Chemometric Methods Research Articles

Impact of ambient conditions on the minor nutrients content in spirulina “Arthrospira platensis” (AP) extracted from Lake Chad revealed by fluorescence spectroscopy coupled with chemometric methods

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.

Evaluation of a Combined Thermal/Chemical Wood Modification by the Use of FTeIR Spectroscopy and Chemometric Methods

In this study, the combined effects of chemical (with tricine and bicine) and thermal treatments were investigated. The modifications which appeared in the wood structure were evaluated by infrared spectroscopy and chemometric methods (principal component analysis and hierarchical cluster analysis). After the treatment, about 6e7% of WPG was identified in treated samples, but further thermal treatment decreased the WPG to about 5%. The modifications appearing in the spectra were mostly related to increase of the intensities of the bands assigned to C]O groups but also to NeH and CeN groups, with shifting of some bands to higher wave number values.

Fluorescence hyperspectral imaging technology combined with chemometrics for kiwifruit quality attribute assessment and non-destructive judgment of maturity

Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the ‘Convolutional Neural Network’-‘Multilayer Perceptron’ (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA–CNN–MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracytest of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%–20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.

Authentication of the shelf-life of NPH injectable insulins samples using NIR and MS spectral fingerprints

Insulin is essential for regulating glucose levels, and its hormonal deficiency leads to diabetes. Treatment involves the use of different types of insulin tailored to individual needs. Properly stored and transported Neutral Protamine Hagedorn (NPH) insulin remains effective and stable, highlighting the importance of quality control to ensure its efficacy and patient safety. This study aims to develop an analytical method to evaluate the shelf-life of NPH injectable insulin samples while preserving packaging integrity. The approach consisted of analyzing intact insulin samples, with and without expired shelf-life dates, using near-infrared spectroscopy (NIR) obtained from two instruments: a benchtop and a portable device. These measurements were coupled with chemometric methods, specifically single-class classification algorithms. The classifiers considered were Data Driven-Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS). For benchtop NIR, the DD-SIMCA method, with Savitzky-Golay smoothing preprocessing (41-point window) and baseline offset (BO), achieved excellent results, reaching 100% sensitivity, specificity, and efficiency in both training and test sets. As for portable NIR, the most effective methods were DD-SIMCA and OC-PLS, using datasets preprocessed with Savitzky-Golay smoothing with first derivative, second-order polynomial (SGD-1st-2nd), and 9-point windows combined with Standard Normal Variate (SNV), and Savitzky-Golay smoothing filters (SGS) using second-order polynomial and 7-point windows combined with baseline offset (BO) and Multiplicative Scattering Correction (MSC). The results were corroborated by mass spectrometry, correlating the structural changes of degraded insulin with NIR data. The proposed methodology is a promising tool for non-destructive, rapid, and cost-effective authentication of the preservation state of NPH insulin, aligning with green chemistry principles and allowing for in-situ evaluation, thereby preserving medication integrity.

Exploring environmental sustainability through dimension reduction algorithms: A cost-effective UV spectroscopic method for simultaneous determination of veterinary binary mixtures of doxycycline hydrochloride and tylosin tartrate

This study presents a cost-effective and environmentally friendly approach for the simultaneous determination of a veterinary binary mixture comprising doxycycline hydrochloride (DOX) and tylosin tartrate (TYZ) utilizing UV spectroscopy alongside dimension reduction algorithms (DRAs). Seventeen DRAs were evaluated, and their performances were compared based on four metrics: mean squared error (MSE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination (R2). Based on the performance indices, Isomap algorithm demonstrated the highest predictive capacity among all DRAs. MSE, MAE, MedAE and R2 values of (0.38, 0.28, 0.19 and 0.999) and (0.08, 0.26, 0.22 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 4.67–30 μg mL−1 for DOX. MSE, MAE, MedAE and R2 values of (0.54, 0.34, 0.19 and 0.994) and (0.07, 0.19, 0.07 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 3.51–24 μg mL−1 for TYZ. The developed method underwent validation utilizing the accuracy profile approach. An ecological impact assessment was carried out employing six greenness evaluation tools: The Green Solvent Selection Tool (GSST), National Environmental Methods Index (NEMI), the Assessment of Green Profile (AGP), carbon footprint analysis, Analytical Greenness Calculator (AGREE), and Complementary Green Analytical Procedure Index (Complex GAPI). Additionally, we applied blueness and whiteness assessments using Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms, respectively. The proposed method demonstrated higher GSST scores, a more "green" profile in NEMI, a superior AGP profile, and better environmental sustainability in Complex GAPI. The calculated carbon footprint value was 0.0002 kg CO2 equivalent per sample, The AGREE score was 0.87, BAGI was assessed at 72.5, and the whiteness assessment by the RGB12 algorithm was 89.6. Statistical comparison of the proposed method with a previously reported HPLC method for dosage form analysis revealed no significant differences at a 95% confidence level. This study highlights the novelty of combining UV spectroscopy with dimension reduction algorithms, offering significant advancements over traditional UV and chemometric methods for the analysis of these drugs. This approach not only enhances the efficiency and accuracy of determining active ingredients in pharmaceutical dosage forms but also contributes to environmental sustainability.

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380–1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops’ health, thus minimizing crop loss and optimizing yields.

Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds

The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chromatography–mass spectrometry, headspace gas chromatography-ion mobility spectrometry, E-nose, E-tongue, quantitative descriptive analysis, and free amino acid and 5′-nucleotide analyses to achieve reliable discrimination of four salted goose breeds. Volatile and non-volatile compounds and sensory characteristics and intelligent sensory characteristics were analyzed. A preliminary composite dataset was generated in InfoGAN and provided to several base classifiers for training. The prediction results were fused via dynamic weighting to produce an integrated model prediction. An ablation study demonstrated that ensemble learning was indispensable to improving the generalization capability of the model. The framework has an accuracy of 95%, a root mean square error (RMSE) of 0.080, a precision of 0.9450, a recall of 0.9470, and an F1-score of 0.9460.

A novel evaluation mode of Nao An Capsules using multi-wavelength fusion fingerprint combined with quantitative analysis of multi-components by a single marker method and antioxidant activity

Nao An Capsules (NAC) are utilized in the acute phase of cerebral thrombosis and are appropriate for patients in the recovery stage exhibiting symptoms of qi deficiency and blood stasis. Nevertheless, there has been a lack of recent research on the overall quality control of NAC. The objective of this study is to establish a comprehensive quality control strategy for NAC. This article presents a methodology based on the average linear quantitative fingerprinting method (ALQFM) in conjunction with multi-wavelength high performance liquid chromatography (HPLC) fusion fingerprinting, quantized Fourier transform infrared (FT-IR) spectroscopy and ultraviolet (UV) spectral techniques to comprehensively evaluate NAC from both qualitative and quantitative perspectives. Subsequently, quantitative analysis of multi-components by a single marker (QAMS) method and the Standard Curve Method (SCM) were used to quantitatively determine 10 quality markers (Q-markers) in 22 batches of samples, with the results indicating that QAMS is a reliable method for quantifying active ingredients. Chemometrics methods such as principal component analysis (PCA) and principal component analysis (HCA) were also used to analyze the quality of samples. To achieve a thorough and methodical evaluation, a combined assessment of the three fingerprints was executed. Subsequently, an in vitro antioxidant activity experiment was performed utilizing the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging technique. By employing a partial least squares (PLS) model, the correlation between HPLC fingerprint chromatography and antioxidant activity was investigated. The study found that out of the 59 common peaks in the five-wavelength fusion fingerprint spectra of NAC, 33 peaks exhibit antioxidant activity. In this study, multi-wavelength fusion HPLC, quantized FT-IR and UV were jointly used to effectively identify functional groups and fingerprint information in compounds. It can fully and quickly obtain the fingerprint information of the compounds in the sample. NAC multi-components quantification and overall quality evaluation were performed using ALQFM and QAMS and chemometrics methods. We have developed a new approach for comprehensive, rapid, and cost-effective quality control of traditional Chinese medicine (TCM) compound preparations.

Forensic discrimination of fiber microspectrophotometry data by resampling and repeating two-sample hypothesis testing

Textile fibers are important forensic trace evidence in criminal investigations. Colored textile fibers are often analyzed using non-destructive microspectrophotometry. However, when the spectra of questioned and known samples are very similar, it is difficult to compare the spectra visually. Chemometric methods, such as principal component analysis and agglomerative hierarchical clustering, have been applied to microspectrophotometry data. In this study, we used two-sample hypothesis testing as a chemometric approach for discrimination of fiber samples without reducing or eliminating the dimensionality of the data. Cotton fibers dyed with vat dyes were analyzed by microspectrophotometry. We examined suitable data types and statistical methods for test fiber samples by resampling and repeating hypothesis testing. A combination of spectral data type and statistical method was identified that provided a good balance of type I (false exclusion) and type II (false inclusion) error rates in two-sample hypothesis testing of the fibers.The main advantage of our chemometric approach is that an appropriate method for samples can be selected by using the type I and II error rates. Furthermore, our method can use all the measured data for the calculation without reducing or eliminating the dimensionality of the data, so it is not necessary to reduce variables or smooth the spectrum. This statistical method also requires no training set. Therefore, two-sample hypothesis testing could be applied to fibers of different types, colors, or materials from those used in this study. Resampling and repeating two-sample hypothesis testing technique is promising as a new chemometric approach for comparing microspectrophotometry spectra.

An integrated study of quality variations for Eucommia ulmoides Oliver staminate flower processed with different drying treatments by untargeted metabolomics coupled with chemometrics

Eucommia ulmoides Oliver staminate flower (EUF) has been increasingly served as a potential feedstock for nutraceuticals. An integrated approach of untargeted metabolomics based on gas chromatography hyphenated with mass spectrometry (GC-MS) and ultrahigh performance liquid chromatography hyphenated with high resolution mass spectrometry (UHPLC-HRMS) coupled with advanced chemometric method was applied to investigate the metabolic variations of EUF obtained from various drying treatments. The metabolomics analysis was conducted automatically in our newly developed data analysis platform AntDAS. Chemometric methods were further used to provide additional insight into the chemical differences of EUF. 34 volatile and semi-volatile as well as 89 nonvolatile compounds were identified and demonstrated significant differences among diverse drying treatments. Moreover, among which 27 volatile and semi-volatile compounds along with 35 nonvolatile compounds were verified qualitatively by standards. The results revealed that shade drying was more effective for the preservation of volatile and semi-volatile substances, including sugars and vitamins. Freeze drying was conducive for preserving nonvolatile compounds, such as iridoids, flavonoids, and organic acids. The content of alcohols, amino acids, and nucleosides was abundant in heat-pump drying, oven drying, and hot air drying, respectively. In addition, hot air, heat pump, and oven drying methods showed a moderate level of the majority of bioactive components and suitable for industrialization process. This work potentially highlights the possibility of using chemometrics in conjunction with untargeted GC-MS and UHPLC-HRMS analysis for quality assessment of EUF.

Chemometric analysis using infrared spectroscopy and PCA-LDA for early diagnosis of Fusarium oxysporum in tomato

AbstractThe interaction of phytopathogenic organisms and plants generates physiological and biochemical changes in the latter. However, the effects on the plants are rarely visible in the infection first stages. Novel optical techniques can help to improve the early detection of phytopathogenic organisms in tomato without the plant sacrifice. In this work, infrared spectroscopy and chemometric methods were used to determinate the effects of Fusarium oxysporum in tomato plants cultivated in pots, analyzing fully expanded leaves. Fusarium oxysporum was molecular identified and its pathogenicity was tested in vitro. Three plants treatments were evaluated for 55 days post infection in pots in greenhouse under semi-controlled conditions: control, water stress, and fungal inoculated (1 × 108 conidia/mL). Phenotypical results were followed twice a week for eight weeks; the phenotypical characteristics were very similar in almost all sampling times except in height, especially in the first 27 days post infection, after this time the height was similar in the three treatments. The stalk and root-dried matter analysis do not show statistical differences; however, the infrared results, acquired from the adaxial surface of leaves, show differences in peaks associated with salicylic acid, jasmonic acid, abscisic acid, and proline in the first 27 days post infection. The principal component analysis–linear discriminant analysis were used to distinguish subtle biochemical changes between the three treatments, facilitating the early detection of the pathogen and its monitoring over time.

Metabolomic Profiling of Guadua Species and Its Correlation with Antioxidant and Cytotoxic Activities.

Bamboo plants are widely used in Asian traditional medicine for various health issues and exhibit therapeutic potential. Guadua species are renowned bamboos for their high phenolic compound content, including flavonoids and hydroxycinnamic acid derivatives, and possess noteworthy biological properties. Despite this, there is a notable scarcity of research on the chemical and biological aspects of Latin American bamboo leaf extracts (BLEs), especially concerning the Guadua genus. This study aimed to employ a metabolomics approach to integrate the phytochemical and activity profiles of BLEs to identify potential bioactive markers. We determined the metabolic fingerprints of 30 BLEs through HPTLC, HPLC-DAD, UHPLC-QTOF-MS, and 1H-NMR analyses and screened for antioxidant and cytotoxic activities using ABTS, DPPH, and MTT methods. Ultimately, correlation analyses were performed by using chemometric methods and molecular networking. Our findings present a comprehensive chemical characterization, encompassing 40 flavonoids and 9 cinnamic acid derivatives. Notably, most of these compounds have been reported for the first time within the genus, signifying novel discoveries. Additionally, certain compounds identified in other species of the subfamily Bambusoideae provide valuable comparative insights. These compounds demonstrated a significant correlation with antioxidant potential, with values exceeding 100 and 30 μmol of TE/g of extract for ABTS and DPPH, respectively, in the samples. Extracts from G. incana and G. angustifolia exhibited potent cytotoxic effects with IC50 values of 1.23 and 4.73 μg/mL against HCT-116 colon cancer cells, respectively. Notably, glycosylated flavones showed a strong correlation with cytotoxicity. These new findings significantly contribute to our understanding of the chemical composition and biological properties of these often overlooked bamboo species, providing them with important added value and alternative use.

High-coverage characterization and discovery of molecular markers for quality control of natural fragrant plant extracts using UPLC-HRMS-based untargeted metabolomics.

The chemical components of natural fragrant plant extracts are of high complexity, and the strategies for quality control (QC) and further discovery of fragrance mechanisms still need to be further investigated. This study integrated the strategies and methods of untargeted metabolomics and chemometrics and statistical modeling to attain the goal. The techniques of reversed-phase and HILIC analysis of ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) were simultaneously used to collect data in both positive and negative ion modes. The pattern analysis of fingerprints and discovery of characteristic molecular markers for QC analysis were comprehensively employed to reach in-depth analysis of the quality variation and discovery of differential molecules among natural fragrant plant extracts. The former uses fingerprint technique to analyze their overall similarities and differences, and the latter comprehensively discovers molecular substances characterizing the chemical characteristics of fragrant extracts with the help of metabolomics and univariate and multivariate methods. The findings are expected to be used as the molecular markers in product manufacturing, sales, and consumption to achieve accurate quality control and recognition of targeted molecules for potential quality monitoring using spectroscopy techniques. In this work, 27 natural fragrant extracts were applied as examples, and their chemical components were comprehensively analyzed with discovery of markers for quality control. After data integration, 1178 molecules were annotated, and 267 differential metabolite molecules with the values of variable importance in the projection (VIP) larger than 1.0 were found. The results show that the method proposed in this work is of great significance for high-coverage analysis, QC marker discovery, and aroma mechanism elucidation, which has potential applications in the areas of food, cosmetics, pharmaceuticals, tobacco, and others.

Visual detection of aldehyde gases using a silver-loaded paper-based colorimetric sensor array

The small molecule aldehydes are volatile organic compounds (VOCs), possessing cytotoxicity and carcinogenicity. Long-term exposure can pose a serious threat to human health. Based on an in-situ reduction colorimetric method to generate silver nanoparticles and induce colorimetric response, we proposed a silver-loaded paper-based colorimetric sensor array for visually detecting and differentiating five relatively common trace small molecule aldehyde gases. The silver ions are immobilized onto a porous filter paper and stabilized by complexing agents of branched polyethyleneimine, ethylenediamine, and 1,6-diaminohexane, respectively. The as-fabricated sensor array expresses remarkable stability and capacity to resist humidity. The qualitative analysis reveals that the sensor array has excellent selectivity for aldehyde gases and displays remarkable anti-interference ability. The quantitative analysis indicates that the sensor array exhibits superior sensitivity for five aldehyde gases, with limits of detection (LODs) of 9.0 ppb for formaldehyde (FA), 3.1 ppm for acetaldehyde (AA), 3.5 ppm for propionaldehyde (PA), 23.8 ppb for glutaric dialdehyde (GD), and 71.5 ppb for hydroxy formaldehyde (HF), respectively. Importantly, these LODs are all comfortably below their respective permissible exposure limits. A unique colorimetric response fingerprint is observed for each analyte. Standard chemometric methods illustrate that the sensor array has excellent clustering capability for these aldehyde gases. Additionally, the sensor array's response is irreversible and possesses outstanding performance for cumulative monitoring. This colorimetric sensor array based on silver ions reduced to silver nanoparticles offers a novel detection method for the continuous, ultrasensitive, and visual detection of trace airborne pollutants.

Correction methods for visible near-infrared spectroscopy in water pH detection to mitigate temperature influence

The pH value is an important indicator for determining water quality, and visible near-infrared (Vis-NIR) spectroscopy is an ideal technique for rapid detection. The Vis-NIR spectrum of water is greatly affected by temperature. When using Vis-NIR spectroscopy to detect the pH value of water, temperature interference will affect the prediction accuracy of the water pH value. In this study, several chemometric methods developed in recent years were used to correct the spectra of distilled water samples under temperature interference to improve the prediction accuracy of water pH values. Specifically, this includes the external parameter orthogonalization (EPO) algorithm and the generalized least square weighting (GLSW) algorithm. Spectra of distilled water samples at different temperatures in the 400–1049 nm range were collected, and the correction effects of the EPO algorithm and the GLSW algorithm on the spectra after temperature changes, as well as the prediction errors of water pH values, were tested and examined, compared with traditional full-temperature spectral correction models. The results show that both the EPO algorithm and the GLSW algorithm methods require only a small number of representative samples to obtain filtering matrices. By correcting the spectra of distilled water samples at all temperatures to the reference temperature level, good correction effects can be achieved. Additionally, the prediction accuracy of water pH values of the models corrected by both methods is better than that of the traditional method, and the complexity of the models is also greatly reduced.

Fabrication of Aluminum Foil Integrated Pegylated Gold Nanoparticle Surface-Enhanced Raman Scattering Substrate for the Detection and Classification of Uropathogenic Bacteria.

Rapid detection and classification of pathogenic microbes for food hygiene, healthcare, environmental contamination, and chemical and biological exposures remain a major challenge due to nonavailability of fast and accurate detection methods. The delay in clinical diagnosis of the most frequent bacterial infections, particularly urinary tract infections (UTIs), which affect about half of the population at least once in their lifetime, can be fatal if not detected and treated appropriately. In this work, we have fabricated aluminum (Al) foil integrated pegylated gold nanoparticles (AuNPs) as a potential surface-enhanced Raman scattering (SERS) substrate, which is used for the detection and classification of uropathogens, namely, E. coli, S. aureus, and P. aeruginosa directly from the culture without any pretreatment. The substrate is first drop cast with bacterial pellets and then pegylated AuNPs, and the interaction of two on Al foil base gives identifiable characteristic Raman peaks with good reproducibility. With the use of chemometric methods such as principal component analysis (PCA), the Al foil-based SERS substrate offers a quick, effective detection and classification of three strains of UTI bacteria with the least bacterial concentration (105 cells mL-1) necessary for clinical diagnosis. In addition, this substrate was able to detect E. coli positive clinical samples by giving SERS fingerprint information directly from centrifuged urine samples within minutes. The stability of pegylated AuNPs provides for its application at the point of care with rapid and easy detection of uropathogens as well as the possibility of advancement in healthcare applications.

Quantitative Analysis of Pb in Soil Using Laser-Induced Breakdown Spectroscopy Based on Signal Enhancement of Conductive Materials.

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

Exploring chemical markers and identifying phenolic markers using a metabolomics strategy and chemometrics to study the different origins of defatted Coix seed

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds.

Discrimination of pressed sesame oil: A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods

This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints. The goal of this study was to provide a reliable tool for assessing the quality of sesame oil. The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200–330 nm. Various classification and prediction methods, including linear discrimination analysis (LDA), support vector machines (SVM), soft independent modeling of class analogy (SIMCA), partial least squares regression (PLSR), support vector machine regression (SVR), and back-propagation neural network (BPNN), were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil. The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil, achieving an accuracy of 96.15%, a sensitivity of 97.87%, and a specificity of 80%. For quantitative analysis, BPNN yielded the best prediction results, with an R2 value of 0.99, RMSEP of 2.34%, and RPD value of 10.60 (LOD of 8.60% and LOQ of 28.67%). Overall, the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.

Vibrational spectroscopy data fusion for enhanced classification of different milk types

The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies.