A Study on the Application of Near Infrared Hyperspectral Chemical Imaging for Monitoring Moisture Content and Water Activity in Low Moisture Systems

Achata, Eva M.; Esquerre, Carlos; O’Donnell, Colm P.; Gowen, Aoife

doi:10.3390/molecules20022611

Cited by 34 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Figure displays the mean spectra for each pure hydrogel and experimental replicate. The raw absorbance spectra (Figure A) exhibit features associated to the water present in each gel, with broad peaks centered at 978 nm, 1181 nm, and 1454 nm, related to the first overtone of the symmetric and asymmetric OH stretching vibrations, to combination of OH bending and first overtone of OH stretching vibrations, and combinations of the first overtone OH bending vibrations, respectively . Highly hydrated hydrogels displayed signal saturation from 1384 to 1650 nm, and this region appeared to be the least reproducible among experimental replicates.…”

Section: Resultsmentioning

confidence: 99%

Hydration of hydrogels studied by near‐infrared hyperspectral imaging

Caponigro

Marini

Gowen

2017

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

Hydrogels are an important class of biomaterials that can absorb large quantities of water. In this study, changes in hydration of natural hydrogels (agar, chitosan, gelatin, starch, and blends of each with chitosan) during storage and rehydration were studied by using near-infrared hyperspectral imaging (NIR-HSI). Moisture content was calculated based on changes in sample weight during hydration. The NIR-HSI data were acquired by using a push-broom system operating in diffuse reflectance in the wavelength range 943 to 1650 nm. A novel synthesis method was developed to enable common preparation of each hydrogel. Mean spectra obtained from the hyperspectral images were analyzed, and predictive models for moisture content were developed by using partial least squares regression. Models were compared in predictive performance by using an independent validation set of data. The optimal model in predictive performance was a 1 latent variable partial least squares regression model developed on second derivative and mean centered pseudoabsorbance data in the wavelength range 943 to 1272 nm. This model was applied to pixel spectra from samples in the validation set to inspect spatial variations during dehydration and rehydration. Challenges associated with NIR-HSI of hydrogels with a large variation in moisture content are discussed.

show abstract

Section: Resultsmentioning

confidence: 99%

Hydration of hydrogels studied by near‐infrared hyperspectral imaging

Caponigro

Marini

Gowen

2017

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overall, the Savitzky-Golay first derivative did not generate accuracies as high as the MSC pre-processing did. Achata et al, (2015) and Ambrose et al, (2016) also showed that MSC generated better results than derivative pre-processing while analyzing NIR data [21,31]. This phenomenon can be explained by the fact that derivative pre-processing was sensitive to the high-frequency noises, and the accuracies could be affected by this fraction of noise after derivative processing.…”

Section: Discriminant Models Based On Full-range Wavelength Variablesmentioning

confidence: 88%

“…where R 0 was the original reflectance intensity in each wavelength from the seed samples, W was the reflectance intensity from the internal gold reference flag when the light source was turning on, and D was the reflectance intensity from the internal gold reference flag when the light source was turning off. To minimize the effects of temperature and moisture fluctuations during spectral acquisition [31], the spectrometer instrument was fully preheated and placed in an environment with a temperature of 25 ± 1 • C and a relative humidity of 50 ± 3%. The seed samples were also placed in the same environment for more than 30 days before the collection of the spectra.…”

Section: Ft-nir Spectroscopy Acquisitionmentioning

confidence: 99%

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

et al. 2019

View full text Add to dashboard Cite

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

show abstract

“…The NIR-HSI system consisted of an InGaAs camera (320×240 pixels; Sensors Unlimited, Inc., Princeton, NJ, USA), a spectrograph (Spectral Imaging Ltd., Oulu, Finland), five halogen lamps (3×50 W and 2×20 W), a cylindrical light diffuser, moving base and a computer (Hernández-Hierro et al, 2014).The speed of the moving base was set at 3 mm/s (spatial resolution 0.28×0.28 mm pixel size) and 20 mm/s (spatial resolution 0.3×0.3 mm pixel size) for the Vis-NIR and NIR systems respectively. Calibration of both systems was carried out as follows: 50 scan lines of black reference (Ib) were acquired and averaged by taking a measurement after covering the spectrograph lens with a cap; a white tile with a known reflectance (Rw) was placed on the moving base and used as a "white" reference (Iw) by averaging 50 scan lines and finally the signal from the sample (Is) was converted and stored as reflectance (R) according to Equation 1 (Achata et al, 2015).…”

Section: Hyperspectral Imaging Systemsmentioning

confidence: 99%

Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

Achata

Inguglia

Esquerre

et al. 2019

Journal of Food Engineering

View full text Add to dashboard Cite

Hyperspectral imaging in the visible and near infrared spectral range (450-1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and nonbrined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957-1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450-960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSE p 1.9%, R 2 p 0.92) and cooked (RMSE p 2.6%, R 2 p 0.83) samples used the 957-1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed.

show abstract

A Study on the Application of Near Infrared Hyperspectral Chemical Imaging for Monitoring Moisture Content and Water Activity in Low Moisture Systems

Cited by 34 publications

References 12 publications

Hydration of hydrogels studied by near‐infrared hyperspectral imaging

Hydration of hydrogels studied by near‐infrared hyperspectral imaging

Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

Contact Info

Product

Resources

About