In-situ monitoring of saccharides removal of alcohol precipitation using near-infrared spectroscopy

As unsafe components in herbal medicine (HM), saccharides can affect not only the drug appearance and stabilization, but also the drug efficacy and safety. The present study focuses on the in-line monitoring of batch alcohol precipitation processes for saccharide removal using near-infrared (NIR) spectroscopy. NIR spectra in the 4000–10,000-cm[Formula: see text] wavelength range are acquired in situ using a transflectance probe. These directly acquired spectra allow characterization of the dynamic variation tendency of saccharides during alcohol precipitation. Calibration models based on partial least squares (PLS) regression have been developed for the three saccharide impurities, namely glucose, fructose, and sucrose. Model errors are estimated as the root-mean-square errors of cross-validation (RMSECVs) of internal validation and root-mean-square errors of prediction (RMSEPs) of external validation. The RMSECV values of glucose, fructose, and sucrose were 1.150, 1.535, and 3.067[Formula: see text]mg[Formula: see text]mL[Formula: see text], and the RMSEP values were 0.711, 1.547, and 3.740[Formula: see text][Formula: see text], respectively. The correlation coefficients [Formula: see text] between the NIR predictive and the reference measurement values were all above 0.94. Furthermore, NIR predictions based on the constructed models improved our understanding of sugar removal and helped develop a control strategy for alcohol precipitation. The results demonstrate that, as an alternative process analytical technology (PAT) tool for monitoring batch alcohol precipitation processes, NIR spectroscopy is advantageous for both efficient determination of quality characteristics (fast, in situ, and requiring no toxic reagents) and process stability, and evaluating the repeatability.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

Applied Spectroscopy ◽

10.1177/0003702817727016 ◽

2017 ◽

Vol 71 (11) ◽

pp. 2427-2436 ◽

Cited By ~ 5

Author(s):

Mi Lei ◽

Long Chen ◽

Bisheng Huang ◽

Keli Chen

Keyword(s):

Root Mean Square Error ◽

Least Squares ◽

Partial Least Squares ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Support Vector ◽

Mean Square ◽

Characteristic Spectrum

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

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Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of Gastrodiae rhizoma with Multiparameters and Chemometrics

Journal of Analytical Methods in Chemistry ◽

10.1155/2020/8847277 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Yamin Zuo ◽

Jing Yang ◽

Chen Li ◽

Xuehua Deng ◽

Shengsheng Zhang ◽

...

Keyword(s):

Near Infrared ◽

Multivariate Calibration ◽

Process Analytical Technology ◽

Nir Spectroscopy ◽

Principal Component ◽

Great Promise ◽

Gastrodiae Rhizoma ◽

Mean Square Errors ◽

Analytical Technology ◽

Hydroxybenzyl Alcohol

Steaming is a vital unit operation in traditional Chinese medicine (TCM), which greatly affects the active ingredients and the pharmacological efficacy of the products. Near-infrared (NIR) spectroscopy has already been widely used as a strong process analytical technology (PAT) tool. In this study, the potential usage of NIR spectroscopy to monitor the steaming process of Gastrodiae rhizoma was explored. About 10 lab scale batches were employed to construct quantitative models to determine four chemical ingredients and moisture change during the steaming process. Gastrodin, p-hydroxybenzyl alcohol, parishin B, and parishin A were modeled by different multivariate calibration models (SMLR and PLS), while the content of the moisture was modeled by principal component regression (PCR). In the optimized models, the root mean square errors of prediction (RMSEP) for gastrodin, p-hydroxybenzyl alcohol, parishin B, parishin A, and moisture were 0.0181, 0.0143, 0.0132, 0.0244, and 2.15, respectively, and correlation coefficients ( R p 2 ) were 0.9591, 0.9307, 0.9309, 0.9277, and 0.9201, respectively. Three other batches’ results revealed that the accuracy of the model was acceptable and that was specific for next drying step. In addition, the results demonstrated the method was reliable in process performance and robustness. This method holds a great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online monitoring and quality control in the TCM industrial steaming process.

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Development of observation-based global multilayer soil moisture products for 1970 to 2016

Earth System Science Data ◽

10.5194/essd-13-4385-2021 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4385-4405

Author(s):

Yaoping Wang ◽

Jiafu Mao ◽

Mingzhou Jin ◽

Forrest M. Hoffman ◽

Xiaoying Shi ◽

...

Keyword(s):

Soil Moisture ◽

Root Mean Square ◽

Land Surface ◽

Soil Layer ◽

Added Value ◽

Surface Model ◽

Mean Square ◽

Wide Range ◽

Mean Square Errors

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

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Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations

Processes ◽

10.3390/pr9111942 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1942

Author(s):

Kamaranga H. S. Peiris ◽

Xiaorong Wu ◽

Scott R. Bean ◽

Mayra Perez-Fajardo ◽

Chad Hayes ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Grain Sorghum ◽

Calibration Model ◽

Mean Square ◽

Starch Properties ◽

End Uses

Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R2) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R2 = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R2 = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R2 = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (<5%) and normal amylose (>15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.

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Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

Remote Sensing ◽

10.3390/rs12213503 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3503 ◽

Cited By ~ 1

Author(s):

Volkan Senyurek ◽

Fangni Lei ◽

Dylan Boyd ◽

Ali Cafer Gurbuz ◽

Mehmet Kurum ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Correlation Coefficient ◽

Root Mean Square ◽

Temporal Resolution ◽

Mean Square ◽

In Situ Observations ◽

Spatio Temporal ◽

Mean Square Errors

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

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Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy

Journal of Analytical Methods in Chemistry ◽

10.1155/2014/741571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Xin-fang Xu ◽

Li-xing Nie ◽

Li-li Pan ◽

Bian Hao ◽

Shao-xiong Yuan ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Multivariate Regression ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Mean Square ◽

Least Squares Regression ◽

Regression Methods

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.

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Fast Determination of Dioscin in Dioscorea zingiberensis C.H.Wright by PLS-NIR Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.2079 ◽

2013 ◽

Vol 807-809 ◽

pp. 2079-2084 ◽

Cited By ~ 3

Author(s):

Cai Xia Xie ◽

Rui Liu ◽

Hai Yan Gong ◽

Jing Wei Lei ◽

Xiao Yan Duan ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Root Mean Square ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Rapid Determination ◽

Nir Spectroscopy ◽

Calibration Model ◽

Mean Square ◽

Dioscorea Zingiberensis

A rapid determination method was builded about content of dioscin in Dioscorea zingiberensis C. H. Wright by near-infrared spectroscopy (NIRS) technology. The calibration model were builded through comparison of the content of dioscin in Dioscorea zingiberensis C. H. Wright and the near infrared spectroscopy of Dioscorea zingiberensis C. H. Wright with partial least squares. The internal correlation coefficient of cross-validation (R2) was 0.99208, root-mean-square error of cross-validation (RMSECV) was 0.0104, and external root mean square prediction deviation RMSEP was 0.0105, and the predictive value of the aversge relative error was 4.12%.

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Rapid and Simultaneous Measurement of Praeruptorin A, Praeruptorin B, Praeruptorin E, and Moisture Contents in Peucedani Radix Using Near-Infrared Spectroscopy and Chemometrics

Journal of AOAC International ◽

10.5740/jaoacint.19-0126 ◽

2020 ◽

Vol 103 (2) ◽

pp. 504-512

Author(s):

Yijuan Hu ◽

Hongjian Zhang ◽

Weiqing Liang ◽

Pan Xu ◽

Kelang Lou ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Mean Square ◽

Determination Coefficient ◽

Moisture Contents ◽

Calibration Models

Abstract Background: Peucedani Radix is a popular traditional Chinese medicine herb with a long history in China. Praeruptorin A (PA), praeruptorin B (PB), and praeruptorin E (PE) are usually taken as important quality indexes of Peucedani Radix. Objective: To establish a rapid method for simultaneous determination of PA, PB, PE, and moisture contents in Peucedani Radix using near-infrared (NIR) spectroscopy and chemometrics. Methods: One hundred twenty Peucedani Radix samples were analyzed with HPLC as a reference method. The NIR spectral scanning range was from 12000 cm−1 to 4000 cm−1. Partial least squares (PLS) regression algorithm was used to establish calibration models. Three variable selection methods were investigated, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and Monte Carlo uninformative variable elimination (MCUVE). The performances of the established models were evaluated by root-mean-square error (RMSEC) and determination coefficient (Rc2) of calibration set, root-mean-square error (RMSEP) and determination coefficient (Rp2) of prediction set, and residual predictive deviation (RPD). Results: A clear ranking of the performance of the calibration models could be as follows: CARS-PLS > MCUVE-PLS > VIP-PLS > Full-PLS. For CARS-PLS, Rp2, RMSEP, and RPD of the prediction set are as follows: 0.9204, 0.0860%, and 3.5850 for PA; 0.8011, 0.0431%, and 2.0868 for PB; 0.8043, 0.0367%, and 2.1569 for PE; and 0.9249, 0.3350%, and 3.6551 for moisture, respectively. Conclusions: The NIR spectroscopy combined with CARS-PLS calibration models could be used for rapid and accurate determination of PA, PB, PE, and moisture contents in Peucedani Radix samples.

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Near infrared spectroscopy of Eucalyptus pellita for foliar nutrients and the potential for real-time monitoring of trees in fertiliser trial plots

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211007971 ◽

2021 ◽

pp. 096703352110079

Author(s):

Agustan Alwi ◽

Roger Meder ◽

Yani Japarudin ◽

Hazandy A Hamid ◽

Ruzana Sanusi ◽

...

Keyword(s):

Near Infrared ◽

Acacia Mangium ◽

Nir Spectroscopy ◽

Nutrient Status ◽

Significant Loss ◽

Eucalyptus Pellita ◽

Operational Decision Making ◽

Fertiliser Application ◽

Planting Distance

Eucalyptus pellita F. Muell. has become an important tree species in the forest plantations of SE Asia, and in Malaysian Borneo in particular, to replace thousands of hectares of Acacia mangium Willd. which has suffered significant loss caused by Ceratocystis manginecans infection in Sabah, Malaysia. Since its first introduction at a commercial scale in 2012, E. pellita has been planted in many areas in the region. The species replacement requires new silvicultural practices to induce the adaptability of E. pellita to grow in the region and this includes relevant research to optimise such regimes as planting distance, pruning, weeding practices and nutrition regimes. In this present study, the nutritional status of the foliage was investigated with the aim to develop near infrared spectroscopic calibrations that can be used to monitor and quantify nutrient status, particularly total foliar nitrogen (N) and phosphorus (P) in the field. Spectra acquired on fresh foliage in situ on the tree could be used to predict N and P with accuracy suitable for operational decision-making regards fertiliser application. If greater accuracy is required, spectra acquired on dry, milled foliage could be used to predict N and P within a relative error of 10% (R2c, r2CV, RMSEP, RPD = 0.77, 0.71, 0.02 g 100/g, 1.9 for foliar P and = 0.90, 0.88, 0.21 g 100/g, 3.0 for foliar N on dry, milled foliage). The ultimate application of this is in situ nutrient monitoring, particularly to aid longitudinal studies in fertiliser trial plots and forest operations, as the non-destructive nature of NIR spectroscopy would enable regular monitoring of individual leaves over time without the need to destructively sample them. This would aid the temporal and spatial analysis of field data.

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