Talanta Open最新文献

We used a microfluidic paper-based analytical device (μPAD) to investigate the influence that zinc reduction exerts on the determination of nitrite and nitrate ions in natural water samples. The μPAD consists of layered channels for the reduction of nitrate to nitrite with zinc powder and the subsequent detection of nitrite with Griess reagent. The amount of zinc, number of layers, and reaction time for the reduction were optimized to obtain an intense signal for nitrate. Initially, the sensitivity to nitrate corresponded to 55% that of nitrite, which implied an incomplete reduction. We found, however, that zinc decreased the sensitivity to nitrite in both the μPAD and spectrophotometry. The sensitivity to nitrite was decreased by 48% in spectrophotometry and 68% in the μPAD following the reaction with zinc. One of the reasons for the decreased sensitivity is attributed to the production of ammonia, as we elucidated that both nitrite and nitrate produced ammonia via the reaction with zinc. The results suggest that the total concentration of nitrite and nitrate must be corrected by constructing a calibration curve for nitrite with zinc, in addition to developing curves for nitrate with zinc and for nitrite without zinc. Using these calibration curves, the absorbance at different concentration ratios of nitrite and nitrate ions could be reproduced via calculation using the calibration curves with zinc for nitrite and nitrate. Eventually, the developed μPAD was applied to the determination of nitrite and nitrate ions in natural water samples, and the results were compared with those using a conventional spectrophotometric method. The results of the μPAD are in good agreement with those of conventional spectrophotometry, which suggests that the μPAD is reliable for the measurement of nitrite and nitrate ions in natural water samples.

Whiskey is a traditional distilled product produced on the island of Ireland that is currently experiencing a major expansion in export volume. Different styles of Irish whiskey exist, however only some very limited published information exists on the volatile congener profile of the different styles of Irish whiskey. Such information is potentially indispensable from a quality, flavour and authenticity perspective. As gas chromatography is the established analytical method of choice to identify volatile congeners, this study compared headspace-solid phase microextraction (HS-SPME) using conventional gas chromatography single quadrupole mass spectrometry, to HS-SPME and HS-SPME-Arrow using comprehensive two-dimensional gas chromatography (GC×GC) modulated using reverse flow in tandem with time-of-flight mass spectrometry (TOFMS) detection. Six representative Irish whiskey samples were evaluated consisting of new make spirit and mature whiskies representing Single Malts, Pot Stills and Blends. The number of volatile congeners identified in these samples by HS-SPME/HS-SPME Arrow GC×GC-TOFMS was approximately twice that detected by conventional HS-SPME GCMS. In total 145 unique individual volatile congeners, excluding ethanol were identified, with the majority consisting of esters, but also benzenes, alcohols, aldehydes, terpenoids, furans, ketones, alkanes, alkenes, norisoprenoids, acetals, acids, lactones and phenols. The use of HS-SPME Arrow GC×GC-TOFMS significantly enhances the number of volatile congeners that can be identified and therefore provides much more information that can convey insights into product quality, consistency, flavour and also for authentication purposes.

Rice is the most important staple crop for more than half of the world's population. As rice quality can deteriorate during storage, methods that can effectively classify rice according to its storage duration are essential. However, existing methods of assessing rice storage time are time-consuming, laborious, and incompatible with modern industrial processing technologies. Therefore, we investigated the ability of near-infrared spectroscopy combined with machine learning algorithms to distinguish rice storage duration. A total of 482 rice samples were analyzed, which included 74, 100, and 308 samples produced during 2015–2016, 2017–2018, and 2020–2021, respectively. Five pre-processing methods were initially applied to the spectra to enhance the accuracy of the discrimination model. Subsequently, two-dimensional correlation spectroscopy and competitive adaptive reweighted sampling (CARS) were used to extract the characteristic spectra associated with storage time. Finally, three pattern recognition methods (K-nearest neighbor analysis, linear discriminant analysis, and least squares support vector machine (LS-SVM)) were compared for their effectiveness in constructing classification models. The results indicated that the best model for identifying the storage duration of rice was established after spectral pre-processing with the standard normal variate and first derivative, using the CARS algorithm to select feature wavelengths, and applying the LS-SVM modeling method, which together yielded correct identification rates of 99.72 % and 91.67 % for the calibration and validation sets, respectively. Thus, we propose near-infrared spectroscopy coupled with machine learning algorithms as an effective approach for classifying rice according to storage duration, which can facilitate evaluations of rice freshness in the market.

Global health is seriously threatened by an increase in antibiotic resistance among ESKAPE pathogens- E- E. faecium, S- S.aureus, K- K.pneumoniae, A-A.baumannii, P- P.aeruginosa, and E-Enterobacter. The resistance of many bacteria to traditional antibiotics is increasing, making the search for novel approaches critical. In order to minimize the effect of these diseases, early detection, diagnosis, and treatment are important. However, there are drawbacks to traditional detection techniques such molecular-based, biochemical, and microbiological assays. These include the inability to detect on-site, as well as their time-consuming, expensive, and labour-intensive nature. Viral agents that target bacteria exclusively, known as bacteriophages, have shown promise in combating over infections resistant to antibiotics. Bacteriophage-based biosensors are adaptable to many environmental conditions and offer special features such as host specificity and ability to identify active infections. They're very accurate, very specific, and have quick assay times, which makes them beneficial tools for detection. Also, phages are more easily produced than antibodies and can withstand high pH, temperature, and chemical solvents. The potential of bacteriophage-based biosensors in the fight against ESKAPE pathogens is highlighted by this review. Bacteriophage-based biosensors provide simplified detection processes in contrast to conventional approaches, which makes them invaluable in environmental and clinical situations. Numerous platforms, including electrochemical, magnetoelastic, quartz crystal microbalance, and surface plasmon resonance sensors, being investigated for their potential use to detect pathogenic bacteria in a range of sample types.

Potency testing in the cannabis industry is one of the most important tests currently performed. Due to its financial importance, there exists a lot of pressure to achieve potency values that are as high as possible, while staying within the realm of what is morally correct to perform. This paper explains how potency testing is achieved and how differences due to lack of standardization may exist. The paper then explores measurement error, or uncertainty, and suggests reference material may be the largest contributor to this uncertainty in the market today.

This comprehensive review investigates the dynamic landscape of trace elemental analysis methodologies applied to plants and food-based extracts. The exploration spans from the inception of techniques to the latest procedures, contributing to heightened precision and sensitivity in elemental detection. According to the WHO, herbal plants and medicine from varied soil compositions serve as crucial therapeutic agents for 70–80 % of the world's population. Yet, their susceptibility to trace element toxicity poses a significant risk to human health. Rising population and increased food demands have led to environmental pollution, contaminating the food chain through unintended activities like industrialization, mining, and pesticide production. The elemental composition of plants and derived extracts is central to comprehending nutritional profiles, evaluating product quality, and ensuring food safety. Methodological advancements, progressing from manual procedures to sophisticated technologies such as X-ray fluorescence (XRF), neutron activation analysis (NAA), inductively coupled plasma optical emission spectroscopy (ICP OES), and inductively coupled plasma mass spectrometry (ICP-MS), are delineated. Recent strides in paper-based electrochemical sensors are highlighted for their distinctive capabilities and elucidation of associated advantages and limitations.

Moreover, the review delves into innovative sample preparation methodologies, encompassing microwave-assisted digestion and solid-phase microextraction, to amplify the efficiency of elemental extraction and subsequent analysis. Integrating data analytics and machine learning in elucidating complex elementary datasets is explored, underscoring the potential for heightened accuracy and automation in trace elemental analysis. This review compiles literature data, summarizing sample preparation methods for various herbal parts (roots, soil, stems, bark, fruits, food). Standard protocols from WHO, United States Pharmacopeia-National Formulary (USP-FR), Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy (AYUSH) are considered. Lead, Cadmium, Mercury, and Arsenic are the primary toxic elements of concern in herbal medicines. This review furnishes valuable insights tailored for researchers, analysts, and policymakers actively involved in advancing the domain of trace elemental analysis in plants and food-based extract.

The false declaration of honey authenticity requires the use of rapid and efficient analytical tools in order to be detected. In this study, the use of a green, rapid and emerging approach for food authentication, FT-Raman spectroscopy, proved to obtain reliable and efficient honey botanical and harvesting year differentiation models, when the spectroscopic data was processed by employing a supervised statistical method, namely Partial Least Squares Discriminant Analysis (PLS-DA). The peaks and bands present in the Raman spectra were discussed based on honey composition. In order to increase the efficiency of the models, different preprocessing methods were used and a variable reduction step was employed. The new authentication approach is capable of distinguishing among four botanical sources and two harvesting periods of honey with a correct prediction rate higher than 97 %. The Raman markers that proved to contribute the most to the discrimination were correlated with the honey composition.

Background

Visceral Leishmaniasis is a neglected tropical disease with a high rate of infection and mortality in affected areas. Around 50,000 to 90,000 new cases of visceral leishmaniasis (VL) are estimated every year. Individuals asymptomatic for the disease should also be considered in epidemiological surveillance of the disease, as they can help spread the parasite. Thus, the development of low-cost diagnosis methods that allow the identification of infected and asymptomatic individuals is required, especially in developing countries where this disease is endemic.

Results

In this work, we developed an immunosensor for recognizing anti-Leishmania antibodies in asymptomatic individuals and avoiding cross-reaction with Chagas disease (CD). For that, we used carbon-based screen-printed electrodes, modified with graphene oxide and gold. Reproducibility was assessed by calculating the relative standard deviation (RSD < 5 %) from cyclic voltammograms of [Fe(CN)₆]^3-/4− using three different electrodes, screen-printed carbon electrodes (DPR-110) and graphene modified screen-printed electrodes (DPR-110 GPH) were purchased from DropSens (Oviedo, Asturias, Spain).

Significance

As an electrochemical methodology, we use cyclic voltammetry. After the tests were carried out, we considered that carbon electrodes adsorbed with reduced graphene oxide and modified with gold nanoparticles were the best platforms for detecting anti-Leishmania antibodies. In the study carried out, the limit of quantification (LOQ) for anti-Leishmania antibodies was established at 16.75 mg/mL, while the limit of detection (LOD) was 5.58 mg/mL. These limits indicate the minimum antibody concentration values that can be quantified and detected accurately and reliably in the analyzed sera.

Tin contamination in waters due to mining and natural activities in high concentrations can threaten human health. This research presents the development of a sensitive and selective colorimetric sensor in aqueous, paper, and gel-based to detect Sn²⁺. The development of such sensors is promising, with attractive advantages such as intense color, fast naked-eye response, and simple continuous fabrication. The addition of Sn²⁺ ions will change the color of the medium because curcumin (Cur) interacts with Sn²⁺, causing a decrease in free Cur, silver nanoparticles (AgNPs) becoming less stable, and a change in particle size. Colorimetric changes in Sn²⁺ were achieved by visual inspection within 10 min for aqueous-based and 20 min for paper and gel-based. The good linear relationship (R² = 0.9999) between Sn²⁺ and Δ absorption with a detection limit of up to 66.99 µg/L. This method is relatively scalable in determining Sn²⁺ and shows good recovery between 80 and 105 %. This colorimetric sensor gives good sensitivity to Sn²⁺ metal ions which is expected to become the basic technology for developing in-situ sensors to monitor Sn²⁺ levels in tin industrial waste.

With a rise in the prominence of cannabis usage, due to its widespread availability and varying legal status, there has been an increased emphasis on the differentiation of cannabinoids present within cannabis using various analytical techniques. The present study aimed to exploit the capability of Raman microscopy to collect high-quality spectra of seven cannabinoid analytical standards, followed by their classification using linear discriminant analysis (LDA) and a novel transfer learning approach. Additionally, the experimental Raman spectra of delta-9-tetrahydrocannabinol (Δ9-THC), cannabidiol (CBD), and cannabichromene (CBC) were compared to simulated spectra from density functional theory calculations (DFT) to connect the spectral features to the underlying vibrational motions. A microscopical approach enabled the determination of the optimal sampling areas to collect Raman spectra for the nonacidic and acidic cannabinoids. An initial visualization of the data using principal component analysis (PCA) confirmed the spectral differences observable by visual comparisons of the spectra of the cannabinoid standards. The application of LDA implemented with a 5-fold cross-validation with 10 repeats, resulted in a classification accuracy of 99.83 %. For the transfer learning approach, the artificial intelligence (AI) model training was conducted in less than 10 min in a graphical processing unit (GPU) environment. All seven cannabinoids were successfully classified into respective classes based on scalograms transformed from Raman spectra, with 100 % classification accuracy. The average prediction probability for correct classification was 99.31 %. The classification outcome provided by the AI model included both prediction labels and probability, which provided a comprehensive evaluation of the samples.