Pub Date : 2022-10-25DOI: 10.1177/09670335221130433
Jianqiang Zhang, Jun Yang, Jin Chen, Junxun Hu, Shuangyan Yang
The rapid recognition of the sources of the drugs can provide valuable clues and provide the basis for determining the nature of a drug case. Here, a novel recognition method was put forward to identify the source of methamphetamine drugs rapidly and non-destructively by using a hand-held near infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. The accuracy, precision, sensitivity, and F-score were higher with the proposed ML-ELM algorithm than in traditional linear discriminant analysis (LDA), extreme learning machine (ELM) classification, and partial least squares (PLS) regression algorithms. The prediction accuracy of ML-ELM algorithm is 25.0%, 15.3% and 18.1% higher than that of LDA, ELM and PLS regression, respectively. The ML-ELM models for recognizing the different sources of methamphetamine drugs had the best generalization ability and prediction results. The experimental results indicated that the combination of hand-held NIR technology and ML-ELM algorithm can recognize the different sources of methamphetamine drugs rapidly, accurately, and non-destructively.
{"title":"Rapid recognition of different sources of methamphetamine drugs based on hand-held near infrared spectroscopy and multi-layer-extreme learning machine algorithms","authors":"Jianqiang Zhang, Jun Yang, Jin Chen, Junxun Hu, Shuangyan Yang","doi":"10.1177/09670335221130433","DOIUrl":"https://doi.org/10.1177/09670335221130433","url":null,"abstract":"The rapid recognition of the sources of the drugs can provide valuable clues and provide the basis for determining the nature of a drug case. Here, a novel recognition method was put forward to identify the source of methamphetamine drugs rapidly and non-destructively by using a hand-held near infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. The accuracy, precision, sensitivity, and F-score were higher with the proposed ML-ELM algorithm than in traditional linear discriminant analysis (LDA), extreme learning machine (ELM) classification, and partial least squares (PLS) regression algorithms. The prediction accuracy of ML-ELM algorithm is 25.0%, 15.3% and 18.1% higher than that of LDA, ELM and PLS regression, respectively. The ML-ELM models for recognizing the different sources of methamphetamine drugs had the best generalization ability and prediction results. The experimental results indicated that the combination of hand-held NIR technology and ML-ELM algorithm can recognize the different sources of methamphetamine drugs rapidly, accurately, and non-destructively.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"337 - 344"},"PeriodicalIF":1.8,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42134503","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}
Pub Date : 2022-10-21DOI: 10.1177/09670335221124612
K. Esbensen, N. Abu-Khalaf
Non-representative sampling of materials, lots and processes intended for near infrared (NIR) analysis is often contributing hidden additions to the full Measurement Uncertainty (MUtotal = TSE + TAENIR). The Total Sampling Error (TSE) can dominate over the Total Analytical Error (TAENIR) by factors ranging from 5 to 10 to even 25 times, depending on material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material analysed. This review (Parts 1 and 2), extensively referenced with easily available complementing literature, presents a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or maximally reduced) in order to achieve, and to be able to document, fully minimised MUtotal. The more irregular and pervasive the heterogeneity, the higher the number of increments needed to reach ‘fit-for-purpose representativity’. A particular focus is necessary regarding the sampling bias, which is fundamentally different from the well-known analytical bias. Whereas the latter can easily be subjected to bias correction, the sampling bias is non-correctable by any posteori means, notably not by chemometrics, nor statistics. Instead, all sampling operations must be designed to exclude the so-called Incorrect Sampling Errors (ISE), which are the hidden bias-generating agents. The key element in this endeavour is representative sampling and sub-sampling before analysis, as laid out by the Theory of Sampling (TOS), which is presented here in a novel compact fashion along with a complement of selected examples and demonstrations. TOS includes a safeguard facility, termed the Replication Experiment (RE), which enables estimation of the total sampling-plus-analysis uncertainty level (MUtotal) associated with NIR analysis (the RE is, for practical and logistical reasons, found in Part 2). Neglecting the TSE effects from the before-analysis domain is lack of due diligence. TOS to the fore!
{"title":"Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1: Generalised theory of sampling","authors":"K. Esbensen, N. Abu-Khalaf","doi":"10.1177/09670335221124612","DOIUrl":"https://doi.org/10.1177/09670335221124612","url":null,"abstract":"Non-representative sampling of materials, lots and processes intended for near infrared (NIR) analysis is often contributing hidden additions to the full Measurement Uncertainty (MUtotal = TSE + TAENIR). The Total Sampling Error (TSE) can dominate over the Total Analytical Error (TAENIR) by factors ranging from 5 to 10 to even 25 times, depending on material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material analysed. This review (Parts 1 and 2), extensively referenced with easily available complementing literature, presents a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or maximally reduced) in order to achieve, and to be able to document, fully minimised MUtotal. The more irregular and pervasive the heterogeneity, the higher the number of increments needed to reach ‘fit-for-purpose representativity’. A particular focus is necessary regarding the sampling bias, which is fundamentally different from the well-known analytical bias. Whereas the latter can easily be subjected to bias correction, the sampling bias is non-correctable by any posteori means, notably not by chemometrics, nor statistics. Instead, all sampling operations must be designed to exclude the so-called Incorrect Sampling Errors (ISE), which are the hidden bias-generating agents. The key element in this endeavour is representative sampling and sub-sampling before analysis, as laid out by the Theory of Sampling (TOS), which is presented here in a novel compact fashion along with a complement of selected examples and demonstrations. TOS includes a safeguard facility, termed the Replication Experiment (RE), which enables estimation of the total sampling-plus-analysis uncertainty level (MUtotal) associated with NIR analysis (the RE is, for practical and logistical reasons, found in Part 2). Neglecting the TSE effects from the before-analysis domain is lack of due diligence. TOS to the fore!","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"291 - 310"},"PeriodicalIF":1.8,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46015330","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}
Pub Date : 2022-10-10DOI: 10.1177/09670335221130469
T. Fujimoto
Wood is a typical viscoelastic material that shows a clear mechanical hysteresis loop during cyclic loading, which implies the irreversibility of the process and is important for the processing and long-term utility of wood. Changes in the physical state of wood were examined during multiple tensile load-unload cycles based on the eigenvalue distribution of the near-infrared spectra. The set of eigenvalues H = {λ1, λ2, …, λ n }, calculated from the spectral matrix successively acquired during the cycling test, was treated as the Hamiltonian, which represents the energy eigenstate of the wood. Using statistical physics and random matrix theory, the variation in the physical state of wood was discussed from both macroscopic and microscopic perspectives. Unlike traditional methods, the energy state of wood can be followed in real time during cyclic loading; in other words, the Helmholtz free energy and Shannon entropy varied with load changes. The commutator, defined by the density and diagonal matrix of H, could be used to quantitatively evaluate the irreversible changes in wood during the cyclic processes. The proposed method is independent of a specific coordinate system, and can therefore be applied using a wide variety of chemical information other than that obtained from the near-infrared spectra.
{"title":"Monitoring the physical state of wood during multiple tensile load-unload cycles by the eigenvalue distribution of near infrared spectra","authors":"T. Fujimoto","doi":"10.1177/09670335221130469","DOIUrl":"https://doi.org/10.1177/09670335221130469","url":null,"abstract":"Wood is a typical viscoelastic material that shows a clear mechanical hysteresis loop during cyclic loading, which implies the irreversibility of the process and is important for the processing and long-term utility of wood. Changes in the physical state of wood were examined during multiple tensile load-unload cycles based on the eigenvalue distribution of the near-infrared spectra. The set of eigenvalues H = {λ1, λ2, …, λ n }, calculated from the spectral matrix successively acquired during the cycling test, was treated as the Hamiltonian, which represents the energy eigenstate of the wood. Using statistical physics and random matrix theory, the variation in the physical state of wood was discussed from both macroscopic and microscopic perspectives. Unlike traditional methods, the energy state of wood can be followed in real time during cyclic loading; in other words, the Helmholtz free energy and Shannon entropy varied with load changes. The commutator, defined by the density and diagonal matrix of H, could be used to quantitatively evaluate the irreversible changes in wood during the cyclic processes. The proposed method is independent of a specific coordinate system, and can therefore be applied using a wide variety of chemical information other than that obtained from the near-infrared spectra.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"345 - 351"},"PeriodicalIF":1.8,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47858930","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}
Pub Date : 2022-10-05DOI: 10.1177/09670335221130430
Y. Roggo, Lizbeth Martínez, A. Peinado, S. Matero
Blending process is a critical unit operation in the pharmaceutical industry during the solid dosage form production. Near infrared (NIR) spectroscopy is a powerful analytical tool to assess the blend homogeneity in real-time. In this paper, a new methodology for blending process monitoring and for end point confirmation is proposed. Quantitative procedure validation and maintenance of NIR procedures are time-consuming activities that can prevent the adoption of PAT tools in the pharmaceutical industry. Clearly, there is a need in the industry for simpler and more intuitive qualitative blend monitoring analytical procedure that are easy to build, validate and maintain. The method introduced herein consists of tracking the trend of the Coefficient of Determination (CD) between a mean reference spectrum from a homogeneous batch and the NIR spectra that are recorded during the blending operation. Four formulations of commercial products were selected from different scales–including low dosage solid form-to show the usefulness of the method. In addition, this analytical procedure is tested with data from two different types of spectrometers (diode array instruments). Method calibration was performed with five batches (representing expected process variability) for each product: one for the computation of the homogeneous batch target spectrum and four to compute the limit of the CD values related to anticipated and acceptable homogeneity. Method validation was performed with homogeneous batches and with challenge spectra for assessing the specificity of the method. Real-world examples (e.g. technical, validation batches and clinical batches) were presented in order to demonstrate that this method is able to detect inhomogeneous batches. The new qualitative method presented in this paper is useful for determination of the blending endpoint, in assessing the blend uniformity in real-time and in increasing process understanding during early development and troubleshooting. Graphical Abstract
{"title":"Near infrared spectroscopy for blend uniformity monitoring: An innovative qualitative application based on the coefficient of determination","authors":"Y. Roggo, Lizbeth Martínez, A. Peinado, S. Matero","doi":"10.1177/09670335221130430","DOIUrl":"https://doi.org/10.1177/09670335221130430","url":null,"abstract":"Blending process is a critical unit operation in the pharmaceutical industry during the solid dosage form production. Near infrared (NIR) spectroscopy is a powerful analytical tool to assess the blend homogeneity in real-time. In this paper, a new methodology for blending process monitoring and for end point confirmation is proposed. Quantitative procedure validation and maintenance of NIR procedures are time-consuming activities that can prevent the adoption of PAT tools in the pharmaceutical industry. Clearly, there is a need in the industry for simpler and more intuitive qualitative blend monitoring analytical procedure that are easy to build, validate and maintain. The method introduced herein consists of tracking the trend of the Coefficient of Determination (CD) between a mean reference spectrum from a homogeneous batch and the NIR spectra that are recorded during the blending operation. Four formulations of commercial products were selected from different scales–including low dosage solid form-to show the usefulness of the method. In addition, this analytical procedure is tested with data from two different types of spectrometers (diode array instruments). Method calibration was performed with five batches (representing expected process variability) for each product: one for the computation of the homogeneous batch target spectrum and four to compute the limit of the CD values related to anticipated and acceptable homogeneity. Method validation was performed with homogeneous batches and with challenge spectra for assessing the specificity of the method. Real-world examples (e.g. technical, validation batches and clinical batches) were presented in order to demonstrate that this method is able to detect inhomogeneous batches. The new qualitative method presented in this paper is useful for determination of the blending endpoint, in assessing the blend uniformity in real-time and in increasing process understanding during early development and troubleshooting. Graphical Abstract","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"322 - 336"},"PeriodicalIF":1.8,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43235771","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}
Pub Date : 2022-10-04DOI: 10.1177/09670335221124611
K. Esbensen, N. Abu-Khalaf
Non-representative sampling of materials, lots and processes intended for NIR analysis is often fraught with hidden contributions to the full Measurement Uncertainty MUtotal = TSE + TAENIR. The Total Sampling Error (TSE) can dominate over the Total Analytical Error TAENIR by factors of 5 to 10 to even 25 times, depending on the degree of material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material actually analysed. Part 1 presented a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or reduced maximally), especially the sampling bias, as a prerequisite to achieve fully representative sampling. The key for this is the Theory of Sampling (TOS), which is presented in two parts in a novel compact fashion. Part 2 introduces (i) application of TOS to process sampling, specifically addressing and illustrating how this manifests itself in the realm of PAT, Process Analytical Technology, and (ii) an empirical safeguard facility, termed the Replication Experiment (RE), with which to estimate the effective sampling-plus-analysis uncertainty level (MUtotal) associated with NIR analysis. The RE is a defence against compromising the analytical responsibilities. Ignorance, either caused by lack of awareness or training, or by wilful neglect, of the demand for TSE minimisation, is a breach of due diligence concerning analysis QC/QA. Part 2 ends with a special focus on: “What does all this TOS mean specifically for NIR analysis?”. The answer to this question will perhaps surprise many. There is nothing special that need worrying NIR analysts relative to professionals from all other analytical modalities; all that is needed is embedded in the general TOS framework. Still, this review concludes by answering a set of typical concerns from NIR practitioners.
{"title":"Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy","authors":"K. Esbensen, N. Abu-Khalaf","doi":"10.1177/09670335221124611","DOIUrl":"https://doi.org/10.1177/09670335221124611","url":null,"abstract":"Non-representative sampling of materials, lots and processes intended for NIR analysis is often fraught with hidden contributions to the full Measurement Uncertainty MUtotal = TSE + TAENIR. The Total Sampling Error (TSE) can dominate over the Total Analytical Error TAENIR by factors of 5 to 10 to even 25 times, depending on the degree of material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material actually analysed. Part 1 presented a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or reduced maximally), especially the sampling bias, as a prerequisite to achieve fully representative sampling. The key for this is the Theory of Sampling (TOS), which is presented in two parts in a novel compact fashion. Part 2 introduces (i) application of TOS to process sampling, specifically addressing and illustrating how this manifests itself in the realm of PAT, Process Analytical Technology, and (ii) an empirical safeguard facility, termed the Replication Experiment (RE), with which to estimate the effective sampling-plus-analysis uncertainty level (MUtotal) associated with NIR analysis. The RE is a defence against compromising the analytical responsibilities. Ignorance, either caused by lack of awareness or training, or by wilful neglect, of the demand for TSE minimisation, is a breach of due diligence concerning analysis QC/QA. Part 2 ends with a special focus on: “What does all this TOS mean specifically for NIR analysis?”. The answer to this question will perhaps surprise many. There is nothing special that need worrying NIR analysts relative to professionals from all other analytical modalities; all that is needed is embedded in the general TOS framework. Still, this review concludes by answering a set of typical concerns from NIR practitioners.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"311 - 321"},"PeriodicalIF":1.8,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45834818","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}
Pub Date : 2022-10-01DOI: 10.1177/09670335221119721
A. Tugnolo, A. Pampuri, V. Giovenzana, A. Casson, R. Guidetti, R. Beghi
The present research aims to evaluate the performance of an optical pre-prototype based on light emitting diode, (450–860 nm) to quantify table tomatoes’ quality features in a rapid and non-destructive way (Solanum lycopersicum L., Marinda F1). A total of 200 samples were analysed. Calibration of the pure near infrared (NIR, 960–1650 nm) and visible/near infrared (VIS/NIR, 400–1000 nm) commercial spectrometers to estimate the main tomato quality parameters, i.e. moisture content (MC) and total soluble solids (TSS), was performed by using PLS regression. Since no substantial differences were highlighted between the two commercial devices, to reduce the complexity while keeping the performance of the model using the whole spectra (1647 variables for VIS/NIR), a cost-effective pre-prototype was designed and built by using 12 bands in the VIS/NIR optical range. The pre-prototype shows slightly lower performance, resulting in RMSEP values of 2% and 1.45 °Brix for MC and TSS respectively, compared to RMSEP values of 1% and 1.19 °Brix for the VIS/NIR device (using the entire spectrum). Moreover, no significant differences at 95% were highlighted by using Passing-Bablok regression. In conclusion, the pre-prototype performance can be considered sufficiently accurate to allow an initial field screening of the trend of the analysed parameters (MC and TSS) using a new generation of simplified optical sensors.
{"title":"Test of a light emitting diode fully integrated pre-prototype spectrometer for rapid evaluation of table tomato (Solanum lycopersicum L., Marinda F1) quality","authors":"A. Tugnolo, A. Pampuri, V. Giovenzana, A. Casson, R. Guidetti, R. Beghi","doi":"10.1177/09670335221119721","DOIUrl":"https://doi.org/10.1177/09670335221119721","url":null,"abstract":"The present research aims to evaluate the performance of an optical pre-prototype based on light emitting diode, (450–860 nm) to quantify table tomatoes’ quality features in a rapid and non-destructive way (Solanum lycopersicum L., Marinda F1). A total of 200 samples were analysed. Calibration of the pure near infrared (NIR, 960–1650 nm) and visible/near infrared (VIS/NIR, 400–1000 nm) commercial spectrometers to estimate the main tomato quality parameters, i.e. moisture content (MC) and total soluble solids (TSS), was performed by using PLS regression. Since no substantial differences were highlighted between the two commercial devices, to reduce the complexity while keeping the performance of the model using the whole spectra (1647 variables for VIS/NIR), a cost-effective pre-prototype was designed and built by using 12 bands in the VIS/NIR optical range. The pre-prototype shows slightly lower performance, resulting in RMSEP values of 2% and 1.45 °Brix for MC and TSS respectively, compared to RMSEP values of 1% and 1.19 °Brix for the VIS/NIR device (using the entire spectrum). Moreover, no significant differences at 95% were highlighted by using Passing-Bablok regression. In conclusion, the pre-prototype performance can be considered sufficiently accurate to allow an initial field screening of the trend of the analysed parameters (MC and TSS) using a new generation of simplified optical sensors.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"279 - 287"},"PeriodicalIF":1.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44983282","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}
Pub Date : 2022-10-01DOI: 10.1177/09670335221130425
A. R. Othman, S. Zain, K. Low
It is essential to control the quality of diesel products so that they comply with relevant fuel specifications, however, the quality assessments rely upon conventional wet chemical analyses that are costly and time consuming. Rapid, simultaneous quality measurement enabling immediate online optimisation for process control and blending offers tremendous cost savings by minimising product quality give-away, shipment demurrage, tank inventory, and laboratory analysis. In this study, the use of near infrared spectroscopy and chemometrics demonstrates a straightforward workflow for simultaneous determination of the petroleum diesel’s boiling point at 95% recovery (T95), flash point (FP), cloud point (CP), and cetane index (CI) calibration development. It involved appropriate spectral region selection, calibration/validation set partition, data pre-processing, regression modelling and validation. Based on the calibration and validation results, the supervised learning models that are obtained from a combination region of 4000–4800 cm−1 on a randomly selected calibration set managed to deliver promising predictive performance in terms of coefficient of determination for prediction (r2P/T95 ≥ 0.94, r2P/FP ≥ 0.89, r2P/CP ≥ 0.89, r2P/CI ≥ 0.993), root mean square error of prediction (RMSEP (T95) ≤ 5.2°C, RMSEP (FP) ≤ 2.0°C, RMSEP (CP) ≤ 2.4°C, RMSEP (CI) ≤ 0.3), and ratio of performance deviation (RPD (T95) ≥ 3.7, RPD (FP) ≥ 3.0, RPD (CP) ≥ 2.9, RPD (CI) ≥ 11). Regardless of principal component regression or partial least square regression on either the multiplicative scattering corrected spectra or Savitzky-Golay second derivative spectra, the developed models met respective ASTM reproducibility requirements, and were considered adequate for immediate quality assessment of diesel.
{"title":"Multivariate calibration strategy in simultaneous determination of temperature properties of petroleum diesel by near infrared spectrometry","authors":"A. R. Othman, S. Zain, K. Low","doi":"10.1177/09670335221130425","DOIUrl":"https://doi.org/10.1177/09670335221130425","url":null,"abstract":"It is essential to control the quality of diesel products so that they comply with relevant fuel specifications, however, the quality assessments rely upon conventional wet chemical analyses that are costly and time consuming. Rapid, simultaneous quality measurement enabling immediate online optimisation for process control and blending offers tremendous cost savings by minimising product quality give-away, shipment demurrage, tank inventory, and laboratory analysis. In this study, the use of near infrared spectroscopy and chemometrics demonstrates a straightforward workflow for simultaneous determination of the petroleum diesel’s boiling point at 95% recovery (T95), flash point (FP), cloud point (CP), and cetane index (CI) calibration development. It involved appropriate spectral region selection, calibration/validation set partition, data pre-processing, regression modelling and validation. Based on the calibration and validation results, the supervised learning models that are obtained from a combination region of 4000–4800 cm−1 on a randomly selected calibration set managed to deliver promising predictive performance in terms of coefficient of determination for prediction (r2P/T95 ≥ 0.94, r2P/FP ≥ 0.89, r2P/CP ≥ 0.89, r2P/CI ≥ 0.993), root mean square error of prediction (RMSEP (T95) ≤ 5.2°C, RMSEP (FP) ≤ 2.0°C, RMSEP (CP) ≤ 2.4°C, RMSEP (CI) ≤ 0.3), and ratio of performance deviation (RPD (T95) ≥ 3.7, RPD (FP) ≥ 3.0, RPD (CP) ≥ 2.9, RPD (CI) ≥ 11). Regardless of principal component regression or partial least square regression on either the multiplicative scattering corrected spectra or Savitzky-Golay second derivative spectra, the developed models met respective ASTM reproducibility requirements, and were considered adequate for immediate quality assessment of diesel.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"237 - 245"},"PeriodicalIF":1.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42269278","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}
Pub Date : 2022-10-01DOI: 10.1177/09670335221130428
Fei Teng, Jianbo Ji, Yue Yang, Haina Wang
In this study, a new method was developed for the determination of praziquantel (PZQ) enantiomers in solution. Praziquantel, as a highly effective and low-toxic broad-spectrum antiparasitic drug developed in the 1970s, is the first choice for the etiology of schistosomiasis treatment recommended by the World Health Organization. It is by far the most effective anti-schistosomiasis drug. PZQ is a chiral drug with a chiral carbon atom and two enantiomers, of which R-PZQ is the main contributor of the anti-schistosome effect. The quantitative model was established based on near infrared (NIR) spectroscopy combined with the partial least square (PLS) method. Using sucrose as a chiral selector, the collected spectral information was processed by the second derivative and Savitzky-Golay smoothing filter, and comprehensively analyzed in the two bands of 1816.9–1884.3 nm and 1405.3–1425.4 nm to establish a good PLS regression model. Internal cross-validation of the model was carried out. In principle, the enantiomeric excess could be determined as low as 1.33%. The mole fraction of S-PZQ determined by HPLC was used as a reference method, and three batches of samples from the same manufacturer were used for independent external validation with an error of ± 4%. The results showed that this quantitative model could be used to determine the enantiomer content of the chiral drug PZQ. It realized the rapid and sensitive analysis of PZQ tablets and provided a new strategy for the quality analysis of chiral drugs.
{"title":"A useful quantitative model for determining the optical purity of praziquantel enantiomers based on near infrared spectroscopy with partial least squares","authors":"Fei Teng, Jianbo Ji, Yue Yang, Haina Wang","doi":"10.1177/09670335221130428","DOIUrl":"https://doi.org/10.1177/09670335221130428","url":null,"abstract":"In this study, a new method was developed for the determination of praziquantel (PZQ) enantiomers in solution. Praziquantel, as a highly effective and low-toxic broad-spectrum antiparasitic drug developed in the 1970s, is the first choice for the etiology of schistosomiasis treatment recommended by the World Health Organization. It is by far the most effective anti-schistosomiasis drug. PZQ is a chiral drug with a chiral carbon atom and two enantiomers, of which R-PZQ is the main contributor of the anti-schistosome effect. The quantitative model was established based on near infrared (NIR) spectroscopy combined with the partial least square (PLS) method. Using sucrose as a chiral selector, the collected spectral information was processed by the second derivative and Savitzky-Golay smoothing filter, and comprehensively analyzed in the two bands of 1816.9–1884.3 nm and 1405.3–1425.4 nm to establish a good PLS regression model. Internal cross-validation of the model was carried out. In principle, the enantiomeric excess could be determined as low as 1.33%. The mole fraction of S-PZQ determined by HPLC was used as a reference method, and three batches of samples from the same manufacturer were used for independent external validation with an error of ± 4%. The results showed that this quantitative model could be used to determine the enantiomer content of the chiral drug PZQ. It realized the rapid and sensitive analysis of PZQ tablets and provided a new strategy for the quality analysis of chiral drugs.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"246 - 253"},"PeriodicalIF":1.8,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48405989","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}
Pub Date : 2022-09-06DOI: 10.1177/09670335221117301
Y. Japarudin, R. Meder, M. Lapammu, A. Alwi, Mark Brown
Tree improvement programmes benefit from measured data for multiple phenotypes in order to potentially gain maximum genetic leverage for selection. Diameter at breast height, tree height and tree form have traditionally dominated the measured phenotype due to their ease of measurement, and the fact that logs are traded by volume. More complex traits, including those that potentially offer economic benefit in terms of quality and end-product performance such as strength and stiffness, are more difficult to measure in standing trees and are frequently overlooked. These traits are therefore in need of rapid methods of assessment. Tree improvement of Eucalyptus pellita in Malaysian Borneo for solid wood and veneer product utilisation is one such example of where selection for improved stiffness is desirable. Genetic trials of E. pellita were assessed using acoustic velocity measurements at several intervention points, including the standing tree, fallen stem, logs and boards, along with near infrared spectroscopic measurement of the final test samples. Calibrations were developed for modulus of elasticity (MOE), modulus of rupture (MOR) and compression parallel to the grain, using reference values obtained from 3-point bending of small clearwood test samples obtained from the trees following felling and sawing to ensure back-to-log recovery of the test sample location. Dynamic MOE calculated from the standing tree acoustic velocity showed good correlation with the mean MOE from static bending for the wood in the butt log, representing the location where standing tree acoustic velocity measurements were obtained. The Savitzky-Golay second derivative pre-treatment yields the best performing calibration for the microNIR and MPA on ground wood for MOE (R2Cal = 0.76, r2CV = 0.80, r2Pred = 0.46, RMSEC and RMSECV = 1.4 GPa, RMSEP = 2.3 GPa, LV = 3) for the microNIR and R2Cal = 0.98, r2CV and r2Pred = 0.70, RMSEC = 0.5 GPa, RMSECV and RMSEP =1.5 GPa, LV = 4 for the MPA.
{"title":"Non-destructive evaluation of strength and stiffness of Eucalyptus pellita. A comparison of near infrared spectroscopy and acoustic wave velocity assessment","authors":"Y. Japarudin, R. Meder, M. Lapammu, A. Alwi, Mark Brown","doi":"10.1177/09670335221117301","DOIUrl":"https://doi.org/10.1177/09670335221117301","url":null,"abstract":"Tree improvement programmes benefit from measured data for multiple phenotypes in order to potentially gain maximum genetic leverage for selection. Diameter at breast height, tree height and tree form have traditionally dominated the measured phenotype due to their ease of measurement, and the fact that logs are traded by volume. More complex traits, including those that potentially offer economic benefit in terms of quality and end-product performance such as strength and stiffness, are more difficult to measure in standing trees and are frequently overlooked. These traits are therefore in need of rapid methods of assessment. Tree improvement of Eucalyptus pellita in Malaysian Borneo for solid wood and veneer product utilisation is one such example of where selection for improved stiffness is desirable. Genetic trials of E. pellita were assessed using acoustic velocity measurements at several intervention points, including the standing tree, fallen stem, logs and boards, along with near infrared spectroscopic measurement of the final test samples. Calibrations were developed for modulus of elasticity (MOE), modulus of rupture (MOR) and compression parallel to the grain, using reference values obtained from 3-point bending of small clearwood test samples obtained from the trees following felling and sawing to ensure back-to-log recovery of the test sample location. Dynamic MOE calculated from the standing tree acoustic velocity showed good correlation with the mean MOE from static bending for the wood in the butt log, representing the location where standing tree acoustic velocity measurements were obtained. The Savitzky-Golay second derivative pre-treatment yields the best performing calibration for the microNIR and MPA on ground wood for MOE (R2Cal = 0.76, r2CV = 0.80, r2Pred = 0.46, RMSEC and RMSECV = 1.4 GPa, RMSEP = 2.3 GPa, LV = 3) for the microNIR and R2Cal = 0.98, r2CV and r2Pred = 0.70, RMSEC = 0.5 GPa, RMSECV and RMSEP =1.5 GPa, LV = 4 for the MPA.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"270 - 278"},"PeriodicalIF":1.8,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42254548","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}
Pub Date : 2022-08-03DOI: 10.1177/09670335221117300
Joel B. Johnson
This proof-of-concept study aimed to investigate the potential of using near infrared (NIR) spectroscopy to discriminate between genera of Gonipterini weevil. NIR spectra (10,000–4,000 cm−1) were collected from 15 Gonipterini specimens, comprising three genera and five species. Principal component analysis (PCA) highlighted the inter-specific variation in NIR spectra, with separation observed between most species across the first two principal components. Partial least squares discriminant analysis (PLS-DA) could be used to differentiate between the genera (78% accuracy), although support vector machine (SVM) modelling gave improved accuracy (91%). The results support the prospect of NIR spectroscopy for the rapid discrimination between Gonipterini genera.
{"title":"Discrimination of Gonipterini weevil genera using near infrared spectroscopy","authors":"Joel B. Johnson","doi":"10.1177/09670335221117300","DOIUrl":"https://doi.org/10.1177/09670335221117300","url":null,"abstract":"This proof-of-concept study aimed to investigate the potential of using near infrared (NIR) spectroscopy to discriminate between genera of Gonipterini weevil. NIR spectra (10,000–4,000 cm−1) were collected from 15 Gonipterini specimens, comprising three genera and five species. Principal component analysis (PCA) highlighted the inter-specific variation in NIR spectra, with separation observed between most species across the first two principal components. Partial least squares discriminant analysis (PLS-DA) could be used to differentiate between the genera (78% accuracy), although support vector machine (SVM) modelling gave improved accuracy (91%). The results support the prospect of NIR spectroscopy for the rapid discrimination between Gonipterini genera.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"264 - 269"},"PeriodicalIF":1.8,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42774182","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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