Pub Date : 2024-08-27DOI: 10.1177/09670335241269014
Joy Sim, Cushla McGoverin, Indrawati Oey, Russell Frew, Biniam Kebede
Over the past decade, there has been overwhelming interest in rapid and routine origin tracing and authentication methods, such as near infrared (NIR) spectroscopy. In a systematic and comprehensive approach, this study coupled NIR with advanced machine learning models to explore the origin classification of coffee at various scales (continental to regional level). Speciality green coffee beans were sourced from three continents, eight countries, and 22 regions. The dispersive bulk NIR spectra were used for spectral registration in the reflectance mode, and the obtained spectra were preprocessed with extended multiplicative scatter correction and mean centering. The classical linear partial least squares-discriminant analysis (PLS-DA) adequately predicted origin at the continental and country level, and showed promise at the regional level. Non-linear machine learning models improved predictions further, with the best accuracy found using random forest with accuracies up to 0.99. Discriminating wavelength regions and constituents were identified at each origin scale, with more minor wavelength regions selected by random forest. This proof of concept work demonstrated the potential of NIR spectroscopy coupled with machine learning for rapid origin classification of coffee from the continental to the regional level.
{"title":"Non-linear machine learning coupled near infrared spectroscopy enhanced model performance and insights for coffee origin traceability","authors":"Joy Sim, Cushla McGoverin, Indrawati Oey, Russell Frew, Biniam Kebede","doi":"10.1177/09670335241269014","DOIUrl":"https://doi.org/10.1177/09670335241269014","url":null,"abstract":"Over the past decade, there has been overwhelming interest in rapid and routine origin tracing and authentication methods, such as near infrared (NIR) spectroscopy. In a systematic and comprehensive approach, this study coupled NIR with advanced machine learning models to explore the origin classification of coffee at various scales (continental to regional level). Speciality green coffee beans were sourced from three continents, eight countries, and 22 regions. The dispersive bulk NIR spectra were used for spectral registration in the reflectance mode, and the obtained spectra were preprocessed with extended multiplicative scatter correction and mean centering. The classical linear partial least squares-discriminant analysis (PLS-DA) adequately predicted origin at the continental and country level, and showed promise at the regional level. Non-linear machine learning models improved predictions further, with the best accuracy found using random forest with accuracies up to 0.99. Discriminating wavelength regions and constituents were identified at each origin scale, with more minor wavelength regions selected by random forest. This proof of concept work demonstrated the potential of NIR spectroscopy coupled with machine learning for rapid origin classification of coffee from the continental to the regional level.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"43 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175110","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}
Petroleum pollution in soil is very damaging to the areas affected by the accidental release of petroleum hydrocarbons and has destructive impacts on natural resources and environmental health. Therefore, its monitoring and analysis are critical, however, due to the cost and time associated with chemical approaches, finding a quick and cost-effective analytical method is valuable. This study was conducted to evaluate the potential of using visible near infrared (Vis-NIR) spectroscopy to predict total petroleum hydrocarbons (TPH) in polluted soils around the Shadegan ponds, in southern Iran. One hundred soil samples showing various degrees of pollution were randomly collected from topsoil (0–10 cm). The soil samples were analyzed for TPH using Vis-NIR reflectance spectroscopy in the laboratory and then following application of preprocessing transformation, partial least squares PLS regression as well as two machine learning models including random forest (RF), and support vector machine (SVM) were examined. The results showed that the reflectance values at 1725 nm and 2311 nm, respectively, served as indicative TPH reflectance features, exhibiting weaker reflection with rising TPH. Among the preprocessing methods, the baseline correction method indicated the highest performance than the others. According to the evaluation model criteria in the validation dataset, the efficiency of the three selected models was observed in the following order SVM > RF > PLS regression. The SVM model provided the best performance in the validation dataset with r2 = 0.85, root mean of square (RMSEP = 1.59 %, and the ratio of prediction to deviation (RPD) = 2.6. Overall, this study provided strong evidence supporting the considerable potential of Visible-NIR spectroscopy as a rapid and cost-effective technique for estimating TPH levels in oil-contaminated soils, surpassing traditional chemical analytical methods. Applying the mid-infrared spectrum (MIR) in combination with Visible-NIR data is expected to provide more comprehensive and accurate results when assessing soils in polluted areas, thereby enhancing the accuracy and reliability of the results across a diverse range of soil types.
{"title":"Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils","authors":"Fereshteh Karimian, Shamsollah Ayoubi, Banafsheh Khalili, Seyed Ahmad Mireei, Yaseen Al-Mulla","doi":"10.1177/09670335241269168","DOIUrl":"https://doi.org/10.1177/09670335241269168","url":null,"abstract":"Petroleum pollution in soil is very damaging to the areas affected by the accidental release of petroleum hydrocarbons and has destructive impacts on natural resources and environmental health. Therefore, its monitoring and analysis are critical, however, due to the cost and time associated with chemical approaches, finding a quick and cost-effective analytical method is valuable. This study was conducted to evaluate the potential of using visible near infrared (Vis-NIR) spectroscopy to predict total petroleum hydrocarbons (TPH) in polluted soils around the Shadegan ponds, in southern Iran. One hundred soil samples showing various degrees of pollution were randomly collected from topsoil (0–10 cm). The soil samples were analyzed for TPH using Vis-NIR reflectance spectroscopy in the laboratory and then following application of preprocessing transformation, partial least squares PLS regression as well as two machine learning models including random forest (RF), and support vector machine (SVM) were examined. The results showed that the reflectance values at 1725 nm and 2311 nm, respectively, served as indicative TPH reflectance features, exhibiting weaker reflection with rising TPH. Among the preprocessing methods, the baseline correction method indicated the highest performance than the others. According to the evaluation model criteria in the validation dataset, the efficiency of the three selected models was observed in the following order SVM > RF > PLS regression. The SVM model provided the best performance in the validation dataset with r<jats:sup>2</jats:sup> = 0.85, root mean of square (RMSEP = 1.59 %, and the ratio of prediction to deviation (RPD) = 2.6. Overall, this study provided strong evidence supporting the considerable potential of Visible-NIR spectroscopy as a rapid and cost-effective technique for estimating TPH levels in oil-contaminated soils, surpassing traditional chemical analytical methods. Applying the mid-infrared spectrum (MIR) in combination with Visible-NIR data is expected to provide more comprehensive and accurate results when assessing soils in polluted areas, thereby enhancing the accuracy and reliability of the results across a diverse range of soil types.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"69 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175111","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 : 2024-08-13DOI: 10.1177/09670335241269005
Patil R Kiran, Parth Jadhav, G Avinash, Pramod Aradwad, Arunkumar TV, Rakesh Bhardwaj, Roaf A Parray
The esteemed Alphonso mango, cherished in India for its taste, saffron color, texture, and extended shelf life, holds global commercial appeal. Unfortunately, the prevalent spongy tissue disorder in Alphonso mangoes results in a soft and corky texture, with up to 30% of mangoes within a single batch affected. This issue leads to outright rejection during export due to delayed disorder identification. The current assessment method involves destructive sampling, causing substantial fruit loss, and lacks assurance for overall batch quality. In light of the mentioned challenges, this current study focuses on utilizing visible-near infrared (Vis-NIR) spectroscopy as a non-invasive method to assess the internal quality of mangoes. It also enables innovative classification models for automated binary categorization (healthy vs spongy tissue-affected). Through preprocessing and principal component analysis of spectral reflectance data, wavelength ranges of 670–750 nm, 900–970 nm, and 1100–1170 nm were identified for distinguishing healthy and damaged mangoes. Soft independent modelling of class analogy (SIMCA) modelling is a novel approach that can be used to classify mango into healthy and spongy tissue-affected categories for better postharvest management. The accuracy of SIMCA models for classifying mangoes into healthy and spongy tissue-affected classes was highest in the wavelength regions of 670–750 nm and 900–970 nm, being 94.4% and 96.7%, respectively. The spectral reflectance between wavelength region 650–970 nm gave significant and visible differentiation between all stages of spongy tissue, that is, mild, medium, and severe. Furthermore, the application of Vis-NIR spectroscopy alongside SIMCA modelling offers a viable avenue for examining internal abnormalities resulting from diseases or injuries in fruits, broadening its utility for diverse inspection needs.
{"title":"Detection and classification of spongy tissue disorder in mango fruit during ripening by using visible-near infrared spectroscopy and multivariate analysis","authors":"Patil R Kiran, Parth Jadhav, G Avinash, Pramod Aradwad, Arunkumar TV, Rakesh Bhardwaj, Roaf A Parray","doi":"10.1177/09670335241269005","DOIUrl":"https://doi.org/10.1177/09670335241269005","url":null,"abstract":"The esteemed Alphonso mango, cherished in India for its taste, saffron color, texture, and extended shelf life, holds global commercial appeal. Unfortunately, the prevalent spongy tissue disorder in Alphonso mangoes results in a soft and corky texture, with up to 30% of mangoes within a single batch affected. This issue leads to outright rejection during export due to delayed disorder identification. The current assessment method involves destructive sampling, causing substantial fruit loss, and lacks assurance for overall batch quality. In light of the mentioned challenges, this current study focuses on utilizing visible-near infrared (Vis-NIR) spectroscopy as a non-invasive method to assess the internal quality of mangoes. It also enables innovative classification models for automated binary categorization (healthy vs spongy tissue-affected). Through preprocessing and principal component analysis of spectral reflectance data, wavelength ranges of 670–750 nm, 900–970 nm, and 1100–1170 nm were identified for distinguishing healthy and damaged mangoes. Soft independent modelling of class analogy (SIMCA) modelling is a novel approach that can be used to classify mango into healthy and spongy tissue-affected categories for better postharvest management. The accuracy of SIMCA models for classifying mangoes into healthy and spongy tissue-affected classes was highest in the wavelength regions of 670–750 nm and 900–970 nm, being 94.4% and 96.7%, respectively. The spectral reflectance between wavelength region 650–970 nm gave significant and visible differentiation between all stages of spongy tissue, that is, mild, medium, and severe. Furthermore, the application of Vis-NIR spectroscopy alongside SIMCA modelling offers a viable avenue for examining internal abnormalities resulting from diseases or injuries in fruits, broadening its utility for diverse inspection needs.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"149 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175112","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 : 2024-08-12DOI: 10.1177/09670335241268928
Yun Zhang, Jun Liu, Zheng lin Tan, Ming Yi Jiang
Near infrared (NIR) spectroscopy is sensitive to physical conditions such as sample temperature, meaning that rapid detection methods based on NIR spectroscopy are significantly influenced by temperature. To address this challenge, symbolic regression was employed to mitigate the effects of temperature. The Weighted Windowed Adaptive Optimization algorithm was proposed and combined with the Sequential Projection Algorithm to extract temperature-related feature points and remove redundant data. Subsequent 3D modeling of these feature points revealed that absorbance alterations due to temperature comprised two distinct segments. Consequently, based on symbolic regression, the temperature standardization algorithm was devised to generate piecewise equations. This algorithm surpassed genetic programming and non-segmented methods in performance metrics. The piecewise function equations generated by the algorithm were used to regress the absorbance at different temperatures to the standard temperature. Non-dairy cream, with different indigo pigment contents, was temperature standardized using a piecewise function to obtain spectra at two standard temperatures; 18°C and 28°C. The r2 for the quantitative regression model improved from 0.71 to 0.95 at 18°C and from 0.63 to 0.85 at 28°C. The temperature standardization method offers interpretable equations for spectra that model the complex changes with temperature, factoring out the temperature variation, thereby facilitating the practical use of NIR spectroscopy in rapid detection applications.
{"title":"A method to standardize the temperature for near infrared spectra of the indigo pigment in non-dairy cream based on symbolic regression","authors":"Yun Zhang, Jun Liu, Zheng lin Tan, Ming Yi Jiang","doi":"10.1177/09670335241268928","DOIUrl":"https://doi.org/10.1177/09670335241268928","url":null,"abstract":"Near infrared (NIR) spectroscopy is sensitive to physical conditions such as sample temperature, meaning that rapid detection methods based on NIR spectroscopy are significantly influenced by temperature. To address this challenge, symbolic regression was employed to mitigate the effects of temperature. The Weighted Windowed Adaptive Optimization algorithm was proposed and combined with the Sequential Projection Algorithm to extract temperature-related feature points and remove redundant data. Subsequent 3D modeling of these feature points revealed that absorbance alterations due to temperature comprised two distinct segments. Consequently, based on symbolic regression, the temperature standardization algorithm was devised to generate piecewise equations. This algorithm surpassed genetic programming and non-segmented methods in performance metrics. The piecewise function equations generated by the algorithm were used to regress the absorbance at different temperatures to the standard temperature. Non-dairy cream, with different indigo pigment contents, was temperature standardized using a piecewise function to obtain spectra at two standard temperatures; 18°C and 28°C. The r<jats:sup>2</jats:sup> for the quantitative regression model improved from 0.71 to 0.95 at 18°C and from 0.63 to 0.85 at 28°C. The temperature standardization method offers interpretable equations for spectra that model the complex changes with temperature, factoring out the temperature variation, thereby facilitating the practical use of NIR spectroscopy in rapid detection applications.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"2 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175122","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 : 2024-07-27DOI: 10.1177/09670335241242644
Fujie Zhang, Shanshan Li, Lei Shi, Lixia Li, Xiuming Cui
The rapid determination of moisture content in Panax notoginseng taproot (PNT) was determined using a portable near infrared spectrometer (900∼1700 nm). First, to reduce baseline offset of the spectra Savitzky-Golay and standard normal variate transformation were combined to preprocess the original spectral data. Then, competitive adaptive reweighting sampling and bootstrapping soft shrinkage (BOSS) were employed to extract feature wavelengths that could characterize the moisture content information of PNT respectively. Finally, the least square support vector regression (LSSVR) model was established based on feature spectra and full spectra. To improve the prediction accuracy of the model, a LSSVR model based on the arithmetic optimization algorithm (AOA) was proposed, and the optimization results were compared with those of the snake optimizer and particle swarm optimization. The results indicated that the best prediction model was BOSS-AOA-LSSVR, with r2 and RMSEP values of 0.96 and 0.03%, respectively. Thus, it is feasible to predict the moisture content of Panax notoginseng taproot by portable near infrared spectroscopy in combination with BOSS-AOA-LSSVR. The results show that portable near infrared spectroscopy can be used to predict the moisture content of Panax notoginseng taproot, which provides a theoretical basis for the rapid and non-destructive detection of the moisture content of Panax notoginseng taproots.
{"title":"Moisture content of Panax notoginseng taproot predicted using near infrared spectroscopy","authors":"Fujie Zhang, Shanshan Li, Lei Shi, Lixia Li, Xiuming Cui","doi":"10.1177/09670335241242644","DOIUrl":"https://doi.org/10.1177/09670335241242644","url":null,"abstract":"The rapid determination of moisture content in Panax notoginseng taproot (PNT) was determined using a portable near infrared spectrometer (900∼1700 nm). First, to reduce baseline offset of the spectra Savitzky-Golay and standard normal variate transformation were combined to preprocess the original spectral data. Then, competitive adaptive reweighting sampling and bootstrapping soft shrinkage (BOSS) were employed to extract feature wavelengths that could characterize the moisture content information of PNT respectively. Finally, the least square support vector regression (LSSVR) model was established based on feature spectra and full spectra. To improve the prediction accuracy of the model, a LSSVR model based on the arithmetic optimization algorithm (AOA) was proposed, and the optimization results were compared with those of the snake optimizer and particle swarm optimization. The results indicated that the best prediction model was BOSS-AOA-LSSVR, with r<jats:sup>2</jats:sup> and RMSEP values of 0.96 and 0.03%, respectively. Thus, it is feasible to predict the moisture content of Panax notoginseng taproot by portable near infrared spectroscopy in combination with BOSS-AOA-LSSVR. The results show that portable near infrared spectroscopy can be used to predict the moisture content of Panax notoginseng taproot, which provides a theoretical basis for the rapid and non-destructive detection of the moisture content of Panax notoginseng taproots.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"171 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863218","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 : 2024-07-01Epub Date: 2023-04-06DOI: 10.1177/17585732231165526
Anthony K Chiu, Kendrick J Cuero, Amil R Agarwal, Samuel I Fuller, R Timothy Kreulen, Matthew J Best, Uma Srikumaran
Background: Alcohol use disorder (AUD) is the most prevalent substance use disorder in the United States. However, the current literature on AUD as a preoperative risk factor for Total Shoulder Arthroplasty (TSA) outcomes is limited. The purpose of this study was to identify the association of AUD with revision rates and 90-day postoperative complications in TSA.
Methods: A retrospective study was conducted using the PearlDiver database. Patients diagnosed with AUD were identified. Patients in remission or with underlying cirrhosis were excluded. Outcomes included 2-year revision, 90-day readmission, 90-day emergency, and 90-day post-operative medical complications. Analysis was performed with univariate chi-squared tests followed by multivariable logistic regression.
Results: A total of 59,261 patients who underwent TSA for osteoarthritis were identified, with 1522 patients having a diagnosis of AUD. Multivariable logistic regression showed that patients with AUD were more likely to undergo 2-year all-cause revision (OR = 1.49, p = 0.007), 2-year aseptic revision (OR = 1.47, p = 0.014), 90-day hospital readmission (OR = 1.57, p = 0.015), and 90-day transient mental disorder (OR = 2.13, p = 0.026).
Conclusions: AUD is associated with increased rates of 2-year revision surgery, as well as 90-day readmission and 90-day transient mental disorder following primary TSA for osteoarthritis. These findings may assist orthopedic surgeons in counseling patients with AUD during the pre-operative course.
{"title":"The association of alcohol use disorder with revision rates and post-operative complications in total shoulder arthroplasty.","authors":"Anthony K Chiu, Kendrick J Cuero, Amil R Agarwal, Samuel I Fuller, R Timothy Kreulen, Matthew J Best, Uma Srikumaran","doi":"10.1177/17585732231165526","DOIUrl":"10.1177/17585732231165526","url":null,"abstract":"<p><strong>Background: </strong>Alcohol use disorder (AUD) is the most prevalent substance use disorder in the United States. However, the current literature on AUD as a preoperative risk factor for Total Shoulder Arthroplasty (TSA) outcomes is limited. The purpose of this study was to identify the association of AUD with revision rates and 90-day postoperative complications in TSA.</p><p><strong>Methods: </strong>A retrospective study was conducted using the PearlDiver database. Patients diagnosed with AUD were identified. Patients in remission or with underlying cirrhosis were excluded. Outcomes included 2-year revision, 90-day readmission, 90-day emergency, and 90-day post-operative medical complications. Analysis was performed with univariate chi-squared tests followed by multivariable logistic regression.</p><p><strong>Results: </strong>A total of 59,261 patients who underwent TSA for osteoarthritis were identified, with 1522 patients having a diagnosis of AUD. Multivariable logistic regression showed that patients with AUD were more likely to undergo 2-year all-cause revision (OR = 1.49, <i>p</i> = 0.007), 2-year aseptic revision (OR = 1.47, <i>p</i> = 0.014), 90-day hospital readmission (OR = 1.57, <i>p</i> = 0.015), and 90-day transient mental disorder (OR = 2.13, <i>p</i> = 0.026).</p><p><strong>Conclusions: </strong>AUD is associated with increased rates of 2-year revision surgery, as well as 90-day readmission and 90-day transient mental disorder following primary TSA for osteoarthritis. These findings may assist orthopedic surgeons in counseling patients with AUD during the pre-operative course.</p>","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"6 1","pages":"250-257"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88324821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.
{"title":"Enhancing quality control in emulsion-type sausage production: Predicting chemical composition of intact samples with near infrared spectroscopy","authors":"Pitiporn Ritthiruangdej, Kanithaporn Vangnai, Sumaporn Kasemsumran, Supapich Somboonying, Pimwaree Charoensin, Arisara Hiriotappa, Papawarin Lowleraha","doi":"10.1177/09670335241240518","DOIUrl":"https://doi.org/10.1177/09670335241240518","url":null,"abstract":"This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"87 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311618","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}
A non-invasive near infrared (NIR) spectroscopic method was developed for the quantitative moisture determination in a lyophilized injection formulation. The calibration samples were prepared by exposing lyophilized samples at different temperatures and relative humidity. The samples from different scales and different process parameters were considered for adding robustness to the model. The NIR spectra were collected using a Fourier- transform (FT) NIR with a diffuse reflectance probe and the same samples were further analyzed by the Karl Fisher (KF) method for moisture content. The pre-treated NIR spectra were used for quantitative method development for moisture content. Partial least squares (PLS) regression was used to develop calibrations in the 5600-4950 cm−1 region with calibration coefficient of determination (R2) of 0.96 and root mean square error of calibration (RMSEC) of 0.149. The model was cross-validated internally using the Kernel algorithm with r2 = 0.96 and RMSECV = 0.15. The accuracy of the NIR method against the KF method, precision, and reproducibility were good and the model was robust in predicting different external validation samples. This work allowed NIR as an alternative measurement for moisture analysis as well as facilitate 100% monitoring before packaging and save the cost of sample and time of KF analysis.
{"title":"Near infrared spectroscopy for determination of moisture content in lyophilized formulation","authors":"Aruna Khanolkar, Pranita Pawale, Viraj Thorat, Bhaskar Patil, Gautam Samanta","doi":"10.1177/09670335241240309","DOIUrl":"https://doi.org/10.1177/09670335241240309","url":null,"abstract":"A non-invasive near infrared (NIR) spectroscopic method was developed for the quantitative moisture determination in a lyophilized injection formulation. The calibration samples were prepared by exposing lyophilized samples at different temperatures and relative humidity. The samples from different scales and different process parameters were considered for adding robustness to the model. The NIR spectra were collected using a Fourier- transform (FT) NIR with a diffuse reflectance probe and the same samples were further analyzed by the Karl Fisher (KF) method for moisture content. The pre-treated NIR spectra were used for quantitative method development for moisture content. Partial least squares (PLS) regression was used to develop calibrations in the 5600-4950 cm<jats:sup>−1</jats:sup> region with calibration coefficient of determination (R<jats:sup>2</jats:sup>) of 0.96 and root mean square error of calibration (RMSEC) of 0.149. The model was cross-validated internally using the Kernel algorithm with r<jats:sup>2</jats:sup> = 0.96 and RMSECV = 0.15. The accuracy of the NIR method against the KF method, precision, and reproducibility were good and the model was robust in predicting different external validation samples. This work allowed NIR as an alternative measurement for moisture analysis as well as facilitate 100% monitoring before packaging and save the cost of sample and time of KF analysis.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"25 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311597","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}
Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction (MSC), first derivative (1stDer), second derivative (2ndDer), and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.
六亚甲基四胺（HA）作为一种原材料被广泛应用于医疗、化工、工业和军事领域，对其进行快速定量分析对这些领域的生产工艺非常重要。近红外（NIR）光谱技术具有高效、低成本、无损检测和便捷等优点，是定量分析 HA-乙酸（HAc）溶液中 HA 浓度的有力技术，在六元和八元生产中具有应用潜力。为了提高近红外光谱校准的检测精度，研究人员研究了一系列预处理算法和变量选择方法。筛选了标准正态变异（SNV）、乘法信号校正（MSC）、一阶导数（1stDer）、二阶导数（2ndDer）和离散小波变换（DWT）的 46 种不同组合。比较了四种变量选择方法（连续投影算法（SPA）、无信息变量消除（UVE）、竞争性自适应加权采样（CARS）和多元宇宙优化（MVO））的效果。最后，通过将基于联合 x-y 距离（SPXY）算法的样本集分配与随机森林（RF）校准模型相结合，建立了一个模型（SPXY-SNV-1stDer-DWT-MVO-RF），并首次将 MVO 与近红外技术相结合。该模型的定标集决定系数（R2）、定标集均方根误差（RMSEC）、预测集决定系数（r2）和预测集均方根误差（RMSEP）分别为 1.000、0.04%、0.999 和 0.05%。该研究证明了利用近红外光谱对 HA 进行定量检测的新颖性和可行性，并为通过优化算法来优化定量分析模型提供了宝贵的启示，表明近红外光谱技术在相关领域具有巨大的应用潜力。
{"title":"Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques","authors":"Hui Chao, Shichuan Qian, Zhi Wang, Xin Sheng, Xinping Zhao, Zhiyan Lu, Xiaoxia Li, Yinguang Xu, Shaohua Jin, Lijie Li, Kun Chen","doi":"10.1177/09670335241242659","DOIUrl":"https://doi.org/10.1177/09670335241242659","url":null,"abstract":"Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction (MSC), first derivative (1stDer), second derivative (2ndDer), and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R<jats:sup>2</jats:sup>), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r<jats:sup>2</jats:sup>), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"157 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311422","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 : 2024-02-17DOI: 10.1177/09670335241232093
Wang Honghong, Yuan Hui, Xiong Zhixin
In order to share multivariate calibration models of gasoline research octane number (RON) between different near infrared spectrometers, a novel calibration transfer method, namely combination of screening consistent wavelengths and direct standardization (SWCSS-DS) was proposed. Firstly, screening wavelengths with consistent and stable signals (SWCSS) between instruments was used to select the wavelengths with best stability, and then direct standardization (DS) further corrected the systematic errors that still exist after the SWCSS was implemented. The spectra of 120 standard gasoline samples collected on two near infrared spectrometers of the same type were investigated in detail to verify the validity of the new algorithm. Compared results of other transfer methods such as SWCSS, Slope/Bias (S/B), direct standardisation (DS), and piecewise direct standardization (PDS), the root mean squared error for prediction (RMSEP) of SWCSS-DS algorithm for target samples was decreased from 5.75 to 0.295, and the Akaike information criterion (AIC) value decreased from 1516 to 640, which were better than those of the SWCSS, S/B, DS and PDS algorithms. Therefore, the joint algorithm of SWCSS-DS has not only improved the universality of the master model, but also reduced the dimension of the spectral matrix and calibration equation, that would provide a more efficient model transfer strategy for the practical applications.
{"title":"Transfer of near infrared calibration for gasoline octane number based on screening consistent wavelengths combined with direct standardization algorithm","authors":"Wang Honghong, Yuan Hui, Xiong Zhixin","doi":"10.1177/09670335241232093","DOIUrl":"https://doi.org/10.1177/09670335241232093","url":null,"abstract":"In order to share multivariate calibration models of gasoline research octane number (RON) between different near infrared spectrometers, a novel calibration transfer method, namely combination of screening consistent wavelengths and direct standardization (SWCSS-DS) was proposed. Firstly, screening wavelengths with consistent and stable signals (SWCSS) between instruments was used to select the wavelengths with best stability, and then direct standardization (DS) further corrected the systematic errors that still exist after the SWCSS was implemented. The spectra of 120 standard gasoline samples collected on two near infrared spectrometers of the same type were investigated in detail to verify the validity of the new algorithm. Compared results of other transfer methods such as SWCSS, Slope/Bias (S/B), direct standardisation (DS), and piecewise direct standardization (PDS), the root mean squared error for prediction (RMSEP) of SWCSS-DS algorithm for target samples was decreased from 5.75 to 0.295, and the Akaike information criterion (AIC) value decreased from 1516 to 640, which were better than those of the SWCSS, S/B, DS and PDS algorithms. Therefore, the joint algorithm of SWCSS-DS has not only improved the universality of the master model, but also reduced the dimension of the spectral matrix and calibration equation, that would provide a more efficient model transfer strategy for the practical applications.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"23 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956300","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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