Rapid determination of phytosterols by NIRS and chemometric methods

Rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy

Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV-Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample's enzyme activity data were linked with each sample's original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong relationship between NIRs and CRL/ZIF-8 enzyme activity. As a result, the established model could be implemented to quantify the enzyme activity of CRL/ZIF-8 quickly by including more variations of natural samples. The prediction method is simple, rapid, and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in enzymology and spectroscopy.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

The Lack of Standardization and Pharmacological Effect Limits the Potential Clinical Usefulness of Phytosterols in Benign Prostatic Hyperplasia

The prevalence of benign prostatic hyperplasia (BPH) markedly increases with age. Phytotherapeutic approaches have been developed over time owing to the adverse side effects of conventional medications such as 5-reductase inhibitors and α1-adrenergic receptor antagonists. Therefore, dietary supplements (DS) containing active compounds that benefit BPH are widely available. Phytosterols (PSs) are well recognized for their role in maintaining blood cholesterol levels; however, their potential in BPH treatment remains unexplored. This review aims to provide a general overview of the available data regarding the clinical evidence and a good understanding of the detailed pharmacological roles of PSs-induced activities at a molecular level in BPH. Furthermore, we will explore the authenticity of PSs content in DS used by patients with BPH compared to the current legislation and appropriate analytical methods for tracking DS containing PSs. The results showed that PSs might be a useful pharmacological treatment option for men with mild to moderate BPH, but the lack of standardized extracts linked with the regulation of DS containing PSs and experimental evidence to elucidate the mechanisms of action limit the use of PSs in BPH. Moreover, the results suggest multiple research directions in this field.

A Review of Machine Learning for Near-Infrared Spectroscopy

The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.

Nutraceutical and Pharmaceutical Behavior of Bioactive Compounds of Miracle Oilseeds: An Overview

India plays an important role in the production of oilseeds, which are mainly cultivated for future extraction of their oil. In addition to the energic and nutritional contribution of these seeds, oilseeds are rich sources of bioactive compounds (e.g., phenolic compounds, proteins, minerals). A regular and moderate dietary supplementation of oilseeds promotes health, prevents the appearance of certain diseases (e.g., cardiovascular diseases (CVDs), cancers) and delays the aging process. Due to their relevant content in nutraceutical molecules, oilseeds and some of their associated processing wastes have raised interest in food and pharmaceutical industries searching for innovative products whose application provides health benefits to consumers. Furthermore, a circular economy approach could be considered regarding the re-use of oilseeds’ processing waste. The present article highlights the different oilseed types, the oilseeds-derived bioactive compounds as well as the health benefits associated with their consumption. In addition, the different types of extractive techniques that can be used to obtain vegetable oils rich from oilseeds, such as microwave-assisted extraction (MAE), ultrasonic-assisted extraction (UAE) and supercritical fluid extraction (SFE), are reported. We conclude that the development and improvement of oilseed markets and their byproducts could offer even more health benefits in the future, when added to other foods.

Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation

Near-infrared spectroscopy (NIRS) is an excellent process analytical technology (PAT) tool for active pharmaceutical ingredient (API) quantification during fluidized granulation. Therefore, a portable near-infrared spectrometer combined with a new innovative method of extended iterative optimization technique (EIOT) was used to in-line monitor the API content uniformity during fluidized bed granulation. The principal component analysis (PCA) and partial least squares regression (PLSR) were also used to characterize and predict API concentration with changes from 75% to 125% of the label claim to prove the superiority of EIOT. The API content prediction accuracy of the EIOT method was verified through offline High Performance Liquid Chromatography (HPLC) measurement. Also, the spatial distribution of API in granules was visualized by Raman imaging technology. The results showed that the established NIRS method was suitable for the prediction of API content in fluidized bed granulation, which provides a new idea for the determination of API content during granulation.

Use of ATR-FTIR Spectroscopy and Chemometrics for the Variation of Active Components in Different Harvesting Periods of Lonicera japonica

Lonicera japonica Thunb is a commonly used Chinese herbal medicine, which belongs to the family Caprifoliaceae. The active components varied greatly during bud development. Research on the variation of the main active components is significant for the timely harvesting and quality control of Lonicera japonica. In this study, the attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with the chemometric method was performed to investigate the variability of different harvesting periods of Lonicera japonica. The preliminary characterization from ATR-FTIR fingerprints showed various characteristic absorption peaks of the main active components from the different harvesting times, such as flavonoids, organic acids, iridoids, and volatile oils. Additionally, principal component analysis (PCA) scatter plots showed that there was a clear clustering trend in the samples of the same harvesting period, and the samples of the different harvesting periods could be well distinguished. Finally, further analysis by the orthogonal partial least-squares discriminant analysis (OPLS-DA) showed that there were regular changes in flavonoids, phenolic acids, iridoids, and volatile oils in different harvesting periods. Therefore, ATR-FTIR, as a novel and convenient analytical method, could be applied to evaluate the quality of Lonicera japonica.

Rapid prediction method of α-Glycosidase inhibitory activity of Coreopsis tinctoria extract from different habitats by near infrared spectroscopy

α-Glucosidase is one of the main enzymes causing elevated blood glucose, and Coreopsis tinctoria extract can be used as a natural inhibitor of α-Glucosidase. Therefore, a new method was proposed for predicting the inhibitory activity on α-Glucosidase of Coreopsis tinctoria extract based on near infrared spectroscopy. The absorbance of the inhibitory system was measured by ultraviolet spectroscopy, which was used to study the inhibitory activity on a-glucosidase of Coreopsis tinctoria extract. The near infrared spectra of the solid samples were collected. By selecting spectral preprocessing and optimizing spectral bands, a rapid prediction model of the inhibitory activity was established by partial least squares regression. The root mean square error of cross-validation (RMSECV), correlation coefficient (R) value and the ratio of prediction to deviation (RPD) value were used as indicators of the evaluation model. The near infrared spectrum model was established by combining the best spectral preprocessing of the continuous wavelet transform (CWT) and the best spectral band. The root mean square error of cross-validation (RMSECV) of this model was 0.815%, the correlation coefficient (R) value was 0.942, and the ratio of prediction to deviation (RPD) was 3.0. The root mean square error of prediction (RMSEP) of the model by prediction set was 0.819%, the correlation coefficient (R) value was 0.950, and the RPD was 3.2. The model shows that the fitting relationship between the predicted inhibition value and the reference inhibition value of the near infrared spectral model is good. The results showed that there was a good correlation between near infrared spectroscopy and the inhibitory activity of Coreopsis tinctoria extract. Thus, the established model was robust and effective and could be used for rapid quantification of α-Glucosidase inhibitory activity. The prediction method is simple and rapid, and can be extended to study the inhibition of other medicinal plants.

Bioactive compounds of pequi pulp and oil extracts modulate antioxidant activity and antiproliferative activity in cocultured blood mononuclear cells and breast cancer cells

Background

Pequi (Caryocar brasiliense Camb.) is a fruit from Brazilian Cerrado rich in bioactive compounds, such as phytosterols and tocopherols, which can modulate the death of cancer cells.

Objective

In the present study, the main bioactive compounds of hydrophilic and lipophilic extracts of pequi oil and pulp were identified and were verified if they exert modulatory effects on oxidative stress of mononuclear cells cocultured with MCF-7 breast cancer cells.

Study design

Identification and quantification of the main compounds and classes of bioactive compounds in pequi pulp and oil, hydrophilic, and lipophilic extracts were performed using spectroscopy and liquid chromatographic methods, while the beneficial effects, such as antioxidant capacity in vitro, were determined using methods based on single electron transfer reaction or hydrogen atom transfer, while for antioxidant and antiproliferative activities ex vivo, 20 healthy volunteers were recruited. Human peripheral blood mononuclear cells (MN) were collected, and cellular viability assay by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide), superoxide anion evaluation, and CuZn-superoxide dismutase determination (CuZn-SOD) in MN cells, MCF-7 cells, and coculture of MN cells and MCF-7 cells in the presence and absence of pequi pulp or oil hydrophilic and lipophilic extracts were performed.

Results

In the hydrophilic extract, the pequi pulp presented the highest phenolic content, while in the oil lipophilic extract, it had the highest content of carotenoids. The main phytosterol in pequi oil was β-sitosterol (10.22 mg/g), and the main tocopherol was γ-tocopherol (26.24 μg/g sample). The extracts that had highest content of bioactive compounds stimulated blood mononuclear cells and also improved SOD activity. By evaluating the extracts against MCF-7 cells and coculture, they showed cytotoxic activity.

Conclusion

The results support the anticarcinogenic activity of pequi extracts, in which the pequi pulp hydrophilic extracts presented better immunomodulatory potential.

Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics

The purpose of the study is to present a nondestructive qualitative and quantitative approach of hard-shell capsule using near-infrared (NIR) spectroscopy combined with chemometrics. The Yaobitong capsule (YBTC) was used for demonstration of the proposed approach and the NIR spectra were collected using a handheld fiber probe (FP) without the damage of capsule shell. By comparing the differences and similarities of the NIR spectra of capsule shells, contents and intact capsules, a preliminary conclusion can be drawn that the NIR spectra contained the information of the contents. Characteristic variables were selected by competitive adaptive weighted resampling (CARS) method, and least squares support vector machine (LSSVM) method based on particle swarm optimization (PSO) algorithm was applied to the construction of quantitative models. The relative standard error of prediction (RSEP) values of five saponins including notoginsenoside R1, ginsenoside Rg1, Re, Rb1, and Rd were 3.240%, 5.468%, 5.303%, 5.043%, and 3.745%, respectively. In addition, for qualitative model, three different types of adulterated capsules were designed. The model established by data driven version of soft independent modeling of class analogy (DD-SIMCA) demonstrated a satisfactory result that all adulterated capsules were identified accurately after an appropriate number of principal components (PCs) were chosen. The results indicated that although the NIR spectra collection was affected by capsule shell, sufficient content information can be obtained for quantitative and qualitative analysis after combining with chemometrics. It further proved that acquired NIR spectra do contain the effective component information of the capsule. This study provided a reference for the rapid nondestructive quality analysis of traditional Chinese medicine (TCM) capsule without damaging capsule shell.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

From Laboratory Tests to the Ecoremedial System: The Importance of Microorganisms in the Recovery of PPCPs-Disturbed Ecosystems

The presence of a wide variety of emerging pollutants in natural water resources is an important global water quality challenge. Pharmaceuticals and personal care products (PPCPs) are known as emerging contaminants, widely used by modern society. This objective ensures availability and sustainable management of water and sanitation for all, according to the 2030 Agenda. Wastewater treatment plants (WWTP) do not always mitigate the presence of these emerging contaminants in effluents discharged into the environment, although the removal efficiency of WWTP varies based on the techniques used. This main subject is framed within a broader environmental paradigm, such as the transition to a circular economy. The research and innovation within the WWTP will play a key role in improving the water resource management and its surrounding industrial and natural ecosystems. Even though bioremediation is a green technology, its integration into the bio-economy strategy, which improves the quality of the environment, is surprisingly rare if we compare to other corrective techniques (physical and chemical). This work carries out a bibliographic review, since the beginning of the 21st century, on the biological remediation of some PPCPs, focusing on organisms (or their by-products) used at the scale of laboratory or scale-up. PPCPs have been selected on the basics of their occurrence in water resources. The data reveal that, despite the advantages that are associated with bioremediation, it is not the first option in the case of the recovery of systems contaminated with PPCPs. The results also show that fungi and bacteria are the most frequently studied microorganisms, with the latter being more easily implanted in complex biotechnological systems (78% of bacterial manuscripts vs. 40% fungi). A total of 52 works has been published while using microalgae and only in 7% of them, these organisms were used on a large scale. Special emphasis is made on the advantages that are provided by biotechnological systems in series, as well as on the need for eco-toxicological control that is associated with any process of recovery of contaminated systems.

Fast determination of oxides content in cement raw meal using NIR-spectroscopy and backward interval PLS with genetic algorithm

Determining oxides content in cement raw meal with near infrared (NIR) spectroscopy, associated with partial least square (PLS) regression, is fast and potential for cement industry to realize cement raw material proportioning control. However, it has hardly been studied. Backward interval PLS (biPLS) with genetic algorithm (GA-biPLS) were applied to select characteristic variables closely related to the concentration of oxide of interest to establish calibration model. The optimal GA-biPLS models showed that the determination coefficient (R_p²) and root mean square error of prediction (RMSEP) were 0.8857 and 0.0994 for CaO, 0.8718 and 0.1044 for SiO₂, 0.7417 and 0.0693 for Al₂O₃, 0.5404 and 0.0387 for Fe₂O₃, correspondingly. These results indicate that GA-biPLS can select less variables with better prediction performance by comparison with PLS and biPLS, the NIR spectroscopy combined with GA-biPLS algorithm is a fast, accurate and reliable alternative method for determination of oxides content in cement raw meal.