1,721,014 research outputs found
Electrochemical biosensors for the detection of pathogenic bacteria in food
No full textBiosensors for the detection of pathogenic bacteria in food are a promising alternative to conventional methods of analysis. This review focuses on the electrochemical biosensors reported in recent years for use with food samples. It highlights the performance parameters of these sensors, and provides a critical discussion of current and future trends, including future commercialization
Application of spectrometric technologies in the monitoring and control of foods and beverages
Full text linkIn order to obtain high-quality products and gain a competitive advantage, food producers seek improved manufacturing processes, particularly when physicochemical and sensory properties add significant value to the product [...]
From spectroscopic data variability to optimal preprocessing: leveraging multivariate error in almond powder adulteration of different grain size
No full textAnalysing samples in their original form is increasingly crucial in analytical chemistry due to the need for efficient and sustainable practices. Analytical chemists face the dual challenge of achieving accuracy while detecting minute analyte quantities in complex matrices, often requiring sample pretreatment. This necessitates the use of advanced techniques with low detection limits, but the emphasis on sensitivity can conflict with efforts to simplify procedures and reduce solvent use. This article discusses the shift towards green analytical methods, focusing on portable spectroscopic techniques in the near-infrared (NIR) region. A case study involving the prediction of adulteration in almond flour with bitter almond flour illustrates the importance of particle size and the integration between the sample and the instrument. The study emphasizes the necessity of investigating the multivariate error associated with raw data to enhance data preprocessing strategies. This research provides valuable insights for professionals in the field, presenting a methodology applicable to a broad range of analytical applications while underscoring the critical role of raw data analysis in achieving accurate and reliable results
Biosensors and Smart Analytical Systems in Food Quality and Safety: Status and Perspectives
Full text linkThe primary focus of research in food production revolves around ensuring food quality and safety [...
Improving the quality of 63Cu/65Cu ratio determination by ICP-QMS through a careful evaluation of instrumental performances
No full textAn alternative validated analytical protocol for the determination of 63Cu/65Cu isotopic ratios via ICPQMS
is here proposed. Preliminary experiments and a careful evaluation of the instrumental
performances have led us to propose a markedly different protocol from methods reported in literature:
a modified bracketing method was chosen over the utilization of a long-term standard bracketing
approach or the utilization of internal standards. As a result, accurate ratios with relative standard
deviations as low as 0.025% can be obtained within an overall machine time (samples, blanks and
standards) of 1 h per sample. Noticeably, this precision is only 2–5 times higher than the one attainable
for 63Cu/65Cu ratio determinations with sector-field multi-collector machines. The required machine
time is markedly lower than the one necessary for typical standard bracketing protocols (1 h vs. 2–4 h
per sample, respectively). In contrast with internal standard based methods, the required machine time
is definitely lower, but with our protocol no time consuming sample purification pretreatments are
needed. The protocol has been validated and used for the determination of 63Cu/65Cu isotopic ratios on
various copper minerals coming from historical mines spread along the Italian Apennines and Alps
Understanding variability and calibration challenges in NIR miniaturized sensors: the impact of particle size and analytical session in almond powder analysis
Full text linkThe increasing application of miniaturized Near-Infrared (NIR) sensors highlights their potential for rapid, non-destructive, and cost-effective analysis, particularly in food industry. These portable instruments are often marketed as easy-to-use solutions, intended for use by non-specialists rather than analytical chemistry experts, which has contributed to their widespread adoption. This study investigates the contamination of bitter almond in almond powder using various low-cost miniaturized NIR sensors, including the SCiO sensor, two NeoSpectra Micro Development Kits, and the NeoSpectra Scanner, with and without the Rotator accessory. Almond powders with different levels of contamination of bitter almond (0–100 wt%) were analysed, and Principal Component Analysis (PCA) was used as an initial data screening step, showing the importance of particle size, thus providing a valuable quality control in this type of measurements. Partial Least Squares (PLS) regression models were developed to predict the percentage of contamination of bitter almonds and to evaluate the performance of each NIR sensor. The best regression models were obtained using the NeoSpectra Scanner spectrometrer being to predict concentration values with an error around 2.5% and a limit of detection around 4.5% of bitter almond in almond powder. Performance discrepancies were observed between sensors of the same type and model, as well as across different experimental sessions. These results emphasize the importance of understanding the limitations of miniaturized NIR sensors, while also highlighting their effectiveness, affordability, and portability, which make them a valuable and reliable tool for on-site food safety applications
Direct analysis of volumetric absorptive micro sampling (VAMS) devices by ATR-FT-MIR and chemometric analysis: a new challenge
No full textVolumetric Absorptive Micro Sampling (VAMS) strategy, in its simplicity, has made a major contribution to the development of at-home sampling strategies. Mainly used for blood analysis, it absorbs a fixed volume of sample. Folded into its cover, the VAMS device dries, and it can be sent to a lab via mail. In this article, for the first time in our knowledge, we explored the possibility to use this sampling strategy to expand the scope of VAMS to other samples than clinical ones. In this way we used VAMS to sample and analyze milk, which is one of the most important and analyzed samples all over the world. VAMS devices were employed to sample commercial milk samples from Italy, Switzerland and Spain, and for the first time the device was directly analyzed by ATR-FT-IR to predict protein, carbohydrate and fat content in the milk samples. Samples were collected in different sessions from different persons and analyzed by different lab operators to include in the models these sources of variability. Multivariate regression was used to correlate ATR-FT-IR spectra with the investigated properties: models were validated with external validation
Navigating the complexity: Managing multivariate error and uncertainties in spectroscopic data modelling
Full text linkSpectroscopy and chemometrics, supported by computer science, have yielded promising outcomes, as evidenced by trends observed in literature searches. However, while researchers meticulously construct chemometric models for exploratory, quantitation and classification purposes, the investigation of data quality, particularly error analysis, remains less frequent. Understanding and quantifying measurement errors is crucial for robust spectroscopic modeling and uncertainty estimation. By unraveling complexities related to multivariate errors and
uncertainties in spectroscopic data, the scientific community is empowered to extract reliable information from spectroscopic analyses, paving the way for enhanced analytical practices. This review underscores the necessity for the scientific community to integrate error analysis and uncertainty estimation into multivariate analysis methods, offering tailored solutions for diverse data types and analysis objectives
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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