Extraction Approach Research Articles

Hibiscus sabdariffa-loaded nanostructured lipid carriers (NLC) for fortified nutraceutical milks

The Hibiscus sabdariffa (H. sabdariffa) belongs to the Malvaceae family, and originates from Africa. It is characterized by a high polyphenols content (e.g., quercetin and anthocyanins), and has recognized bioactivity, that makes this plant an interesting resource for possible addition of its extracts to dairy beverages and obtain functional beverages. Nonetheless, it may be observed that many of these phenolic compounds are not stable depending on the environmental conditions. In this work, we propose the use of nanostructured lipid carriers (NLC) as a carrier for the loading of H. sabdariffa extracts, for further incorporation in daily beverages. Polyphenols-enriched extracts from H. sabdariffa were obtained using two distinct extraction approaches: by (i) microwave-assisted extraction (MAE), or by (ii) pressurized liquid extraction (PLE). The obtained extracts were then loaded into NLC to obtain two distinct samples identified as: HS-MAE-NLC and HS-PLE-NLC. The developed nanoparticles were then incorporated into a dairy beverage for the production of fortified milks identified with the same names. The long-term stability, texture properties and in vitro release profile were evaluated. The results show that the fortified milks, HS-MAE-NLC and HS-PLE-NLC, were stable under stress conditions attributed to the enhanced stability provided by the protein content present in the milk. The in vitro release profile of quercetin and anthocyanins from NLC-enriched dairy beverage was more prolonged than the milk containing the non-loaded extract, fitting better to the Korsmeyers–Peppas model. From our results, NLC may be considered a potential approach to formulate photoprotective, anti-inflammatory, and antioxidant bioactives from H. sabdariffa adding the extracts into dairy beverages to increase their bioavailability, and bioactivity.

A custom-built deep learning approach for text extraction from identity card images

<span>Information found on an identity card is needed for different essential tasks and manually extracting this information is time consuming, resource exhaustive and may be prone to human error. In this study, an optical character recognition (OCR) approach using deep learning techniques is proposed to automatically extract text related information from the image of an identity card in view of developing an automated client onboarding system. The OCR problem is divided into two main parts. Firstly, a custom-built image segmentation model, based on the U-net architecture, is used to <span>detect the location of the text to be extracted. Secondly, using the location of the identified text fields, a (CRNN) based on long short-term memory (LSTM) cells is trained to recognise the characters and build words. Experimental results, based on the national identity card of the Republic of Mauritius, demonstrate that our approach achieves higher accuracy compared to other studies. Our text detection module has an intersection over union (IOU) measure of 0.70 with a pixel accuracy of 98% for text detection and the text recognition module achieved a mean word recognition accuracy of around 97% on main fields of the identity card.</span></span>

A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques

A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques

Optimization of microalgae protein extraction from Scenedesmus obliquus and investigating its functional properties

The objective of this work was to evaluate alkaline treatment with enzymatic approaches for the optimal extraction of proteins from Scenedesmus obliquus microalgae. The experiments were designed with an emphasis on the alkaline treatment period and the enzyme-to-substrate ratio utilizing response surface methodology (RSM) with central composite design (CCD). Higher enzyme concentrations led to better extraction rates and efficiency, according to the findings. Cellulase demonstrated extraction efficiencies between 16.88% and 21.42%, but the multi-enzyme complex had efficiencies ranging from 15.28% to 27.87%. With an extraction rate of 19.13%, alkaline treatment was less advantageous than enzymatic treatment due to its longer procedure. Microalgal proteins' functional characteristics were also looked at. These proteins demonstrated foaming capacity, oil absorption, and emulsification ability, making them suitable alternatives to animal proteins in food systems such as egg white and soybean. Although microalgal protein powder had lower foam capacity (20%), stability (25%), fat absorption capacity (124%), and emulsification ability compared to egg white powder, it still exhibited favorable functional properties, highlighting its potential for various food applications. Further enhancement of the functional properties can be achieved by optimizing the environmental conditions during extraction.

Screening of leaf extraction and storage conditions for eco-metabolomics studies.

Mass spectrometry-based plant metabolomics is frequently used to identify novel natural products or study the effect of specific treatments on a plant's metabolism. Reliable sample handling is required to avoid artifacts, which is why most protocols mandate shock freezing of plant tissue in liquid nitrogen and an uninterrupted cooling chain. However, the logistical challenges of this approach make it infeasible for many ecological studies. Especially for research in the tropics, permanent cooling poses a challenge, which is why many of those studies use dried leaf tissue instead. We screened a total of 10 extraction and storage approaches for plant metabolites extracted from maize leaf tissue across two cropping seasons to develop a methodology for agroecological studies in logistically challenging tropical locations. All methods were evaluated based on changes in the metabolite profile across a 2-month storage period at different temperatures with the goal of reproducing the metabolite profile of the living plant as closely as possible. We show that our newly developed on-site liquid-liquid extraction protocol provides a good compromise between sample replicability, extraction efficiency, material logistics, and metabolite profile stability. We further discuss alternative methods which showed promising results and feasibility of on-site sample handling for field studies.

There's Something in What We Eat: An Overview on the Extraction Techniques and Chromatographic Analysis for PFAS Identification in Agri-Food Products.

Per- and polyfluorinated alkyl substances (PFASs) are a group of anthropogenic chemicals used in a range of industrial processes and consumer products. Recently, their ubiquitous presence in the environment as well as their toxicological effects in humans have gained relevant attention. Although the occurrence of PFASs is widely investigated in scientific community, the standardization of analytical method for all matrices still remains an important issue. In this review, we discussed extraction and detection methods in depth to evaluate the best procedures of PFAS identification in terms of analytical parameters (e.g., limits of detection (LODs), limits of quantification (LOQs), recoveries). Extraction approaches based on liquid-liquid extraction (LLE), alkaline digestion, and solid phase extraction (SPE), followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) analysis are the main analytical methods applied in the literature. The results showed detectable recoveries of PFOA and PFOS in meat, milk, vegetables, eggs products (90.6-101.2% and of 89.2-98.4%), and fish (96-108%). Furthermore, the low LOD and LOQ values obtained for meat (0.00592-0.01907 ng g-1; 0.050 ng g-1), milk (0.003-0.009 ng g-1; 0.010-0.027 ng g-1), fruit (0.002-0.009 ng g-1; 0.006-0.024 ng g-1), and fish (0.00369-0.017.33 ng g-1; 0.05 ng g-1) also confirmed the effectiveness of the recent quick, easy, cheap, effective, rugged, and safe method (QuEChERS) for simple, speedy, and sensitive ultra-trace PFAS analysis.

Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media

In today’s fast-paced world, the rates of stress and depression present a surge. People use social media for expressing their thoughts and feelings through posts. Therefore, social media provide assistance for the early detection of mental health conditions. Existing methods mainly introduce feature extraction approaches and train shallow machine learning (ML) classifiers. For addressing the need of creating a large feature set and obtaining better performance, other research studies use deep neural networks or language models based on transformers. Despite the fact that transformer-based models achieve noticeable improvements, they cannot often capture rich factual knowledge. Although there have been proposed a number of studies aiming to enhance the pretrained transformer-based models with extra information or additional modalities, no prior work has exploited these modifications for detecting stress and depression through social media. In addition, although the reliability of a machine learning (ML) model’s confidence in its predictions is critical for high-risk applications, there is no prior work taken into consideration the model calibration. To resolve the above issues, we present the first study in the task of depression and stress detection in social media, which injects extra-linguistic information in transformer-based models, namely, bidirectional encoder representations from transformers (BERT) and MentalBERT. Specifically, the proposed approach employs a multimodal adaptation gate for creating the combined embeddings, which are given as input to a BERT (or MentalBERT) model. For taking into account the model calibration, we apply label smoothing. We test our proposed approaches in three publicly available datasets and demonstrate that the integration of linguistic features into transformer-based models presents a surge in performance. Also, the usage of label smoothing contributes to both the improvement of the model’s performance and the calibration of the model. We finally perform a linguistic analysis of the posts and show differences in language between stressful and nonstressful texts, as well as depressive and nondepressive posts.

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

Navigation aids play a crucial role in guiding ship navigation and marking safe water areas. Therefore, ensuring the accurate and efficient recognition of a navigation aid’s state is critical for maritime safety. To address the issue of sparse features in navigation aid data, this paper proposes an approach that involves three distinct processes: the extension of rank entropy space, the fusion of multi-domain features, and the extraction of hidden features (EFE). Based on these processes, this paper introduces a new LSTM model termed EFE-LSTM. Specifically, in the feature extension module, we introduce a rank entropy operator for space extension. This method effectively captures uncertainty in data distribution and the interrelationships among features. The feature fusion module introduces new features in the time domain, frequency domain, and time–frequency domain, capturing the dynamic features of signals across multiple dimensions. Finally, in the feature extraction module, we employ the BiLSTM model to capture the hidden abstract features of navigational signals, enabling the model to more effectively differentiate between various navigation aids states. Extensive experimental results on four real-world navigation aid datasets indicate that the proposed model outperforms other benchmark algorithms, achieving the highest accuracy among all state recognition models at 92.32%.

Controls of Human Humanity and its Purposes in Light of the Hadiths of the Two Sahihs: A Recent Analytical Study

The study aims to highlight human humanity's controls and purposes by identifying their inherent manifestations from prophetic Sunni sources. It also identifies several controls on human responsibility and purposes through Al-Ahadith Alsahihyn, emphasizing the importance of authentic Sunni sources in their preservation and control. To achieve its objectives, the study followed the collective approach, based on the graduation of the prophetic texts under study collected and selected from the Bukhari and Muslim health as the most important codes of the perceived prophetic year. The study also used the extractive analytical approach: the study of prophetic conversations on human disciplines, purposes, and analysis, to address precise values. The study found several findings, most notably the diversity of human responsibility controls and their multiplicity in the prophetic year. Religious aspects, including social aspects, Islam's desire to form an integrated Islamic personality in all spheres of its life; the study also found the essential purpose of human humanity in the prophetic year, including achieving the goal for which it was created: God's worship is blessed and uplifting, realizing the principle of exclusion in the architecture of the universe and utilizing its treasures.

Modern ecofriendly approach for extraction of luteolin natural dye from weld for silk fabric and wool yarn dyeing

The increasing apprehension regarding the ecological effects of synthetic dyes has prompted a transition towards sustainable and environmentally friendly substitutes in the textile sector. This research examines the eco-friendly use of a natural pigment obtained from the weld flower for dyeing of silk fabric and wool yarn. The study investigates the use of microwave-assisted extraction to acquire the luteolin based pigment found in the weld flower. An assessment is conducted to evaluate the effectiveness of microwave radiation in improving the extraction process, emphasizing its capacity to accelerate and enhance the production of the natural colorant. Moreover, the process of optimizing dyeing conditions is carried out utilizing a response surface approach, with a specific focus on parameters including temperature, time, pH, and salt content. The study also investigates the influence of mordants, such as iron salt, and aluminum salt, on improving color fastness and attaining bright and enduring hues. The study expands its research to examine the impacts of plant-based extracts, such as madder root, and oak, as bio-mordants for producing a wide array of colors. The study additionally highlights the favorable influence on color fastness, exhibiting significant enhancements in light fastness, wash fastness, and rubbing fastness for both silk and wool. The measurable results validate the effectiveness of the suggested environmentally friendly dyeing techniques, provide numerical evidence of their suitability for sustainable textile manufacturing.

Sustainable Valorization of Industrial Cherry Pomace: A Novel Cascade Approach Using Pulsed Electric Fields and Ultrasound Assisted-Extraction.

In this study, a two-stage cascade extraction process utilizing pulsed electric fields (PEF) (3 kV/cm, 10 kJ/kg) for initial extraction, followed by ultrasound (US) (200 W, 20 min)-assisted extraction (UAE) in a 50% (v/v) ethanol-water mixture (T = 50 °C, t = 60 min), was designed for the efficient release of valuable intracellular compounds from industrial cherry pomace. The extracted compounds were evaluated for total phenolic content (TPC), flavonoid content (FC), total anthocyanin content (TAC), and antioxidant activity (FRAP), and were compared with conventional solid-liquid extraction (SLE). Results showed that the highest release of bioactive compounds occurred in the first stage, which was attributed to the impact of PEF pre-treatment, resulting in significant increases in TPC (79%), FC (79%), TAC (83%), and FRAP values (80%) of the total content observed in the post-cascade PEF-UAE process. The integration of UAE into the cascade process further augmented the extraction efficiency, yielding 21%, 49%, 56%, and 26% increases for TPC, FC, TAC, and FRAP, respectively, as compared to extracts obtained through a second-stage conventional SLE. HPLC analysis identified neochlorogenic acid, 4-p-coumaroylquinic, and cyanidin-3-O-rutinoside as the predominant phenolic compounds in both untreated and cascade-treated cherry pomace extracts, and no degradation of the specific compounds occurred upon PEF and US application. SEM analysis revealed microstructural changes in cherry pomace induced by PEF and UAE treatments, enhancing the porosity and facilitating the extraction process. The study suggests the efficiency of the proposed cascade PEF-UAE extraction approach for phenolic compounds from industrial cherry pomace with potential applications to other plant-based biomasses.

Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach.

Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context. This study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately. Existing approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model's performance. We demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F1-score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases. The proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts.

Automatic Detection of Epileptic Seizures from EEG Signals Using Artificial Intelligence Methods

Epilepsy is a neurological disorder in which involuntary contractions, sensory abnormalities, and changes occur as a result of abrupt and uncontrolled discharges in the neurons in the brain, which disrupt the systems regulated by the brain. In epilepsy, abnormal electrical impulses from cells in various brain areas are noticed. The accurate interpretation of these electrical impulses is critical in the illness diagnosis. This study aims to use different machine-learning algorithms to diagnose epileptic seizures. The frequency components of EEG data were extracted using parametric approaches. This feature extraction approach was fed into machine learning classification algorithms, including Artificial Neural Network (ANN), Gradient Boosting, and Random Forest. The ANN classifier was shown to have the most significant test performance in this investigation, with roughly 97% accuracy and a 91% F1 score in recognizing epileptic episodes. The Gradient Boosting classifier, on the other hand, performed similarly to the ANN, with 96% accuracy and a 93% F1 score.

LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement

Understanding road structures is crucial for autonomous driving. Intricate road structures are often depicted using lane graphs, which include centerline curves and connections forming a Directed Acyclic Graph (DAG). Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG. Recent research highlights Transformer-based language models' impressive sequence prediction abilities, making them effective for learning graph representations when graph data are encoded as sequences. However, existing studies focus mainly on modeling vertices explicitly, leaving edge information simply embedded in the network. Consequently, these approaches fall short in the task of lane graph extraction. To address this, we introduce LaneGraph2Seq, a novel approach for lane graph extraction. It leverages a language model with vertex-edge encoding and connectivity enhancement. Our serialization strategy includes a vertex-centric depth-first traversal and a concise edge-based partition sequence. Additionally, we use classifier-free guidance combined with nucleus sampling to improve lane connectivity. We validate our method on prominent datasets, nuScenes and Argoverse 2, showcasing consistent and compelling results. Our LaneGraph2Seq approach demonstrates superior performance compared to state-of-the-art techniques in lane graph extraction.

STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models

Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies. They heavily rely on task-specific training data in the form of (passage, target structure) pairs to obtain reasonable performance. However, obtaining such data through human annotation is costly, leading to a pressing need for low-resource information extraction approaches that require minimal human labeling for real-world applications. Fine-tuning supervised models with synthesized training data would be a generalizable method, but the existing data generation methods either still rely on large-scale ground-truth data or cannot be applied to complicated IE tasks due to their poor performance. To address these challenges, we propose STAR, a data generation method that leverages Large Language Models (LLMs) to synthesize data instances given limited seed demonstrations, thereby boosting low-resource information extraction performance. Our approach involves generating target structures (Y) followed by generating passages (X), all accomplished with the aid of LLMs. We design fine-grained step-by-step instructions to obtain the initial data instances. We further reduce errors and improve data quality through self-reflection error identification and self-refinement with iterative revision. Our experiments show that the data generated by STAR significantly improve the performance of low-resource event extraction and relation extraction tasks, even surpassing the effectiveness of human-curated data. Human assessment of the data quality shows STAR-generated data exhibit higher passage quality and better align with the task definitions compared with the human-curated data.

A novel hybrid method for calculating the fetal heart rate from the non-invasive abdominal Electrocardiogram signal

Continuous monitoring of fetal health requires the accurate calculation of the Fetal Heart Rate (FHR) by efficiently isolating fetal Electrocardiogram (fECG) from non-invasive abdominal Electrocardiogram (aECG). There are numerous solutions proposed for this problem, having its own restrictions. This paper introduces a novel three-stage hybrid adaptive approach for fECG extraction and FHR determination from aECG. The proposed framework is an amalgamation of independent component analysis (ICA), adaptive exponential functional link network (AEFLN), and novel wavelet thresholding (NWT). The proposed work aims to determine the best quality fetal R-peaks for calculating fetal heart rate (FHR). ICA is a blind source separation technique that uses all the information from the multichannel inputs to determine the maternal Electrocardiogram (mECG) and enhanced aECG. In the next stage, a non-linear adaptive filter, AEFLN, is used for the extraction of fECG. This approach requires no additional algorithm for polarity reversal and time alignment before AEFLN. At last, NWT determines the fetal R-peak envelopes from the residual fECG. The R-peak envelopes are used to analyze FHR, and its accuracy was accessed using the FHR trace. So far, FastICA-AEFLN-NWT has not been used as a hybrid method for fECG extraction and fetal R-peak detection. The algorithm’s effectiveness was tested on both real databases from Daisy and Physionet and synthetic databases from Physionet. The proposed hybrid method attains 95.29 % of sensitivity (SE), 96.68 % of Positive Predictive Value (PPV), 95.92 % of F1 score, and FHR measurement (FHRmeas) is 29.72 bpm2. The proposed methodology outperforms the state-of-art methods in fECG extraction.

AI-assisted literature exploration of innovative Chinese medicine formulas.

Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives. Methods: We present a novel iterative keyword extraction approach for discerning diverse herbs in historical TCM texts from the Pu-Ji Fang copies. Utilizing association rules, we uncover previously unexplored herb pairs. To bridge classical TCM herbal pairs with modern genetic relationships, we conduct gene-herb searches in PubMed and statistically validate this genetic literature as supporting evidence. We have expanded on the present work by developing a generative language model for suggesting innovative TCM formulations based on textual herb combinations. Results: We collected associations with 7,664 PubMed cross-search entries for gene-herb and 934 for Shenqifuzheng Injection as a positive control. We analyzed 16,384 keyword combinations from Pu-Ji Fang's 426 volumes, employing statistical methods to probe gene-herb associations, focusing on examining differences among the target genes and Pu-Ji Fang herbs. Conclusion: Analyzing Pu-Ji Fang reveals a historical focus on flavor over medicinal aspects in TCM. We extend our work on developing a generative model from classical textual keywords to rapidly produces novel herbal compositions or TCM formulations. This integrated approach enhances our comprehension of TCM by merging ancient text analysis, modern genetic research, and generative modeling.

Synthesis and assessment of spherogranular composite tin pyrophosphate antimonate adsorbent for selective extraction of rubidium and cesium from salt lakes

In this study, a spherogranular porous composite tin pyrophosphate antimonate (SSbPP) adsorbent was developed for the efficient extraction of rubidium (Rb) and cesium (Cs) resources. This granular composite adsorbent was prepared using the anti-solvent method with polyvinyl chloride (PVC) as carrier and the incorporated SSbPP powder as adsorbent. The composition, molecular structure, and adsorption performance of SSbPP, along with the microstructure and adsorption behavior of the granulated PVC-SSbPP adsorbent, were comprehensively investigated. The results showed that the SSbPP adsorbent with superior performance was synthesized at a Sn:Sb:P molar ratio of 0.5:0.25:2. The adsorption behavior of PVC-SSbPP was evaluated using the pseudo-second-order kinetic model and Langmuir isotherm. The maximal adsorption capacities of PVC-SSbPP for Rb+ and Cs+ were found to be 81.3 mg g−1 and 95.23 mg g−1 at optimal condition, respectively. The PVC-SSbPP also exhibited high adsorption selectivity for Rb+/Cs+ in the presence of competitive metal ions such as K+, Na+, Li+, Mg2+ and Ca2+. Furthermore, through dynamic adsorption-desorption experiment using a fixed-bed column, a low-concentration Rb+/Cs+ solution (10 mg L−1) could be concentrated by approximately 140 times. This research offers a promising approach for the selective extraction of low-concentration Rb and Cs resources from highly saline solutions.

A Three-Dimensional Damaged Region Contour Extraction Approach for Cold Spray Repair

A Three-Dimensional Damaged Region Contour Extraction Approach for Cold Spray Repair

Green Extraction Approach for Isolation of Bioactive Compounds in Wild Thyme (Thymus serpyllum L.) Herbal Dust-Chemical Profile, Antioxidant and Antimicrobial Activity and Comparison with Conventional Techniques.

The aim of this study was to provide a chemical profile and determine the antioxidant and antimicrobial activity of the essential oil (EO) and lipid extracts of Thymus serpyllum L. herbal dust obtained via conventional (hydrodistillation (HD) and Soxhlet extraction (SOX)) and novel extraction techniques (supercritical fluid extraction (SFE)). In addition, a comparative analysis of the chemical profiles of the obtained EO and extracts was carried out, as well as the determination of antioxidant, antibacterial and antifungal activity of the lipid extracts. According to the aforementioned antioxidant and antimicrobial activities and the monoterpene yield and selectivity, SFE provided significant advantages compared to the traditional techniques. In addition, SFE extracts could be considered to have great potential in terms of their utilization in the pharmaceutical and cosmetic industries, as well as appropriate replacements for synthetic additives in the food industry.