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To study the identification mechanism of microplastics in agricultural environmental media by molecular spectroscopy, two typical media, soil and fishmeal, were selected for this study. Three common microplastics, PE, PP, and PS, were used as research objects. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric methods were used to explore microplastics' identification and analysis effects in different agricultural media and reveal their identification mechanisms. PCA analysis revealed that different soil types would affect the identification results of microplastics. PLS-DA discriminant analysis showed that the accuracy of NIR spectroscopy technology in identifying microplastics in different types of soil decreased in the order of sand > loam > clay. In contrast, the rule of MIR spectroscopy technology was the opposite. The sensitivity and specificity of the three microplastics in the NIR model of sand and the infrared spectroscopy model of fishmeal reached 1.000. NIR spectroscopy technology is suitable for identifying microplastics in soil, while infrared spectroscopy technology is more suitable for identifying microplastics in fish meal. Furthermore, based on the VIP values of each wavelength point in the spectrum, the characteristic bands that have essential contributions to identifying microplastics in soil were screened out. The NIR spectra of 4500–4300 cm − 1 , 4300–3900 cm − 1 and 7100–5800 cm − 1 are the most essential characteristic bands for identifying microplastics in clay, loam, and sand, respectively. The MIR of 3000–2900 cm − 1 and 700–650 cm − 1 were the most essential characteristic bands for identifying microplastics in soils, and the overlap of the characteristic spectra of the three soils reached 59.45%. The NIR spectra of 6050–5600 cm − 1 , 4700–4000 cm − 1 and the MIR spectra of 2300–1900 cm − 1 and 800–400 cm − 1 are the most essential characteristic bands for the identifying microplastics in fishmeal. This study provides a more suitable technical "solution" for identifying microplastics in environmental media, which is of great significance for improving the accuracy of molecular spectroscopic identification in environmental media and clarifying the interaction between microplastics and environmental media. [ABSTRACT FROM AUTHOR]

In this investigation, a pioneering approach involving the fusion of matrix fragments-related spectral data was proposed to improve the underperformance observed in raw meat and bone meal (MBM) when employed for species discrimination analysis. Initially, the MBM matrix was characterized as a binary mixture comprising bone fragment (BF) and meat fragment (MF). Subsequently, the disparities in near infrared (NIR), mid infrared (MIR), and Raman spectra between BF and MF samples were individually identified and elucidated. Following, the spectral fusion data related to matrix fragments were synthesized and subjected to analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for species-specific evaluation. The suggested data fusion strategy was authenticated by its capacity to facilitate improved differentiation within the principal component space, along with reduced classification errors in PLS-DA. Further, complementarity of matrix fragments-related spectral variables for MBM species discrimination analysis was explicitly scrutinized and contributions to MBM derived from four species were meticulously traced. Additionally, the proposed analytical strategy for MBM could serve as a reference for the spectral characterization of other agricultural materials with complex matrices. • Spectral fusion data of MF and BF were firstly used to trace MBM species. • The proposed fusion strategy greatly improved the MBM species-specific analysis. • Contributions of fragments-related spectral variables for tracing MBM species were analyzed in detail. [ABSTRACT FROM AUTHOR]