Researching | GPXYGPFX Volume 44 Issue 8,2024

SHEN Yong-cai, NIU Yu-fan, KONG De-feng, YE Yang..., ZHANG Shou-biao, LI Da-chuang, TAN Ming-sheng, HUANG Yan-qing, ZHAO Zhi-hao, ZI Peng-fei, ZHANG Xiao-hui and WEN Fei|Show fewer author(s)

Impurity behavior study is very important in magnetic confined fusion research as impurity may cause the dilution of fuel ions, affect the power balance, and degrade plasma performance. Spectroscopic diagnostic is important for impurity measurement and transport study in fusion devices. Spectroscopy in the vacuum ultra Impurity behavior study is very important in magnetic confined fusion research as impurity may cause the dilution of fuel ions, affect the power balance, and degrade plasma performance. Spectroscopic diagnostic is important for impurity measurement and transport study in fusion devices. Spectroscopy in the vacuum ultraviolet (VUV) range offers a useful tool for investigating impurity radiation from low-temperatureareasof edge tokamak plasma. To meet the impurity measurement requirement in fusion research, a Seya-Namioka spectrometer was designed, and the main parts of the spectrometer are adjustable width incident slits, concave gratings, and a detector). The luminous flux of the spectrometer is adjusted through an adjustable width of the incident slit, which is of the linear guide type. The position of the incident slit baffle is adjusted through the linear guide to achieve slit width adjustment. The grating surface is coated with aluminum (Al) and magnesium fluoride (MgF2) to enhance the refractive efficiency. The usable wavelength range of the grating is 50~460 nm, and the optical path optimization design is carried out for the 50~50 nm wavelength range. The spectrometers detector was chosen as a deeply cooled back-illuminated charge-coupled device (CCD). By turning the grating turntable to rotate the grating and change the diffraction angle, spectral observations in the 50~250 nm range can be achieved. Based on the parameters of the concave grating, the specific optical path was determined, and the relationship between wavelength and grating rotation angle, as well as the line dispersion rate at different wavelengths, was analyzed. According to the theory of concave grating imaging, the spectral resolution of the system was calculated and analyzed. The optimal exit arm was determined to be 205 mm by analyzing the spectral resolution under different exit arms. The effects of incident slit width, pixel size, aberration, and diffraction limit on spectral resolution were analyzed with an exit arm of 205 mm and an incident slit of 20 μm. The main contributions to the spectral resolution are the entrance slit width, pixel size, aberration, and diffraction limit. The diffraction limit has the smallest effect on spectral resolution, which can be ignored. Due to the size of the detector pixel, the width of the exit slit has a great impact on the spectral resolution, which remains at about 0.09~0.10 nm and increases slowly in the 50~250 nm wavelength range. The total spectral resolution is between 0.121 nm and 0.122 nm. The width of the incident slit will be adjustable from 10 to 1 000 μm, depending on the intensity of the incident light. The wider the width of the incident slit, the worse the overall spectral resolution will be. In actual measurement, the luminous flux and spectral resolution should be considered. Wavelength calibration and performance tests were performed by using a low-pressure mercury lamp and microwave plasma light source. The wavelength calibration of the spectrometer can be completed based on the zero-order spectrum and the characteristic wavelength of the mercury lamp (Hg Ⅰ 185 nm). The zero-order spectrum position is defined as the diffraction angle zero position. The angular position of Hg Ⅰ 185 nm in the spectrometer is determined according to the formula. The spectral resolution was 0.124 3 at 185 nm by Gauss fitting of the Hg Ⅰ (185 nm) spectral line, which is close to the calculated value. By comparing the spectral resolution of Hg Ⅰ 185 nm under different exit arms through experiments and theoretical calculations, it was further verified that the instrument achieved the best spectral resolution at the exit-arm length of 205 mm. Further performance tests of the spectrometer were carried out using a microwave plasma discharge light source device. It has been verified that the spectrometer has good detection ability in the 50~250 nm wavelength range, based on observing nitrogen, oxygen, and helium lines emitted by microwave plasma light sources..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2158 (2024)

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Research on Identification of Corn Storage Years Based on Raman Spectroscopy

HUANG Ji-chong, SONG Shao-zhong, LIU Chun-yu, XU Li-Jun..., CHEN An-liang, LU Ming, GUO Wen-jing, MIAO Zhuang, LI Chang-ming, TAN Yong and LIU Zhe|Show fewer author(s)

In this study, Songyuan Grain Group Storages corn grain reserves from various years were used as samples, and a three-stage micro Raman spectrometer was used to gather the spectra of individual grains of corn from multiple conditions. The regularity of its internal components changes over time was investigated using t In this study, Songyuan Grain Group Storages corn grain reserves from various years were used as samples, and a three-stage micro Raman spectrometer was used to gather the spectra of individual grains of corn from multiple conditions. The regularity of its internal components changes over time was investigated using the molecular spectra produced by Density Functional Theory (DFT). The DFT theory optimised the chemical structure, and the vibration frequency associated with each molecules bond was analyzed. The partial peak location was then determined by comparing the vibration frequency to the Raman spectra of corn grains as measured in practice. Strong Raman peaks were discovered in the original spectra after pretreatment at 476, 1 006, 1 156, 1 459, 1 519, 1 597, and 1 633 cm-1. It indicated the presence of lignin, lutein, and corn starch. Measurements were made of the Raman spectra of longitudinal corn sections from various years. After spectral normalisation, the relative peak intensities and areas of all peak sites were determined. In addition to the surface-based Raman peaks, fresh Raman peaks in the 850～1 450 cm-1 range had also been discovered. With more storage years, distinct changes occur in the starch contents Raman intensity, peak area, and FWHM at 475, 863, 944, 1 260, 1 338, and 1 378 cm-1. It demonstrates how its molecular structure, substance, and makeup have distinctly altered. This modification essentially fits the corn starch aging process. The Raman peaks at 1 080 and 1 130 cm-1 revealed a significant variation in the vertical section Raman spectra of maize across three years. While the Raman peak at 1 192 cm-1 only occurs in the spectrum of maize stored in 2020, the peak strength and FWHM value varies to variable degrees, the Raman peak at 1 080 cm-1 is only present in the spectrum of corn stored in 2018 and 2019. It suggests that the reciprocal conversion of specific carbohydrate compounds in starch can be utilized as a distinctive peak to distinguish between corn samples from various years. The peak strength and peak area values were assessed by linear fitting with the year after the physical properties of the peak sites associated withlutein content were compared. The coefficients were above 0.9, and the fitting degree was good, showing that the lutein concentration declined linearly as storage duration increased. The loss of lutein has varied impacts on the vibration of chemical bonds, with the stretching vibration of the CC double bond being the most affected. It is discovered by comparing and evaluating the spectrum of lutein acquired from theoretical calculation. This study investigates the changes in the Raman spectra of corn with time using density functional theory calculations and experimental comparisons. It gives a definite basis for identifying corn years using Raman spectroscopy. It increases the application of spectroscopy in agricultural product quality detection and analysis by analysing the internal composition variations of maize in different years..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2166 (2024)

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Retrieval of Plant Carotenoids and Chlorophyll Contents With Model Constraints and Machine Learning

TANG Fu-rui, XU Yuan-yuan, GENG Yan, CAI Gu-bin..., YANG Fan, LI Yu-chen and JI Ying|Show fewer author(s)

The chlorophyll and carotenoid content is an important indicator for evaluating the health status of plants. The PROSPECT model, coupled with machine learning, has been widely used to retrieve the biochemical properties of vegetation. However, the application of the coupled model is limited due to the differences betwe The chlorophyll and carotenoid content is an important indicator for evaluating the health status of plants. The PROSPECT model, coupled with machine learning, has been widely used to retrieve the biochemical properties of vegetation. However, the application of the coupled model is limited due to the differences between the leaf-directional hemispherical reflectance factor (DHRF) spectra and the bidirectional reflectance factor (BRF) spectra. This paper utilizes the leaf spectral database of North American plant (EcoSIS) as the experimental dataset and introduces the PROSPECT model as an additional constraint for machine learning. This approach creates a hybrid dataset by employing wavelet continuous wavelet transform (CWT) to generate the wavelet coefficient spectrum and the derivative spectrum generated by the first-order derivative (FD). Three kinds of feature extraction algorithms, namely competitive adaptive reweighting algorithm (CARS), successive projection algorithm (SPA), and principal component analysis (PCA) were applied to extract spectral features for chlorophylls and carotenoids in the full-spectral domains and the subdomain of VNIR spectroscopy. Based on the above 12 combinations of different methods, artificial neural network (ANN) prediction models for chlorophyll and carotenoids were separately established. The results show that the simulated data under the constraint of the PROSPECT model enhanced the quality of the training set for machine learning to a certain extent. Additionally, the spectra processed by the first-order derivatives and wavelet transforms were able to reduce better the bias between the simulated spectra of the DHRF and the measured spectra of the BRF. The best inversion of leaf chlorophyll is achieved with the FD+CARS combination in the whole spectral domain, yielding a test set R2 of 0.806 4 and RMSE of 2.911 4. Meanwhile, the CWT+CARS combination in the VNIR spectral sub-domain offers the best results for leaf carotenoids, with a test set R2 of 0.797 2 and RMSE of 0.414 1. The proposed method can provide researchers with a reference to extract biochemical characteristics of plant leaves more accurately and efficiently from BRF spectra and other near-end reflectance images..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2174 (2024)

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Application of Hyperspectral Imaging Technology in the Identification of Composite Adulteration Type in Beef Balls

KONG Li-qin, NIU Xiao-hu, WANG Cheng-lei, FENG Yao-ze, and ZHU Ming

The complexity of the meat processing process presents significant challenges in detecting adulteration in meat products. This study uses hyperspectral technology to identify and analyze adulteration in beef meatballs. To establish the models, different proportions (20%, 40%, and 80%) of pork/chicken were added to minc The complexity of the meat processing process presents significant challenges in detecting adulteration in meat products. This study uses hyperspectral technology to identify and analyze adulteration in beef meatballs. To establish the models, different proportions (20%, 40%, and 80%) of pork/chicken were added to mince beef to obtain single adulterated samples, respectively. Subsequently, pork and chicken were mixed in 2∶8, 5∶5, and 8∶2 ratios to prepare samples for composite adulteration under three different gradients (20%, 40%, and 80%). In addition, fried adulterated beef balls were also prepared to test the applicability of classification models. Hyperspectral data of the adulterated samples were collected and preprocessed using five different methods. Adulteration identification models were developed using the Extreme Learning Machine Classification (ELMC) and Support Vector Classification (SVC) algorithms. Feature wavelengths were extracted using the Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS), developing corresponding simplified models. The results showed that the performance of the raw/cooked beef ball classification models SVC model based on full wavelength was better than that of ELMC. In contrast, the simplified model based on characteristic wavelength showed a contrary trend. For the classification of raw beef balls, the ELMC model (SPA-ELMC-Raw) established based on the 44 characteristic wavelengths selected by SPA yielded the best performance, with classification accuracies of 97.17% for both the calibration set and prediction set. For the classification of cooked beef balls, the ELMC model (CARS-ELMC-Cooked) established based on the 38 characteristic wavelengths selected by CARS showed the highest performance, with classification accuracies of 97.17% and 96.23% for the calibration set and prediction set, respectively. The results indicated that hyperspectral imaging technology proves to be an effective, rapid, and accurate method for discriminating between different types of adulteration in raw and cooked meat. This provides a strong theoretical basis for developing portable detection equipment..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2183 (2024)

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Saturation Intensity Analysis of LIF Received Optical Power of Oil-In-Water Emulsion in Emulsified Oil Spill on Sea Surface

MA Qin-yong, CUI Yong-qiang, WANG Zhi-wei, and KONG Ling-fu

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2192 (2024)

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X-Ray Fluorescence Technology Based on Monolithic Polycapillary X-Ray Focusing Lens in Helium Atmosphere

LI Hui-quan, SUN Xue-peng, SHAO Shang-kun, YUAN Tian-yu..., HUA Lu, ZHONG Yu-chuan, LIU Zhi-guo and SUN Tian-xi|Show fewer author(s)

When X-ray fluorescence (XRF) is used in analyzing the element composition of the sample in air atmosphere, the strong absorption from the air to the low-energy characteristic X-rays from such low atomic number (Z) elements as Cl, K, Ca, and so on, deeply affect the analysis for them.To avoid the strong absorption from

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2198 (2024)

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Discrimination of Chuzhou Chrysanthemum Tea Grades Using Noise Discriminant C-Means Clustering

WU Bin, XIE Chen-ao, CHEN Yong, WU Xiao-hong, and JIA Hong-wen

Near-infrared (NIR) spectroscopy detection technology can reflect the measured samples organic chemical composition and structural information by detecting the spectral features in the NIR region. During the material composition analysis, NIR spectroscopy often involves a significant amount of wavelength data, resulti Near-infrared (NIR) spectroscopy detection technology can reflect the measured samples organic chemical composition and structural information by detecting the spectral features in the NIR region. During the material composition analysis, NIR spectroscopy often involves a significant amount of wavelength data, resulting in relatively high data dimensions. Furthermore, spectra are susceptible to phenomena such as overlap and redundancy, which impact the models performance. Therefore, we proposed a noise discriminant C-means clustering (NDCM) algorithm that combined fast generalized noise clustering (FGNC) and fuzzy linear discrimination analysis (FLDA). NDCM can realize the extraction of data identification information and data space compression in the fuzzy clustering process, which can achieve higher clustering accuracy. The fuzzy membership degree and the cluster centers obtained by fuzzy C-means clustering (FCM) on the near-infrared spectral data of Chuzhou chrysanthemum tea are used as the initial fuzzy membership degree and initial clustering centers of NDCM, respectively, so that NDCM has the advantages of fast clustering speed and high accuracy. The FCM algorithm is sensitive to noisy data, while the NDCM algorithm can perform better when dealing with noisy data in spectra. In this study, 240 samples of Chuzhou chrysanthemum tea with three quality grades, namely special grade, first grade and second grade, were selected as experimental samples. A portable NIR spectrometer (NIR-M-F1-C) was used to collect the NIR spectra of Chuzhou chrysanthemum tea, and they are the 400-dimensional data. At first, the NIR spectra were pretreated with Savitzky-Golay filtering and multivariate scattering correction (MSC) to reduce spectral scattering and noise. Secondly, the dimensionality of the spectral data was reduced by principal component analysis (PCA), and the dimensionality of the data after PCA reduction was 6. Next, linear discriminant analysis (LDA) was applied to extract the discriminant information in the spectral data of Chuzhou Chrysanthemum tea and further transform the data space dimension into 2 dimensions. Finally, three algorithms, i.e. FCM, FGNC and NDCM, were utilized to perform cluster analysis on the processed data to accurately classify chrysanthemum tea. The experimental results exhibited that when the weight index m=2.5, the clustering accuracy rates of FCM, FGNC and NDCM were 92.42%, 98.48%, and 100%, respectively. The clustering time of NDCM was slightly longer compared to FGNC. FCM had 27 iterations to reach convergence, while FGNC and NDCM took 13 and 10 times, respectively. NIR spectroscopy combined with MSC, Savitzky-Golay filtering, PCA, LDA and NDCM can provide a clustering model to accurately identify Chuzhou chrysanthemum tea quality..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2202 (2024)

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Estimation of Eggplant Leaf Nitrogen Content Based on Hyperspectral Imaging and Convolutional Auto-Encoders Networks

WANG Hao-yu, WEI Zi-yuan, YANG Yong-xia, HOU Jun-ying..., SUN Zhang-tong and HU Jin|Show fewer author(s)

Leaf nitrogen content (LNC) is crucial for assessing plant growth status and photosynthetic capacity. Accurate LNC can aid in the rational control of nitrogen fertilizer application, which is critical for achieving efficient agricultural production. Chemical analysis methods can accurately detect nitrogen content. Howe Leaf nitrogen content (LNC) is crucial for assessing plant growth status and photosynthetic capacity. Accurate LNC can aid in the rational control of nitrogen fertilizer application, which is critical for achieving efficient agricultural production. Chemical analysis methods can accurately detect nitrogen content. However, it often requires destructive sampling and cumbersome steps, which are difficult to measure in real-time. Spectral technology can enable nondestructive detection of LNC, but the high dimensionality and noise inherent in spectral data make accurate estimation challenging for precision agriculture. To achieve accurate prediction of nitrogen content in eggplant leaves, this paper proposed a feature extraction method of spectral data based on hyperspectral imaging (HSI) technology and a one-dimensional convolutional autoencoder network (CAE). The proposed method utilized pixel-level spectral data to train the CAE, fully utilizing the HSI data of leaves. This can extract deep features that retain local spectral features related to the nitrogen content distribution on the leaf surface, reducing data dimension, filtering out noise, and enhancing the accuracy and stability of the nitrogen content prediction model. In this paper, we set up four nitrogen application gradients for eggplant, obtained leaf samples with varying nitrogen content using culture, and measured their HIS data. Multiple scattering correction algorithm was used for data preprocessing. The HSI-CAE method, competitive adaptive reweighting (CARS) algorithm, and random frog (RF) algorithm were used to extract spectral datas deep features and characteristic wavelengths, respectively. The partial least squares regression(PLSR)models were built based on these features. The influence of deep features and different feature wavelength combinations on the accuracy of the prediction model was compared to determine the optimal feature extraction method. The results were as follows: the test set determination coefficient of the prediction model, established by using deep features from the CAE encoder of different depths, was greater than 0.85. When 28-dimensional features were output, the test set determination coefficient was 0.910 2, and the root mean square error was 3.118 9 mg·g-1. It was found that the CAE-PLSR model has the best prediction performance, which verified the feasibility and superiority of the HSI-CAE feature extraction method for estimating nitrogen content in eggplant leaves. In conclusion, the HSI-CAE feature extraction method can efficiently analyze HSI data and extract its deep features. These features contain information highly related to nitrogen content. The deep feature modeling used in this research greatly reduced the complexity of the model. It effectively improved the accuracy of the nitrogen content prediction model, providing a new way of implementing accurate prediction of nitrogen content based on the HSI technology..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2208 (2024)

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Rapid Determination of Chlorella Sorokiniana Lutein Production Based on Snapshot Multispectral Feature Wavelengths

SHEN Ying, ZHAN Xiu-xing, HUANG Chun-hong, XIE You-ping..., GUO Cui-xia and HUANG Feng|Show fewer author(s)

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2216 (2024)

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Spectral Feature Band Selection and Interval Partial Least Squares Modeling of Short-Range Nitrification Process

SONG Yu, LI Wei-hua, XUE Tong-zhan, YU Li, and SHEN Hui-yan

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2224 (2024)

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Effect of Temperature and Light on Fluorescence Characteristics of Mineral Transformer Oil for EMU

ZHAO En-bo, SHI Ze-lin, LIU Yun-peng, and LI Chen-xi

The oil leakage fault detection of traction transformers isa crucial component of Electrical Multiple Unit (EMU) operation fault detection. In the near-infrared image of the bottom plate collected by the trouble of moving the EMU detection system, the characteristics of leakage transformer oil traces are similar to tho The oil leakage fault detection of traction transformers isa crucial component of Electrical Multiple Unit (EMU) operation fault detection. In the near-infrared image of the bottom plate collected by the trouble of moving the EMU detection system, the characteristics of leakage transformer oil traces are similar to those of water traces, which cannot be distinguished by the trouble inspectors and image recognition algorithms, resulting in a high false alarm rate of the oil leakage fault and affecting the operation and maintenance efficiency of the train.Water lacks fluorescence properties, whereas mineral transformer oil exhibits ultraviolet fluorescence, so fluorescence imaging can differentiate the two traces. Moreover, varying operating seasons, regions, and periodsof EMU expose the leaked transformer oil to diverse environmental conditions, affecting its fluorescence properties. The fluorescence properties of the leaked oil decide the choice of excitation light source wavelength and imaging band for the fluorescence imaging system.Hence, samples under different temperatures and light conditions are first prepared to explore the fluorescence properties of mineral transformer oil used by EMU and the impact of two major environmental factors, temperature, and light, on its fluorescence properties. Then, the three-dimensional fluorescence spectra of the samples were collected by the fluorescence spectrometer and subsequently analyzed, the fluorescence characteristics of the samples and the effect of temperature and illumination on the location and intensity of fluorescence characteristic peaks. The results show that the mineral transformer oil for EMU has two strong fluorescence excitation/emission peaks located at 350/382 and 350/402 nm, respectively. The increase in temperature causes a decrease in fluorescence intensity but does not change the position of the fluorescence peaks. The increase of light intensity and the extension of light duration cause a decrease in fluorescence intensity but do not significantly change the position of fluorescence peaks. The experimental results provide experimental evidence and data support for fluorescence imaging to detect the leakage fault of mineral transformer oil for EMU..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2233 (2024)

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Extraction Spectrophotometric Determination of Octadecylamine in Potassium Chloride

SONG Zhong-mei, LU Miao, ZHANG Hui-fang, ZHANG Peng-rui..., FANG De-zhen, MA Liang, LIU Qing-qing, YU Xue-feng, LIU Hai-ning, YE Xiu-shen, MA Zhen, PENG Wen-bo and WU Zhi-jian|Show fewer author(s)

Octadecylamine is a flotation collector commonly used to produce potassium chloride by cold crystallization-positive flotation, and its dosage will significantly affect the flotation separation efficiency. In addition, octadecylamine will adsorb on the surface of potassium chloride and inevitably remain in potassium ch Octadecylamine is a flotation collector commonly used to produce potassium chloride by cold crystallization-positive flotation, and its dosage will significantly affect the flotation separation efficiency. In addition, octadecylamine will adsorb on the surface of potassium chloride and inevitably remain in potassium chloride products, which is inconducive to developing high-purity potassium salt products. To meet the requirement of determination of octadecamide potency in potassium chloride, an extraction spectrophotometry was developed, based on the principle of van der Waals force and hydrogen bonding between octadecylamine and bromophenol blue with butyl acetate as extractant and bromophenol blue sodium salt as chromogenic agent. The effects of solution pH, dodecyl morpholine, co-existing salt, equilibrium time, and the amount of chromogenic agent on the concentration measurement of octachylamine were investigated. The results show that when the solution pH increases from 3 to 9, the absorbance of the extract decreases because the complexation between octachylamine and bromophenol blue weakens. The polarity of the complex molecules increases. When the solution pH is less than 5, dodecyl morpholine and bromophenol blue can also form colored complexes. While pH is between 6 and 9, the absorbance tends to zero. The ionic strength of the coexisting solutions of potassium chloride, sodium chloride, potassium sulfate and magnesium chloride increases, weakening the hydrogen bonding. Still, the salting-out effect of the complex molecules is enhanced, and the trace Li+, NH+4 and B in the solution have little effect on the absorbance. The absorbance does not conform to Lambert-Beer law when the amount of chromogenic agent is too large. Besides, equilibrium time has little effect on the absorbance. The determination conditions in potassium chloride solution are as follows: The solution pH is 6, the ionic strength is 1 mol·L-1, and the amount of 2 mmol·L-1 chromogenic agent is 0.5 mL. 5 mL butyl acetate is added to the 25 mL aqueous solution adjusted by buffer after 5 min of reaction for extraction. After 2 min of delamination equilibrium, the absorbance of the extraction solution is tested at 458 nm. The working curve is A=0.049 49c+0.066 24 (R2=0.992 3, ε=1.33×104 L·mol-1·cm-1, 0~10 mg·L-1). The relative standard deviation of this method was 0.33%~2.63%, the mean relative error was -0.90%, and the mean relative error of the system of octadecylamine and dodecylmorpholine was -0.25%. The content of octadecamide in the filter liquor derived from washing potassium chloride after positive flotation was 8.66 mg·L-1 and recoveres were 95.5%~106% by this method. The extraction spectrophotometry has been proven to be appropriate for detecting the concentration of octadecamide in the production of potassic fertilizer in salt lakes..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2240 (2024)

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Determination of Chromium Content in Nickel Base Alloy by Matrix Matching Calibration Method of Inductively Coupled Plasma Atomic Emission Spectrometry

LIU Jie, TAN Sheng-nan, QI Zhen-nan, REN Ling-ling..., GE Jing-jing and SUN Zhong-hua|Show fewer author(s)

Chromium has multiple roles in nickel-based alloy and has outstanding contributions to increasing the wear resistance of alloy coating. At the same time, chromium can form a dense oxidation protective film under the action of high-temperature gas, which significantly improves the alloys resistance to high-temperature Chromium has multiple roles in nickel-based alloy and has outstanding contributions to increasing the wear resistance of alloy coating. At the same time, chromium can form a dense oxidation protective film under the action of high-temperature gas, which significantly improves the alloys resistance to high-temperature oxidation and thermal fatigue performance. However, in the case of high chromium content, chromium easily forms harmful phases with titanium, aluminum, molybdenum, and other elements in the alloy and reduces its strength. So, the quasi-determined value of chromium in nickel-base alloy is very important. Nickel base alloy samples are digested by microwave digestion using hydrofluoric acid, hydrochloric acid, and nitric acid. The matrix is matched by inductively coupled plasma atomic emission spectrometry, and the calibration solution is prepared to determine-chromiums net strength in the nickel-base alloys analysis spectrum line to calculate the content. Given the interference of high-content elements such as Ni, Mo, and Fe in nickel-base alloys on the determination of Cr content, 266.602 nm with fewer interference elements and moderate sensitivity was selected as the analytical spectral line; the matrix effect and the interference effect of coexisting elements were systematically investigated. When preparing the calibration solution, the elements with content greater than 5% were matched with the same content. Prepare the low standard calibration solution Kl Crfor the chromium element, prepare the high standard calibration solution Kh Crfor the chromium element, prepare the sample solution KS Cr, and use the ICP-OES spectrometer to determine the net strength of the low standard calibration solution Kl Cr, the sample solution KS Cr, and the high standard calibration solution Kh Cron the analytical spectrum line in turn, and calculate the chromium content according to the formula. The methods accuracy was evaluated using standard reference materials, and the reliability of the method was verified by comparative analysis using the YS/T 539.4-2009 standard. The results showed that the limit of detection (LOD) and limit of quantification (LOQ) of the method were 0.05 and 0.10 μg·mL-1, respectively, and the relative standard deviation (RSD) of the determination result is less than 2.5%. The relative error (RE) of the comparison analysis result between the measured value and the certified value of the standard reference material is less than 2.5%. This method is easy to operate and accurate to detect. It broadens the detection range for the determination of chromium content in nickel-based alloys by ICP-OES. The lower detection limit is reduced from 2.0% to 0.1%, and the upper limit of detection range is increased from 30% to 33.5%. It greatly improves the efficiency and accuracy of the detection of chromium content in nickel-base alloys. It is suitable for determining chromium content in various brands of nickel-base alloys..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2250 (2024)

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Quantitative Fourier Infrared Spectroscopy Model of a Single CO2 System Under High Temperature and Pressure and Its Application to Natural Inclusions

HUANG Ya-hao, XUE Yi-fan, WEN Zhi-gang, CHEN Jun-lin..., QIAO Zhan-feng and LIU Yi-cheng|Show fewer author(s)

Fourier transform infrared spectroscopy is a commonly used in-situ non-destructive analysis technique for qualitatively identifying natural fluid inclusion components. The quantitative analysis of temperature-pressure components of natural inclusions is the focus, as well as the difficulty and development direction of Fourier transform infrared spectroscopy is a commonly used in-situ non-destructive analysis technique for qualitatively identifying natural fluid inclusion components. The quantitative analysis of temperature-pressure components of natural inclusions is the focus, as well as the difficulty and development direction of laser spectroscopy geological applications. The quantitative method and Fourier transform infrared spectroscopy model for natural fluid inclusions have not been established. The infrared spectra of a single CO2 system with two stretching vibration peaks of 2ν2+ν3 and ν1+ν3 were collected and observed at 40～80 ℃ and 60～500 bar for the first time by high-pressure capillary silicon tube packaging technology combined with Fourier infrared spectrometer. The infrared spectral parameters at different temperature and pressure ranges were obtained. The peak area and peak displacement of the Fourier infrared spectrum under this system were obtained by fitting and calculation. The calibration model of the density and peak area, pressure, and peak area of the Fourier infrared spectrum fluid under the 2ν2+ν3 and ν1+ν3 stretching vibration bimodal system of CO2 was established. The variation characteristics of bimodal stretching vibration of 2ν2+ν3 and ν1+ν3 of CO2 with temperature and pressure are clarified, and the variation of peak displacement with temperature and pressure is discussed. Using Fourier transform infrared spectroscopy (FT-IR) of a single CO2 component, the densities of pure CO2 inclusions in quartz fractures of the Huangliu Formation reservoir in the Yinggehai Basin were calculated at 40 ℃ by collecting the double peak area of stretching vibration of the inclusions. At the same time, the Raman spectra of the inclusions were collected at room temperature, and the densities calculated by two different in-situ analysis methods were compared to verify the feasibility of FT-IR quantitative analysis. Compared with Raman quantitative analysis, Fourier transform infrared spectroscopy quantitative analysis is not limited by experimental instruments and environment. Its quantitative method can avoid errors caused by different laboratories where the instrument is located..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2256 (2024)

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Qualitative Modeling Method of Mango Species in Near Infrared Based on Attention Mechanism Residual Neural Network

WANG Shu-tao, WAN Jin-cong, LIU Shi-yu, ZHANG Jin-qing, and WANG Yu-tian

In recent years, the rapid development of modern spectral detection technology is closely related to deep learning. As an end-to-end model, the deep neural network can get more information from the spectra, thus improving the robustness of the model. A one-dimensional residual neural network (1D-AD-ResNet-18) model bas

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2262 (2024)

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Discrimination of Lettuce Storage Time Based on Near-Infrared Spectroscopy Combined With Fuzzy Uncorrelated QR Analysis

HU Cai-ping, FU Zhao-min, XU Hong-jia, WU Bin, and SUN Jun

Lettuce is one of the vegetables that people often eat, and the storage time of lettuce is an important factor affecting the freshness of lettuce. Therefore, it is necessary to develop a simple, fast, and non-destructive method to identify the storage time of lettuce. Near-infrared spectroscopy (NIR) can quickly and ac Lettuce is one of the vegetables that people often eat, and the storage time of lettuce is an important factor affecting the freshness of lettuce. Therefore, it is necessary to develop a simple, fast, and non-destructive method to identify the storage time of lettuce. Near-infrared spectroscopy (NIR) can quickly and accurately detect the near-infrared spectrum of lettuce to realize the non-destructive identification of lettuce storage time. However, noise and redundant signals are in the NIR spectral data collected by the near-infrared spectrometer. To eliminate the noise information of the spectrum and extract the feature information, a novel method was proposed to identify the storage time of lettuce based on NIR spectroscopy and fuzzy uncorrelated QR analysis (FUQRA). Firstly, principal component analysis (PCA) was used to reduce the dimension of the original spectral data from 1557 dimensions to 22 dimensions. Secondly, after the feature vectors are obtained by fuzzy uncorrelated discriminant transformation (FUDT), the discriminant vector matrix is established by using the feature vectors, and the final discriminant vector matrix is obtained by QR decomposition. Finally, the k-nearest neighbor algorithm was utilized for classification. 60 fresh lettuce samples were selected as the research object. Firstly, the NIR spectral data of lettuce samples were collected by Antaris Ⅱ near-infrared spectrometer and detected once every 12 hours. Secondly, multivariate scatter correction (MSC) was used to reduce the noise signal in the NIR spectra. To verify the effectiveness of the proposed method, the experimental results were compared by four classification models: principal component analysis (PCA) combined with a K-nearest neighbor (KNN) algorithm, PCA and fuzzy linear discriminant analysis (FLDA) combined with KNN algorithm, PCA and fuzzy uncorrelated discriminant transformation (FUDT) combined with KNN algorithm and PCA and FUQRA combined with KNN algorithm. The classification accuracies produced by different values of the weight indexm were studied, and the most appropriate parameters were selected: m=2, K=3. Under this condition, the classification accuracies of the four algorithms were 43.33%, 96.67%, 96.67%, and 98.33%, respectively. It can be seen that compared with the other three algorithms, FUQRA can better realize the identification of lettuce storage time..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2268 (2024)

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Deep Leaning Classification of Novel Coronavirus Raman Spectra Enhanced by CGAN

ZHANG Yin, FENG Cheng-cheng, XIA Qi, HU Ting, and YUAN Li-bo

The emergence of the novel coronavirus has caused significant losses in the global economy and public safety. The need for efficient detection and diagnosis has become urgent as the virus continues to inflict damage worldwide. Accurate and fast diagnosis of the novel coronavirus is critical for epidemic prevention and The emergence of the novel coronavirus has caused significant losses in the global economy and public safety. The need for efficient detection and diagnosis has become urgent as the virus continues to inflict damage worldwide. Accurate and fast diagnosis of the novel coronavirus is critical for epidemic prevention and control. With the development of technology, deep learning has made many breakthroughs in detection and recognition, attracting widespread attention from researchers. However, deep learning requires a large amount of data for model training, and the collection of Raman spectra data for the novel coronavirus is limited by devices and environmental factors, making it difficult to obtain large amounts of data. The limited training data can hinder the training of deep learning models and limit their accuracy, resulting in poor performance in actual detection. To address this problem, this study introduces the CGAN adversarial network to automatically extract features from Raman spectra data and generate new spectra to expand the dataset for the novel coronavirus. These methods can effectively increase the training sets size and improve the models accuracy. Using the enhanced Raman spectra dataset and deep neural networks, as well as traditional machine learning methods, including logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors algorithm, the diagnosis of COVID-19 is performed. The experimental results show that the deep neural network has a prediction accuracy of 98% for whether a patient is infected with the novel coronavirus, which is higher than traditional machine learning algorithms, demonstrating the superiority of deep learning models in Raman spectra detection of the novel coronavirus. We also compared the performance of the datasets before and after augmentation in different models, proving the effectiveness of data augmentation. Compared with traditional detection methods, this method is non-invasive, fast, and accurate, providing a new approach for biomedical detection of the novel coronavirus. The method proposed in this study can assist in rapidly and accurately detecting the novel coronavirus. It can be applied not only to the diagnosis of the novel coronavirus but also to the diagnosis of other diseases, having practical application value..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2273 (2024)

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Spectral Molecular Band Detection Based on W Geometry

CHEN Fen, WANG Ying, and LIU Fu-yao

This study focuses on identifying and detecting spectral molecular bands caused by changes in internal energy levels of molecules, which contributes to the research of stellar spectral types and parameter estimation. First, considering the curve trend of molecular bands, pseudo molecular bands that have a W shape but a

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2279 (2024)

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Classification of Star Spectrum Based on Multi-Scale Feature Fusion

HAN Bo-chong, SONG Yi-han, and ZHAO Yong-heng

In recent years, there has been a significant increase in the number of observed astronomical spectra. This has led to greater demands for automatic classification and analysis of spectra in large-scale spectroscopic surveys. In this study, we introduce a multi-scale feature fusion-based stellar spectral classification In recent years, there has been a significant increase in the number of observed astronomical spectra. This has led to greater demands for automatic classification and analysis of spectra in large-scale spectroscopic surveys. In this study, we introduce a multi-scale feature fusion-based stellar spectral classification model (MSFnet) that leverages the strengths of Convolutional Neural Networks (CNNs) in classification tasks and employs a multi-scale feature fusion module to extract spectral features at various scales for the prediction of stellar spectral types. The proposed MSFnet architecture consists primarily of a multi-scale feature fusion module and a CNN with four convolutional layers, two max-pooling layers, and one fully connected layer. To mitigate overfitting, dropout is incorporated into the model, enhancing its robustness by reducing dependence on specific local features. The dataset employed in this study is derived from the LAMOST DR9 database. Before training, data preprocessing is performed, which includes uniform resampling of spectra and min-max normalization. The experiment uses Python 3.9 and the PyTorch deep learning framework to build the network. The experimental section is divided into two parts: the first part investigates the relationship between the number of layers in the CNN, the number of feature maps, and the classification accuracy; the second part compares the performance of the proposed MSFnet model and the Resnet18 model using evaluation metrics such as precision (P), recall (R), and F1 score. Both models training, validation, and test sets are split according to a 6∶2∶2 ratio to maintain consistency in training samples. Results demonstrate that the highest accuracy is achieved using a CNN with four convolutional layers and 16 feature maps. Based on this finding, we propose the MSFnet model, which combines the feature fusion module with the CNN. Compared to the 18-layer residual neural network model, the MSFnet model has a more straight forward structure and performs similarly on the evaluation metrics. The performance in the metrics above is on par with that of the Resnet18 model. Furthermore, it demonstrates superior classification efficacy for spectra types A, F, and K, accompanied by enhanced speed. With an accuracy of nearly 97% on the test set, the MSFnet model outperforms traditional CNN and Resnet18 models, indicating its potential to improve the accuracy of automatic spectral classification significantly..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2284 (2024)

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Spectroscopic Analysis and New Findings of Jade Artifacts Unearthed From the Beiyinyangying Site in the Shanghai Museum Collection

GU Xian-zi, ZHENG Xin-yu, ZHANG Yue-feng, YI Chuan-zhen, and QIU Zhi-li

Since their excavation nearly 70 years ago, the jade artifacts from the Beiyinyangying site have been lacking in scientific testing and analysis. To address this issue, 13 jade artifacts from the Beiyinyangying site in the Shanghai Museum collection underwent non-destructive testing and analysis using a super-depth-of-

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2289 (2024)

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Inversion of Chlorophyll and Water Content of Hami Melon Leaves Based on Spectrophotometry on Study

LI Long-jie, SHI Yong, and GUO Jun-xian

In order to predict the water content and chlorophyll content of cantaloupe leaves quickly and accurately and improve the accurate management level of Cantaloupe crops, the leaves of cantaloupe in three different growth stages, namely the growing stage, the flowering stage, and the fruiting stage, were selected as expe In order to predict the water content and chlorophyll content of cantaloupe leaves quickly and accurately and improve the accurate management level of Cantaloupe crops, the leaves of cantaloupe in three different growth stages, namely the growing stage, the flowering stage, and the fruiting stage, were selected as experimental research objects by using the spectrophotometry technology. The correlation changes of leaf temperature, leaf water content, and chlorophyll content with LAB eigenvalues of color space were studied in three different collection periods: 9:00—10:00, 14:00—15:00, and 20:00—21:00, respectively. The least square method (LS) was used to preprocess the changes in temperature, water content, chlorophyll content, and color eigenvalues of different samples, and the eigenvalues with the best fit were selected for regression analysis and prediction model verification. The results showed that① Leaf temperature, leaf water content, and chlorophyll content had different color eigenvalues under different parameters. ② For leaves with 84%~93% moisture content, leaf temperature, and chlorophyll content were negatively correlated with leaf moisture content. ③ the chlorophyll content and leaf water content and color space LAB, there is a linear correlation. As the leaf water content -rises, L is on the rise, and the color becomes shallow gradually with light green leaves; with the increase of chlorophyll, L has a downward trend, showing the leaf color gradually deepens with black-green, exists in all types of sample data, L B positive. ④ Through model prediction and evaluation, random forest (RF), partial least squares (PLS), support vector machine (SVM), and LASSO can be used to predict chlorophyll content effectively. Among the chlorophyll prediction models, RF had the best prediction performance, R2c=0.939, RMSEC=0.868 and MAE=0.686, R2p=0.915, RMSEP=1.194 and MAE=0.942. ⑤ Through model prediction and evaluation, RF, PLS, AdaBoost, and polynomial regression (POLYNOMIAL) can effectivelypredict leaf water contents. In the prediction model of leaf moisture content, the POLYNOMIAL prediction performance is the best, R2c=0.884, RMSEC=0.005 9 and MAE=0.005 2, R2p=0.920 and RMSEP=0.006 2 and MAE=0.005 7. The spectrophotometry method can effectively and rapidly determine leaf water and chlorophyll content, which is expected to provide an optional feasible method for nondestructive, rapid, and accurate determination of leaf water and chlorophyll content..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2296 (2024)

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Hyperspectral Prediction of Soil Organic Matter Content Using CARS-CNN Modelling

LI Hao, YU Hao, CAO Yong-yan, HAO Zi-yuan..., YANG Wei and LI Min-zan|Show fewer author(s)

Convolutional Neural Network (CNN) has a great advantage in data feature extraction, as it can fully acquire data features and has better generalization than traditional models. This study used a hyperspectral prediction method and modeling of Soil Organic Matter (SOM) content based on CNN. Using 320 soil samples from Convolutional Neural Network (CNN) has a great advantage in data feature extraction, as it can fully acquire data features and has better generalization than traditional models. This study used a hyperspectral prediction method and modeling of Soil Organic Matter (SOM) content based on CNN. Using 320 soil samples from Shangzhuang Experimental Station, Changping District, Beijing, 807 spectral bands within 350~1 700 nm in the visible-near-infrared (VIS-NIR) were extracted, and the spectral data were denoised and transformed by the multivariate scattering correction (MSC) and the first-order differential transform. Successive projection algorithm (SPA) and competitive adaptive reweighted Sampling (CARS) were used to screen the sensitive wavelengths to realize the dimensionality reduction of the spectral data, respectively. To solve the problems of poor generalization of traditional means as well as the complexity and overload of deep CNN networks, based on the CARS and SPA algorithms, a shallow CNN model prediction based on 6 convolutional layers is proposed, and 1D-CNN1 and 1D-CNN2 with different convolutional sizes and number of convolutions are compared to find the optimal network parameters. By comparing the performance of VGG16, Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Random Forests (RF) to build a prediction model in the feature wavelength and the full waveform. The optimal model was determined. The results show that compared with the full-spectrum band and SPA filtering algorithms, the model based on CARS filtering feature wavelength modeling performs better, and the number of bands is compressed to 8% of the full-wavelength band, which effectively realizes the dimensionality reduction of the spectral data. Comparing the full-band data, 1D-CNN1 and 1D-CNN2 based on CARS screening wavelengths performed better, with the model predicted R2 improved by 0.028 and 0.018, respectively, and the RMSE reduced by 0.150 and 0.107 g·kg-1, respectively. Overall, the 1D-CNN1 model based on CARS performs the best, with the predicted R2=0.846 and the RMSE decreased by 0.150 g·kg-1, respectively 0.846, and RMSE=3.145 g·kg-1, which reduces the network load while improving the model accuracy, and also proves that small-size convolution outperforms a larger number of large-size convolutions for better acquisition of data features. The SOM content prediction model is established by CARS screening feature wavelengths combined with shallow CNN, which provides a method and reference for establishing a high-precision SOM content prediction model..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2303 (2024)

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Prediction of Total Nitrogen Content in Brown Soil Based on Hyperspectral and Combined Prediction Model

ZHANG Xiu-quan, MA Shi-xing, LI Zhi-wei, ZHENG De-cong, and SONG Hai-yan

Accurately grasping the total nitrogen content of farmland soil is significant for evaluating soil fertility and applying nitrogen fertilizer reasonably. To comprehensively utilize the advantages of each single prediction Model, improve the overall prediction performance, reduce the variance of the model, and improve t Accurately grasping the total nitrogen content of farmland soil is significant for evaluating soil fertility and applying nitrogen fertilizer reasonably. To comprehensively utilize the advantages of each single prediction Model, improve the overall prediction performance, reduce the variance of the model, and improve the robustness, this study takes farmland brown soil as the research object, and based on near-infrared and visible hyperspectral data, puts forward a Combined prediction model based on standard deviation. CPM was used to predict soil total nitrogen content. Savitzky-Golay smoothing and first-order differential transformation are applied to the original hyperspectral data, and a tree model is used for feature band extraction. Using five single prediction models, Decision Tree Regression (DTR) (Model 1), Gaussian Kernel Regression (GKR) (Model 2), Random Forest Regression (RF) (Model 3), LASSO Regression (Model 4), and Multi-Layer Perceptron (MLP) (Model 5), a combination prediction model is established through a linear combination of single prediction models. The results indicate that: (1) The weights of the five single prediction models in the combined prediction model are obtained by generalized reduced gradient optimization algorithm: ω*1=0.407，ω*2=0.378，ω*3=0.215，ω*4=0，ω*5=0; (2) For all data, the predictive effectiveness of five single prediction models and combined prediction models for predicting soil total nitrogen content is M, respectively M1=0.855，M2=0.856，M3=0.847，M4=0.785，M5=0.796，MCPM=0.880, compared to the maximum predictive validity of a single model, the predictive validity of the combination prediction model has increased by 2.924%; (3) For all data, the prediction accuracy and standard deviation of soil total nitrogen content based on five single prediction models and combined prediction models are E(A1)=0.924，σ(A1)=0.075，E(A2)=0.928，σ(A2)=0.077，E(A3)=0.923，σ(A3)=0.082，E(A4)=0.882，σ(A4)=0.109，E(A5)=0.889，σ(A5)=0.104，E(ACPM)=0.937，σ(ACPM)=0.066, compared to the maximum prediction accuracy of a single model, the combination prediction model has improved prediction accuracy by 0.970% and model stability by 12.000%, making it an optimal combination prediction model. The combined prediction model can effectively estimate the total nitrogen content of farmland brown soil based on visible-near-infrared spectral data and can provide a basis and reference for the rapid monitoring of the total nitrogen content of farmland soil..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2310 (2024)

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Prediction of Total Nitrogen Content of Lettuce Based on UAV Multi-Spectral Vegetation Index

LIAN Bing-rui, LI Ya-hao, ZHANG Jing, LI Chang-qing..., YANG Xiao-dong, WANG Ji-qing, ZOU Guo-yuan, Thompson Rodney and YANG Jun-gang|Show fewer author(s)

China has a huge and intensive open-field vegetable production system. However, serious issues such as low water and nitrogen use efficiencies, as well as excessive fertilizer application, limit the sustainability of the system. To improve the efficiency of production and enhance accurate fertilization in large-scale v China has a huge and intensive open-field vegetable production system. However, serious issues such as low water and nitrogen use efficiencies, as well as excessive fertilizer application, limit the sustainability of the system. To improve the efficiency of production and enhance accurate fertilization in large-scale vegetable cultivation systems, this study was conducted with open-field lettuce. Three treatments of no nitrogen (N0), low nitrogen (N1) and high nitrogen (N2) were established. An unmanned aerial vehicle (UAV) equipped with a multi-spectral camera was used to establish correlations between three multi-spectral vegetation indices (NDVI, RVI, and SAVI) and lettuce chlorophyll content, biomass, crop nitrogen uptake, and total nitrogen content. Models to predict total nitrogen content for single growth stage and multiple growth stages were developed. The results showed that: (1) during rosette and heading stages, NDVI, RVI and SAVI values increased with the amount of applied nitrogen, but that during harvest stage, maximum values occurred with the N1 treatment; (2) NDVI showed a significant correlation with lettuce yield, nitrogen uptake and chlorophyll content during heading stage; and the total nitrogen content of lettuce was significantly correlated with chlorophyll content at p<0.01 level, with a correlation coefficient (R) of 0.51. When considering multiple growth stages together, NDVI values showed a significant correlation with lettuce yield, chlorophyll content, nitrogen uptake, and total nitrogen content at p<0.001 level, with correlation coefficients of 0.85, 0.82, 0.81, and 0.71, respectively. (3) Relationships of exponential, linear, logarithmic and power functions were fitted to the corresponding datasets, and the best prediction model of total nitrogen content for lettuce (N%=16.52ln(NDVI)+73.514) was established in the multiple growth stages. Using the lettuce total nitrogen content prediction model to obtain modeled values of total nitrogen content for the area of a commercial production field on the same farm, the average relative error was 3.2%, RMSE=0.556 6, NRMSE=0.010 8, showing accurate estimation of total nitrogen content. The results show that the model had good accuracy and that it is feasible to diagnose vegetable nitrogen content using unmanned aerial vehicle multi-spectral remote sensing..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2318 (2024)

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Effects of Different Light Qualities on Spectral Characteristics and Mineral Elements of the Pholiota Adiposa

LIU Yi-han, GUO Yuan, ZHENG Wen-gang, ZHANG Xin..., GUO Wen-zhong, WANG Ming-fei and CHEN Xiao-li|Show fewer author(s)

The fruiting body of edible fungi is sensitive to light in the development stage, and there are no studies on the response of growth and nutritional quality of Pholiota adiposa to different spectral irradiation. In this study, Pholiota adipose was grown in an artificial light-type plant factory with pure white light as The fruiting body of edible fungi is sensitive to light in the development stage, and there are no studies on the response of growth and nutritional quality of Pholiota adiposa to different spectral irradiation. In this study, Pholiota adipose was grown in an artificial light-type plant factory with pure white light as control (CK), and four treatments were set up: pure green (G), pure blue (B), pure red (R), and red-blue (RB). The light cycle was 12 h light /12 h darkness. The optimum light quality was determined by analyzing the effects of different light qualities on the traits, spectral characteristics, and mineral elements of Pholiota adiposa. The results showed that red light significantlypromoted the growth of Pholiota adiposestipe and the increase of fruiting body quality by 78.4% and 90.0%, respectively, compared with the control (p<0.05). Blue light significantly increased stipe diameter and pileus diameter by 22.8% and 19.1%, respectively, compared with the control (p<0.05). There was no significant difference in the thickness of the pileus between different treatments, indicating that the light quality had little effect on the pileus thickness. Compared with the control, the Hue value of Pholiota adiposepileus was increased in all treatments (5.3%~28.9% increase), while the Hue value of the stipe was decreased in all treatments (26.3%~46.7% decrease). The color spectral parameters such as C, MACRI, and PRI value of the pileus and stipe of Pholiota adiposa increased under green light treatment, which is more conducive to the coloring of Pholiota adiposa than other light qualities. In actual production, the shapes and colorationof the stipe and pileus can be adjusted by changing the light quality according to different market demands. Compared with the control, all light treatments increased the content of P and K-elements in the Pholiota adipose pileus (by 4%~15% and 7%~16%, respectively) and decreased the content of K, Ca, Mg, Na, and Mn elements in the stipe to different degrees. All light treatments decreased the accumulation of Ca and Na elements in the fruit body of Pholiota adiposa, and the accumulation of other elements except Ca and Na was increased and reached the highest level under green light. Therefore, green light is more favorable to mineral element accumulation in Pholiota adiposa than other light qualities. This study provides a theoretical basis for regulating the light environment in the factory production of the specialty mushroom Pholiota adiposa..

Spectroscopy and Spectral Analysis

Publication Date: Oct. 11, 2024
Vol. 44, Issue 8, 2326 (2024)

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Non-Contact Characterization Method of Birefringence Crystal Based on Terahertz Time-Domain Spectroscopy

YUAN Yuan, ZHANG Tian-yao, ZHANG Zhao-hui, ZHAO Xiao-yan..., LI Xing-yue, LI Bo-yang, WU Xian-hao, XU Bo, YAN Jian-feng, SUN Pu and CAO Can|Show fewer author(s)

The anisotropic detection technology of crystals is gradually developing towards non-contact and non-destructive methods. Terahertz radiation has broad prospects in studying birefringence of anisotropic materials due to its large penetration depth and non-ionization characteristics for many dielectric materials. Quartz The anisotropic detection technology of crystals is gradually developing towards non-contact and non-destructive methods. Terahertz radiation has broad prospects in studying birefringence of anisotropic materials due to its large penetration depth and non-ionization characteristics for many dielectric materials. Quartz, sapphire, liquid crystal, and metamaterials containing sub-wavelength structures exhibit terahertz birefringence. As a common material in polarization functional devices, the parameter measurement is of great significance for developing terahertz devices. The extraction of material birefringence often depends on previous knowledge, such as optical axis direction and crystal thickness. The materials optical axis direction characterizes its anisotropys preferred direction. The appropriate Jones vector can be selected according to experience for crystals with known optical axis orientation. In practical applications, only the terahertz waves linear polarization direction, optical axis, and detection axis can be selected. It is easy to measure the ordinary and extraordinary light and calculate their refractive index directly from the time domain signal. For materials with unknown optical axis direction, it is necessary to rotate the sample to measure in different orientations. In addition, the extraction of birefringence depends on the thickness of the materials. The measured value obtained by vernier caliper or micrometer is quite different from the true value, and it is easy to cause scratches on the samples surface. At the same time, whether it is sample rotating or thickness measurement, human operation will introduce uncertainty for birefringence characterization. Based on the Terahertz time-domain spectroscopy (THz-TDS), a non-contact measurement method for the optical axis direction and the thickness of the birefringent crystal is developed in this paper. The complete refractive index properties of the crystal can be obtained without relying on the crystals prior parameters.The automatic positioning of the optical axis is realized by controlling the rotation of the sample and the action of the optical delay line. The iterative approximation algorithm of the transfer function is used to extract the crystal thickness and complete refractive index information.To validate our method, the (10-10) oriented sapphire, which exhibits birefringence at terahertz frequencies, was selected. We extracted the average extraordinary and ordinary refractive indices of sapphire in the frequency range of 0.3～1.5 THz, which is 3.08±0.02 and 3.39±0.02, respectively. The birefringence is -0.31±0.02, and the absorption spectrum was plotted. The results show that our method avoids sample damage and positioning errorscaused by manual operation and improves the efficiency, stability, and accuracy of terahertz birefringence extraction. It is significant to the polarization-sensitive terahertz measurement technology and its application..

Spectroscopy and Spectral Analysis