Journal articles: 'Synergy Interval PLS (siPLS)'

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Zhang, Lu, Long Xue, Mu Hua Liu, and Jing Li. "Nondestructive Detection of Soluble Solids Content of Nanfeng Mandarin Orange Using VIS-NIR Spectroscopy." Advanced Materials Research 361-363 (October 2011): 1634–37. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.1634.

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This study demonstrated how VIS-NIR spectroscopy can be used in the quantitative, noninvasive probing of soluble solids content (SSC) of mandarin orange. Total 197 mandarin oranges were divided into calibration set (133 samples) and prediction set (64 samples). Multiple scatter correction (MSC) was used to preprocess the collected visible and near infrared (Vis-NIR) spectra (350-1800nm) of mandarin orange. Partial least square (PLS), interval partial least square (IPLS) and synergy interval partial least square (SIPLS) methods were applied for constructing predictive models of SSC. Experimental results showed that the optimal SIPLS model obtained with 10 PLS components and the optimal combinations of intervals were number 5,7,8,9. The correlation coefficient (r) between the predicted and actual SSC was 0.9265 and 0.8577 for calibration and prediction set, respectively. The root mean square error of calibration (RMSEC) and prediction (RMSEP) set was 0.4890 and 0.7113, respectively. In conclusion, the combination of Vis-NIR spectroscopy and SIPLS methods can be used to provide a technique of noninvasive, convenient and rapid analysis for SSC in fruit.

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El-Alamin, Maha Mahmoud Abou, Maha Abd Elrahman Sultan, Maha Hegazy, Alastair William Wark, and Marwa Mohamed Azab. "Pure component contribution (PCCA) and synergy interval partial least squares (siPLS) algorithms for efficient resolution and quantification of overlapped signals; an application to novel antiviral tablets of daclatasvir, sofosbuvir and ribavirin." European Journal of Chemistry 10, no. 4 (December 31, 2019): 350–57. http://dx.doi.org/10.5155/eurjchem.10.4.350-357.1899.

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Daclatasvir (DAC), sofosbuvir (SOF) and ribavirin (RIB) have been recently co-formulated in tablet dosage form for the treatment of Hepatitis C virus infections. In this work, the resolution and quantitation of overlapped spectral signals was achieved by both univariate and multivariate algorithms. Pure component contribution algorithm (PCCA) as a novel approach was applied along with factor based partial least squares (PLS) algorithms using both full range and synergistic intervals (siPLS). Each drug could be determined at its λmax using PCCA, while PLS and siPLS were used for multivariate determination of the three components. Good linear relationships were obtained in the ranges of 5.45-16.35, 4.40-44.00 and 5.50-35.00 µg/mL for DAC, SOF and RIB, respectively, by PCCA. The PLS and siPLS models were built for the three compounds each in the concentration range of 2.00-10.00, 10.00-20.00 and 10.00-26.00 µg/mLfor DAC, SOF and RIB, respectively. Validation of the proposed methods was ascertained according to ICH guidelines for PCCA and through the use of internal and external validation sets for PLS and SiPLS models. The three methods were successfully applied for determination of DAC, SOF and RIB in pure form and in tablets.

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Ai, Shi Rong, Rui Mei Wu, Lin Yuan Yan, and Yan Hong Wu. "Measurement of the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion Based on near Infrared Spectroscopy." Advanced Materials Research 301-303 (July 2011): 1093–97. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.1093.

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This study attempted the feasibility to determine the ratio of tea polyphenols to amino acids in green tea infusion using near infrared (NIR) spectroscopy combined with synergy interval PLS (siPLS) algorithms. First, SNV was used to preprocess the original spectra of tea infusion; then, siPLS was used to select the efficient spectra regions from the preprocessed spectra. Experimental results showed that the spectra regions [7 8 18] were selected, which were out of the strong absorption of H2O. The optimal PLS model was developed with the selected regions when 6 PCs components were contained. The RMSEP value was equal to 0.316 and the correlation coefficient (R) was equal to 0.8727 in prediction set. The results demonstrated that NIR can be successfully used to determinate the ration of tea polyphenols to amino acids in green tea infusion.

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He, Yang-Chun, Sheng Fang, and Xue-Jiao Xu. "Simultaneous determination of acesulfame-K, aspartame and stevioside in sweetener blends by ultraviolet spectroscopy with variable selection by sipls algorithm." Macedonian Journal of Chemistry and Chemical Engineering 31, no. 1 (June 15, 2012): 17. http://dx.doi.org/10.20450/mjcce.2012.53.

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A chemometric-assisted UV absorption spectroscopic method is proposed for the simultaneous determination of acesulfame-K, aspartame and stevioside in raw powder mixtures of commercial sweeteners. The synergy interval partial least squares (siPLS) algorithm was applied to select the optimum spectral range and their combinations. The utilization of spectral region selection aims to construct better partial least squares (PLS) model than that established from the full-spectrum range. The results show that the siPLS algorithm can find out an optimized combination of spectral regions, yielding lower relative standard error of prediction (RSEP) and root mean square error of prediction (RMSEP), as well as simplifying the model. The RMSEP and RSEP obtained after selection of intervals by siPLS were 0.1330 μg·ml–1 and 1.50 % for acesulfame-K, 0.2540 μg·ml–1 and 1.64 % for aspartame, 1.4041 μg·ml–1 and 2.03 % for stevioside respectively. The recovery values range from 98.12 % to 101.88 % for acesulfame-K, 98.63 % to 102.96% for aspartame, and 96.38 % to 104.04 % for stevioside respectively.

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Silva, Fabiana E. B. da, Érico M. M. Flores, Graciele Parisotto, Edson I. Müller, and Marco F. Ferrão. "Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations." Anais da Academia Brasileira de Ciências 88, no. 1 (March 4, 2016): 1–15. http://dx.doi.org/10.1590/0001-3765201620150057.

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An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.

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Sarrafi, Amir H. M., Elahe Konoz, and Maryam Ghiyasvand. "Simultaneous Detemination of Atorvastatin Calcium and Amlodipine Besylate by Spectrophotometry and Multivariate Calibration Methods in Pharmaceutical Formulations." E-Journal of Chemistry 8, no. 4 (2011): 1670–79. http://dx.doi.org/10.1155/2011/292346.

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Resolution of binary mixture of atorvastatin (ATV) and amlodipine (AML) with minimum sample pretreatment and without analyte separation has been successfully achieved using a rapid method based on partial least square analysis of UV–spectral data. Multivariate calibration modeling procedures, traditional partial least squares (PLS-2), interval partial least squares (iPLS) and synergy partial least squares (siPLS), were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. The simultaneous determination of both analytes was possible by PLS processing of sample absorbance between 220-425 nm. The correlation coefficients (R) and root mean squared error of cross validation (RMSECV) for ATV and AML in synthetic mixture were 0.9991, 0.9958 and 0.4538, 0.2411 in best siPLS models respectively. The optimized method has been used for determination of ATV and AML in amostatin commercial tablets. The proposed method are simple, fast, inexpensive and do not need any separation or preparation methods.

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Mahanty, Biswanath, and Angel P. John. "Development of Robust Partial Least Squares Regression Model for Spectroscopic Determination of Diclofenac Sodium in Environmental Samples." Current Analytical Chemistry 16, no. 3 (May 15, 2020): 241–49. http://dx.doi.org/10.2174/1573411015666181128143727.

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Background: Diclofenac (DCF) is an important widely used non-steroidal antiinflammatory drug. Disposal of expired formulation, excretion from administered dose, the poor performance of sewage treatment process, contributes to its frequent detection in environment. Analysis of DCF in environmental sample requires time consuming pretreatment, extraction steps. Though, UV absorption analysis of DCF is simple but spectral interference of soil organic matter is a problem. The aim of this paper is to establish appropriate partial least square chemometric model for DCF quantitation through variable selection, and validation of analytical method through multivariate figure of merit analysis. Methods: Spectral data of DCF spiked soil solution is recorded and variants of partial least squares (PLS) regression viz., backward-interval PLS (biPLS), synergy-interval PLS (siPLS) and genetic algorithm (GA) based PLS models (GA-PLS) are developed from autoscaled and 2nd order differential spectrum. Prediction fidelity of the selected models was evaluated from a blind-folded semi-synthetic spectral data. The method was validated through figures of merit estimates, such as selectivity, analytical sensitivity, limits of detection and quantitation. Results: The siPLS model developed offered the minimum root mean square error of crossvalidation (RMSECV) of 0.1896 mg/l and 0.1910 mg/l for autoscaled data (9 variables) and derivative spectra (12 variables), respectively. Refinement of the derivative spectrum with GA offered a simplified model (RMSECV:0.1712, 10 variable). Conclusion: The GA based variable selection for PLS regression analysis offers robust analytical tool for DCF in environmental samples. Further research is warranted to model variable interference in spectral data unknown to analyst in priori.

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Li, Chunxu, Jinghan Zhao, Yaoxiang Li, Yongbin Meng, and Zheyu Zhang. "Modeling and Prediction of Soil Organic Matter Content Based on Visible-Near-Infrared Spectroscopy." Forests 12, no. 12 (December 20, 2021): 1809. http://dx.doi.org/10.3390/f12121809.

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In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.

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Chen, Tao, Zhi Li, Fang Rong Hu, and Wei Mo. "Quantitative Analysis of Mixtures Using Terahertz Time-Domain Spectroscopy and Different PLS Algorithms." Advanced Materials Research 804 (September 2013): 23–28. http://dx.doi.org/10.4028/www.scientific.net/amr.804.23.

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This paper attempted the feasibility to determine component concentrations in multicomponent mixtures with terahertz time-domain spectroscopy (THz-TDS) combined with different partial least-squares regression (PLS) algorithms. First, THz absorbance spectra for 75 ternary mixtures of anhydrous theophylline, lactose monohydrate and magnesium stearate were investigated using THz-TDS in the frequency range from 0.1 to 3.0 THz, then four different PLS methods, including interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS) and moving window PLS (mwPLS), were employed to perform quantitative analysis of anhydrous theophylline concentrations in ternary mixtures. The performance of mwPLS model is the best in contrast to other PLS models and full spectrum PLS. The optimal model was achieved with higher correlation coefficient for calibration (RC) of 0.9842, higher correlation coefficient for prediction (RP) of 0.9851, lower root mean square error of cross-validation (RMSECV) of 3.8241, and lower root mean square error of prediction (RMSEP) of 4.1540. Experimental results demonstrate that THz spectroscopy combined with PLS algorithms could be successfully applied as an effective nondestructive tool for the quantitative analysis of component concentrations in multicomponent mixtures, and mwPLS is an ideal method for reducing the complexity and improving the performance of the model.

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Yan, Xiaoli, Yujie Xie, Jianhua Chen, Tongji Yuan, Tuo Leng, Yi Chen, Jianhua Xie, and Qiang Yu. "NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea." Foods 11, no. 19 (September 23, 2022): 2976. http://dx.doi.org/10.3390/foods11192976.

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Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (RP = 0.9407, RPD = 3.00), FAA (RP = 0.9110, RPD = 2.21) and TP/FAA (RP = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.

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Pei, Yanling, Zhisheng Wu, Xinyuan Shi, Xiaoning Pan, Yanfang Peng, and Yanjiang Qiao. "NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid." Journal of Innovative Optical Health Sciences 08, no. 06 (October 27, 2015): 1550023. http://dx.doi.org/10.1142/s1793545815500236.

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Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

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Lei, Mi, Long Chen, Bisheng Huang, and Keli Chen. "Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine." Applied Spectroscopy 71, no. 11 (September 21, 2017): 2427–36. http://dx.doi.org/10.1177/0003702817727016.

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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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Marques Junior, Jucelino Medeiros, Aline Lima Hermes Muller, Edson Luiz Foletto, Adilson Ben da Costa, Cezar Augusto Bizzi, and Edson Irineu Muller. "Determination of Propranolol Hydrochloride in Pharmaceutical Preparations Using Near Infrared Spectrometry with Fiber Optic Probe and Multivariate Calibration Methods." Journal of Analytical Methods in Chemistry 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/795102.

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A method for determination of propranolol hydrochloride in pharmaceutical preparation using near infrared spectrometry with fiber optic probe (FTNIR/PROBE) and combined with chemometric methods was developed. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). The treatments based on the mean centered data and multiplicative scatter correction (MSC) were selected for models construction. A root mean square error of prediction (RMSEP) of 8.2 mg g−1was achieved using siPLS (s2i20PLS) algorithm with spectra divided into 20 intervals and combination of 2 intervals (8501 to 8801 and 5201 to 5501 cm−1). Results obtained by the proposed method were compared with those using the pharmacopoeia reference method and significant difference was not observed. Therefore, proposed method allowed a fast, precise, and accurate determination of propranolol hydrochloride in pharmaceutical preparations. Furthermore, it is possible to carry out on-line analysis of this active principle in pharmaceutical formulations with use of fiber optic probe.

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Li, Chunxu, Yaoxiang Li, Yanzheng Zhao, Zheyu Zhang, and Zichun Wang. "Mechanical Property Prediction of Larix gmelinii Wood Based on Vis-Near-Infrared Spectroscopy." Forests 13, no. 12 (November 25, 2022): 1995. http://dx.doi.org/10.3390/f13121995.

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Larix gmelinii is the major tree species in Northeast China. The wood properties of different Larix gmelinii are quite different and under strong genetic controls, so it can be better improved through oriented breeding. In order to detect the longitudinal compressive strength (LCS), modulus of rupture (MOR) and modulus of elasticity (MOE) in real-time, fast and non-destructively, a prediction model of wood mechanical properties with high precision and stability is constructed based on visible-near-infrared spectroscopy (Vis-NIRS) technology. The featured wavelengths were selected with the algorithms of competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least squares (SiPLS) and their combinations. The prediction models were then developed based on the partial least square regression (PLSR). The predictive ability of models was evaluated with coefficient of determination (R2) and root mean square error (RMSE). It indicated that CARS performed the best among the four methods examined in terms of wavelength-variable selection. The combined featured wavelength selecting method of SiPLS-CARS showed better performance than the single wavelength selection method. The optimal models of LCS, MOR and MOE are the SiPLS-CARS-PLSR model, with the R2 of the calibration set and the validation set are both greater than 0.99, and RMSE the smallest. The NIR optimal models for wood mechanical properties predictions has high predictive accuracy and good robustness.

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Feng, Xue, Li Jie Zhao, and Yu Chen Zhang. "Experimental Platform for Feature Selection of Signal of Ball Mill." Applied Mechanics and Materials 263-266 (December 2012): 412–15. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.412.

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Ball mill load monitoring and rational parameters setting are important to ensure the ball mill long-term stable operation. Although vibration and acoustic signal of shell contain plenty of information about mill load, it is difficult to select the feature of them in time domain. Due to the high dimensionality and colinearity, models based on frequency spectrum are complex and with a low generalization and the irrelevant spectral variables deteriorate their quality. This paper use Synergy Interval Partial Least-Squares Regression(SiPLS) to select the feature frequency bands of vibration and acoustical, which are directly relevant to the parameters of ball mill load, and build effective prediction models. The experimental platform combines the strengths of MATLAB with the benefits of C#.net to implement the functions of frequency feature selection, feature modeling and load parameters prediction. Test results show that the platform selects the frequency spectrum feature effectively, and the generalization of mill load models are improved.

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Bilal, Muhammad, Zou Xiaobo, Muhmmad Arslan, Haroon Elrasheid Tahir, Yue Sun, and Rana Muhammad Aadil. "Near infrared spectroscopy coupled chemometric algorithms for prediction of the antioxidant activity of peanut seed (Arachis hypogaea)." Journal of Near Infrared Spectroscopy 29, no. 4 (April 28, 2021): 191–200. http://dx.doi.org/10.1177/0967033520979425.

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In the present research work, near infrared (NIR) spectroscopy coupled with chemometric algorithms such as partial least-squares (PLS) regression and some effective variable selection algorithms (synergy interval-PLS (Si-PLS), Backward interval-PLS (Bi-PLS), and genetic algorithm-PLS (GA-PLS)) were used for the quantification of antioxidant properties of peanut seed samples including, amongst others, total phenolic content, total flavanoid content and total antioxidant capacity. The developed models were assessed using coefficients of determination for the calibration (R2) and prediction (r2); root mean standard error of cross-validation, RMSECV; root mean square error of prediction, RMSEP and residual predictive deviation, RPD. The efficiency of the developed model was significantly enhanced with the use of Si-PLS, Bi-PLS, and GA-PLS as compared to the classical PLS model. The R2 for calibration and r2 for prediction varied from 0.76 to 0.95 and 0.72 to 0.94, respectively. The obtained results revealed that NIR spectroscopy, coupled with different chemometric algorithms, has the potential to be used for rapid assessment of the antioxidant properties of peanut seed.

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Zuo, Xiaobo, Sheng Fang, and Xianli Liang. "Synergy Interval Partial Least Square (siPLS) with Potentiometric Titration Multivariate Calibration for the Simultaneous Determination of Amino Acids in Mixtures." Advance Journal of Food Science and Technology 6, no. 11 (November 10, 2014): 1209–18. http://dx.doi.org/10.19026/ajfst.6.187.

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Arslan, Muhammad, Zou Xiaobo, Hu Xuetao, Haroon Elrasheid Tahir, Jiyong Shi, Moazzam Rafiq Khan, and Muhammad Zareef. "Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)." Journal of Near Infrared Spectroscopy 26, no. 5 (August 23, 2018): 275–86. http://dx.doi.org/10.1177/0967033518795597.

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Fourier-transform near infrared spectroscopy coupled with chemometric algorithms was applied comparatively for the quantification of chemical compositions in black wolfberry. The compositional parameters, i.e. total flavonoid content, total anthocyanin content, total carotenoid content, total sugar, and total acid were performed for quantification. Model results were evaluated using the correlation coefficients of determination for calibration (R2) and prediction (r2), root-mean-square error of prediction and residual predictive deviation. The findings revealed that the performances of models based on variable selection such as synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS were better than the classical PLS. The performance of the developed models yielded 0.88 ≤ R2 ≤ 0.97, 0.87 ≤ r2 ≤ 0.94 and 1.75 ≤ RPD ≤ 4.00. The overall results showed that the FT-NIR spectroscopy in conjunction with chemometric algorithms could be used for the quantification of the chemical composition of black wolfberry samples.

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Wu, Xin, Guanglin Li, and Fengyun He. "Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing." Foods 10, no. 6 (June 7, 2021): 1315. http://dx.doi.org/10.3390/foods10061315.

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The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.

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Chu, Xuan, Wei Wang, Chunyang Li, Xin Zhao, and Hongzhe Jiang. "Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions." Journal of Innovative Optical Health Sciences 11, no. 02 (February 19, 2018): 1850006. http://dx.doi.org/10.1142/s1793545818500062.

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In this paper, a methodology based on characteristic spectral bands of near infrared spectroscopy (1000–2500[Formula: see text]nm) and multivariate analysis was proposed to identify camellia oil adulteration with vegetable oils. Sunflower, peanut and corn oils were selected to conduct the test. Pure camellia oil and that adulterated with varying concentrations (1–10% with the gradient of 1%, 10–40% with the gradient of 5%, 40–100% with the gradient of 10%) of each type of the three vegetable oils were prepared, respectively. For each type of adulterated oil, full-spectrum partial least squares partial least squares (PLS) models and synergy interval partial least squares (SI-PLS) models were developed. Parameters of these models were optimized simultaneously by cross-validation. The SI-PLS models were proved to be better than the full-spectrum PLS models. In SI-PLS models, the correlation coefficients of predition set (Rp) were 0.9992, 0.9998 and 0.9999 for adulteration with sunflower oil, peanut oil and corn oil seperately; the corresponding root mean square errors of prediction set (RMSEP) were 1.23, 0.66 and 0.37. Furthermore, a new generic PLS model was built based on the characteristic spectral regions selected from the intervals of the three SI-PLS models to identify the oil adulterants, regardless of the adultrated oil types. The model achieved with Rp[Formula: see text] 0.9988 and RMSEP [Formula: see text] 1.52. These results indicated that the characteristic near infrared spectral regions could determine the level of adulteration in the camellia oil.

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Guo, Zhiming, Chuang Guo, Quansheng Chen, Qin Ouyang, Jiyong Shi, Hesham R. El-Seedi, and Xiaobo Zou. "Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics." Sensors 20, no. 7 (April 9, 2020): 2130. http://dx.doi.org/10.3390/s20072130.

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It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

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Jiang, Hui, Guohai Liu, Congli Mei, Shuang Yu, Xiahong Xiao, and Yuhan Ding. "Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 97 (November 2012): 277–83. http://dx.doi.org/10.1016/j.saa.2012.06.024.

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Wang, Fuyun, Hao Lin, Peiting Xu, Xiakun Bi, and Li Sun. "Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis." Foods 10, no. 9 (September 14, 2021): 2176. http://dx.doi.org/10.3390/foods10092176.

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This work presents a novel work for the detection of the freshness of eggs stored at room temperature and refrigerated conditions by the near-infrared (NIR) spectroscopy and multivariate models. The NIR spectroscopy of diffuse transmission and reflection modes was used to compare the quantitative and qualitative investigation of egg freshness. It was found that diffuse transmission is more conducive to the judgment of egg freshness. The linear discriminant analysis model (LDA) for pattern recognition based on the diffuse transmission measurement was employed to analyze egg freshness during storage. NIR diffuse transmission spectroscopy showed great potential for egg storage time discrimination in normal atmospheric conditions. The LDA model discrimination rated up to 91.4% in the prediction set, while only 25.6% of samples were correctly discriminated among eggs in refrigerated storage conditions. Furthermore, NIR spectra, combined with the synergy interval partial least squares (Si-PLS) model, showed excellent ability in egg physical index prediction under normal atmospheric conditions. The root means square error of prediction (RMSEP) values of Haugh unit, yolk index, and weight loss from predictive Si-PLS models were 4.25, 0.031, and 0.005432, respectively.

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Yang, Yue, Lei Wang, Yongjiang Wu, Xuesong Liu, Yuan Bi, Wei Xiao, and Yong Chen. "On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 182 (July 2017): 73–80. http://dx.doi.org/10.1016/j.saa.2017.04.004.

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Zhang, Zhen-yu, Ying-jun Wang, Hui Yan, Xiang-wei Chang, Gui-sheng Zhou, Lei Zhu, Pei Liu, Sheng Guo, Tina T. X. Dong, and Jin-ao Duan. "Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy." Journal of Analytical Methods in Chemistry 2021 (January 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/8875876.

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Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

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Luo, Yijia, Juan Dong, Xuewei Shi, Wenxia Wang, Zhuoman Li, and Jingtao Sun. "Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy." International Journal of Food Engineering 17, no. 5 (February 5, 2021): 365–75. http://dx.doi.org/10.1515/ijfe-2020-0198.

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Abstract Determination of Cabernet Sauvignon grapes quality plays an important role in commercial processing. In this research, a rapid approach based on near infrared spectroscopy was proposed to the determination of soluble solids content (SSC), pH, and total phenol content (TPC) in entire bunches of Cabernet Sauvignon grapes. Standardized normal variate (SNV) and competitive adaptive weighted sampling (CARS), genetic algorithm (GA), and synergy interval partial least squares (si-PLS) were used to optimize the spectral data. With optimal combination input, the prediction accuracy of partial least squares regression (PLSR) and support vector regression (SVR) models was compared. The results showed that these models based on variable optimization method could predict well the SSC, pH, and TPC of Cabernet Sauvignon grapes. The correlation coefficient of prediction for SSC, pH, and TPC had reached more than 0.85. This work provides an alternative to analyze the chemical parameters in whole bunch of Cabernet Sauvignon grape.

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Zeng, Miao-Na, and Shao-Yan Zheng. "Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder." Journal of Near Infrared Spectroscopy, October 28, 2020, 096703352096669. http://dx.doi.org/10.1177/0967033520966695.

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Maca ( Lepidium meyenii Walp.) is a cruciferous edible and medicinal plant rich in nutrients. As maca demand in the international market is gradually increasing, dishonest people have been using low-priced alternatives to either adulterate or falsify maca and increase their profit. Existing methods to identify and quantify adulterated maca are laborious, expensive, destructive, time-consuming, and environmentally unfriendly. Thus, it is imperative to develop a method to overcome these problems to effectively authenticate maca products. We combine near infrared spectroscopy with chemometrics to classify and quantify maca powder adulteration by turnip and radish powder. Different maca samples were adulterated with turnip and radish powder individually at different percentages (5–95%). Specifically, discriminant analysis based on a support vector machine provides a classification accuracy of 100%, allowing near infrared spectroscopy to be used to distinguish maca powder adulteration. Furthermore, to calibrate a regression model, we evaluated the partial least squares (PLS), interval PLS, and synergy interval PLS (siPLS). The siPLS models were determined as the best models for the quantification of maca powder adulterated with turnip and radish powder, in which the correlation coefficients were both 0.97 with root-mean-square error of prediction values of 5.79% and 5.85% for the two models, respectively. The combination of near infrared spectroscopy and chemometrics can provide a fast, simple, and environment-friendly analytical method for identifying and quantifying the properties of maca.

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Zhao, Na, Zhisheng Wu, Chunying Wu, Shuyu Wang, and Xueyan Zhan. "Performance evaluation of variable selection methods coupled with partial least squares regression to determine the target component in solid samples." Journal of Near Infrared Spectroscopy, May 12, 2022, 096703352210972. http://dx.doi.org/10.1177/09670335221097236.

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Variable selection can improve the robustness and prediction accuracy of partial least squares (PLS) regression models and decrease the calculation time by selecting the optimal subset of variables in multivariate calibration. In this study, the performance of two variable selection methods for wavelength interval and individual wavelength coupled with partial least squares regression are investigated by employing the experimental data of asiaticoside (AS) and madecassoside (MS) contents in centella total glucosides (CTG) and a public dataset of corn. The studied variable selection methods include interval partial least squares regression ( iPLS), backward interval partial least squares ( biPLS), synergy interval partial least squares regression ( siPLS), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE) and variable importance in projection (VIP). The results show that the implementation of variable selection methods improved the performance of the model compared with full-spectrum modeling. All variable selection methods improved the prediction of AS or MS contents in CTG. When latent variables for PLS models are less than 10 in the practical application, the RPD value of AS models by iPLS method is 7.5, and the RPD value of MS models by biPLS method is 2.9. The results of wavelength interval selection are better than individual wavelength selection, especially for iPLS and biPLS. The same results were obtained with the public data for moisture in corn, and the RPD value of biPLS model of moisture is 1.6. Therefore, the wavelength interval selection methods, such as iPLS or biPLS, are appropriate for improving the PLS model’s accuracy and robustness to determine the target components’ contents in solid samples.

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Fares, Michel Y., Nada S. Abdelwahab, Maha A. Hegazy, Maha M. Abdelrahman, and Ghada M. El-Sayed. "Comparative Chemometric Manipulations of UV-spectrophotometric Data for the Efficient Resolution and Determination of Overlapping Signals of Cyclizine and its Impurities in its Pharmaceutical Preparations." Journal of AOAC INTERNATIONAL, September 19, 2022. http://dx.doi.org/10.1093/jaoacint/qsac113.

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Abstract Background Cyclizine (CYZ), a commonly used antiemetic drug has two pharmacopeial toxic impurities, 1-Methylpiperazine (MPZ) and diphenylmethanol (DPM). When CYZ parenteral formulations are administered intravenously, both impurities are poisonous, toxic, and harmful to the human body. Objective Cyclizine was determined along its hazardous impurities MPZ and DPM by green multivariate calibration using UV-spectroscopic data. Methods Three multivariate algorithms were used to resolve and quantify overlapped spectral signals: principal component regression (PCR), partial least squares (PLS), and synergistic intervals partial least squares (siPLS). A concentration set containing 16 distinct combinations of CYZ, MPZ, and DPM was randomly prepared, and the absorbance values of the concentration set were determined using the 376 point-wavelength set with an interval of 0.2 nm between 200 and 275 nm. Results Good linear correlations were established for CYZ, MPZ, and DPM in the concentration ranges of 5.00-25.0, 0.50-2.50, and 0.50-2.50 µg/mL, respectively. The ideal spectral range and associated combinations were chosen based on the lowest root mean error of prediction (RMSEP) and correlation coefficient values (r). The siPLS approach performed better than the PCR and PLS models. The combination of four subintervals, 1, 3, 4, and 7, demonstrated the greatest effect, with RMSEP values of 0.0272, 0.0053, and 0.0315 for CYZ, MPZ, and DPM, respectively, and correlation coefficients of 0.9991, 0.9999, and 0.9997, in order. Various assessment tools were used to evaluate and measure the greenness profile of the established methods. The proposed methods were validated using internal and external validation sets. Conclusion The three methods were effectively used to determine CYZ in its pure form and parenteral formulations, as well as its toxic impurities. The acquired results were compared statistically to those obtained using the reported HPLC method.

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Arslan, Muhammad, Zou Xiaobo, Haroon Elrasheid Tahir, Hu Xuetao, Allah Rakha, Muhammad Zareef, Emmanuel Amomba Seweh, and Sajid Basheer. "NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)." International Journal of Food Engineering 15, no. 3-4 (March 16, 2019). http://dx.doi.org/10.1515/ijfe-2018-0148.

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AbstractIn this work, near-infrared spectroscopy coupled the classical PLS and variable selection algorithms; synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS for rapid measurement of the antioxidant activity of Chinese dates. The chemometric analysis of antioxidant activity assays was performed. The built models were investigated using correlation coefficients of calibration and prediction; root mean square error of prediction, root mean square error of cross-validation and residual predictive deviation (RPD). The correlation coefficient for calibration and prediction sets and RPD values ranged from 0.8503 to 0.9897, 0.8463 to 0.9783 and 1.86 to 4.88, respectively. In addition, variable selection algorithms based on efficient information extracted from acquired spectra were superior to classical PLS. The overall results revealed that near-infrared spectroscopy combined with chemometric algorithms could be used for rapid quantification of antioxidant content in Chinese dates samples.

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ZENG, Shupeng, Xiaohong WU, Bin WU, Haoxiang ZHOU, and Meng WANG. "Rapid determination of cadmium residues in tomato leaves by Vis-NIR hyperspectral and Synergy interval PLS coupled Monte Carlo method." Food Science and Technology 43 (2023). http://dx.doi.org/10.1590/fst.113422.

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Journal articles on the topic 'Synergy Interval PLS (siPLS)'

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