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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]

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]

Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud. [ABSTRACT FROM AUTHOR]

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.)

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.)

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.)

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.)

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.)

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).)

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

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

Wild edible boletes mushrooms are regarded as a delicacy in many countries and regions due to their rich nutritional contents and strong aromatic compounds. This study aimed to identify 445 samples of 11 boletes species collected from Yunnan and Sichuan provinces through molecular analysis. Using simultaneous distillation-extraction (SDE) combined with gas chromatography-mass spectrometry (GC-MS), 97 volatile compounds were identified. Chemometric methods were then applied to analyze the heterogeneity of these volatile compounds among the different species. The results showed that, 22 and 21 volatile compounds were selected using variable importance in projection (VIP > 1) and relative odor activity values (ROAV > 0.1), respectively. Partial least squares discrimnatint analysis (PLS-DA) was then employed to develop pattern recognition models for 11 species, which demonstrated strong identification performance. Furthermore, correlation heat maps, volcano plots, and Fisher linear discriminant analysis identified five volatile organic compounds, including methyl (9E)-9-octadecenoate, 2, 6-dimethylpyrazine, 1-decen-3-one, furfural, and methional as markers for distinguishing 11 boletes species. Ultimately, the rapid content prediction models of partial least squares regression (PLSR) were established by combining Fourier Transform Near-Infrared Spectroscopy (FT-NIR) with the concentrations of these five marker compounds. These findings provide a methodological strategy for the effective species identification of wild edible mushrooms and the rapid prediction of their characteristic aroma compounds., 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.)

Background & Aim: Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples., Methods & Materials: Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy., Results: The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively., Conclusion: The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Aurantii Fructus (AF) and Aurantii Fructus Immaturus (AFI) are distinct herbs outlined by the Chinese Pharmacopoeia. They are sourced from the same plant but harvested at different times, resulting in differences in efficacy. It is important to avoid mixing them clinically and to distinguish between the two. Furthermore, dissimilar cultivation conditions may cause variability in the quality of herbs, so it is vital to differentiate drugs from dissimilar origins. In this study, two plants, AF and AFI from different provinces, were comparatively analyzed based on High Performance Liquid Chromatography (HPLC) fingerprints and classified using chemometric methods. The results indicate that the two medicines can be clearly distinguished. Also, AF and AFI grown in different locations can be distinguished. Ten chemical markers were screened, and their variations were determined, including eriocitrin, narirutin, naringin, meranzin hydrate, naringenin, hesperidin, nobiletin, tangeretin, neohesperidin, and poncirin. Subsequent network pharmacology correlated the screened chemical components with the biological network of the organism. The material basis of the difference in efficacy of the two homologous herbs was explored from the perspective of changes in chemical composition. This study provides a reference for formulating quality evaluation standards for AF and AFI and lays a foundation for the efficacy-related quality research of the two.

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putňový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing., 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 medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples., 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.)

Euphorbiae pekinensis Radix (EPR) is a traditional Chinese herb commonly used to treat edema, pleural effusion, and ascites. However, counterfeit and adulterated products often appear in the market because of the homonym phenomenon, similar appearance, and artificial forgery of Chinese herbs. This study comprehensively evaluated the quality of EPR using multiple methods. The DNA barcode technique was used to identify EPR, while the UPLC-Q-TOF-MS technique was utilized to analyze the chemical composition of EPR. A total of 15 tannin and phenolic acid components were identified. Furthermore, UPLC fingerprints of EPR and its common counterfeit products were established, and unsupervised and supervised pattern recognition models were developed using these fingerprints. The backpropagation artificial neural network and counter-propagation artificial neural network models accurately identified counterfeit and adulterated products, with a counterfeit ratio of more than 25%. Finally, the contents of the chemical markers 3,3'-di-O-methyl ellagic acid-4'-O-β-D-glucopyranoside, ellagic acid, 3,3'-di-O-methyl ellagic acid-4'-O-β-d-xylopyranoside, and 3,3'-di-O-methyl ellagic acid were determined to range from 0.05% to 0.11%, 1.95% to 8.52%, 0.27% to 0.86%, and 0.10% to 0.42%, respectively. This proposed strategy offers a general procedure for identifying Chinese herbs and distinguishing between counterfeit and adulterated products., (© 2024 John Wiley & Sons Ltd.)

Background: There is an increasing interest of the scientific community in developing innovative methodologies for their analysis needs within a green analytical chemistry framework. UV spectrophotometry is one of the most promising eco-friendly methods, which is integrated with advanced chemometric tools to enhance the selectivity of the analysis of complex mixtures with severe overlapped signals., Objective: Simultaneous determination of a triple-combination of pseudoephedrine hydrochloride (PSE), carbinoxamine maleate (CRX), and paracetamol (PAR) using an artificial intelligence system and multivariate calibration methods. This combination has been recently recommended for COVID-19 home-treated patients as part of a symptomatic treatment., Methods: Namely, the suggested models are artificial neural networks, partial least-squares, and principal component regression. The proposed algorithms were optimized and developed with the aid of a five-level, three-factor experimental design., Results: The investigated methods were applied over the concentration range of 100-180 μg/mL, 18-16 μg/mL, and 4-12 μg/mL for PSE, CRX, and PAR, respectively. The models' validation results demonstrated excellent recoveries (around 98 to 102%), signaling the approaches' outstanding resolution capacity for the cited compounds in the presence of common excipients. The outcomes of the studied methods were statistically compared to the official approaches, and no significant difference was found., Conclusions: The suggested models were efficiently employed to determine the selected drugs in their combined tablets without any initial separation steps. The impact of these methods on the environment was evaluated via greenness tools: namely, the National Environmental Method Index, Raynie and Driver's green assessment method, Analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric., Highlights: Green chemometric quality assessment of PSE, CRX, and PAR in their pure and pharmaceutical dosage forms. The established approaches are innovative, sustainable, smart, fast, selective, and cost-effective. These models are potential green nominees for routine analysis of the investigated mixture in quality control laboratories., (© The Author(s) 2024. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions., Objective: This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions., Methods: The models were produced under different conditions to distinguish the origins distinctly. Ten preprocessing methods have been used to preprocess the original spectra (OS) and to select the most optimal spectral preprocessing method. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables., Results: The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate., Conclusion: The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua., Highlights: The utilization of NIR spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating QA measures., (© The Author(s) 2024. Published by Oxford University Press on behalf of AOAC INTERNATIONAL. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Quality evaluation of Huang-qin is significant to ensure its clinical efficacy., Objective: This study aims to establish an accurate, rapid and comprehensive Huang-qin quality evaluation method to overcome the time-consuming and laborious shortcomings of traditional herbal medicine quality assessment methods., Methods: The contents of baicalin, baicalein and scutellarin in Huang-qin from five different origins were analyzed by FT-IR and NIR spectra combined with multivariate data technology. The quality of Huang-qin from different origins was evaluated by TOPSIS and consistency analysis based on the content of three active ingredients. The correlation between ecological factors and the accumulation of active ingredients was explored., Results: Satisfactory prediction results of PLS models were obtained. Relatively, the model based on FT-IR combined with the PLS regression method has higher R 2 and smaller RMSE than the NIR combined with the PLS method. TOPSIS and consistency analysis results showed that the quality of Huang-qin from different geographical origins was significantly different. The results showed that the quality of Huang-qin produced in Shanxi Province was the best among the five origins studied. The results also found that the quality of Huang-qin in different growing areas of the same origin was not completely consistent. The correlation study showed that altitude, sunshine duration and rainfall were the main factors that caused the quality difference of medicinal materials in different geographical origins., Conclusion: This study provides a reference for the rapid quantitative analysis of the active components of herbal medicine and the quality evaluation of them., (© 2024 John Wiley & Sons Ltd.)

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]

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]

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.

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

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.)

Herbicide-resistant weed species are one of the largest threats to modern agriculture, as ineffective weed control results in significant yield losses or increased costs through alternatives such as mechanical methods. Palmer amaranth (Amaranthus palmeri S. Watson) has been one of the most troublesome weeds. Its identification through the adoption of sitespecific weed management systems will help farmers select more appropriate control options to reduce costs and improve efficacy, resulting in increased farm revenue. In this study, a pixel-wised method was evaluated for the classification of Palmer amaranth and soybean (Glycine max L.). A pushbroom hyperspectral imagery acquisition system was used to collect imagery from 224 spectral bands ranging from 400 to 1000 nm. Greenhouse experiments were conducted in three different runs. Greenhouse-grown plants were evaluated to generate predictive models from paired samples generated with 56 replications in each run. Data collection occurred weekly when Palmer amaranth plants were between approximately 2.5 and 12.7 cm tall. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were tested to classify Palmer amaranth and soybean. Half of the dataset (Palmer amaranth = 42, soybean = 42) was used to train the models, and the other half (Palmer amaranth = 42, soybean = 42) was used to test model performance. Preliminary results showed that the PLS-DA model with PLS factors as input had a cumulative variation (R2Y(cum)) of 60% and predictive ability (Q2Y(cum)) of 60%. The SIMCA model showed a cumulative variation of 85% and a predictive ability of 82%. Overall, this study illustrated the capability of hyperspectral imagery to classify Palmer amaranth and soybean, which will increase the efficiency of weed control in modern agriculture. [ABSTRACT FROM AUTHOR]

As a traditional Chinese medicine (TCM), Allii Macrostemonis Bulbus (AMB) is a key herb for the treatment of thoracic paralytic cardiac pain, but its quality evaluation method has not yet been fully clarified. In this study, chromatographic fingerprints of AMB were developed using solid-phase extraction-high-performance liquid chromatography-evaporative light scattering detection (SPE-HPLC-ELSD) to evaluate the quality of AMB from various origins and processing methods. This was achieved by employing chemical pattern recognition techniques and verifying the feasibility and applicability of the quality evaluation of AMB through the quantitative analysis of multi-components via a single-marker (QAMS) method. Through the analysis of the fingerprints of 18 batches of AMB, 30 common peaks were screened, and 6 components (adenosine, syringin, macrostemonoside T, macrostemonoside A, macrostemonoside U, and macrostemonoside V) were identified. Moreover, three differential markers (macrostemonoside A, macrostemonoside T, and macrostemonoside U) were screened out using chemometrics techniques, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Subsequently, a QAMS method was established for macrostemonoside T and macrostemonoside U using macrostemonoside A as an internal reference. The results demonstrate the method's accuracy, reproducibility, and stability, rendering it suitable for the quality evaluation of AMB. This study provides a theoretical basis for drug quality control and the discovery of quality markers for AMB., Competing Interests: The authors declare no competing financial interests.