Hyperspectral Imaging Spectroscopy Research Articles

The application of PRISMA hyperspectral satellite imagery in the delineation of distinct hydrothermal alteration zones in the Chilean Andes: The Marimaca IOCG and the Río Blanco-Los Bronces Cu-Mo porphyry districts

This study provides an overview of the application of hyperspectral imagery acquired by the Italian satellite mission “PRecursore IperSpettrale della Missione Applicativa” (PRISMA), for mapping hydrothermal and supergene mineral alteration zones associated with different Cu deposits in the Chilean Andes. Study areas include the Marimaca Copper Project in the Naguayán district (Antofagasta Province), hosting Late Jurassic – Early Cretaceous iron oxide-copper–gold mineralizations and the Río Blanco-Los Bronces district (Santiago Region) known for its Late Miocene – Early Pliocene copper-molybdenum porphyry deposits. These mineral systems exhibit hydrothermal alteration haloes extending over several kilometers, with upward and outward mineral zonation.To characterize the surface-exposed alteration, the satellite imagery was processed using a multiple feature extraction workflow targeting the relative abundances and compositions of specific supergene and hydrothermal alteration minerals, including Fe-oxides and hydroxides (hematite-goethite), phyllosilicates (micas-kaolinite-chlorite), hydroxyl-bearing sulfates (alunite-natroalunite-jarosite) and epidote. The results reveal a zonation from proximal sulfates (natroalunite to alunite) and Al-rich white mica (ranging from 0.3 km-wide for the area identified to the east of the Marimaca deposit to 1 km-wide in correspondence of the Los Sulfatos deposit, respectively) to distal Al-poor white mica (0.2 to 0.5 km-wide, respectively) and an outer chlorite-epidote zone. In the Marimaca Project area, the structurally-controlled alteration evolves west to east from chlorite-rich to phengitic white mica (Al-poor). A NNE-SSW trending supergene leached cap covers a 6 km-long and up to 2 km-wide area from north to south. Our study documents how spaceborne hyperspectral imaging spectroscopy can support mineral exploration by enabling non-invasive reconnaissance mapping of the outcropping rocks, providing specific targeting information for follow-up field surveys and drilling campaigns.

Optimizing clinical O2 saturation mapping using hyperspectral imaging and diffuse reflectance spectroscopy in the context of epinephrine injection.

Clinical determination of oxygen saturation (sO2) in patients is commonly performed via non-invasive optical techniques. However, reliance on a few wavelengths and some form of pre-determined calibration introduces limits to how these methods can be used. One example involves the assessment of sO2 after injection of local anesthetic using epinephrine, where some controversy exists around the time it takes for the epinephrine to have an effect. This is likely caused by a change in the tissue environment not accounted for by standard calibrated instruments and conventional analysis techniques. The present study aims to account for this changing environment by acquiring absorption spectra using hyperspectral imaging (HSI) and diffuse reflectance spectroscopy (DRS) before, during, and after the injection of local anesthesia containing epinephrine in human volunteers. We demonstrate the need to account for multiple absorbing species when applying linear spectral unmixing in order to obtain more clinically relevant sO2 values. In particular, we demonstrate how the inclusion of water absorption greatly affects the rate at which sO2 seemingly drops, which in turn sheds light on the current debate regarding the time required for local anesthesia with epinephrine to have an effect. In general, this work provides important insight into how spectral analysis methods need to be adapted to specific clinical scenarios to more accurately assess sO2.

Fast fingerprint scanning and chemical composition identification of a large-scale material surface by a combined HSI-LIBS operation

An experimental solution that combines hyperspectral imaging (HSI) and laser-induced breakdown spectroscopy (LIBS) applied to large-scale material surface chemical analysis is investigated..

Hyperspectral imaging coupled with machine learning for classification of anthracnose infection on mango fruit

Anthracnose is the major plant disease causing an economic loss of mango fruit. Anthracnose symptom is not visible at a quiescent stage and the infected fruit often enters the food chain before the infection is known. Detection of a pre-symptomatic anthracnose infection is thus, crucial to prevent the infected fruit from entering the food chain. This research applied hyperspectral imaging (HSI) spectroscopy integrated with machine learning (ML) including principal component analysis (PCA) and support vector machine (SVM) for rapid identification of quiescent infection of anthracnose in mango fruit. Mango fruit (Nam Dok Mai Si Thong) was artificially infected with Colletotrichum gloeosporioides and stored at 20 °C and 90 % RH. The HSI was used to collect the spectral and spatial data of the samples. PCA and SVM were respectively performed to explore the hyperspectral data and to classify different symptom severities. The obtained spectral data can be recognized as fingerprints ascribing to the metabolites produced by C. gloeosporioides and the decomposed fruit tissues caused by the fungal infection. The HSI integrated with ML was able to not only detect the anthracnose infection at a latent stage before the onset of disease symptoms but also correctly classify different symptom severities. The symptom maps were also constructed using false-color image processing to simplify the data visualization of different symptom severities. The capability of detecting a pre-symptomatic anthracnose infection is a key advantage of the developed ML-assisted HSI.

Data fusion of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging to predict quality parameters of yellow flesh “Jintao” kiwifruit

The internal quality of kiwifruit, in terms of soluble solid content (SSC), flesh firmness (FF), and dry matter (DM), is widely recognised as a key feature for fruit sorting and pre-harvest assessment. Furthermore, flesh hue (FH) is another important parameter to consider for yellow flesh kiwifruits. NIR and VIS/NIR spectroscopic techniques are valuable alternatives for rapid and non-destructively prediction of all these quality parameters in fruit. Accordingly, the aim of this work was to build a partial least square (PLS) regression models to estimate SSC, FF, FH, and DM of yellow fleshed Actinida chinensis (Jintao) starting from Vis/NIR hyperspectral imaging (400–1000 nm) and FT-NIR (800–2500 nm) spectroscopy data. To take advantage of the complementary information of the two different spectral ranges, data fusion strategies were investigated to concatenate the data before PLS models. In particular, two different sequential fusion methods were used: low-level data fusion based on the concatenation of the pretreated spectra, and mid-level feature fusion characterised by the concatenation of features (scores) obtained by principal component analysis (PCA) or PLS models developed considering individually each data set. For all quality parameters, the best results were achieved by adopting the second approach of mid-level data fusion (PLS scores), reporting RP2 (test set validation) of 0.914 (RMSEP=0.97°Brix), 0.843 (RMEP=1.82°H), 0.866 (RMSEP=9.41N), and 0.854 (RMSEP = 0.64%) for SSC, FH, FF, and DM, respectively. Furthermore, with respect to the PLS models from the individual data sets, the results reported a mean RMSEP reduction of 16.0 ± 4.8%, confirming the potential of the data fusion in improving the PLS prediction power for the quality parameter of kiwifruit.

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.

Identification of black plastics with terahertz time-domain spectroscopy and machine learning

Several optical spectroscopy and imaging techniques have already proven their ability to identify different plastic types found in household waste. However, most common optical techniques feasible for plastic sorting, struggle to measure black plastic objects due to the high absorption at visible and near-infrared wavelengths. In this study, 12 black samples of nine different materials have been characterized with Fourier-transform infrared spectroscopy (FTIR), hyperspectral imaging, and terahertz time-domain spectroscopy (THz-TDS). While FTIR validated the plastic types of the samples, the hyperspectral camera using visible and near-infrared wavelengths was challenged to measure the samples. The THz-TDS technique was successfully able to measure the samples without direct sample contact under ambient conditions. From the recorded terahertz waveforms the refractive index and absorption coefficient are extracted for all samples in the range from 0.4 to 1.0 THz. Subsequently, the obtained values were projected onto a two-dimensional map to discriminate the materials using the classifiers k-Nearest Neighbours, Bayes, and Support Vector Machines. A classification accuracy equal to unity was obtained, which proves the ability of THz-TDS to discriminate common black plastics.

A Rapid and Nondestructive Detection Method for Rapeseed Quality Using NIR Hyperspectral Imaging Spectroscopy and Chemometrics

In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image by the regional growth algorithm. The kernel partial least square regression was used to build prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. The results showed that the correlation coefficients were 0.9461, 0.9503, 0.9572, and 0.9335, whereas the root mean square errors of prediction were 0.5514%, 0.5680%, 2.8113%, and 10.3209 µmol/g for crude protein content, oil content, erucic acid content, and glucosinolate content, respectively. It demonstrated that NIR hyperspectral imaging is a promising tool to determine rapeseed quality parameters in a rapid and non-invasive manner.

Do beaver ponds increase methane emissions along Arctic tundra streams?

Beaver engineering in the Arctic tundra induces hydrologic and geomorphic changes that are favorable to methane (CH4) production. Beaver-mediated methane emissions are driven by inundation of existing vegetation, conversion from lotic to lentic systems, accumulation of organic rich sediments, elevated water tables, anaerobic conditions, and thawing permafrost. Ground-based measurements of CH4 emissions from beaver ponds in permafrost landscapes are scarce, but hyperspectral remote sensing data (AVIRIS-NG) permit mapping of ‘hotspots’ thought to represent locations of high CH4 emission. We surveyed a 429.5 km2 area in Northwestern Alaska using hyperspectral airborne imaging spectroscopy at ∼5 m pixel resolution (14.7 million observations) to examine spatial relationships between CH4 hotspots and 118 beaver ponds. AVIRIS-NG CH4 hotspots covered 0.539% (2.3 km2) of the study area, and were concentrated within 30 m of waterbodies. Comparing beaver ponds to all non-beaver waterbodies (including waterbodies >450 m from beaver-affected water), we found significantly greater CH4 hotspot occurrences around beaver ponds, extending to a distance of 60 m. We found a 51% greater CH4 hotspot occurrence ratio around beaver ponds relative to nearby non-beaver waterbodies. Dammed lake outlets showed no significant differences in CH4 hotspot ratios compared to non-beaver lakes, likely due to little change in inundation extent. The enhancement in AVIRIS-NG CH4 hotspots adjacent to beaver ponds is an example of a new disturbance regime, wrought by an ecosystem engineer, accelerating the effects of climate change in the Arctic. As beavers continue to expand into the Arctic and reshape lowland ecosystems, we expect continued wetland creation, permafrost thaw and alteration of the Arctic carbon cycle, as well as myriad physical and biological changes.

Post-fire assessment of heating temperatures experienced by concrete using short video imaging, hyperspectral imaging and laser-induced breakdown spectroscopy

This study develops rapid post-fire analysis methods to predict the heating temperature to which the concrete was exposed. Short video imaging (SVI), hyperspectral imaging (HSI) and laser-induced breakdown spectroscopy (LIBS) were used for the first time to obtain spectra of concrete after high-temperature exposure (100–800 °C). The differences in colour levels and spectroscopic signals due to varying temperatures were observed. To handle the complex relationship between spectra and temperatures, machine learning models were used to extract meaningful information from spectra for the quantification of temperature. Furthermore, domain knowledge related to concrete composition and LIBS signal was integrated into machine learning to improve quantification performance. The highest coefficients of determination for prediction achieved based on SVI, HSI and LIBS measurements were 91.4, 94.8 and 98.6, respectively. These results demonstrate that SVI, HSI and LIBS can be fast and viable methods for assessing the fire temperature that the concrete has experienced.

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques.

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

Towards circular plastics: Density and MFR prediction of PE with IR spectroscopic techniques

The high variety of tailor fitted molecular structures of polyethylene (PE) is very beneficial to fulfill requirements of various applications, however it poses a difficulty in the mechanical recycling of post-consumer PE products. To improve the quality of PE recyclates and increase the amounts of recyclates that can be used in new products, separation of PE waste by density and melt flow rate (MFR) during mechanical sorting is essential. Therefore, 25 virgin PE grades were used to manufacture compression molded plates that were then characterized by means of Attenuated Total Reflection - Fourier transformed IR (ATR-FTIR) and near IR (NIR) spectroscopy, NIR hyperspectral imaging and dual-comb spectroscopy. The results were used to build partial least squares regression (PLS) models to predict MFR and density. ATR-FTIR and laboratory NIR spectroscopy provided sufficient information to predict the density value of PE, whereas the MFR assessments was not possible. The PLS model from the industrial NIR data also only allowed the density-based classification of virgin PE grades. The PLS models built from transmission and reflectance dual comb spectroscopy infrared (DCS-IR) of selected samples clearly showed that density and MFR prediction can be carried out with high accuracy. As DCS-IR could be implemented on plastic sorting systems using a conveyor belt, the addition of this sensor in mechanical sorting line would lead to a significantly higher quality of recycled PE with narrow well-defined density and MFR ranges. Such an improvement would immensely support the targeted recycling rates and amount by the European Union and would make a significant step towards circular plastics.

Non-invasive study of the dyes and pigments in the Chinese woodblock New Year paintings

Woodblock New Year painting is a traditional Chinese folk art that flourished during the 19th and early 20th centuries. These paintings feature a wide variety of colors, particularly after the introduction of synthetic colorants from Europe in the late 19th century. The scientific identification of dyes and pigments used in these paintings can provide valuable insights into the socio-historical context of the time. However, the history of materials used in the printing of New Year paintings is still lacking adequate information. In this study, we employed a combination of spectroscopic and imaging techniques, including Macro X-ray fluorescence imaging (MA-XRF), hyperspectral imaging, and Raman spectroscopy to identify the dyes and pigments used in two Yangliuqing New Year paintings. Our analysis revealed the presence of both traditional pigments and synthetic colorants, such as vermilion, Prussian blue, and methyl/crystal violet. We also discovered several color-matching techniques used to create light pink, light orange, and dark purple hues. These findings suggest that synthetic colorants were gradually replacing traditional pigments in China due to the growth of global trade at that time. Furthermore, our research provides valuable information for the conservation, preservation, and display of New Year paintings.

Prediction of soil organic carbon in soil profiles based on visible–near-infrared hyperspectral imaging spectroscopy

Complete and coherent soil profile information is important for studying soil morphological characteristics, vertical variation patterns of soil physicochemical properties, and identifying soil types. However, traditional soil survey methods use a limited number of discrete sample points at different depths as a data source to explore the correlation between soil depth and soil properties. Due to the variability of soil properties at vertical depth, there is a strong uncertainty in the conclusions drawn from the limited sample points. With the development of near-Earth remote sensing technology, the near-Earth hyperspectral imaging system can acquire hyperspectral images of soil profiles and characterize the fine texture differences and spectral variations of soil with continuous spectral curves and images, which is useful for inversion of soil properties. Soil organic carbon (SOC) is one of the important physicochemical properties to identify the genetic horizon, therefore, this paper explored the ability of visible–near-infrared (Vis-NIR) hyperspectral imaging spectroscopy to predict SOC in soil monoliths. The dataset contained five soil monoliths retrieved from the upper Yellow River, China. A random forest (RF) model was developed for predicting organic carbon, and the organic carbon content in the genetic horizon was used to establish a depth function to obtain the continuous content variation with the depth. These differences with and without depth function modeling, and the effect of three equal-spacing sampling schemes (1, 5, and 10 cm) on the depth function and the accuracy differences in five spectral statistics of the maximum (max), mean, median, mode, and minimum (min) within the image region of interest (ROI) were compared. The results indicated that (1) the depth function under the 1-cm sampling scheme predicted organic carbon the best, (2) the mode of the spectra within the ROI could yield a comparable or even a higher accuracy than that obtained with the mean, and (3) imaging spectroscopy could be used to visualize the distribution of organic carbon on soil profiles with relatively uniform morphology. These results indicated that imaging spectroscopy could be used for predicting organic carbon in undisturbed soil profiles.

A novel hyperspectral remote sensing tool for detecting and analyzing human materials in the environment: a geoenvironmental approach to aid in emergency response

Geology is the backdrop against which environmental disasters, emergencies and conflict occur. A search and discovery tool is developed to aid in investigations, search and rescue operations, and emergency response operations. The primary goal is to provide a software tool for the interpretations of hyperspectral remote sensing images in the context of investigations and emergency response operations in a wide range of outdoor settings (e.g., streams, semiarid settings, urban). Data is also translatable to indoor forensic hyperspectral imaging and reflective spectroscopy work. Data was collected on geologic materials, human materials, and other relevant items by staff and the accompanying software tool was developed by L3Harris Geospatial. A description of the spectral search and discovery tool is provided and is a customized ENVI extension written using the IDL programming language designed to help users find custom targets within hyperspectral imagery. The tool is free of charge and can be accessed here.

Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology.

Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases.

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. The proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

Critical Analysis of the Materials Used by the Venetian Artist Guido Cadorin (1892–1976) during the Mid-20th Century, Using a Multi-Analytical Approach

The present study sought to expand on and confirm the already available information on the painting materials used by the Venetian artist Guido Cadorin (1892–1976). A multi-analytical approach was employed in the study of six tempera grassa easel paintings and one casein tempera on a panel signed by the artist and belonging to the International Gallery of Modern Art Ca’ Pesaro in Venice, Italy, which dated from 1921 to 1951. The aim of the research was to identify the painting materials, observe the evolution of the color palette through time and assess the state of conservation. Non-invasive imaging and/or spectroscopic techniques were employed, such as hyperspectral imaging spectroscopy (HSI) and Raman spectroscopy. Microsamples were also collected from the edges and detached areas of the canvases that were studied through three non-destructive techniques, namely optical microscopy (OM), energy dispersive x-ray fluorescence spectrometry (EDXRF) and attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR), and one destructive technique, namely gas chromatography-mass spectrometry (GC/MS). The results allowed the inference of the color palette used to render the artist’s paints, composition of the preparation layer, and characterization of the binding media and varnish layers. Moreover, the state of conservation of the artworks was determined. Thus, the outcome of this research enriches the painter’s profile and might aid the International Gallery of Modern Art Ca’ Pesaro in Venice, Italy in the planning of future conservation treatments in accordance with the guidelines of good practices in art conservation.

Recent advances on the methods developed for the identification and detection of emerging contaminant microplastics: a review.

The widespread use of plastics, popular for their versatility and cost-efficiency in mass production, has led to their essential role in modern society. Their remarkable attributes, such as flexibility, mechanical strength, lightweight, and affordability, have further strengthened their importance. However, the emergence of microplastics (MPs), minute plastic particles, has raised environmental concerns. Over the last decade, numerous studies have uncovered MPs of varying sizes in diverse environments. They primarily originate from textile fibres and cosmetic products, with large plastic items undergoing degradation and contributing as secondary sources. The bioaccumulation of MPs, with potential ingestion by humans through the food chain, underscores their significance as environmental contaminants. Therefore, continuous monitoring of environmental and food samples is imperative. A range of spectroscopic techniques, including vibrational spectroscopy, Raman spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, hyperspectral imaging, and nuclear magnetic resonance (NMR) spectroscopy, facilitates the detection of MPs. This review offers a comprehensive overview of the analytical methods employed for sample collection, characterization, and analysis of MPs. It also emphasizes the crucial criteria for selecting practical and standardized techniques for the detection of MPs. Despite advancements, challenges persist in this field, and this review suggests potential strategies to address these limitations. The development of effective protocols for the accurate identification and quantification of MPs in real-world samples is of paramount importance. This review further highlights the accumulation of microplastics in various edible species, such as crabs, pelagic fish, finfish, shellfish, American oysters, and mussels, shedding light on the extreme implications of MPs on our food chain.

Different variable selection and model updating strategies about sex classification of silkworm pupae

When the model was applied to discriminate the sex of silkworm pupae of new species, the accuracy was not satisfied. Rebuilding the model need abundant samples and was time consuming. Updating the original model with few samples was usually used, which could also gave high performance. Hence, different model updating strategies were explored via visible (VIS)/near-infrared (NIR) hyperspectral imaging (HSI) spectroscopy in this paper. The calibration model including 150 samples was established on two species (221B-403, 223B-404) and the prediction set had 50 samples. Different variable selection methods including uninformative variable elimination (UVE) and synergy interval-partial least-squares (SiPLS) were adopted. Then partial least squares-discriminant analysis (PLS-DA) and extreme learning machine (ELM) were used to build the model. The best performance were given by UVE-PLSDA and SiPLS-PLSDA models with the accuracy of 98%. When SiPLS-PLSDA model was explored to discriminate the sex of new species “9312-shanheB”, the accuracy was just 70.83%. Different sample selecting methods, containing Kennard Stone (KS) algorithm, Manhattan distance, Euclidean distance and Chebyshev distance, were used to update the original calibration model. The updated SiPLS-PLSDA model based on KS gave the highest performance with 100% when used to discriminate the sex of silkworm pupa with different species in the same year. In addition, the updated model reached the accuracy of 94.44% when used to discriminate the sex of silkworm pupa with different species in different year. It indicated that the proposed updating strategy could be further effectively explored to differentiate the sex of other new species.