Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this...
This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and s...
The difficulty in controlling the quality of Korla pears is the main factor limiting their market value. The key to solving this problem is to detect the color of Korla pears quickly and accurately. This study employed near-infrared spectroscopy (NIR...
In the present investigation, we have devised a hyperspectral imaging (HSI) apparatus to assess the chemical characteristics and freshness of the yellow croaker (Larimichthys polyactis) throughout its storage period. This system operates within the s...
Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni),...
Traditional chemical analysis of plant tissue is time-consuming, costly, and poses risks due to exposure to toxic gases, highlighting the need for faster, low-cost, and safer alternatives. Vis-NIR spectroscopy, combined with machine learning, offers...
Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the re...
The preprocessing of infrared spectra can significantly improve predictive accuracy for protein, carbohydrate, lipid, or other nutrition components, yet optimal preprocessing selection is typically empirical, tedious, and dataset specific. This study...
Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance s...
Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accu...
Xinyu pear is one of the important pear cultivars in China. Owing to its rich nutritional composition, high quality, and distinctive flavor, it is highly favored in the market. In this study, near-infrared spectroscopy was employed to determine the s...
As part of China’s Tianwen-1 Mars mission, the Mars Surface Composition Detector (MarSCoDe) instrument on the Zhurong rover adopts laser-induced breakdown spectroscopy (LIBS) to perform chemical component detection of the materials on the Marti...
Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel object...
The current methods that use hyperspectral remote sensing imagery to extract and monitor marine oil spills are quite popular. However, the automatic extraction of endmembers from hyperspectral imagery remains a challenge. This paper proposes a data f...
Demand for rapid and cost-effective soil analysis has increased the use of spectroscopy, particularly in the visible–near-infrared (Vis–NIR) and mid-infrared (MIR) regions. Using 8304 soil samples from the United States Department of Agri...
Infrared spectroscopy provides an efficient, robust, and multivariate means to measure phenolic levels during red-wine fermentations. However, its use is currently limited to off-line sampling. In this study, partial least squares (PLS) regression wa...
Side-channel analysis (SCA) attacks constantly improve and evolve. Implementations are therefore designed to withstand strong SCA adversaries. Different side channels exhibit varying statistical characteristics of the sensed or exfiltrated leakage, a...
Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases a...
Accurate monitoring of substrate moisture content and electrical conductivity (EC) is essential for irrigation and nutrient management in soilless strawberry cultivation. However, conventional sensor-based approaches are limited in spatial coverage....
Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-...
Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved sit...
In this study, partial least squares regression (PLSR) models were developed using no preprocessing, traditional preprocessing, multi-preprocessing 5-range, multi-preprocessing 3-range, a genetic algorithm (GA), and a successive projection algorithm...
Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Poly...
Excitation–emission matrix (EEM) fluorescence spectroscopy, when combined with machine learning algorithms, has emerged as a highly promising tool for non-invasive biomedical diagnosis, demonstrating significant potential across various applica...
A portable spectral detector for water quality assessment was developed, utilizing potassium nitrate and ammonium chloride standard solutions as the subjects of investigation. By preparing solutions with differing concentrations, spectral data rangin...
Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data ana...
The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay...
Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) has been increasingly utilized for fine-scale surface characterization and quantitative retrieval due to its capability of capturing dense spectral information at ultra-high spatial reso...
Liquid chromatography–tandem mass spectrometry (LC-MS/MS) is widely employed in pesticide residue analysis. Machine learning methods for automated spectral interpretation depend on large, well-curated training datasets; however, publicly availa...
This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll con...
The emergence of new-generation hyperspectral satellites offers more potential for mapping soil properties. This study presents the first assessment of EnMAP (Environmental Mapping and Analysis Program) hyperspectral imagery for soil organic matter (...
Aflatoxin B1 (AFB1) contamination in maize poses serious risks to food and feed safety; however, conventional laboratory-based assays are often constrained by time and throughput for large-scale screening. This study proposes a hyperspectral imaging...
Hyperspectral imaging (HSI) has potential for forensic entomology, but its practical use is limited by manual region-of-interest (ROI) delineation before spectral extraction. This step is time-consuming, operator-dependent, and difficult to standardi...
Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food...
Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and non-destructive prediction of this key quality parameter, t...
In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible–near-i...
Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a sp...
Urban freshwater ecosystems affected by eutrophication and recurrent algal blooms require monitoring approaches capable of representing optical complexity and spatial heterogeneity. This study evaluated an integrated workflow combining proximal in si...
The electrically evoked compound action potential (ECAP) is a crucial physiological signal used by clinicians to evaluate auditory nerve functionality. Clean ECAP recordings help to accurately estimate auditory neural activity patterns and ECAP magni...
This study presents a configurable speech signal preprocessing and feature-extraction workflow for identifying candidate acoustic biomarkers in acute heart failure. The workflow was evaluated in 12 patients hospitalized with acute heart failure. Shor...
Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aeria...
Efficient and accurate identification of spectra from large databases remains a critical challenge in spectroscopic analysis. Previous coarse-to-fine frameworks, typically combining Principal Component Analysis (PCA)-based preprocessing and k-d tree...
Accurate estimation of the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) is important for improving the representation of photosynthetic capacity in terrestrial carbon-cycle models. Leaf hyperspectral spectroscopy prov...
Soil heavy metal contamination has attracted widespread concern globally, with nickel (Ni) posing distinct environmental and human health risks. However, high-precision prediction of soil Ni concentrations at a large scale remains inadequately explor...
In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multi...
Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infr...
Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has pr...
Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consu...
This paper studies an advanced machine learning method, specifically few-shot classification with meta-learning, applied to distributed acoustic sensing (DAS) data. The study contributes two key aspects: (i) an investigation of different pre-processi...
Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide o...
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