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This study aimed to investigate the differences in volatile composition and aroma of different fractions of Osmanthus fragrans absolute purified by molecular distillation. Gas chromatography-mass spectrometry (GC-MS) combined with orthogonal partial least-squares discrimination analysis (OPLS-DA), P-value, variable importance in the projection (VIP), and relative odor activity value (ROAV) were used for analysis. The light, heavy, and residue fractions of Osmanthus fragrans absolute were analyzed. A total of 73 volatile components belonging to 8 major categories were detected in the three fractions, including 22 alcohols, 5 aldehydes, 10 acids, 3 phenols, 5 ketones, 17 esters, 5 alkanes, and 6 olefins. Alcohols were the main contributors to the volatile components in each fraction, and the types and relative contents of volatile components significantly differed among the fractions (P1) of the three fractions, the key aroma contributing components (ROAV>1) of the 14 light fractions, 9 recombinant fractions, and 11 residue fractions were analyzed by the aroma profile analysis. Light fractions exhibited more intense floral, sweet, and pine woody aromas compared to recombinant and residue fractions. The light fractions had the highest types and relative contents of volatile components and showcased the most intense Osmanthus fragrans floral flavor. This study provided a theoretical basis for the comprehensive utilization of different fractions of Osmanthus fragrans absolute through molecular distillation using GC-MS combined with chemometrics methods.

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Efficient utilization of organic fertilizer through advanced technology and development of regional agriculture according to local conditions are a crucial method for the development of organic dry farming. Combining sheep manure with millet cultivation can significantly enhance millet quality. Unlike traditional detection methods, hyperspectral imaging offers a rapid, non-destructive means of analyzing samples, therefore, it has great potential in achieving rapid detection of millet crude fat and protein. In order to provide reference and theoretical support for improvement of millet cultivation practices and rapid assessment of nutrient content in millet through hyperspectral imaging, in this study, hyperspectral image data of 358 millet samples were collected, traditional detection methods were used to determine the content of crude fat and crude protein. Simultaneously, chemometrics methods were utilized to predict the content of the two components. The results indicated that the partial least squares regression (PLSR) model established by employing the successive projections algorithm(SPA) and iteratively retaining informative variables (IRIV) had the highest accuracy. The correlation coefficient (R²) predicted by the model for crude protein content was 0.88, with a root mean squared error(RMSE) of 0.59 and a relative percent deviation (RPD) of 1.99. The visualization representations by the regression model vividly illustrated the accumulation patterns of crude fat and protein at various application rates of sheep manure, and it was determined that applying 90 m3 of fertilizer per hectare was the optimal application amount. [ABSTRACT FROM AUTHOR]

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs. [ABSTRACT FROM AUTHOR]

Introduction: Scrophulariae Radix (SR) has been extensively used in traditional Chinese medicine (TCM) for thousands of years. However, the processing methods and production areas of Scrophularia ningpoensis have undergone notable historic changes. Thus, their effects on the bioactive constituents of SR still need to be studied further. Objectives: This study aimed to establish an objective and comprehensive method to identify the correlation of bioactive constituents of SR with variety, place of origin and processing method for evaluating their qualities. Methodology: An accurate and rapid high‐performance liquid chromatography‐diode array detector (HPLC‐DAD) method for the simultaneous determination of 11 marker components (aucubin, harpagide, 6‐O‐methyl‐catalpol, harpagoside, verbascoside, isoverbascoside, angoroside C, cinnamic acid, l‐tyrosine, l‐phenylalanine, and l‐tryptophan) was established to evaluate the quality of SR for the first time. In addition, the effects of different production areas and processed methods on the target compounds were studied by analysing 66 batches of SR samples with chemometrics methods, including similarity evaluation of chromatographic fingerprints of TCM, principal component analysis (PCA), and partial least squares‐discriminant analysis (PLS‐DA). Results: Compared with "sweating", short‐term "steaming" and "slice‐drying" could largely preserve the bioactive constituents of SR. When using the model established through PLS‐DA, five components were identified as the most significant variables for discrimination. Furthermore, the score plots of PCA and the similarity evaluation revealed that variety had a more notable influence on the quality of SR than the place of origin. Conclusion: An objective approach of HPLC fingerprint coupled with chemometrics analysis and quantitative assessment could be applied to discriminate different processed SR and evaluate the qualities of SR rapidly. An accurate and comprehensive HPLC‐DAD method for the simultaneous determination of 11 marker components was established to identify the correlation of bioactive constituents of SR with variety, place of origin, and processing methods. In addition, chemometrics methods, including similarity evaluation of chromatographic fingerprints of TCM, PCA, and PLS‐DA, were used to analyze 66 batches of SR samples based on the target compounds. The above work could be applied to discriminate different processed SR and evaluate the qualities of SR rapidly. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for swift detection of AFB 1 in peanuts using millimetre wave technology. The research team devised a portable millimetre-wave detection device employing a double-external-difference mixing structure. The device measured millimetre-wave transmission coefficients in the 20 GHz - 40 GHz frequency range for peanut samples. Results showed that the PCA-KNN model excelled in qualitative AFB 1 detection, achieving 100 % accuracy in the prediction set. In quantitative analysis, by condensing the feature variables into a 16-dimensional space, the BOSS-PSO-SVR model enhanced performance. Compared to the full transmission coefficient SVR model, the BOSS-PSO-SVR model exhibited improved coefficients of determination (R P 2), reducing root mean square error of prediction (RMSEP) from 36.49 μ g ∙ kg − 1 to 19.08 μ g ∙ kg − 1 , and enhancing relative prediction deviation (RPD) from 3.17 to 6.06. This study concludes that the integration of a custom miniaturized millimetre-wave device with appropriate chemometric methods facilitates rapid and accurate detection of peanut AFB 1 levels. • Development of a new portable millimeter-wave detection unit. • Realize 100% accuracy of peanut AFB 1 qualitative detection. • Select the optimal subset of variables to achieve quantitative detection of peanut AFB 1. [ABSTRACT FROM AUTHOR]

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity. [ABSTRACT FROM AUTHOR]

Aflatoxin B1 (AFB1) is carcinogenic and highly susceptible to production in wheat. In this study, the quantitative detection of contaminant AFB1 in wheat was investigated by Raman spectroscopy combined with chemometric method realization. Firstly, Savitzky-Golay smoothing (SG) and baseline calibration methods were used to perform the necessary preprocessing of the collected raw Raman spectra. Then, three variable optimization methods, i.e., competitive adaptive reweighted sampling (CARS), iteratively variable subset optimization (IVSO), and bootstrap soft shrinkage (BOSS), were applied to the preprocessed wheat Raman spectra. Finally, partial least squares regression (PLSR) models were developed to determine AFB1 in wheat samples. The results showed that all three variable optimization algorithms significantly improved the predictive performance of the models. The BOSS-PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination (R P 2 ) was 0.9927, the root mean square error of prediction (RMSEP) was 2.4260 μg/kg, and the relative prediction deviation (RPD) was 11.5250, respectively. In conclusion, the combination of Raman spectroscopy and chemometrics can realize the rapid quantitative detection of AFB1 in wheat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R 2 ) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models' RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Boletus bainiugan has high nutritional and economic values. As one of the potential medicinal active ingredients, nucleosides have important research significance. Porcini mushrooms fraud is frequently detected on the market, including substitute inferior into superior and lack of geographical origin's certification. This behavior results in economic loss and health damage to consumers. Fourier transform near-infrared (FT-NIR) spectroscopy is a fast, efficient and reliable analytical tool. In the present study, the effect of source environment (climatic factors) on nucleoside content is analyzed for the first time. Then, the FT-NIR spectroscopy to study the origin traceability and content prediction of Boletus bainiugan are utilized. The results indicate that the nucleoside content is associated with precipitation and temperature. The combination of synchronous two-dimensional correlation spectroscopy (2DCOS) with residual neural networks (ResNet) model obtains the precise identification of the origin of Boletus bainiugan, with an accuracy of 100%. In the prediction models of content for uridine, guanosine, and adenosine, the optimal coefficient of determination of predictive set (R 2 P ) is 0.901, and the optimum residual prediction deviation (RPD) is 3.178. FT-NIR spectroscopy has proven to be an environmentally friendly and non-destructive analytical tool for accurate origin traceability and content prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Grammosciadium platycarpum Boiss. & Hausskn (Family 'Apiaceae') is a crop rich in essential oil and widely used in food and perfume industries. This study aimed to investigate the diversity of phytochemical traits and antimicrobial potency in G. platycarpum Boiss. & Hausskn populations collected from fourteen geographical regions in Iran. The aim was to identify the compounds of the essential oil and extract of the aerial parts, to investigate its antimicrobial properties, and to select the best population for domestication, cultivation and future breeding programs. The aerial parts of the plant were used to extract and determine the content and constituents of the essential oil. The essential oil content (EOC) exhibited a range from 0.09 to 0.46%. TAK population showed the maximum and QAS population revealed the minimum EOC. Based on GC-MS and GC analysis, 91.63 to 98.50% of the essential compounds of different populations of G. platycarpum Boiss. & Hausskn were identified. The main chemical groups identified in the essential oil include hydrocarbon monoterpenes (22.79-46.15%), oxygenated monoterpenes (0.87-31.05%), hydrocarbon sesquiterpenes (25.50-61.04%) and oxygenated sesquiterpenes (5.75-19.52%). Based on the results, (Z, E)-α-Farnesene (13.29-53.71%), linalool (0.44-30.56%), limonene (5.84-31.14%), α-Farnesene (0.71-22.39%), β-pinene (5.10-18.48%), and Caryophyllene (2.95-17.87%) were the major compounds of the essential oil. Chlorogenic acid, ferulic acid, and rutin were detected as the major phenolic compounds using HPLC. The essential oil of ABH, JOL and GAR populations as well as the extracts of MAG, OSH and JOL populations showed great antibacterial activity against E. coli and S. aureus. The high diversity observed among different populations of G. platycarpum Boiss. & Hausskn provides good potential for selecting the best populations and using them in domestication projects, cultivation, and breeding programs., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Declaration of competing interest: The authors declare no conflict of interest., (© 2025. The Author(s).)

The Zika disease caused by the Zika virus was declared a Public Health Emergency by the World Health Union (WHO), with microcephaly as the most critical consequence. Aiming to reduce the spread of the virus, biopharmaceutical organizations invest in vaccine research and production, based on multiple platforms. A crescent vaccine production approach is based on virus-like particles (VLP), for not having genetic material in its composition, hypoallergenic and non-mutant character. For bioprocess, it is essential to have means of real-time monitoring, which can be assessed using process analysis techniques such as Near-infrared (NIR) spectroscopy, that can be combined with chemometric methods, like Partial-Least Squares (PLS) and Artificial Neural Networks (ANN) for prediction of biochemical variables. This work proposes a biochemical Zika VLP upstream production at-line monitoring model using NIR spectroscopy comparing sampling conditions (with or without cells), analytical blank (air, ultrapure water), and spectra pre-processing approaches. Seven experiments in a benchtop bioreactor using recombinant baculovirus/Sf9 insect cell platform in serum-free medium were performed to obtain biochemical and spectral data for chemometrics modeling (PLS and ANN), composed by a random data split (80 % calibration, 20 % validation) for cross-validation of the PLS models and 70 % training, 15 % testing, 15 % validation for ANN. The best models generated in the present work presented an average absolute error of 1.59 × 10 5  cell/mL for density of viable cells, 2.37 % for cell viability, 0.25 g/L for glucose, 0.007 g/L for lactate, 0.138 g/L for glutamine, 0.18 g/L for glutamate, 0,003 g/L for ammonium, and 0.014 g/L for potassium., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Sugars (saccharides) are sweet-tasting carbohydrates that are abundant in foods and play very important roles in living organisms, particularly as sources and stores of energy, and as structural elements in cellular membranes. They are desirable therapeutic targets, as they participate in multiple metabolic processes as fundamental elements. However, the physicochemical characterization of sugars is a challenging task, mostly due to the structural similarity shared by the large diversity of compounds of this family. The need for fast, accurate enough, and cost-effective analytical methods for these substances is of extreme relevance, in particular because of the recently increasing importance of carbohydrates in Medicine and food industry. With this in view, this work focused on the development of chemometric models for semi-quantitative analysis of samples of different types of sugars (glucose, galactose, mannitol, sorbose and fructose) using infrared spectra as data, as an example of application of a novel approach, where the Principal Component Analysis (PCA) score plots are used to estimate the composition (weight-%) of the mixtures of the sugars. In these plots, polygonal geometric shapes emerge in the vectorial space of the most significant principal components, that allow grouping different types of samples on the vertices, edges, faces and interior of the polygons according to the composition of the samples. This approach was applied successfully to mixtures of up to 5 sugars and shown to appropriately extract the compositional information from the hyper-redundant complex spectral data. Thought the method has been applied here to a specific problem, it shall be considered as a general procedure for the semi-quantitative analysis of other types of mixtures and applicable to other types of data reflecting their composition. In fact, the methodology appears as an efficient tool to solve three main general problems: (i) use hyper-redundant (in variables) data, as spectral information, directly and with minimum pre-treatment, to evaluate semi-quantitatively the composition of mixtures; (ii) do this for systems which produce data that can be considered rather similar; and (iii) do it for a number of substances present in the mixtures that might be greater than that usually considered in chemistry, which in general is limited to 3 components. In addition, this work also demonstrates that, similarly to the developed analysis based on the PCA score plots, the Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) chemometric method can also be used successfully for the qualitative (when used without any previous knowledge of the components present in the samples) or semi-quantitative (when the pure components spectral profiles are provided as references) analyses of mixtures of (at least) up to 5 distinct sugars., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ANNA LUIZA BIZERRA DE BRITO, RUI FAUSTO MARTINS RIBEIRO DA SILVA LOURENCO, LUIS PEDRO DA FRANCA E SILVA CALEIRAS VIEGAS has patent Chemometrics Protocol for Semi-Quantitative Analysis of Mixtures Using Spectral Data or Other Composition Dependent Hyper-Redundant Data pending to 119690. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

As a new deep-processing garlic product with notable health benefits, the accurate discrimination of processing stages and prediction of key physicochemical constituents in black garlic are vital for maintaining product quality. This study proposed a novel method utilizing hyperspectral imaging technology to both rapidly monitor the processing stages and quantitatively predict changes in the key physicochemical constituents during black garlic processing. Multiple methods of noise reduction and feature screening were used to process the acquired hyperspectral information. To differentiate processing stages, pattern recognition methods including linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine classification (SVC) analysis were utilized, achieving a discriminant accuracy of up to 98.46 %. Furthermore, partial least squares regression (PLSR) and support vector machine regression (SVR) analysis were performed to achieve quantitative prediction of the key physicochemical constituents including moisture and 5-HMF. PLSR models outperformed SVR models, with correlation coefficient of prediction of 0.9762 and 0.9744 for moisture and 5-HMF content, respectively. The current study can not only offer an effective approach for quality detection and assessment during black garlic processing, but also have a positive significance for the advancement of black garlic related industries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Ethnopharmacological Relevance: Flos Trollii (FT) is the dried flower of Trollius Chinensis Bunge of Ranunculaceae with the pharmacological properties of anti-inflammatory, antibacterial, antiviral, anti-oxidative. The herb FT is not only a traditional Chinese medicine (TCM) but also an extensively utilized ethnic medicine, employed by diverse ethnic groups including Mongolian, Tibetan, and Kazakh., Aim of Study: FT was taken as an example to construct a strategy of quality markers (Q-markers) identification based on effect, property flavor material basis, and rapid quantitative evaluation using near-infrared (NIR) spectroscopy and chemometric methods of TCM., Materials and Methods: Initially, the anti-inflammatory efficacy of FT from three places of origin was evaluated using the RAW264.7-cell inflammatory model, and the bitter property flavor was characterized using an electronic tongue. The high-performance liquid chromatography(HPLC) fingerprint of FT was generated, and the quality of FT from different origins was evaluated employing chemometrics. Next, potential anti-inflammatory and bitter property flavor compounds were screened utilizing a fingerprinting-effect relationship and fingerprinting-property flavor relationship model using partial least squares regression (PLSR). The Q-markers of the FT were confirmed based on the testability principle. Then, a swift, uncomplicated, and precise Q-marker content of the FT prediction model was developed by adopting NIR., Results: The main common fingerprinting peaks affecting FT's efficacy and property flavor were screened. Five of these compounds, 2″-O-beta-L-galactopyranosylorientin, orientin, vitexin, veratric acid, and isoquercitrin, characterized using HPLC and ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS), could be regarded as Q-markers of FT. Q-marker content of the FT prediction model developed adopting NIR spectroscopy was rapid and effective., Conclusion: According to the strategy proposed in this study, a quantitative NIR spectroscopic method to identify Q-markers could be a tool to improve the QC efficiency of TCM., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with R P 2 , RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this study, the technique of fluorescence spectroscopy coupled with chemometric methods is used to analyse samples of Lake Chad Spirulina "Arthrospira platensis" (AP), either harvested and conditioned by using the traditional method at different seasons or industrially processed. The content of minor fluorescent nutrients is investigated. To this end, fluorescence excitation-emission matrices (EEMs) of 46 AP samples are recorded in aqueous solution. Synchronous fluorescence (SF) spectra are extracted from these EEMs and their important features are compared to those of PARAFAC methods. Synchronous fluorescence scanning allows different AP samples to be characterized in a single scan. The SF and PARAFAC methods yielded two groups of fluorescent compounds; the first group, consisting of vitamin-like molecules, shows excitation/emission (ex/em) peaks at 340/460, 390/462, 370/440 and 450/526 nm, attributed to caffeic acid, vitamin K, E and riboflavins respectively, while the second group, consisting of pigments, shows ex/em peaks at 610/654, 590/630 and 570/644 nm, attributed to phycocyanins, C-phycocyanin and allophycocyanin. Our fluorescence data showed that while both vitamins and pigments are present in AP during the rainy season, only fluorescent components of vitamin-like compounds are present during the dry season. PCA methods allowed classifying different AP samples according to their geographic origin and harvesting season. Fluorescence spectroscopy therefore appears to be a powerful technique for rapidly assessing the chemical composition of AP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Traditional viscosity measurements for carrageenan are laborious, present practical and environmental challenges, and fail to provide structure-property understanding for application and manufacturing development. We hypothesize that integrating Size Exclusion Chromatography (SEC) with Multi-Angle Light Scattering (MALS) and online viscometry, combined with chemometric techniques, can develop a more efficient and environmentally friendly method for determining the apparent viscosity of carrageenan solutions. To test this hypothesis, predictive chemometric models were developed using SEC-MALS data for carrageenan extracted from four different seaweed species. By integrating SEC-MALS with Partial Least Squares (PLS) regression, key molecular parameters such as hydrodynamic radius, intrinsic viscosity, and molecular mass were identified as significant influencers of viscosity. The model for carrageenan from Eucheuma denticulatum yielded the lowest prediction error (RMSEP 8.4), while those for carrageenan extracted from Kappaphycus alvarezii or from several species of the Chondrus genus showed higher errors due to κ-carrageenan sensitivity. For carrageenan extracted from seaweed of the Gigartina genus, incorporating the root mean square radius resulted in a low prediction error of 10. This study concludes that integrating SEC-MALS with PLS regression effectively identifies key molecular parameters influencing carrageenan viscosity, enhancing structure-property understanding and providing a reliable analytical method for optimizing quality control and application in various industries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R 2 ), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Snake melon (Cucumis melo var. flexuosus, CM) is a gourd with health-promoting nutritional traits and unexplored phytochemicals. This study aims to comprehensively investigate the phytoconstituents in the fruits, leaves, roots, seeds, and stems of CM, using liquid chromatography-quadrupole time-of-flight tandem mass spectrometry. Consequently, 118 metabolites were identified, encompassing phenolic compounds, flavonoids, megastigmanes, lignans, cucurbitacins, and fatty acids. Multivariate data analysis revealed differences in the metabolite composition of CM organs and correlated these variations with the potential in-vitro anti-inflammatory properties assessed against RAW 264.7 macrophages through the down-regulation of cyclo-oxygenase-Ⅱ, nuclear factor-kappa B, and tumor necrosis factor-α. The results indicated that leaf and seed extracts showed the highest anti-inflammatory activity due to their enrichment in several flavonoids, phenolic glycosides, and a megastigmane. These findings emphasize the health benefits of CM organs as potential functional foods and functional food by-products, serving as a natural source for developing new anti-inflammatory agents., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The aim of this study was to create rapid and sustainable instrumental methods for screening virgin olive oils (VOOs) to support the Panel test. The Panel test is the official sensory method used in EU regulations to determine the commercial category of VOOs. The Panel test is based on a time-consuming and expensive approach, so reducing the number of samples to be analysed is crucial. Spectroscopy offers a potential solution for quickly determining VOOs composition and predicting their quality grade. In this context, three spectroscopic techniques were explored: Near-Infrared (NIR), Fourier-Transform Infrared (FT-IR), and Raman spectroscopy. A dataset of 100 VOOs samples, categorized into the three official grades (extra virgin, EVOO, virgin, VOO and lampante, LOO) established in EU, based on the Panel test results, was analysed. An initial analysis of all spectra revealed typical for triacylglycerols molecular vibrations and not good variability between types of samples, indicating low specificity. However, FT-IR data paired with two different Partial Least Squares-Discriminant Analysis (PLS-DA) models - one differentiating LOO from non-LOO (VOO and EVOO) and another distinguishing LOO from VOO - yielded promising results. Cross-validation indicated successful sample classification with percentages ranging from 81% to 96%, in which LOO vs. no-LOO model showed the highest performance. These findings suggest that FT-IR coupled with chemometric analysis holds promise, particularly for discriminating LOO (inedible) from the higher-quality grade VOO and EVOO categories. Further research efforts are needed to possibly make the herein developed models more robust and potentially extend their application to differentiate all three VOO quality grades., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Mushrooms not only serve as a source of a wide range of nutrients in the structure of the human diet, but they have also received a great deal of attention in the field of biopharmaceuticals because of their wide range of medicinal benefits. Rapid quality certification of boletus (porcini) mushrooms is particularly important as a health food and as a potential source of medicines before purchase and production. Infrared (IR) spectroscopy is commonly used for rapid qualitative and quantitative analyses of foods and herbs. The Ultra Performance Liquid Chromatography (UPLC) combined with systematic fingerprinting quantification was used to analyze the quality consistency of Boletus edulis (B. edulis) from different geographic sources, and a method based on Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometrics for origin traceability and rapid prediction of nucleoside quality marker content of B. edulis dried slices was developed with the aim of achieving rapid, lossless, high-throughput and green quality authentication of raw materials for pharmaceutical products., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R 2 p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R 2 p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Heat stress in hyper-prolific lactating sows is recognised as a factor reducing feed intake, milk production, and welfare, with significant losses in farm productivity. Individual capacities for body thermoregulation during environmental hyperthermia determine the adaptation of the animal during long and recurrent events. This study aimed to evaluate the ability of attenuated total reflectance (ATR) mid infrared (MIR) spectroscopy as a high-throughput method to identify markers of stress in plasma and milk collected from lactating sows under heat stress conditions fed with two levels of protein in the diet defined as low (16%) and standard (20%). The MIR spectra were analysed using linear discriminant analysis (LDA) and principal component analysis and validated using cross-validation. The results obtained indicated that MIR spectroscopy, in combination with chemometrics, was able to identify changes in the spectra associated with heat stress in wavenumbers corresponding with amide groups (proteins) (highest loadings observed in the regions between1065 and 1635 cm -1 ), lipids and unsaturated fatty acids (regions between 1746 and 3063 cm -1 ), lipo-polysaccharides (in 1247 cm -1 ) and carbohydrates (around the region1050 cm -1 ). These results also indicated that the information provided by these wavenumbers can be used as metabolic markers of the adaptation of the sows to hyperthermia. It was concluded that MIR spectroscopy is a rapid and inexpensive tool capable of detecting and evaluating the main biochemical changes of hyperthermia on lactating sows, facilitating the development of palliative management strategies such as dietary manipulations., Competing Interests: Declarations. Ethics approval: All animals and experimental protocols in this study received approval from the Animal Ethics Committee of the University of Queensland (Ethics ID: 2020/AE000340). The experimental procedures adhered to the Australian Code for the Care and Use of Animals for Scientific Purposes (8th edition; National Health and Medical Research Council, 2013). Conflict of interest: The authors declare no conflict of interest. Declaration of generative AI and AI-assisted technologies in the writing process: During the preparation of this work the author(s) did not use any AI and AI-assisted technologies., (© 2024. The Author(s).)

Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Schisandrae Chinensis Fructus (SCF), a traditional Chinese medicine, has been used in treating virtual injury and strain since ancient times. The Chinese Pharmacopoeia reveals that SCF includes raw (RSCF) and vinegar-processed (VSCF) decoction pieces., Objective: This study developed an effective method combining the electronic eye (e-eye), electronic tongue (e-tongue), and chemometrics to discriminate RSCF and VSCF from the perspective of chemical composition, color, and taste., Material and Methods: First, RSCF were collected and processed into VSCF, and their color parameters, e-tongue sensory properties, high-performance liquid chromatography (HPLC) and ultra-HPLC (UPLC) characteristic fingerprints, and nominal ingredients were determined. Multivariate statistical analyses, including principal component, linear discriminant, similarity, and partial least squares discriminant analyses, were conducted., Results: HPLC and UPLC fingerprints were established, demonstrating a > 0.900 similarity. The content determination indicated increased schisantherin A, schisantherin B, and schisandrin A contents in VSCF. The e-eye data demonstrated a > 1.5 total color difference before and after processing ΔE* ab , indicating the significantly changed sample color and appearance before and after processing. The e-tongue technology was used to quantitatively characterize the taste of RSCF and VSCF. The t-test revealed significantly reduced sourness, aftertaste-bitter, and aftertaste-astringent values of SCF after vinegar processing. Principal component and partial least squares discriminant analyses indicated that e-eye and e-tongue realize the rapid RSCF and VSCF identification., Conclusion: The proposed comprehensive strategy of electronic eye and electronic tongue combined with chemometrics demonstrated satisfactory results with high efficiency, accuracy, and reliability. This can be developed into a novel and accurate method for discriminating RSCF and VSCF., (© 2024 John Wiley & Sons Ltd.)

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

The effect of solvents with different polarities on the recovery of phytochemicals (carotenoids, capsaicinoids, and phenolic compounds) from habanero pepper (Capsicum chinense) and their association with antioxidant activity (ABTS •+ and DPPH) was evaluated through Ultra-Performance-Liquid Chromatography coupled with a Photodiode Array Detector and a Electrospray Ionization Mass Spectrometry (UPLC-PDA-ESI-MS)-based chemometric analysis, including linear correlation, multiple linear regression, and principal component analysis (PCA). The solvent polarity scale was established according to solvent dielectric constants (ɛ). Color variation (ΔE) was used to determine the presence of carotenoids, with the highest ΔE obtained using low-polarity solvents (hexane and ethyl acetate). A high content of capsaicin and dihydrocapsaicin was recovered with acetone (4.29 and 3.76 mg g⁻¹ dry weight, respectively). Phenolic compounds such as N-caffeoyl putrescine and derivatives of luteolin and apigenin were identified through mass spectrometry. A high recovery (26.54-31.74 mg GAE g⁻¹ dry weight) of these compounds was obtained using intermediate-polarity solvents. The PCA revealed clustering of solvents based on their affinity for extracting specific compounds and their association with antioxidant activity. A significant correlation was observed between ΔE and DPPH, indicating that carotenoid pigments exhibited higher DPPH radical inhibition capacity than other compounds. Total phenolic content (TPC) and phenolic compounds (phenolpolyamides, hydroxycinnamic acids, and hydroxybenzoic acids) were clustered with the ABTS •+ radical inhibition assay. The information obtained is crucial for selecting suitable solvents in the extraction and purification protocols of bioactive compounds. It is also valuable for conducting plant metabolomic analyses and for studies focused on determining the effects of bioactive compounds in food, pharmaceutical, and cosmeceutical applications. PRACTICAL APPLICATION: The results describe the characteristics of the extracts obtained using different solvents. Therefore, the information may be useful for establishing extraction protocols for phytochemical compounds in fruits from Capsicum chinense for various purposes, such as metabolomic analysis, the recovery of specific compounds with antioxidant activity, and food applications., (© 2024 Institute of Food Technologists.)

Introduction: Magnoliae officinalis cortex (MOC) is an important traditional Chinese medicine (TCM), and both raw and stir-fried MOC were commonly used in clinic., Objectives: This study aimed to discriminate MOC and MOC stir-fried with ginger juice (MOCG) using an integrated approach combining liquid chromatography/mass spectrometry (LC/MS), gas chromatography/mass spectrometry (GC/MS), intelligent sensors, and chemometrics., Methods: The sensory characters of the samples were digitalized using intelligent sensors, i.e., colorimeter, electronic nose, and electronic tongue. Meanwhile, the chemical profiles of the samples were analyzed using LC/MS and GC/MS methods. Chemometric models were constructed to discriminate samples of MOC and MOCG based on not only the sensory data but also the chemical data., Results: The differential sensory characters (L* and b* from colorimeter, ANS from electronic tongue, W1S and W2S from electronic nose) and the differential chemical compounds (26 and 11 compounds from LC/MS and GC/MS, respectively) were discovered between MOC and MOCG. Furthermore, twelve differential compounds showed good relations with differential sensory characters. Finally, artificial neural network models were established to discriminate samples of MOC and MOCG, in which W1S, W2S, ANS, b*, and 10 differential compounds were among the top 10 important variables, respectively., Conclusion: Samples of MOC and MOCG can be discriminated not only by the digitalized data of color, taste, and scent detected by intelligent sensors but also by chemical information obtained from LC/MS and GC/MS using chemometrics. The variations in sensory characters and chemical compounds between MOC and MOCG partially resulted from the Maillard reaction products and the oxidation of some compounds in the stir-frying process., (© 2024 John Wiley & Sons Ltd.)

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain., Competing Interests: Declarations. Ethics Approval: Not related. Consent to Participate: Not related. Consent for Publication: Not related. Competing Interests: The authors declare no competing interests., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted R P 2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 μg∙kg -1 ., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: This study aimed to validate a method for characterizing and quantifying the multi-elemental profiles of different insect flours to enable their distinction, identification, and quality assessment. The focus was on three insect species: cricket ( Acheta domesticus ), buffalo worm ( Alphitobius diaperinus ), and mealworm ( Tenebrio molitor )., Methods: Mealworms were powdered in the laboratory through mechanical processing. Sample analysis involved acid digestion using a microwave digester, followed by profiling with Inductively Coupled Plasma Mass Spectrometry (ICP-MS). This technique enabled rapid, multi-elemental analysis at trace levels. Chemometric methods, including Principal Component Analysis (PCA) for exploratory analysis, Covariance Selection-Linear Discriminant Analysis (CovSel-LDA), alongside forward stepwise LDA classification methods, were applied and compared., Results: ICP-MS accurately detected elements at micro trace levels. Both classification models, based on different variable selection methods and externally validated on a test set comprising 45% of the available samples, proved effective in classifying samples based on slightly different pools of trace elements. CovSel-LDA selected Mg and Se, whereas the stepwise-LDA focused on Mg, K, and Mn., Conclusions: the validated methods demonstrated high accuracy and generalizability, supporting their potential use in food industry applications. This model could assist in quality control, facilitating the introduction of insect-based flour into European and international markets as novel foods., Competing Interests: The authors declare no conflicts of interest.

Introduction: Moringa oleifera is a plant with high antioxidant content in its leaves, flowers and seeds. It attracts the attention of researchers with the effect of its nutritional and medical advantages., Objective: The purpose of the current study is to propose a deep eutectic solvent (DES)-based ultrasound-assisted extraction of bioactive substances from M. oleifera leaves by the application of a chemometric study., Methodology: A total of 18 different choline chloride-based DESs were prepared by using several hydrogen bond donors (glucose, sucrose, glycerol, ethylene glycol, urea and dimethyl urea) with various molar ratios (1:1, 1:2 and 2:1) by addition of diluents (water and 50% methanol) or alone. In order to decide the best DES combination, principal component analysis (PCA) was applied. The response surface method (RSM) was used as statistical experimental design approach through the Box-Behnken design., Results: The best phenolic (TPC), flavonoid (TFC) and antioxidant activity yields of M. oleifera leaf extract were found to be 19.102 mg-GAE, 10.47 mg-CE and 24.404 mg-TEAC per gram dried leaf under the optimal conditions (50% water content, 20% amplitude, 15 min time). The model fitting has been also found reliable depending on the statistical indicators such as p-value (<0.0001), coefficients of determination (R 2  = 0.9827, 0.9916 and 0.9864) and root mean square error (RMSE = 1.0562, 2.4656 and 0.7713)., Conclusions: A chemometric study through PCA was carried out to determine the similarities and differences between the solvent groups, and the ethylene glycol-based DES (1:2, molar ratio) with the addition of water showed the best performance., (© 2023 John Wiley & Sons Ltd.)

Microplastic (MP) pollution in water has become one of the most important global problems of our time. The development of appropriate and rapid analysis techniques is of great importance at the beginning of the studies aimed at solving this problem. In the presented study, in order to perform the qualitative and quantitative analysis of MP forms of polyamide (PA), polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET), which are known to be most abundant in water, in a fast and easy way, new Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy methods were tried to be developed by utilizing chemometric methods. While principal component analysis (PCA) was applied for qualitative analyses, partial least squares (PLS) models were created for quantitative analyses. Raw, 1st, and 2nd order derivatives of all spectra and their spectra with different levels of smoothing points were taken and 24 different chemometric models were created for each MP. In interpreting the statistical performances of the developed PCA and PLS models, different parameters were used. According to the obtained results, the qualitative discrimination of all polymer types was successfully achieved. It was determined that the PLS models developed for the quantitative determination of mixtures consisting of different concentrations of MP types could not be at the desired level. However, it was determined that the PLS models developed for PA, PE, PP, and PET, where the normal spectrum was used, could give quantitatively accurate results, albeit partially., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Kimchi is a traditional Korean dish made from fermenting vegetables. The fermentation process is crucial for enhancing its quality and flavor during storage. Approaches such as hyperspectral imaging (HSI) and chemometrics (PLS, partial least square; SVR, support vector regression) including principal component analysis (PCA), and 2-dimensional correlation spectroscopy (2D-COS) can detect key physical and chemical components and changes in total soluble solids (TSS), pH, titratable acidity (TA), salinity, and lactic acid bacteria (LAB). Multivariate analytical models were developed to predict the quality properties using full and characteristic wavelengths and preprocessed data. The results showed that the ratio of prediction to deviation (RPD) values of the PLS prediction model constructed using the full wavelengths of TSS, salinity, pH, TA, and LAB were 1.57, 2.33, 2.79, 2.91, and 2.73, respectively. The Savitzky Golay 1st derivative preprocessed SVR model established based on characteristic wavelengths (951, 1020, 1139, 1174, 1216, 1321, and 1384 nm) extracted by PCA and a 2D-COS matrix showed the best results and increased efficiency in predicting pH (R p 2  = 0.9166, RPD = 3.281) and the number of LAB (R p 2  = 0.8488, RPD = 2.466). Additionally, the visualization process accurately illustrated the distribution of various quality indicators of kimchi across different periods. These results demonstrate that our proposed HSI strategy successfully assessed the degree of kimchi fermentation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

As a reliable method, Laser induced breakdown spectroscopy (LIBS) is widely applied to elemental analysis in different fields. In this research, LIBS was used to analyze pharmaceutical tablets. For this purpose, spectra from eight samples of three pharmaceutical classes were acquired; and after peaks recognition, they were investigated by principal component analysis (PCA) and artificial neural network (ANN). According to the results, due to the similarity of the main elements of different samples, it is difficult to distinguish them by spectra; therefore, PCA was used to make a better comparison between the samples. Apart from that, unknown samples were identified and predicted through the ANN. [ABSTRACT FROM AUTHOR]

Medicinal and aromatic plants are alternative products to synthetics because of their antioxidant, antimicrobial, anti-inflammatory and antidiabetic effects. The objective of this study is to investigate the automated solvent extraction (ASE) process parameters for the extraction of bioactive-rich substances from purple basil (Ocimum basilicum L.). Process optimization in relation to total phenolic content (TPC), total flavonoid content (TFC), and total anthocyanin content (TAC) was performed through a chemometric approach. The ASE system was designed, modelled and optimized by 3-factor and 3-level Box-Behnken design of Response Surface method (RSM). Antioxidant activity of the samples were measured by 2 different free radical scavenging activity assays (ABTS and DPPH). By using principal component analysis (PCA) to the dataset, the impact of interactions between the parameters was also evaluated according to their antioxidant activity, TPC, TFC and TAC levels. The optimal ASE conditions (0.3 g of purple basil, 19 min of immersion time and 66 % ethanol solution) provided the highest yields of TPC (98.888 mg-GAE/g-DM), TFC (27.033 mg-CE/g-DM) and TAC (11.556 mg-C3G/g-DM), which were verified by satisfactory validation findings (the error<2 %)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Azo dyes, such as tartrazine and sunset yellow, are widely used as affordable and stable food colorants. Accurate quantification is crucial in foods for regulatory monitoring to ensure compliance with safety standards and minimize health risks. This study developed a low-cost and eco-friendly method using digital images and chemometrics for the simultaneous determination of these dyes in food samples. The best prediction results were achieved by applying partial least squares regression to RGB + Grayscale+HSI color histograms, with R 2 of 0.9977, 0.9989, RMSEP of 0.21, 0.10 mg/L and REP of 1.6, 1.0 % for tartrazine and sunset yellow, respectively. The method was successfully applied for determination of tartrazine and sunset yellow in soft drink samples, producing results comparable to those obtained from the HPLC method. This innovative approach provides a practical and reliable alternative for monitoring the dye concentrations, supporting both food manufacturers and health authorities in ensuring compliance with safety standards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The unique fatty acid composition of BSF larvae oil makes it suitable for various applications, including use in animal feed, aquaculture, biodiesel production, biomaterials, and the food industry. Determination of BSF larvae composition usually requires analytical methods with chemicals, thus needing emerging techniques for fast characterization of its composition. In this study, Near Infrared Hyperspectral Imaging (NIR-HSI) (928 - 2524 nm) coupled with chemometrics was applied to predict the lipid content and fatty acid composition in intact black soldier fly (BSF) larvae. Partial Least Squares Regression (PLSR) and Support Vectors Machine Regression (SVMR) models, combined with two variable selection methods, Interval Partial Least Squares (iPLS) and Bootstrapping Soft Shrinkage (BOSS), were compared. PLSR reached a good performance to predict myristic acid with Root Mean Square Error in prediction (RMSEP) = 0.45 %, while SVMR reached values of Ratio to Prediction Deviation (RPD) > 3 to predict total lipid content, lauric acid, myristic acid, palmitic acid and oleic acid. In addition, selecting wavelength by BOSS improved PLSR models (6 - 15 % increases in RPD), while iPLS improved SVMR model to predict palmitic acid (16 % increases in RPD). The study emphasizes the advantages of NIR-HSI as a non-invasive, rapid method for lipid and fatty acid quantification, which can be highly valuable for industrial applications such as monitoring BSF larvae feeding systems to ensure high-quality oil production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Congelex Laxative Granules is an in-house preparation of Hebei Provincial Hospital of Traditional Chinese Medicine. This study aims to establish the HPLC fingerprint of Congelex Laxative Granules and evaluate its quality using chemometric methods. The Agilent Eclipse Plus C18 column and a methanol-water gradient elution system were employed, with detection at 224 nm. The High-performance liquid chromatography (HPLC) analysis of 20 batches of samples successfully established a fingerprint with 17 common peaks and a similarity exceeding 0.95. Seven main active components, including salidroside, echinacoside, acteoside, specnuezhenide, wedelolactone, aurantio-obtusin, and chrysophanol, were quantitatively analyzed. Hierarchical Cluster Analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to comprehensively evaluate sample quality. Results indicated that the 20 batches could be divided into two categories, with consistent results from PCA and HCA. The OPLS-DA model was stable and reliable, identifying salidroside, acteoside, and chrysophanol as key differential markers. The conclusion shows that the established fingerprint and content determination method provide an accurate and reliable tool for the quality control and comprehensive evaluation of Congelex Laxative Granules., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

For the first time, advanced chemometric models were utilized to determine florescence induced by carbon dots. In an endeavor to regulate anthelmintic drug usage by facilitating the determination of veterinary formulations in animals' biological fluids, a novel fluorometric-assisted chemometric method has been developed for detecting two nonfluorescent drugs, Ivermectin (IVR) and Clorsulon (CLR). The method relies on the linear quenching effect of the drugs on the fluorescence intensity of carbon dots (CDs) synthesized from natural sources. Despite the significant overlap, chemometric models such as partial least squares (PLS) and artificial neural networks (ANN), assisted by genetic algorithms (GA), successfully resolved the issue and achieved high-precision recovery of both drugs. The method demonstrates a linearity range of 50-6000 ng/mL, rendering it suitable for determining both drugs in biological animal fluids. To ensure practical application, the method was applied to veterinary formulations and spiked animal plasma, yielding satisfactory results. Finally, a comparison of the proposed method with official ones revealed no significant differences. According to principles of white analytical chemistry (WAC), the method also obeys sustainability rules. The method was therefore proven to be a novel, safe and applicable alternative approach for this formulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Lu'an Guapian (LAGP) is a renowned green tea, with its price significantly higher when picked before the Qingming Festival compared to after, posing risks of confusion and counterfeiting. This study proposed using an excitation-emission matrix (EEM) fluorescence method combined with chemometrics for rapid identification of tea picked before and after Qingming Festival. Firstly, the differences among the EEM fingerprints of different tea samples were analyzed using the alternating trilinear decomposition (ATLD) algorithm. To determine the differences between LAGP before and after Qingming Festival, the total contents of ten main components in tea were detected, and their effects on the EEM fluorescence fingerprint of tea were analyzed using correlation heatmaps. Finally, two chemometric algorithms, partial least squares discriminant analysis (PLS-DA) and k-nearest neighbor (k-NN), were used to classify LAGP before and after Qingming Festival, achieving a classification accuracy of 100% for the training set, test set, and prediction set. To further explore the potential of this method, LAGP was further classified in detail according to four detailed picking periods, achieving an accuracy of over 83%. The same chemometric algorithm was used to classify the data based on the high-performance liquid chromatography (HPLC) method, yielding results comparable to those of the EEM-based method, though slightly inferior. Variable importance projection (VIP) analysis shows that catechin analogs are the main contributors to the classification of LAGP. The results demonstrated the EEM method's significant potential in identifying the picking time of green tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Fragrant rapeseed oils and traditional pressed oils are increasingly popular in China owing to their sensory advantages. Many fragrant rapeseed oils are labeled by different fragrance types; however, the scientific basis for these differences is lacking. To identify the distinctive aroma and achieve fragrance classification, the sensory characteristics and aroma components of nine different fragrant rapeseed oils were analyzed via sensory evaluation and gas-chromatography-mass spectrometry-olfactometry. A total of 35 aroma compounds were found to contribute to the overall aroma. By using chemometrics methods, rapeseed oils were categorized into three fragrance styles: “strong fragrance,” “umami fragrance,” and “delicate fragrance.” In total, 10 aroma compounds were predicted to be the most effective compounds for distinguishing sensory characteristics of fragrant rapeseed oil. According to our results, this approach has excellent potential for the fragrance classification and quality control of rapeseed oil.

Eucalyptus essential oil is widely valued for its therapeutic properties and extensive commercial applications, with its chemical composition significantly influenced by species variety, geographical origin, and environmental conditions. This study aims to develop a reliable method for identifying the geographical origin and variety of eucalyptus oil samples through the application of advanced analytical techniques combined with chemometric methods. Essential oils from Eucalyptus globulus and Eucalyptus camaldulensis were analyzed using Gas Chromatography-Flame Ionization Detection (GC-FID) and Fourier Transform Infrared (FTIR) Spectroscopy. Chemometric analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (O2PLS-DA) and Hierarchical Cluster Analysis (HCA), were utilized to classify the oils based on their volatile compound profiles. Notably, O2PLS-DA was applied directly to the raw FTIR data without additional spectral processing, showcasing its robustness in handling unprocessed data. For geographical origin determination, the GC-FID model achieved a Correct Classification Rate (CCR) of 100%, with 100% specificity and 100% sensitivity for both calibration and validation sets. FTIR spectroscopy achieved a CCR of 100%, specificity of 100%, and sensitivity of 100% for the calibration set, while the validation set yielded a CCR of 95.83%, specificity of 99.02%, and sensitivity of 94.44%. In contrast, the analysis based on species variety demonstrated 100% accuracy across all metrics CCR, specificity, and sensitivity-for both calibration and validation using both techniques. These findings underscore the effectiveness of volatile and infrared spectroscopy profiling for quality control and authentication, providing robust tools for ensuring the consistency and reliability of eucalyptus essential oils in various industrial and therapeutic applications.

The extraction process plays a crucial role in the production of Tibetan medicines. This study focused on assembling a set of online near-infrared (NIR) spectroscopy detection devices for the extraction of medicinal herbs. The original infrared device was transformed into an online detection system. After evaluating the stability of the system, we applied online NIR spectroscopy monitoring to the flavonoid contents (total flavonoids, quercetin-3-O-sophoroside, and luteolin) of Meconopsis quintuplinervia Regel. during the ultrasonic extraction process and determined the extraction endpoint. Nine batches of samples were employed to construct quantitative and discriminant models, half of the remaining two batches of samples are used for external verification. Our research shows that the residual predictive deviation (RPD) values of total flavonoids, quercetin-3-O-sophoroside and luteolin models exceeded 2.5. The R values for external verification of the three ingredients were above 0.9, with RPD values generally exceeding 2 and RSEP values within 10 %, demonstrating the model's strong predictive performance. Most of the extraction endpoints of the flavonoid components in M. quintuplinervia ranged from 18 to 58 min, with high consistency between the predicted extraction endpoints of the external validation, suggesting accurate determination of extraction endpoints based on predicted values. This study can provide a reference for the online NIR spectroscopy quality monitoring of the extraction process of Chinese and Tibetan herbs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near-infrared spectroscopy to non-invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high-quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near-infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R 2 ) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Tempeh is an antioxidant-rich soybean fermentation product from Java, Indonesia. Cooking methods have an impact on the nutritional value and bioactivity of food., Objective: This study aims to investigate how the cooking process affects the metabolites and antioxidant activity in tempeh using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)., Methods: A nontargeted UHPLC-HRMS metabolomics and chemometric analysis were used to evaluate metabolite profiles and antioxidant activity changes because of food processing in tempeh., Results: The score plots of tempeh produced by boiling and frying methods displayed a distinct separation from raw tempeh, revealing that the cooking process altered the metabolite composition of tempeh. Due to processing, L-glutamic acid, L-pyroglutamic acid, DL-glutamine, and D-( +)-proline became the most affected metabolites on tempeh. There were 70 metabolites that showed antioxidant activity using the DPPH assay; 23 metabolites significantly differ from DPPH and control for antioxidant activity for all processing tempeh. Metabolites with significantly different antioxidant activity in raw and processed tempeh were dominated by flavonoids, vitamin E, and bioactive lipids., Conclusion: The DPPH antioxidant assay using UHPLC-HRMS is promising as a fast antioxidant assay by simplifying the conventional DPPH antioxidant assay. Further, it can be used to identify the name of metabolites responsible for its antioxidant activity., Competing Interests: Declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Spices played crucial and variable roles in traditions, culture, history, religious ceremonials and festivals along with fetching food flavor and microbial protection globally due to presence of structurally unique and multi-natured chemotypes. Their existence in dishes portrayed key roles in improving shelf life by regulating spoilage of cuisine with different synergistic mechanism. Histo-anatomically (A) sowa exhibited distinguished cellular attributes which created remarkable differences with T. ammi. HPTLC chemometrics of both fruits revealed several peaks for active metabolomics with unique isocratic combination of menstruum. GC-MS study of hydro-distillate exhibited presence of monoterpenic cyclic and aromatic hydrocarbons, alcoholic and ketonic groups along with phenolic derivative that covers majorly 90% of total metabolites. Combined essential oils (EOs 1 + 2) of both fruits showed excellent antimicrobial activity against various clinical pathogenic strains such as K. pneumoniae at 10 µL/mL, S. aureus at 2.5 µL/mL, E. coli and E. faecalis at 1.25 µL/mL, and MRSA and Bcereus at 0.625 µL/mL and (C) albicans at 0.312 µL/mL as the MIC. The antioxidant study of (EOs 1 + 2) with maximum inhibition percentage to DPPH assay was 84.02 ± 1.05 at 100 µg/mL, and minimal inhibition was 72.31 ± 0.63 at 5 µg/mL with IC 50 value 4.69 ± 0.22 µg/mL, while ABTS assay extreme was 79.15 ± 2.14 µg/mL and minimal was 67 ± 1.34 with the IC 50 value of 18.37 ± 0.15 µg/mL, in superoxide assay uppermost inhibition was 81.03 ± 0.27 µg/mL and lowest was at 65.16 ± 3.15 with the IC 50 value 16.46 ± 0.54, and H 2 O 2 radical scavenging activity, predominant value was 78.01 ± 0.47 and least was 64.1 ± 2.01 with IC 50 15.58 ± 0.34. These comparative key diagnostic features and synergistic effect of multicomponent natural antimicrobials will provide profound intellect of ancient utility and further scientists to explore their multiple mechanistic modality and application in food and beverages industry., (© 2024. The Author(s).)