Rapid and accurate detection of total nitrogen (TN) and total phosphorus (TP) in dairy cow slurry is essential for guiding the scientific application of slurry to fields, preventing soil nutrient imbalance and water pollution, and promoting sustainable agricultural development and resource utilization of slurry. However, the complex composition and high moisture content of slurry, along with the high dimensionality and nonlinear characteristics of its spectral data, pose challenges to accurate and efficient prediction. In this study, near-infrared spectroscopy was combined with partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and deep neural network (DNN) to establish TN and TP prediction models for dairy cow slurry using different spectral preprocessing methods (MSC, SG, and MSC + SG). The optimal preprocessing methods for TN and TP were determined according to model performance. Furthermore, uninformative variable elimination (UVE) and bootstrap soft shrinkage (BOSS) were employed to extract characteristic wavelength variables and construct models, and the performances of different models were compared to identify the optimal one. Results showed that the DNN model achieved the best performance in TN and TP prediction, with Rp2 values of 0.92 and 0.75, and RPD values of 3.49 and 2.01, respectively, significantly outperforming PLSR and XGBoost. When using UVE and BOSS, both methods yielded comparable predictive performance; however, BOSS achieved a higher degree of data compression and modeling efficiency, showing greater potential for practical applications. Overall, the BOSS-DNN approach demonstrated remarkable advantages in predicting complex components of dairy slurry and provides a new technical pathway and theoretical foundation for rapid on-site nutrient detection of livestock and poultry slurry.

Aiming at the problems of spectral overlap caused by the coexistence of multiple microplastics (MPs) in soil and low efficiency of traditional detection methods, this study explores the feasibility of an efficient detection method combining near-infrared (NIR) spectroscopy and machine learning (ML) for the simultaneous qualitative and quantitative analysis of multiple MPs in soil. Taking polypropylene (PP), polyethylene terephthalate (PET) and polyvinyl chloride (PVC) as target pollutants, NIR spectra were collected for eight types of samples (MPs-Free, single/two/three types of MPs-contaminated). After spectral preprocessing with the multivariate scatter correction + standard normal variate (MSC + SNV) method, four ML models, namely partial least squares (PLS), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed for the qualitative and quantitative analysis of soil MPs, and their performance was systematically compared. In qualitative classification, all ML models achieved excellent performance with overall accuracies higher than 94

The rapid identification of licorice seed varieties has great significance for guiding large-scale cultivation practices and ensuring quality control in the licorice industry. This study aims to develop a rapid, efficient, nondestructive classification of licorice seed varieties based on a portable near infrared (NIR) spectrometer combined with machine learning (ML) algorithms. Three varieties of medicinal licorice seeds -Glycyrrhiza uralensis Fisch., Glycyrrhiza inflata Bat., and Glycyrrhiza glabra L., -were prepared, and their NIR spectra were collected using a portable NIR spectrometer (900-1700 nm). The raw spectra were then preprocessed by combining SavitzkyGolay (SG) smoothing with multiplicative scattering correction (MSC), and characteristic variables were extracted via Variable Combination Population Analysis (VCPA), Bootstrapping Soft Shrinkage (BOSS), and Variable Iterative Space Shrinkage Approach (VISSA), respectively. Furthermore, four ML methods, including partial least squares discriminant analysis (PLSDA), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to establish classification models for the three varieties of licorice seeds. Among all the constructed models, our findings show that the VISSA-BPNN model, based on 55 characteristic variables selected from the 198 original wavelength variables, achieved the highest accuracy of 92.75% in identifying licorice seed varieties. The results highlight the effectiveness of the portable NIR spectrometer particularly when paired with VISSA-BPNN in enabling reliable and rapid identification of licorice seed varieties.

To investigate the adsorption behavior and molecular mechanisms of polyacrylonitrile (PAN) microplastics (MPs) toward phenanthrene (PHE) and its four derivatives: PHE-CH3, PHE-CHO, PHECl, and PHE-NO2, the adsorption kinetics, isotherms, and interaction mechanisms of the above five target pollutants were systematically studied and compared by macroscopic adsorption experiments combined with microscopic spectral technology. The results showed that the adsorption capacity of PAN for the target pollutants follows the order: PHE-NO2 > PHE-Cl > PHE-CH3 > PHE-CHO > PHE, and the adsorption process reached equilibrium within 8 h. Kinetic and isotherm models further revealed that the type of derivatives had a significant impact on the adsorption behavior: PHE, PHE-CHO, PHECl, and PHE-NO2 were mainly adsorbed through chemical adsorption, while the adsorption of PHE-CH3 was dominated primarily by physical diffusion. Fourier transform infrared spectroscopy and two-dimensional correlation spectroscopic analyses revealed that different derivatives regulated intermolecular forces such as hydrophobic interactions, hydrogen bonding, halogen bonding, and π-π stacking, thereby governing the adsorption pathways and efficiency. This study provides theoretical insights at the mechanistic level for understanding the combined pollution behavior of MPs and PHE and its derivatives, offering important reference value for ecological risk assessment.

This study systematically investigated the adsorption of polycyclic aromatic hydrocarbons (PAHs) (naphthalene (NAP), phenanthrene (PHE), benzo[ghi]perylene (BghiP)) on UV-aged polyvinyl chloride (PVC) microplastics (MPs) in single/coexistent systems, clarifying their competitive adsorption mechanisms via macroscopic experiments and microscopic spectroscopy. UV aging modified PVC's physical structure and enhanced its PAHs adsorption capacity. The adsorption process may be jointly controlled by physical pore filling and interfacial chemical interactions, and the relative contribution of the latter tends to increase after UV aging. In competition, adsorption affinity followed BghiP > PHE > NAP (matching hydrophobicity) with pore-filling as a key factor. Two-dimensional correlation spectroscopy (2D-COS) revealed different response priorities of CCl and CH bonds in single and competitive adsorption systems, and selective Cl-π polar coupling between polarized CCl bonds and PAH conjugated planes further governs the benzene-ring vibration response sequence: BghiP > NAP > PHE. UV aging regulates the adsorption pathways and kinetics of PAHs on PVC MPs but does not alter the competitive priority determined by PAHs' intrinsic physicochemical properties. These findings provide new insights into the composite pollution behavior and ecological risks of MPs in natural environments.

Microplastics (MPs), as an emerging pollutant, have become a potential threat to the global ecological environment and human health due to their small particle size, wide distribution, easy interaction with other pollutants in the environment, and transmission along the food chain. As a powerful analytical technique, two-dimensional correlation spectroscopy (2D-COS) can provide information on intermolecular interactions and dynamic changes, thus exhibiting unique advantages in MPs research. This review focuses on 2D-COS applications in MPs studies, including chemical structure and functional group characterization, aging or degradation mechanism elucidation, MPs-pollutants interaction analysis, and novel applications in biogeochemical processes (e.g., plant-MPs interactions, biofilm-modulated transformation). Finally, we discuss the current limitations of 2D-COS and prospect its future application trends in MPs research.

Sudan Red I is an illegal food colorant that can enhance the color intensity of egg yolks. Rapid detection of Sudan Red in egg yolks is of great significance. In this study, a near-infrared spectrometer was used to collect spectral data from 60 unadulterated egg yolk samples and 102 adulterated samples containing Sudan Red I at concentrations ranging from 0.5 to 10 mg (100 g). After spectral analysis and data preprocessing, the sample dataset was divided into training and test subsets Ita 3:1 ratio, Qualitative and quantitative models were then built to detect Sudan Red I in egg yolks. The models were Evaluated using prediction accuracy, calibration, and prediction R? coefficients (R-c(2)/R-p(2)), and root mean square errors (RMSEC/RMSEP). For qualitative analysis, the Partial Least Squares Discriminant Analysis (PLS-DA) algorithm was used to classify igg samples as adulterated with Sudan Red 1. After data preprocessing using the Standard Normal Variate (SNV) Fransformation, the model achieved optimal performance, with accuracy rates of 98.3% for the training set and 97.6% for the fest set. For quantitative analysis, the Competitive Adaptive Reweighted Sampling (CARS) method was first used to select characteristic wavelengths from the spectral data. Then, regression models were established using the linear Partial Least Squares Regression (PLSR) and the nonlinear Back-Propagation Artificial Neural Network (BP-ANN) algorithms to predict Sudan Red I content. The PLSR model showed better performance, with R-c (2) of 0.98. R-p(2) of 0.98. RMSEC of 0.79, and in egg yolks.

The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected. NIR spectral properties of chicken feeds, three MPs of PP, PVC and PET, MPs-contaminated chicken feeds were firstly investigated, and principal component analysis was carried out to reveal the effect of MPs on spectra of chicken feed. Moreover, the raw spectral data were pre-processed by multiplicative scattering correction (MSC) and standard normal variate (SNV), and the characteristic variables were selected using the competitive adaptive re-weighted sampling (CARS) algorithm and the successive projections algorithm (SPA), respectively. On this basis, four machine learning methods, namely partial least squares discriminant analysis (PLSDA), back propagation neural network (BPNN), support vector machine (SVM) and random forest (RF), were used to establish discriminant models for MPs-contaminated chicken feed, respectively. The overall results indicated that SPA was a powerful tool to select the characteristic wavelength. SPA-SVM model was proved to be optimal in all constructed models, with a classification accuracy of 96.26% for unknow samples in test set. The results show that it is not only feasible to combine NIR spectroscopy with machine learning for rapid detection of microplastics in chicken feed, but also achieves excellent analysis results.

The accumulation of microplastics (MPs) in the body through biological cycles leads to persistent health risks owing to their degradation resistance. Enhanced toxicity arises from co-occurrence with polycyclic aromatic hydrocarbons (PAHs) that adsorb onto MPs in aquatic environments. Elucidation of MP-PAH adsorption mechanisms requires precise, simultaneous, in situ measurements of two critical parameters, i.e., the dynamic morphologies of MP particles and PAH concentrations. However, there is no comprehensive technology available that can simultaneously measure changes in both of these physical and chemical parameters. This report describes a digital holographic microscopy-based method for simultaneously measuring the 3D MP morphologies and PAH concentration changes in real time. A simple digital off-axis holographic microscopic system is used in combination with a phase difference method to measure the refractive index of the solution to determine the PAH concentration. The measurement results of the phase difference method are consistent with those obtained using a fluorescence spectrophotometer, and the signal-to-noise ratio is 38% higher. The developed technique enables real-time measurements of the PAH concentration changes to derive their adsorption kinetics, thermodynamics, and isothermal models, as verified by adsorption experiments involving various MPs in naphthalene solutions. This work presents a simple and purely optical method for simultaneous physical-chemical characterization in aqueous solutions, thus advancing measurement capabilities for analyzing environmental interfacial processes. The proposed methodology demonstrates broad applicability for co-monitoring morphological and chemical parameters in liquid-phase systems, which expands the scope of optical measurement techniques in biological and environmental sciences.

The aim of this study was to present a rapid qualitative and quantitative analysis approach of microplastics (MPs) in chicken meat based on near infrared (NIR) diffuse reflectance spectroscopy. First, the NIR reflectance spectra of all samples were measured, and the spectral properties of non- and MPs-contaminated chicken meat were investigated. Then, the data driven soft independent modeling of class analogy (DD-SIMCA) was used to build models for the three target classes. The results showed that it was possible to identify the non-contaminated and MPs-contaminated chicken meat samples with 88.46-100% of sensitivity and 82-98.11% of specificity. Moreover, the competitive adaptive reweighted sampling (CARS) was extracted the characteristic variables. Finally, PLS, BPNN and linear SVM regression models were built for the determination of concentration of polypropylene (PP) and polyvinyl chloride (PVC) in chicken meat using the characteristic variables and the full-spectrum, respectively. PLS and linear SVM achieve similar results with high coefficient of determination of prediction (Rp2) and low root mean square error of prediction (RMSEP). CARS-PLS models obtained the best performance, with the Rp2 of 0.97 and 0.99 for PP and PVC, the RMSEP of 0.04% and 0.02% for PP and PVC, respectively. This is the first study to investigate the feasibility of detection of MPs in chicken meat using NIR spectroscopy coupled with DD-SIMCA and machine learning algorithms. The results showed that NIR spectroscopy for the rapid detection of MPs in chicken were feasible and achieved excellent analysis results.

Yolk color is a key indicator of egg quality, as customers prefer eggs with intensely yellow yolks, which also signal nutrient richness. At present, the commonly used method for yolk color detection is to open the eggs and evaluate the yolk color using the Roche yolk color fan (RYCF), so developing a non-destructive method for discrimination of yolk color is of great significance. In order to overcome the human subjectivity associated with RYCF based on yolk color scoring, a machine vision method was built to classify the yolk color grades more objectively and precisely. In this work, a total of 150 egg samples with yolk color scores from 5 to 11 were collected and the near-infrared (NIR) spectral data of intact eggs and egg yolks were gathered independently, while the true scores of yolk color grades were acquired using the machine vision system as the target set for modeling. Finally, different regression prediction models for egg yolk color grades were constructed using chemometric Partial Least Squares (PLS) and machine learning techniques, such as Temporal Convolutional Network - Gated Recurrent Unit-Attention (TCN-GRU-Attention), Least Squares Support Vector Machines (LSSVM) and Convolutional Neural Network-Bidirectional Long Short Term Memory-Adaptive Boosting (CNN-BiLSTM-Adaboost). For the intact egg and separated yolk spectral data, the results show that the PLS model achieved the best prediction accuracy in the test set, with R2 values of 0.9035 and 0.9274, and the root mean square errors (RMSE) were 0.3665 and 0.2933, respectively, which accomplished the non-destructive quantitative detection of egg yolk color scores.

Polycyclic aromatic hydrocarbons (PAHs) are widely distributed in soil and water, but fluorescence spectroscopy for PAHs is often interfered with organic matter in the environment. The aim of this paper is to evaluate a correction method using combined spectral technology in an environment where humic acids and PAHs coexist. In the present work, humic acids and benzo[ghi]perylene were analyzed in various concentrations using fluorescence and near-infrared (NIR) spectroscopy from single and mixed samples. The NIR prediction model of humic acids in mixed samples was established based on synergy interval partial least squares, and the standard curve of fluorescence spectra for humic acids was established at 478 nm (characteristic wavelength of benzo[ghi]perylene). The fluorescence intensity of humic acids in the mixed sample was predicted from the content derived from the NIR spectra. The final correction was carried out by their exclusion from the fluorescence of the mixture at the same wavelength. The corrected fluorescence intensity was linearly correlated with the concentration of benzo[ghi]perylene with R2 = 0.8362, while R2 = 0.3538 before correction. These results give a new insight into the calibration modeling of the combined spectral method. Graphical abstract This is a visual representation of the abstract.

The properties of soil matrix have an impact on the fluorescence intensity of polycyclic aromatic hydrocarbons (PAHs), which restricts the application of fluorescence spectral technology in detecting PAHs in soil. The present study explored the mechanism of the influence of soil matrix properties on the fluorescence intensity of PAHs from the perspective of specific surface area (SSA). A three-factor three-level experimental design was adopted for investigating the relationship between soil matrix properties, PAH fluorescence intensity, and soil SSA. The typical benzo[ghi]perylene pollutant in soil as the research object, 27 soil samples with different sand content, moisture content, humic acid content, and given benzo[ghi]perylene concentration (2 mg/g) were prepared. On the basis of obtaining the fluorescence spectra and SSA data of soil samples, statistical analyses of fluorescence intensity and SSA were investigated in relation to the soil matrix properties. The statistical results showed that the soil matrix properties had a significant influence (P < 0.05) on the fluorescence intensity and SSA. Furthermore, combined with the technology of fluorescence microscopy imaging, the influence mechanism of soil matrix properties on fluorescence intensity was revealed. The soil matrix properties affected the soil SSA, resulting in a change of benzo[ghi]perylene concentration in the soil surface at the probe window, thereby affecting the fluorescence intensity.

Microplastics (MPs), an emerging pollutant, widely co-occur with polycyclic aromatic hydrocarbons (PAHs) in the environment. Therefore, the interaction between MPs and PAHs has been the focus of much attention in recent years. In this study, three types of MPs, i.e., polypropylene, polystyrene, and poly(vinyl chloride), with the same main chain were selected as the adsorbents, with phenanthrene (PHE) as the representative PAHs. The adsorption mechanisms were explored from the perspective of the molecular spectral level using a combination of Fourier transform infrared spectroscopy (FT-IR) with a two-dimensional correlation technique. The adsorption kinetics results showed that the adsorption of PHE on the three MPs was dominated by chemisorption. However, the FT-IR analysis results indicated that no new covalent bond was created during the adsorption process. Based on the above research, a generalized two-dimensional (2D) correlation spectral technique was employed to investigate the sequence of functional group changes during the adsorption process for different MPs. Furthermore, the hybrid 2D correlation spectral technique explored the effect of side groups attached to the main chain molecules of MPs on adsorption. The results showed that for all three MPs, the functional groups in the side chain have a higher affinity for PHE, which is due to their higher hydrophobicity. This study provides a feasible way to analyze the adsorption of pollutants on MPs, and the results are important for understanding the adsorption interaction between PAHs and MPs in the aquatic environment.

The paper takes graduate students in the field of agricultural engineering and information technology at the College of Engineering and Technology of Tianjin Agricultural University as an example, and combines with graduate students in Food Science and Environmental Science majors. Based on interdisciplinary research projects, the photoelectric detection laboratory and agricultural environment laboratory were as platforms, and a large team of supervisors and graduate students from different colleges and majors were formed. The interdisciplinary project enables students to broaden their knowledge areas while complementing each other's strengths, cultivating professional qualities and skills in related majors, and multiple majors, laying a solid foundation for future career development.

Fluorescence spectroscopy has been used for rapid detection of PAHs in soil, but soil organic matter (SOM) produces strong interference to the fluorescence intensity of PAHs, which restricts the application of fluorescence spectroscopy for rapid detection of PAHs in soil. A correction method of reducing the interference of SOM on PAHs fluorescence intensity was proposed combining fluorescence and near-infrared (NIR) spectroscopy. Six soil samples with different concentrations of humic acid (HA) at a given phenanthrene concentration (5 mg/g) were prepared and scanned for obtaining the fluorescence and NIR diffuse reflectance spectra. The spectral data showed that the fluorescence intensity and NIR diffuse reflectance had an approximate trend with the change of HA concentration. It was found that the NIR diffuse reflection at 4672 cm(-1) as a calibration factor could effectively reduce the interference of HA on the fluorescence intensity of phenanthrene. Subsequently, a standard curve for the quantitative analysis of phenanthrene in soil was established based on the fluorescence intensity before and after calibration. For the unknown samples, the predicted average relative errors of the standard curves before and after calibration were 27.46 % and 9.00 %, respectively. The results showed that the proposed correction method could reduce the interference of HA on the quantitative analysis of PAHs, and provide a reference for eliminating the interference constraint of fluorescence spectroscopy technique for rapid real-time detection of PAHs in soil.

In order to effectively provide ideological and political education to students in the teaching process of analytical instrument courses, the master's students were taken as the research object in agricultural engineering at Tianjin Agricultural University. The ideological and political elements and moral education functions contained in the course were deeply explored and extracted in terms of the teaching content. The typical ideological and political cases were established, and the outline has been supplemented and revised. On this basis, the organic integration of ideological and political cases with professional knowledge is achieved through teaching design, and achieved the unity of explicit education and implicit education.

采集不同玉米自交系的近红外光谱和中红外光谱,研究其一维光谱特性.以不同自交系玉米为外扰,构建同谱2T2D近/中红外相关谱和异谱 2T2D近-中红外相关谱,根据相关强度大小来确定不同玉米自交系之间的亲缘关系.结果表明,相对于一维光谱,2T2D相关谱能提取不同玉米自交系更多的光谱信息,可有效确定玉米自交系的亲缘关系.该方法可推广到其他作物上,具有较好的应用前景.

A qualitative analysis of melamine-adulterated milk was proposed based on two-trace two-dimensional (2T2D) auto-correlation spectra. The concentration of melamine was used as external perturbation, and 40 adulterated samples of each brand with different concentrations of melamine (0.01 g/L to 1 g/L) were configured. Four brands of milk were used to configure experimental samples, including Guangming brand, Mengniu brand, Sanyuan brand and Wandashan brand. Spectroscopic data of pure milk and melamine-adulterated milk were measured by infrared (IR) (80-4000 cm-1) spectrophotometer. 2T2D auto-correlation spectral technology combined with least squares support vector machine (LS-SVM) method was used for qualitative analysis. The two strongest auto-correlation peaks in the auto-correlation spectra were selected for modeling. For Guangming brand, the intensities of auto-correlation at two wave numbers 2898 cm-1 and 2972 cm-1 were selected as independent variables. For Mengniu brand, the intensities of auto-correlation at two wave numbers 2852 cm-1 and 2920 cm-1 were selected. For Sanyuan brand, the intensities of auto-correlation at two wave numbers 2900 cm-1 and 2974 cm-1 were selected. For Wandashan brand, the intensities of auto-correlation at two wave numbers 2900 cm-1 and 2974 cm-1 were selected. For four brands fused together, the intensities of auto-correlation at two wave numbers 2900 cm-1 and 2974 cm-1 were selected. For each brand, the accuracy of qualitative analysis was 100 %. For four brands fused together, the accuracy of qualitative analysis was 99.05 %. In this way, it greatly reduced the amount of data to be processed. This study showed that 2T2D auto-correlation spectral technology combined with LS-SVM method was perfect for the discrimination of melamine-adulterated milk.

Fluorescence spectroscopy has been widely used to detect polycyclic aromatic hydrocarbons (PAHs) in the environment. However, the interference of coexisting humic acids (HA) in the environment poses a great challenge to the qualitative and quantitative detection of PAHs using fluorescence spectroscopy. In this study, the spectral properties of benzo [ghi] perylene (BGP) and HA were investigated based on fluorescence and UV-vis spectroscopy combined with two-dimensional (2D) correlation analysis. Under the external disturbance of HA concentration, the homo-2D (fluorescence, UV-visible) correlation and hetero-2D fluorescence-UV-visible correlation spectral characteristics of the mixed samples of HA and BGP were studied, and the effect of HA on the fluorescence of BGP was investigated. It can be inferred that the fluorescence peak at 478 nm come from BGP, and the fluorescence peaks at 442 nm and 533 nm, UV absorption peak at 233 nm come from HA. Meanwhile, asynchronous two-trace two-dimensional (2T2D) fluorescence correlation slice spectra at 533 nm were obtained. The slice spectral intensity at 478 nm was extracted to quantify the BGP concentration in mixture. The results showed that the slice spectral intensity and BGP concentration had a good linear relationship with the coefficient of determination R2 = 0.96. This research provides a way to further study the separation method of HA and PAHs or explore the correction method of the effect of HA on PAHs.