Derivative FTIR spectroscopy for cluster analysis and classification of morocco olive oils

Rapid and nondestructive identification of adulterate capsules by NIR spectroscopy combined with chemometrics

This study aims to develop a rapid and non-destructive method to identify counterfeit and substandard drugs, addressing the critical need for better quality control in drug production. According to the reasons for counterfeit products in actual production, the commonly used solid preparation excipients such as HPMC, MCC, Mg-St and Pregelatinized Starch, as well as three chemical drugs with similar efficacy to Guizhi-Fuling (GZFL) Capsule as adulterants, including Aspirin, Ibuprofen and Sinomenine Hydrochloride were selected and designed as adulteration samples with different levels of adulteration. NIR spectra were collected in a non-invasive mode and analyzed by one-class classification methods. The feasibility of using Near-infrared (NIR) spectroscopy as a detection method to qualitatively identify adulterated samples was explored at three packaging levels of powder, intact capsules and capsules in PVC. The differences between the samples were analyzed by NIR spectra comparison, cluster analysis and principal component analysis. The performance of SVM, OCPLS and DD-SIMCA models in dealing with the authentication of genuine and counterfeit products was established and compared. The results show that the spectra contain sample information and the adulterated samples could be discriminated correctly by established models. Moreover, applying appropriate spectral preprocessing methods can further improve the model's performance. In addition, a PLS regression model was developed to predict the adulteration levels of the three packing level samples, which yielded satisfactory results. This study highlights the potential of NIR spectroscopy combined with Chemometrics as a rapid and non-destructive testing analysis method to accurately identify counterfeit and substandard drugs, thereby ensuring drug quality.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Classification of Amazonian fast-growing tree species and wood chemical determination by FTIR and multivariate analysis (PLS-DA, PLS)

Fast-growing trees like Capirona, Bolaina, and Pashaco have the potential to reduce forest degradation because of their ecological features, the economic importance in the Amazon Forest, and an industry based on wood-polymer composites. Therefore, a practical method to discriminate specie (to avoid illegal logging) and determine chemical composition (tree breeding programs) is needed. This study aimed to validate a model for the classification of wood species and a universal model for the rapid determination of cellulose, hemicellulose, and lignin using FTIR spectroscopy coupled with chemometrics. Our results showed that PLS-DA models for the classification of wood species (0.84 ≤ R² ≤ 0.91, 0.12 ≤ RMSEP ≤ 0.20, accuracy, specificity, and sensibility between 95.2 and 100%) were satisfied with the full spectra and the differentiation among these species based on IR peaks related to cellulose, lignin, and hemicellulose. Besides, the full spectra helped build a three-species universal PLS model to quantify the principal wood chemical components. Lignin (RPD = 2.27, [Formula: see text] = 0.84) and hemicellulose (RPD = 2.46, [Formula: see text] = 0.83) models showed a good prediction, while cellulose model (RPD = 3.43, [Formula: see text] = 0.91) classified as efficient. This study showed that FTIR-ATR, together with chemometrics, is a reliable method to discriminate wood species and to determine the wood chemical composition in juvenile trees of Pashaco, Capirona, and Bolaina.

Screening of polymer types and chemical weathering in macro- and meso-plastics found on lake and river beaches using a combined chemometric approach

In the environment, synthetic polymers, commonly known as "plastics", are well-known to undergo various chemical weathering processes, which modify their surface chemistry by introducing new functional groups. Such changes are important to monitor, as they can severely influence the toxicity caused by plastic debris. Therefore, in this study, two chemometric models are proposed to accelerate the chemical classification of macro- and meso-plastics found in the environment. For this purpose, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied on preprocessed infrared spectra of 83 plastic fragments found on public lake and river beaches. HCA associated all beach samples with a known plastic, whereas PCA enabled the association of only 39.8% (33 out of 83) of the beach samples with a known plastic. However, both techniques agreed on 93.9% of the samples identified. According to PCA and HCA results, polypropylene and polyethylene were the most frequently identified polymers in the samples. PCA turned out to be a very promising tool for fast screening of weathered plastics, since the distance of samples from the polypropylene cluster in the PCA plot was correlated with weathering. This was later confirmed by employing other characterization techniques such as micro-Raman, X-ray photoelectron spectroscopy and scanning electron microscopy. Finally, future experiments should focus on the applicability of the proposed combined chemometric approach for very small microplastics (<100 μm), as they have more important effects than larger plastics on aquatic ecosystems.

Central retinal thickness fluctuations in patients treated with anti-VEGF for neovascular age related macular degeneration

PURPOSE

This is a retrospective, single-center, non randomized interventional real life study, investigating the correlation between variability of central retinal thickness (CRT) and functional outcomes during 2 years of anti-VEGF therapy in patients treated for neovascular age related macular degeneration (nAMD).

BACKGROUND

CRT fluctuations can depend on various factors such as the correct timing of injections, the therapeutic algorithm, and the number of injections (NI) performed; it is important to understand if CRT fluctuations are responsible for worse visual outcomes and consequently to identify the correct ways to avoid or reduce them.

METHODS

Forty-one patients were treated for nAMD with aflibercept: 0.5 mg intravitreal aflibercept was administered every 4 weeks during the first 3 months, then bimonthly over the first year, and after the first year adopting a PRN regimen. Standard deviation of CRT (CRT/SD), BCVA, and NI were recorded. Correlation studies were performed by Pearson's test, Ancova, and Principal Component Analysis.

RESULTS

A negative correlation was found between CRT/SD and final BCVA. In patients who lost more than 15 letters, CRT/SD mean was significantly higher in comparison with patients who lost less than 15 letters. Patients with final BCVA >65 letters showed lower CRT/SD values compared to patients with final BCVA ⩽65 letters. Multivariate analysis confirmed that in patients with higher baseline BCVA, improvement of BCVA was correlated to NI, and lower values of CRT fluctuations were observed.

CONCLUSIONS

CRT fluctuations, even after an appropriate NI given per year, significantly influence BCVA; a proactive treatment algorithm appears crucial when treating patients with nAMD.

Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

Optimizing setup of scan number in FTIR spectroscopy using the moment distance index and PLS regression: application to soil spectroscopy

Vibrational spectroscopy such as Fourier-transform infrared (FTIR), has been used successfully for soil diagnosis owing to its low cost, minimal sample preparation, non-destructive nature, and reliable results. This study aimed at optimizing one of the essential settings during the acquisition of FTIR spectra (viz. Scans number) using the standardized moment distance index (SMDI) as a metric that could trap the fine points of the curve and extract optimal spectral fingerprints of the sample. Furthermore, it can be used successfully to assess the spectra resemblance. The study revealed that beyond 50 scans the similarity of the acquisitions has been remarkably improved. Subsequently, the effect of the number of scans on the predictive ability of partial least squares regression models for the estimation of five selected soil properties (i.e., soil pH in water, soil organic carbon, total nitrogen, cation exchange capacity and Olsen phosphorus) was assessed, and the results showed a general tendency in improving the correlation coefficient (R²) as the number of scans increased from 10 to 80. In contrast, the cross-validation error RMSECV decreased with increasing scan number, reflecting an improvement of the predictive quality of the calibration models with an increasing number of scans.

Preliminary study on the potential application of Fourier-transform mid-infrared for the evaluation of overall quality and authenticity of Moroccan virgin olive oil

BACKGROUND

Olive oil provides a wide range of health-promoting compounds. The quality of olive oil is an even more complex concept as it is affected by several factors, such as variety, season, stage of maturation, extraction processing, and so on. The main objective of this study was to determine the potential of chemical and mid-infrared spectroscopy techniques to determine the quality and authenticity of virgin olive oil (VOO). For this, we studied 41 VOOs originating from five regions of Morocco (Fez/Meknes, Eastern, Northern, Beni-Mellal/Khenifra, and Marrakech/Safi) and produced using different agricultural and technological conditions during two successive crop seasons (2015-2016 and 2016-2017).

RESULTS

By applying principal component analysis and factorial discriminant analysis with leave-one-out validation to the mid-infrared spectroscopy, clear discrimination between VOO samples according to their geographic origin and variety was observed, with correct classification rates of 91.87% and 91.87% being observed respectively. The application of partial least-squares regression to mid-infrared and chemical data sets allowed excellent prediction of free acidity, peroxide value, k₂₇₀ , and chlorophyll level with R² of 0.99, 0.97, 0.98, and 0.93 respectively, and good prediction of k₂₃₂ (R² = 0.84).

CONCLUSION

The results demonstrate that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool for evaluating the overall quality and authenticity of VOO. © 2020 Society of Chemical Industry.

Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

The correlation of physicochemical properties of edible vegetable oils by chemometric analysis of spectroscopic data

This work aimed to investigate and compare the composition and the physicochemical properties of 18 different sources of edible vegetable oils. A systematic study on the correlation between composition and physical properties was performed using Fourier Transform Infrared (FTIR) Spectroscopy and fatty acid chromatographic analysis. Principal component analysis of FTIR spectra is performed to classify edible oils concerning their physical properties. The results demonstrate the potentiality of the method associated with multivariate statistics analysis as powerful, fast, and non-destructive tools for characterization and quality control of edible vegetable oils.

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.

Use of FTIR Spectroscopy and Chemometrics with Respect to Storage Conditions of Moldavian Dragonhead Oil

Oils often have similar properties and can be difficult to identify based on color, smell or taste alone. The present paper suggests the use of Fourier-transform infrared spectroscopy (FTIR) in combination with chemometric methods to explore similarities and differentiate between samples of Moldavian dragonhead oil subjected to different storage conditions. Dragonhead is a plant characterized by very good honey output and ease of cultivation. Principal component analysis (PCA) was applied to a standard, full range of FTIR spectra. Additionally, hierarchical cluster analysis (HCA) was employed to explore the organization of the samples in groups relative to their “proximity” (similarity), by way of Euclidean distance measurement. PC1 and PC2 accounted respectively for 85.4% and 10.1% of the total data variance. PC1 and PC2 were strongly, negatively correlated within the entire spectral range; the only exception was the region corresponding to νs(-C-Hvst, -CH2) vibrations (aliphatic groups in triglycerides), where PC2 was positively correlated. The use of FTIR spectral analysis revealed noticeable differences in the intensity of bands characteristic of the ageing processes (markers of oxidative processes, etc.) taking place in oleaginous samples and related to the processes of fatty acids oxidation.

The impact of the order of derivative spectra on the performance of pattern recognition methods. Classification of medicinal plants according to the phylum

Data pre-processing is an important strategy in chemometrics and related fields because in many cases the transformation of data has a great effect on the performance of the method (model). However, a careful examination of the literature clearly points out that only very few systematic studies are dedicated to the effect of the derivative spectra on the performance of the pattern recognition methods. This comprehensive study compares the impact of the order of derivative spectra and other data pre-processing procedures (normalization and standardization) on the performance of cluster analysis, principal component analysis and discriminant analysis applied for characterization and classification of medicinal plants according to their phylum using UV spectra. The efficiency of the pre-processing methods was estimated by comparing the accuracy of classification and prediction measured by internal cross-validation. Derivatization method (1st order) resulted in the best classification (100%) of medicinal plants according to their phylum (Pteridophyte, Magnoliophyte and Spermatophyte) as compared to other pre-processing methods (normalized spectra-71.4%, standardized spectra-76.2% and original spectra-78.6%).

Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000-550 cm^-1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

Evaluating the freezing impact on the proximate composition of immature cowpea (Vigna unguiculata L.) pods: classical versus spectroscopic approaches

BACKGROUND

Freezing represents a common conservation practice regarding vegetal foodstuffs. Since compositional features need to be monitored during storage, the development of rapid monitoring tools suitable for assessing nutritional characteristics arises as a pertinent issue. In this study, cowpea (Vigna unguiculata L.) pods, both fresh and after 6 and 9 months of freezing at -18 °C, were evaluated by high-performance liquid chromatography for their content of protein as well as of essential and nonessential amino acids, while their Fourier transform infrared spectra in the mid infrared (MIR) and near infrared (NIR) ranges were concomitantly registered to assess the feasibility of this approach for the traceability of these frozen matrices.

RESULTS

For the NIR interval, the application of the 1st derivative to the spectral data retrieved the best results, while for lower concentrations the application of the Savitzky-Golay algorithm was indispensable to achieve quantification models for the amino acids. MIR is also suitable for this purpose, though being unable to quantify amino acids with concentrations below 0.07 mmol g^-1 dry weight, irrespective of the data treatment used.

CONCLUSIONS

The spectroscopic approach constitutes a methodology suitable for monitoring the impact of freezing on the nutritional properties of cowpea pods, allowing accurate quantification of the protein and amino acid contents, while NIR displayed better performance. © 2017 Society of Chemical Industry.

Spectrophotometric versus NIR-MIR assessments of cowpea pods for discriminating the impact of freezing

BACKGROUND

Freezing represents an important storage method for vegetal foodstuffs, such as cowpea pods, and thus the impact of this process on the chemical composition of these matrices arises as a prominent issue. In this sense, the phytochemical contents in frozen cowpea pods (i.e. at 6 and 9 months) have been compared with fresh cowpea pods material, with the samples being concomitantly assessed by Fourier-transform infrared spectroscopy (FTIR), both mid-infrared (MIR) and near infrared (NIR), aiming to evaluate the potential of these techniques as a rapid tool for the traceability of these matrices.

RESULTS

A decrease in phytochemical contents during freezing was observed, allowing the classification of samples according to the freezing period based on such variations. Also, MIR and NIR allowed discrimination of samples: the use of the first derivative demonstrated a better performance for this purpose, whereas the use of the normalized spectra gave the best correlations between the spectra and specific contents. In both cases, NIR displayed the best performance.

CONCLUSION

Freezing of cowpea pods leads to a decrease of phytochemical contents, which can be monitored by FTIR spectroscopy, both within the MIR and NIR ranges, whereas the use of this technique, in tandem with chemometrics, constitutes a suitable methodology for the traceability of these matrices. © 2017 Society of Chemical Industry.

Classification and identification of Rhodobryum roseum Limpr. and its adulterants based on fourier-transform infrared spectroscopy (FTIR) and chemometrics

Fourier-transform infrared spectroscopy (FTIR) with the attenuated total reflectance technique was used to identify Rhodobryum roseum from its four adulterants. The FTIR spectra of six samples in the range from 4000 cm-1 to 600 cm-1 were obtained. The second-derivative transformation test was used to identify the small and nearby absorption peaks. A cluster analysis was performed to classify the spectra in a dendrogram based on the spectral similarity. Principal component analysis (PCA) was used to classify the species of six moss samples. A cluster analysis with PCA was used to identify different genera. However, some species of the same genus exhibited highly similar chemical components and FTIR spectra. Fourier self-deconvolution and discrete wavelet transform (DWT) were used to enhance the differences among the species with similar chemical components and FTIR spectra. Three scales were selected as the feature-extracting space in the DWT domain. The results show that FTIR spectroscopy with chemometrics is suitable for identifying Rhodobryum roseum and its adulterants.

Ultraviolet spectroscopy combined with ultra-fast liquid chromatography and multivariate statistical analysis for quality assessment of wild Wolfiporia extensa from different geographical origins

A quality assessment system comprised of a tandem technique of ultraviolet (UV) spectroscopy and ultra-fast liquid chromatography (UFLC) aided by multivariate analysis was presented for the determination of geographic origin of Wolfiporia extensa collected from five regions in Yunnan Province of China. Characteristic UV spectroscopic fingerprints of samples were determined based on its methanol extract. UFLC was applied for the determination of pachymic acid (a biomarker) presented in individual test samples. The spectrum data matrix and the content of pachymic acid were integrated and analyzed by partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The results showed that chemical properties of samples were clearly dominated by the epidermis and inner part as well as geographical origins. The relationships among samples obtained from these five regions have been also presented. Moreover, an interesting finding implied that geographical origins had much greater influence on the chemical properties of epidermis compared with that of the inner part. This study demonstrated that a rapid tool for accurate discrimination of W. extensa by UV spectroscopy and UFLC could be available for quality control of complicated medicinal mushrooms.

Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review

Today virgin and extra-virgin olive oil (VOO and EVOO) are food with a large number of analytical tests planned to ensure its quality and genuineness. Almost all official methods demand high use of reagents and manpower. Because of that, analytical development in this area is continuously evolving. Therefore, this review focuses on analytical methods for EVOO/VOO which use fast and smart approaches based on chemometric techniques in order to reduce time of analysis, reagent consumption, high cost equipment and manpower. Experimental approaches of chemometrics coupled with fast analytical techniques such as UV-Vis spectroscopy, fluorescence, vibrational spectroscopies (NIR, MIR and Raman fluorescence), NMR spectroscopy, and other more complex techniques like chromatography, calorimetry and electrochemical techniques applied to EVOO/VOO production and analysis have been discussed throughout this work. The advantages and drawbacks of this association have also been highlighted. Chemometrics has been evidenced as a powerful tool for the oil industry. In fact, it has been shown how chemometrics can be implemented all along the different steps of EVOO/VOO production: raw material input control, monitoring during process and quality control of final product.

Using the liquid-chromatographic-fingerprint of sterols fraction to discriminate virgin olive from other edible oils

A method to discriminate virgin olive oil from other edible vegetable oils such as, sunflower, pomace olive, rapeseed, canola, corn and soybean, applying chemometric techniques to the liquid chromatographic representative fingerprint of sterols fraction, is proposed. After a pre-treatment of the LC chromatogram data - including baseline correction, smoothing signal and mean centering - different unsupervised and supervised pattern recognition procedures, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares-discriminant analysis (PLSDA), have been applied. From the information obtained from PCA and HCA, two groups can be clearly distinguished (virgin olive and the rest of vegetable oils tested) which have been used to discriminate between two defined classes by means of a PLSDA model. Five latent variables (LVs) explained 76.88% of X-block variance and 95.47% of the defined classes block (γ-block) variance. A root mean square error for calibration and cross validation of 0.10 and 0.22 respectively, confirmed these results and a root mean square error for prediction of 0.15 evidences that the classification model proposed presents an adequate prediction capability. The contingency table also shows the good performance of the model, proving the capability of the LC-R-FpM, to discriminate virgin olive from other vegetable edible oils.