scholarly journals Assessment of quality defects in macadamia kernels using NIR spectroscopy

2004 ◽  
Vol 55 (4) ◽  
pp. 471 ◽  
Author(s):  
John Guthrie ◽  
Colin Greensill ◽  
Ray Bowden ◽  
Kerry Walsh
Keyword(s):  
Moisture Content ◽  
Oil Content ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Insect Damage ◽  
Mould Growth ◽  
Reference Analysis ◽  
Calibration Models ◽  
Calibration Coefficients

Spectral data were collected of intact and ground kernels using 3 instruments (using Si-PbS, Si, and InGaAs detectors), operating over different areas of the spectrum (between 400 and 2500 nm) and employing transmittance, interactance, and reflectance sample presentation strategies. Kernels were assessed on the basis of oil and water content, and with respect to the defect categories of insect damage, rancidity, discoloration, mould growth, germination, and decomposition. Predictive model performance statistics for oil content models were acceptable on all instruments (R2 > 0.98; RMSECV < 2.5%, which is similar to reference analysis error), although that for the instrument employing reflectance optics was inferior to models developed for the instruments employing transmission optics. The spectral positions for calibration coefficients were consistent with absorbance due to the third overtones of CH2 stretching. Calibration models for moisture content in ground samples were acceptable on all instruments (R2 > 0.97; RMSECV < 0.2%), whereas calibration models for intact kernels were relatively poor. Calibration coefficients were more highly weighted around 1360, 740 and 840 nm, consistent with absorbance due to overtones of O-H stretching and combination. Intact kernels with brown centres or rancidity could be discriminated from each other and from sound kernels using principal component analysis. Part kernels affected by insect damage, discoloration, mould growth, germination, and decomposition could be discriminated from sound kernels. However, discrimination among these defect categories was not distinct and could not be validated on an independent set.It is concluded that there is good potential for a low cost Si photodiode array instrument to be employed to identify some quality defects of intact macadamia kernels and to quantify oil and moisture content of kernels in the process laboratory and for oil content in-line. Further work is required to examine the robustness of predictive models across different populations, including growing districts, cultivars and times of harvest.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Determine of Moisture Content for Rough Rice Single Kernel by Visible/NIR Spectroscopy

2014 ◽  
Vol 931-932 ◽  
pp. 1549-1554 ◽  
Author(s):  
Adcha Heman ◽  
Ching Lu Hsieh
Keyword(s):  
Moisture Content ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Regression Methods ◽  
Average Accuracy ◽  
Rough Rice ◽  
Calibration Models ◽  
Plsr Model

Moisture content (MC) of rough rice directly affects rice quality and its market value. This study applied spectroscopy both in visible 400-700 nm and NIR 700-1050 nm bands to record spectrum of rough rice single kernel (SK). Tainan No.11 medium rice randomly collected from field. After machine harvested, it was used in the tests and they were conditioned by oven to five MC levels ranging from 10.2 to 35.9%. Two regression methods, multiple linear regressions (MLR) and partial least square regression (PLSR), were applied to develop calibration models. Among 7 tested models were found that PLSR model of first differential with 21 gap points, which are rc=0.98, SEC=1.1% for calibration and rp=0.96, SEP=1.9% for prediction. The results suggested average accuracy for the best model was about 98.4% in 400-1050 nm wavelength.

scholarly journals Spectral Range and Wood Surface Impacts on Equilibrium Moisture Content Estimation in Thermally Modified Beech Wood by FT-NIR Spectroscopy

2021 ◽  
Vol 1208 (1) ◽  
pp. 012022
Author(s):  
Nebojša Todorović
Keyword(s):  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Cross Section ◽  
Near Infrared ◽  
Prediction Models ◽  
Beech Wood ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Least Squares Regression ◽  
The Cross

Abstract Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares regression (PLS-R) were tested for the possibility of equilibrium moisture content (EMC) prediction in thermally modified beech wood (Fagus moesiaca C.). The samples were modified for 4h at temperatures of 170, 190 and 210 °C. After thermal modification, the samples were kept in a climatic chamber until EMC was reached. FT-NIR spectra (100 scans and 4 cm-1) were collected on the cross-section and radial surfaces at four points. PLS – R models were developed for four spectral regions: the first overtone, the second overtone, the third overtone and the combination band region. Applied thermal treatment caused a decrease of EMC by 42 % at 170 °C, by 53 % at 190 °C, and by 62 % at 210 °C. Principal component analysis (PCA) indicated that there is a difference both between treatments and between wood surfaces. The results of the spectra taken from the radial surface were, in all models, better than the spectra of the cross-section. Related to chemical changes, the first and second overtone region play an important role in the calibrations. The best prediction models for EMC of thermally modified beech wood were obtained from radial surface spectra in the first (Rp2=0.86, RPD=2.69) and second overtone region (Rp2=0.87, RPD=2.70). The obtain results could contribute to the development of predictive models in monitoring of EMC which could significantly improve the quality of industrial production of thermally modified wood.

scholarly journals Rapid assessment of oil and moisture content in seeds of oil flax using IR spectrometry

2020 ◽  
Vol 3 (183) ◽  
pp. 63-70
Author(s):  
S.G. Efimenko ◽  
◽  
S.K. Efimenko ◽  
Keyword(s):  
Moisture Content ◽  
High Speed ◽  
Oil Content ◽  
Near Infrared ◽  
Coat Color ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Flax Seeds

We used near-infrared reflectance spectroscopy (NIRS) to assess biochemical parameters in whole oil flax seeds, regardless of differences in seed coat color of the samples. At the first stage of work, the set the task to develop calibration models for the MATRIX-I IR analyzer to determine the oil and moisture content in flax seeds. The carried out the research in the laboratory of biochemistry on brown and yellow seed samples of oil flax, grown in 2015-2020 in various agro-ecological conditions of the Russian Federation. We determined the oil content on an AMV 1006M NMR analyzer in accordance with the GOST 8.597- 2010 measurement procedure; we assessed the moisture content by the standard method of GOST 10856- 96. We used the results of determination of the oil and moisture content of the seeds of test lot in accordance with the accuracy indicator of the calibration of GOST 32749-2014 to verify the reliability of the developed models. We received the best indicators of the quality of calibration models (root-mean-square prediction error, coefficient of determination and the value of the residual deviation of prediction for the rank displayed on the graph) by determining the oil content (RMSEP = 0.27 %, R2 = 99.2 and RPD = 11.2) and moisture content (RMSEP = 0.06 %, R2 = 99.9 and RPD = 39). In the OPUS LAB program we developed the “Flax 51” method for mass analysis based on the developed calibration models for the determination of oil and moisture content in whole oil flax seeds (9-20 g) in a sample cell with a diameter of 51 mm. It enables the quick carrying out a preliminary assessment of the breeding material at a high speed – more than 120 samples in 7 hours without seed destruction.

scholarly journals Near-Infrared Spectroscopy for Anticounterfeiting Innovative Fibers

ISRN Textiles ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Jing Cao ◽  
Suraj Sharma
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Woven Fabrics ◽  
Effective Technique ◽  
Textile Materials ◽  
Synthetic Fibers ◽  
Qualitative And Quantitative ◽  
Calibration Models

Near-infrared (NIR) spectroscopy has gained increased attention for the qualitative and quantitative evaluation of textile and polymer products. Many NIR instruments have been commercialized to identify the natural and synthetic fibers; however, there is a strong need to have NIR database of these high-performance fibers to detect contraband textile materials rapidly and quantitatively. In this study, NIR spectra of PLA, Kevlar, Spandex and Sorona woven fabrics were collected and studied by several calibration models to identify the fibers. The results indicated that these four innovative fibers had been successfully distinguished by their NIR spectra in combination with preprocessing of 1/X transformation, SNV, and 2nd Savitzky-Golay derivative as well as principal-component-analysis (PCA-) based chemometric methods. Our promising results suggest that NIR spectroscopy is an effective technique to anticounterfeit innovative fibers.

scholarly journals Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy

2021 ◽  
Author(s):  
Michael J. Burns ◽  
Jonathan S. Renk ◽  
David P. Eickholt ◽  
Amanda M. Gilbert ◽  
Travis J. Hattery ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Nir Spectroscopy

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

scholarly journals Electrical Capacitance Characteristics of Wood Chips at Low Frequency Ranges: A Cheap Tool for Quality Assessment

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3494
Author(s):  
Jakub Lev ◽  
Václav Křepčík ◽  
Egidijus Šarauskis ◽  
František Kumhála
Keyword(s):  
Moisture Content ◽  
Linear Models ◽  
Economic Value ◽  
Wood Chip ◽  
Principal Component ◽  
Dissipation Factor ◽  
Industrial Applications ◽  
Wood Chips ◽  
Electrical Capacitance ◽  
Low Frequencies

Moisture content is one of the most important parameters related to the quality of wood chips that affects both the calorific and economic value of fuel chips. For industrial applications, moisture content needs to be detected quickly. For this purpose, various indirect moisture content measurement methods (e.g., capacitance, NIR, microwave, ECT, X-ray CT, and nuclear MR) have been investigated with different results in the past. Nevertheless, determining wood chip moisture content in real time is still a challenge. The main aim of this article was therefore to analyze the dielectric properties of wood chips at low frequencies (10 kHz–5 MHz) and to examine the possibility of using these properties to predict wood chip moisture content and porosity. A container-type probe was developed for this purpose. The electrical capacitance and dissipation factor of wood chips with different moisture content was measured by an LCR meter at 10 kHz, 50 kHz, 100 kHz, 500 kHz, 1 MHz, and 5 MHz frequencies. Wood chip porosity was also measured using a gas displacement method. Linear models for moisture content and porosity prediction were determined by backward stepwise linear regression. Mathematical model was developed to better understand the physical relationships between moisture content, porosity, and electrical capacitance. These models were able to predict the moisture content of observed quantities of wood chips with the required accuracy (R2 = 0.9−0.99). This finding opens another path to measuring the moisture content and porosity of wood chips in a relatively cheap and fast way and with adequate precision. In addition, principal component analysis showed that it is also possible to distinguish between individual wood chip fraction sizes from the information obtained.

Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 382-385
Author(s):  
D. Thumrongchote
Keyword(s):  
Moisture Content ◽  
Cocos Nucifera ◽  
Nir Spectroscopy ◽  
Grocery Stores ◽  
Absorbance Spectrum ◽  
Coconut Tree ◽  
Cocos Nucifera L ◽  
The Cost ◽  
Non Destructive ◽  
Low Glycemic Index

Coconut sugar is a local sugar from the blossoms of a coconut tree. It has been considered a healthy sugar due to its low glycemic index. There is an attempt to add other sugar to it to lower the cost. Thus, this research aimed to identify Thai coconut sugar and to establish models for predicting the moisture content of coconut sugar by using FT-NIR spectroscopy. Thai coconut sugar samples were purchased from local grocery stores in four provinces, online, and the community market. Their moisture contents were varied and equilibrated for 24 hrs prior to the measurements of moisture and FT-NIR spectra. The results showed that FT-NIR spectra of Thai coconut sugar differ from sucrose, glucose and fructose at the absorbance spectrum of 5379-5011 cm-1 . FT-NIR spectroscopy of 54 known moisture samples of Thai coconut sugar was used to obtain a model to predict moisture content. The predicted equation, using the PLS technique with the Spectrum Quant program, was found to give a standard error of prediction (SEP) 0.077% (less than 0.10%), indicating a non-destructive method of accurately and precisely predicting moisture levels in the coconut sugar. The results obtained suggested that FTNIR spectroscopy has the potential to be used as a tool to identify Thai coconut sugar accurately. It can rapidly predict the moisture content in the sample which will be useful in quality control standards.

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