Rapid and non-destructive detection of aflatoxin contamination of peanut kernels using visible/near-infrared (Vis/NIR) spectroscopy

2018 ◽  
Author(s):  
Haibo Yao ◽  
Zuzana Hruska ◽  
Yongliang Liu ◽  
Kanniah Rajasekaran ◽  
Deepak Bhatnagar ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Aflatoxin Contamination ◽  
Non Destructive

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.

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

2016 ◽  
Vol 24 (6) ◽  
pp. 517-528 ◽  
Author(s):  
Susanna Pulkka ◽  
Vincent Segura ◽  
Anni Harju ◽  
Tarja Tapanila ◽  
Johanna Tanner ◽  
...  
Keyword(s):  
Scots Pine ◽  
High Throughput ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tree Breeding ◽  
Decay Resistance ◽  
Increment Core ◽  
Automatic Selection ◽  
Non Destructive ◽  
Selection Of

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

scholarly journals Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling

Molecules ◽  
2020 ◽  
Vol 25 (8) ◽  
pp. 1845 ◽  
Author(s):  
Kiah Edwards ◽  
Marena Manley ◽  
Louwrens C. Hoffman ◽  
Anel Beganovic ◽  
Christian G. Kirchler ◽  
...  
Keyword(s):  
South African ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Muscle Type ◽  
Game Species ◽  
Analysis Techniques ◽  
Hierarchical Modelling ◽  
Analysis Models ◽  
Non Destructive

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908–1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%–93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%–100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy’s potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

Estimation of gelatin content in historical papers using NIR spectroscopy

2011 ◽  
Author(s):  
M Ormsby ◽  
T Barrett ◽  
J. B. Lang ◽  
J. Mazurek ◽  
M. Schilling
Keyword(s):  
Gas Chromatography ◽  
Amino Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Destructive Sampling ◽  
Percentage Points ◽  
Gelatin Concentration ◽  
The Difference ◽  
Gas Chromatography Mass Spectroscopy ◽  
Non Destructive

<p>Gelatin sizing was a key ingredient during the handpapermaking era. The gelatin concentration in historical papers has never been well documented, however, because measuring the gelatin content required destructive sampling. In this project we developed a non-destructive method using near infrared (NIR) spectroscopy. Gelatin concentrations of 40 historical papers from the 15<sup>th</sup>-18<sup>th</sup> centuries were determined from amino acid (AA) concentrations by using gas chromatography/mass spectroscopy. These values were combined with NIR spectra from the same papers to generate a model for predicting concentrations of unknowns. If a NIR measurement predicted a gelatin concentration in the range 0-6 percent then there is a 95% probability that the difference between the NIR model value and a destructive AA measurement would be between -1.6 and +1.3 percentage points. For 6-8 percent there is a 95% probability the difference would be between -2.0 and +1.5 percentage points, and for 8-12 percent the difference is between -3.0 and +2.0 percentage points. In a study of 159 specimens from books, loose leaves, and artworks printed from 1460-1791, the means for all papers were quite high in the 15<sup>th</sup> century and dropped an average of 20% every 50 years. Possible explanations for the decline are offered.</p>

scholarly journals Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials

2014 ◽  
Vol 43 (24) ◽  
pp. 8200-8214 ◽  
Author(s):  
Marena Manley
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Biological Materials ◽  
Destructive Analysis ◽  
Non Destructive

Principles, interpretation and applications of near-infrared (NIR) spectroscopy and NIR hyperspectral imaging are reviewed.

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