A. Martínez-Cuazitl, M. M. Mata-Miranda, Miguel Sanchez-Brito, Daniel Valencia-Trujillo, Amanda M. Avila-Trejo, R. Delgado-Macuil, Consuelo Atriano-Colorado, Francisco Garibay-Gonzalez, V. Sánchez-Monroy, G. J. Vázquez-Zapién
The wide range of symptoms of the coronavirus disease 2019 (COVID-19) makes it challenging to predict the disease evolution using a single parameter. Therefore, to describe the pathophysiological response to SARS-CoV-2 infection in hospitalized patients with severe COVID-19, we compared according to survival or death, the sociodemographic and clinical characteristics, the biochemical and immunological attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectra from saliva samples and their correlation with chemometric findings. Herein, we demonstrate that ATR-FTIR spectroscopy allows the description of the events related to cell damage, such as lipids biogenesis and the secondary structure of proteins associated with lactate dehydrogenase and albumin levels. Moreover, humoral (IgM) and cellular (IFN-γ, TNF-α, IL-10, and IL-6) responses were also increased in patients who died from COVID-19.
{"title":"Clinical, Biochemical, and ATR-FTIR Spectroscopic Parameters Associated with Death or Survival in Patients with Severe COVID-19","authors":"A. Martínez-Cuazitl, M. M. Mata-Miranda, Miguel Sanchez-Brito, Daniel Valencia-Trujillo, Amanda M. Avila-Trejo, R. Delgado-Macuil, Consuelo Atriano-Colorado, Francisco Garibay-Gonzalez, V. Sánchez-Monroy, G. J. Vázquez-Zapién","doi":"10.1155/2023/3423183","DOIUrl":"https://doi.org/10.1155/2023/3423183","url":null,"abstract":"The wide range of symptoms of the coronavirus disease 2019 (COVID-19) makes it challenging to predict the disease evolution using a single parameter. Therefore, to describe the pathophysiological response to SARS-CoV-2 infection in hospitalized patients with severe COVID-19, we compared according to survival or death, the sociodemographic and clinical characteristics, the biochemical and immunological attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectra from saliva samples and their correlation with chemometric findings. Herein, we demonstrate that ATR-FTIR spectroscopy allows the description of the events related to cell damage, such as lipids biogenesis and the secondary structure of proteins associated with lactate dehydrogenase and albumin levels. Moreover, humoral (IgM) and cellular (IFN-γ, TNF-α, IL-10, and IL-6) responses were also increased in patients who died from COVID-19.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"8 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84364496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S Kumar, Zoltan K. Nagy, G. Reklaitis, Marcial Gonzalez
Raman spectroscopy is one of the important process analytical technology tools available for implementation in the continuous manufacturing of oral solid dosages. The aim of this study was to investigate several practical considerations in generating real-time measurements using Raman spectrometer at a tablet press feed frame, including the effects of fluorescence interference, photobleaching, feed-frame rpm, and material particle size. Fluorescence, in particular, is a significant drawback of Raman spectroscopy, compared to the use of near-infrared spectroscopy. Potential material sparing strategies were also investigated, including using stationary powders for calibration and isolation of feed-frame materials. Acetaminophen was used as the main active pharmaceutical ingredient (API), and microcrystalline cellulose (MCC) and lactose were used as excipients. The fluorescent behavior of MCC at 785 nm laser wavelength was reported and discussed. Raman spectra of a blend of MCC and acetaminophen and lactose and acetaminophen were collected at the feed frame of the tablet press. A series of preprocessing steps applied to remove the fluorescence interference was found to be effective, including the use of standard normal variate, subtraction of spectra of fluorescent material, baseline correction, and smoothing. Three different PLS models were prepared for different scenarios and their performances were compared. The models were able to predict the concentration of acetaminophen with root mean squared error prediction (RMSEP) of 0.29% w/w when there was no fluorescence interference and 0.57% w/w when there was fluorescence interference in background spectra. The study demonstrated the feasibility of using Raman spectroscopy for API concentration prediction even in the case of fluorescent interference and showed that Raman measurements were robust; that is, they were not much affected by feed-frame rpm and excipient particle size.
{"title":"Considerations in Raman Spectroscopy for Real-Time API Concentration Measurement at Tablet Press Feed Frame","authors":"S Kumar, Zoltan K. Nagy, G. Reklaitis, Marcial Gonzalez","doi":"10.1155/2023/8631288","DOIUrl":"https://doi.org/10.1155/2023/8631288","url":null,"abstract":"Raman spectroscopy is one of the important process analytical technology tools available for implementation in the continuous manufacturing of oral solid dosages. The aim of this study was to investigate several practical considerations in generating real-time measurements using Raman spectrometer at a tablet press feed frame, including the effects of fluorescence interference, photobleaching, feed-frame rpm, and material particle size. Fluorescence, in particular, is a significant drawback of Raman spectroscopy, compared to the use of near-infrared spectroscopy. Potential material sparing strategies were also investigated, including using stationary powders for calibration and isolation of feed-frame materials. Acetaminophen was used as the main active pharmaceutical ingredient (API), and microcrystalline cellulose (MCC) and lactose were used as excipients. The fluorescent behavior of MCC at 785 nm laser wavelength was reported and discussed. Raman spectra of a blend of MCC and acetaminophen and lactose and acetaminophen were collected at the feed frame of the tablet press. A series of preprocessing steps applied to remove the fluorescence interference was found to be effective, including the use of standard normal variate, subtraction of spectra of fluorescent material, baseline correction, and smoothing. Three different PLS models were prepared for different scenarios and their performances were compared. The models were able to predict the concentration of acetaminophen with root mean squared error prediction (RMSEP) of 0.29% w/w when there was no fluorescence interference and 0.57% w/w when there was fluorescence interference in background spectra. The study demonstrated the feasibility of using Raman spectroscopy for API concentration prediction even in the case of fluorescent interference and showed that Raman measurements were robust; that is, they were not much affected by feed-frame rpm and excipient particle size.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"105 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74341581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinfei Wu, Juan Zhai, Wanchang Lai, Hongjian Lin, Chenhao Zeng, Runqiu Gu, Shaoqin Li, Yunrui Jiang, Jie Shi, Bo Zhang
NaI(Tl) detectors are frequently operated under unstable temperature conditions when used in an open environment. Temperature changes would result in a peak shift and spectral distortion during measurement. Two easy-to-implement methodologies are proposed to stabilize the measured spectrum without the necessity of adjusting the gain, which are a correction algorithm for temperature-caused peak-shift based on multiple characteristic peak area weighting factors and an interpolation correction algorithm based on multicharacteristic peak sequence. Both of them can be used when the relative channel displacement of characteristic peaks in the spectrum due to temperature changes is not constant. Experimental data obtained under controlled temperature conditions in the laboratory were adopted to correct a spectrum, with joint consideration of some known characteristic peaks, such as 40K, U (214Bi), or Th (208Tl) peaks. Through constructing a reversible temperature coefficient matrix, one can easily obtain the coefficients of the n-th polynomial describing the influence of temperature on peak position, which presents their nonlinear mathematical relationship. Then, corrections of these two effects can also be easily calculated. Comparing the experimental results, peak positions before and after correction, it is proved that the interpolation correction algorithm based on multicharacteristic peak sequence has better correction accuracy, but the temperature-caused peak shift correction algorithm based on the multicharacteristic peak area weighting factor has a shorter calibration time.
在开放环境中，NaI(Tl)探测器经常在不稳定的温度条件下工作。温度变化会导致测量过程中的峰移和光谱失真。提出了两种易于实现的无需调整增益即可稳定测量光谱的方法，即基于多特征峰面积加权因子的温度引起的峰移校正算法和基于多特征峰序列的插值校正算法。当光谱中特征峰的相对通道位移由于温度变化而不恒定时，两者都可以使用。采用在实验室控制温度条件下获得的实验数据，并联合考虑一些已知的特征峰，如40K, U (214Bi)或Th (208Tl)峰，对光谱进行校正。通过构造一个可逆的温度系数矩阵，可以很容易地得到描述温度对峰值位置影响的第n个多项式的系数，从而表示出它们之间的非线性数学关系。然后，这两种效应的修正也可以很容易地计算出来。对比实验结果和校正前后的峰值位置，证明基于多特征峰序列的插值校正算法具有更好的校正精度，而基于多特征峰面积加权因子的温度引起的峰移校正算法校正时间更短。
{"title":"Nonlinear Correction Methods of Temperature-Caused Peak Shift for a NaI(Tl) Gamma-Ray Spectrometer","authors":"Jinfei Wu, Juan Zhai, Wanchang Lai, Hongjian Lin, Chenhao Zeng, Runqiu Gu, Shaoqin Li, Yunrui Jiang, Jie Shi, Bo Zhang","doi":"10.1155/2023/1590667","DOIUrl":"https://doi.org/10.1155/2023/1590667","url":null,"abstract":"NaI(Tl) detectors are frequently operated under unstable temperature conditions when used in an open environment. Temperature changes would result in a peak shift and spectral distortion during measurement. Two easy-to-implement methodologies are proposed to stabilize the measured spectrum without the necessity of adjusting the gain, which are a correction algorithm for temperature-caused peak-shift based on multiple characteristic peak area weighting factors and an interpolation correction algorithm based on multicharacteristic peak sequence. Both of them can be used when the relative channel displacement of characteristic peaks in the spectrum due to temperature changes is not constant. Experimental data obtained under controlled temperature conditions in the laboratory were adopted to correct a spectrum, with joint consideration of some known characteristic peaks, such as 40K, U (214Bi), or Th (208Tl) peaks. Through constructing a reversible temperature coefficient matrix, one can easily obtain the coefficients of the n-th polynomial describing the influence of temperature on peak position, which presents their nonlinear mathematical relationship. Then, corrections of these two effects can also be easily calculated. Comparing the experimental results, peak positions before and after correction, it is proved that the interpolation correction algorithm based on multicharacteristic peak sequence has better correction accuracy, but the temperature-caused peak shift correction algorithm based on the multicharacteristic peak area weighting factor has a shorter calibration time.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"48 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79333976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to the combined impact of many factors, the regression relationship often shows nonlinearity. Moreover, the relationship between the single temporal image and the measured data are not transplantable in time and space, which makes it difficult to construct a more general model for the remote sensing (RS) estimation of SSM. In order to solve this problem, the back propagation (BP) neural network (NN) with an excellent nonlinear mapping ability is introduced to determine the relationship between the characteristic band/index and the measurement data. In the BPNN model, the optical and TIR RS data in different periods were taken as the input parameters, and the in situ soil moisture data were treated as the output parameter. There are 12 schemes designed to retrieve SSM. The key findings of study were as follows: (1) the BPNN model could retrieve SSM with a high accuracy that indicates the correlation coefficient between the estimated and measured soil moisture as 0.9001 and (2) the SSM retrieval model based on the BPNN can be applied to estimate the SSM with different spatial resolution values.
{"title":"Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas","authors":"Dayou Luo, Xingping Wen, P. He","doi":"10.1155/2023/5887177","DOIUrl":"https://doi.org/10.1155/2023/5887177","url":null,"abstract":"Most of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to the combined impact of many factors, the regression relationship often shows nonlinearity. Moreover, the relationship between the single temporal image and the measured data are not transplantable in time and space, which makes it difficult to construct a more general model for the remote sensing (RS) estimation of SSM. In order to solve this problem, the back propagation (BP) neural network (NN) with an excellent nonlinear mapping ability is introduced to determine the relationship between the characteristic band/index and the measurement data. In the BPNN model, the optical and TIR RS data in different periods were taken as the input parameters, and the in situ soil moisture data were treated as the output parameter. There are 12 schemes designed to retrieve SSM. The key findings of study were as follows: (1) the BPNN model could retrieve SSM with a high accuracy that indicates the correlation coefficient between the estimated and measured soil moisture as 0.9001 and (2) the SSM retrieval model based on the BPNN can be applied to estimate the SSM with different spatial resolution values.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"27 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72680810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yaoyao Hu, Jun Chang, Yiting Li, Wenchao Zhang, Xiaoxiao Lai, Quanquan Mu
Snapshot hyperspectral imaging technology is increasingly used in agricultural product monitoring. In this study, we present a 9× local zoom snapshot hyperspectral imaging system. Using commercial spectral sensors with spectrally resolved detector arrays, we achieved snapshot hyperspectral imaging with 14 wavelength bands and a spectral bandwidth of 10–15 nm. An experimental demonstration was performed by acquiring spatial and spectral information about the fruit and Drosophila. The results show that the system can identify Drosophila and distinguish well between different types of fruits. The results of this study have great potential for online fruit classification and pest identification.
{"title":"High Zoom Ratio Foveated Snapshot Hyperspectral Imaging for Fruit Pest Monitoring","authors":"Yaoyao Hu, Jun Chang, Yiting Li, Wenchao Zhang, Xiaoxiao Lai, Quanquan Mu","doi":"10.1155/2023/2286867","DOIUrl":"https://doi.org/10.1155/2023/2286867","url":null,"abstract":"Snapshot hyperspectral imaging technology is increasingly used in agricultural product monitoring. In this study, we present a 9× local zoom snapshot hyperspectral imaging system. Using commercial spectral sensors with spectrally resolved detector arrays, we achieved snapshot hyperspectral imaging with 14 wavelength bands and a spectral bandwidth of 10–15 nm. An experimental demonstration was performed by acquiring spatial and spectral information about the fruit and Drosophila. The results show that the system can identify Drosophila and distinguish well between different types of fruits. The results of this study have great potential for online fruit classification and pest identification.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"48 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77346771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination ( R v 2 ), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination ( R p 2 ), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR’s R p 2 decreased by 0.0181, SVR’s R v 2 decreased by 0.01, and 1DCNN’s R v 2 increased by 0.0009 and R p 2 decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value.
{"title":"Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy","authors":"Tuo Guo, Fengjie Xu, Jinfang Ma, Fahuan Ge","doi":"10.1155/2022/6875022","DOIUrl":"https://doi.org/10.1155/2022/6875022","url":null,"abstract":"Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination ( R v 2 ), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination ( R p 2 ), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR’s R p 2 decreased by 0.0181, SVR’s R v 2 decreased by 0.01, and 1DCNN’s R v 2 increased by 0.0009 and R p 2 decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"13 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76225297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheng-Tao Su, Bin Li, Hai Yin, Ji-Ping Zou, Feng Zhang, Yan-De Liu
Crown pears are an important economic crop, but their quality and economy are seriously affected by the different levels of damage. To improve the overall quality of crown pears, sorting of crown pears with different levels of damage is required. However, there are some shortcomings in the traditional detection methods, such as low efficiency and large error. Therefore, the hyperspectral technology was used to discriminate between sound and 3 different levels of damage (defined as level I, II, and III damage, respectively) of crown pears in this study. To improve the discriminatory accuracy of the model, absorbance (A) spectra and Kubelka–Munk (K-M) spectra were added to reflectance (R) spectra. The three spectra were pretreated; then, the partial least squares discriminant analysis (PLS-DA) model and the support vector machine (SVM) model were established to discriminate the crown pears with different levels of damage. The results of the discriminant model show that the discrimination accuracy of the SVM based on R, A, and K-M spectra is higher than that of PLS-DA of them; the A-RAW-SVM model has the best discrimination performance with an overall discrimination accuracy of 100% for the test and 98.98% for calibration sets, respectively. Finally, the spectra were selected by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) to obtain the characteristic wavelengths, and the SVM models were built based on the filtered R, A, and K-M. Their discrimination results show that the A-RAW-CARS-SVM model has the best discrimination ability, and the discrimination accuracies of the test and calibration sets of the model are 96.88% and 100%, respectively. The results show that the best discrimination of different levels of damage of crown pears is the SVM model based on a spectra. This study provides a theoretical basis and experimental basis for detecting the damage of crown pears using hyperspectral.
{"title":"Identification of Damage in Pear Using Hyperspectral Imaging Technology","authors":"Cheng-Tao Su, Bin Li, Hai Yin, Ji-Ping Zou, Feng Zhang, Yan-De Liu","doi":"10.1155/2022/9094249","DOIUrl":"https://doi.org/10.1155/2022/9094249","url":null,"abstract":"Crown pears are an important economic crop, but their quality and economy are seriously affected by the different levels of damage. To improve the overall quality of crown pears, sorting of crown pears with different levels of damage is required. However, there are some shortcomings in the traditional detection methods, such as low efficiency and large error. Therefore, the hyperspectral technology was used to discriminate between sound and 3 different levels of damage (defined as level I, II, and III damage, respectively) of crown pears in this study. To improve the discriminatory accuracy of the model, absorbance (<i>A</i>) spectra and Kubelka–Munk (<i>K</i>-<i>M</i>) spectra were added to reflectance (<i>R</i>) spectra. The three spectra were pretreated; then, the partial least squares discriminant analysis (PLS-DA) model and the support vector machine (SVM) model were established to discriminate the crown pears with different levels of damage. The results of the discriminant model show that the discrimination accuracy of the SVM based on <i>R</i>, <i>A,</i> and <i>K</i>-<i>M</i> spectra is higher than that of PLS-DA of them; the A-RAW-SVM model has the best discrimination performance with an overall discrimination accuracy of 100% for the test and 98.98% for calibration sets, respectively. Finally, the spectra were selected by the competitive adaptive reweighted sampling (CARS) and the uninformative variables elimination (UVE) to obtain the characteristic wavelengths, and the SVM models were built based on the filtered <i>R</i>, <i>A,</i> and <i>K</i>-<i>M</i>. Their discrimination results show that the A-RAW-CARS-SVM model has the best discrimination ability, and the discrimination accuracies of the test and calibration sets of the model are 96.88% and 100%, respectively. The results show that the best discrimination of different levels of damage of crown pears is the SVM model based on <i>a</i> spectra. This study provides a theoretical basis and experimental basis for detecting the damage of crown pears using hyperspectral.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"1 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138528250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Teye, C. Amuah, K. Atiah, R. Darko, K. Amoah, E. Afutu, Rebecca Owusu
The rise in population growth worldwide requires efficient management of agricultural lands through the correct determination of authentic fertilizers. In this current study, a rapid on-site detection technique was developed by using portable NIR spectroscopy in the wavelength range of 740–1070 nm together with optimum multivariate algorithms to identify fertilizer integrity (unexpired, expired, and adulterated) as well as quantify the levels (10–50%) of adulteration. NIR models were built based on support vector machine (SVM) and random forest (RF) for identification, while different types of partial least square regression (PLS, iPLS, Si-PLS, and GaPLS) were used for quantification purposes. The models were evaluated according to identification rate (Rt), coefficient of correlation in prediction (Rpre2), and root mean square error of prediction (RMSEP). For the identification of the integrity of the fertilizer, among the mathematical pretreatments used, the first derivative (FD) together with SVM gave above 99.20% identification rate in both calibration and prediction sets. For the quantification of the adulterants, Si-PLS was found to be superior and showed an excellent predictive potential of Rpre2 = 0.95–0.98 and RMSEP = 0.069–0.11 for the two fertilizer types used. The overall results indicated that a handheld NIR spectrometer together with appropriate algorithms could be employed for fast and on-site determination of fertilizer integrity.
{"title":"Feasibility Study on the Use of a Portable NIR Spectrometer and Multivariate Data Analysis to Discriminate and Quantify Adulteration in Fertilizer","authors":"E. Teye, C. Amuah, K. Atiah, R. Darko, K. Amoah, E. Afutu, Rebecca Owusu","doi":"10.1155/2022/1412526","DOIUrl":"https://doi.org/10.1155/2022/1412526","url":null,"abstract":"The rise in population growth worldwide requires efficient management of agricultural lands through the correct determination of authentic fertilizers. In this current study, a rapid on-site detection technique was developed by using portable NIR spectroscopy in the wavelength range of 740–1070 nm together with optimum multivariate algorithms to identify fertilizer integrity (unexpired, expired, and adulterated) as well as quantify the levels (10–50%) of adulteration. NIR models were built based on support vector machine (SVM) and random forest (RF) for identification, while different types of partial least square regression (PLS, iPLS, Si-PLS, and GaPLS) were used for quantification purposes. The models were evaluated according to identification rate (Rt), coefficient of correlation in prediction (Rpre2), and root mean square error of prediction (RMSEP). For the identification of the integrity of the fertilizer, among the mathematical pretreatments used, the first derivative (FD) together with SVM gave above 99.20% identification rate in both calibration and prediction sets. For the quantification of the adulterants, Si-PLS was found to be superior and showed an excellent predictive potential of Rpre2 = 0.95–0.98 and RMSEP = 0.069–0.11 for the two fertilizer types used. The overall results indicated that a handheld NIR spectrometer together with appropriate algorithms could be employed for fast and on-site determination of fertilizer integrity.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"2 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80472150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Pinto Corujo, Pavel Michal, Rod Wesson, D. P. Amarasinghe, A. Rodger, N. Chmel
Background fluorescence remains the biggest challenge in Raman spectroscopy because of the consequent curvature of the baseline and the degradation of the signal-to-noise ratio of the Raman signal. While the concentrations of the fluorophore impurities are usually too low to be detected by other analytical methods, they are often sufficient to prevent Raman data collection. Among the different existing methods to remove the fluorescence signal, photobleaching remains the most popular due to its simplicity. However, using the spectrometer laser to photobleach is far from optimal. Most commercially available instruments have little or no choice of wavelength, and their output powers are in many cases not suitable for highly fluorescent samples such as those from biological systems (e.g., proteins). In this article, we assess practical aspects of photobleaching such as the apparent reversibility of the process and the effect of convection currents due to what we speculate to be temperature gradients across the bulk of the solution. We also introduce an affordable custom made external photobleaching unit with a choice of excitation wavelength and demonstrate its viability with a highly fluorescent bovine serum albumin protein solution, which had proved most challenging for Raman spectroscopy as it contained ∼10% w/w impurities.
{"title":"Reduction of Background Fluorescence from Impurities in Protein Samples for Raman Spectroscopy","authors":"Marco Pinto Corujo, Pavel Michal, Rod Wesson, D. P. Amarasinghe, A. Rodger, N. Chmel","doi":"10.1155/2022/1928091","DOIUrl":"https://doi.org/10.1155/2022/1928091","url":null,"abstract":"Background fluorescence remains the biggest challenge in Raman spectroscopy because of the consequent curvature of the baseline and the degradation of the signal-to-noise ratio of the Raman signal. While the concentrations of the fluorophore impurities are usually too low to be detected by other analytical methods, they are often sufficient to prevent Raman data collection. Among the different existing methods to remove the fluorescence signal, photobleaching remains the most popular due to its simplicity. However, using the spectrometer laser to photobleach is far from optimal. Most commercially available instruments have little or no choice of wavelength, and their output powers are in many cases not suitable for highly fluorescent samples such as those from biological systems (e.g., proteins). In this article, we assess practical aspects of photobleaching such as the apparent reversibility of the process and the effect of convection currents due to what we speculate to be temperature gradients across the bulk of the solution. We also introduce an affordable custom made external photobleaching unit with a choice of excitation wavelength and demonstrate its viability with a highly fluorescent bovine serum albumin protein solution, which had proved most challenging for Raman spectroscopy as it contained ∼10% w/w impurities.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"133 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89067716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. In this paper, we propose a wavelength selection algorithm based on the ant colony optimization (ACO), in which the absolute value of the regression coefficient of the multiple linear regression (MLR) model is used as the basis for evaluating the importance of wavelengths, and the absolute value of the regression coefficient after full wavelength MLR modeling is used as the initial pheromone value of the ant colony optimization (MLR-ACO). In each iteration, the absolute value of the regression coefficient corresponding to each wavelength of the individual with the highest fitness value is used as the basis for a pheromone update. The crossover operator is introduced in MLR-ACO (MLR-ACO-GA), and the individuals with the top 100 fitness values in MLR-ACO are used as the initial population of the genetic algorithm (GA). A selected frequency of wavelengths greater than the threshold among MLR-ACO individuals is calculated. A number of coarse interval points are generated according to the selected frequency, and a coarse crossover operation is performed at the coarse interval points. Fine crossover points are randomly generated within the coarse interval, and fine crossover operations are performed within the coarse interval to exploit the potential of combining excellent individuals in MLR-ACO with each other as much as possible. MLR-ACO can well solve the problem of traditional ACO initial pheromone scarcity, and MLR-ACO-GA can avoid MLR-ACO falling into a local optimum to a certain extent and be more flexible in the selection of the number of wavelengths, which can give full play to the advantages of MLR-ACO.
{"title":"A New Method for Spectral Wavelength Selection Based on Multiple Linear Regression Combined with Ant Colony Optimization and Genetic Algorithm","authors":"Qing Huang, Heru Xue, Jiangping Liu, Xinhua Jiang","doi":"10.1155/2022/2440518","DOIUrl":"https://doi.org/10.1155/2022/2440518","url":null,"abstract":"Wavelength selection is one of the key steps in quantitative spectral analysis, which reduces the computation time while also improving the prediction accuracy of the model. In this paper, we propose a wavelength selection algorithm based on the ant colony optimization (ACO), in which the absolute value of the regression coefficient of the multiple linear regression (MLR) model is used as the basis for evaluating the importance of wavelengths, and the absolute value of the regression coefficient after full wavelength MLR modeling is used as the initial pheromone value of the ant colony optimization (MLR-ACO). In each iteration, the absolute value of the regression coefficient corresponding to each wavelength of the individual with the highest fitness value is used as the basis for a pheromone update. The crossover operator is introduced in MLR-ACO (MLR-ACO-GA), and the individuals with the top 100 fitness values in MLR-ACO are used as the initial population of the genetic algorithm (GA). A selected frequency of wavelengths greater than the threshold among MLR-ACO individuals is calculated. A number of coarse interval points are generated according to the selected frequency, and a coarse crossover operation is performed at the coarse interval points. Fine crossover points are randomly generated within the coarse interval, and fine crossover operations are performed within the coarse interval to exploit the potential of combining excellent individuals in MLR-ACO with each other as much as possible. MLR-ACO can well solve the problem of traditional ACO initial pheromone scarcity, and MLR-ACO-GA can avoid MLR-ACO falling into a local optimum to a certain extent and be more flexible in the selection of the number of wavelengths, which can give full play to the advantages of MLR-ACO.","PeriodicalId":17079,"journal":{"name":"Journal of Spectroscopy","volume":"72 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79954278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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