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Simultaneous quantitative analysis of indomethacin and benzoic acid in gel using ultra-violet-visible spectrophotometry and chemometrics
摘要： BACKGROUND: In order to manufacture pharmaceutical products, real-time monitoring in the manufacturing process is necessary, but large equipment cost is required to achieve it. OBJECTIVE: The aim of this research is to use ultra-violet-visible spectroscopy along with chemometrics procedure to simultaneously quantitative analysis of indomethacin (IMC) and benzoic acid (BA) in the gel during pharmaceutical manufacturing process. METHODS: The gel preparations were contained of 0.1–1.5% IMC, 0.015–0.225% BA, 2% carbopol? 941 and 95% ethanol solution. The calibration models were constructed using the partial least square regression (PLS). RESULTS: The relationships of the measured and predicted concentrations for both IMC and BA had linear plots. The developed PLS calibration models were used to monitor the IMC and BA concentrations during mixing of the gels by the planetary centrifugal and conventional mixers, respectively. IMC and BA were gradually dispersed, dissolved and completely homogeneous within 30 min by the centrifugal mixer. In contrast, IMC and BA were slowly dispersed, dissolved and completely homogeneous at more than 60 min by the conventional mixer. CONCLUSIONS: The ultra-violet-visible spectrophotometric method couples with multivariate chemometric techniques for quantitative data analysis were successfully applied for the simultaneous determination of major component IMC and trace component BA in the gel.
关键词： benzoic acid,indomethacin,partial least square regression,Ultra-violet-visible spectroscopy,process analysis technology,process monitoring
更新于2025-09-23 15:22:29
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Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods
摘要： Honey adulteration is a major issue in food production, which may reduce the effective components in honey and have a detrimental effect on human health. Herein, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two common types of adulteration, including mixing acacia honey with high fructose corn syrup (HFCS) and rape honey, were quantified with univariate analysis and partial least squares regression (PLSR). In addition, the variable importance was tested with univariable analysis and feature selection methods (genetic algorithm (GA), variable importance in projection (VIP), selectivity ratio (SR)). The results indicated that emissions from Mg II 279.58, 280.30 nm, Mg I 285.25 nm, Ca II 393.37, 396.89 nm, Ca I 422.70 nm, Na I 589.03, 589.64 nm, and K I 766.57, 769.97 nm had compact relationship with adulterant content. Best models for detecting the adulteration ratio of HFCS 55, HFCS 90, and rape honey were achieved by SR-PLSR, VIP-PLSR, and VIP-PLSR, with root-mean-square error (RMSE) of 8.9%, 8.2%, and 4.8%, respectively. This study provided a fast and simple approach for detecting honey adulteration.
关键词： partial least square regression,laser-induced breakdown spectroscopy,adulteration,feature variable,honey
更新于2025-09-23 15:19:57
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Laser-induced fluorescence spectroscopy for early disease detection in grapefruit plants
摘要： Both biotic and abiotic stress causes considerable decrease in chlorophyll content in plant leaves which provide the means of early disease diagnosis. The emergence of disease affects the fluorescence of phenolic compounds and chlorophyll which have been appeared at 530, 686 and 735 nm. It has been found that the intensity of emission band of phenolic compounds at 530 nm increases and that of chlorophyll at 735 nm decreases with the onset of disease. Statistical analysis through principal component analysis (PCA) and partial least square regression (PLSR) has been performed which demonstrated the classification of apparently healthy leaf sites with diseased ones which provide the basis for the detection of disease at early stages. PLSR model was validated through the coefficient of determination (R2), standard error of prediction (SEP) and standard error of calibration (SEC) with the values 0.99, 0.394 and 0.401 which authenticated the model. The prediction accuracy of the model was evaluated through root mean square error in prediction (RMSEP) of 0.14 by predicting 22 unknown emission spectra of different leaf sites. Both PCA and PLSR models produced similar results and proved fluorescence spectroscopy as an excellent tool for early disease detection in plants.
关键词： Early disease diagnosis,Principal component analysis (PCA),Chlorophyll fluorescence,Partial least square regression (PLSR),Phenolic compounds
更新于2025-09-19 17:13:59
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[IEEE 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Nagoya, Japan (2019.7.13-2019.7.15)] 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Salt Content Prediction System of Dried Sea Cucumber (Beche-de-mer) Based on Visual Near-Infrared Imaging
摘要： Dried sea cucumber (Beche-de-mer) is a culinary food that is considered luxurious and delicious, especially in China, Korea, and Japan, so the price is quite high. Dried sea cucumber (Beche-de-mer) also has high commercial value and high nutritional value. Their quality determines dried sea cucumber (Beche-de-mer) prices on international markets. One of the parameters that determine its quality is salt content. The excessive salt content in Dried sea cucumber (Beche-de- mer) can cause health problems such as hypertension, stroke, digestive system disorders, etc. Therefore, this paper will discuss a prediction system for measuring salt content in Dried sea cucumber (Beche-de-mer) using hyperspectral imaging technique. This system uses reflectance mode with a wavelength from 400 to1000 nm. The hardware from the prediction system for measuring salt content is motors to generate, hyperspectral camera system, two 150 W halogen lamps, Teflon tables, and personal computer link. Then, the PLSR algorithm is applied to the prediction system model at full wavelength. The prediction model is used to obtain the predicted value of salt content. Then the results of the prediction model are compared with the data references obtained by the mercury nitrate method. The root means square errors and correlation coefficient are used to evaluate the prediction system performance of salt content. The best result of the prediction system in this work is to have a correlation coefficient of 0.99 and root mean square errors of 0.27, respectively, with the number of PLS component is 25. Based on the results of this work, the proposed system can be used as an alternative method of measuring the salt content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability.
关键词： Hyperspectral Imaging,Salt Content,Dried Sea Cucumber,PLSR (Partial Least Square Regression)
更新于2025-09-12 10:27:22
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[IEEE 2019 6th International Conference on Instrumentation, Control, and Automation (ICA) - Bandung, Indonesia (2019.7.31-2019.8.2)] 2019 6th International Conference on Instrumentation, Control, and Automation (ICA) - Moisture Content Prediction System of Dried Sea Cucumber (Beche-de-mer) Based on Visual Near-Infrared Imaging
摘要： Dried sea cucumber (Beche-de-mer), the product after cleaning, boiling, salting, and drying, is as delicious and healthy food. Dried sea cucumber (Beche-de-mer) also has a high market price and the highest nutritional value of all seafood products. Moisture content in dried sea cucumber (Beche-de-mer) can affect the international market prices of dried sea cucumber to decline. This condition takes place because the moisture content is one of the parameters that determine the quality of dried sea cucumber. Therefore, this research will discuss a prediction system for measuring moisture content in dried sea cucumber (Beche-de-mer) using hyperspectral imaging technique. This system uses reflectance mode with the wavelength from 400 to 1000 nm. The hardware from the prediction system for measuring moisture content is motors to generate, hyperspectral camera system, two 150 W halogen lamps, Teflon table, and personal computer link. Then, the PLSR algorithm is applied to the prediction system model at full wavelength. The prediction model is used to obtain the predicted value of moisture content. Then the results of the prediction model are compared with the data references obtained by the gravimetric method. The root means square errors and correlation coefficient are used to evaluate the prediction system performance of moisture content prediction. The best result of the prediction system in this work is to have a correlation coefficient of 0.99 and root mean square errors of 0.92% respectively, with the number of PLS component is 30. Based on the results of this research, the proposed system can be used as an alternative method of measuring the moisture content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability
关键词： Hyperspectral Imaging,Moisture content,Partial Least Square Regression,Dried Sea Cucumber (Beche-de-mer)
更新于2025-09-11 14:15:04