Journal of Near Infrared Spectroscopy最新文献

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"title":"Improving feedstock quality control in formaldehyde-based resin and wood-based panel production through near infrared spectroscopy","authors":"Roberto Magalhães, N. Paiva, J. Ferra, Fernão D Magalhães, F. G. Martins","doi":"10.1177/09670335241228407","DOIUrl":"https://doi.org/10.1177/09670335241228407","url":null,"abstract":"To assure the quality control of industrial processes, it is important to adopt reproducible and efficient methodologies. Spectroscopic methods, such as near infrared (NIR), are a good option as they are fast and may be used to indirectly estimate multiple physicochemical properties. In this study, NIR spectra of key feedstock samples used in the production of formaldehyde-based resin and wood-based panels (WBP), namely urea, ammonium sulfate, ammonium nitrate, sodium hydroxide, and acetic acid, were acquired. Multivariate data analysis was applied to establish the correlation between the spectra and the properties being measured. Quantitative models were then created using partial least squares (PLS) regression to predict the concentrations of feedstock samples. This study presents quantitative models that were created by combining spectra measured on two probes, which achieved similar prediction results as single-probe based models. The performances of the best models were compared with the reference methods for each of the evaluated samples. For the samples under study, the proposed approach is suitable for routine analysis across multiple equipment configurations using the same quantitative model. NIR spectroscopy combined with chemometric models could be a valuable complement to support in-line raw material monitoring and plant digitalization in the wood panels industry.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605551","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}

{"title":"A hyperspectral band selection algorithm for identifying high oleic acid peanuts","authors":"Hui Shao, Xingyun Li, Long Sun, Cheng Wang, Yuxia Hu","doi":"10.1177/09670335231225817","DOIUrl":"https://doi.org/10.1177/09670335231225817","url":null,"abstract":"High oleic acid peanuts have higher oleic acid content and stronger oxidation stability than common peanuts, but their appearances are similar, which imposes difficulties for classifying. Based on this, the study aims to classify high oleic acid peanut to ensure its purity by using hyperspectral imaging technology. However, classification accuracy and efficiency are limited given the large amount of redundant information of hyperspectral images. The band iteration algorithm (BIA) is proposed to select characteristic bands by reducing the redundant information between spectral bands for the peanut classification. Hyperspectral images with 616 bands (from 400 nm to 1100 nm) of 126 high oleic acid peanuts and 126 common peanuts were collected. Then, BIA selected optimal bands as characteristic bands from adjacent bands according to the classification accuracy of each band subsets. Thirdly, three classification models, namely linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA), were employed to compare the performance of BIA with successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The experimental results show that BIA can effectively improve the classification ability of spectral data. The BIA-PLS-DA model had the best classification efficiency, and the accuracy of the test set reached 93.26%. For peanut individuals, only one peanut sample was misclassified with a classification error rate of 1.43%.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139532028","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}

{"title":"Predicting oil content of Australian beauty leaf tree kernel samples using near infrared spectroscopy combined with chemometrics","authors":"Rahul Sreekumar, N. Ashwath, D. Cozzolino, KB Walsh","doi":"10.1177/09670335231225820","DOIUrl":"https://doi.org/10.1177/09670335231225820","url":null,"abstract":"This study was conducted to evaluate the ability of near infrared (NIR) spectroscopy to estimate oil content, and per cent of cake, resin and residue in beauty leaf tree ( Calophyllum inophyllum L.) kernel samples. Fruits were collected from various geographical locations of tropical Australia (from Rockhampton to Darwin) and air dried before the kernels were manually separated from the fruits. Kernel samples were oven dried, crushed (5–10 mm) and their NIR spectra collected using a Fourier transform (FT) NIR instrument where the same batch of kernels were used to extract oil using a screw press. Calibration models between the NIR spectra and reference data were developed using partial least squares (PLS) regression. The cross-validation statistics including the coefficient of determination (r2) and standard error in cross validation (SECV) were 0.83 (SECV: 2.39%) for oil content, 0.89 (SECV: 2.81%) for cake, 0.88 (SECV: 1.92%) for resin and 0.79 (SECV: 2.15%) for residue, respectively. This research showed that NIR spectroscopy can be used as an alternative, faster and low-cost technique to predict oil content, per cent of cake, resins and residues in various genotypes of beauty leaf tree. Further studies should be carried out to increase the sample size and chemical variation, as well as to evaluate different methods of oil extraction (e.g., solvent extraction) to improve the reliability of the calibration models.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139441164","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}

{"title":"Classification of <i>Listeria</i> species using near infrared hyperspectral imaging","authors":"Rumbidzai T Matenda, Diane Rip, Paul J Williams","doi":"10.1177/09670335231213951","DOIUrl":"https://doi.org/10.1177/09670335231213951","url":null,"abstract":"Near infrared (NIR) hyperspectral imaging and multivariate data analysis was evaluated for its potential to detect and classify Listeria species. Three Listeria species, namely L. monocytogenes (ATCC 23074), L. innocua (ATCC 33090) and L. ivanovii (ATCC 19119) were grown for single colonies on Brain Heart Infusion agar and imaged in the NIR range of 950–2500 nm. Principal component analysis (PCA) was used for data exploration and to establish pattern recognition. Images were pre-processed with standard normal variate correction and the Savitzky-Golay smoothing technique (third order polynomial with 15 points). Two approaches to data analysis, that is object-wise and pixel-wise analysis, were investigated for discriminant analysis. The PCA score plot showed slight separation between the three groups with L. monocytogenes and L. ivanovii grouping close together. It was possible to visualise separation along PC3 (5.64% sum of squares (SS)) and PC4 (3.44% SS). Based on the loadings, differences in bacteria were attributed to teichoic acids, protein, and carbohydrate composition in the bacterial cell wall within the wavelength range 1000–1900 nm. Using extracted spectral data from the hypercubes, partial least squares discriminant analysis was employed for further classification. Classification accuracies above 90% were achieved for L. monocytogenes, L. innocua and L. ivanovii. This was true for data analysed using both pixel-wise analysis and object-wise analysis. The results demonstrated that hyperspectral imaging has notable potential to classify bacteria within the Listeria genus. Nonetheless, in order to improve model efficiency, model optimisation and incorporation of more bacterial strains need to be investigated in further research.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241250","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}

{"title":"Active learning sample selection - based on multicriteria","authors":"Zhonghai He, Kun Shen, Xiaofang Zhang","doi":"10.1177/09670335231211618","DOIUrl":"https://doi.org/10.1177/09670335231211618","url":null,"abstract":"In multivariate calibration problems, model performance is affected significantly by the calibration samples used during model building. In recent years, active learning methods have become one of the best methods for sample selection. However, most active learning methods only select instances from prediction uncertainty or sample space distance, and these single-criteria methods tend to select undesired samples. In addition, sample density characterizes the spatial information carried by the sample, but few studies in quantitative analysis utilize sample density alone to select calibration samples. Considering these issues, based on the k-means clustering algorithm, this paper proposes an active learning sample selection method (DIDAL), which combines the three criteria of diversity, informativeness and sample density. The most representative sample is iteratively selected for - addition to the calibration set for modeling and estimating the chemical concentration of analytes. Soybean meal and soy sauce samples were analyzed by DIDAL and compared with existing sample selection methods. The prediction results show that the DIDAL algorithm significantly outperforms several existing algorithms and is close to the performance of full-sample modeling. A model with high prediction accuracy can be constructed by selecting only a few samples using the DIDAL method.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135392765","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}

{"title":"A new hydrochloric acid 2-0 band analysis: A two temperature study","authors":"Alexandre E Santos, Laiz R Ventura, Carlos E Fellows","doi":"10.1177/09670335231200998","DOIUrl":"https://doi.org/10.1177/09670335231200998","url":null,"abstract":"A new study of the 2−0 band of the hydrochloric acid molecule is performed by high resolution Fourier-transform absorption spectroscopy in the near infrared region. The spectra were measured at two different temperatures, 293 K and 315 K, for different pressures at each temperature. The spectral linewidths were analysed in a two-step procedure, being first performed by directly measuring the linewidth and second by fitting each spectral line to a model line profile, using Gaussian, Loretzian and Voigt profiles. A study of the profiles that best describe the spectral line fits is carried out in this work. The behavior of the spectral lines self-broadening and their corresponding self-induced shifts were studied for different values of rotational quantum numbers. The analysis are performed for both isotopes of the molecule and the self-broadening and self-shift coefficients are presented.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136213034","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}

{"title":"Investigation of <i>Fusarium</i> damage in wheat using hyperspectral imaging: An independent component analysis approach","authors":"Mohammad Nadimi, Fernando AM Saccon, Ahmed Elrewainy, Dennis Parcey, Sherif S Sherif, Jitendra Paliwal","doi":"10.1177/09670335231202258","DOIUrl":"https://doi.org/10.1177/09670335231202258","url":null,"abstract":"With the continuously growing world population in the 21st century, the agri-food industry is in dire need of adopting rapid, eco-friendly, and reliable technologies to improve the quantity, quality, and safety of agri-food products to fulfill the world's future food needs. Hyperspectral imaging (HSI), a technique to glean a sample's spectral and spatial information, is an emerging non-destructive technique that can characterize the quality parameters of agri-food products such as Fusarium damage. Despite its vast potential, HSI systems suffer from enormous data sizes, requiring high computational time and power. One potential solution to overcome the aforementioned challenge is to reduce the data size by removing redundant information. However, detecting small optimum features from a large dataset is not trivial. To this end, an exploratory novel HSI data reduction and analysis technique was investigated and validated to identify Fusarium damage in wheat kernels. Wheat samples at three moisture contents (19, 27, and 35%, wet basis) and seven infection levels (ranging from 0 to 56 days after infection) were imaged at 256 equally spaced wavelengths from 820 to 1666 nm. Firstly, complete HSI data was utilized to successfully characterize sound and Fusarium-damaged wheat kernels using independent component analysis (ICA) algorithm. Then, a genetic algorithm optimization approach was used to reduce the data to ten wavelengths for ICA-based analysis. This data reduction approach reduced the computation time to approximately 1.31% of the original time taken for analyzing the full HSI data without compromising the performance of the system. This preliminary study suggests that such wavelength tailoring could reduce the complexity and price of the imaging hardware, e.g., the use of inexpensive non-tunable filters, and less expensive computational hardware, thereby enabling fast and affordable real-time exploration and sorting of grains. This study, while exploratory, fosters advancements in HSI data processing and identifies certain limitations that open new avenues for future research.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960507","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}

{"title":"Predicting starch content of cassava with near infrared spectroscopy in Ugandan cassava germplasm","authors":"Babirye Fatumah Namakula, Ephraim Nuwamanya, Michael Kanaabi, Enoch Wembambazi, Robert Sezi Kawuki","doi":"10.1177/09670335231194739","DOIUrl":"https://doi.org/10.1177/09670335231194739","url":null,"abstract":"In Uganda, efforts are underway to improve starch content through conventional breeding as a strategy for increasing adoption of new cassava varieties for both food and industry. However, only few samples can be quantified, limiting the gains in breeding. A database of 115 clones was used to evaluate the potential of Near infrared spectroscopy to predict starch content in cassava. Starch content ranged from 21.48 to 73.97% dry basis. The performance of standard normal variate and de-trend with second derivative calculated on two data points and smoothing plus combination of standard multiplicative scatter correction with second derivative calculated on two data points and smoothing were the best fit mathematical treatments for the calibrations developed. Near infrared spectroscopy predictions for starch content (R 2 = 0.85, and r 2 = 0.55) developed were reliable, thus usable for screening of cassava starch content at early stages of breeding.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135864717","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}