scholarly journals Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3483 ◽  
Author(s):  
Youngwook Seo ◽  
Hoonsoo Lee ◽  
Changyeun Mo ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
...  
Keyword(s):  
Spectral Data ◽  
Fluorescence Imaging ◽  
Color Image ◽  
Median Filter ◽  
Partial Least Square ◽  
Least Square ◽  
Fecal Matter ◽  
On Line ◽  
Contaminated Area ◽  
Multispectral Fluorescence Imaging

Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.

Method for predicting lignocellulose components in jute by transformed FT-NIR spectroscopic data and chemometrics

2019 ◽  
Vol 34 (1) ◽  
pp. 1-9 ◽  
Author(s):  
M. Nashir Uddin ◽  
Sohan Ahmed ◽  
Swapan Kumer Ray ◽  
M. Saiful Islam ◽  
Ariful Hai Quadery ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Spectral Data ◽  
Spectroscopic Data ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Chemical Compositions ◽  
Nondestructive Technique ◽  
Jute Fibers ◽  
Calibration Techniques

Abstract In this investigation, a nondestructive technique has been developed for determining chemical composition of jute fiber by chemometric modeling with pretreated FT-NIR spectroscopic data. The chemical composition of jute fibers in wet chemical method were, 58 to 61.80 % α-cellulose, 13.0 to 21.90 % lignin, 9.89 to 16.8 % pentosan and 79.02 to 88.33 % holocellulose. FT-NIR spectral data from range 9000–4000 cm−1 of all jute samples were collected from the instrument. Spectral data of jute samples were pretreated with second order derivatives (SOD), standard normal variate (SNV) techniques and both together were used before calibration. Two chemometric calibration techniques: partial least square regression (PLSR) and artificial neural network (ANN) were assessed for predicting chemical compositions of Jute fibers. Result shows that prediction efficiency ({\text{R}^{2}}) of ANN varies from 72–99 % for calibration, validation and test datasets. However, by PLSR, {\text{R}^{2}} are much higher and consistent than those by earlier one. For α-cellulose, lignin, pentosan and holocellulose {\text{R}^{2}} values hover around 95–99 %. Thereby, a non-destructive, simple and cost effective novel method is being proposed to determine chemical compositions of jute with pretreated FT-NIR spectral data and chemometric calibration techniques.

scholarly journals National spectral data and learning algorithms for potentially toxic elements modelling in forest soil horizons

2020 ◽  
Author(s):  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon ◽  
Eyal Ben Dor ◽  
Raphael A. Viscarra Rossel ◽  
Lubos Boruvka
Keyword(s):  
Czech Republic ◽  
Spectral Data ◽  
Forest Soils ◽  
Toxic Elements ◽  
Potentially Toxic Elements ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
The Czech Republic ◽  
Organic Horizons

Forest ecosystems are among the main parts of the biosphere; however, they have been endangered from the significant elevation and harmful effects of air and soil pollutants, including potentially toxic elements (PTEs). The concentration of PTEs in forest soils varies not only laterally but also vertically with depth. Forest surface organic horizons are of particular interest in forest ecosystem monitoring due to their role as stable adsorbents of the deposited atmospheric substances. Therefore, the main purpose of this study was to conduct rapid examinations of forest soils PTEs (Cr, Cu, Pb, Zn, and Al), testing the capability of VIS--NIR spectroscopy coupled with machine learning (ML) techniques (partial least square regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and fully connected neural network (FNN), a deep learning (DL) approach, in forest organic horizons. One-thousand-and-eighty forested sites across the Czech Republic at two soil layers, defining the fragmented (F) and humus (H) organic horizons, were investigated (total 2160 samples). PTEs as well as total Fe and SOC, as auxiliary data, were conventionally and spectrally determined and modelled in the combined organic horizons (F + H) and in each individual horizon using the ML and DL algorithms. Results indicated that the concentration of all PTEs was higher in the horizon H compared to the F horizon. Although the spectral reflectance of samples tended to decrease with increased PTEs concentration. Strongly significant positive correlations between all PTEs and total Fe in all horizons were obtained, which were higher in the H and F + H horizons than the F horizon. The highest correlations of PTEs with the spectra were at 460--590~nm, which is mostly linked to the presence of Fe-oxide. These results show the importance of Fe for spectral prediction of PTEs. Cr and Al were the most accurately predicted elements, regardless of the applied learning technique. SVMR provided the best results in assessing the H horizon (e.g., R\(^2\) = 0.88 and root mean square error (RMSE) = 3.01~mg/kg, and R\(^2\) = 0.82 and RMSE = 1682.25~mg/kg for Cr and Al, respectively); however, FNN predicted the combined F + H horizons the best (R\(^2\) = 0.89 and RMSE = 2.95~mg/kg, and R\(^2\) = 0.86 and RMSE = 1593.64~mg/kg for Cr and Al, respectively) due to the larger number of samples. In the F horizon, almost no parameters were predicted adequately. This study shows that given the availability of larger sample sizes, FNN can be a more promising technique compared to ML methods for assessment of Cr and Al concentration based on national spectral data in the forests of the Czech Republic.

scholarly journals Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 5001 ◽  
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Monte Carlo ◽  
Spectral Data ◽  
Near Infrared ◽  
Selection Procedure ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Tolerance Interval ◽  
Wavelength Selection ◽  
Wrapper Method

The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.

scholarly journals EVALUASI PENERIMAAN PENGGUNA COMPUTER BASED TEST DENGAN PENDEKATAN TAM DI KULON PROGO

Respati ◽  
2017 ◽  
Vol 12 (34) ◽  
Author(s):  
Sukasna Sukasna ◽  
Kusrini Kusrini ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Partial Least Square ◽  
Least Square ◽  
On Line ◽  
Computer Based ◽  
Acceptance Model

Abstrak –Pelaksanaan ujian nasional di SMP, SMA / SMK sederajat yang dilaksanakan olehKementerian Pendidikan dan Kebudayaan Republik Indonesia selalu mengalami perubahan, baik darisegi manfaat, teknis pelaksanaan, banyaknya model paket soal sampai dengan cara mengoreksi hasilujian. Seiring dengan perkembangan teknologi ujian nasional kini mulai dilaksanakan dengan sarana komputer secara on line atau lebih dikenal dengan istilah Computer BasedTest ( CBT). Tujuan penelitian ini adalah untuk menganalisis hubungan antara satu variabel denganvariabel lainnya atau bagaimana suatu variabel mempengaruhi variabel lainnya dengan pendekatankuantitatif. Pendekatan yang digunakan dalam penelitian ini adalah Technology Acceptance Model(TAM) I dengan metode SEM program PLS. Untuk menguji hipotesis dalam penelitian ini menggunakanmetode Analisis Component Based SEM atau Partial Least Square (PLS). Hasil yang diperoleh daripenelitian ini ditemukannya pengaruh yang signifikan dan tidak signifikan dari variabel eksternal sertabeberapa persepsi yang berpengaruh terhadap pelaksanaan CBT di Kulon Progo.Kata kunci: TAM, pengalaman, kerumitan, kesesuian tugas, sikap pengguna

On-line characterization of wood chip brightness and chemical composition by means of visible and near-infrared spectroscopy

Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Guillaume Hans ◽  
Bruce Allison
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Wood Chip ◽  
Pulp Mill ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
External Parameter ◽  
Contact Measurement ◽  
On Line

Abstract Historically, on-line and real-time measurement of wood chip properties in the pulp and paper industry has been a challenge and has hampered the development of advanced process control strategies. In this study, visible and near-infrared (VIS-NIR) spectroscopy is investigated as a means to characterize wood chip brightness and chemical composition (i.e. extractives, lignin and holocellulose content) on-line. The estimated standard error on the holocellulose reference measurement was significantly reduced using data reconciliation. VIS-NIR calibration models were developed using partial least square regression. Derivative and baseline correction were found to be the most appropriate pre-processing methods. Model desensitization to the influence of moisture content and temperature by means of external parameter orthogonalization resulted in more robust models critical for on-line applications under harsh industrial conditions. Wavelength selection improved model accuracy for all properties. A comparison of two different spectrometer and probe combinations demonstrated that, after wavelengths selection, a non-contact measurement of wood chips performs as well as a contact measurement of wood powder for monitoring chemical composition. On-line prediction of wood chip brightness and chemical composition using the developed VIS-NIR models was demonstrated over 7 months in a kraft pulp mill processing both hardwood and softwood chips.

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