Pengcheng Nie

Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors

Compared with the chemical analytical technique, the soil nitrogen acquisition method based on near infrared (NIR) sensors shows significant advantages, being rapid, nondestructive, and convenient. Providing an accurate grasp of different soil types, sensitive wavebands could enhance the nitrogen estimation efficiency to a large extent. In this paper, loess, calcium soil, black soil, and red soil were used as experimental samples. The prediction models between soil nitrogen and NIR spectral reflectance were established based on three chemometric methods, that is, partial least squares (PLS), backward interval partial least squares (BIPLS), and back propagation neural network (BPNN). In addition, the sensitive wavebands of four kinds of soils were selected by competitive adaptive reweighted sampling (CARS) and BIPLS. The predictive ability was assessed by the coefficient of determination R2 and the root mean square error (RMSE). As a result, loess (0.93<Rp2<0.95,0.066 g/kg<RMSEp<0.075 g/kg) and calcium soil (0.95<Rp2<0.96, 0.080 g/kg<RMSEp<0.102 g/kg) achieved a high prediction accuracy regardless of which algorithm was used, while black soil (0.79<Rp2<0.86, 0.232 g/kg<RMSEp<0.325 g/kg) obtained a relatively lower prediction accuracy caused by the interference of high humus content and strong absorption. The prediction accuracy of red soil (0.86<Rp2<0.87, 0.231 g/kg<RMSEp<0.236 g/kg) was similar to black soil, partly due to the high content of iron–aluminum oxide. Compared with PLS and BPNN, BIPLS performed well in removing noise and enhancing the prediction effect. In addition, the determined sensitive wavebands were 1152 nm–1162 nm and 1296 nm–1309 nm (loess), 1036 nm–1055 nm and 1129 nm–1156 nm (calcium soil), 1055 nm, 1281 nm, 1414 nm–1428 nm and 1472 nm–1493 nm (black soil), 1250 nm, 1480 nm and 1680 nm (red soil). It is of great value to investigate the differences among the NIR spectral characteristics of different soil types and determine sensitive wavebands for the more efficient and portable NIR sensors in practical application.

Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy

The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3–2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information.

Research on the Effects of Drying Temperature on Nitrogen Detection of Different Soil Types by Near Infrared Sensors

Soil is a complicated system whose components and mechanisms are complex and difficult to be fully excavated and comprehended. Nitrogen is the key parameter supporting plant growth and development, and is the material basis of plant growth as well. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near infrared sensors are widely used for rapid detection of nutrients in soil. However, soil texture, soil moisture content and drying temperature all affect soil nitrogen detection using near infrared sensors. In order to investigate the effects of drying temperature on the nitrogen detection in black soil, loess and calcium soil, three kinds of soils were detected by near infrared sensors after 25 °C placement (ambient temperature), 50 °C drying (medium temperature), 80 °C drying (medium-high temperature) and 95 °C drying (high temperature). The successive projections algorithm based on multiple linear regression (SPA-MLR), partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were used to model and analyze the spectral information of different soil types. The predictive abilities were assessed using the prediction correlation coefficients (RP), the root mean squared error of prediction (RMSEP), and the residual predictive deviation (RPD). The results showed that the loess (RP = 0.9721, RMSEP = 0.067 g/kg, RPD = 4.34) and calcium soil (RP = 0.9588, RMSEP = 0.094 g/kg, RPD = 3.89) obtained the best prediction accuracy after 95 °C drying. The detection results of black soil (RP = 0.9486, RMSEP = 0.22 g/kg, RPD = 2.82) after 80 °C drying were the optimum. In conclusion, drying temperature does have an obvious influence on the detection of soil nitrogen by near infrared sensors, and the suitable drying temperature for different soil types was of great significance in enhancing the detection accuracy.

Rapid Determination of Thiabendazole Pesticides in Rape by Surface Enhanced Raman Spectroscopy

Thiabendazole is widely used in sclerotium blight, downy mildew and black rot prevention and treatment in rape. Accurate monitoring of thiabendazole pesticides in plants will prevent potential adverse effects to the Environment and human health. Surface Enhanced Raman Spectroscopy (SERS) is a highly sensitive fingerprint with the advantages of simple operation, convenient portability and high detection efficiency. In this paper, a rapid determination method of thiabendazole pesticides in rape was conducted combining SERS with chemometric methods. The original SERS were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS and thiabendazole pesticides in rape. As a result, the SERS enhancing effect based on silver Nano-substrate was better than that of gold Nano-substrate, where the detection limit of thiabendazole pesticides in rape could reach 0.1 mg/L. Moreover, 782, 1007 and 1576 cm−1 could be determined as thiabendazole pesticides Raman characteristic peaks in rape. The prediction effect of thiabendazole pesticides in rape was the best (Rp2 = 0.94, RMSEP = 3.17 mg/L) after the original spectra preprocessed with 1st-Derivative, and the linear relevance between thiabendazole pesticides concentration and Raman peak intensity at 782 cm−1 was the highest (R2 = 0.91). Furthermore, five rape samples with unknown thiabendazole pesticides concentration were used to verify the accuracy and reliability of this method. It was showed that prediction relative standard deviation was 0.70–9.85%, recovery rate was 94.71–118.92% and t value was −1.489. In conclusion, the thiabendazole pesticides in rape could be rapidly and accurately detected by SERS, which was beneficial to provide a rapid, accurate and reliable scheme for the detection of pesticides residues in agriculture products.

Spectral Characterization and Molecular Dynamics Simulation of Pesticides Based on Terahertz Time-Domain Spectra Analyses and Density Functional Theory (DFT) Calculations

This work provides the experimental and theoretical fundamentals for detecting the molecular fingerprints of six kinds of pesticides by using terahertz (THz) time-domain spectroscopy (THz-TDS). The spectra of absorption coefficient and refractive index of the pesticides, chlorpyrifos, fipronil, carbofuran, dimethoate, methomyl, and thidiazuron are obtained in frequencies of 0.1–3.5 THz. To accurately describe the THz spectral characteristics of pesticides, the wavelet threshold de-noising (WTD) method with db 5 wavelet fucntion, 5-layer decomposition, and soft-threshold de-noising was used to eliminate the spectral noise. The spectral baseline correction (SBC) method based on asymmetric least squares smoothing was used to remove the baseline drift. Spectral results show that chlorpyrifo had three characteristic absorption peaks at 1.47, 1.93, and 2.73 THz. Fipronil showed three peaks at 0.76, 1.23, and 2.31 THz. Carbofuran showed two peaks at 2.72 and 3.06 THz. Dimethoate showed three peaks at 1.05, 1.89, and 2.92 THz. Methomyl showed five peaks at 1.01, 1.65, 1.91, 2.72, and 3.20 THz. Thidiazuron showed four peaks at 0.99, 1.57, 2.17, and 2.66 THz. The density functional theory (DFT) of B3LYP/6-31G+(d,p) was applied to simulate the molecular dynamics for peak analyzing of the pesticides based on isolated molecules. The theoretical spectra are in good agreement with the experimental spectra processed by WTD + SBC, which implies the validity of WTD + SBC spectral processing methods and the accuracy of DFT spectral peak analysis. These results support that the combination of THz-TDS and DFT is an effective tool for pesticide fingerprint analysis and the molecular dynamics simulations.

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm−1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm−1: y = 672.26x + 5748.4, R2 = 0.9948; 1008 cm−1: y = 1155.4x + 8740.2, R2 = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300–1700 cm−1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp2 = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy

Deltamethrin is widely used in pest prevention and control such as red spiders, aphids, and grubs in strawberry. It is important to accurately monitor whether the deltamethrin residue in strawberry exceeds the standard. In this paper, density functional theory (DFT) was used to theoretically analyze the molecular structure of deltamethrin, gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were used to enhance the surface enhanced Raman spectroscopy (SERS) detection signal. As a result, the theoretical Raman peaks of deltamethrin calculated by DFT were basically similar to the measured results, and the enhancing effects based on AuNPs was better than that of AgNPs. Moreover, 554, 736, 776, 964, 1000, 1166, 1206, 1593, 1613, and 1735 cm−1 could be determined as deltamethrin characteristic peaks, among which only three Raman peaks (736, 1000, and 1166 cm−1) could be used as the deltamethrin characteristic peaks in strawberry when the detection limit reached 0.1 mg/L. In addition, the 500–1800 cm−1 SERS of deltamethrin were analyzed by the partial least squares (PLS) and backward interval partial least squares (BIPLS). The prediction accuracy of deltamethrin in strawberry (Rp2 = 0.93, RMSEp = 4.66 mg/L, RPD = 3.59) was the highest when the original spectra were pretreated by multiplicative scatter correction (MSC) and then modeled by BIPLS. In conclusion, the deltamethrin in strawberry could be qualitatively analyzed and quantitatively determined by SERS based on AuNPs enhancement, which provides a new detection scheme for deltamethrin residue determination in strawberry.

Predicting pork freshness using multi-index statistical information fusion method based on near infrared spectroscopy

In this work, the near infrared spectroscopy (NIR) technology was applied to nondestructively evaluate the freshness of pork. The total volatile basic-nitrogen (TVB-N) and pH value of pork were detected as freshness evaluation indicators. A multi-index statistical information fusion (MSIF) modeling method based on variable selection was proposed to evaluate pork freshness. In the experiment, the proposed MSIF was compared with other state-of-the-art variable selection methods. Results showed that the proposed method achieved the best generalization performance and stability. The prediction correlation coefficient (Rval) and root mean square error (RMSEP) of MSIF were: Rval = 0.8618 and RMSEP = 3.910 for TVB-N content, Rval = 0.9379 and RMSEP = 0.1046 for pH value. The research demonstrated that NIR combined with MSIF has the potential for rapid and nondestructive determination of pork freshness, and so hopefully to provide a promising tool for monitoring meat quality and enriching the extracted information from food industry.