scholarly journals The Effect of Micromixer Geometry on the Diameters of Emulsion Droplets: NIR Spectroscopy and Artificial Neural Networks Modeling

2021 ◽  
Vol 4 (1) ◽  
pp. 26
Author(s):  
Tamara Jurina ◽  
Ivana Čulo ◽  
Maja Benković ◽  
Jasenka Gajdoš Kljusurić ◽  
Davor Valinger ◽  
...  
Keyword(s):  
Droplet Size ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tween 20 ◽  
Dispersed Phase ◽  
Total Flow ◽  
Flow Rates ◽  
Feret Diameter ◽  
Oil In Water ◽  
Artificial Neural

In this work, teardrop micromixer and swirl micromixer were used for preparation of oil-in-water (O/W) emulsions with Tween 20 and PEG 2000 as emulsifiers (concentrations: 2% and 4%) at different total flow rates (20–280 µL/min). Stability of the prepared O/W emulsions was evaluated based on the droplet size of the dispersed phase. For determination of the droplet size, the average Feret diameter was used. Furthermore, near infrared (NIR) spectra of all prepared samples were collected. Obtained results showed that the change in the droplet size followed the same trend for both micromixers used in the experiment. At higher total flow rates, emulsification resulted in smaller values of the average Feret diameter. Values of the average Feret diameter were higher for emulsions prepared in the swirl micromixer, compared to the teardrop micromixer. Artificial Neural Network (ANNs) models, based on the recorded NIR spectra of emulsions, were developed to predict the droplet size of the dispersed phase. The obtained ANN models have high values of R2 for training, test, and validation, with small error values and show that NIR spectroscopy, in combination with ANNs, could be efficiently used for evaluation of the stability of oil-in-water emulsions.

Fast Nondestructive Measurement and Discrimination of Tablet Based on Vis/NIR Spectroscopy and Chemometrics

2011 ◽  
Vol 48-49 ◽  
pp. 506-510
Author(s):  
Yong Ni ◽  
Yong Ni Shao ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Short Wave ◽  
Nondestructive Measurement ◽  
Discrimination Rate ◽  
Ann Models ◽  
Artificial Neural

This paper presents methods based on chemometrics analysis to select the optimal model for variety discrimination of ginkgo (Ginkgo biloba L.) tablets by using a visible/short-wave near-infrared spectroscopy (Vis/NIRS) system. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325-1075nm using a spectrograph. Principal component artificial neural network (PC-ANN) was used to identify the tablet varieties. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN. Independent component analysis (ICA) was executed to select several optimal wavelengths based on loading weights. The absorbance values log (1/R), corresponding to the wavelengths of 481nm, 1000nm, 460nm, 572nm, 658nm, 401nm, 998nm, 996nm, 468nm and 661nm were then chosen as the input data of artificial neural network (IC-ANN), and the discrimination rate was reached at 95.6%, which was better than PC-ANN. The results indicated that ginkgo tablets discrimination was good based on the both methods.

scholarly journals Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 267 ◽  
Author(s):  
Yousef Abbaspour-Gilandeh ◽  
Sajad Sabzi ◽  
Brahim Benmouna ◽  
Ginés García-Mateos ◽  
José Luis Hernández-Hernández ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Range ◽  
Artificial Bee Colony ◽  
Near Infrared ◽  
Effective Properties ◽  
Starch Content ◽  
Nir Spectroscopy ◽  
Bee Colony ◽  
Artificial Neural

Non-destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near-infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively.

Neural Network Based Prediction of Soluble Solids Concentrationin Oriental Melon Using VIS/NIR Spectroscopy

2021 ◽  
Vol 37 (4) ◽  
pp. 653-663
Author(s):  
Sang-Yeon Kim ◽  
Suk-Ju Hong ◽  
Eungchan Kim ◽  
Chang-Hyup Lee ◽  
Ghiseok Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Prediction Performance ◽  
Soluble Solids ◽  
Ann Model ◽  
Oriental Melon ◽  
Artificial Neural

Highlights Non-destructive soluble solids content prediction model for oriental melon was developed based on NIR spectrum data. Not only the classical ML or Neural-Network methods, but also the mixture of both techniques have also been tried. Comparing the various pre-processing methods, the MSC-PLS-ANN model showed the best results. MSC-PLS-ANN model demonstrated 6% of improvement in RMSE score over the PLSR model, which is commonly used in commercial products Abstract. Models for predicting the soluble solids concentration (SSC) of oriental melons were developed and evaluated by applying near infrared spectroscopy and an artificial neural network technique. For the evaluation, a total of 300 oriental melons, both ripe and unripe, were mixed together and sampled. To develop an SSC prediction model, the actual SSC values of specimens having the same spectra as those of the visible/near infrared wavelength bands were measured. The measured spectra were preprocessed using eight methods [Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Robust Normal Variate, Savitzky-Golay 1st and 2nd; Min-Max Normalization; Robust Normalization; Standardization], and the SSC prediction model was developed by applying three techniques (Partial Least Squared Regression [PLSR], Artificial Neural Network [ANN], and Convolutional Neural Network [CNN]). Among them, the PLSR technique also applied a Variable Importance in Projection (VIP) method for wavelength selection. Among the PLSR-based SSC prediction models, the SNV-preprocessed PLSR model showed the best SSC prediction performance (RMSEtest, 0.67; R2test, 0.81). Among the ANN-based models, the MSC-preprocessed PLS-ANN model showed the best SSC prediction performance (RMSEtest: 0.63, R2test: 0.83). Among the CNN-based models, the DeepSpectra model was applied, but showed the lowest prediction performance (RMSEtest: 0.79, R2test: 0.74). In conclusion, among the three SSC prediction algorithms tested in this study, the PLS-ANN-based prediction model showed the best SSC prediction performance, which was found to be higher than that of the PLSR-based SSC prediction model applied to the sugar sorters currently used in agricultural products at processing centers. Keywords: Artificial Neural Network, Convolution Neural Network, Korean melon, VIP-PLSR, VIS/NIR spectroscopy.

scholarly journals Stabilization and Release of Palm Tocotrienol Emulsion Fabricated Using pH-Sensitive Calcium Carbonate

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 358
Author(s):  
Phui Yee Tan ◽  
Beng Ti Tey ◽  
Eng Seng Chan ◽  
Oi Ming Lai ◽  
Hon Weng Chang ◽  
...  
Keyword(s):  
Calcium Carbonate ◽  
Droplet Size ◽  
Entrapment Efficiency ◽  
Ph Sensitive ◽  
Emulsion System ◽  
Ph Responsive ◽  
Small Droplet ◽  
Homogenization Time ◽  
Oil In Water ◽  
Ph Range

Calcium carbonate (CaCO3) has been utilized as a pH-responsive component in various products. In this present work, palm tocotrienols-rich fraction (TRF) was successfully entrapped in a self-assembled oil-in-water (O/W) emulsion system by using CaCO3 as the stabilizer. The emulsion droplet size, viscosity and tocotrienols entrapment efficiency (EE) were strongly affected by varying the processing (homogenization speed and time) and formulation (CaCO3 and TRF concentrations) parameters. Our findings indicated that the combination of 5000 rpm homogenization speed, 15 min homogenization time, 0.75% CaCO3 concentration and 2% TRF concentration resulted in a high EE of tocotrienols (92.59–99.16%) and small droplet size (18.83 ± 1.36 µm). The resulting emulsion system readily released the entrapped tocotrienols across the pH range tested (pH 1–9); with relatively the highest release observed at pH 3. The current study presents a potential pH-sensitive emulsion system for the entrapment and delivery of palm tocotrienols.

Multi-Site Cr3+ Occupation Related Broadband NIR Luminescence in Cr3+-doped Li3Mg2NbO6

CrystEngComm ◽  
2021 ◽  
Author(s):  
Fen Xiao ◽  
Chengning Xie ◽  
Shikun Xie ◽  
Rongxi Yi ◽  
Huiling Yuan ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Luminescent Materials ◽  
Optical Source ◽  
Nir Emission ◽  
Nir Luminescence

Broadband near infrared (NIR) luminescent materials have attracted great attention recently for the advance smart optical source of NIR spectroscopy. In this work, a broadband NIR emission from 650 nm...

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

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

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

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