Advanced Chemometrics Research Articles

Are We There Yet? Is There Such a Thing as an Expert Calibration System for Vibrational Spectroscopy?

The development of an expert calibration system (ECS) for spectroscopic-based process analytical chemistry would be a significant advancement aimed at automating the creation of high-quality calibration models for standard zero-order and first-order calibrations as well as multidimensional imaging applications. The concept of ECS would seek to reduce the reliance on users’ extensive knowledge of chemometrics all the while leveraging their domain knowledge and understanding of specific sample physical and chemical properties. By providing automated tools and guidance, an ECS would aim to streamline the calibration process, improve calibration transfer, enhance operator efficiency, and improve the overall consistency and reliability of analytical results produced using advanced chemometrics and machine earning techniques. In this article, we resurrect the discussion from nearly three decades ago.

Non-destructive Near-infrared Hyperspectral Imaging in Food Technologies with a Focus on Monitoring Seed Viability

Seed qualities including viability and germination significantly influence the quantity and the quality of harvest. Technological means to assess seed qualities and attributes of seed-derived food products are varied. This paper highlights the use of infrared hyperspectral imaging (NIR- HSI) in food quality control, authentication, safety, process monitoring, shelf-life prediction, ingredients analysis, allergens detection and food sorting and grading. It also shows a particular application of NIR-HSI for the monitoring of nutrient content of sprouts and germinated seeds for industrial processing of foods with high nutritional values. The paper further reviews the applications of NIR-HSI to predict seed viability and germination. The non-destructive, rapid, and high-throughput capability of NIR-HSI were demonstrated through research works combining the NIR-HSI technology with chemometrics tools to reach more than 90% prediction rate. These relatively high rates may depend on the storage conditions or the stringency of the artificial aging conditions applied to parts of the seeds. However, the NIR-HSI has also proven efficient using naturally aged seeds with the prediction rates up to 90% correct classification, demonstrating the high capability of the technology. In combination with advanced chemometrics tools, some components of emerging technologies such as traditional machine learning and deep learning models have been added to increase the efficiency of NIR_HSI. Overall, the research works reviewed in this paper and which cover several food crops and food products showed that NIR-HSI is set to reach new heights in monitoring seed viability for improved seed stock management, crop production and innovation in the food industry.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Development of a Voltammetric Methodology Based on an Artificial Neural Network for the Quantification of Sodium Diclofenac in Pharmaceutical Samples

Sodium diclofenac is a widely used anti-inflammatory drug that can cause heart diseases if consumed constantly in high doses. Consequently, it is essential to have strict control of the amounts of this active principle in pharmaceutical products. The combination of electroanalytical techniques with advanced chemometrics has risen as a viable alternative for the exact and precise determination of active principles even in the presence of chemical interferences. In this research, an artificial neural network (ANN) for the voltammetric quantification of diclofenac in the presence of paracetamol, pyridoxine, and caffeine is presented, using a carbon paste electrode modified with multilayer carbon nanotubes and titanium dioxide nanoparticles. Cyclic voltammetry is performed to study the effect of the interferences on diclofenac response. Subsequently, a set of diclofenac standards and interferents was prepared using a fractional factorial design to build the response model and perform differential pulse voltammetry to produce the data of the input layer of the ANN. The ANN developed was able to predict the concentration of diclofenac even in the presence of the interferences, since multiple correlation coefficients of 0.9917 and 0.8387 were obtained for training and test data in the analysis of pharmaceutical samples with a recovery percentage of 95.9%.

A novel 3D-fluorescence sensing strategy based on HN-chitosan polymer probe for rapid identification and quantification of potential adulteration in saffron.

Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry.

Laser-induced fluorescence spectroscopy enhancing pistachio nut quality screening

Food safety and quality has become increasingly important in our society, driving the development of novel optical food sensing technologies. Optical sensing technologies have shown good product identification performances, while offering a non-destructive measurement and the possibility for in-line applications. They are, however, currently often limited in their sensitivity and product variability. We therefore pursue the development of a novel pistachio nut screening methodology offering a multi-defect detection, simultaneously detecting shells, tree parts and aflatoxins, by combining fluorescence spectroscopy with advanced chemometrics. Specifically, both one- and two-photon induced fluorescence are investigated, in combination with Linear Discriminant Analysis, Quadratic Discriminant Analysis and K-Nearest Neighbors algorithm, enabling to optimize both the hardware and software parameters. Optimal results were obtained combining the fluorescence spectra using 385 nm excitation with Quadratic Discriminant Analysis, showing a classification accuracy of 99.2% for the healthy pistachio kernels, together with a false positive rate of only 0.8%. This excellent classification accuracy, while considering a multi-defect challenge, is exceeding the state-of-the-art, paving the way towards an improved pistachio screening, benefitting the food processing industry.

Label-Free Cytometric Evaluation of Mitosis via Stimulated Raman Scattering Microscopy and Spectral Phasor Analysis.

Hyperspectral stimulated Raman scattering (SRS) microscopy is a robust imaging tool for the analysis of biological systems. Here, we present a unique perspective, a label-free spatiotemporal map of mitosis, by integrating hyperspectral SRS microscopy with advanced chemometrics to assess the intrinsic biomolecular properties of an essential process of mammalian life. The application of spectral phasor analysis to multiwavelength SRS images in the high-wavenumber (HWN) region of the Raman spectrum enabled the segmentation of subcellular organelles based on innate SRS spectra. Traditional imaging of DNA is primarily reliant on using fluorescent probes or stains which can affect the biophysical properties of the cell. Here, we demonstrate the label-free visualization of nuclear dynamics during mitosis coupled with an evaluation of its spectral profile in a rapid and reproducible manner. These results provide a snapshot of the cell division cycle and chemical variability between intracellular compartments in single-cell models, which is central to understanding the molecular foundations of these fundamental biological processes. The evaluation of HWN images by phasor analysis also facilitated the differentiation between cells in separate phases of the cell cycle based solely on their nuclear SRS spectral signal, which offers an interesting label-free approach in combination with flow cytometry. Therefore, this study demonstrates that SRS microscopy combined with spectral phasor analysis is a valuable method for detailed optical fingerprinting at the subcellular level.

Effect of solvent extraction on the antioxidant and phytochemical profiles of ellagitannins from “wonderful” pomegranate peel: an advanced chemometrics analysis

Effect of solvent extraction on the antioxidant and phytochemical profiles of ellagitannins from “wonderful” pomegranate peel: an advanced chemometrics analysis

Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa

BackgroundHemp and marijuana are the two major varieties of Cannabis sativa. While both contain Δ9-tetrahydrocannabinol (THC), the primary psychoactive component of C. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate that C. sativa containing greater than 0.3% THC is classified as marijuana, while plant material that contains less than or equal to 0.3% THC is hemp. Current methods to determine THC content are chromatography-based, which requires extensive sample preparation to render the materials into extracts suitable for sample injection, for complete separation and differentiation of THC from all other analytes present. This can create problems for forensic laboratories due to the increased workload associated with the need to analyze and quantify THC in all C. sativa materials.MethodThe work presented herein combines direct analysis in real time—high-resolution mass spectrometry (DART-HRMS) and advanced chemometrics to differentiate hemp and marijuana plant materials. Samples were obtained from several sources (e.g., commercial vendors, DEA-registered suppliers, and the recreational Cannabis market). DART-HRMS enabled the interrogation of plant materials with no sample pretreatment. Advanced multivariate data analysis approaches, including random forest and principal component analysis (PCA), were used to optimally differentiate these two varieties with a high level of accuracy.ResultsWhen PCA was applied to the hemp and marijuana data, distinct clustering that enabled their differentiation was observed. Furthermore, within the marijuana class, subclusters between recreational and DEA-supplied marijuana samples were observed. A separate investigation using the silhouette width index to determine the optimal number of clusters for the marijuana and hemp data revealed this number to be two. Internal validation of the model using random forest demonstrated an accuracy of 98%, while external validation samples were classified with 100% accuracy.DiscussionThe results show that the developed approach would significantly aid in the analysis and differentiation of C. sativa plant materials prior to launching painstaking confirmatory testing using chromatography. However, to maintain and/or enhance the accuracy of the prediction model and keep it from becoming outdated, it will be necessary to continue to expand it to include mass spectral data representative of emerging hemp and marijuana strains/cultivars.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability.

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

Spelt authenticity assessment using a rapid and simple Fourier transform infrared spectroscopy (FTIR) method combined to advanced chemometrics

Spelt authenticity assessment using a rapid and simple Fourier transform infrared spectroscopy (FTIR) method combined to advanced chemometrics

Thorough Investigation of the Phenolic Profile of Reputable Greek Honey Varieties: Varietal Discrimination and Floral Markers Identification Using Liquid Chromatography-High-Resolution Mass Spectrometry.

Honey is a highly consumed commodity due to its potential health benefits upon certain consumption, resulting in a high market price. This fact indicates the need to protect honey from fraudulent acts by delivering comprehensive analytical methodologies. In this study, targeted, suspect and non-targeted metabolomic workflows were applied to identify botanical origin markers of Greek honey. Blossom honey samples (n = 62) and the unifloral fir (n = 10), oak (n = 24), pine (n = 39) and thyme (n = 34) honeys were analyzed using an ultra-high-performance liquid chromatography hybrid quadrupole time-of-flight mass spectrometry (UHPLC-q-TOF-MS) system. Several potential authenticity markers were revealed from the application of different metabolomic workflows. In detail, based on quantitative targeted analysis, three blossom honey markers were found, namely, galangin, pinocembrin and chrysin, while gallic acid concentration was found to be significantly higher in oak honey. Using suspect screening workflow, 12 additional bioactive compounds were identified and semi-quantified, achieving comprehensive metabolomic honey characterization. Lastly, by combining non-targeted screening with advanced chemometrics, it was possible to discriminate thyme from blossom honey and develop binary discriminatory models with high predictive power. In conclusion, a holistic approach to assessing the botanical origin of Greek honey is presented, highlighting the complementarity of the three applied metabolomic approaches.

Counterfeit fifty Ringgit Malaysian banknotes authentication using novel graph-based chemometrics method

Counterfeiting, in particular, the forgery of banknotes is a serious crime problem and has become a great challenge to the global economies. The forensic science experts have been utilizing chemical technique such as infrared spectroscopy to analyze genuine and counterfeit banknotes. Nevertheless, chemometrics techniques are required to further discriminate the banknotes. In this paper, an advanced fuzzy graph chemometrics method is used to discriminate genuine and counterfeit fifty Ringgit Malaysian (RM50) banknotes. The development of the technique, namely chemometrics fuzzy autocatalytic set (c-FACS) is presented in this paper, together with the results and its comparison to principal component analysis (PCA) method. The results from the c-FACS analysis showed distinct patterns and features of the counterfeit banknotes in the c-FACS plot. Furthermore, the new method is faster than PCA in authentication analysis of counterfeit banknotes. Hence, the c-FACS provides better performance in terms of computing time as compared to PCA, and has the potential in assisting the investigation involving counterfeit banknotes.

Classification of Groundnut Oil Using Advanced ATR-MIR Spectroscopy and Chemometrics

Classification of Groundnut Oil Using Advanced ATR-MIR Spectroscopy and Chemometrics

A Review on Laser-Induced Breakdown Spectroscopy in Different Cancers Diagnosis and Classification

Due to the new demonstrations of Laser-induced breakdown spectroscopy (LIBS) applicability in a surprisingly wide variety of applications, the use of LIBS as a medical diagnostic tool is steadily gaining momentum. Especially in different cancer diseases, LIBS has the potential to become a fast and valuable analytical tool. We addressed LIBS equipment and quantitative analytical procedures, and signal enhancement techniques for improving element detection. For detailed aspects of applications, we reviewed the recent progress of LIBS in different cancer diseases diagnoses by using different tissues and medical fluid as samples. To fulfill the high demands in the medical industry and overcome the severe tissue sample problem, it is proposed that the chemometric and signal amplification techniques for quantitative analysis should be employed, and robust and effective LIBS devices should be developed. This overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.

SERS and advanced chemometrics – Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment

The enormous development and expansion of antibiotic-resistant bacterial strains impel the intensive search for new methods for fast and reliable detection of antibiotic susceptibility markers. Here, we combined DNA-targeted surface functionalization, surface-enhanced Raman spectroscopy (SERS) measurements, and subsequent spectra processing by decision system (DS) for detection of a specific oligonucleotide (ODN) sequence identical to a fragment of blaNDM-1 gene, responsible for β-lactam antibiotic resistance. The SERS signal was measured on plasmonic gold grating, functionalized with capture ODN, ensuring the binding of corresponded ODNs. Designed DS consists of a Siamese neural network (SNN) coupled with robust statistics and Bayes decision theory. The proposed approach allows manipulation with complex multicomponent samples and predefine the desired detection level of confidence and errors, automatically determining the number of required spectra and samples. In constant to commonly used classification-type SNN, our method was applied to analyze samples with compositions previously “unknown” to DS. The detection of targeted ODN was performed with ≥99% level of confidence up to 3 × 10−12 M limit on the background of 10−10 M concentration of similar but not targeted ODNs.

A Study on the Volatile Compounds in Elaeagnus angustifolia L. Flowers during Flowering Season by Gas Chromatography-Mass Spectrometry Coupled with Advanced Chemometrics

The flowers of Elaeagnus angustifolia L. have been used as a homologous variety in China, whose quality seriously relies on the compositions during the flowering period. Unfortunately, studies on the variations of volatile compounds during the flowering season are rarely reported. Herein, a gas chromatography-mass spectrometry-based untargeted metabolomic methodology was proposed for the comprehensive analysis of volatile compounds in E. angustifolia flowers to classify various flowering stages. Samples from four flowering stages were collected, including the initial bloom stage, pre-full bloom stage (70–80% of flowers), full bloom stage, and ending of the bloom stage. Simultaneous distillation extraction was used for the extraction of volatile compounds in the flowers, which was then analyzed by a newly developed chemometric data analysis tool, autoGCMSDataAnal. An advantage of the developed methodology is that compounds can be accurately screened and identified. Finally, 59 compounds that showed significant difference among four flowering stages were screened and 31 compounds were identified. Sample clustering results from principal component analysis and hierarchical clustering analysis suggested that flowers from the pre-full bloom stage and full bloom stage may be more suitable when used as raw materials for industrial products.

MALDI-TOF-MS integrated workflow for food authenticity investigations: An untargeted protein-based approach for rapid detection of PDO feta cheese adulteration

Advances in Matrix-assisted Laser Desorption/Ionization -Time-Of-Flight Mass Spectrometry (MALDI-TOF-MS) have led to its supremacy for complex assessment of food authenticity studies, like dairy products fraud, holding promise for the discovery of potential authenticity (bio)markers. In this study, an integrated untargeted protein-based workflow in combination with advanced chemometrics is presented, to address authenticity challenges in PDO feta cheese which is legally manufactured by the mixture of sheep/goat milk. Potential markers attributed to specific animal origin were found from protein profiles acquired for authentic feta and white cheeses (prepared from cow milk), belonging to 4 kDa–18.5 kDa mass area. Rapid detection of feta cheese adulteration from cow milk was also achieved down to 1% adulteration level. The discriminative models showed high predictive ability for feta cheese authenticity (Q2 = 0.920, RMSEE = 0.053) and its adulteration (Q2 = 0.835, RMSEE = 0.121), introducing a reliable approach in routine analysis. The methodology was successfully applied in detection of cow milk in sheep yoghurt.

Preparation of microwave‐processed oil‐free potato snacks with assorted natural flavours: Application of advanced chemometrics

SummaryGenerally, potato chips are produced by deep‐fat frying with 35%–40% oil in the final product, which is considered unhealthy. Therefore, we attempted to prepare microwave‐processed oil‐free potato snacks, fat‐free alternative for potato chips, assorted with natural flavours (tomato, mint and pepper) and evaluated their proximate composition, microbiological analysis and sensory evaluation coupled with advanced chemometrics. The proximate analysis revealed the presence of carbohydrates (53–58 g/100 g), energy (190–214 kcal/100 g) and negligible fat (≤0.15%) with a microbial load of 2.50–9 CFU/100 g. Samples assorted with mint and tomato flavours received higher sensory scores (7.65–8.80) for all sensory attributes compared with control (6.61–7.55). From the chemometric analysis, the high similarities in samples assorted with mint and tomato flavours confirmed by principal component analysis loadings of 0.69 and 0.71, respectively, and hierarchical clustering (cluster 1 with 2 sub‐clusters). Therefore, the microwave processing can be a suitable technique to produce oil‐free potato snacks with natural flavourings while preserving their quality attributes.