precision demonstrated robust consistency, with values spanning 5.0-6.1% and 5.03-6.08%, respectively, offering quicker results compared to those from HPLC-MS, promising a safer assessment of herbal and food products.

Rapid non-destructive monitoring and quality assessment of the fumigation process of Shanxi aged vinegar based on Vis-NIR hyperspectral imaging combined with multiple chemometric algorithms.

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Development and validation of colorimetric-assisted chemometrics methods based on the localized gold nanoparticles surface plasmon resonance for fast simultaneous estimation of anti-hepatitis C virus drugs in their combined dosage form: A comparative study with HPLC method.

The present work describes a developed analytical method based on a colorimetric assay using gold nanoparticles (AuNPs) along with chemometric techniques for the simultaneous estimation of sofosbuvir (SOF) and ledipasvir (LED) in their synthetic mixtures and tablet dosage form. The applied chemometric approaches were continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM). Characterization of AuNPs and AuNPs in combination with the drug was performed by UV-vis spectrophotometer, transmission electron microscopy (TEM), dynamic light scattering (DLS), and Fourier transform infrared (FTIR) spectroscopy. In the CWT method, the zero amplitudes were determined at 427 nm with Daubechies wavelet family for SOF (zero crossing point of LED) and 440 nm with Symlet wavelet family for LED (zero crossing point of SOF) over the concentration range of 7.5-90.0 µg/L and 40.0-100.0 µg/L with coefficients of determination (R2) of 0.9974 and 0.9907 for SOF and LED, respectively. The limit of detection (LOD) and limit of quantification (LOQ) of this method were found to be 7.92, 9.96 µg/L and 12.02, 30.2 µg/L for SOF and LED, respectively. In the LS-SVM model, the mean percentage recovery of SOF and LED in synthetic mixtures was 98.29 % and 99.25 % with root mean square error of 2.392 and 1.034, which were obtained by the optimization of regularization parameter (γ) and width of the function (σ) based on the cross-validation method. The proposed methods were also applied for the determination concentration of SOF and LED in the combined dosage form, recoveries were higher than 95 %, and relative standard deviation (RSD) values were lower than 0.4 %. The achieved results were statistically compared with those obtained from the high-performance liquid chromatography (HPLC) technique for the concurrent estimation of components through one-way analysis of variance (ANOVA), and no significant difference was found between the suggested approaches and the reference one. According to these results, simplicity, high speed, lack of time-consuming process, and cost savings are considerable benefits of colorimetry along with chemometrics methods compared to other ways.

Contamination of trace, non-essential/heavy metals in nutraceuticals/dietary supplements: A chemometric modelling approach and evaluation of human health risk upon dietary exposure.

Across the world, nutraceuticals/dietary supplements are commonly consumed without medical supervision, and believing these products are harmless to health. However, these products may contain trace (TMs) and non-essential/heavy metals (nHMs) as contaminants at levels higher than the recommended daily allowance (RDA), which can be hazardous to human health. Consequently, it is crucial to assess the levels of these metals to ensure the safety of these products. This study aimed to analyze the concentration of TMs (Mn, Cu and Zn) and nHMs (Al, Cr, Ni, Cd and Pb) in nutraceuticals/dietary supplements. Metal analysis was conducted using inductively coupled plasma-optical emission spectrometry (ICP-OES). Multivariate and bivariate analysis including principle component analysis (PCA), hierarchical cluster analysis (HCA) and Pearson correlation coefficient (PCC) were applied to understand inter-metal association and sources of these metals. Concentration ranges for TMs were found as, Mn (0.2-4.3 mg/kg), Cu (0.11-2.54 mg/kg), and Zn (0.1-22.66 mg/kg) while the nHMs concentration ranges were: Al (0.046-3.336 mg/kg), Cr (0.11-1.63 mg/kg), Ni (0.18-0.72 mg/kg), Cd (0.04-0.92 mg/kg), and Pb (0.18-1.08 mg/kg). The levels of tolerable dietary intake (TDI) for Cr and Ni, and the provisional tolerable monthly intake (PTMI) limit for Cd, exceeded the values set by the World Health Organization (WHO) and the European Food Safety Authority (EFSA). The estimation of the target hazard quotient (THQ <1), hazard index (HI < 1) and cumulative cancer risk (CCR <1 ✕ 10-3) indicated no significant non-carcinogenic and carcinogenic health risks associated with consuming these products. Therefore, the primary recommendation from this study is to use the nutraceuticals/dietary supplements should be under the supervision of dietitian.