Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

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.

The Impact of Harvesting Mechanization on Oolong Tea Quality

Mechanization is the inevitable future of tea harvesting, but its impact on tea chemistry and quality remains uncertain. Our study examines untargeted metabolomic data from 185 oolong tea products (Tieguanyin) made from leaves harvested by hand or machine based on UPLC-QToF-MS analysis. The data revealed a minimum 50% loss for over half of the chemicals in the machine-harvested group, including catechins, theaflavin, gallic acid, chlorogenic acid, and kaempferol-3-gluocside. Integrating sensory evaluation, OPLS-DA identified the six most important metabolites as significant contributors to sensory decline caused by harvesting mechanization. Furthermore, our research validates the possibility of using DD-SIMCA modelling with untargeted metabolomic data for distinguishing handpicked from machine-harvested tea products. The model was able to achieve 93% accuracy. This study provides crucial insights into the chemical and sensory shifts during mechanization, along with tools to manage and monitor these changes.

Cancer detection in dogs using rapid Raman molecular urinalysis

Introduction

The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor.

Methods

Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software.

Results and discussion

The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.

Chemometric Evaluation of RI-Induced Phytochemicals in Phaseolus vulgaris Seeds Indicate an Improvement on Liver Enzymes in Obese Rats

Liver enzymes alterations (activity or quantity increase) have been recognized as biomarkers of obesity-related abnormal liver function. The intake of healthy foods can improve the activity of enzymes like aspartate and alanine aminotransferases (AST, ALT), γ-glutaminyl transferase (GGT), and alkaline phosphatase (ALP). Beans have a high concentration of several phytochemicals; however, Restriction Irrigation (RI) during plant development amends their synthesis. Using chemometric tools, we evaluated the capacity of RI-induced phytochemicals to ameliorate the high activity of liver enzymes in obese rats. The rats were induced with a high-fat diet for 4 months, subsequently fed with 20% cooked beans from well-watered plants (100/100), or from plants subjected to RI at the vegetative or reproduction stage (50/100, 100/50), or during the whole cycle (50/50) for 3 months. A partial least square discriminant analysis indicated that mostly flavonols have a significant association with serum AST and ALT activity, while isoflavones lowered GGT and ALP. For AST and ALT activity in the liver, saponins remained significant for hepatocellular protection and flavonoids remained significant as hepatobiliary protectants by lowering GGT and ALP. A principal component analysis demonstrated that several flavonoids differentiated 100/50 treatment from the rest, while some saponins were correlated to 50/100 and 50/50 treatments. The intake of beans cultivated under RI improves obesity-impaired liver alterations.

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

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

Migration Studies and Endocrine Disrupting Activities: Chemical Safety of Cosmetic Plastic Packaging

The endocrine activity and endocrine disruptor (ED) chemical profiles of eleven plastic packaging materials covering five major polymer types (3PET, 1HDPE, 4LDPE, 2 PP, and 1SAN) were investigated using in vitro cell-based reporter-gene assays and a non-targeted chemical analysis using gas chromatography coupled to mass spectrometry (GC-MS). To mimic cosmetic contact, six simulants (acidic, alkaline, neutral water, ethanol 30%, glycerin, and paraffin) were used in migration assays performed by filling the packaging with simulant. After 1 month at 50 °C, simulants were concentrated by Solid Phase Extraction (SPE) or Liquid-Liquid Extraction (LLE). The migration profiles of seven major endocrine disrupting chemicals detected from GC-MS in the different materials and simulants were compared with Estrogen Receptor (ER) and Androgen Receptor (AR) activities. With low extraction of ED chemicals in aqueous simulants, no endocrine activities were recorded in the leachates. Paraffin was shown to be the most extracting simulant of antiandrogenic chemicals, while glycerin has estrogenic activities. Overall, ED chemical migration in paraffin was correlated with hormonal activity. The NIAS 2,4-di-tert-butyl phenol and 7,9-di-tert-butyl1-oxaspiro (4,5) deca-6,9-diene-2,8-dione were two major ED chemicals present in all polymers (principally in PP and PE) and in the highest quantity in paraffin simulant. The use of glycerin and liquid paraffin as cosmetic product simulants was demonstrated to be relevant and complementary for the safety assessment of released compounds with endocrine activities in this integrated strategy combining bioassays and analytical chemistry approaches.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Using Molecular Spectroscopic Techniques (NIR and ATR-FT/MIR) Coupling with Various Chemometrics to Test Possibility to Reveal Chemical and Molecular Response of Cool-Season Adapted Wheat Grain to Ergot Alkaloids

The objectives of this study were to explore the possibility of using near infrared (NIR) and Fourier transform mid-infrared spectroscopy-attenuated total reflectance (ATR-FT/MIR) molecular spectroscopic techniques as non-invasive and rapid methods for the quantification of six major ergot alkaloids (EAs) in cool-season wheat. In total, 107 wheat grain samples were collected, and the concentration of six major EAs was analyzed using the liquid chromatography-tandem mass spectrometry technique. The mean content of the total EAs-ergotamine, ergosine, ergometrine, ergocryptine, ergocristine, and ergocornine-was 1099.3, 337.5, 56.9, 150.6, 142.1, 743.3, and 97.45 μg/kg, respectively. The NIR spectra were taken from 680 to 2500 nm, and the MIR spectra were recorded from 4000-700 cm-1. The spectral data were transformed by various preprocessing techniques (which included: FD: first derivative; SNV: standard normal variate; FD-SNV: first derivative + SNV; MSC: multiplicative scattering correction; SNV-Detrending: SNV + detrending; SD-SNV: second derivative + SNV; SNV-SD: SNV + first derivative); and sensitive wavelengths were selected. The partial least squares (PLS) regression models were developed for EA validation statistics. Results showed that the constructed models obtained weak calibration and cross-validation parameters, and none of the models was able to accurately predict external samples. The relatively low levels of EAs in the contaminated wheat samples might be lower than the detection limits of the NIR and ATR-FT/MIR spectroscopies. More research is needed to determine the limitations of the ATR-FT/MIR and NIR techniques for quantifying EAs in various sample matrices and to develop acceptable models.

Detection and discrimination of sedative-hypnotics in spiked beverage dry residues using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics

Drugs-facilitated crimes (DFCs) involve the incapacitation of victims under the influence of drugs. Conventionally, a drug administration act is often determined through the examination of biological samples; however, dry residues from any surface, such as drinking glass if related to a DFC could be a potential source of evidence. This study was aimed to establish an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics for the determination of spiked sedative-hypnotics from dry residues of a drug-spiked beverage. In this study, four sedative-hypnotics, namely diazepam, ketamine, nimetazepam, and xylazine were examined using ATR-FTIR spectroscopy. Subsequently, the ATR-FTIR profiles were compared and decomposed by principal component analysis (PCA) followed by linear discriminant analysis (LDA) for their detection and discrimination. Visual comparison of ATR-FTIR profiles revealed distinct spectra among the tested drugs. An initial unsupervised exploratory PCA model indicated the separation of four main sedative-hypnotics clusters, and the proposed PCA score-LDA model had allowed for a 100% accurate classification. Discrimination of sedative-hypnotics from a dry beverage previously spiked with these drugs was also possible upon an additional extraction procedure. In conclusion, ATR-FTIR coupled with PCA score-LDA model was useful in detecting and discriminating sedative-hypnotics, including those that had been previously spiked into a beverage.

Metabolic profiling on the analysis of different parts of Schisandra chinensis based on UPLC-QTOF-MS with comparative bioactivity assays

The Schisandra chinensis is an important edible plant, and previous phytochemical research focused on the S. chinensis fruit (SF) due to its long history as traditional Chinese medicine. Schisandra chinensis fruit was used as an astringent tonic to astringe the lungs and the kidneys, replenish energy, promote the production of body fluids, tonify the kidney, and induce sedation. The components of S. chinensis, such as its stems (SS), leaves (SL), and roots (SR), have drawn little attention regarding their metabolites and bioactivities. In this study, a strategy of combining a chemical database with the Progenesis QI informatics platform was applied to characterize the metabolites. A total of 332 compounds were tentatively identified, including lignans, triterpenoids, flavonoids, tannins, and other compound classes. Heatmap and principal component analysis (PCA) showed remarkable differences in different parts of the plants. By multiple orthogonal partial least-squares discriminant analyses (OPLS-DA), 76 compounds were identified as potential marker compounds that differentiate these different plant parts. Based on the variable influence on the projection score from OPLS-DA, the active substances including gomisin D, schisandrol B, schisantherin C, kadsuranin, and kadlongilactone F supported the fact that the biological activity of the roots was higher than that of the fruit. These substances can be used as marker compounds in the plant roots, which likely contribute to their antioxidant and anti-inflammatory activities. The plant roots could be a new medicinal source that exhibits better activity than that of traditional medicinal parts, which makes them worth exploring.

Texture staling of pound cakes assessed by front face fluorescence spectroscopy in tandem with chemometric analysis

Two vegetal oils have been selected for the formulation of pound cakes, rapeseed oil (RO) and palm oil (PO). The freshness of these products has been monitored during 58 days of storage. The RO pound cakes of 58 days as well as PO pound cakes presented higher values (p < .05) of hardness (N), Young's modulus (N.cm^-2 ) and compressive stress (N.cm^-2 ) than those aged of 5 days. For both vitamin A and tryptophan spectra, the maximum of fluorescence intensity varied according to the aging period. For both RO and PO recipes, lipid oxidation indicators (peroxide value, para-anisidine value, and total oxidation value [TOTOX]) were interestingly correlated to Young's modulus (R²  = 0.99) as well as compressive stress (R²  = 0.99), using polynomial model. The application of partial least squares regression to the vitamin A spectra provided excellent predictive results for both compressive stress (R²  = 0.99) and TOTOX (R²  = 0.99). Good results were observed for tryptophan spectra for the prediction of TOTOX (R²  = 0.77). Thus, the use of front face fluorescence spectroscopy may provide rapid and accurate results for the monitoring of pound cakes freshness throughout storage.

Raman spectroscopy mapping of changes in the organization and relative quantities of cell wall polymers in bast fiber cell walls of flax plants exposed to gravitropic stress

Flax is an important fiber crop that is subject to lodging. In order to gain more information about the potential role of the bast fiber cell wall in the return to the vertical position, 6-week-old flax plants were subjected to a long-term (6 week) gravitropic stress by stem tilting in an experimental set-up that excluded autotropism. Stress induced significant morphometric changes (lumen surface, lumen diameter, and cell wall thickness and lumen surface/total fiber surface ratio) in pulling- and opposite-side fibers compared to control fibers. Changes in the relative amounts and spatial distribution of cell wall polymers in flax bast fibers were determined by Raman vibrational spectroscopy. Following spectra acquisition, datasets (control, pulling- and opposite sides) were analyzed by principal component analysis, PC score imaging, and Raman chemical cartography of significant chemical bonds. Our results show that gravitropic stress induces discrete but significant changes in the composition and/or spatial organization of cellulose, hemicelluloses and lignin within the cell walls of both pulling side and opposite side fibers.

Correlations between phytochemical fingerprints of Moringa oleifera leaf extracts and their antioxidant activities revealed by chemometric analysis

INTRODUCTION

Moringa oleifera Lam. is widely cultivated and applied in tropical and subtropical areas. Numerous studies have been focused on the antioxidant capacity of M. oleifera leaves, but its correlated bioactive phytochemicals remain elusive.

OBJECTIVE

In order to search for the corresponding chemical compounds from M. oleifera leaves responsible for their antioxidant activity, the correlations between phytochemical fingerprints of 15 batches of M. oleifera leaves and their antioxidant activities were investigated by using chemometric analysis.

MATERIAL AND METHODS

Fifteen batches of M. oleifera leaves were extracted with 90% ethanol solution, and their phytochemical fingerprints and antioxidant activities were estimated by using high-performance liquid chromatography-ultraviolet-electrospray ionisation tandem mass spectrometry (HPLC-UV/ESI-MS/MS), and three detected methods, namely 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) assay and ferric-reducing antioxidant power (FRAP) assay, respectively. Chemometric analysis was then applied to reveal the correlations between their phytochemical fingerprints and corresponding antioxidant capacity.

RESULTS

Fifteen M. oleifera leaf extracts exhibited strong antioxidant activities, in which 24 common compounds were identified by LC-MS. Furthermore, the partial least squares (PLS) analysis indicated that compounds 14, 16, 18 and 23 were the main potential effective components in at least two antioxidant tests. They were identified as kaempferol 3-O-rutinoside, quercetin 3-O-(6″-malonyl-glucoside), kaempferol 3-O-glucoside, and quercetin derivative, respectively.

CONCLUSION

The correlations between phytochemical fingerprints of M. oleifera leaf extracts and their corresponding antioxidant capacities were revealed by chemometric analysis, which provides an alternative method for screening for potential bioactive compounds with antioxidant capacity from M. oleifera leaves.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.

A rapid and nondestructive approach for forensic identification of cigarette inner liner papers using shift-excitation Raman difference spectroscopy and chemometrics

In forensic science, cigarettes are considered as crucial physical evidence because it helps to establish the connection between the criminal and the crime scene. In the present study, SERDS has been used for the examination of 25 different brands or series of cigarette inner liner paper. The discrimination power is calculated by using three methods, i.e., visual discrimination of the spectra, hierarchical cluster analysis (HCA) and principal component analysis (PCA). They are 100.00%, 92.42% and 100.00%, respectively. Cigarette inner liner paper samples were divided into four categories based on HCA and assignment of Raman special peaks: (1) talcum powder, (2) zinc oxide, (3) talcum powder and zinc oxide and (4) zinc oxide and barium sulfate. The PCA-FDA model was constructed for identifying the unknown samples, it delivered 100.00% calibration accuracy and validation accuracy. The results suggest that SERDS combined with the chemometric methods is a rapid, nondestructive and accurate method for the differentiation of cigarette inner liner papers.

Characterization of poultry house dust using chemometrics and scanning electron microscopy imaging

Poultry house dust is composed of fine particles which likely originate from a diverse range of materials such as feed, litter, excreta, and feathers. Little is known about the contribution of these sources to broiler house airborne dust so the present study was designed to identify the relative contributions of these sources. Samples of feed, excreta, feather, and bedding, known mixtures of these and settled dust from 28 broiler chicken flocks were tested for the concentration of 18 chemical elements. A chemometrics approach (the application of multivariate statistical techniques to chemical analysis data) was used to identify the primary source material in broiler chicken house dust samples. Scanning electron microscopy (SEM) was also used to analyze dust sample particulates based on examination of source materials. Excreta was found to be the main component of broiler chicken house dust, both by SEM and chemometric analysis. SEM of experimental flock dust between 7 and 35 days of age (d) revealed that the contribution of excreta to dust increased with age from 60% at 7 d to 95% at 28 d (P < 0.001). The proportion of bedding and feed in dust declined with age while the contribution of feather material remained low throughout. This study demonstrates that excreta provides the bulk of the material in poultry dust samples with bedding material, feed and feather material providing lower proportions. The relative contributions of these materials to dust varies with age of birds at dust collection. Additional research is required to determine the health and diagnostic implications of this variation.

Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) Regression in the Quantitative Analysis of Respirable Crystalline Silica by Fourier-Transform Infrared Spectroscopy (FTIR)

Respirable crystalline silica (RCS) overexposure can lead to the development of silicosis which is a chronic, irreversible, potentially fatal respiratory disease. The most significant prerequisite for any silica exposure control plan is an accurate occupational exposure assessment. The results of crystalline silica analysis are often affected by other mineral interferences and are influenced by an analyst's knowledge of mineralogy to accurately interpret infrared spectra and correct matrix interferences. Partial least squares (PLS) and artificial neural networks (ANNs) are two multivariate calibration methods to overcome the problem of spectral interferences without the need for an analyst intervention. The performance of these two methods in quantitative analysis of quartz in the presence of mineral interferences was evaluated and compared in this study. Fifty mixtures with different crystalline silica content ratios were prepared by mixing quartz with four common mineral interferences including kaolinite, albite, muscovite, and amorphous silica. Fourier-transform infrared spectra of the mixtures were split into training and test datasets. The optimal architecture of the ANN model was achieved using a two-level full factorial design experiment and data were modeled using ANN and PLS regression analysis. Root mean squared error of prediction values of 1.69 and 6.12 µg quartz for ANN and PLS models, respectively, revealed the fact that the both models performed very well in quantitative analysis of quartz in the presence of mineral interferences, with a better relative performance of the ANN model which can be related to the inherent nonlinear predictive ability of ANNs. Given the excellent predictive ability of the ANN model which can deal with a completely overlapped peak without any need of user's intervention, it is recommended that the ANN model be optimized in future studies and utilized for reliable and rapid on-field assessment of RCS exposure.

Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs

BACKGROUND

Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species.

PURPOSE

To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control.

METHODS

A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed.

RESULTS

A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly.

CONCLUSION

These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.

Survey of Phenolic Acids, Flavonoids and In Vitro Antioxidant Potency Between Fig Peels and Pulps: Chemical and Chemometric Approach

In the present study, chromatic coordinates, phenolic acids, flavonoids and antioxidant capacity assessed by 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonate (ABTS) and lipid peroxidation inhibition capacity (LPIC) essays and their relative IC50 were investigated in 25 fig cultivars growing in Morocco. The aims of this study were to determine (i) the variation in these compounds among light and dark-colored cultivars, (ii) their partitioning between fruit peel and pulp and (iii) to display network connections among these variables. Twelve phenolic compounds (PCs) were isolated in peel extract versus eight in pulp samples. Anthocyanins, mainly cyanidin-3,5-diglucoside and cyanidin-3-O-rutinoside, were the predominant compounds in peels, where the mean concentrations were 75.90 ± 18.76 and 77.97 ± 18.95 µg/g dw, respectively. On the other hand, (−)-epicatechin and cyanidin-3-O-rutinoside were the major compounds in the pulp extracts, where the mean values were 5.23 ± 4.03 and 9.01 ± 5.67 µg/g dw, respectively. A two-dimensional hierarchically clustered heatmap was applied to the dataset to explore correlations in the dataset and similarities between cultivars, without dimensionality reduction. Results showed that anthocyanins, particularly pelargonidin-3-O-rutinoside, cyanidin-3,5-diglucoside and cyanidin-3-O-rutinoside, were the main contributors to the peels’ free radical scavenging capacity. This capacity was particularly higher in the peel of dark-colored figs compared to the fruit pulp. The local cultivar “INRA 1301” showed the most promising phenolic profile due to its very high levels of almost all detected PCs, especially (−)-epicatechin, quercetin-3-O-rutinoside, quercetin-3-O-glucoside, cyanidine-3,5-diglucoside, cyanidine-3-O-rutinoside and pelargonidin-3-O-rutinoside (54.66, 141.08, 35.48, 494.08, 478.66, 12.56 µg/g dw, respectively). Having the darkest figs in the collection (L* = 25.72, c* = 22.09 and h° = 20.99), this cultivar has also combined promising IC50 values, which were of 19.85, 40.58 and 124.78 µg/mL for DPPH, ABTS and LPIC essays, respectively.

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box–Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 µL·L⁻¹). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.

Vitagenic Effect of Specific Bioactive Fractions of Rhodiola with Trachurus sp. Extract Against Oxidative Stress-Induced Aging in Human Amnion Derived Epithelial Cell Line: In View of a Novel Senolytic

BACKGROUND

Rhodiola rosea is a herb that has been used in traditional medicine for several years, and LF is a class of lipoproteins derived from the fish Trachurus sp. (LF-T), which exhibits known anti-inflammatory activity.

OBJECTIVE

Investigating the anti-aging effect of Rhodiola specific bioactive fractions cluster in combination with LF-T (R-L compound) in H₂O₂ mediated oxidative stress-induced human amnion derived epithelial cell line - FL cells as normal human cell line.

METHODS

FL cells were treated with H₂O₂ to induce cellular aging, followed by treatment of the RL compound to study its anti-aging characteristics. Based on the proliferation rate, 0.05% and 0.1% concentration of R-L compound was determined using MTT assay. Anti-aging and anti-oxidant assays, ABTS, DPPH, Hyaluronidase activity Nitric Oxide, Lipid Peroxidase, and Superoxide Dismutase were performed. qPCR for anti-aging genes and matrix metalloproteinase genes were analyzed.

RESULTS

FL cells treated with R-L compound exhibited increased proliferation rate and free-radical reduction. Decreased Hyaluronidase enzyme activity and regulation of genes such as SIRT1, KLOTHO, SERPINA 6, MMP 9, and MMP 2 expression depicted the anti-aging role of the R-L compound. Chemometric profiling of the R-L compound revealed that aromatic compounds and unsaturated fatty acids along with their derivatives, were present predominantly, which might have attributed to the potent oxidative stress impeded aging activity.

CONCLUSION

Specific Bioactive Fractions of Rhodiola in combination with LF-T obtained from Trachurus sp. involve in the regulation of aging genes and might be a novel approach to prevent the cells from oxidative stress damage and also it might avert the aging of cells.

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm⁻¹ and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.

Assessing the Impact of Oil Types and Grades on Tocopherol and Tocotrienol Contents in Vegetable Oils with Chemometric Methods

The consumption of vegetable oil is an important way for the body to obtain tocols. However, the impact of oil types and grades on the tocopherol and tocotrienol contents in vegetable oils is unclear. In this study, nine types of traditional edible oils and ten types of self-produced new types of vegetable oil were used to analyze eight kinds of tocols. The results showed that the oil types exerted a great impact on the tocol content of traditional edible oils. Soybean oils, corn oils, and rapeseed oils all could be well distinguished from sunflower oils. Both sunflower oils and cotton seed oils showed major differences from camellia oils as well as sesame oils. Among them, rice bran oils contained the most abundant types of tocols. New types of oil, especially sacha inchi oil, have provided a new approach to obtaining oils with a high tocol content. Oil refinement leads to the loss of tocols in vegetable oil, and the degree of oil refinement determines the oil grade. However, the oil grade could not imply the final tocol content in oil from market. This study could be beneficial for the oil industry and dietary nutrition.

Electronic Nose for Monitoring Odor Changes of Lactobacillus Species during Milk Fermentation and Rapid Selection of Probiotic Candidates

Probiotic bacteria have been associated with a unique production of aroma compounds in fermented foods but rapid methods for discriminating between foods containing probiotic, moderately probiotic, or non-probiotic bacteria remain aloof. An electronic nose (e-nose) is a high-sensitivity instrument capable of non-invasive volatile measurements of foods. In our study, we applied the e-nose to differentiate probiotic, moderately probiotic, and non-probiotic Lactobacillus bacteria strains at different fermentation time points (0th, 4th, and 11th) of milk fermentation. The pH of the changing milk medium was monitored with their corresponding increase in microbial cell counts. An e-nose with two gas chromatographic columns was used to develop classification models for the different bacteria groups and time points and to monitor the formation of the aromatic compounds during the fermentation process. Results of the e-nose showed good classification accuracy of the different bacteria groups at the 0th (74.44% for column 1 and 82.78% for column 2), the 4th (89.44% for column 1 and 92.22% for column 2), and the 11th (81.67% for column 1 and 81.67% for column 2) hour of fermentation. The loading vectors of the classification models showed the importance of some specific aroma compounds formed during the fermentation. Results show that aroma monitoring of the fermentation process with the e-nose is a promising and reliable analytical method for the rapid classification of bacteria strains according to their probiotic activity and for the monitoring of aroma changes during the fermentation process.

N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil

The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Rapid Classification and Quantification of Camellia (Camellia oleifera Abel.) Oil Blended with Rapeseed Oil Using FTIR-ATR Spectroscopy

Currently, the authentication of camellia oil (CAO) has become very important due to the possible adulteration of CAO with cheaper vegetable oils such as rapeseed oil (RSO). Therefore, we report a Fourier transform infrared (FTIR) spectroscopic method for detecting the authenticity of CAO and quantifying the blended levels of RSO. In this study, two characteristic spectral bands (1119 cm^-1 and 1096 cm^-1) were selected and used for monitoring the purity of CAO. In combination with principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares regression (PLSR) analysis, qualitative and quantitative methods for the detection of camellia oil adulteration were proposed. The results showed that the calculated I₁₁₁₉/I₁₀₉₆ intensity ratio facilitated an initial check for pure CAO and six other edible oils. PCA was used on the optimized spectral region of 1800-650 cm^-1. We observed the classification of CAO and RSO as well as discrimination of CAO with RSO adulterants. LDA was utilized to classify CAO from RSO. We could differentiate and classify RSO adulterants up to 1% v/v. In the quantitative PLSR models, the plots of actual values versus predicted values exhibited high linearity. Root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSECV) values of the PLSR models were 1.4518%-3.3164% v/v and 1.7196%-3.8136% v/v, respectively. This method was successfully applied in the classification and quantification of CAO adulteration with RSO.

Chemosystematic evaluation of Amazonian freshwater sponges

Freshwater sponges can be considered a promising new source of bioactive compounds for the pharmaceutical industry; however, the research on their chemical composition is still in the incipient stage. We evaluated the most endemic Amazonian freshwater sponge species from the Drulia and Metania genera by untargeted metabolomic approaches, based on UHPCL-HRMS, in order to identify chemical markers and explore the diversity of specialized metabolites. The use of untargeted approaches allowed us to observe subsets of metabolites that enabled the characterization of, not only each genus, but also, of each species. Freshwater sponge species presented themselves as rich sources of fatty acids and sterols, which were putatively identified. These metabolites were suggested as chemical markers for further targeted metabolomic studies.

Development of an FTIR based chemometric model for the qualitative and quantitative evaluation of cane sugar as an added sugar adulterant in apple fruit juices

A Fourier Transform Infrared Spectroscopy based chemometric model was evaluated for the rapid identification and estimation of cane sugar as an added sugar adulterant in apple fruit juices. For all the ninety samples, spectra were acquired in the mid-infrared range (4000 cm^-1-400 cm^-1). The spectral analysis provided information regarding the distinctive variable region, which lies in the range of 1200cm^-1 to 900cm^-1, designated as fingerprint region for the carbohydrates. A specific peak in the fingerprint region was observed at 997cm^-1 in all the adulterated samples and was undetectable in pure samples. Based on different levels of cane sugar adulteration (5, 10, 15, and 20%), principal component analysis showed the clustering of samples and further helped us in compression of data by selecting wavenumbers with maximum variability based on the loading line plot. Supervised classification methods (SIMCA and LDA) were evaluated based on their classification efficiencies for a test set. Though SIMCA showed 100% classification efficiency (Raw data set), LDA was able to classify the test set with an accuracy of only 96.67% (Raw as well as Transformed data set) between pure and 5% adulterated samples. For the quantitative estimation, calibration models were developed using partial least square regression (PLS-R) and principal component regression method (PCR) methods. PLS-1^st derivative showed a maximum coefficient of determination (R²) with a value of 0.991 for calibration and 0.992 for prediction. The RMSECV, RMSEP, LOD and LOQ observed for PLS-1^st derivative model were 0.75% w/v, 0.61% w/v, 1.28%w/v and 3.88%w/v, respectively. The coefficient of variation as a measure of precision (repeatability) was also determined for all models, and it ranged from 0.23% to 1.83% (interday), and 0.25% to 1.43% (intraday).

Structure-activity relationships study on biological activity of peptides as dipeptidyl peptidase IV inhibitors by chemometric modeling

The aim of this study is to identify the potential descriptors affecting the inhibitory activity of the peptides inhibiting dipeptidyl peptidase IV (DPP-IV). This study provides important information for assessing the biological activity of the new peptide sequences of food origin or making structural modifications to the current inhibitors to improve their performance. For this purpose, the chemometric method describing the relationship between the structure of food peptides and their biological activity (structure-activity relationship [SAR]) was used to theoretically predict the potential of bioactivity of peptides. Data on the physicochemical properties of amino acids in the dipeptides acting as inhibitors of DPP-IV were collected and analyzed for using these properties as descriptors in further analysis. A total of 252 dipeptide sequences with confirmed DPP-IV inhibitory activity available in the BIOPEP-UWM database were included in the analysis, and 16 descriptors defining individual amino acids (such as molecular weight, polarity, hydropathicity, bulkiness, buried residue, and acceptable and normalized frequency of alpha-helix and beta-sheet) were identified. Based on this information, a data matrix was constructed and used in the chemometric analysis (principal component analysis and multiple linear regression). From the SAR model created, a multiple regression equation was derived to predict the biological activity of the dipeptide DPP-IV inhibitors.

Online Monitoring of Solutions Within Microfluidic Chips: Simultaneous Raman and UV-Vis Absorption Spectroscopies

Microfluidics is an appealing analytical tool in the global effort to close the nuclear fuel cycle. Using a microfluidic chip permits the analysis of greatly reduced sample volumes compared to what is necessary for traditional analytical methods. There is a commensurate reduction in disposal volume and cost. The development of novel sensors is necessary to take full advantage of the microchip configuration, where optical-spectroscopy-based approaches offer a powerful route to characterize chemical composition. This study uses simultaneously applied UV-vis and micro-Raman spectroscopies adapted to function on the microscale to analyze in situ both the Nd³⁺ (UV-vis-active) and HNO₃ (Raman-active) concentrations in the same sample. An adjustable translation platform was designed to hold the micro-Raman probe above and perpendicular to the chip face and the UV-vis probe in the plane of the chip. These complimentary spectral techniques when processed through multivariate partial least-squares (PLS) models gave an accurate picture of the widely varying solution concentrations as a function of time for each solution component. Solution matrix effects can drastically alter analyte signatures as measured by both UV-vis absorbance and Raman spectroscopy. PLS methods successfully modeled these spectral changes and accurately measured concentrations of components of interest within the microfluidic chip.

Qualitative and quantitative evaluation of Oroxylum indicum (L.) Kurz by HPLC and LC-qTOF-MS/MS

Oroxylum indicum, as a popular functional Chinese herbal medicine for reducing hyperactivity, relieving sore throat, smoothing the liver and adjusting stomach, mainly contains flavonoids. In this study, we aimed to establish a fast and sensitive method that enables to analyze the chemical components in O. indicum qualitatively and quantitatively. First, a total of 42 components were characterized by LC-quadrupole time-of-flight (qTOF)-tandem mass spectrometry (MS/MS), including 23 flavonoid glycosides, 13 flavonoids and six other types of compounds. Then, 17 characteristic components of the 19 common peaks in the chromatographic fingerprints of O. indicum were confirmed. Fifty samples were classified into two groups by hierarchical clustering analysis and orthogonal partial least squares-discriminant analysis, which also identified the 10 main chemical markers responsible for differences between samples. Last, the quantitative analysis of multiple components with a single marker method was established for simultaneous determination of six main active components in O. indicum by LC-UV with oroxin B was chosen as internal reference substance. Finally, a rapid and efficient method integrating HPLC with LC-electrospray ionization-qTOF-MS/MS analysis was established to comprehensively discriminate and assess the quality of O. indicum samples.

Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals

Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.

varieties: chemometric analysis

Eight different Serbian genotypes were analysed for their polyphenol, carotenoid, vitamin C content and evaluated for their antioxidant properties. The highest content of biologically important carotenoids such as lutein (4.58 mg/10 g), lycopene (160.64 mg/10 g) and β-carotene (189.64 mg/10 g) were detected in the genotype S606. Rutin was the most abundant phenolic compound in all tastes samples, but its content is highest in the genotype S615 (1424.30 µg/100 g dw). All tomato samples were the great source of vitamin C, where the sample S615 stood out (68.54 mg AA g^-1 of dw). Their content of antioxidant compounds suggested that genotypes S606 and S615 showed the best antioxidant potential. Principal component analysis (PCA) and Partial least squares (PLS) were applied to analyse results. The results obtained in the present study could be of considerable interest for breeding programmes wishing to select tomato genotypes with high biological and nutritional properties.

Exploratory Monitoring of the Quality and Authenticity of Commercial Honey in Ecuador

Honey is one of the oldest sweetening foods and has economic importance, making this product attractive to adulteration with cheap sugars. This can cause a critical problem in the honey industry and a possible health risk. The present work has the aim of evaluating the authenticity of honey commercialized in two different provinces of Ecuador (Pichincha and Loja) by performing physicochemical and spectroscopic analyses. For this study 25 samples were collected from different places and markets and characterized by water, sucrose, reducing sugars and electric conductivity measurement. Also, their Raman and Infrared (IR) spectra were recorded and analysed using a Principal Component Analysis (PCA) in order to verify the quality of the honeys. In addition, a screening of several pesticides was performed in order to verify possible chemical threats to human health and honey bees. It was found that 8 samples have a deviation from the Standard established parameters. Two of them have a high difference in the content of sucrose and reducing sugars, which are located deviated from all the other samples in the PCA of the applied vibrational spectroscopy (IR/Raman), shaping two clear clusters. The results show that Raman and IR spectroscopy is appropriate techniques for the quality control of honey and correlates well with the physicochemical analyses.

Simultaneous qualitative and quantitative evaluation of Toddalia asiatica root by using HPLC-DAD and UPLC-QTOF-MS/MS

INTRODUCTION

Coumarin and alkaloids are the major bioactive constituents of Toddalia asiatica, playing an important role in various biological activities such as anti-inflammatory, analgesic, anti-bacterial and anti-tumour.

OBJECTIVE

To establish a method that will simultaneously determine the coumarins and alkaloids compounds in T. asiatica and identify their characteristic fragmentation patterns, while combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of T. asiatica samples.

METHODOLOGY

Qualitative characterisation of coumarins and alkaloids compounds in the methanol extracts of T. asiatica was determined by ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS). Quantitative analysis relies on high-performance liquid chromatography with a diode array detector (HPLC-DAD).

RESULTS

A total of 59 components were characterised by UPLC-QTOF-MS/MS, including 29 coumarin, 25 alkaloids, one phenolic acid and four flavonoids. While the 19 characteristic components out of 23 common peaks in the chromatographic fingerprints of T. asiatica were confirmed. Quantitative analysis of seven major compounds from 18 samples were simultaneously detected by HPLC-DAD at wavelengths of 280 nm. The samples were classified into three groups by hierarchical clustering analysis (HCA) combined with principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) which screened out the main chemical markers responsible for the samples differences.

CONCLUSION

Fingerprints combined with chemometrics and chemical identification are a simple, rapid and effective method for the quality control of T. asiatica.

Quantifying Graphene Oxide Reduction Using Spectroscopic Techniques: A Chemometric Analysis

The surface chemistry of graphene oxide (GO) can be modified by the chemical reduction of oxygen-containing groups using L-ascorbic acid (L-AA). Being able to "tune" the surface hydrophobicity of GO in a controlled manner, with a well-defined level of reduction, provides a valuable tool for understanding and controlling interactions with hydrophobic surfaces. Numerous analytical and chemical methods have been used to determine the extent of reduction in chemically reduced graphene oxide (CRGO) samples. However, many of these methods are limited by their laborious nature, cost, or lack of sensitivity in resolving oxygen content in samples that have only been reduced for short periods of time, making them inappropriate for rapid use with multiple samples. Here, we have used ultraviolet (UV), Raman, and attenuated total reflection infrared (ATR-IR) spectroscopy to monitor the chemical reduction of GO. These three techniques are simple, rapid, nondestructive, accurate, and widely available. The data set from each technique has been correlated and modeled against a reference data set (carbon to oxygen ratio obtained from elemental analysis) using partial least squares regression (PSLR). Using this approach, the chemical reduction of GO was quantified from UV (r² = 0.983, RMSE_CV = 0.049), Raman (r² = 0.961, RMSE_CV = 0.073) and ATR-IR (r² = 0.993, RMSE_CV = 0.032) data. ATR-IR enabled identification of the different oxygen-containing groups on GO, and coupled with chemometric modeling, provides an excellent approach for the routine quantitative analysis of the chemical reduction of GO.

Chemical and Chemometric Methods for Halal Authentication of Gelatin: An Overview

The issue of food authenticity has become a concern among religious adherents, particularly Muslims, due to the possible presence of nonhalal ingredients in foods as well as other commercial products. One of the nonhalal ingredients that commonly found in food and pharmaceutical products is gelatin which extracted from porcine source. Bovine and fish gelatin are also becoming the main commercial sources of gelatin. However, unclear information and labeling regarding the actual sources of gelatin in food and pharmaceutical products have become the main concern in halal authenticity issue since porcine consumption is prohibited for Muslims. Hence, numerous analytical methods involving chemical and chemometric analysis have been developed to identify the sources of gelatin. Chemical analysis techniques such as biochemical, chromatography, electrophoretic, and spectroscopic are usually combined with chemometric and mathematical methods such as principal component analysis, cluster, discriminant, and Fourier transform analysis for the gelatin classification. A sample result from Fourier transform infrared spectroscopy analysis, which combines Fourier transform and spectroscopic technique, is included in this paper. This paper presents an overview of chemical and chemometric methods involved in identification of different types of gelatin, which is important for halal authentication purposes.

Disentangling the Complexity of a Hexa-Herbal Chinese Medicine Used for Inflammatory Skin Conditions-Predicting the Active Components by Combining LC-MS-Based Metabolite Profiles and in vitro Pharmacology

Objectives: The purpose of this study is to investigate the anti-inflammatory activity of a hexa-herbal Chinese formula (HHCF) using spontaneously immortalized human epidermal keratinocytes (HaCaT) and to predict the active components by correlating the LC-MS-based metabolite profiles of the HHCF and its 12 varied formulae with their anti-inflammatory activity using partial least-squares regression analysis. Methods: The HHCF comprises the rootstock of Scutellaria baicalensis, Rheum tanguticum, Sophora flavescens, the root bark of Dictamnus dasycarpus, the bark of Phellodendron chinense, and the fruit of Kochia scoparia in equal proportions. Its 12 varied formulae were developed by uniform design with varied proportions of the component botanical drugs. The decoctions of the HHCF and its 12 varied formulae were profiled using liquid chromatography (LC) combined with triple quadrupole mass spectrometry (MS) and their effects on tumor necrosis factor (TNF)-α -plus-interferon (IFN)-γ-induced C-C motif chemokine ligand 17 (CCL17) production in HaCaT were investigated. Partial least-squares regression analysis was conducted to assess the relationship between the LC-MS-based metabolite profiles of the decoctions to anti-CCL17 production in HaCaT. Results: Compounds with potential to promote anti-CCL17 production in HaCaT were identified (e.g., berberine, pyrogallol and catechin dimers) as a result of the developed model and their potential to act as anti-inflammatory agents were also supported by relevant literature. Conclusion: This promising approach should assist in the screening process of active components from complex Chinese herbal preparations and will better inform the necessary pharmacological experiments to take forward.

Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) with Raman Imaging Applied to Lunar Meteorites

Lunar meteorites provide a more random sampling of the surface of the Moon than do the returned lunar samples, and they provide valuable information to help estimate the chemical composition of the lunar crust, the lunar mantle, and the bulk Moon. As of July 2014, ∼96 lunar meteorites had been documented and ten of these are unbrecciated mare basalts. Using Raman imaging with multivariate curve resolution-alternating least squares (MCR-ALS), we investigated portions of polished thin sections of paired, unbrecciated, mare-basalt lunar meteorites that had been collected from the LaPaz Icefield (LAP) of Antarctica-LAP 02205 and LAP 04841. Polarized light microscopy displays that both meteorites are heterogeneous and consist of polydispersed sized and shaped particles of varying chemical composition. For two distinct probed areas within each meteorite, the individual chemical species and associated chemical maps were elucidated using MCR-ALS applied to Raman hyperspectral images. For LAP 02205, spatially and spectrally resolved clinopyroxene, ilmenite, substrate-adhesive epoxy, and diamond polish were observed within the probed areas. Similarly, for LAP 04841, spatially resolved chemical images with corresponding resolved Raman spectra of clinopyroxene, troilite, a high-temperature polymorph of anorthite, substrate-adhesive epoxy, and diamond polish were generated. In both LAP 02205 and LAP 04841, substrate-adhesive epoxy and diamond polish were more readily observed within fractures/veinlet features. Spectrally diverse clinopyroxenes were resolved in LAP 04841. Factors that allow these resolved clinopyroxenes to be differentiated include crystal orientation, spatially distinct chemical zoning of pyroxene crystals, and/or chemical and molecular composition. The minerals identified using this analytical methodology-clinopyroxene, anorthite, ilmenite, and troilite-are consistent with the results of previous studies of the two meteorites using electron microprobe analysis. To our knowledge, this is the first report of MCR-ALS with Raman imaging used for the investigation of both lunar and other types of meteorites. We have demonstrated the use of multivariate analysis methods, namely MCR-ALS, with Raman imaging to investigate heterogeneous lunar meteorites. Our analytical methodology can be used to elucidate the chemical, molecular, and structural characteristics of phases in a host of complex, heterogeneous geological, geochemical, and extraterrestrial materials.

Quality Variation of Goji (Fruits of Lycium spp.) in China: A Comparative Morphological and Metabolomic Analysis

Goji (fruits of Lycium barbarum L. and L. chinense Mill.) has been used in China as food and medicine for millennia, and globally has been consumed increasingly as a healthy food. Ningxia, with a semi-arid climate, always had the reputation of producing best goji quality (daodi area). Recently, the increasing market demand pushed the cultivation into new regions with different climates. We therefore ask: How does goji quality differ among production areas of various climatic regions? Historical records are used to trace the spread of goji production in China over time. Quality measurements of 51 samples were correlated with the four main production areas in China: monsoon (Hebei), semi-arid (Ningxia, Gansu, and Inner Mongolia), plateau (Qinghai) and arid regions (Xinjiang). We include morphological characteristics, sugar and polysaccharide content, antioxidant activity, and metabolomic profiling to compare goji among climatic regions. Goji cultivation probably began in the East (Hebei) of China around 100 CE and later shifted westward to the semi-arid regions. Goji from monsoon, plateau and arid regions differ according to its fruit morphology, whereas semi-arid goji cannot be separated from the other regions. L. chinense fruits, which are exclusively cultivated in Hebei (monsoon), are significantly lighter, smaller and brighter in color, while the heaviest and largest fruits (L. barbarum) stem from the plateau. The metabolomic profiling separates the two species but not the regions of cultivation. Lycium chinense and samples from the semi-arid regions have significantly (p < 0.01) lower sugar contents and L. chinense shows the highest antioxidant activity. Our results do not justify superiority of a specific production area over other areas. Instead it will be essential to distinguish goji from different regions based on the specific morphological and chemical traits with the aim to understand what its intended uses are.

Rennet-induced coagulation of raw and heated camel and cow milk gels determined by instrumental techniques: effects of added calcium and phosphate

BACKGROUND

The potentiality of the front-face fluorescence spectroscopy and rheological measurements combined with chemometric tools to characterise the structure evolution during coagulation of raw and preheated camel and cow milk at 50 and 70 °C with/without added calcium and phosphate was evaluated. Tryptophan and vitamin A fluorescence spectra were collected during the gelation of milk at 0, 10, 20, 30, 40, and 45 min after the addition of rennet-induced coagulation. In parallel, the storage modulus (G'), loss modulus (G″) and tan delta (tan δ) were determined using low amplitude oscillation shear analysis.

RESULTS

The principal component analysis (PCA) applied to the normalised tryptophan spectra allowed the gels made with camel milk to be differentiated from those of cow milk on the one hand, and to monitor protein structure modifications during the gelation, on the other hand. The common components and specific weights analysis (CCSWA) applied jointly to the fluorescence and rheological data sets permitted a clear separation of raw milk gels from those preheated at 50 and 70 °C.

CONCLUSION

The front-face fluorescence spectroscopy method coupled with multi-variate statistical analyses showed a high capacity for studying changes in the micelle structure throughout the rennet-induced coagulation process. © 2017 Society of Chemical Industry.

Raman Microspectroscopic Mapping with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) Applied to the High-Pressure Polymorph of Titanium Dioxide, TiO2-II

The high-pressure, α-PbO₂-structured polymorph of titanium dioxide (TiO₂-II) was recently identified in micrometer-sized grains recovered from four Neoarchean spherule layers deposited between ∼2.65 and ∼2.54 billion years ago. Several lines of evidence support the interpretation that these layers represent distal impact ejecta layers. The presence of shock-induced TiO₂-II provides physical evidence to further support an impact origin for these spherule layers. Detailed characterization of the distribution of TiO₂-II in these grains may be useful for correlating the layers, estimating the paleodistances of the layers from their source craters, and providing insight into the formation of the TiO₂-II. Here we report the investigation of TiO₂-II-bearing grains from these four spherule layers using multivariate curve resolution-alternating least squares (MCR-ALS) applied to Raman microspectroscopic mapping. Raman spectra provide evidence of grains consisting primarily of rutile (TiO₂) and TiO₂-II, as shown by Raman bands at 174 cm^-1 (TiO₂-II), 426 cm^-1 (TiO₂-II), 443 cm^-1 (rutile), and 610 cm^-1 (rutile). Principal component analysis (PCA) yielded a predominantly three-phase system comprised of rutile, TiO₂-II, and substrate-adhesive epoxy. Scanning electron microscopy (SEM) suggests heterogeneous grains containing polydispersed micrometer- and submicrometer-sized particles. Multivariate curve resolution-alternating least squares applied to the Raman microspectroscopic mapping yielded up to five distinct chemical components: three phases of TiO₂ (rutile, TiO₂-II, and anatase), quartz (SiO₂), and substrate-adhesive epoxy. Spectral profiles and spatially resolved chemical maps of the pure chemical components were generated using MCR-ALS applied to the Raman microspectroscopic maps. The spatial resolution of the Raman microspectroscopic maps was enhanced in comparable, cost-effective analysis times by limiting spectral resolution and optimizing spectral acquisition parameters. Using the resolved spectra of TiO₂-II generated from MCR-ALS analysis, a Raman spectrum for pure TiO₂-II was estimated to further facilitate its identification.

Combining NMR Spectroscopy and Chemometrics to Monitor Structural Features of Crude Hep-arin

Because of the complexity and global nature of the heparin supply chain, the control of heparin quality during manufacturing steps is essential to ensure the safety of the final active pharmaceutical ingredient (API). For this reason, there is a need to develop consistent analytical methods able to assess the quality of heparin early in production (i.e., as the crude heparin before it is purified to API under cGMP conditions). Although a number of analytical techniques have been applied to characterize heparin APIs, few of them have been applied for crude heparin structure and composition analyses. Here, to address this issue, NMR spectroscopy and chemometrics were applied to characterize 88 crude heparin samples. The samples were also analyzed by strong anion exchange HPLC (SAX-HPLC) as an orthogonal check of the purity levels of the crudes analyzed by NMR. The HPLC data showed that the chemometric analysis of the NMR data differentiated the samples based on their purity. These orthogonal approaches differentiated samples according their glycosaminoglycan (GAG) composition and their mono and disaccharide composition and structure for each GAG family (e.g., heparin/heparan, dermatan sulfate, and chondroitin sulfate A). Moreover, quantitative HSQC and multivariate analysis (PCA) were used to distinguish between crude heparin of different animal and tissue sources.

(Danshen) Based on HPLC Fingerprints and HPLC-MSn Coupled with Chemometrics

Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MSⁿ. Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.

Evaluation of the quality of herbal teas by DART/TOF-MS

The paper focuses on the optimization, settings and validation of direct analysis in real time coupled with time-of-flight detector when used for the evaluation of the quality of selected herbal teas (fennel, chamomile, nettle, linden, peppermint, thyme, lemon balm, marigold, sage, rose hip and St. John's wort). The ionization mode, the optimal ionization temperature and the type of solvent for sample extraction were optimized. The characteristic compounds of the analysed herbal teas (glycosides, flavonoids and phenolic and terpenic substances, such as chamazulene, anethole, menthol, thymol, salviol and hypericin) were detected. The obtained mass spectra were evaluated by multidimensional chemometric methods, such as cluster analysis, linear discriminate analysis and principal component analysis. The chemometric methods showed that the single variety herbal teas were grouped according to their taxonomic affiliation. The developed method is suitable for quick identification of herbs and can be potentially used for assessing the quality and authenticity of herbal teas. Direct analysis in real time/time-of-flight-MS is also suitable for the evaluation of selected substances contained in the mentioned herbs and herbal products. Copyright © 2017 John Wiley & Sons, Ltd.

Using Fourier Transform Near-Infrared Spectroscopy to Predict the Mechanical Properties of Thermally Modified Southern Pine Wood

This study seeks to estimate the mechanical properties of thermally modified wood (TMW) by using near-infrared (NIR) spectroscopy to measure 80 samples in three-point bending tests. Near-infrared spectra collected from the transverse, radial, and tangential sections of wood, coupled with chemometric techniques, were used to predict the mechanical properties of southern pine wood, from which NIR models were constructed based on partial least squares and corresponding cross-validation. The coefficient of determination between NIR transverse section spectra, as well as two mechanical properties of wood samples, modulus of rupture (MOR) and modulus of elasticity (MOE), were above 0.92 and greater than values for other sections. Spectral data from the transverse sections were richer than data from other sections, and thus, a model based on transverse sections better predicts the mechanical properties of wood. A close relationship between the values for mechanical properties (MOE and MOR) and the NIR spectra of thermally modified southern pine wood can be demonstrated, which provides the potential to predict the mechanical properties of untreated and thermally modified wood.

Improved Discrimination for Brassica Vegetables Treated with Agricultural Fertilizers Using a Combined Chemometric Approach

Multielement and stable isotope (δ(13)C, δ(15)N, δ(2)H, δ(18)O, (207)Pb/(206)Pb, and (208)Pb/(206)Pb) analyses were combined to provide a new chemometric approach to improve the discrimination between organic and conventional Brassica vegetable production. Different combinations of organic and conventional fertilizer treatments were used to demonstrate this authentication approach using Brassica chinensis planted in experimental test pots. Stable isotope analyses (δ(15)N and δ(13)C) of B. chinensis using elemental analyzer-isotope ratio mass spectrometry easily distinguished organic and chemical fertilizer treatments. However, for low-level application fertilizer treatments, this dual isotope approach became indistinguishable over time. Using a chemometric approach (combined isotope and elemental approach), organic and chemical fertilizer mixes and low-level applications of synthetic and organic fertilizers were detectable in B. chinensis and their associated soils, improving the detection limit beyond the capacity of individual isotopes or elemental characterization. LDA shows strong promise as an improved method to discriminate genuine organic Brassica vegetables from produce treated with chemical fertilizers and could be used as a robust test for organic produce authentication.

Diffuse reflectance near infrared-chemometric methods development and validation of amoxicillin capsule formulations

Objective:

The aim of present study was to establish near infrared-chemometric methods that could be effectively used for quality profiling through identification and quantification of amoxicillin (AMOX) in formulated capsule which were similar to commercial products. In order to evaluate a large number of market products easily and quickly, these methods were modeled.

Materials and Methods:

Thermo Scientific Antaris II near infrared analyzer with TQ Analyst Chemometric Software were used for the development and validation of the identification and quantification models. Several AMOX formulations were composed with four excipients microcrystalline cellulose, magnesium stearate, croscarmellose sodium and colloidal silicon dioxide. Development includes quadratic mixture formulation design, near infrared spectrum acquisition, spectral pretreatment and outlier detection. According to prescribed guidelines by International Conference on Harmonization (ICH) and European Medicine Agency (EMA) developed methods were validated in terms of specificity, accuracy, precision, linearity, and robustness.

Results:

On diffuse reflectance mode, an identification model based on discriminant analysis was successfully processed with 76 formulations; and same samples were also used for quantitative analysis using partial least square algorithm with four latent variables and 0.9937 correlation of coefficient followed by 2.17% root mean square error of calibration (RMSEC), 2.38% root mean square error of prediction (RMSEP), 2.43% root mean square error of cross-validation (RMSECV).

Conclusion:

Proposed model established a good relationship between the spectral information and AMOX identity as well as content. Resulted values show the performance of the proposed models which offers alternate choice for AMOX capsule evaluation, relative to that of well-established high-performance liquid chromatography method. Ultimately three commercial products were successfully evaluated using developed methods.