Honey is a natural sweetening agent widely used in food products and as a daily diet ingredient, but it also has medicinal properties. An increase in the demand for honey has resulted in adulteration ...by different sugar syrups. Authentication is therefore important for consumer confidence. This comprehensive overview covers known syrup adulterants and the analytical methodologies adopted for their detection in honey. For instance, TLC is the oldest method for honey analysis. C-isotope methods such as SCIRA, which can differentiate adulterated honey from C3 plants, are explained. Common analytical techniques such as HPAEC, GC, and HPLC are discussed. Advanced techniques, including IR, NMR, and Raman spectroscopy, which enhance the analysis process for larger numbers of samples, are also presented. Finally, Q-TOF-MS is addressed as a metabolomics-based detection method, since it has recently gained momentum following the increase in different adulterants that make detection more difficult.
•SCIRA differentiates honey adulterated by C3 plant adulterants.•GC and HPAEC is an affordable, easy technique to detect HFCS.•HPLC can detect C3 and C4 starch syrups and rice syrups.•IR-based analysis, NMR, and Raman spectroscopy speed up the detection of honey adulterants.•Q-TOF-MS for metabolomics-based detection is able to detect different adulterants simultaneously.
Consumption of large volumes of toxic organic solvents in chemical processes can trigger both health and environmental problems. Therefore, “green chemistry” is getting increasingly important. ...Significant efforts have already been devoted to reduction of the utilization of hazardous organic solvents and continuation of these attempts remains a viable task. In this context, a diverse range of more sustainable solvents including deep eutectic solvents, bio-based solvents, supercritical carbon dioxide, and gas-expanded liquids has been introduced and their applications explored in both research and industry. Our review focuses on the properties, environmental effects, toxicity, and applications of these solvents in the extraction of natural bioactive compounds described in papers published since 2018. Moreover, some common sustainable extraction techniques including supercritical fluid, microwave-assisted, and ultrasound-assisted extractions are covered and compared in terms of the extraction efficiency. In addition, some common chemometric approaches that are widely used for the optimization of the processes related to the extraction of bioactive compounds are also presented.
•Fundamental physicochemical properties of some sustainable solvents are discussed.•Applications of the solvents in the extraction of bioactive molecules are presented.•Strategies focused on decrease in the environmental impact of solvents are covered.
Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the ...data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600–1000 nm spectra with Rp, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 oBx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.
•Novel NIR transmittance spectroscopy quantitatively detected the degree of watercore in apple.•The characteristic spectral variables of apple watercore disease detection were studied.•Multiple variables selection simplified and improved the performance of models.•NIR spectroscopy is advantageous as the fast, non-destructive measurements.
LIBS-based classification has experienced an ever-increasing interest in the last few years. LIBS is a well-suited technique for classification tasks based on elemental fingerprinting, providing fast ...simultaneous multi-element analysis with stand-off, online, and portable capabilities. The topic of classification gained even more momentum due to the current hype on machine learning, big data, and chemometrics. Nevertheless, with many LIBS users not being data scientists by training, classification algorithms are often used and considered “black boxes,” hindering the adequate application of these tools. This review provides a comprehensive introduction and overview of the steps necessary (e.g., normalization, background correction, feature selection) to go from recorded data to a well-performing classifier. Additionally, the basic principles, advantages, and limitations of the most used machine learning algorithms reported in LIBS-classification literature are discussed. Finally, the review offers an overview of the literature published in the field, highlighting the great diversity of applications.
•Classification, discrimination, and identification of samples using LIBS data.•Data preprocessing strategies (normalization, feature selection, …).•Intuitive introduction to the most common machine learning algorithms used in LIBS.•Overview of LIBS-based classification literature.
Hyperspectral image analysis. A tutorial Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa
Analytica chimica acta,
10/2015, Volume:
896
Journal Article
Peer reviewed
This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate ...exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed.
Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares – Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case.
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•Comprehensive tutorial of Hyperspectral Image analysis.•Hierarchical discrimination of six classes of plastics containing flame retardant.•Step by step guidelines to perform class-modeling on hyperspectral images.•Fusion of multivariate data analysis and digital image processing methods.•Promising methodology for real-time detection of plastics containing flame retardant.
This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), ...and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.
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•A data fusion system based on optimal sensor array was developed.•The fermentation process was identified by physicochemical parameter results.•The visualization results of PCA confirmed the fermentation process.•Multiple feature sets were evaluated by DT、RF、SVC for effective classification.•SVR and PLSR successfully predicted crucial physicochemical parameters.
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•Visible-near infrared hyperspectral imaging was proposed to detect breast cancer.•Multivariate curve resolution was extracted pure profiles of healthy and cancerous tissue.•Partial ...least square-discriminant analysis was discriminated healthy from cancerous samples.•Hyperspectral imaging is a reliable and non-invasive technique for breast cancer detection.
This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400–950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.
The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and ...quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.
•The Vis-NIR system accurately detected and quantified the adulteration percentage in cacao powder.•Unsupervised techniques (PCA and HCA) showed a clear arrangement by type and percentage of adulteration.•Supervised techniques (RF and SVM) successfully classified samples with 100% accuracy.•The PLS-Regression model with the Boruta algorithm provided the best results for quantification.•An online platform is available for users to access our models for detecting and quantifying cocoa powder adulteration.
Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the ...scientific community on the use of DL for spectral data modelling. This paper provides a critical and comprehensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral data modelling. Although this work focuses on DL for the modelling of near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be expanded to cover other spectral techniques. Finally, empirical guidelines on the best practice for the use of DL for the modelling of spectral data are provided.
•Status of deep learning for NIR data modelling is reviewed.•The hypes and benefits of deep learning for NIR data are highlighted.•Recommendations on fair DL practices are presented.
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