scholarly journals Penggunaan UV-Vis Spektroskopi dan Kemometrika untuk Uji Keaslian Kopi Codot Lampung

2021 ◽  
Vol 26 (4) ◽  
pp. 479-489
Author(s):  
Meinilwita Yulia ◽  
Kurnia Rimadhanti Ningtyas ◽  
Diding Suhandy
Keyword(s):  
Extraction Procedure ◽  
Classification Model ◽  
Coefficient Of Determination ◽  
Distilled Water ◽  
Pls Regression ◽  
Roasted Coffee ◽  
Vis Spectroscopy ◽  
Specialty Coffee ◽  
Uv Visible ◽  
High Level

Codot coffee from Tanggamus, Lampung is one of Indonesian specialty coffee with a very limited production. In this research, an authentication study for the Codot ground roasted coffee was conducted using UV-vis spectroscopy and chemometrics. A total of 330 samples of pure and adulterated Codot coffee was prepared. The adulterated Codot coffee samples were intentionally created by adding a regular coffee (non-Codot coffee) into pure Codot coffee samples with three levels of adulterations: low (10-20%), medium (30-40%), and high level (50-60%). All samples were 0,29 mm in particle size. The extraction procedure was performed with hot distilled water (98°C). The spectral data of coffee samples were acquired using a benchtop UV-visible spectrometer in the range of 190-1100 nm using a transmittance mode. The result showed that the pure and adulterated samples could be discriminated along PC1 and PC2 axis. The classification model was developed using LDA with 90,91% of accuracy could be obtained. The LDA model was used to classify the new samples and resulted in a sensitivity (SEN) of 100%, specificity (SPEC) of 76,67%, precision (PREC) of 78,13%, and accuracy (ACC) of 87,27% could be obtained. Using PLS regression, a PLS model was developed to quantify the percentages of Codot coffee adulteration and resulted in high of coefficient of determination both in calibration and validation (R2kal = 0,99 and R2val = 0,98). These results showed that UV-vis spectroscopy and chemometrics are suitable for authentication of Codot specialty coffee with RMSEP = 2,68% and RPD in prediction of 6,49.   Keywords: authentication, LDA, PCA, PLS regression, UV-vis spectroscopy

scholarly journals Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Molecules ◽  
2021 ◽  
Vol 26 (20) ◽  
pp. 6091
Author(s):  
Meinilwita Yulia ◽  
Diding Suhandy
Keyword(s):  
Regression Models ◽  
Spectroscopic Method ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Error Prediction ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Roasted Coffee ◽  
Vis Spectroscopy ◽  
Plsr Model

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques

1998 ◽  
Vol 1635 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Wael H. Awad ◽  
Bruce N. Janson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Linear Regression ◽  
Prediction Models ◽  
Washington State ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Training Process ◽  
Truck Accidents ◽  
High Level

Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results.

Neptunium Speciation in Humic Acid-rich Clay Water upon Interaction With Radioactive Waste Glass Samples

2002 ◽  
Vol 757 ◽  
Author(s):  
V. Pirlet ◽  
P. Van Iseghem
Keyword(s):  
Humic Substances ◽  
Humic Acids ◽  
Specific Interaction ◽  
Waste Glass ◽  
High Level Waste ◽  
Boom Clay ◽  
Dissolution Tests ◽  
Vis Spectroscopy ◽  
Doped Glasses ◽  
High Level

ABSTRACTOrganic complexes of actinides are known to occur upon interaction of high level waste glass and Boom Clay which is a potential host rock formation for disposal of high level waste in Belgium. The solubility and mobility of 237Np, one of the most critical radionuclides, can be affected by the high dissolved organic carbon content of the Boom Clay porewater through complexation with the humic substances. The influence of humic substances on the Np behaviour is considered through dissolution tests of Np-doped glasses in Boom Clay water and through fundamental study of the specific interaction between Np(IV) and the humic acids using spectroscopic techniques. High Np(IV) concentrations are found in the glass dissolution tests. These concentrations are higher than what we should expect from the solubility of Np(OH)4, the solubility limiting solid phase predicted under the reducing conditions and pH prevailing in Boom Clay. Studying the specific interaction of Np(IV) with humic acids in Boom Clay porewater, high soluble Np concentrations are also measured and two main tetravalent Np-humate species are observed by UV-Vis spectroscopy. The two species are interpreted in terms of mixed hydroxo-humate complexes, Np(OH)xHA with x = 3 or 4. These species are the most likely species that can form according to the pH working conditions. Using thermodynamic simplified approaches, high complexation constants, i.e. log β131 and log β141 respectively equal to 46 and 51.6, are calculated for these species under the Boom Clay conditions.Comparing the spectroscopic results of the dissolution tests with the study of the interaction of Np(IV) with humic substances, we can conclude that the complexation of Np(IV) with the humic acids may occur and increases the solubility of Np(OH)4 upon interaction of a Np-doped glass and the Boom Clay porewater.

Predictive evaluation of multicomponent direct compress model tablets by integrating sphere UV-Vis spectroscopy and chemometrics

2021 ◽  
pp. 1-12
Author(s):  
Yuta Otsuka ◽  
Suvra Pal
Keyword(s):  
Regression Models ◽  
Process Analytical Technology ◽  
Compaction Pressure ◽  
Principal Component ◽  
Magnesium Stearate ◽  
Dicalcium Phosphate ◽  
Integrating Sphere ◽  
Pls Regression ◽  
Standard Normal Variate ◽  
Vis Spectroscopy

BACKGROUND: Control of the pharmaceutical manufacturing process and active pharmaceutical ingredients (API) is essential to product formulation and bioavailability. OBJECTIVE: The aim of this study is to predict tablet surface API concentration by chemometrics using integrating sphere UV-Vis spectroscopy, a non-destructive and contact-free measurement method. METHODS: Riboflavin, pyridoxine hydrochloride, dicalcium phosphate anhydrate, and magnesium stearate were mixed and ground with a mortar and pestle, and 100 mg samples were subjected to direct compression at a compaction pressure of 6 MPa at 7 mm diameter. The flat surface tablets were then analyzed by integrating sphere UV-Vis spectrometry. Standard normal variate (SNV) normalization and principal component analysis were applied to evaluate the measured spectral dataset. The spectral ranges were prepared at 300–800 nm and 500–700 nm with SNV normalization. Partial least squares (PLS) regression models were constructed to predict the API concentrations based on two previous datasets. RESULTS: The regression vector of constructed PLS regression models for each API was evaluated. API concentration prediction depends on riboflavin absorbance at 550 nm and the excipient dicalcium phosphate anhydrate. CONCLUSION: Integrating sphere UV-Vis spectrometry is a useful tool to process analytical technology.

Removal of organic dye compounds in water and wastewater samples based on covalent organic frameworks -titanium dioxide before analysis by UV-VIS spectroscopy

2021 ◽  
Vol 4 (01) ◽  
pp. 58-67
Author(s):  
Aida Bahadori Bahadori ◽  
Mehdi Ranjbar Corresponding
Keyword(s):  
Titanium Dioxide ◽  
Carbon Nanostructures ◽  
Solid Phase ◽  
Cost Effective ◽  
Combustion Method ◽  
Organic Dye ◽  
Coefficient Of Determination ◽  
Covalent Organic Frameworks ◽  
Methylene Orange ◽  
Vis Spectroscopy

A simple and rapid microwave-assisted combustion method was developed to synthesize homogenous carbon nanostructures (HCNS). This research presents a new and novel nanocomposite structures for removal of methylene red (2-(4- Dimethylaminophenylazo)benzoic acid), methylene orange (4-[4-(Dimethylamino)phenylazo]benzenesulfonic acid sodium salt) and methylene blue (3,7-bis(Dimethylamino)phenazathionium chloride)with semi degradation-adsorption solid phase extraction (SDA-SPE) procedure before determination by UV-VIS spectroscopy. A covalent organic frameworks (COFs) with high purity were synthesized and characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results indicated that the self-assembled carbon nanostructures (COFs) synthesized with the cost-effective method which was used as a novel adsorbent for adsorption of dyes after semi-degradation of methylene red, orange and blue (1-5 mg L-1) as an organic dye by titanium dioxide (TiO2) nanoparticales in presence of UV radiation. Based on results, the COFs/TiO2 has good agreement with the Langmuir adsorption isotherm model with favorite coefficient of determination (R2= 0.9989). The recovery of dye removal based on semi-degradation/adsorption of COFs/TiO2 and adsorption of COFs were obtained 98.7% and 48.3%, respectively (RSD less than 5%). The method was validated by spiking dye to real samples.

scholarly journals Investigasi pengaruh pengenceran sampel madu pada proses klasifikasi madu menggunakan uv spectroscopy dan kemometrika

2021 ◽  
Vol 26 (2) ◽  
pp. 72
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia ◽  
Kusumiyati Kusumiyati ◽  
Siti Suharyatun ◽  
Sri Waluyo
Keyword(s):  
Spectral Data ◽  
Analytical Methods ◽  
Uv Spectroscopy ◽  
Honey Sample ◽  
Distilled Water ◽  
Pca Analysis ◽  
Spectra Analysis ◽  
Uv Visible ◽  
Sample Dilution

One form of honey adulteration is label adulteration for some premium honey such as uniflora honey from the honeybee species Trigona sp. One of the analytical methods that are currently developing and have the potential to perform the classification of premium honey in Indonesia is the UV spectroscopy method. In this study, an investigation was carried out on the effect of dilution on the performance of UV spectroscopy in the process of classifying Indonesian honey with different honeybees. A total of 4 types of honey samples with 10 samples each were used in this study. The honey sample was then diluted using distilled water. Each type of honey was given two dilution treatments, namely 1:20 (volume: volume) dilution of 5 samples and 1:40 (volume: volume) dilution of 5 samples. Spectral data were taken using a UV-visible spectrometer with a wavelength of 190-1100 nm (Genesys™ 10S UV-Vis, Thermo Scientific, USA) using the transmittance mode. The results of spectra analysis generally show that the sample with a 1:20 dilution has a higher absorbance intensity for both the original and modified spectra. The PCA results for each dilution showed that the honey samples could be separated into four different clusters for both 1:20 and 1:40 dilutions. The results of PCA analysis using all samples showed that the honey samples were classified into eight different clusters showing a significant effect of differences in honey sample dilution on the classification process of honey samples based on differences in the types of honeybees.

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