scholarly journals Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

2020 ◽  
Vol 25 (4) ◽  
pp. 564-573
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia ◽  
Kusumiyati Kusumiyati
Keyword(s):  
Apis Mellifera ◽  
Phenolic Compounds ◽  
Spectral Data ◽  
Uv Spectroscopy ◽  
Low Cost ◽  
Moving Average ◽  
Apis Dorsata ◽  
Classification Rate ◽  
Standard Normal Variate ◽  
Standard Normal

In this research, spectral data in UV region (200-400 nm) alongside PCA and SIMCA chemometrics were used to classify two types of honey obtained from different honeybees (Apis dorsata versus Apis mellifera). A total of 200 Durian monofloral honey samples from Apis dorsata and 120 samples for Longan monofloral honey from Apis mellifera were prepared. Therefore, spectral data were recorded based on the following parameters: range of acquisition 200-400 nm, transmittance mode, and interval 1 nm. In addition, the original spectra were transformed using three different algorithms: moving average smoothing with 11 segments, standard normal variate (SNV), and Savitzky-Golay 1st derivative with 11 segments and 2 ordos. The result of PCA using transformed spectra in the range of 250-400 nm explained the possibility of clearly separating Durian and Longan honey along the PC1 axis, with 98% variance, while the SIMCA showed a 100% proper classification rate for all prediction samples. In addition, several important wavelengths were identified alongside high x-loadings values at 270 and 300 nm. These results were closely related to the absorbance of important phenolic compounds in honey, including benzoic, salicylic, and aryl-alyphatic acids. The results demonstrate a probability to establish simple and low-cost honey authentication systems, using UV spectroscopy and chemometrics on free-chemical in sample preparations. Keywords: authentication, Apis dorsata, Apis mellifera, SIMCA, UV spectroscopy

Classification of Floral Origins of Honey by NIR and Chemometrics

2012 ◽  
Vol 605-607 ◽  
pp. 905-909 ◽  
Author(s):  
Xiu Ying Liang ◽  
Xiao Yu Li ◽  
Wen Jun Wu
Keyword(s):  
Spectral Data ◽  
Spectral Region ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Classification Rate ◽  
Standard Normal Variate ◽  
Mean Centering ◽  
Scattering Correction ◽  
Standard Normal

Near-infrared (NIR) spectroscopy combined with chemometrics methods has been investigated to discriminate type of honey. 147 NIR spectra of six floral origins of honey samples were collected within 4000~10000cm-1 spectral region. Spectral data were compressed using partial least squares (PLS). Back propagation neural networks (BPNN) models were constructed to distinguish the type of honey. Six spectral data pretreatments including first derivative, first derivatives followed by mean centering(MC), second derivatives, Savitzky-Golay smoothing, standard normal variate transformation (SNV) and multiplicative scattering correction (MSC) were compared to establish the optimal models for honey discrimination. Savitzky-Golay smoothing proved more effective than the other data pretreatments. BPNN models were developed within the full spectral region, 5303～6591cm-1 and 7012～10001cm-1, respectively. Results have shown that the highest(100%) classification rate was achieved within 5303～6591cm-1 wave range. Our results indicated that NIR spectroscopy with chemometrics techniques can be applied to classify rapidly honeys of different floral origin.

scholarly journals The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 915
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Spectral Data ◽  
Uv Spectroscopy ◽  
Moving Average ◽  
Food Product ◽  
Classification Method ◽  
Economic Losses ◽  
Food Fraud ◽  
Authentication System ◽  
Uv Visible

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.

scholarly journals Identification of fresh and expired ground roasted robusta coffee using UV-visible spectroscopy and chemometrics

2018 ◽  
Vol 197 ◽  
pp. 09003 ◽  
Author(s):  
Meinilwita Yulia ◽  
Diding Suhandy
Keyword(s):  
Moving Average ◽  
Principal Component ◽  
Analysis Data ◽  
Visible Spectroscopy ◽  
Roasted Coffee ◽  
Classification Rate ◽  
Robusta Coffee ◽  
Standard Normal Variate ◽  
Smoothing Window ◽  
Uv Visible

The freshness of ground roasted coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted coffee must be taken into account for acceptability of coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted robusta coffee. A number of 200 samples of robusta fresh coffee and 200 samples of robusta expired coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted robusta coffee with a correct classification rate of 100%.

scholarly journals Investigation on Texture Changes and Classification between Cold-Fresh and Freeze-Thawed Tan Mutton

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dongdong Li ◽  
Yaling Peng ◽  
Haihong Zhang
Keyword(s):  
Near Infrared ◽  
Successive Projections Algorithm ◽  
Classification Rate ◽  
Full Spectrum ◽  
Original Spectrum ◽  
Standard Normal Variate ◽  
Scattering Correction ◽  
Standard Normal ◽  
Validation Set

To study the texture, microstructural changes, and classification of cold-fresh (C-F), freeze-thawed once (F-T0), and freeze-thawed twice Tan mutton (F-Tt), the aforementioned three types of Tan mutton were subjected to near-infrared hyperspectrum scanning, scanning electron microscopy, and TPA testing. The original spectrum of Tan mutton was obtained at a wavelength range of 900∼1,700 nm after hyperspectrum scanning; a spectrum fragment ranging from 918 nm to 1,008 nm was intercepted, and the remaining original spectrum was used as a studied spectrum (“full spectrum” hereafter). The full spectrum was pretreated by SNV (standard normal variate), MSC (multiple scattering correction), and SNV + MSC and then extracted feature wavelengths by SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) algorithm, and 25 feature wavelengths were obtained. By combining these feature wavelengths with classified variables, the SNV + MSC−CARS−PLS-DA (partial least squares-discriminate analysis, PLS-DA) and SNV + MSC−SPA−PLS-DA models for classification of C-F and F-T Tan mutton were established. In contrast, SNV + MSC−CARS−PLS-DA yielded the highest classification rate of 98% and 100% for calibration set and validation set, respectively. The results indicated that the texture and surface microstructure of F-T Tan mutton deteriorated, and more worsely with F-T time. SNV+MSC-CARS-PLS-DA could be well used to classify C-F, F-T0, and F-Tt Tan mutton.

Demonstration of environmental chemistry with microscale chemistry and a low-cost four-led based photom

2018 ◽  
Vol 16 (4) ◽  
pp. 510
Author(s):  
Wai Kin Kee ◽  
Wing Hong Chan
Keyword(s):  
Phenolic Compounds ◽  
Environmental Chemistry ◽  
Low Cost ◽  
Light Sources ◽  
Electrochemical Degradation

<span>In this article, a four-LED based photometer, in which four LEDs are used as light sources, are demonstrated to be a useful instrument for the study of pollution problems caused by phenols and of their remediation by electrochemical degradation method and the iron (II) catalyzed homogeneous Fenton’s reaction. The fate of phenols can be monitored by the photometer via the 4-aminoantipyrine method. The results revealed that the latter method was a superior method to treat the phenolic compounds.</span>

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

Design and Implementation of Wireless Voltage Monitoring System Based on Zigbee

2017 ◽  
Vol 12 (3) ◽  
pp. 83-96 ◽  
Author(s):  
Luo Xiaohui
Keyword(s):  
Monitoring System ◽  
Single Phase ◽  
Low Cost ◽  
Moving Average ◽  
Voltage Sensor ◽  
Wireless Transmission ◽  
Phase Voltage ◽  
Medium Voltage ◽  
Wireless Monitoring ◽  
Three Phase

This paper proposed a low cost wireless monitoring system based on ZigBee wireless transmission, and designed a new floating voltage sensor which is suitable for the monitoring of medium voltage and high voltage(MV/HV) public equipment. The system used TI-CC2530 as the controller, proposed a new moving average voltage sensing(MAVS) algorithm by reasonable assumptions, and adopted algorithms to perform the theoretical analysis for the single phase and three-phase voltage. At last, the author carried out a practical experiment on the wireless floating voltage sensor under the voltage up to 30kV, the experimental results showed that the proposed low cost wireless sensor can achieve a good voltage monitoring function, and the error is less than 3%.

Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements

2021 ◽  
Author(s):  
Friederike Kaestner ◽  
Magdalena Sut-Lohmann ◽  
Thomas Raab ◽  
Hannes Feilhauer ◽  
Sabine Chabrillat
Keyword(s):  
Heavy Metal ◽  
Random Forest ◽  
Metal Concentration ◽  
Strong Dependence ◽  
Scatter Correction ◽  
Heavy Metal Concentration ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Second Derivatives ◽  
Standard Normal

&lt;p&gt;Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.&lt;/p&gt;&lt;p&gt;At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.&lt;/p&gt;

scholarly journals Some complexes of N-aryl furfural nitrones with Co (II), Ni (II), Cu (II), Zn (II) and Cd (II) chlorides

2016 ◽  
Vol 4 (1) ◽  
pp. 10
Author(s):  
Amer A. Taqa
Keyword(s):  
Spectral Data ◽  
General Formula ◽  
Elemental Analysis ◽  
13C Nmr ◽  
High Frequency ◽  
Metal Ion ◽  
Uv Spectroscopy ◽  
Active Atom ◽  
Tetrahedral Complexes

Some new metal(II) dichloride complexes with the ligands substituted nitrones of the general formula [ML2Cl2], where M= Co(II), Ni(II), Cu(II), Zn(II) and Cd(II), L=OCH=CHCH=C-CH=N(O)C6H4X (X=H,p-CH3,CH3O,CH3CO,F,Cl,and Br) have been prepared and characterized by elemental analysis, IR,1H,13C NMR and Vis/Uv spectroscopy. The IR spectral data showed that the nitrone ligands coordinated with the metal ion through the most active atom of the N-oxide to give square planner coordinate (Cu,Ni,) complexes and (Zn,Cd,Co) tetrahedral complexes. No correlation was observed between the N-O vibrations stretching high frequency ν (N-O) of the complexes and the Hammet (σ) constants.

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