An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate

Effect of orange fruit peel extract concentration on the synthesis of zinc oxide nanoparticles

In this investigation, the impact of reducing agent concentration on the synthesis of zinc oxide nanoparticles (ZnO NPs) was examined. During the synthesis, an assessment of ionic conductivity was carried out, revealing a significant increase in conductivity prior to the introduction of the reducing agent, followed by a sharp decrease upon its addition. Characterization of the ZnO NPs involved ultraviolet-visible spectroscopy, scanning electron microscopy, Fourier-transform infrared spectroscopy, and, X-ray diffraction analysis. The outcomes suggest that the characteristics of the ZnO NPs are influenced by the concentration of the reducing agent during the synthesis process. Notably, the ZnO NPs synthesized with a higher concentration of reducing agent exhibited a narrower optical band gap and increased surface energy. Furthermore, employing a concentration of 0.5 v/v resulted in the rapid production of NPs with relatively uniform sizes. Conversely, concentrations below 0.5 v/v lead to slow formation, while concentrations exceeding 0.5 v/v yielded non-uniform NPs.

On photokinetics under polychromatic light

Since the dawn of photochemistry 150 years ago, photoreactions have been conducted under polychromatic light. However, despite the pivotal role that photokinetics should naturally play for such reactive photosystems, the literature lacks a comprehensive description of that area. Indeed, one fails to identify explicit model integrated rate laws for these reactions, a characteristic type for their kinetic behavior, or their kinetic order. In addition, there is no consensus in the community on standardized investigative tools to evaluate the reactivity of these photosystems, nor are there venues for the discussion of such photokinetic issues. The present work is a contribution addressing some of these knowledge gaps. It proposes an unprecedented general formula capable of mapping out the kinetic traces of photoreactions under polychromatic light irradiation. This article quantitatively discusses several reaction situations, including the effects of initial reactant concentration and the presence of spectator molecules. It also develops a methodology for standardizing actinometers and defines and describes both the spectral range of highest reactivity and the photonic yield. The validity of the model equation has been proven by comparing its results to both theoretical counterparts and those generated by fourth-order Runge-Kutta numerical calculations. For the first time, a confirmation of the Φ -order character of the kinetics under polychromatic light was established.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

Non-linear shrinking of linear model errors

BACKGROUND

Artificial neural networks (ANNs) can be a powerful tool for spectroscopic data analysis. Their ability to detect and model complex relations in the data may lead to outstanding predictive capabilities, but the predictions themselves are difficult to interpret due to the lack of understanding of the black box ANN models. ANNs and linear methods can be combined by first fitting a linear model to the data followed by a non-linear fitting of the linear model residuals using an ANN. This paper explores the use of residual modelling in high-dimensional data using modern neural network architectures.

RESULTS

By combining linear- and ANN modelling, we demonstrate that it is possible to achieve both good model performance while retaining interpretations from the linear part of the model. The proposed residual modelling approach is evaluated on four high-dimensional datasets, representing two regression and two classification problems. Additionally, a demonstration of possible interpretation techniques are included for all datasets. The study concludes that if the modelling problem contains sufficiently complex data (i.e., non-linearities), the residual modelling can in fact improve the performance of a linear model and achieve similar performance as pure ANN models while retaining valuable interpretations for a large proportion of the variance accounted for.

SIGNIFICANCE AND NOVELTY

The paper presents a residual modelling scheme using modern neural network architectures. Furthermore, two novel extensions of residual modelling for classification tasks are proposed. The study is seen as a step towards explainable AI, with the aim of making data modelling using artificial neural networks more transparent.

Photometric flow injection analysis of As(III) by using a homemade, LED-based flow-cell device and methyl orange reagent

Inorganic arsenic especially As(III) is considered a carcinogenic substance and its measurement is important in water samples. In this work, an inexpensive flow injection analysis system was designed for the photometric determination of As(III) at low concentrations. For this purpose, a light-emitting diode (LED) based photometer with a miniaturized detector, was fabricated and used as a determination apparatus and methyl orange was used as a detecting reagent. The fabricated photometer employed the LEDs, as a light source and the light detector. The λ_max of emission for emitter and detector LEDs were 525 and 625 nm, respectively. Determination of As (III) was based on its inhibition effect on the redox reaction between methyl orange and X₂ (Cl₂ or Br₂). The decolorization of the reaction products in the FIA system was monitored using the homemade flow cell detector. Analytical figures of merit including linear responses ranging from 0.03 to 3.0 mg/l of As(III) (r = 0.994), detection limits of 0.007 mg/l As(III), RSD% of 1.5% (n = 7), low reagent consumption per determination, and sampling throughput of 50 determinations per hour were achieved.