# Assessment of microplastic contamination in shrimp utilizing multispectral imaging, fluorescence, and infrared spectroscopy

Sureerat Makmuang, Abderrahmane Aït-Kaddour

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## To cite this version:

Sureerat Makmuang, Abderrahmane Aït-Kaddour. Assessment of microplastic contamination in shrimp utilizing multispectral imaging, fluorescence, and infrared spectroscopy. Journal of Food Composition and Analysis, 2025, 146, pp.107864. 10.1016/j.jfca.2025.107864. hal-05525817

HAL Id: hal-05525817

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Submitted on 24 Feb 2026

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# Assessment of microplastic contamination in shrimp utilizing multispectral imaging, fluorescence, and infrared spectroscopy

Sureerat Makmuang<sup>a, b</sup>, Abderrahmane Aït-Kaddour<sup>a, b, *</sup>

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## ARTICLE INFO

**Keywords:**

Microplastic
Shrimp
Fluorescence
Infrared
Multispectral
Chemometrics

## ABSTRACT

This research explores the potential of three spectroscopic techniques: Mid-Infrared (MIR) spectroscopy, fluorescence spectroscopy, and multispectral imaging (MSI) in predicting and categorizing microplastic (MP) contamination levels in minced shrimp. The shrimp samples were mixed with varying concentrations of poly-styrene (PS), polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET) microplastics at 11 different levels, ranging from 0 % to 10 % w/w. These levels included 0 %, 0.04 %, 0.08 %, 0.12 %, 0.16 %, 0.20 %, 0.60 %, 0.80 %, 1 %, 5 %, and 10 % w/w. Partial least squares regression (PLSR) was utilized to predict the level of MP contamination. The results showed that MSI and fluorescence spectroscopy effectively predicted the four types of MP, resulting in R<sup>2</sup><sub>p</sub> values ranging from 0.61 to 0.98 and root mean square error of prediction (RMSEP) values ranging between 0.43 and 2.89. On the other hand, partial least squares discriminant analysis (PLSDA) was employed to classify the contamination level of shrimp into three groups: low, medium, and high concentrations. PLSDA demonstrated exceptional classification accuracy ranging from 70.37 % to 98.15 % for all MP contamination using the three techniques (MSI, MIR, and fluorescence spectroscopy). These findings emphasize the effectiveness of spectroscopic techniques in detecting and quantifying microplastic contamination in shrimp, serving as a valuable tool for ensuring food safety and addressing environmental concerns associated with microplastics in the seafood supply chain.

## 1. Introduction

Each year, an estimated 8.8 million metric tons of plastic debris enter the oceans, breaking down into microplastics (MPs)—plastic particles under 5 mm—that pose severe environmental and health risks. Due to their size, composition, toxicity, and ability to absorb toxic substances, MPs present a considerable threat to marine ecosystems (Fernández Severini et al., 2020; Keshavarzifard et al., 2021). MPs are ingested by over 220 marine species, leading to bioaccumulation and food web contamination, which raises concerns for human health and marine biodiversity (Piarulli et al., 2022).

Shrimp are valuable subjects for studying MP contamination due to their ecological and economic significance (Niemcharoen et al., 2022). This study focuses on shrimp as a case study for assessing MP contamination, but the methodologies developed could be further applied to other biological samples, such as fish and seafood. Given shrimp's susceptibility to MP accumulation, these techniques are relevant for analyzing similar contamination in marine organisms' safety (Fernández

Severini et al., 2020). By showcasing the effectiveness of these methods in shrimp, we emphasize their potential for broader application in understanding MP contamination and supporting seafood safety and environmental health.

So far, one of the major bottlenecks and challenges in workflows for quantifying MP particles in marine samples is the purification of MPs from natural particles and the analysis of the polymer types of the particles. This difficulty arises mainly due to the small size and diverse chemical compositions of MPs (Beck et al., 2023). Furthermore, water often contains various organic and inorganic materials, making it challenging to identify MPs' spectral signatures, leading to many studies first extracting MPs before analysis (Gebejes et al., 2024). Many researchers use chemical digestion, such as a 10 % potassium hydroxide (KOH) solution or 30 % hydrogen peroxide ($H_2O_2$), to digest shrimp tissues before spectroscopy analysis, allowing for MP identification without biological interference (Fernández Severini et al., 2020; Keshavarzifard et al., 2021). However, this method is time-consuming and labor-intensive. Spectroscopy offers a rapid, accurate alternative, enabling direct MP

<footnote><sup>a</sup> Université Clermont Auvergne, INRAE, VetAgroSup, UMRF, Lempdes 63370, France</footnote>

<footnote><sup>b</sup> Department of Food Technology, Faculty of Agroindustrial Technology, University of Padjadjaran, Sumedang, Jawa Barat 45363, Indonesia</footnote>

<footnote>* Correspondence to: VetAgro Sup campus Agronomique, 89 Avenue de l'Europe, Lempdes 63370, France.</footnote>

<footnote>E-mail address: abderrahmane.aitkaddour@vetagro-sup.fr (A. Aït-Kaddour).</footnote>
detection and comprehensive polymer identification to assess their
sources and environmental impacts (Beck et al., 2023).

Mid-infrared (MIR) spectroscopy identifies and quantifies chemical
compounds by analyzing molecular vibrations, providing a compre-
hensive view of sample composition. In 2020, Huang et al. used MIR
spectroscopy to detect MPs, specifically polystyrene (PS) and polyvinyl
chloride (PVC), in chicken meat at contamination levels of 0.02–10 %.
Using chemometric techniques, including principal component analysis
(PCA), the study demonstrated that MIR spectroscopy effectively
detected higher contamination levels (1–10 %), showcasing its potential
as a non-destructive tool for food contamination assessment (Huang
et al., 2020). Multispectral imaging (MSI) technique complements this
by capturing data across multiple wavelengths, allowing for the visu-
alization of spatial variations in chemical composition that may indicate
adulteration (Aït-Kaddour et al., 2020). In 2024, Giovanni Bragato et al.
(2024) developed a non-invasive method for detecting MPs using mul-
tispectral imaging (MSI) combined with PCA. The study showed that
MSI can detect high-density polyethylene (HDPE) fragments as small as
0.5 mm in complex backgrounds (Bragato et al., 2024). Additionally,
fluorescence spectroscopy provides high sensitivity for quickly detecting
low levels of adulteration. For example, Yan et al. (2024) studied
MP-derived dissolved organic matter (MP-DOM) and its interactions
with pharmaceuticals chloramphenicol (CAP) and carbamazepine (CBZ)
using fluorescence and two-dimensional correlation spectroscopy. The
results showed that MP-DOM had a higher binding capacity for CAP than
for CBZ, emphasizing the role of specific fluorescent components in
binding behavior. Together, these techniques enable rapid,
non-destructive assessments of MP contamination in biological samples,
supporting consumer safety and product integrity across the seafood
industry.

To the best of our knowledge, this study is the first to systematically
compare the effectiveness of three spectroscopic techniques—MIR
spectroscopy, fluorescence spectroscopy, and multispectral imaging
(MSI)—which are based on different principles of light-material in-
teractions (Boukria et al., 2023), for the direct detection and quantifi-
cation of microplastics in shrimp without prior chemical digestion. The
primary objectives of this research are: (1) to assess the accuracy and
sensitivity of each technique across a range of MP contamination levels;
(2) to identify key spectral features and patterns associated with
different MP types; and (3) to evaluate the feasibility of rapid,
non-destructive MP detection methods applicable to complex biological
matrices. While shrimp serves as the model organism due to its rele-
vance in the seafood supply chain, the analytical framework developed
herein is designed to be broadly applicable to other seafood products,
thereby supporting improved monitoring of microplastic contamination
and promoting seafood safety and environmental health.

## 2. Materials and methods

### 2.1. Sample preparation

The frozen shrimp were purchased from a supermarket (Cora,
Lempdes, France) and transported to the institution laboratory (VetAgro
Sup Campus, Lempdes, France) for analysis. Upon arrival, the shrimp
were stored at – 20 °C until the study was conducted. Before the anal-
ysis, the frozen shrimp were thawed at room temperature and cleaned by
removing the shell, head, the black vein along the dorsal side (intestinal
tract), as well as the white vein on the ventral side. The shrimp were
then rinsed with distilled water to eliminate any potential impurities or
contaminants. This preparation was essential to remove contaminants,
such as the intestinal and ventral veins, which could affect the accuracy
of the study. The cleaned shrimp were finely minced by hand using a
knife. After mincing, the shrimp were placed in glass Petri dishes and air-
dried at room temperature for 2 h. Subsequently, they were dried with
compressed air (Atlas Copco, Nacka, Sweden) for 30 min to reduce
moisture. This process ensured the effective removal of water,

minimizing the risk of light scattering during spectroscopic measure-
ments. By eliminating moisture, the procedure allowed the spectral data
to accurately represent the composition and concentration of MPs,
avoiding artifacts or distortions caused by water-related interference.
This step was crucial for ensuring the reliability and precision of the
spectroscopic analysis.

Regarding plastics, common types used in daily products—including polyethylene terephthalate (PET), polyethylene (PE), polypropylene (PP), and polystyrene (PS)—were collected. These plastic samples were manually cut into small fragments measuring between 1–4 mm using clean, stainless-steel scissors to mimic MP particles found in environmental samples. To ensure all particles fell within the desired size range for analysis, a filtration step was carried out. The cut fragments were passed through a 1 mm mesh sieve. Particles larger than 4 mm were excluded during cutting, while those smaller than 1 mm passed through the sieve and were discarded. This process ensured that only plastic fragments within the 1–4 mm range were retained. The selected MP size range of 1–4 mm was chosen to reflect the natural ingestion and digestive capacities of the shrimp species studied, ensuring biological relevance (Braid et al., 2012; Devriese et al., 2015; Hossain et al., 2020; Murray and Cowie, 2011). Additionally, this range served as a proof of concept to demonstrate the sensitivity and reliability of our methodology for detecting and quantifying MPs in a realistic context.

### 2.2. Contamination of MPs in minced shrimp

To investigate the effects of MP contamination on minced shrimp, we employed a systematic approach to create controlled mixtures of shrimp and various types of MPs, including PS, PP, PE, and PET. Each type of MP was carefully processed into small fragments measuring between 1 and 4 mm, mimicking the sizes commonly found in marine environments. The minced shrimp served as the base for contamination, with MPs added in specific concentrations ranging from 0 % (pure minced shrimp) to 10 % (w/w), including 0.04 %, 0.08 %, 0.12 %, 0.16 %, 0.20 %, 0.60 %, 0.80 %, 1 %, 5 %, and 10 %. This concentration range was selected to encompass both environmentally relevant levels and elevated concentrations used for method validation, enabling a robust evaluation of detection capabilities across a broad contamination spectrum. It allowed for a comprehensive analysis of the impact of MPs at both low and high contamination levels. MP particles (1–4 mm) were manually incorporated into the minced shrimp samples using gloved hands to maintain sample integrity and prevent external contamination. No mechanical homogenizers or blenders were used at any stage. Instead, the mixtures were manually stirred in glass containers using a non-reactive, easy-to-clean stainless-steel rod for approximately 2–3 minutes. Stirring was performed with consistent circular and back-and-forth motions to break up any MP clumps and promote uniform dispersion. This method, based on preliminary trials, ensured effective mixing without damaging the shrimp tissue or altering the physical structure of the MP particles, thereby providing reliable and controlled contamination levels for subsequent analysis. To ensure the reliability of our experimental procedures, we implemented quality assurance (QA) measures, including strict adherence to procedural guidelines and sourcing materials from verified suppliers. Quality control (QC) measures included visual assessments and digital imaging of the mixed samples to confirm uniform MP incorporation at different concentrations. A digital image of MP incorporation in minced shrimp at varying concentrations is shown in Fig. S2–S5. All images represent samples after the manual mixing process, with no additional homogenization performed. This approach was intended to simulate practical sample preparation while allowing a controlled evaluation of MP incorporation and its effects on the biochemical composition and overall quality. However, we acknowledge that our current assessment of MP distribution relied primarily on qualitative observations through visual inspection and imaging, without applying quantitative homogeneity metrics. In future work, we plan to incorporate replicate sampling and statistical
analyses—such as standard deviation and coefficient of variation—to quantitatively assess MP distribution and enhance the reliability of our sample preparation.

## 2.3. Mid-infrared (MIR) spectroscopy

MIR Spectroscopy involves the absorption of infrared radiation, leading to molecular vibrations that identify chemical compounds based on their unique spectral signatures. It is particularly effective for quantifying microplastics in biological samples due to its sensitivity to polymer molecular groups (Beck et al., 2023; Fernández Severini et al., 2020). Herein, MIR spectra were recorded using thirty-two scans at a resolution of $4 \text{ cm}^{-1}$ within the range of 4000–1000 $\text{cm}^{-1}$ on a Tensor II Series Fourier transform spectrometer (Bruker, Billerica, MA, USA) equipped with an Attenuated Total Reflectance (ATR) accessory featuring a dug and a grip. The ATR cell consists of a horizontal ZnSe crystal with six reflections and an incidence angle of 45°. Before each measurement, the spectrum of the ZnSe crystal was recorded and used as the background. After equilibration at room temperature, 10 g of each concentration of individual MP contaminants mixed with minced shrimp samples were randomly placed on the crystal until it was completely covered. Three spectra were recorded for each sample concentration, with each spectrum being the average of thirty-two scans. For each contamination level, four replicates were conducted, meaning each sample would yield $3 \times 4 = 12$ spectra. Since each MP has 11 samples, this results in a total of $11 \times 12 = 132$ MIR spectra for each microplastic contamination. The spectra were subtracted from the background air spectrum. After each scan, a new reference background air spectrum was recorded. Following every measurement, the ATR crystal was cleaned with ethanol and distilled water, then dried using soft tissue and compressed air (Atlas Copco, Nacka, Sweden) before the next sample was applied. Cleanliness was verified by collecting a background spectrum and comparing it to the previous one. Spectra were recorded as absorbance values at each data point in triplicate. Spectral data collection and manipulation were conducted using OPUS software version 7.5 (Bruker, Billerica, MA, USA) (Boukria et al., 2023).

## 2.4. Fluorescence spectroscopy

Fluorescence Spectroscopy detects light emitted from a sample after it absorbs photons. This method provides high sensitivity and selectivity for identifying low levels of microplastics, as the emitted light characteristics depend on specific fluorophores present (Aït-Kaddour et al., 2018; Yan et al., 2024). Herein, synchronous fluorescence (SF) spectra were acquired using a FluoroMax-4 system, which boasts a signal-to-noise ratio of 3000:1 (Jobin-Yvon, Horiba, Longjumeau, France). The system is equipped with a solid sample holder with a 60° angle of incidence for the excitation radiation. SF spectra were collected at various offsets ($\Delta\lambda$: 20, 40, 60, 80, 100, and 120 nm) by placing the MP contamination samples in a quartz cell ($30 \times 1 \times 1$ mm). These specific offsets were selected based on our previous successful studies (Aït-Kaddour et al., 2018; Boukria et al., 2023), which demonstrated that this range effectively enhances spectral discrimination in complex biological matrices such as milk and meat for authenticity and adulteration analysis. The offset range of 20–120 nm, with 20 nm steps, is widely used in fluorescence spectroscopy as it provides a balance between spectral resolution and data quality. Offsets that are too small may generate redundant or highly correlated data, while excessively large offsets can lead to the loss of crucial spectral details or decreased signal intensity. This offset range ensures comprehensive spectral profiling of microplastic types while maintaining both sensitivity and selectivity across different fluorophore responses.

Fluorescence spectra were recorded in four replicates per offset for each of the 11 contamination levels tested for a given microplastic type (i.e., 0%, 0.04%, 0.08%, 0.12%, 0.16%, 0.20%, 0.60%, 0.80%, 1%, 5%, and 10% w/w), totaling 44 spectra per MP type at each offset.

For instance, 44 spectra were collected for PET at 20 nm, another 44 at 40 nm, and so forth, with the same procedure applied to PE, PP, and PS. This study focused on individual MP mixtures, comparing their fluorescence properties across different offsets to identify unique characteristics, as offset values impact the excitation-emission relationship and may enhance specific MP detection. By systematically comparing spectra offset by offset, the study aimed to understand how the offset influences the fluorescence behavior of each MP type and to optimize conditions for predicting and distinguishing MP contamination in shrimp. This detailed analysis helps develop accurate and sensitive methods for detecting and characterizing MP contamination in complex environmental samples. All data was collected and manipulated using FluorEssence software (version 3.5, 2010) from Horiba Scientific (Jobin-Yvon). Four acquisitions were performed for each sample (Boukria et al., 2023).

## 2.5. Multispectral image (MSI) spectroscopy

MSI captures images across multiple wavelengths, allowing for the identification of materials based on their spectral profiles and visualizing spatial variations in chemical composition. This technique helps detect MPs in complex backgrounds, enhancing the analysis (Aït-Kaddour et al., 2024; Boukria et al., 2023; Bragato et al., 2024). Herein, high-resolution images of minced shrimp samples with varying MP contamination levels were captured using a VideometerLab2® (Videometer A/S, Denmark). This device, equipped with a multispectral camera (Point Gray Research, Scorpion SCOR-20SOM, $1200 \times 1200$ pixels), records 17 images across the VIS-NIR spectrum, from 435 to 970 nm. Before capturing images, an “auto light” setting was applied to optimize the LED strobing duration and prevent pixel saturation. Calibration involved recording three plates in sequence: a white plate for reference, a dark plate for background correction, and a dotted plate for geometric alignment. During image capture, both pure and microplastic-contaminated shrimp samples were placed in a controlled dark environment by lowering a hollow sphere with the camera onto the sample platform. Each sample was sequentially illuminated by strobing the 17 LEDs, creating a data cube for each sample. Three acquisitions were performed for each sample. Herein, the spectra for one replicate were collected from an area of approximately $450 \times 500$ pixels, resulting in a total of 225,000 MSI spectra per replicate. After acquisition, the spectra were preprocessed and averaged to create a single spectrum for each replicate, resulting in 17 averaged values per spectrum. With 11 samples per MP, this yielded a total of $11 \times 3 = 33$ MSI spectra for each type of MP contamination.

## 2.6. Data preprocessing

Accurate spectral data collection and analysis are crucial for studying MP contamination, especially regarding particle size effects. In MSI, smaller MP particles provide clearer spectral signatures, while larger particles can obscure signals. Fluorescence spectroscopy's emission characteristics also vary with size, necessitating careful calibration. Similarly, MIR spectroscopy shows size-dependent behavior, with smaller particles exhibiting stronger absorbance due to a greater surface area-to-volume ratio, whereas larger particles produce weaker signals. To address these variations, adjustments were made in our methodology to account for particle size differences during spectral collection. A drying process was implemented to eliminate moisture, reducing noise and scattering effects. Meticulous instrument calibration ensured precise data acquisition, while controlled experimental conditions minimized the impact of external factors like temperature and humidity. Preprocessing involves a series of steps designed to prepare raw data for analysis. This crucial phase in spectroscopic analysis enhances the quality and clarity of spectral data, making it easier to extract valuable information. As a result, it leads to more accurate and reliable outcomes during the classification process. In this study, preprocessing included
outlier detection using the interquartile range (IQR) method, Savitzky-Golay filtering to smooth the spectral data, and standard normal variate (SNV) transformation to account for variations in sample characteristics (Makmuang et al., 2021). During the initial data preprocessing stage, the IQR is calculated by finding the difference between the 75th percentile (Q3) and the 25th percentile (Q1) of the dataset. This calculation helps identify data points that significantly deviate from the norm, known as outliers. Outliers are defined as points that have a Euclidean distance from the class centroid exceeding 1.5 times the IQR. Removing these outliers is essential to ensure that subsequent analyses are based on accurate and representative data (Makmuang et al., 2023, 2024). The spectra were further processed to enhance data quality. This included applying a Savitzky-Golay smoothing filter (polynomial order: 2; smoothing points range: 11) to reduce noise while preserving important spectral features. Additionally, artifacts from sources such as instrument noise and environmental factors were removed, which improved the signal-to-noise ratio and facilitated the identification of relevant features. Then, standard normal variate (SNV) baseline correction was applied to account for background interference and to normalize the spectra. This widely used technique for scatter correction addresses variations caused by factors such as surface light scattering, differences in sample particle size, and fluctuations in the optical path during measurement. By mitigating these external influences, we ensure that the spectral data remains accurate and reliable for subsequent analysis (Makmuang et al., 2024).

## 2.7. Partial least squares regression

Partial Least Squares Regression (PLSR) is a powerful statistical method for predicting contamination levels in various fields, including food safety and environmental monitoring. One of the main advantages of PLSR is managing multicollinearity in datasets with highly correlated predictor variables. PLSR also reduces dimensionality while preserving essential information, enabling accurate modeling of complex relationships between predictor and response variables (Gowen et al., 2011; Marchesi et al., 2023).

This study utilized PLSR to evaluate the effectiveness of three wavelength techniques for predicting MP contamination in minced shrimp across concentrations from 0 % to 10 % w/w. The analysis focused on 0 % (representing pure minced shrimp), 0.04 %, 0.08 %, 0.12 %, 0.16 %, 0.20 %, 0.60 %, 0.80 %, 1 %, 5 %, and 10 % w/w. The calibration set comprised of 0 %, 0.04 %, 0.08 %, 0.16 %, 0.60 %, 1 %, and 10 % w/w. These calibration points were chosen to span the entire contamination range, anchoring the model with data points reflecting low and high MP contamination scenarios. To evaluate the model's generalizability, the validation set was composed of intermediate concentrations—0.12 %, 0.20 %, 0.80 %, and 5 % w/w—which fall within the overall calibration range but were not included in the calibration dataset. This design ensures that the model is tested on previously unseen data while maintaining relevance to the trained concentration range. Additionally, calibration and validation samples were prepared and measured independently to minimize potential overlap or bias.

The modeling process involved calibration, cross-validation using a leave-one-out method, and validation with an external sample set. The optimal number of latent variables (LVs) was determined by constructing and evaluating PLSR models with varying LVs through cross-validation on the calibration set. In this approach, each sample is tested individually while the others serve as the training set. The optimal number of LVs is chosen based on the model with the lowest root mean square error of cross-validation (RMSECV), ensuring high predictive accuracy and reducing the risk of overfitting (Galtier et al., 2011). The chosen optimal number of LVs was subsequently used to predict the validation set. The accuracy of the PLSR model was assessed using the coefficient of determination (R²). Additionally, the precision of the analytical method was evaluated with the root mean square error of calibration (RMSEC) and the root mean square error of prediction

(RMSEP). A better predictive ability was indicated by higher values of the coefficient of determination for both validation (R²p) and calibration (R²c), as well as lower values of RMSEP and RMSECV for the prediction models (Rahmania et al., 2015; Zareef et al., 2018).

$$ \text{RMSEP} = \sqrt{\frac{\sum_{i=1}^{m} (\hat{Y}_i - Y_i)^2}{M - 1}} \qquad (1) $$

$$ \text{RMSEP} = \sqrt{\frac{\sum_{i=1}^{n} (\hat{Y}_i - Y_i)^2}{N}} \qquad (2) $$

$Y_i$ and $\hat{Y}_i$ represent the actual and predicted values of microplastic contamination in the minced shrimp, respectively. M and N indicate the number of samples in calibration and validation sets, respectively (Rahmania et al., 2015).

Furthermore, the limits of detection (LOD) and quantification (LOQ) for each MP type were determined using PLSR following standard methodologies (Clua-Palau et al., 2020; Tsanaktsidou et al., 2020; Ytsma and Dyar, 2022), allowing for a comparative evaluation of analytical sensitivity across various spectroscopic methods used in this study.

$$ \text{LOD} = \frac{3 \times \text{SD}}{m} \qquad (3) $$

$$ \text{LOQ} = \frac{10 \times \text{SD}}{m} \qquad (4) $$

Where SD is the standard deviation of the residuals, and m is the slope of the calibration curve (Clua-Palau et al., 2020; Tsanaktsidou et al., 2020; Ytsma and Dyar, 2022).

## 2.8. Partial least squares discriminant analysis (PLSDA)

Partial least squares discriminant analysis (PLSDA) is a statistical method used for classification tasks, particularly effective for complex datasets with large predictor sets, such as spectral data from spectroscopy (Boukria et al., 2023; Paul et al., 2019). It is a recognized classification method in chemometrics, differing from PLSR in how it handles the response variable (Galtier et al., 2011). In PLSR, the response variable is continuous, meaning it can take any value within a specified range. For example, when predicting the concentration of a contaminant in a food sample, the response variable might range from 0 to a maximum concentration level. In contrast, PLSDA is tailored for scenarios where the response variable is categorical, representing distinct classes or groups. For instance, in food safety, the response variable might categorize samples as “adulterated” or “not adulterated.” Similarly, in studies of MPs, the response variable could indicate the “presence” or “absence” of MPs in biological samples.

In this study, the division of calibration and validation sets for PLS-DA classification was designed to ensure that each concentration group (low, medium, and high) was appropriately represented. The concentration levels for each group were selected based on both the practical relevance of microplastic contamination in food matrices and the sensitivity of the detection method. The low concentration group included 0 %, 0.04 %, 0.12 %, and 0.16 % w/w, representing trace to minimal contamination levels; the medium concentration group consisted of 0.2 % and 0.8 % w/w, reflecting moderate contamination where quantification becomes increasingly important; and the high concentration group included 1 % and 10 % w/w to represent higher contamination scenarios. For the validation set, intermediate concentrations (0.08 %, 0.6 %, and 5 % w/w) were chosen. In PLS-DA, the validation process uses concentrations that differ from the calibration set but remain within its range. Unlike PLSR, which focuses on specific concentrations, PLS-DA evaluates the model’s ability to categorize samples into three groups: low, medium, and high concentrations, determined using spectral data from MIR, MSI, and fluorescence. In the
context of this study, medium concentration refers to intermediate levels of MP contamination in minced shrimp, specifically 0.2 % and 0.8 % w/ w. These values fall between trace contamination (low) and highly spiked levels (high), and were selected to represent realistic, moderate contamination scenarios. Grouping the data into low, medium, and high categories enabled the classification model (PLS-DA) to assess its ability to discriminate across a spectrum of contamination levels, particularly in cases where differences may be more subtle. Before selecting the best PLSDA model, each model underwent calibration and was cross-validated. A validation set was chosen based on the concentration subsets within the calibration set. Regarding the calibration process, the PLS-DA model is constructed using a training dataset that includes both the predictor variables—features derived from biological samples, such as fish, other seafood, and their spectral data—and the known response variable. To ensure robustness and prevent overfitting, cross-validation is conducted using the "leave-one-out" method. Finally, the model is tested on external samples not included in the training or validation datasets, which is crucial for assessing accuracy and reliability. The quality of the model is determined by evaluating its accuracy (the ratio of correctly classified observations to the total number of observations), recall (the ratio of true positives to the total actual positives in the data), and specificity (the ratio of true negatives to total negatives in the data) (Boukria et al., 2023; Jin et al., 2022). High sensitivity, specificity, and recall indicate high PLS-DA model performance.

$$ Accuracy = (TP + TN)/(TP + FP + FN + TN) \times 100\% \quad (3) $$

<figure><img src="image_4.png" /><figcaption>Fig. 1. Mean MIR spectra of microplastic contamination in minced shrimp at concentrations ranging from 0 % to 10 % w/w after performing SNV: (A) PS, (B) PP, (C) PE, and (D) PET. The variance plot is shown at the bottom, highlighting the MIR spectral regions with high variance across contamination levels. Regions exceeding one standard deviation (1 SD) were selected as key wavelengths for further analysis.</figcaption></figure>

$$ Recall = TP/(TP + FN) \times 100\% \quad (4) $$

$$ Specificity = TN/(TN + FP) \times 100\% \quad (5) $$

where TP is defined as true positive, FN is defined as false negative, TN is defined as true negative, and FP is defined as false positive (Aït-Kaddour et al., 2024; Boukria et al., 2023; Makmuang et al., 2021).

All computational procedures were conducted using MATLAB R2019b (The MathWorks, Natick, Massachusetts, U.S.A.) with an in-house coding algorithm.

## 3. Results and discussion

### 3.1. Mid-infrared (MIR) spectra

MIR spectroscopy was employed to analyze minced shrimp samples spiked with varying concentrations of common MPs including PS, PP, PE, and PET, at contamination levels ranging from 0 % to 10 % by weight (w/w).

Fig. 1 displays the mean MIR spectra collected over the range of 1000–4000 cm⁻¹, following preprocessing with Savitzky-Golay smoothing and standard normal variate (SNV) correction. The control group (pure minced shrimp at 0 % w/w) and MP-contaminated samples are presented for comparison. Fig. 1A–D illustrate the mean spectra and corresponding variance for PS, PP, PE, and PET, respectively. The

<figure><img src="image_5.png" /></figure>
variance plots (shown at the bottom) indicate that areas with variation greater than one standard deviation (1 SD), which may serve as potential spectral markers for differentiating MP contamination levels. These spectral regions are considered critical for identifying and quantifying microplastic presence within shrimp samples (Makmuang et al., 2021).

Notably, the results showed similar spectral features across all MPs, including the -OH stretching region (3176 –3252 cm⁻¹) associated with hydroxyl groups in shrimp, and the C=O stretching absorption (1502–1669 cm⁻¹) possibly linked to carbonyl groups in proteins. A peak around 1550 cm⁻¹ corresponds to amide II vibrations in proteins. These shared spectral bands indicate common vibrational characteristics among the different MP mixtures in shrimp. Crucially, we observed that higher contamination levels of PS (Fig. 1A) and PET (Fig. 1D)

corresponded to increased peak intensity at 1640 and 1550 cm⁻¹. This suggests an enhancement of protein signals, likely due to interactions between the amide bonds in proteins and the functional groups of the microplastics, which may influence the spectral characteristics of organic materials (Fu et al., 2014; Rohman et al., 2011). This agrees with different studies that demonstrated that PS and PET can interact with organic proteins. For example, Krishna de Guzman et al. (2023) demonstrated that pepsin can interact with PS, and this interaction leads to structural alterations of pepsin, inhibition of pepsin activity, and impairment of pepsin digestibility (Krishna de Guzman et al., 2023). Furthermore, Gligorijevic et al. (2024) reported that ovalbumin could interact with PS and PET MPs (Gligorijevic et al., 2024).

In contrast, contamination with PP and PE led to decreased

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Mean fluorescence spectra of microplastic contamination in minced shrimp at concentrations ranging from 0 % to 10 % w/w after performing SNV: (A) PS at offset (Δλ) 40 nm, (B) PP at offset (Δλ) 20 nm, (C) PE at offset (Δλ) 20 nm, and (D) PET at offset (Δλ) 120 nm. The variance plot is shown at the bottom, highlighting the fluorescence spectral regions with high variance across contamination levels. Regions exceeding one standard deviation (1 SD) were selected as key wavelengths for further analysis.</figcaption></figure>
absorbance at 1640 cm⁻¹ and 1550 cm⁻¹ as contamination levels increased, likely due to a dilution effect or weak interactions with protein amide regions. Unlike PET and PS, PP and PE lack functional groups to enhance protein signals, resulting in declining protein absorbance with higher concentrations. Additional changes in the 1500–1300 cm⁻¹ further reflect MP type and concentration effects (Huang et al., 2020; Kong and Yu, 2007). A prominent peak at 1480–1400 cm⁻¹ corresponds to CH and CH₂ bending, a feature common to nearly all organic matter (Parvin et al., 2022; Xu et al., 2019). Lower signals at 1040–1085 cm⁻¹ for PP and PS indicate C–H rocking vibrations, confirming CH₂ and CH₃ groups. These peaks highlight aliphatic hydrocarbon functional groups and C–C stretching, aiding polymer backbone analysis (Parvin et al., 2022; Rohman et al., 2011; Signoret et al., 2019; Xu et al., 2019). These findings indicate that varying levels of microplastic contamination can significantly impact shrimp's biochemical composition, potentially compromising quality and safety as a food product. This has critical implications for subsequent statistical analyses, such as partial least squares regression (PLSR), principal component analysis (PCA), and partial least squares discriminant analysis (PLSDA), which will help further evaluate the associations between microplastic contamination and the qualitative characteristics of shrimp.

## 3.2. Fluorescence spectroscopy

Synchronous fluorescence spectroscopy was used to detect contamination in minced shrimp by measuring emitted fluorescence at various wavelengths while exciting the sample with a specific wavelength. The "offset" (Δλ), the difference between excitation and emission wavelength, was optimized to enhance detection accuracy and selectivity for distinguishing fluorescent species within the shrimp mixture (Boukria et al., 2023, 2020; Loudiyi et al., 2022).

Fig. 2 shows the mean fluorescent spectra of MP contamination in minced shrimp, highlighting optimal offsets for each MP type: 40 nm for PS, 20 nm for both PP and PE and 120 nm for PET. These offsets were chosen based on the best PLSR model results as shown in Tables S1–S4. Fig. 2(A)–(D) displays these spectra along with computed variance at the bottom, where variations exceeding one standard deviation may indicate potentially different MP mixtures. Consequently, the results revealed several significant fluorescence peaks at specific wavelengths: 350–390 nm (emission: 390–430 nm) and 433–447 nm (emission: 473–487 nm) for the PS mixture; 371–415 nm (emission: 391–435 nm) for the PP mixture; 384–435 nm (emission: 404–455 nm) and 482–527 nm (emission: 502–547 nm) for the PE mixture; and 302–350 nm (emission: 422–470 nm) and 378–422 nm (emission: 490–542 nm) for the PET mixture.

Fluorescence in shrimp arises from naturally occurring compounds like proteins, pigments (e.g., fluorescent proteins and carotenoids), and biogenic amines, which reflect the shrimp's biochemical state and environmental conditions (Das and Mishra, 2023; Zhang et al., 2023; Zhou et al., 2023). These compounds may change as the shrimp deteriorates, with fluorescence signals influenced by pH, color, and temperature (Zhou et al., 2023). Trends in fluorescence intensity at excitation/emission peaks, increasing or decreasing with MP concentrations, can provide insights into interactions and characteristics of the MPs.

Fig. 2A shows that PS contamination in minced shrimp is linked to benzene rings enabling π–π* transitions, contributing to fluorescence. The fluorescence peak at 350–390 nm arises from aromatic structures, which respond well to excitation within the 300–400 nm range (Gao et al., 2015). As PS concentration increases, fluorescence intensity decreases in the 350–390 nm region, likely due to quenching effects from enhanced energy transfer among PS molecules. Conversely, intensity increases in the 433–487 nm range, suggesting stronger emission at higher concentrations, possibly from cumulative effects or π–π* transitions in the benzene rings.

PP exhibits low fluorescence due to its non-aromatic structure. In Fig. 2B, fluorescence intensity (371–415 nm excitation, 391–435 nm emission) decreases with higher PP concentrations, likely due to quenching from molecular aggregation, energy dissipation as heat, and reduced light penetration in the shrimp matrix (Gao et al., 2015; Tran et al., 2019).

Similar to PP, PE lacks aromatic groups, leading to low inherent fluorescence. However, in Fig. 2C, fluorescence intensity (384–435 nm excitation, 404–455 nm emission) increases with higher PE concentrations, suggesting enhanced light interactions. This correlation indicates the potential of this excitation/emission pair for detecting and quantifying PE contamination in shrimp samples.

For the PET mixture, excitation at 302–350 nm and emission at 422–470 nm, as well as excitation at 378–422 nm and emission at 490–542 nm, corresponding to PET's aromatic terephthalate groups, which are highly responsive to UV excitation. These distinct emission bands, linked to aromatic rings and ester groups in PET's structure, confirm its presence in shrimp samples and provide clear spectral differentiation from other polymers.

Fluorescence spectra (Fig. 2) reveal that MP contamination significantly alters the fluorescent molecules in minced shrimp. Interactions between MPs and the shrimp matrix likely modify intrinsic compounds, such as proteins and pigments, causing shifts in emission profiles, peak intensities, and wavelengths. These changes reflect the physicochemical impact of contamination on shrimp (Boukria et al., 2020). Fluorescence spectroscopy proves valuable for detecting MPs and assessing their effects on shrimp's biochemical composition, aiding in monitoring seafood quality and safety.

## 3.3. Multispectral data

The average absorbance spectral data for minced shrimp samples at varying MP contamination levels was obtained using the MSI technique, covering the 430–970 nm range. Fig. 3(A–D) shows average MSI spectra for PS, PP, PE, and PET mixtures, with similar broad peaks across samples but notable intensity differences in PE (Fig. 3C). Variance calculations identified overtone regions with high variance as potential markers for distinguishing MP contamination levels.

Fig. 3(A) PS exhibits absorbance peaks at 435–445 nm, 645–660 nm, and 940–970 nm. The peaks at 435–445 nm are possibly associated with protein pigments (Li et al., 2023), while the 940–970 nm corresponds to the second overtone of O–H stretching in the water (Jia et al., 2017; Li et al., 2023; Xiong et al., 2015). This range shows an inverse relationship with PS concentration, suggesting structural interactions, while the 645–660 nm increases proportionally with PS levels, reflecting changes in the sample's color characteristics (Jia et al., 2017).

Fig. 3(B) shows that the PP mixture has an absorbance at 435–450 nm, linked to protein pigments (Li et al., 2023), and at 590–700 nm, corresponding to the third overtone of O–H stretching related to water content (Jia et al., 2017). Both peaks directly correlate with %PP levels, indicating PP's impact on shrimp's biochemical composition. Conversely, the 940–970 nm band, linked to O–H stretching in water, inversely correlates with %PP contamination (Li et al., 2023), suggesting that higher levels of PP disrupt water's hydrogen bonding within the shrimp matrix. This inverse relationship indicates that increased PP reduces the vibrational intensity at this wavelength, reflecting the detrimental impact of microplastic contamination on the shrimp's biochemical interactions.

In Fig. 3(C), the PE mixture shows peaks at 435–524 nm likely associated with protein pigments (Li et al., 2023), with a direct relationship to PE concentration, suggesting that increased PE contamination may influence the spectral characteristics of the shrimp samples. The 870–970 nm band is primarily due to overtones and combinations of fundamental vibrations of molecular bonds, significant for detecting water content and other organic molecules (Boukria et al., 2023; Cen and He, 2007).
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Mean MSI spectra of microplastic contamination in minced shrimp at concentrations ranging from 0 % to 10 % w/w after performing SNV: (A) PS, (B) PP, (C) PE, and (D) PET. The variance plot is shown at the bottom, highlighting the MSI spectral regions with high variance across contamination levels. Regions exceeding one standard deviation (1 SD) were selected as key wavelengths for further analysis.</figcaption></figure>

In Fig. 3(D), the PET mixture shows absorbance peaks at 435–470 nm, likely associated with protein pigments, and at 940–970 nm, which reflect the second overtone of O–H stretching in water (Jia et al., 2017; Li et al., 2023; Su and Sun, 2018; Xiong et al., 2015). These peaks correlate with increased %PET levels, indicating that higher PET concentrations affect both protein-related absorbance and water’s vibrational characteristics in the shrimp samples. However, the 590–660 nm peaks, associated with the absorption of light by various organic compounds, including some proteins and lipids (Boukria et al., 2023), show an inverse relationship with %PET contamination—meaning that as PET concentration increases, the intensity of absorbance in this region decreases. These spectral differences highlight the distinct compositions of the various MPs in relation to the

shrimp samples.

### 3.4. Principal component analysis

The PCA score and loading plots in Fig. 4 and Fig. 5, respectively, highlight how well different spectral techniques differentiate contamination levels. It is worth noting that Fig. 4 presents PCA scores and loadings derived from all microplastic-contaminated samples as an unsupervised analysis, without applying predefined group classifications (e.g., control, low, or high concentrations), to reveal the intrinsic variance structure across the dataset. PC1 and PC2 account for most of the variance (Meng et al., 2024), with MSI explaining 90–98 %, fluorescence 87–93 %, and MIR 81–91 %. MSI and fluorescence are
<figure><img src="image_8.png" /><figcaption>Fig. 4. Principal component 1 and 2 score plots of all microplastic-contaminated shrimp samples analyzed using MSI, MIR, and fluorescence (FLUO) techniques.</figcaption></figure>

particularly effective at lower contamination levels, while MIR is slightly less effective for clustering contamination levels individually.

Additionally, we have included a PCA score plot with clearly labeled groups in the Supplementary material (Fig. S1). The samples are divided into three contamination categories: control (0% w/w), low (0.04–0.6% w/w), and high (0.8–10% w/w). Across all three spectroscopic techniques—MSI, fluorescence spectroscopy, and MIR spectroscopy—similar clustering patterns are observed within these groups. This consistent grouping demonstrates that all three methods are reliable and effective for distinguishing and quantifying different levels of Principal component 1 and 2 score plots of all microplastic-contaminated shrimp samples analyzed using MSI, MIR, and fluorescence (FLUO) techniques microplastic contamination in minced shrimp samples.

The loading analysis further illuminated the spectral features contributing to sample differentiation. In PCA, loadings reveal the correlation between original variables (e.g., spectral data from MP contamination) and principal components, identifying key wavelengths sensitive to MP composition changes spectroscopy (Meng et al., 2024).

Fig. 5 presents PCA loadings for PC1 and PC2 across four MP contamination types using MSI, fluorescence (PC1 only, as PC2 lacked informative data), and MIR spectroscopy. In the fluorescence spectroscopy PCA models for four MP (PS, PP, PE, and PET) contamination in

minced shrimp, different excitation bands were observed. For PS, positive bands were identified at 393 nm (20 nm offset), 366 nm (40 nm), 327 nm (60 nm), and 326 nm (80 nm), while negative bands were found at 430 nm (60 nm), 432 nm (80 nm), and 433 nm (100 nm), indicating a high contribution of those bands in the discrimination of the shrimp samples based on their PS contamination level. PP exhibited positive excitation bands at 398 nm (20 nm), 367 nm (40 nm), 356 nm (60 nm), and negative peaks at 486 nm (40 nm), 486 nm (60 nm), and 442 nm (80 nm), suggesting a prevalence of those bands for samples. PE showed positive bands at 315 nm (80 nm), 310 nm (100 nm), and 309 nm (120 nm), with negative bands at 503 nm (20 nm) and others across different offsets. PET showed positive bands at 379 nm (20 nm), 347 nm (40 nm), and 426 nm (80 nm), and negative bands at 467 nm (20 nm), 326 nm (60 nm), and others. The spectral band differences on PC1 maps reveal distinct fluorescence characteristics for each MP type, indicating varied interactions with minced shrimp. This diversity is crucial for accurately identifying contamination types. Analyzing these loadings helps pinpoint key spectral wavelengths, enhancing understanding of chemical changes in minced shrimp due to contaminants.

The PC1 loading plots for PS, PP, and PET in the MSI technique show consistent patterns. PS exhibits positive loadings from 435 to 505 nm and 890–970 nm, with negative loadings from 525 to 870 nm,
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Principal component 1 and 2 loading plots corresponding to the PCA of all microplastic-contaminated shrimp samples analyzed by MSI, MIR, and fluorescence (FLUO) techniques. These plots illustrate the contributions of spectral variables to the principal components. Note that only PC1 is represented for the loadings of the fluorescence technique.</figcaption></figure>

indicating that color differences are significant for distinguishing contamination. Similar trends are observed for PP and PET. Positive loadings in the 900–1050 nm range reflect shrimp's chemical composition, while negative loadings between 500 and 900 nm suggest that increased PS contamination reduces absorbance (Boukria et al., 2023; Jacquot et al., 2015). For PE, positive loadings from 435 to 645 nm enhance shrimp's optical properties, while negative loadings from 660 to 970 nm indicate that higher PE levels decrease absorbance. The PC2 plot for PE highlights inverse relationships, with distinct spectral shifts due to PE contamination. Next, MIR loading analysis shows strong PC1 correlations with variations at 1000–1900 cm⁻¹, linked to organic matter and protein. PC2, capturing minimal variance, emphasizes the 3200–4000 cm⁻¹ range (likely water), highlighting subtler changes not addressed by PC1. Notable shifts in PP, PE, and PET include a negative peak at 3500 cm⁻¹ and a positive peak at 3200 cm⁻¹ for PP and PE, forming a shoulder-like feature, indicating complex interactions between water and the surrounding molecules (Ferreiro et al., 2022; Tsenkova et al., 2015). This suggests that PP and PE's hydrophobic nature (Al Harraq and Bharti, 2022) disrupts water's hydrogen bonding,

reducing water's vibrational intensity and integration within the shrimp matrix. For PET, PC2 exhibits a significant shift from 3500–3200 cm⁻¹, with a positive peak at 3500 cm⁻¹ indicating strong O-H stretching and a negative peak at 3200 cm⁻¹ suggesting weakened hydrogen bonds. This weakening may result from PET's hydrophobic nature, disrupting water's interaction with the shrimp matrix. For PS, variations in the 1500–1700 cm⁻¹ range correlate with PC1, indicating protein-related features, while the 3000–3500 cm⁻¹ range (PC2) reflects changes in hydroxyl groups, possibly linked to the second component of the sample (Fu et al., 2014; Kokot et al., 2002; Olsztyska-Janus et al., 2011). These insights are crucial for assessing MP contamination in seafood.

### 3.5. Partial least squares discriminant analysis

PLS-DA was used to classify MP concentrations into three distinct groups based on their concentration levels: low (0 % w/w, 0.04 % w/w, 0.12 % w/w, and 0.16 % w/w), medium (0.2 % w/w and 0.8 % w/w), and high (1 % w/w and 10 % w/w). Table 1 presents the predictive results of PLS-DA for identifying concentrations of various MPs (PS, PP,
<caption>**Table 1**</caption>

Best predictive results after applying PLSDA to predict the concentration of MPs (including PS, PP, PE, and PET) within minced shrimp (C: Calibration, T: Test, LV: latent variables).

<table><thead><tr><th rowspan="2">Spectral techniques</th><th rowspan="2">MPs</th><th rowspan="2">LV</th><th colspan="2">%Accuracy</th><th colspan="2">%Specificity</th><th colspan="2">%Recall</th></tr><tr><th>C</th><th>T</th><th>C</th><th>T</th><th>C</th><th>T</th></tr></thead><tbody><tr><td rowspan="5">Fluorescent</td><td>PS</td><td>7</td><td></td><td>97.92</td><td>88.89</td><td>98.61</td><td>91.67</td><td>95.83</td><td>83.33</td></tr><tr><td>PP</td><td>3</td><td></td><td>89.59</td><td>83.33</td><td>89.58</td><td>87.50</td><td>79.17</td><td>75</td></tr><tr><td>PE</td><td>3</td><td></td><td>85.42</td><td>88.89</td><td>88.89</td><td>91.67</td><td>72.92</td><td>83.33</td></tr><tr><td>PET</td><td>7</td><td>mean</td><td>100</td><td>83.33</td><td>100</td><td>87.50</td><td>100</td><td>75</td></tr><tr><td></td><td></td><td></td><td>93.23</td><td>86.11</td><td>94.27</td><td>89.59</td><td>86.98</td><td>79.17</td></tr><tr><td rowspan="5">MIR</td><td>PS</td><td>4</td><td></td><td>100</td><td>85.18</td><td>100</td><td>88.89</td><td>100</td><td>77.78</td></tr><tr><td>PP</td><td>11</td><td></td><td>100</td><td>77.78</td><td>100</td><td>83.33</td><td>100</td><td>66.67</td></tr><tr><td>PE</td><td>13</td><td></td><td>100</td><td>98.15</td><td>100</td><td>98.61</td><td>100</td><td>97.22</td></tr><tr><td>PET</td><td>7</td><td>mean</td><td>100</td><td>83.33</td><td>100</td><td>87.50</td><td>100</td><td>75</td></tr><tr><td></td><td></td><td></td><td>100.00</td><td>86.11</td><td>100.00</td><td>89.58</td><td>100.00</td><td>79.17</td></tr><tr><td rowspan="5">MSI</td><td>PS</td><td>9</td><td></td><td>100</td><td>77.78</td><td>100</td><td>83.33</td><td>100</td><td>66.67</td></tr><tr><td>PP</td><td>3</td><td></td><td>91.67</td><td>92.59</td><td>92.59</td><td>94.44</td><td>86.11</td><td>88.89</td></tr><tr><td>PE</td><td>3</td><td></td><td>94.45</td><td>70.37</td><td>96.30</td><td>77.78</td><td>55.56</td><td>88.89</td></tr><tr><td>PET</td><td>3</td><td>mean</td><td>91.67</td><td>70.37</td><td>91.67</td><td>77.78</td><td>83.33</td><td>55.56</td></tr><tr><td></td><td></td><td></td><td>94.45</td><td>77.78</td><td>95.14</td><td>83.33</td><td>81.25</td><td>75.00</td></tr></tbody></table>

PE, and PET) in minced shrimp using fluorescence, MIR, and MSI techniques. The number of latent variables (LVs) used in each model was optimized through cross-validation and is also reported in Table 1.

Fluorescence spectroscopy has shown strong predictive accuracy for detecting MPs, with average accuracy rates is 86.11 %. Similarly, the average specificity is also high at 89.59 %, which indicates effective identification of true negatives. The average recall rates are acceptable at 79.17 %, reflecting the model's capability to correctly identify a reasonable proportion of actual positive cases. Considerably, PS performed exceptionally well, with accuracy, specificity, and recall rates exceeding 95.83 % in the calibration set and 83.33 % in the test set. In overall, fluorescent seems to be very strong for MP classification by PLS-DA. This finding aligns with previous studies, such as those by Hassoun et al. (2019), who reported that fluorescence techniques, particularly when combined with PLS-DA, demonstrated high sensitivity and specificity for detecting contaminants in food products.

MIR spectroscopy showed superior performance with an average of 86.11 % accuracy, 89.59 % specificity, and 79.17 % recall in the validation set. Notably, the calibration set achieved 100 % across all metrics, demonstrating its effectiveness when properly calibrated, making MIR highly beneficial for precise MP detection. This finding aligns with the work of Da Costa Filho et al. (2022), who found that MIR spectroscopy effectively detected higher contamination levels in food products, showcasing its potential as a non-destructive tool for assessing food safety (Da Costa Filho et al., 2022).

MSI demonstrated moderate effectiveness with average metrics of 77.78 % accuracy, 83.33 % specificity, and 75 % recall. Although lower than fluorescence and MIR, MSI remains valuable for rapid screening due to its ability to visualize spatial variations in food composition. As highlighted by Boukria et al. (2023), MSI provides quick acquisition and processing, making it effective for capturing spatial heterogeneity in food matrices (Boukria et al., 2023).

The detailed confusion matrices and classification metrics for both the calibration and validation sets are provided in Supplementary Tables S5–S7 for MIR, fluorescence, and MSI, respectively. Additionally, PLS-DA loading plots for MSI, fluorescence, and MIR spectral data are shown in Supplementary Fig. S6.

Combining fluorescence, MIR, and MSI spectroscopy could provide a comprehensive approach for monitoring MP contamination. This aligns with He and Sun (2015), highlighting the value of integrating multiple techniques and chemometrics for improved detection. PLS-DA analysis underscores the strengths and weaknesses of each method, emphasizing the importance of selecting the right techniques for food safety assessments and suggesting directions for future research.

### 3.6. Partial least squares analysis

In this study, PLSR was utilized to predict MP contamination levels ranging from 0 % to 10 % (w/w) in minced shrimp. Calibration models were developed for each of the three spectroscopic techniques—fluorescence, MIR, and MSI—using the same samples that were adulterated with MPs. Table 2 summarizes the most effective predictive models for each technique and evaluates the performance of PLSR in predicting MP contamination across these methods.

In fluorescence spectroscopy, the $R_p^2$ for the prediction test set ranged from 0.61 to 0.98, indicating a robust correlation between the predicted and actual levels of contamination. The RMSEC varied from 0.5827 to 1.66, while the RMSEP ranged from 1.59 to 2.89. These results demonstrate that fluorescence spectroscopy is an effective method for accurately and precisely quantifying MP contamination across a wide range of levels, particularly for PS and PP, which showed low LOD and LOQ values of 0.62 and 2.07, respectively. However, PE exhibited higher LOD (6.66) and LOQ (22.18), indicating less sensitivity for this technique.

In contrast, MIR showed lower performance, with low $R^2$ and high RMSEP (4.7576), along with high LOD and LOQ values, indicating reduced sensitivity for detecting low MP concentrations. PS demonstrated moderate sensitivity (LOD 0.80, LOQ 2.67), while PP, PE, and PET had even higher values. This suggests that the MIR technique may be less effective in accurately predicting MP contamination, likely attributed to lower sensitivity and specificity compared to fluorescence.

On the other hand, the MSI technique performed commendably, with $R^2$ values for the prediction test set ranging from 0.74 to 0.96. The RMSEC for MSI was reported between 0.1676 and 0.9859, while the

<caption>**Table 2**</caption>

Best predictive results after applying PLSR models to predict the concentration of MPs (including PS, PP, PE, and PET) within minced shrimp (LV: Latent variables).

<table><thead><tr><th>Spectral techniques</th><th>MP type</th><th>LV</th><th>R<sup>2</sup><sub>c</sub></th><th>R<sup>2</sup><sub>p</sub></th><th>RMSEC</th><th>RMSEP</th></tr></thead><tbody><tr><td rowspan="5">Fluorescent</td><td>PS</td><td>5</td><td>0.97</td><td>0.98</td><td>0.5827</td><td>1.5903</td></tr><tr><td>PP</td><td>7</td><td>1.00</td><td>0.84</td><td>0.2028</td><td>2.2905</td></tr><tr><td>PE</td><td>2</td><td>0.76</td><td>0.61</td><td>1.66</td><td>2.25</td></tr><tr><td>PET</td><td>7</td><td>0.98</td><td>0.84</td><td>0.44</td><td>2.89</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">MIR</td><td>PS</td><td>11</td><td>0.99</td><td>0.09</td><td>0.2638</td><td>2.2404</td></tr><tr><td>PP</td><td>8</td><td>0.93</td><td>0.09</td><td>0.9192</td><td>4.7576</td></tr><tr><td>PE</td><td>10</td><td>0.94</td><td>0.10</td><td>0.8663</td><td>3.4131</td></tr><tr><td>PET</td><td>5</td><td>0.91</td><td>0.33</td><td>1.0165</td><td>3.5743</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">MSI</td><td>PS</td><td>6</td><td>0.99</td><td>0.96</td><td>0.2484</td><td>1.0193</td></tr><tr><td>PP</td><td>2</td><td>0.92</td><td>0.74</td><td>0.9859</td><td>1.5264</td></tr><tr><td>PE</td><td>3</td><td>1.00</td><td>0.96</td><td>0.1676</td><td>0.4258</td></tr><tr><td>PET</td><td>3</td><td>0.98</td><td>0.95</td><td>0.4590</td><td>0.4796</td></tr></tbody></table>
RMSEP ranged from 0.4258 to 1.5264. It is worth noting that MSI exhibited good sensitivity for PE with a LOD of 0.53, making it a viable alternative for predicting MP contamination overall.

Fluorescence spectroscopy effectively predicts MP contamination in
minced shrimp using PLSR, promising in PS and PP detection (R²_p = 0.98,
0.84; RMSEP = 1.5903, 2.2905). MSI, however, shows strong predictive
performance across all MP types with high R²_p and low RMSEP values
(Table 2). The poor performance of MIR highlights the importance of
choosing suitable analytical methods based on the specific microplastic
types being assessed for accurate detection in food. These insights are
vital for ensuring food safety and compliance with regulatory standards
for seafood products.

4. Conclusion

This study is the first to combine MIR imaging, fluorescence, and MSI with multivariate analysis (PCA, PLSR, PLS-DA) to detect and quantify microplastic contamination in minced shrimp. The study highlights that fluorescence spectroscopy shows superior performance, particularly for specific types of MPs such as PS and PP. It achieves strong correlation and predictive accuracy metrics, with R² values ranging from 0.61 to 0.98 and RMSEP values between 1.59 and 2.89. The high sensitivity of fluorescence compared to other methods makes it the recommended primary technique for detecting these MPs. In contrast, while multispectral imaging is effective for all types of MPs, including PE and PET, it exhibits lower sensitivity compared to fluorescence and mid-infrared techniques in PLSDA. For the most reliable detection of MP contamination in shrimp, the study recommends prioritizing fluorescence spectroscopy, especially when analyzing PS and PP. However, it's important to note that the prediction performance varies depending on the type of MP being analyzed, and fluorescence spectroscopy may take more time than other methods. Incorporating multispectral imaging alongside fluorescence could enhance overall detection capabilities. While this study effectively demonstrates the potential of spectroscopic methods for detecting and quantifying MPs in a controlled food matrix, the experimental setup does not replicate the biological accumulation processes that occur in natural environments. Future research should aim to validate these techniques using seafood samples containing naturally bioaccumulated MPs to better reflect real-world contamination scenarios and enhance the practical relevance of the findings. Furthermore, refining these analytical methods and exploring their broader applications will be critical for advancing food safety and environmental monitoring. A more comprehensive approach—including a wider range of MP types—and the development of standardized protocols will be essential for practical implementation in the seafood industry.

CRediT authorship contribution statement

**Sureerat Makmuang:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Abderrahmane Aït-Kaddour:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Formal analysis, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research project is supported by the Make Our Planet Great

Again (MOPGA) 2023 funded by the French Ministry for Europe and Foreign Affairs, in collaboration with the French Ministry for Higher Education and Research and implemented by Campus France.

**Appendix A. Supporting information**

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jfca.2025.107864.

Data availability

Data will be made available on request.

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