scholarly journals Near-Infrared Hyperspectral Imaging as a Monitoring Tool for On-Demand Manufacturing of Inkjet-Printed Formulations

2021 ◽  
Vol 22 (6) ◽  
Author(s):  
Sandra Stranzinger ◽  
Matthias Wolfgang ◽  
Emma Klotz ◽  
Otto Scheibelhofer ◽  
Patrizia Ghiotti ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Active Pharmaceutical Ingredient ◽  
Drug Dose ◽  
Metformin Hydrochloride ◽  
Distribution Maps ◽  
Standard Normal Variate ◽  
Gelatin Films ◽  
Board Monitoring

AbstractThis study evaluates the potential use of near-infrared hyperspectral imaging (NIR-HSI) for quantitative determination of the drug amount in inkjet-printed dosage forms. We chose metformin hydrochloride as a model active pharmaceutical ingredient (API) and printed it onto gelatin films using a piezoelectric inkjet printing system. An industry-ready NIR-HSI sensor combined with a motorized movable linear stage was applied for spectral acquisition. Initial API-substrate screening revealed best printing results for gelatin films with TiO2 filling. For calibration of the NIR-HSI system, escalating drug doses were printed on the substrate. After spectral pre-treatments, including standard normal variate (SNV) and Savitzky-Golay filtering for noise reduction and enhancement of spectral features, principal component analysis (PCA) and partial least squares (PLS) regression were applied to create predictive models for the quantification of independent printed metformin hydrochloride samples. It could be shown that the concentration distribution maps provided by the developed HSI models were capable of clustering and predicting the drug dose in the formulations. HSI model prediction showed significant better correlation to the reference (HPLC) compared to on-board monitoring of dispensed volume of the printer. Overall, the results emphasize the capability of NIR-HSI as a fast and non-destructive method for the quantification and quality control of the deposited API in drug-printing applications. Graphical abstract

Detection of Aflatoxin B1 in Pistachio Kernels Using Visible/Near-Infrared Hyperspectral Imaging

2019 ◽  
Vol 62 (5) ◽  
pp. 1065-1074
Author(s):  
Qifang Wu ◽  
Huirong Xu
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Nutrient Content ◽  
Aflatoxin Contamination ◽  
Quantitative Prediction ◽  
Spectra Analysis ◽  
Linear Discriminant ◽  
Standard Normal Variate

Abstract. Pistachios are susceptible to aflatoxin contamination because of their rich nutrient content. Hyperspectral imaging (HSI), a new method for collecting spectral and image information, has been successfully employed in contamination research to classify staple agricultural products, such as maize, that are contaminated with aflatoxins. However, only a few studies have been conducted on the nondestructive discrimination among contaminated nuts using HSI for both qualitative and quantitative purposes. Thus, the feasibility of directly detecting aflatoxin B1 (AFB1) in individual pistachio kernels using visible/near-infrared HSI (VNIR HSI) was explored in this study. A total of 300 pistachio kernels were randomly selected to prepare target samples that were artificially contaminated with 5, 10, 20, 30, or 50 ppb (parts per billion) of AFB1. Principal component analysis (PCA) showed an overall separation trend between the control and all contaminated kernels. Accuracies greater than 90.0% were obtained by linear discriminant analysis (LDA) for samples that were artificially contaminated with different concentrations of AFB1 based on spectra at 694 to 988 nm that had been preprocessed with standard normal variate (SNV) and Savitzky-Golay (SG) smoothing. The correlation coefficients of calibration and validation (rc and rv) from stepwise multiple linear regression (SMLR) models were all >0.9100. Moreover, five key wavelengths (708, 771, 892, 915, and 941 nm) closely associated with AFB1 contamination were identified using principal component spectra analysis. Generally, the results indicated that VNIR HSI could be employed for preliminary screening of pistachio kernels that were artificially contaminated with AFB1, even at the 5 ppb level. However, the quantitative prediction of the specific AFB1 concentration needed to be further improved. Keywords: Aflatoxins, Detection analysis, Hyperspectral information, Pistachios, Visible/near-infrared.

scholarly journals Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2907 ◽  
Author(s):  
Lei Feng ◽  
Susu Zhu ◽  
Chu Zhang ◽  
Yidan Bao ◽  
Pan Gao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Variety Identification ◽  
Support Vector ◽  
Neural Network Models ◽  
K Nearest Neighbors ◽  
Standard Normal Variate

Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.

scholarly journals Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019 ◽  
Vol 9 (18) ◽  
pp. 3926 ◽  
Author(s):  
Yue Zhang ◽  
Hongzhe Jiang ◽  
Wei Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chicken Meat ◽  
Regions Of Interest ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Distribution Maps ◽  
Plsr Model ◽  
Volume Range

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses

2003 ◽  
Vol 11 (4) ◽  
pp. 269-281 ◽  
Author(s):  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Bosoon Park ◽  
R. Jeff Buhr
Keyword(s):  
Real Time ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Ccd Camera ◽  
Sample Population ◽  
Multivariate System ◽  
Ratio Image ◽  
On Line

A method and system for detecting faecal and ingesta contaminants on poultry carcasses were demonstrated. A visible/near infrared monochromator, which measured reflectance and principal component analysis were first used to identify key wavelengths from faecal and uncontaminated skin samples. Measurements at 434, 517, 565 and 628 nm were identified and used for evaluation with a hyperspectral imaging system. The hyperspectral imaging system, which was a line-scan (pushbroom) imaging system, consisted of a hyperspectral camera, fibre-optic line lights, a computer and frame grabber. The hyperspectral imaging camera consisted of a high-resolution charge coupled device (CCD) camera, a prism-grating-prism spectrograph, focusing lens, associated optical hardware and a motorised controller. The imaging system operated from about 400 to 900 nm. The hyperspectral imaging system was calibrated for wavelength, distance and percent reflectance and analysis of calibrated images at the key wavelengths indicated that single-wavelength images were inadequate for detecting contaminants. However, a ratio of images at two of the key wavelengths was able to identify faecal and ingesta contaminants. Specifically, the ratio of the 565-nm image divided by the 517-nm image produced good results. The ratio image was then further processed by masking the background and either enhancing the image contrast with a non-linear histogram stretch, or applying a faecal threshold. The results indicated that, for the limited sample population, more than 96% of the contaminants were detected. Thus, the hyperspectral imaging system was able to detect contaminants and showed feasibility, but was too slow for real-time on-line processing. Therefore, a multivariate system operating at 565 and 517 nm, which should be capable of operating at real-time on-line processing speed, should be used. Further research with such a system needs to be conducted.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

scholarly journals Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 603
Author(s):  
Lukáš Krauz ◽  
Petr Páta ◽  
Jan Kaiser
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Hyperspectral Image Processing ◽  
Different Types ◽  
The Difference ◽  
Hyperspectral Camera

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

scholarly journals Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

2020 ◽  
Author(s):  
Mohammad Al Ktash ◽  
Otto Hauler ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Near Infrared ◽  
Dominant Role ◽  
Principal Component ◽  
Industrial Applications ◽  
Acquisition Time ◽  
Hydroxyl Groups ◽  
Short Acquisition Time ◽  
Different Types

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

Prediction of water content in biscuits using near infrared hyperspectral imaging spectroscopy and chemometrics

2020 ◽  
Vol 28 (3) ◽  
pp. 140-147
Author(s):  
Eloïse Lancelot ◽  
Philippe Courcoux ◽  
Sylvie Chevallier ◽  
Alain Le-Bail ◽  
Benoît Jaillais
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Content ◽  
Hyperspectral Imaging ◽  
Analysis Of Variance ◽  
Near Infrared ◽  
Imaging Spectroscopy ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Water Contents

The possibility of using near infrared hyperspectral imaging spectroscopy to quantify the water content in commercial biscuits was investigated. Principal component analysis was successfully applied to hyperspectral images of commercial biscuits to monitor their water contents. Variables were selected and water contents quantified using analysis of variance, followed by multiple linear regression, and the results were compared with those obtained with variable importance in projection partial least squares. Initially equal to 212, the number of variables after application of analysis of variance was equal to 10. Analysis of variance–multiple linear regression gave better results: the coefficient of determination (R2) was higher than 0.92 and the root mean square error of validation was less than 0.015. The “prediction images” obtained were very relevant and can be used to study biscuit defects. The methodology developed could be implemented at the industrial level for biscuit quality control and for online monitoring of the uniform distribution of water in the superficial layer of biscuits.

Development of a polarized hyperspectral imaging system for investigation of absorption and scattering properties

2019 ◽  
Vol 27 (4) ◽  
pp. 314-329 ◽  
Author(s):  
Jun-Li Xu ◽  
Alexia Gobrecht ◽  
Nathalie Gorretta ◽  
Daphné Héran ◽  
Aoife A Gowen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Polarized Light ◽  
Superior Performance ◽  
Sand Sample ◽  
Least Squares Regression ◽  
Distribution Maps ◽  
Spectral Curves ◽  
Standard Normal Variate ◽  
Model Matrix

This study was carried out to investigate the feasibility of an original polarized hyperspectral imaging setup in the spectral range of 400–1100 nm for enhancement of absorbance signal measurement on highly scattering samples. Spatial response and spectral calibration have been verified, indicating the consistency of this system and reliability of the acquired data. Model samples consisting of layered sand were prepared and used to uncover the hidden spectral information. In the model matrix, sand worked as scattering particle and dye E141 as absorbing material. Cross ([Formula: see text]) and parallel ([Formula: see text]) reflectance signals, along with the back-scattered reflectance, [Formula: see text]([Formula: see text]+[Formula: see text]) and the weakly scattered reflectance [Formula: see text] ([Formula: see text]−[Formula: see text]) spectra were computed and compared. Results demonstrated that cross-polarized images showed more subsurface information from the second layer due to the rejection of the superficial reflectance, while weakly scattered reflectance ([Formula: see text]) preserved only the surface information from the first layer. In addition, polarized light spectroscopy absorbance based on Dahm's equation in the frame of the representative layer theory and standard normal variate preprocessing [Formula: see text] spectra were also obtained from the prepared model matrix. The visual inspection of spectral curves revealed that [Formula: see text] and PoLiS absorbance showed two narrow peaks at 405 nm and 630 nm that were less impacted by multi-scattering effects. Partial least squares regression models were developed to predict dye concentration in the mixture sample. Consistent with the spectral profiles, [Formula: see text] and PoLiS absorbance presented the best model performances with determination of coefficients of prediction ([Formula: see text]) equal to 0.96 and 0.95, respectively. The resulting distribution maps of S1/S2 sand sample again confirmed the superior performance of [Formula: see text] and PoLiS absorbance, manifesting their better ability to reveal chemically related information. The overall results obtained in this research showed that the developed polarized-hyperspectral imaging system coupled with scattering correction methods has great potential for the analysis of powdered or turbid samples.

Non-Destructive Assessment of Edible Seed Chemical Content by Near-Infrared Hyperspectral Imaging for the Improvement of Nutritional Traits in Bottle Gourd (Lagenaria Siceraria)

2021 ◽  
Vol 7 (5) ◽  
pp. 75-81
Author(s):  
Nadège Aurelie N’dri-Aya ◽  
◽  
Irié Vroh-Bi ◽  
◽  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Bottle Gourd ◽  
Lagenaria Siceraria ◽  
Absorption Bands ◽  
Tropical Regions ◽  
Nutrition Security ◽  
Chemical Content ◽  
Non Destructive

The edible seeds of bottle gourd [Lagenaria siceraria (Molina) Standl.] are rich in oils, proteins and minerals of high nutritional quality. They are highly prized in pan tropical regions where they constitute valuable resources for food and nutrition security. In this study, near-infrared hyperspectral imaging (NIR-HSI) was combined with chemometrics to assess the variability of seed chemical content of African cultivars for the selection of nutritional traits. Six hundred seeds of four accessions belonging to two cultivars were collected from the Ivory Coast (West Africa) and analysed. The NIR-HSI spectra collected on whole seeds in the 1100-2400 nm range revealed that the main absorption bands of the seed chemical content were associated with water, lipids and proteins. The absorbance values between seeds of the same accession in these spectral regions varied up to 1.8 folds. Among the two chemometric tools used, principal component analysis (PCA) did not separate the accessions while Partial Least Squares Discriminant Analysis (PLS-DA) discriminated the accessions with 87.33 % to 94.67 %, and the cultivars with 90 % to 92 % correct classification. Seed oils from bottle gourd are for instance rich in linoleic acid which is an essential fatty acid for human health. The non-destructive and qualitative determination of the content of single seeds was demonstrated in the study and provides the opportunity to select superior seeds for the improvement of key nutritional traits in bottle gourd. Lagenaria siceraria, near-infrared hyperspectral imaging, seed chemical content, PCA, PLS-DA, nutrition security

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