Emerging Health Risks Associated with the Intake of Microplastics Found in Milk and Dairy Products

Abstract

Microplastic contamination in milk and dairy products is an emerging public health concern due to the potential transfer of polymer particles into the human diet. This study aims to assess the health risks associated with the presence of five major polymers, such as poly(methyl methacrylate), polyurethane, polyester, polyethylene, and polyamide, found in a variety of conventional, organic, and raw milk and dairy products. The risk assessment was performed by calculating several indices, including the polymer risk index, concentration factor, daily plastic intake, the chronic daily exposure dose by ingestion, and the plastic risk index. Statistical analyses, including t-test, Pearson correlations, Multilayer Perceptron Analysis, Principal Component Analysis, Scatterplot Matrix, pairwise comparisons, and Multidimensional Scaling, were performed to establish the emerging risks associated with the consumption of contaminated dairy products. The results indicated significant differences in risk parameters between certain product categories, with yogurts, both conventional and organic, showing consistently higher associations with poly(methyl methacrylate), polyurethane, polyester, and polyamide contamination. Strong positive correlations between microplastic concentration and intake-related parameters have confirmed a robust exposure-risk relationship. The exploratory and predictive analyses have revealed product-specific contamination patterns, but no significant association has been observed between product and polymer types. These findings validate the link between microplastic exposure and human health risk and suggest that targeted monitoring of dairy products with high sensitivity is needed to mitigate potential impacts.

1. Introduction

Milk and dairy products are a vital part of the human diet, providing essential proteins, fats, vitamins, and minerals. However, in addition to nutrients, these products may also contain various chemicals, which can be either natural or added during the production process. Recently, Giosue et al. [1] investigated the relationship between dairy consumption and cardiovascular disease, the leading cause of death worldwide. Other research has suggested a possible link between long-term dairy consumption and a higher risk of developing Parkinson’s disease, which may be due to pesticide residues from feed contaminating the milk [2]. Apart from pesticides, other common contaminants, such as heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs), antibiotics, and microplastics, can modify the chemical composition of milk and diminish its health benefits, thus being classified as contaminants of emerging concern (CECs). This is due to their dual role: they not only exhibit high toxicity, which is yet unknown, but also act as carriers for other harmful contaminants from the carcinogen category.

In recent years, microplastics (MPs) have become a major concern for researchers and health experts worldwide. MPs are particles less than 5 mm in size, appearing in various forms such as fibers, flakes, beads, foam, film, or fragments (which can be irregular, angular, rounded, etc.). These tiny particles, known as secondary MPs, result from the breakdown of plastics, are widespread in the environment, so they are often found in water, air, soil, and even in essential foods consumed daily, such as milk and dairy products, meat, vegetables, and fruits. Microplastics can enter the human body through three main pathways: inhalation, ingestion, and dermal exposure [3,4,5]. MPs from the air are inhaled along with dust and other pollutants [6,7]. These tiny particles are small enough to penetrate deep into the lungs and may even enter the bloodstream [8]. Both indoor and outdoor air pollution contribute to this inhalation exposure, originating from sources such as vehicle tire wear, synthetic textiles, and plastic particles found in cleaning products [9,10,11]. On the other hand, MPs are ingested through the consumption of contaminated food and water [12]. They have been detected in a wide range of food sources, including seafood, table salt, bottled water, and even fresh products like fruits and vegetables [13,14]. For instance, plastic particles can leach into food from packaging and the production process [15,16]. Additionally, microplastics can enter in human body through drinking water.

Microplastics can enter the body through the skin, although this route is less studied. Cosmetic and personal care products that contain microbeads, such as scrubs and toothpastes, can allow microplastics to be absorbed through the skin [17,18]. Additionally, plastic particles from household dust can increase the risk of exposure in terms of dermal absorption [19,20,21]. Briefly, once inside the human body, microplastics can be transported through the circulatory and lymphatic systems to various organs and tissues [22]. Recent studies reported the presence of microplastics in blood [23,24], saliva [25], liver [26], kidneys [27], brain [28,29,30], and placenta [31,32]. Winiarska et al. [33] reported that the smallest particles, known as nanoplastics, can penetrate cells and their nuclei, increasing the risk of cellular and genetic damage.

The lack of information on the potentially harmful effects of microplastics on health, especially on the brain, in terms of a direct cause–effect relationship, represents research topics for several studies/projects. Thus, a recent study, conducted by Forutan et al. [34], investigated the impact of low-density polyethylene (LDPE) microplastics, with dimensions < 30 μm, on the integrity of the blood–brain barrier (BBB), oxidative stress, and neuronal health. Although the results did not reveal significant changes in the water content of the subjects’ brains during monitoring, the integrity of the blood–brain barrier was significantly compromised after three and six weeks of exposure, respectively. Oxidative stress increased in the case of subjects exposed to microplastics, compared to controls, and this aspect was highlighted by the decrease in superoxide dismutase (SOD) levels and the increase in malondialdehyde (MDA). Furthermore, the level of Brain-Derived Neurotrophic Factor (BDNF) significantly decreased in subjects exposed for six weeks. Histological analysis indicated neuronal damage and death in both treatment durations. This study highlights the potential neurotoxic effects of microplastics and emphasizes the need for further research to address their possible health risks [34].

Although the toxicity of microplastics in humans has not yet been fully demonstrated, niche studies have shown altered behavioral effects and malformations in animals used in a preliminary clinical trial [35,36]. MPs can function as potential co-transport agents of pollutants, such as heavy metals and antibiotic-resistant genes in the environment, based on Liu et al. review [37]. Mock J. [38] also revealed that microplastics can cause irritation or even introduce toxins when ingested by the human body, and this is strongly related to MPs’ physical and chemical characteristics, including size, shape, length, but also chemical structure. Lately, other research highlighted that MPs can be considered potential carriers for other CECs (e.g., HMs, PAHs, pesticides, antibiotics, etc.), thus increasing the synergy of MPs’ toxicity with that of contaminants adsorbed and enhancing their harmful impact on ecosystems and humans [39,40,41].

The present research aimed to (i) assess the potential risks that may arise from the consumption of milk and dairy products contaminated with microplastics, and (ii) identify correlations that may emerge among the identified risk factors. The studies carried out by Banica et al. over the last few years have been the subject of several articles [42,43,44] and two patents [45,46], which also constituted the starting point in establishing the aforementioned objectives.

Due to the lack of scientific literature and regulations, this study is characterized by a high degree of novelty because it attempts to establish the risk associated with the ingestion of microplastics by determining some risk indices (e.g., polymer risk index, plastic pollution load index, chronic daily intake through ingestion, etc.), but also a daily and annual estimate of the number of microplastics ingested by both adults and children.

This paper makes an original contribution to the field of food safety by investigating the presence of microplastics in milk and dairy products, a relatively new and insufficiently explored topic in the current scientific literature. Unlike most studies that focus on microplastic contamination in drinking water or air, this research focuses on a category of basic foods, consumed in high amounts worldwide, but less analyzed from the perspective of contamination with MPs. The originality of the paper consists of (i) the application for the first time (in the known literature) of machine learning methods (Multilayer Perceptron Analysis, and the Nearest Neighbor analysis) for classifying the dairy samples according to the content of microplastics; (ii) the use of PCA to reduce dimensionality and highlight the main variables that contribute to the differentiation of samples; (iii) the statistical correlation between the type of dairy product and the level of microplastic contamination; (iv) the provision of a relevant data set for the assessment of potential risks to consumer health. Thus, the research positions itself at the intersection of traditional statistical analysis, food science and technology, and machine learning, proposing a robust methodology, with applicability in monitoring the quality of dairy products and in developing strategies to reduce their microplastic contamination.

In the context in which the specialized literature predominantly deals with microplastic contamination in aquatic products and drinking water, their analysis in the complex matrix of dairy products is insufficiently explored. The work makes a significant contribution to the field, providing both relevant experimental data and a robust and replicable analytical approach.

2. Materials and Methods

2.1. Materials

To achieve the two objectives of the present study, previous research by Banica et al. [42,43,44] contributed remarkable results for a large sample of milk and dairy products. The results were obtained by applying the isolation protocol that was the subject of two patent applications [45,46]. Thus, in previous research, 20 milk samples were analyzed [42], 16 yogurt samples [43], and the third study focused on high-fat dairy products, and 11 butter samples were analyzed [44]. Figure 1 clearly shows the selection of milk and dairy product samples analyzed in previous research [42,43,44] in terms of sample type (i.e., milk, yoghurt, sour cream, and butter), category (i.e., conventional, organic, or raw), fat content (FAT), and packaging type.

2.2. Methods

In the statistical field, many techniques and tools have been developed for data analysis. A crucial component of conducting research or statistical analysis is data analysis and detection/inference. There are several statistical packages for this purpose, and IBM SPSS is one of the statistical programs [47,48]. Statistical analyses were performed by IBM SPSS Statistics v. 26, including t-test, Pearson correlations, Multilayer Perceptron Analysis (MPA), Principal Component Analysis (PCA), Scatterplot Matrix, pairwise comparisons, and Two-Dimensional Common Space (MDS). Statistical analysis was applied to dairy-related risk parameters on human health.

3. Data Analysis

To sustain a healthy life, food is essential for ensuring the harmonious growth and development of humanity [49]. Microplastics (MPs) are currently the focus of researchers due to the rapid pollution of marine and terrestrial ecosystems and implicitly the food industry, but also because of the humans and ecotoxicological harms they cause [42,49,50,51].

Ingestion of microplastics is the main route of human exposure, and risk factors are presented in the following sections. The pollution loading index (PLI) with microplastics and the estimated daily dose (EDI) were calculated in previous research conducted by Banica et al., as follows: for conventional, organic, and raw milk [42], for conventional and organic yogurt [37], and conventional and organic butter and sour cream [44].

The polymer risk index (H) of MPs identified in the analyzed milk and dairy product samples was calculated based on Equation (1) [52,53].

Table 1. Hazard level and risk factors of polymers [52,54].

Polymers	Abbreviation	Si	Hazard Level
Poly(methyl methacrylate)	PMMA	1021	IV
Polyamide	PA	50	II
Polyurethane	PU	13,844	V
Polystyrene	PS	30	II
Polyethylene	PE	11	II

presents the risk factors for polymers identified in the analyzed samples and the hazard level according to the research conducted by Lithner et al. [54].

$$\mathrm{H}=\sum P_i·S_i$$

(1)

where P_i represents the percentage of polymer identified in the analyzed fragment (expressed in %) [42,43,44], and S_i is the risk factor of the polymers established by Lithner et al. [54].

The pollution load index (PLI) of MPs is closely related to the concentration factor (CF_i), which is the ratio between the minimum average polymer concentration (C_i) expressed as n·kg⁻¹ and the reported minimum average concentration (C₀) of MPs (1.68 n·kg⁻¹) [52]. CF_i was calculated based on Equation (2).

$${\mathrm{C}\mathrm{F}}_{\mathrm{i}}={\displaystyle \frac{C_i}{C_0}}$$

(2)

The estimated annual intake (EAI) of microplastics was calculated based on the consumption of milk and dairy products for both adults and children, using Equation (3) [55].

$$\mathrm{E}\mathrm{A}\mathrm{I}=C_i·AIR$$

(3)

where AIR represents the annual rate of consumption of milk and dairy products.

Table 2. Recommended daily intake for milk and dairy products [42,43,44].

Category	Daily Consumption Rate (Ir)
	Milk [L·day−1]	Yogurt [kg·day−1]	Sour Cream [kg·day−1]	Butter [kg·day−1]
Adults	0.750	0.304	0.260	0.130
Children	0.625	0.407	0.200	0.100

presents the recommended daily consumption rate for adults and children, based on which AIR was calculated.

Daily plastic intake (DPI) is a critical concern in medicine and research [56]. DPI was calculated by applying Equation (4) presented by Binelli et al. [57]:

$$\mathrm{D}\mathrm{P}\mathrm{I}=I_r·C_i$$

(4)

To establish the plastic pollution load index (PCF), Equation (5) was applied [58] as follows:

$$\mathrm{P}\mathrm{C}\mathrm{F}={\left(\prod {\mathrm{C}\mathrm{F}}_{\mathrm{i}}\right)}^{{\displaystyle \frac{1}{n}}}$$

(5)

The plastic risk index (PRI) from microplastic (MPs) pollution was calculated using Equation (6) [52,54,59].

$$\mathrm{P}\mathrm{R}\mathrm{I}=\sum \left({\displaystyle \frac{C_i}{P_t}}·S_i\right)$$

(6)

where P_t is the total number of MPs fragments identified in milk and dairy product samples.

To calculate the chronic daily intake through ingestion (CDI_ng), Equation (7) was used [23,60].

$${\mathrm{C}\mathrm{D}\mathrm{I}}_{\mathrm{n}\mathrm{g}}={\displaystyle \frac{C_i·I_r·E_d·E_f}{B_w·A_t}}·CF$$

(7)

where C_i is minimum average polymer concentration; I_r is the daily rate of ingestion of dairy products (Table 2); E_d represents the duration of exposure in years (70 years for adults and 14 years for children); E_f is the frequency of exposure to microplastics (expressed as day·year⁻¹); B_w is the body weight expressed in kilograms (70 kg for adults and 14 kg for children); A_t represents the average exposure time in days; and CF is the conversion factor (1 × 10⁻⁶).

4. Results and Discussion

The toxicity of microplastics has been established by performing specific risk calculations according to data published by Banica et al. in previous research [42,43,44]. Several analytical techniques, including optical microscopy (OM) and the micro-Fourier Transform Infrared spectroscopy (micro-FTIR), were used to identify, quantify, and characterize the MPs in terms of their chemical composition, shape, and size [42,43,44]. Briefly, the total number of MPs identified in the four product categories (milk, yogurt, sour cream, and butter) in terms of the chemical structure of monomers, as a specific unit of polymeric chain, is shown in Figure 2.

According to Figure 2, the most common polymers in the analyzed samples were as follows: PMMA > PA > PU > PS > PE. The mentioned polymers may appear from (i) exfoliation of plastic materials from the surface of packaging used for finished products (milk, yogurt, cream, and butter); (ii) polymer debris, it being well known that cows feed on almost anything (e.g., accidental intake of plastic packaging); and (iii) textile materials from work equipment, or from their use in the process of sanitizing/cleaning/drying animal udders, etc. [42,43,44].

4.1. Risk of Microplastic Intake

According to research conducted by Lithner et al. [54], an H value for milk between 337.4 and 463.0 indicates a medium health risk. In particular, an H value below 337.4 represents a low health risk, and an H value above 463.0 a high health risk. In this regard, Lithner et al. [54] established the five risk levels (very low (≤1), low (≤10), medium (≤100), high (≤1000), and very high (≤10,000) based on H values.

Taking into account the data obtained in the current study, it can be mentioned that the highest H value calculated for polymers identified in milk samples (Table S1) was found in L₂ (i.e., H = 1,408,920.00), and the lowest value was in sample L₆B (i.e., H = 1250.00). According to the H values from Table S1, it can be concluded that these exceeded the limit of H = 1000, specified by Lithner et al. [54], indicating a very high-risk level.

Five polymers (i.e., PA, PU, PMMA, PS, and PE) were identified in the conventional and organic yogurt samples (Figure 2), and for each of them, the H values were calculated (Table S4). According to data shown in Table S2, the highest H value was obtained in the conventional yogurt sample I₁₀ (i.e., H = 1,425,240.00), and the lowest value in the conventional yogurt sample I₅ (i.e., H = 1250.00). PA was not identified in sample I₁₀, PU was identified in five samples, PMMA was absent in four samples, PS was absent in eight yogurt samples, and PE was identified in sample I₉. Yogurt samples I₅ and I₁₁B do not exceed the high health risk level, while the other samples show H values ≤ 10,000, but also values > 10,000 (Table S2).

According to data shown in Figure 2, poly(methyl methacrylate), polyurethane, and polyamide are the three polymers identified in the high-fat dairy samples (sour cream and butter). Considering the data presented in Table S3, it can be noted that polyamide was absent from the sour cream samples but was identified in five conventional and organic butter samples. For the two sour cream samples, the H values are >10,000, indicating a very high-risk level in terms of polymer amount. U₅ recorded the highest value for the risk index (i.e., H = 132,193.00), and U₂ the lowest value (i.e., H = 17,236.00). PU was identified in one butter sample (U₇), PMMA was identified in all high-fat samples, and PA was absent in both sour cream and butter samples. The eleven butter samples have H values > 10,000, indicating a very high-risk level.

The inclusion of a polymer in a high-risk class does not always indicate that it is also harmful. The harm degree of a polymer only indicates whether it is composed of monomers or has additives in its composition that are dangerous to humans, especially after intake. The additives from the polymer composition can be released during the dairy obtaining process through mechanical or thermal decomposition.

The harm degree assigned to each polymer in the research conducted by Lin et al. [52] and taken up in this study represents a rough classification, and the value determined for H may not accurately reflect the absolute differences in danger. Only by calculating the risk index for the analyzed samples can it be said exactly which category and which milk and dairy products are dangerous for consumption.

The MPs intake through contaminated food and the risks associated with their concentration have led to the establishment of well-defined risk categories [61,62]. According to values of concentration factor (CF_i), Ferguson et al. [62] established four risk classes, such as (i) low for values less than 1, (ii) moderate for values between 1 and 3, (iii) high for values from 3 to 6, and (iv) very high for values higher than 6.

The MPs concentration coefficient in milk and dairy product samples is presented in Figure S1a–c. The concentration factor of MPs in the three milk categories was between 0.595 and 4.167 for conventional milk and 0.595 and 3.571 for organic milk; for conventional milk, the minimum value was 1.785, while the maximum was 2.976. The values determined for the CF_i grouped the conventional and organic milk samples into three risk categories: low, moderate, and high. The raw milk samples were included in the moderate risk category, due to the values ranging between 1 and 3.

The yogurt, sour cream, and butter samples have been categorized as posing a dangerous risk based on their calculated concentration factors. Taking into account previous and current studies demonstrating that MPs tend to adhere to the surface of fat globules and migrate with milk fat due to density, processed dairy samples (yogurt, butter, and sour cream) were classified as hazardous due to the large number of MPs identified in the analyzed samples [44]. Hydrophobic interactions between MPs and fat globules are attractive interactions between two hydrophobic components in a water-mediated environment [63].

The estimation of the annual exposure to MPs through exposure to contaminated food is essential (

Table 3. Estimated annual intake of MPs from milk and dairy products.

Dairy Products	Average EAI [MPs·year−1]
	Adults	Children
Conventional Milk	889.69	741.41
Organic Milk	730.91	609.09
Raw Milk	1095.00	912.50
Conventional Yogurt	283,704.55	236,420.45
Organic Yogurt	246,375.00	205,312.50
Sour Cream	54,750.00	45,625.00
Conventional Butter	175,371.09	146,142.58
Organic Butter	205,312.50	171,093.75

) due to the toxic effects that plastics have on humans. MPs tend to accumulate mainly in the lungs, heart, adipose tissue, kidneys, liver, but also in other organs [64,65,66].

The largest amounts of MPs are ingested per year through yogurt consumption. Children are most exposed to ingesting 236,420.45 MPs·year⁻¹ from conventional yogurt and 205,312.50 MPs·year⁻¹ from organic yogurt, while adults ingest 283,704.55 MPs·year⁻¹ from conventional yogurt and 246,375.00 MPs·year⁻¹ from organic yogurt. MPs in large quantities in unprocessed milk can come from cattle feed.

In this regard, Urli et al. [67] raise an alarm regarding the use of plastics in agriculture, which favors the penetration of MPs into animal feed and the transfer of MPs from animals to humans. The research conducted by Urli et al. [67] highlights the presence of MPs in fertilizers used in agriculture and horticulture and the transfer of plastics to soil and water. Plant products (fruits and vegetables) [55], but also plant materials (e.g., grass, cereals, etc.), are the main food of animals and can be another source for the presence of MPs in milk processed products. Atmospheric deposition and airborne particles represent another pathway of MPs intake as well [67,68].

On the other hand, large factories that receive quantities of milk greater than their production capacity apply two milk storage techniques: (i) temporary storage of milk in specially constructed storage tanks; (ii) concentration of milk to obtain powdered milk and its rehydration when it is to be processed. In the research carried out by Da Costa Filho et al. [69], several samples of raw milk, branded milk, and reconstituted powdered milk were analyzed, and the reported results show that most MPs are found in the samples of reconstituted powdered milk. Given that some factories use reconstituted powdered milk in the technological process of obtaining yogurt, it can be considered that the high values calculated for the estimated annual intake of MPs in this category of dairy products can be attributed to the quality of the drinking water used in the rehydration of powdered milk.

After establishing the estimated annual intake of MPs from milk and dairy products, the next calculated index was the daily plastic intake (DPI) using Equation (4). The obtained results are presented in Figure S2a–c.

The recommended daily consumption of milk is 0.750 L·day⁻¹ for adults and 0.625 L·day⁻¹ for children [42]. For conventionally processed milk samples, an average daily plastic intake of 2.438 ± 1.37 n·day⁻¹ for adults and 2.031 ± 1.14 n·day⁻¹ for children was determined. In addition, for organic milk, the average daily plastic intake was 2.00 ± 1.47 n·day⁻¹ for adults and 1.67 ± 1.29 n·day⁻¹ for children. In the case of raw milk, an average DPI of 3.00 ± 0.61 n·day⁻¹ for adults and 2.50 ± 0.51 n·day⁻¹ for children was determined. Binelli et al. [57] reported that the average daily plastic intake was 0.78 particles·mL⁻¹ in milk samples. The DPI values obtained in the present study are three times higher than the results reported by Binelli et al. [57], but the recommended daily milk consumption (I_r) value used in Equation (4) is six times lower, resulting in a double real concentration per L of milk.

The average daily plastic consumption at a daily yogurt consumption of 0.304 kg·day⁻¹ was determined to be 315.055 ± 27.04 n·day⁻¹ for adults, for conventional yogurt, and 273.600 ± 137.30 n·day⁻¹ for organic yogurt, respectively. In the case of children, the daily yogurt consumption is 0.407, and the average daily plastic consumption was calculated to be 421.800 ± 370.91 n·day⁻¹ for conventional yogurt and 366.300 ± 183.82 n·day⁻¹ for organic yogurt, respectively. Higher values were obtained in the case of children than in adults, and a possible explanation may be the larger amount of yogurt recommended for daily consumption. For processed high-fat dairy products, daily intakes of sour cream and butter are presented in Table 2. Daily plastic intake was calculated for two samples of sour cream, 5.200 n·day⁻¹ for adults and 4.000 n·day⁻¹ for children. For adults, in the case of butter intake, the minimum value of DPI was 8.328 ± 4.87 n·day⁻¹ (i.e., conventional butter) and 9.750 ± 4.87 n·day⁻¹ (i.e., organic butter), while for children, the minimum DPI was 6.406 ± 2.70 (conventional butter) and 7.500 ± 3.75 (organic butter). No risk calculations for processed dairy products (i.e., yogurt, sour cream, and butter) have been reported in the scientific literature, which means that they cannot compare the obtained results with others.

The plastic pollution load index (PCF) was calculated for milk and processed product samples. The contamination levels and risk categories for PCF were established by Ibeto et al. [70] and adapted by Binelli et al. [57]. The classification of the analyzed samples was carried out based on the PCF values, and the five levels of stability risk by Ibeto et al. [70] were (i) very low hazard (≤1); (ii) low (≤10); (iii) medium (≤100); (iv) high (≤1000); and (v) very high (≤10,000). For the three milk categories, the PCF values were <10 (1.68 for conventional milk, 1.27 for organic milk, and 2.34 for raw milk) and present a very low risk level. For the sour cream samples, the PCF was slightly higher than 100 (119.04 for sour cream). The highest PCF values were recorded for yogurt (481.14 for conventional yogurt and 472.71 for organic yogurt) and butter (353.23 for conventional butter and 405.60 for organic butter), which categorize the samples as medium-high risk.

In current research, the plastic risk index was also calculated for the polymers identified in the analyzed samples. Each polymer has a risk rating established by Lin et al. [52] in their research: PU has the highest risk factor (S_i = 13,844), followed by PMMA (S_i = 1021), PA (S_i = 50), PS (S_i = 30), and PE (S_i = 10). The results obtained are presented in Figures S3–S5, and the calculations were performed based on Equation (6) according to Figure 2.

The PRI was calculated for each polymer according to the risk factor (S_i). Summing the polymers identified in a sample, the highest plastic risk index was calculated in sample L₁ (6947.00), while the lowest risk index was identified in sample L₆B (50.00).

The risk categories for PRI established by Enyoh et al. [58] and taken over by Binelli et al. [57] helped to classify the 18 milk samples as follows: sample L₆B has a low risk, and samples L₅, L₉, and L₁₀B present a considerable risk. Samples L₄, L₁₁B, and raw milk samples (LF₁, LF₂, LF₃, and LF₄) have a high risk, while samples with PRI > 1200 present a very high risk. Eight of the eighteen milk samples analyzed present an alarming risk; the values obtained ranges from 2138.38 to 6947.00. The average PRI of the 18 samples analyzed (Figure S3) is 2009.46. Binelli et al. [57] reported an average value of 795.10 for the 11 milk samples analyzed. Basaran et al. [59] reported an average PRI of 255 in the 14 samples analyzed. The values reported by Binelli et al. [57] are almost twice as high, and the results reported by Basaran et al. [59] are almost eight times higher than those reported in the present study.

The PRI for the processed yogurt samples analyzed was calculated for the five identified polymers, on the same structure described for the milk samples. The results obtained are presented graphically in Figure S4. The lowest PRI value was identified in sample I₄ (PRI = 40.00), where PA and PS polymers were identified, and the highest PRI value was identified in sample I₁₀ (PRI = 9569.67), where PMMA, PA, and PU polymers were identified. Using the same risk categories described by Binelli et al. [57] for the milk samples analyzed in their study, the yogurt samples analyzed in this study fall into all five risk classes. Three analyzed samples present a low risk with PRI values lower than 150. Four yogurt samples had PRI values between 150 and 300, and were placed the samples in the medium risk class. PRI values between 300 and 600 were identified in three yogurt samples and present a considerable risk (Figure S4).

Two yogurt samples recorded PRI values between 600 and 1200, which classifies the samples in the high-risk class. Six yogurt samples have PRI values higher than 1200 and a very high risk. The average PRI value for the 17 yogurt samples is 1726.34. In the literature, a study by Ling et al. [71] analyzed fruit yogurt, and the risk index of the plastics identified in the analyzed samples was lowered by a PRI value equal to 16. However, it used different values for the toxicity coefficient of PE and PS polymers, so we could not compare the results obtained (Figure S4).

PA, PMMA, and PU are the three polymers identified in the high-fat dairy samples (cream and butter) and for which the PRI was calculated (Figure S5). Since there are no studies in the literature for butter and cream, it was chosen to classify the samples and establish the risk level according to the criteria presented by Binelli et al. [57].

PMMA was identified in only two sour cream samples (S₂ and S₆) and the calculated PRI value was 1021, which means that the samples present a medium risk. The U₁ sample poses a considerable risk. Eight conventional and organic butter samples present a high risk, with PRI values ranging from 600 to 1200. Conventional butter samples U₅ and U₇ obtained PRI values >1200, classifying the two samples as having a very high risk (Figure S5).

Before assessing the potential risk to human health of a product, the route of exposure and the degree of risk must be established [72]. To establish the chronic daily intake dose, the following factors must be taken into consideration: ingestion rate, duration of exposure, body weight, and frequency of exposure. Since the specialized literature does not provide minimum and maximum limits for PRI, the authors proposed specific limitations and risk levels for milk and dairy products. These levels are outlined in

Table 4. Risk levels for the plastics risk index.

Risk Levels	CDIng
Very low risk	<10−8
Low risk	10−8
Medium risk	10−7
High risk	10−6
Very high risk	≥10−5

and apply to both adults and children.

The CDI_ng was calculated for the intake of MPs, for both adults and children, through milk and processed dairy products (Tables S4–S6). Children are more exposed than adults to chronic exposure to MPs through ingesting the milk samples analyzed. Even though children are more exposed due to their body weight, the values obtained are low. According to the limitations and thresholds suggested by the authors of this paper, the milk samples fall into the medium risk (10⁻⁷) and low risk (10⁻⁸) levels (Table S4).

The daily consumption of yogurt for children up to I₄ years of age is 0.407 kg·day⁻¹, which is higher than that of adults, where the daily consumption is 0.304 kg·day⁻¹. The CDI_ng values for children are higher than those obtained for adults, and the risk level for children is very high. In the case of adults, the CDI_ng values fall within the high-risk level. The daily dose of exposure to MPs in the case of yogurt samples raises concerns due to the abundance of MPs identified in the analyzed samples. We note that samples I₁₁ and I₁₁B are part of the category of samples intended for children (Table S5).

The daily chronic exposure dose to MPs was also calculated for processed dairy products with high fat content. A low (10⁻⁸) and medium (10⁻⁷) risk level was established in most samples analyzed. In the case of children, the CDI_ng values place all high-fat dairy samples in the medium risk level. According to Table S6, there are exceptions where the risk level is medium for children and adults in the butter samples analyzed (U₃, U₇, U₈, U₆B, and U₇B).

4.2. Statistical Correlations Between Risk Parameters

To assess the relationships between the studied variables and to test the proposed research assumptions, a series of statistical analyses was performed. Based on the research objectives, hypotheses were formulated as follows:

H1:

There are significant differences among the dairy product samples regarding the risk parameters.

H2:

The types of microplastics are associated with specific types of dairy products.

H3:

There are strong and significant relationships between microplastics and the risk parameters for human health.

A t-test was used to determine if the dairy products were different in terms of the associated risk parameters. In the analysis, all samples were taken into account, including those that have at least one characteristic in common, such as organic/conventional features. Thus, conventional and organic milk were different with p values < 0.05 in terms of H risk indices. Regarding the H risk indices and PRI, different values were obtained for the three analyzed milk categories (i.e., conventional, organic, and raw).

The t-test displayed differences (p < 0.05) between conventional milk and the other conventional dairy products, namely yogurt, sour cream, and butter, mainly in the last case (milk/butter) for all dependent variables. This result may be due to differences in the chemical composition of the two dairy products, and also the daily amounts in which each of them is consumed. It remains that the next stages of analysis and further research are needed to confirm or deny this statement. Additional information could be needed, especially in reference to the sources of potential contamination of certain dairy products with microplastics, because organic milk and organic butter were different (p < 0.05) only in terms of PRI.

The groups’ conventional milk and conventional yogurt were different (p < 0.05) in terms of CF_i, DPI_adults, DPI_children, CDI_ng_adults, and CDI_ng_children. If organic milk and organic yogurt are compared, these two groups were different (p < 0.05) in terms of CF_i, DPI_Adults, DPI_Children, CDI_ng_Adults, and CDI_ng_Children, respectively. The other groups of samples were not different (p > 0.05) when their associated parameters of risk were analyzed by a t-test.

The differences between the average values of the risk factors in relation to both dependent variables (dairy product and microplastic type) are shown in Figure 3.

The risk index of polymers (H) had close values for PA, regardless of the analyzed sample. Within the group of samples, high values of H were determined for conventional milk containing PE and PS, respectively. Significant values of this parameter were also determined for conventional yogurt in relation to PU and PMMA, respectively. The concentration factor (CF_i) reached the highest value in the case of the organic yogurt containing PE. As shown in Figure 3 (CF_i graph), which is correlated with Figure S1b, all five microplastics were detected in this product (i.e., organic yogurt), despite its organic label. The allure of graphics showing the DPI values was similar for both adults and children. The results of the independent samples t-test partially support Hypothesis 1, indicating that significant differences (p < 0.05) exist among certain dairy product samples with respect to the analyzed risk parameters. Specifically, conventional milk differed significantly from conventional yogurt, sour cream, and butter, with the milk–butter comparison showing significant differences for all dependent variables. Similarly, organic milk and organic yogurt differ significantly in terms of CF_i, DPI_Adults, DPI_Children, CDI_ng_Adults, and CDI_ng_Children. However, no significant differences (p > 0.05) were observed for other sample group comparisons, suggesting that the effect is product-dependent rather than consistent across all dairy types.

Further analysis should establish if the packaging and/or the technological process could be responsible for the determined values of the risk factors mentioned above. Implementation of the traceability system could be useful in this sense.

The Chi-Square test did not indicate a significant association between sample type and contaminant type, χ²; (28, N = 109) = 24.37, p = 0.662. This result suggests that the distribution of microplastic types does not differ significantly depending on the type of dairy product sample.

However, there are interesting tendencies provided by the Chi-Square test. Thus, the total PU sample (hazard level V) is distributed as follows: conventional milk 3.7% = conventional yogurt 3.7% > organic milk 2.8% > organic yogurt 1.8% = conventional butter 1.8% > raw milk 0.0% = sour cream 0.0% = organic butter 0.0%. PMMA (hazard level IV) is distributed as follows: conventional yogurt 7.3% = conventional butter 7.3% > conventional milk 5.5% > organic milk 4.6% = organic yogurt 4.6% > raw milk 3.7% > organic butter 2.8% > sour cream 1.8%.

Large deviations between observed count and expected count were determined using the Chi-Square test in the case of PS/raw milk (deviation = + 9), PA/sour cream (deviation = + 7), and PMMA/sour cream (deviation = + 6). This suggests that there may be a relationship (association) between the two variables.

In other cases, the deviation is small; thus, there is probably no association, and the difference is just a random fluctuation.

Cramer’s V is a measure of the strength of the relationship between two nominal variables (in this case, the dairy product and the microplastic type). According to the Chi-Square test, the Cramer’s V value of 0.236 indicates a weak association between the two variables. Even if p > 0.05, a large Cramer’s V value may indicate an interesting pattern, but with a sample size too small for statistical significance.

The Pearson correlation (

Table 5. Pearson correlations.

Variables	H	CFi	DPI_Adults	DPI_Children	PRI	CDIng_Adults	CDIng_Children
H	1	−1.22	−0.82	−0.81	0.491 **	−0.81	−0.81
CFi		1	0.936 **	0.934 **	−0.148	0.936 **	0.934 **
DPI_Adults			1	0.728 **	−0.95	0.933 **	0.795 **
DPI_Children				1	−0.94	0.875 **	0.925 **
PRI					1	−0.95	−0.94
CDIng_Adults						1	0.658 **
CDIng_Children							1

** Correlation is significant at the 0.01 level (two-tailed).

) matrix revealed several strong and statistically significant (p < 0.01) associations among the analyzed variables. A strong positive correlation was observed between CF_i and DPI_Adults (r = 0.936), as well as between CF_i and DPI_Children (r = 0.934), indicating that the concentration factor of ingested microplastics is directly proportional to the daily intake estimates for both adults and children. DPI_Adults and DPI_Children were also strongly correlated (r = 0.728), reflecting the shared dependency of these intake parameters on the measured microplastic levels.

Furthermore, both DPI_Adults (r = 0.933) and DPI_Children (r = 0.875) were strongly associated with CDI_ng_Adults, while DPI_Children showed an even stronger relationship with CDI_ng_Children (r = 0.925). These results emphasize the interdependence of exposure metrics derived from the same microplastic contamination data.

A moderate positive correlation was found between PRI and H (r = 0.491), suggesting that higher hazard values are associated with increased potential risk indices for human health. No significant correlations were observed between PRI and the ingestion-related parameters (CF_i, DPI, and CDI_ng), suggesting that the risk index is influenced more by toxicity-related factors than by direct exposure quantities.

Multilayer Perceptron Analysis (Figure 4) further supported the findings of the Pearson correlation by identifying the dairy products most strongly associated with high DPI_Adults values in relation to the presence of PA, PU, PMMA, and PS microplastics. The normalized importance values indicated that both conventional and organic yogurt had the greatest contribution to elevated DPI_Adults, followed by conventional milk, whereas other dairy products showed much lower scores of importance. This consistent pattern suggests that yogurt, regardless of production system, is the dairy product most susceptible to microplastic contamination, thereby reinforcing the evidence of product-dependent variability in exposure parameters observed in the correlation analysis.

The microplastics associated with high DPI_Adults values were the following: PA, PU, PMMA, and PS, respectively, in both conventional yogurt and organic yogurt. The data confirm, once again, that yogurt was the dairy product most susceptible to microplastic contamination.

The descriptive statistics revealed that the variance as a measure of the data variability ranged in large limits, except for the pollution load index (PLI). Taking into account the relationship existing between PLI and PCF (the plastic pollution load index of each milk sample), it is obvious that the number of plastics revealed in each milk sample has a high impact on the chemical risk of MPs, beyond their chemical composition.

The histograms are useful in representing the data distribution, highlighting how well they fit a normal distribution curve. Figure 5 shows that the data are inhomogeneous in terms of chemical risk of MPs, parameters such as H and PRI registering large asymmetries to the right. Thus, a total of 25% of the analyzed samples are “concentrated’’ in the domain of the highest values of PRI determined.

The histograms of the estimated daily intake for MPs show a greater symmetry of EDI (adults) than EDI (children). Consumption of dairy products varies not only in the amounts ingested but also in the types of food products, as discussed for adults and children. This may explain why 60% of samples are clustered in the median range of EDI values for adults, while only 34% are in that range for children.

The comparative analysis of the distribution of PRI values corresponding to conqualitynd organic products, such as those consumed frequently and in high amounts by people, namely milk and yogurt (Figure 6), reveals that despite the legislation applicable to the ecological production and the related conditions imposed on it, the organic dairy products contain MPs with plastic risk scores that should be a severe focus of the quality control in dairy factories.

The boxplots show measures of central tendency and dispersion of data, detecting extreme values or outliers. To better understand the differences between the conventional and organic dairy products in terms of the chemical risk of MPs, the boxplots of PRI and CFi are designed (Figure 7).

The median value of PRI is lower in the case of organic milk than in the case of conventional milk, but if yogurt and butter are discussed, the situation is opposite. The outliers range over a large domain of values in the case of conventional milk. Certainly, the number of analyzed samples associated with each type of dairy product could mitigate the results of the statistical analysis. The overview of the CF_i and PCF values underlines an interesting aspect: high concentration factor values are associated with samples such as butter and yogurt (conventional and organic too), and not milk. This can be due to the potential protective role of fat against MPs in the case of butter and acidity in the case of yogurt. The pollution load index (PLI) reveals that sour cream and organic butter are the samples characterized by values of this indicator much bigger than 10, indicating risk I.

The boxplots’ analysis could be useful to observe the influence of the packaging material on some parameters determined for evaluation of the chemical risk of MPs (Figure 8).

The PRI values are higher if the milk is packed in Tetra Pak than in glass. The glass seems to be also a suitable material for decreasing the chemical risk of MPs associated with yogurt compared with plastic. Comparatively analyzing the estimated daily intake for MPs, for both adults and children, the conclusion previously mentioned, referring to the potential protective role of some compositional chemical compounds from dairy products towards MPs, is underlined. In addition, the EDI_children values related to yogurt packed in plastic bring back to attention the importance of eliminating plastic as a packaging material in the food industry, because on one hand, the plastic could be a source of MPs, and on the other hand, its interaction with the food matrix could be a source of risk in terms of contamination with MPs.

Factor Analysis was performed to establish a descriptive model for grouping parameters related to the chemical risk of MPs. Principal Component Analysis, selected as the extraction method, showed that the three factors obtained after rotation—namely, PC1, PC2, and PC3—accounted for 75.54% of the data variance (

Table 6. Factor loadings (Varimax normalized) using principal component extraction.

Rotated Component Matrix a
	1	2	3
Eigenvalue	6.54	1.44	1.07
Cumulative variance (%)	52.42	64.91	75.54
H	−0.061	−0.574	−0.058
PRI	−0.180	−0.722	−0.055
CFi	0.918	0.214	0.092
PLI	0.300	0.103	−0.530
PCF	0.918	0.214	0.092
DPI_adults	0.988	−0.003	0.049
DPI_children	0.988	−0.006	0.049
CDIng_adults	0.988	−0.003	0.049
CDIng_children	0.988	−0.006	0.049
EDI_adults	0.444	0.255	0.736
EDI_children	0.603	0.027	0.643

^a Rotation converged in four iterations.

, Figure 9). PC1 explained 52.42% of the variance, while PC2 explained 12.49% and PC3 only 10.62%.

PC1 explained a high percentage of the total variance, the parameters CF_i, PCF, DPI_adults, DPI_children, CDIng_adults, and CDI_children being located in its positive part, in agreement with the results of the Pearson analysis. The plastic risk index (PRI), located in the negative part of PC2, is correlated with the polymer risk index (H). The third component discriminates the dairy products according to the values of the pollution load index of MPs (PLI) and estimated daily intakes for adults and children, respectively.

The PCA binning led to the separation and sorting of the samples into bins. Based on the discrete version of the continuous variables, it could be suggested that the binning finds the most suitable parameters to be determined to evaluate the chemical risk of MPs from the selected dairy products. Thus, H and PRI were retained (PC2), while CFi, PCF, DPI_adults, DPI_children, CDIng_adults, and CDIng_children from PC1 would be unrelated to the differentiation of the samples. According to the equations used for indices calculation, the EDI_adults and EDI_children should be correlated with DPI_adults, DPI_children, CDIng_adults, and CDIng_children from PC1; the PC3 binning can be explained by the different values taken into account: years (Ed and At) and body weight (Bw). The significance of the factors associated with the chemical risk of MPs must be deeply understood.

The Nearest Neighbor analysis was applied to classify both the experimental data (H, PRI, CFi, PLI, and PCF) and the values of descriptors associated with human health (DPI, CDIng, and EDI). The variable Dairy Product was chosen as the Focal Case Identifier. Depending on the parameter of interest in terms of MPs risk, the samples were grouped as can be seen in Figure 10, showing the distances between samples in terms of each risk parameter.

The parameters were plotted in a multidimensional space, the neighboring points being found in the frame of a total of 18 predictors that classify the experimental data. Figure 11 was analyzed to produce the Peers Chart for different vectors. The Peers Charts show the distance metrics between the dairy products if different vectors are discussed.

Thus, the peers with high H values were the conventional milk L₁, the organic milk L₁B, and the conventional butter U₇. At the opposite part of the chart are the located samples I₇ (conventional yoghurt) and U₈ (conventional butter). PLI exhibits a large distribution of the samples on vertical, from organic yogurt I₁₁B to organic milk (L₁₀B and L₁₁B). The Peers Chart related to the parameters characterizing the people’s health shows that some conventional samples of milk (L₆, L₇, and L₈) are neighbors of organic milk (L₁₁B) in terms of DPI_adults. Interesting, if DPI_children is discussed, neighbors such as butter (U₈), conventional milk (L₄), organic milk (L₁B), and raw milk (LF₄) are unveiled, due to the recommended quantities taken into account.

Overall, the integrated statistical approach—encompassing Pearson correlation, Chi-Square testing, correspondence analysis, and Multilayer Perceptron modeling—demonstrated robust and significant associations between microplastic concentrations and human health risk parameters, thereby supporting Hypothesis 3. Although the Chi-Square test did not confirm a statistically significant overall association between dairy product type and microplastic type, consistent patterns emerging from correspondence analysis and predictive modeling, particularly the recurrent susceptibility of both conventional and organic yogurt to PA, PU, PMMA, and PS contamination, provide partial empirical support for Hypothesis 2 and highlight product-dependent variability warranting further investigation.

4.3. Statistical Correlations Between Risk Indices

For a better understanding, the statistical analysis was extended in terms of risk indices correlations. Based on the objectives of the study, the following research hypotheses were formulated, targeting the relationships between milk and dairy product consumption, the presence of MPs, and the potential impact on human health, both among children and adults. The proposed hypotheses are

H4: Exposure indices (EDI, EAI, DPI, and CDIng) are influenced by decontamination indices (PLI, PCF, and CFi) depending on the age category (i.e., adults and children).

H5: Exposure indices are dependent on global risk indices (H and PRI), depending on the age category (i.e., adults and children).

Principal Component Analysis (PCA) is a multivariate statistical technique used to reduce the dimensionality of a complex data set while preserving as much of the original variability as possible. PCA transforms the original, correlated variables into a reduced set of uncorrelated principal components, ordered by the variance explained. This method allows the identification of latent structures in the data and the clustering of variables with similar behavior. In this study, PCA analysis was applied to explore the relationships between risk indices associated with the presence of microplastics in the dairy products analyzed. Principal Component Analysis (PCA) with Varimax orthogonal rotation led to the extraction of three major components, which together explain a significant proportion of the total variance of the data. Table S7 and Figure S6 highlight the clear clustering of variables on three dimensions. Additionally, the Multidimensional Scaling (MDS) was used to represent the relationships of similarity or dissimilarity between variables in a low-dimensional space. By projecting the risk indices associated with microplastics onto a two-dimensional plane (Figure S7), MDS allows the identification and visualization of natural groupings between variables, highlighting the similarities and differences between them. On the other hand, Scatterplot Matrix analysis was used to explore the relationships between risk indices associated with microplastic (MP) ingestion for both adults and children, depending on the type of polymer (Figure S8a,b). Paired comparisons were applied to compare risk indices calculated for adults and children (Figure S9) to detect possible age-related variations in microplastic exposure.

5. Conclusions

This study provides new evidence on the presence of microplastics in milk and dairy products and their potential health implications. The analyses performed highlighted PMMA, PU, PS, PE, and PA as the main polymers present in different categories of dairy products. The calculated risk indices (H, CF_i, EAI, DPI, CDI_ng, and PRI) demonstrated that exposure levels vary substantially depending on the type of product. Yogurts, both conventional and organic, consistently showed higher levels of contamination.

The obtained results showed the existence of clear groupings of samples according to the type of polymer and the intensity of the risk (i.e., PRI values: 6947 for milk, 9570 for yogurt, and 9229 for butter), with the identification of extreme values suggesting high exposures. Comparisons between age categories indicated that, for certain indicators such as CDI_ng and EAI, children present significantly higher values (i.e., in conventional yogurt 9.89·10⁻⁵ n·kg⁻¹·day⁻¹ and 236,420.45 MPs/year, respectively), suggesting an increased vulnerability to microplastic ingestion. The set of statistical analyses provides a solid basis for interpreting the emerging risks associated with the consumption of dairy products contaminated with microplastics and emphasizes the need to consider age differences in exposure assessments.

The statistical analysis supported Hypothesis 1 by confirming significant differences in risk parameters between specific pairs of products, while the strong positive correlations between microplastic concentrations and human health risk indices fully validated Hypothesis 3. At the same time, the tests performed allowed a detailed characterization of the relationships between the risk indices associated with microplastic ingestion, for both adults and children. The applied methods revealed significant correlations between most of the risk indices. The results obtained showed the existence of clear groupings of samples according to polymer type and risk intensity, with the identification of extreme values suggesting high exposures, thus confirming hypotheses H4 and H5. Comparisons between age categories indicated that, for certain indicators such as CDI_ng and EAI, children present significantly higher values, suggesting an increased vulnerability to microplastic ingestion. The set of statistical analyses provides a solid basis for interpreting the emerging risks associated with the consumption of dairy products contaminated with microplastics and highlights the need to consider age differences in exposure assessments. This study has some limitations. Although the total number of samples was relatively high (n = 150), the distribution of the types of dairy products within the group in terms of number and some features (i.e., conventional/organic) was quite different.

These findings highlight the importance of specific monitoring and mitigation strategies for high-risk dairy products. To enhance the statistical power and clarify the relationships between products and contaminants, it is recommended to expand the dataset, include comprehensive polymer characterization, and evaluate seasonal and geographical variations. These measures will improve the accuracy of risk assessments and guide policy decisions aimed at reducing dietary exposure to microplastics.

Practical implications:

The results of this study highlight the need for specific interventions in the dairy production chain, as well as the reduction in plastic waste in unmanaged locations and high accuracy in livestock farms, to reduce microplastic contamination of the environment and minimize human health implications. The consistent association of yogurt, regardless of the production system, with high levels of PA, PU, PMMA, and PS suggests that certain processing or packaging steps may contribute disproportionately to contamination.

From a regulatory perspective, the observed variability in contamination between product types supports the establishment of product-specific monitoring protocols and acceptable threshold levels for microplastics in dairy products. Incorporating microplastic testing into routine food safety assessments, especially for products with high sensitivity, would allow for early detection and mitigation.

Future research also aims to evaluate the risk of emerging contaminants (e.g., heavy metals and PAHs) on milk samples, this being the raw material for obtaining other dairy products.