ABSTRACT We introduce Auditory Analytics, a methodological framework that utilizes data sonification for scientific discovery. Auditory Analytics describes a cycle of collecting and deriving datasets, mapping data to audible signals (sonification), analytical listening, hypothesis formulation, and tool building, where human insights from any stage of the cycle can feed back into further iterations of the cycle in the form of new datasets, alternative mappings, and new models of the original phenomenon. In Auditory Analytics, the remarkable capacity of the human auditory system to extract meaningful information from complex soundscapes across multiple timescales is repurposed for exploring, interpreting, and analyzing data. As an illustration of how Auditory Analytics can be used to uncover relationships and dynamics in physical systems, we describe an earlier study in which we applied this methodology to investigate state transition passages in a molecular dynamics simulation of a small protein. Auditory Analytics helped us identify distinct hydrogen‐bonding patterns associated with different rates of transit between folded and unfolded states, leading to a deeper understanding of the process of protein folding. A single, isolated data mapping—whether visual, auditory, haptic, mathematical, or verbal—provides an incomplete picture of reality; by adding the Auditory Analytics cycle to our portfolio of data interpretation tools, we can build a more complete picture of physical phenomena.

ABSTRACT Multisensory tools are beginning to reformat research and education in chemistry and many other fields. For example, translating infrared spectra into sound (sonification) can unveil molecular facts that the eye might miss. Tactile approaches are used with 3‐D printed scientific data such as electrophoresis gels. These scientific advances are expanding the way we study data and are part of a broader area known as immersive analytics. While immersive analytics cover all the human senses for data immersion, this perspective focuses on data visualization in virtual reality (VR). By visualizing chemometric data information in VR, a human can use their extensively trained pattern recognition and problem‐solving skills to make final analysis decisions. As an example, presented is a feasibility study for one‐class classification with a focus on correcting samples machine learning (ML) identified as false positives (FPs) and false negatives (FNs) that typically reside in the gray zone (class fringe samples) to respective true negatives (TNs) and positives (TPs). Conversely, while not expected in a well‐designed VR universe, it is possible that TP and TN prediction samples in the gray zone could be classified as respective FNs and FPs by decisions in VR. Results are presented for three datasets showing the feasibility of using VR for classification decisions. These datasets are clam contamination and two cancer detection situations. Some brief comments on the potential of using VR to identify local structure within a class are also provided using a quantitative structure–activity relationship (QSAR) dataset.

ABSTRACT Peanuts are an important food ingredient, and quality adulteration may pose health risks to consumers. Therefore, a rapid, accurate, and effective method for detecting peanut adulteration should be developed. This paper proposes a peanut adulteration identification method based on an electronic nose (e‐nose) and a feature complementary calculation network (FCC‐Net). First, volatile organic compound gas data of peanuts with different adulteration ratios are collected using an e‐nose system. Then, leveraging the cross‐sensitivity and temporal dynamic characteristics of the e‐nose data, a feature complementary calculation module (FCCM) is proposed to extract deep gas features. Finally, based on the FCCM, a lightweight FCC‐Net is designed to identify peanuts with varying adulteration levels. Experimental results demonstrate that FCC‐Net outperforms classical lightweight deep learning models and state‐of‐the‐art gas classification methods in terms of accuracy (97.33%), precision (96.92%), and recall (97.61%), while maintaining extremely low parameters (0.0102 M) and computational cost (0.3700 M). The combination of the e‐nose system and FCC‐Net provides an efficient and lightweight solution for peanut quality inspection.

ABSTRACT The adoption of spectroscopy as a process analytical technology (PAT) modality in the pharmaceutical industry and related sectors has enabled advanced monitoring and control of manufacturing processes. Most applications of spectroscopic PAT instruments are dependent on chemometric multivariate data analysis (MVDA) methods to extract the relevant process data from the spectral measurements. However, calibrating and maintaining conventional MVDA methods is often burdensome, as it requires extensive time, material, and financial costs to generate the necessary representative samples and corresponding reference data. This calibration burden can be a barrier to the adoption of spectroscopic PAT in the pharmaceutical industry. Within this article, a classification of MVDA methods referred to as “lean chemometrics” is proposed and formalized. Lean chemometrics are time‐saving, material‐sparing, and cost‐cutting MVDA methods that reduce the calibration burden relative to conventional chemometric methods of choice for spectroscopic PAT. Categories of various MVDA methods that are classifiable as lean chemometric techniques and practical considerations for integration of these techniques with PAT in common pharmaceutical PAT applications are discussed. The intention of lean chemometrics is to raise awareness of solutions that minimize the challenge of calibration burden toward improving spectroscopic PAT adoption.

ABSTRACT The growing complexity and information content of the data, together with the need to understand both the complex structures, relationships, and phenomena present in these data spaces, compounded with the emerging need to understand the results produced by AI tools used to analyze the data, requires development of novel, effective data visualization tools. Much of the growing complexity is reflected in the increasing dimensionality of data spaces, where extended reality (XR) naturally emerges as a candidate to help extend our capability for higher dimensional understanding. However, humans often understand lower dimensionality representations more effectively. Still, XR offers an opportunity for a seamless integration of simulated traditional data displays within the three‐dimensional virtual data spaces, leading to more intuitive and more effective data analytics. In this paper we present an overview of the benefits of seamlessly integrated two‐dimensional and three‐dimensional interactive visual representations embedded in XR spaces, and present three case studies that leverage these approaches for more efficient data analytics.

ABSTRACT Conventional geochemical biomarkers include saturated, aromatic, and carboxylic acid compounds. Saturated and aromatic biomarkers, however, form the foundation of many geochemical studies because of their abundance and intrinsic significance. Although carboxylic acids are also present in sediments and petroleum, they are often found at much lower concentrations, which complicates their isolation when using a single analytical method. For this reason, the application of a modified Brønsted‐based silica gel phase by classical liquid chromatography has the potential to separate and characterize these three classes. However, variations in the factors can compromise effective component separation. A well‐designed experiment can optimize the process by addressing multiple methodological aspects. The Brønsted bases tested to modify the silica gel were sodium and potassium hydroxides. The central composite design (CCD) showed that, besides the expected influence of the volume of Eluent 1 ( n‐ hexane), there is a difference between the base types in the separation of saturated and aromatic compounds. The SiO 2 /KOH phase appears to be more resistant to the influence of Eluent 1. Therefore, the trend in separation efficiency is K > Na. All two silica gel modifications yield a fraction rich in carboxylic acids. The modification of the silica gel with a Brønsted base had no significant effect on the molecular geochemical parameters in the samples. This separation method saves time and material, making it a potential approach for routine analysis of saturated and aromatic biomarkers and carboxylic acids in molecular organic geochemistry.

ABSTRACT The physicochemical and functional properties of wheat can be modified by exposing the whole grains to thermal pretreatment. Conventional analytical methods used to investigate such modifications are expensive, labour‐intensive and may be inaccurate due to interfering compounds. An economical alternative is to use Fourier transform near‐infrared (FT‐NIR) spectroscopy in combination with multivariate data analysis techniques. Analysis of variance simultaneous component analysis (ASCA) is an exploratory data analysis technique used to characterise the effects of experimental design factors on the chemical composition captured in the FT‐NIR spectral data. In this study, two hard wheat cultivars were exposed to 10 different temperatures by means of forced convection continuous tumble roasting. ASCA was applied to the standard normal variate preprocessed spectral data to evaluate the effects of cultivar, roasting temperature and their interaction. All three factors, cultivar, roasting temperature and their interaction, had a significant effect ( p 0.05) on the spectral data. Differences between the roasted wheat cultivars were associated with moisture, starch and aromatic compounds. The association with aromatic structures was supported by the differences in the phenolic contents of the two cultivars. Low roasting temperatures (108°C–150°C) were associated with starch and moisture changes particularly at approximately 1450, 1410 and 1940 nm. Water evaporated from the kernels, and the degree of starch polymerisation decreased. High roasting temperatures (170°C–232°C) were associated with starch and amino acids (ca. 2100 and 2294 nm), which likely underwent structural changes and participated in nonenzymatic browning reactions.

ABSTRACT One of the particularities of spectral imaging when compared to single point spectroscopy is the importance of spectral pretreatments to minimize environmental and sample‐related effects, such as shadows, shapes, or variations in illumination. However, the influence of spectral pretreatments on distance metrics is rarely considered in any great depth. This work explores and discusses the effects of combining different spectral pretreatments with the most commonly used distance metrics. A case study on the classification of recyclable materials is used as an example. MATLAB code scripts and functions are provided and referenced through the article to allow the reader to follow and implement the process step by step. The theoretical basis for the calculation and choice of different pretreatments and distance metrics is explained in depth, and the classification performance of the different combinations of pretreatments and distance metrics is discussed in the light of these. Results show that distance metrics based on angle or correlation (i.e., Spectral Angle Mapper or Spectral Correlation Mapper) could be used with or without pretreatments without significantly impacting the classification results. Classification based on Euclidean and Cityblock distances was the most computationally efficient but was also the most affected by multiplicative effects in the spectra and thus benefited the most from pretreatments such as standard normal variate (SNV) or from combining SNV and second derivative. Lastly, Mahalanobis distance showed the best classification performance for nonpretreated spectra but showed the worst performance for SNV pretreated spectra, illustrating the importance of assessing spectral similarity between calibration and validation datasets on pretreated spectra when using Mahalanobis distance, particularly when applying spectral pretreatments. This work provides practical insights into the effects that the parameters used have on the results of distance‐based classification in terms of the performance of classification models. Considering this issue can greatly improve classification performance when assessing the potential of hyperspectral imaging systems for a particular application.