In the second installment of “The Big Review,” we discuss the physical mechanism behind how molecules absorb infrared (IR) radiation. Because light can be thought of as a wave or a particle, we have two equivalent pictures of IR absorbance. We also discuss the quantum mechanics behind IR absorbance, and how this leads to the different peak types observed in IR spectrum.

In this article, we introduce the concept of a gas exchange device (GED) and how it can be used to monitor organometallic compounds and metallic particles in specialty gases. The GED–inductively coupled plasma–mass spectrometry (GED–ICP-MS) workflow eliminates the need for any offline sample preparation, making it the ideal tool for online metallic impurities monitoring. This technology can be deployed to monitor specialty gas impurities in process chemicals at delivery, distribution points, and point of use.

In light of the escalating demand for enhanced chicken quality and safety, there is an imperative need for an advanced methodology that can accurately and expeditiously ascertain the freshness of chicken. This study endeavors to harness hyperspectral imaging (HSI) technology, in synergy with machine learning and deep learning algorithms, to innovate a non-destructive method for the assessment of chicken freshness. In this study, chicken freshness was categorized into three distinct levels based on a comprehensive range of evaluation criteria specific to chicken freshness. Subsequent to preprocessing the spectra data, a discriminative model for chicken freshness predicated on Visible and Near-Infrared (VNIR,400-1000 nm) and Short-Wave Infrared (SWIR, 900-1700 nm) spectra was formulated utilizing both the raw and the preprocessed datasets. Consequently, key wavelengths were discerned via feature wavelengths selecting within the full spectra wavelengths, culminating in the establishment of the feature-wavelength model. The outcomes indicate that the VNIR-ResNet model, incorporating Normalization preprocessing, outperforms other full-spectra models, boasting an accuracy rate of 98.31%. Following feature wavelengths modeling, the precision of the feature selecting augmented by CARS and SPA was enhanced to 98.87%, respectively. Subsequently, a fusion model is developed through the application of a data fusion technique, the accuracy of data-layer fusion modeling was 98.87%, surpassing that derived from a singular data source, albeit the efficacy of feature-layer fusion modeling fell short of ideal. In summary, considering factors such as the cost and volume of hyperspectral data, the models such as MN-ResNet based on VNIR and MN-CARS-ResNet based on feature selecting emerge as more cost-effective and pragmatic solutions.

This month’s column is a contribution from my daughter Glenna, who recently completed her PhD studies in environmental science from the University of Copenhagen in Denmark. Her article explores the current landscape of global critical raw materials (CRM) trends in research and the applications of atomic spectroscopy (AS), including inductively coupled plasma–mass spectrometry (ICP–MS), inductively coupled plasma–optical emission spectrometry (ICP–OES), and X-ray analytical techniques in their identification of diverse industrial and environmental media, which have been essential in method validation and quantification of CRMs in complex matrices presenting high risks of interference. Some important examples that are presented include rare earth elements (REE) in water leaching purification (WLP) residues that co-occur with radio- active materials, REEs and other metals in acid mine drainage (AMD) environments, REEs in coal combustion (fly ash) residues, arsenic (As) from groundwater treatment sediment, and platinum-group elements (PGE) from sewage sludge. In addition, the article classifies the different techniques in use at each stage of the CRM recovery train, investigate present challenges to each analytical method, and discuss the problem-solving tools used.

To study the effect of various extractants on the structure of peat humic acid, peat humic acid was extracted using NH3·H2O, Na2CO3, NaHCO3, and Na2SO3 via alkali-extraction and acid-precipitation methods. The extracted peat humic acid was characterized using ultraviolet−visible (UV-vis) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, and fluorescence (FL) spectroscopy. The results showed that the extractants had negligible effects on the content of peat humic acids. Na2CO3 was found to be the most effective extractant for isolating humic acids from peat, with the highest content of 66.20% and a yield of 32.67%. Moreover, among the extractants used, NH3·H2O resulted in humic acids with the highest aromaticity, and Na2SO3 yielded humic acids with higher number of hydroxyl groups. NaHCO3 yielded humic acids with the most aliphatic ether and lowest aromaticity. Na2CO3 was the best extractant for isolating humic acids from peat, having the highest content, lowest degree of aromatic polymerization, and highest number of carboxylic acids.

Spectroscopy is publishing a series of feature articles highlighting the lives and careers of the most influential spectroscopists over the past 100 years. These individuals were selected by our Editorial Advisory Board and team of editors to represent the leading figures in spectroscopy over the century. Our second featured Icon of Spectroscopy is Peter R. Griffiths.

The field of analytical chemistry is well established. It’s time to develop the field of analytical biology—an emerging discipline that blends various research fields to provide a holistic view of biological phenomena.

Nickel (Ni[Ⅱ]) was used to catalyze the hydrogen peroxide oxidation of morin to weaken its fluorescence. In Na2B4O7-NaOH buffer solution at pH 10.10, Ni(Ⅱ) has an obvious catalytic effect on the oxidation of morin by H2O2 solution. At the maximum excitation and emission wavelengths (λex = 440 nm and λem = 530 nm), the fluorescence intensity of the system is linearly correlated with the concentration of Ni(II) within a certain range. The linear equation is ΔF = 11463ρ (μg/mL) − 34.70, and the R2 = 0.9992. The linear range is 1.6 × 10-2 − 9.6 × 10-2 μg/mL, and the detection limit was 2.9 × 10-4 μg/mL. Finally, the samples of tap water, Yellow River water and tea were analyzed, with the recoveries being 99.6%, 107.3%, and 104.6%, respectively. This method is suitable for the determination of trace Ni(II) in water samples.

Geochemical samples were digested using polytetrafluoroethylene (PTFE) crucibles with screw caps followed by ICP-MS analysis of 50 elements. Two-point standard curves were plotted using blank solution and national standard reference GSD-9 for stream sediments, and the acid system, digestion conditions, isotopes, and internal standards for each element were optimized. The results showed that the detection limits for most elements were less than 0.2 μg/g, and the relative standard deviations for all elements ranged from 1.1% to 16.4%. The measured values were in agreement with the certified values of national standard references. This method not only significantly reduces the amount of acid used and avoids dust contamination, but also accurately determines some volatile elements. In addition, it has the advantages of easy operation and high pretreatment efficiency, making it very suitable for laboratories with large numbers of samples and multiple elements to be tested.

Early fungal infection of citrus is one of the common diseases found during the storage period of citrus, and fungus that infects citrus will spread to the entire batch of citrus as the degree of infection deepens, causing enormous economic losses. Therefore, early detection of fungal infection of citrus is fundamental. The purpose of this study is to explore the qualitative identification of early fungal infections in citrus by using Fourier transform near-infrared (FT-NIR) combined with a variety of chemometric methods. First, discrete wavelet transform (DWT) is used to filter the noise of the spectral signal, then combined with a PLS-DA model, that helps discriminate healthy from infected Citrus. Subsequently, four different feature variable selection methods were introduced, Then, the linear discriminant analysis (LDA) and support vector machine (SVM) two classifiers were combined to establish a qualitative model for the degree of fungal infection. The modeling results show that the SVM modeling effect is better than LDA, and the DWT-CARS-SVM based on the RBF kernel function has the best result, the accuracy rates of the training set and test set are 100% and 97%. The results indicate that FT-NIR spectroscopy, combined with chemometric methods, is able to distinguish early fungal infections in citrus.